Linear regression models and k-means clustering for statistical analysis of fNIRS data

PubMed Central

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-01-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751

Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy.

PubMed

Zhang, Gaoyang; Li, Penghui; Zhang, Wenfei; Zhao, Jian

2017-05-01

Improvement of the nutritional quality of soybean is usually facilitated by a vast range of soybean germplasm with enough information about their multiple phytonutrients. In order to acquire this essential information from a huge number of soybean samples, a rapid analytic method is urgently required. Here, a nondestructive near-infrared reflectance spectroscopy (NIRS) method was developed for rapid and accurate measurement of 25 nutritional components in soybean simultaneously, including fatty acids palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid, vitamin E (VE), α-VE, γ-VE, δ-VE, saponins, isoflavonoids, and flavonoids. Modified partial least squares regression and first, second, third, and fourth derivative transformation was applied for the model development. The 1 minus variance ratio (1-VR) value of the optimal model can reach between the highest 0.95 and lowest 0.64. The predicted values of phytonutrients in soybean using NIRS technology are comparable to those obtained from using the traditional spectrum or chemical methods. A robust NIRS can be adopted as a reliable method to evaluate complex plant constituents for screening large-scale samples of soybean germplasm resources or genetic populations for improvement of nutritional qualities. Graphical Abstract ᅟ.

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils.

PubMed

Chakraborty, Somsubhra; Weindorf, David C; Li, Bin; Ali Aldabaa, Abdalsamad Abdalsatar; Ghosh, Rakesh Kumar; Paul, Sathi; Nasim Ali, Md

2015-05-01

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R(2)=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils. Copyright © 2015 Elsevier B.V. All rights reserved.

Near-infrared spectral image analysis of pork marbling based on Gabor filter and wide line detector techniques.

PubMed

Huang, Hui; Liu, Li; Ngadi, Michael O; Gariépy, Claude; Prasher, Shiv O

2014-01-01

Marbling is an important quality attribute of pork. Detection of pork marbling usually involves subjective scoring, which raises the efficiency costs to the processor. In this study, the ability to predict pork marbling using near-infrared (NIR) hyperspectral imaging (900-1700 nm) and the proper image processing techniques were studied. Near-infrared images were collected from pork after marbling evaluation according to current standard chart from the National Pork Producers Council. Image analysis techniques-Gabor filter, wide line detector, and spectral averaging-were applied to extract texture, line, and spectral features, respectively, from NIR images of pork. Samples were grouped into calibration and validation sets. Wavelength selection was performed on calibration set by stepwise regression procedure. Prediction models of pork marbling scores were built using multiple linear regressions based on derivatives of mean spectra and line features at key wavelengths. The results showed that the derivatives of both texture and spectral features produced good results, with correlation coefficients of validation of 0.90 and 0.86, respectively, using wavelengths of 961, 1186, and 1220 nm. The results revealed the great potential of the Gabor filter for analyzing NIR images of pork for the effective and efficient objective evaluation of pork marbling.

Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS.

PubMed

Power, Sarah D; Kushki, Azadeh; Chau, Tom

2012-01-01

Near-infrared spectroscopy (NIRS) has been recently investigated for use in noninvasive brain-computer interface (BCI) technologies. Previous studies have demonstrated the ability to classify patterns of neural activation associated with different mental tasks (e.g., mental arithmetic) using NIRS signals. Though these studies represent an important step towards the realization of an NIRS-BCI, there is a paucity of literature regarding the consistency of these responses, and the ability to classify them on a single-trial basis, over multiple sessions. This is important when moving out of an experimental context toward a practical system, where performance must be maintained over longer periods. When considering response consistency across sessions, two questions arise: 1) can the hemodynamic response to the activation task be distinguished from a baseline (or other task) condition, consistently across sessions, and if so, 2) are the spatiotemporal characteristics of the response which best distinguish it from the baseline (or other task) condition consistent across sessions. The answers will have implications for the viability of an NIRS-BCI system, and the design strategies (especially in terms of classifier training protocols) adopted. In this study, we investigated the consistency of classification of a mental arithmetic task and a no-control condition over five experimental sessions. Mixed model linear regression on intrasession classification accuracies indicate that the task and baseline states remain differentiable across multiple sessions, with no significant decrease in accuracy (p = 0.67). Intersession analysis, however, revealed inconsistencies in spatiotemporal response characteristics. Based on these results, we investigated several different practical classifier training protocols, including scenarios in which the training and test data come from 1) different sessions, 2) the same session, and 3) a combination of both. Results indicate that when selecting optimal classifier training protocols for NIRS-BCI, a compromise between accuracy and convenience (e.g., in terms of duration/frequency of training data collection) must be considered.

Multivariate modelling of density, strength, and stiffness from near infared for mature, juvenile, and pith wood of longleaf pine (Pinus Palustris)

Treesearch

Brian K. Via; Todd F. Shupe; Leslie H. Groom; Michael Stine; Chi-Leung So

2003-01-01

In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more...

Real-time process monitoring in a semi-continuous fluid-bed dryer - microwave resonance technology versus near-infrared spectroscopy.

PubMed

Peters, Johanna; Teske, Andreas; Taute, Wolfgang; Döscher, Claas; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg

2018-02-15

The trend towards continuous manufacturing in the pharmaceutical industry is associated with an increasing demand for advanced control strategies. It is a mandatory requirement to obtain reliable real-time information on critical quality attributes (CQA) during every process step as the decision on diversion of material needs to be performed fast and automatically. Where possible, production equipment should provide redundant systems for in-process control (IPC) measurements to ensure continuous process monitoring even if one of the systems is not available. In this paper, two methods for real-time monitoring of granule moisture in a semi-continuous fluid-bed drying unit are compared. While near-infrared (NIR) spectroscopy has already proven to be a suitable process analytical technology (PAT) tool for moisture measurements in fluid-bed applications, microwave resonance technology (MRT) showed difficulties to monitor moistures above 8% until recently. The results indicate, that the newly developed MRT sensor operating at four resonances is capable to compete with NIR spectroscopy. While NIR spectra were preprocessed by mean centering and first derivative before application of partial least squares (PLS) regression to build predictive models (RMSEP = 0.20%), microwave moisture values of two resonances sufficed to build a statistically close multiple linear regression (MLR) model (RMSEP = 0.07%) for moisture prediction. Thereby, it could be verified that moisture monitoring by MRT sensor systems could be a valuable alternative to NIR spectroscopy or could be used as a redundant system providing great ease of application. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of NIR chemical imaging with conventional NIR, Raman and ATR-IR spectroscopy for quantification of furosemide crystal polymorphs in ternary powder mixtures.

PubMed

Schönbichler, S A; Bittner, L K H; Weiss, A K H; Griesser, U J; Pallua, J D; Huck, C W

2013-08-01

The aim of this study was to evaluate the ability of near-infrared chemical imaging (NIR-CI), near-infrared (NIR), Raman and attenuated-total-reflectance infrared (ATR-IR) spectroscopy to quantify three polymorphic forms (I, II, III) of furosemide in ternary powder mixtures. For this purpose, partial least-squares (PLS) regression models were developed, and different data preprocessing algorithms such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC) and 1st to 3rd derivatives were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the standard error of cross-validation (SECV), R(2), and the ratio performance deviation (RPD). Limits of detection (LOD) and limits of quantification (LOQ) of all methods were determined. For NIR-CI, a SECVcorr-spec and a SECVsingle-pixel corrected were calculated to assess the loss of accuracy by taking advantage of the spatial information. NIR-CI showed a SECVcorr-spec (SECVsingle-pixel corrected) of 2.82% (3.71%), 3.49% (4.65%), and 4.10% (5.06%) for form I, II, III. NIR had a SECV of 2.98%, 3.62%, and 2.75%, and Raman reached 3.25%, 3.08%, and 3.18%. The SECV of the ATR-IR models were 7.46%, 7.18%, and 12.08%. This study proves that NIR-CI, NIR, and Raman are well suited to quantify forms I-III of furosemide in ternary mixtures. Because of the pressure-dependent conversion of form II to form I, ATR-IR was found to be less appropriate for an accurate quantification of the mixtures. In this study, the capability of NIR-CI for the quantification of polymorphic ternary mixtures was compared with conventional spectroscopic techniques for the first time. For this purpose, a new way of spectra selection was chosen, and two kinds of SECVs were calculated to achieve a better comparability of NIR-CI to NIR, Raman, and ATR-IR. Copyright © 2013 Elsevier B.V. All rights reserved.

Harvest-time prediction of apple physiological indices using fiber optic Fourier transform near-infrared spectrometer

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2004-12-01

This work evaluates the feasibility of Fourier transform near infrared (FT-NIR) spectrometry for rapid determining the total soluble solids content and acidity of apple fruit. Intact apple fruit were measured by reflectance FT-NIR in 800-2500 nm range. FT-NIR models were developed based on partial least square (PLS) regression and principal component regress (PCR) with respect to the reflectance and its first derivative, the logarithms of the reflectance reciprocal and its second derivative. The above regression models, related the FT-NIR spectra to soluble solids content (SSC), titratable acidity (TA) and available acidity (pH). The best combination, based on the prediction results, was PLS models with respect to the logarithms of the reflectance reciprocal. Predictions with PLS models resulted standard errors of prediction (SEP) of 0.455, 0.044 and 0.068, and correlation coefficients of 0.968, 0.728 and 0.831 for SSC, TA and pH, respectively. It was concluded that by using the FT-NIR spectrometry measurement system, in the appropriate spectral range, it is possible to nondestructively assess the maturity factors of apple fruit.

Prediction of soil organic carbon in a coal mining area by Vis-NIR spectroscopy.

PubMed

Sun, Wenjuan; Li, Xinju; Niu, Beibei

2018-01-01

Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.

Development and Validation of a Near-Infrared Spectroscopy Method for the Prediction of Acrylamide Content in French-Fried Potato.

PubMed

Adedipe, Oluwatosin E; Johanningsmeier, Suzanne D; Truong, Van-Den; Yencho, G Craig

2016-03-02

This study investigated the ability of near-infrared spectroscopy (NIRS) to predict acrylamide content in French-fried potato. Potato flour spiked with acrylamide (50-8000 μg/kg) was used to determine if acrylamide could be accurately predicted in a potato matrix. French fries produced with various pretreatments and cook times (n = 84) and obtained from quick-service restaurants (n = 64) were used for model development and validation. Acrylamide was quantified using gas chromatography-mass spectrometry, and reflectance spectra (400-2500 nm) of each freeze-dried sample were captured on a Foss XDS Rapid Content Analyzer-NIR spectrometer. Partial least-squares (PLS) discriminant analysis and PLS regression modeling demonstrated that NIRS could accurately detect acrylamide content as low as 50 μg/kg in the model potato matrix. Prediction errors of 135 μg/kg (R(2) = 0.98) and 255 μg/kg (R(2) = 0.93) were achieved with the best PLS models for acrylamide prediction in Russet Norkotah French-fried potato and multiple samples of unknown varieties, respectively. The findings indicate that NIRS can be used as a screening tool in potato breeding and potato processing research to reduce acrylamide in the food supply.

A Feasibility Study on Monitoring Residual Sugar and Alcohol Strength in Kiwi Wine Fermentation Using a Fiber-Optic FT-NIR Spectrometry and PLS Regression.

PubMed

Wang, Bingqian; Peng, Bangzhu

2017-02-01

This work aims to investigate the potential of fiber-optic Fourier transform-near-infrared (FT-NIR) spectrometry associated with chemometric analysis, which will be applied to monitor time-related changes in residual sugar and alcohol strength during kiwi wine fermentation. NIR calibration models for residual sugar and alcohol strength during kiwi wine fermentation were established on the FT-NIR spectra of 98 samples scanned in a fiber-optic FT-NIR spectrometer, and partial least squares regression method. The results showed that R 2 and root mean square error of cross-validation could achieve 0.982 and 3.81 g/L for residual sugar, and 0.984 and 0.34% for alcohol strength, respectively. Furthermore, crucial process information on kiwi must and wine fermentations provided by fiber-optic FT-NIR spectrometry was found to agree with those obtained from traditional chemical methods, and therefore this fiber-optic FT-NIR spectrometry can be applied as an effective and suitable alternative for analyses and monitoring of those processes. The overall results suggested that fiber-optic FT-NIR spectrometry is a promising tool for monitoring and controlling the kiwi wine fermentation process. © 2017 Institute of Food Technologists®.

Limited short-term prognostic utility of cerebral NIRS during neonatal therapeutic hypothermia.

PubMed

Shellhaas, Renée A; Thelen, Brian J; Bapuraj, Jayapalli R; Burns, Joseph W; Swenson, Aaron W; Christensen, Mary K; Wiggins, Stephanie A; Barks, John D E

2013-07-16

We evaluated the utility of amplitude-integrated EEG (aEEG) and regional oxygen saturation (rSO2) measured using near-infrared spectroscopy (NIRS) for short-term outcome prediction in neonates with hypoxic ischemic encephalopathy (HIE) treated with therapeutic hypothermia. Neonates with HIE were monitored with dual-channel aEEG, bilateral cerebral NIRS, and systemic NIRS throughout cooling and rewarming. The short-term outcome measure was a composite of neurologic examination and brain MRI scores at 7 to 10 days. Multiple regression models were developed to assess NIRS and aEEG recorded during the 6 hours before rewarming and the 6-hour rewarming period as predictors of short-term outcome. Twenty-one infants, mean gestational age 38.8 ± 1.6 weeks, median 10-minute Apgar score 4 (range 0-8), and mean initial pH 6.92 ± 0.19, were enrolled. Before rewarming, the most parsimonious model included 4 parameters (adjusted R(2) = 0.59; p = 0.006): lower values of systemic rSO2 variability (p = 0.004), aEEG bandwidth variability (p = 0.019), and mean aEEG upper margin (p = 0.006), combined with higher mean aEEG bandwidth (worse discontinuity; p = 0.013), predicted worse short-term outcome. During rewarming, lower systemic rSO2 variability (p = 0.007) and depressed aEEG lower margin (p = 0.034) were associated with worse outcome (model-adjusted R(2) = 0.49; p = 0.005). Cerebral NIRS data did not contribute to either model. During day 3 of cooling and during rewarming, loss of physiologic variability (by systemic NIRS) and invariant, discontinuous aEEG patterns predict poor short-term outcome in neonates with HIE. These parameters, but not cerebral NIRS, may be useful to identify infants suitable for studies of adjuvant neuroprotective therapies or modification of the duration of cooling and/or rewarming.

Quantitative analysis of bayberry juice acidity based on visible and near-infrared spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shao Yongni; He Yong; Mao Jingyuan

Visible and near-infrared (Vis/NIR) reflectance spectroscopy has been investigated for its ability to nondestructively detect acidity in bayberry juice. What we believe to be a new, better mathematic model is put forward, which we have named principal component analysis-stepwise regression analysis-backpropagation neural network (PCA-SRA-BPNN), to build a correlation between the spectral reflectivity data and the acidity of bayberry juice. In this model, the optimum network parameters,such as the number of input nodes, hidden nodes, learning rate, and momentum, are chosen by the value of root-mean-square (rms) error. The results show that its prediction statistical parameters are correlation coefficient (r) ofmore » 0.9451 and root-mean-square error of prediction(RMSEP) of 0.1168. Partial least-squares (PLS) regression is also established to compare with this model. Before doing this, the influences of various spectral pretreatments (standard normal variate, multiplicative scatter correction, S. Golay first derivative, and wavelet package transform) are compared. The PLS approach with wavelet package transform preprocessing spectra is found to provide the best results, and its prediction statistical parameters are correlation coefficient (r) of 0.9061 and RMSEP of 0.1564. Hence, these two models are both desirable to analyze the data from Vis/NIR spectroscopy and to solve the problem of the acidity prediction of bayberry juice. This supplies basal research to ultimately realize the online measurements of the juice's internal quality through this Vis/NIR spectroscopy technique.« less

A comparative study of the use of powder X-ray diffraction, Raman and near infrared spectroscopy for quantification of binary polymorphic mixtures of piracetam.

PubMed

Croker, Denise M; Hennigan, Michelle C; Maher, Anthony; Hu, Yun; Ryder, Alan G; Hodnett, Benjamin K

2012-04-07

Diffraction and spectroscopic methods were evaluated for quantitative analysis of binary powder mixtures of FII(6.403) and FIII(6.525) piracetam. The two polymorphs of piracetam could be distinguished using powder X-ray diffraction (PXRD), Raman and near-infrared (NIR) spectroscopy. The results demonstrated that Raman and NIR spectroscopy are most suitable for quantitative analysis of this polymorphic mixture. When the spectra are treated with the combination of multiplicative scatter correction (MSC) and second derivative data pretreatments, the partial least squared (PLS) regression model gave a root mean square error of calibration (RMSEC) of 0.94 and 0.99%, respectively. FIII(6.525) demonstrated some preferred orientation in PXRD analysis, making PXRD the least preferred method of quantification. Copyright © 2012 Elsevier B.V. All rights reserved.

Dissolution testing of isoniazid, rifampicin, pyrazinamide and ethambutol tablets using near-infrared spectroscopy (NIRS) and multivariate calibration.

PubMed

de Oliveira Neves, Ana Carolina; Soares, Gustavo Mesquita; de Morais, Stéphanie Cavalcante; da Costa, Fernanda Saadna Lopes; Porto, Dayanne Lopes; de Lima, Kássio Michell Gomes

2012-01-05

This work utilized the near-infrared spectroscopy (NIRS) and multivariate calibration to measure the percentage drug dissolution of four active pharmaceutical ingredients (APIs) (isoniazid, rifampicin, pyrazinamide and ethambutol) in finished pharmaceutical products produced in the Federal University of Rio Grande do Norte (Brazil). The conventional analytical method employed in quality control tests of the dissolution by the pharmaceutical industry is high-performance liquid chromatography (HPLC). The NIRS is a reliable method that offers important advantages for the large-scale production of tablets and for non-destructive analysis. NIR spectra of 38 samples (in triplicate) were measured using a Bomen FT-NIR 160 MB in the range 1100-2500nm. Each spectrum was the average of 50 scans obtained in the diffuse reflectance mode. The dissolution test, which was initially carried out in 900mL of 0.1N hydrochloric acid at 37±0.5°C, was used to determine the percentage a drug that dissolved from each tablet measured at the same time interval (45min) at pH 6.8. The measurement of the four API was performed by HPLC (Shimadzu, Japan) in the gradiente mode. The influence of various spectral pretreatments (Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and Savitzky-Golay derivatives) and multivariate analysis using the partial least squares (PLS) regression algorithm was calculated by the Unscrambler 9.8 (Camo) software. The correlation coefficient (R(2)) for the HPLC determination versus predicted values (NIRS) ranged from 0.88 to 0.98. The root-mean-square error of prediction (RMSEP) obtained from PLS models were 9.99%, 8.63%, 8.57% and 9.97% for isoniazid, rifampicin, ethambutol and pyrazinamide, respectively, indicating that the NIR method is an effective and non-destructive tool for measurement of drug dissolution from tablets. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Potential effectiveness of visible and near infrared spectroscopy coupled with wavelength selection for real time grapevine leaf water status measurement.

PubMed

Giovenzana, Valentina; Beghi, Roberto; Parisi, Simone; Brancadoro, Lucio; Guidetti, Riccardo

2018-03-01

Increasing attention is being paid to non-destructive methods for water status real time monitoring as a potential solution to replace the tedious conventional techniques which are time consuming and not easy to perform directly in the field. The objective of this study was to test the potential effectiveness of two portable optical devices (visible/near infrared (vis/NIR) and near infrared (NIR) spectrophotometers) for the rapid and non-destructive evaluation of the water status of grapevine leaves. Moreover, a variable selection methodology was proposed to determine a set of candidate variables for the prediction of water potential (Ψ, MPa) related to leaf water status in view of a simplified optical device. The statistics of the partial least square (PLS) models showed in validation R 2 between 0.67 and 0.77 for models arising from vis/NIR spectra, and R 2 ranged from 0.77 to 0.85 for the NIR region. The overall performance of the multiple linear regression (MLR) models from selected wavelengths was slightly worse than that of the PLS models. Regarding the NIR range, acceptable MLR models were obtained only using 14 effective variables (R 2 range 0.63-0.69). To address the market demand for portable optical devices and heading towards the trend of miniaturization and low cost of the devices, individual wavelengths could be useful for the design of a simplified and low-cost handheld system providing useful information for better irrigation scheduling. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region

PubMed Central

Al-Suhaibani, Nasser; Hassan, Wael; Tahir, Mohammad; Schmidhalter, Urs

2017-01-01

Simultaneous indirect assessment of multiple and diverse plant parameters in an exact and expeditious manner is becoming imperative in irrigated arid regions, with a view toward creating drought-tolerant genotypes or for the management of precision irrigation. This study aimed to evaluate whether spectral reflectance indices (SRIs) in three parts of the electromagnetic spectrum ((visible-infrared (VIS), near-infrared (NIR)), and shortwave-infrared (SWIR)) could be used to track changes in morphophysiological parameters of wheat cultivars exposed to 1.00, 0.75, and 0.50 of the estimated evapotranspiration (ETc). Significant differences were found in the parameters of growth and photosynthetic efficiency, and canopy spectral reflectance among the three cultivars subjected to different irrigation rates. All parameters were highly and significantly correlated with each other particularly under the 0.50 ETc treatment. The VIS/VIS- and NIR/VIS-based indices were sufficient and suitable for assessing the growth and photosynthetic properties of wheat cultivars similar to those indices based on NIR/NIR, SWIR/NIR, or SWIR/SWIR. Almost all tested SRIs proved to assess growth and photosynthetic parameters, including transpiration rate, more efficiently when regressions were analyzed for each water irrigation rate individually. This study, the type of which has rarely been conducted in irrigated arid regions, indicates that spectral reflectance data can be used as a rapid and non-destructive alternative method for assessment of the growth and photosynthetic efficiency of wheat under a range of water irrigation rates. PMID:28829809

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

Tailoring noise frequency spectrum to improve NIR determinations.

PubMed

Xie, Shaofei; Xiang, Bingren; Yu, Liyan; Deng, Haishan

2009-12-15

Near infrared spectroscopy (NIR) contains excessive background noise and weak analytical signals caused by near infrared overtones and combinations. That makes it difficult to achieve quantitative determinations of low concentration samples by NIR. A simple chemometric approach has been established to modify the noise frequency spectrum to improve NIR determinations. The proposed method is to multiply one Savitzky-Golay filtered NIR spectrum with another reference spectrum added with thermal noises before the other Savitzky-Golay filter. Since Savitzky-Golay filter is a kind of low-pass filter and cannot eliminate low frequency components of NIR spectrum, using one step or two consecutive Savitzky-Golay filter procedures cannot improve the determination of NIR greatly. Meanwhile, significant improvement is achieved via the Savitzky-Golay filtered NIR spectrum processed with the multiplication alteration before the other Savitzky-Golay filter. The frequency range of the modified noise spectrum shifts toward higher frequency regime via multiplication operation. So the second Savitzky-Golay filter is able to provide better filtering efficiency to obtain satisfied result. The improvement of NIR determination with tailoring noise frequency spectrum technique was demonstrated by both simulated dataset and two measured NIR spectral datasets. It is expected that noise frequency spectrum technique will be adopted mostly in applications where quantitative determination of low concentration sample is crucial.

A histological evaluation and in vivo assessment of intratumoral near infrared photothermal nanotherapy-induced tumor regression.

PubMed

Green, Hadiyah N; Crockett, Stephanie D; Martyshkin, Dmitry V; Singh, Karan P; Grizzle, William E; Rosenthal, Eben L; Mirov, Sergey B

2014-01-01

Nanoparticle (NP)-enabled near infrared (NIR) photothermal therapy has realized limited success in in vivo studies as a potential localized cancer therapy. This is primarily due to a lack of successful methods that can prevent NP uptake by the reticuloendothelial system, especially the liver and kidney, and deliver sufficient quantities of intravenously injected NPs to the tumor site. Histological evaluation of photothermal therapy-induced tumor regression is also neglected in the current literature. This report demonstrates and histologically evaluates the in vivo potential of NIR photothermal therapy by circumventing the challenges of intravenous NP delivery and tumor targeting found in other photothermal therapy studies. Subcutaneous Cal 27 squamous cell carcinoma xenografts received photothermal nanotherapy treatments, radial injections of polyethylene glycol (PEG)-ylated gold nanorods and one NIR 785 nm laser irradiation for 10 minutes at 9.5 W/cm(2). Tumor response was measured for 10-15 days, gross changes in tumor size were evaluated, and the remaining tumors or scar tissues were excised and histologically analyzed. The single treatment of intratumoral nanorod injections followed by a 10 minute NIR laser treatment also known as photothermal nanotherapy, resulted in ~100% tumor regression in ~90% of treated tumors, which was statistically significant in a comparison to the average of all three control groups over time (P<0.01). Photothermal nanotherapy, or intratumoral nanorod injections followed by NIR laser irradiation of tumors and tumor margins, demonstrate the potential of NIR photothermal therapy as a viable localized treatment approach for primary and early stage tumors, and prevents NP uptake by the reticuloendothelial system.

Detection of urinary estrogen conjugates and creatinine using near infrared spectroscopy in Bornean orangutans (Pongo Pygmaeus).

PubMed

Kinoshita, Kodzue; Kuze, Noko; Kobayashi, Toshio; Miyakawa, Etsuko; Narita, Hiromitsu; Inoue-Murayama, Miho; Idani, Gen'ichi; Tsenkova, Roumiana

2016-01-01

For promoting in situ conservation, it is important to estimate the density distribution of fertile individuals, and there is a need for developing an easy monitoring method to discriminate between physiological states. To date, physiological state has generally been determined by measuring hormone concentration using radioimmunoassay or enzyme immunoassay (EIA) methods. However, these methods have rarely been applied in situ because of the requirements for a large amount of reagent, instruments, and a radioactive isotope. In addition, the proper storage of the sample (including urine and feces) on site until analysis is difficult. On the other hand, near infrared (NIR) spectroscopy requires no reagent and enables rapid measurement. In the present study, we attempted urinary NIR spectroscopy to determine the estrogen levels of orangutans in Japanese zoos and in the Danum Valley Conservation Area, Sabah, Malaysia. Reflectance NIR spectra were obtained from urine stored using a filter paper. Filter paper is easy to use to store dried urine, even in the wild. Urinary estrogen and creatinine concentrations measured by EIA were used as the reference data of partial least square (PLS) regression of urinary NIR spectra. High accuracies (R(2) > 0.68) were obtained in both estrogen and creatinine regression models. In addition, the PLS regressions in both standards showed higher accuracies (R(2) > 0.70). Therefore, the present study demonstrates that urinary NIR spectra have the potential to estimate the estrogen and creatinine concentrations.

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

DOE PAGES

Li, Muyang; Williams, Daniel L.; Heckwolf, Marlies; ...

2016-10-04

In this paper, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These weremore » compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.« less

Prediction of Cell Wall Properties and Response to Deconstruction Using Alkaline Pretreatment in Diverse Maize Genotypes Using Py-MBMS and NIR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Muyang; Williams, Daniel L.; Heckwolf, Marlies

In this paper, we explore the ability of several characterization approaches for phenotyping to extract information about plant cell wall properties in diverse maize genotypes with the goal of identifying approaches that could be used to predict the plant's response to deconstruction in a biomass-to-biofuel process. Specifically, a maize diversity panel was subjected to two high-throughput biomass characterization approaches, pyrolysis molecular beam mass spectrometry (py-MBMS) and near-infrared (NIR) spectroscopy, and chemometric models to predict a number of plant cell wall properties as well as enzymatic hydrolysis yields of glucose following either no pretreatment or with mild alkaline pretreatment. These weremore » compared to multiple linear regression (MLR) models developed from quantified properties. We were able to demonstrate that direct correlations to specific mass spectrometry ions from pyrolysis as well as characteristic regions of the second derivative of the NIR spectrum regions were comparable in their predictive capability to partial least squares (PLS) models for p-coumarate content, while the direct correlation to the spectral data was superior to the PLS for Klason lignin content and guaiacyl monomer release by thioacidolysis as assessed by cross-validation. The PLS models for prediction of hydrolysis yields using either py-MBMS or NIR spectra were superior to MLR models based on quantified properties for unpretreated biomass. However, the PLS models using the two high-throughput characterization approaches could not predict hydrolysis following alkaline pretreatment while MLR models based on quantified properties could. This is likely a consequence of quantified properties including some assessments of pretreated biomass, while the py-MBMS and NIR only utilized untreated biomass.« less

A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance.

PubMed

Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N

2017-07-01

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration.

Baseline Correction of Diffuse Reflection Near-Infrared Spectra Using Searching Region Standard Normal Variate (SRSNV).

PubMed

Genkawa, Takuma; Shinzawa, Hideyuki; Kato, Hideaki; Ishikawa, Daitaro; Murayama, Kodai; Komiyama, Makoto; Ozaki, Yukihiro

2015-12-01

An alternative baseline correction method for diffuse reflection near-infrared (NIR) spectra, searching region standard normal variate (SRSNV), was proposed. Standard normal variate (SNV) is an effective pretreatment method for baseline correction of diffuse reflection NIR spectra of powder and granular samples; however, its baseline correction performance depends on the NIR region used for SNV calculation. To search for an optimal NIR region for baseline correction using SNV, SRSNV employs moving window partial least squares regression (MWPLSR), and an optimal NIR region is identified based on the root mean square error (RMSE) of cross-validation of the partial least squares regression (PLSR) models with the first latent variable (LV). The performance of SRSNV was evaluated using diffuse reflection NIR spectra of mixture samples consisting of wheat flour and granular glucose (0-100% glucose at 5% intervals). From the obtained NIR spectra of the mixture in the 10 000-4000 cm(-1) region at 4 cm intervals (1501 spectral channels), a series of spectral windows consisting of 80 spectral channels was constructed, and then SNV spectra were calculated for each spectral window. Using these SNV spectra, a series of PLSR models with the first LV for glucose concentration was built. A plot of RMSE versus the spectral window position obtained using the PLSR models revealed that the 8680–8364 cm(-1) region was optimal for baseline correction using SNV. In the SNV spectra calculated using the 8680–8364 cm(-1) region (SRSNV spectra), a remarkable relative intensity change between a band due to wheat flour at 8500 cm(-1) and that due to glucose at 8364 cm(-1) was observed owing to successful baseline correction using SNV. A PLSR model with the first LV based on the SRSNV spectra yielded a determination coefficient (R2) of 0.999 and an RMSE of 0.70%, while a PLSR model with three LVs based on SNV spectra calculated in the full spectral region gave an R2 of 0.995 and an RMSE of 2.29%. Additional evaluation of SRSNV was carried out using diffuse reflection NIR spectra of marzipan and corn samples, and PLSR models based on SRSNV spectra showed good prediction results. These evaluation results indicate that SRSNV is effective in baseline correction of diffuse reflection NIR spectra and provides regression models with good prediction accuracy.

Improved determination of particulate absorption from combined filter pad and PSICAM measurements.

PubMed

Lefering, Ina; Röttgers, Rüdiger; Weeks, Rebecca; Connor, Derek; Utschig, Christian; Heymann, Kerstin; McKee, David

2016-10-31

Filter pad light absorption measurements are subject to two major sources of experimental uncertainty: the so-called pathlength amplification factor, β, and scattering offsets, o, for which previous null-correction approaches are limited by recent observations of non-zero absorption in the near infrared (NIR). A new filter pad absorption correction method is presented here which uses linear regression against point-source integrating cavity absorption meter (PSICAM) absorption data to simultaneously resolve both β and the scattering offset. The PSICAM has previously been shown to provide accurate absorption data, even in highly scattering waters. Comparisons of PSICAM and filter pad particulate absorption data reveal linear relationships that vary on a sample by sample basis. This regression approach provides significantly improved agreement with PSICAM data (3.2% RMS%E) than previously published filter pad absorption corrections. Results show that direct transmittance (T-method) filter pad absorption measurements perform effectively at the same level as more complex geometrical configurations based on integrating cavity measurements (IS-method and QFT-ICAM) because the linear regression correction compensates for the sensitivity to scattering errors in the T-method. This approach produces accurate filter pad particulate absorption data for wavelengths in the blue/UV and in the NIR where sensitivity issues with PSICAM measurements limit performance. The combination of the filter pad absorption and PSICAM is therefore recommended for generating full spectral, best quality particulate absorption data as it enables correction of multiple errors sources across both measurements.

Simultaneous multiple wavelength upconversion in a core-shell nanoparticle for enhanced near infrared light harvesting in a dye-sensitized solar cell.

PubMed

Yuan, Chunze; Chen, Guanying; Li, Lin; Damasco, Jossana A; Ning, Zhijun; Xing, Hui; Zhang, Tianmu; Sun, Licheng; Zeng, Hao; Cartwright, Alexander N; Prasad, Paras N; Ågren, Hans

2014-10-22

The efficiency of most photovoltaic devices is severely limited by near-infrared (NIR) transmission losses. To alleviate this limitation, a new type of colloidal upconversion nanoparticles (UCNPs), hexagonal core-shell-structured β-NaYbF4:Er(3+)(2%)/NaYF4:Nd(3+)(30%), is developed and explored in this work as an NIR energy relay material for dye-sensitized solar cells (DSSCs). These UCNPs are able to harvest light energy in multiple NIR regions, and subsequently convert the absorbed energy into visible light where the DSSCs strongly absorb. The NIR-insensitive DSSCs show compelling photocurrent increases through binary upconversion under NIR light illumination either at 785 or 980 nm, substantiating efficient energy relay by these UCNPs. The overall conversion efficiency of the DSSCs was improved with the introduction of UCNPs under simulated AM 1.5 solar irradiation.

Rapid non-destructive assessment of pork edible quality by using VIS/NIR spectroscopic technique

NASA Astrophysics Data System (ADS)

Zhang, Leilei; Peng, Yankun; Dhakal, Sagar; Song, Yulin; Zhao, Juan; Zhao, Songwei

2013-05-01

The objectives of this research were to develop a rapid non-destructive method to evaluate the edible quality of chilled pork. A total of 42 samples were packed in seal plastic bags and stored at 4°C for 1 to 21 days. Reflectance spectra were collected from visible/near-infrared spectroscopy system in the range of 400nm to 1100nm. Microbiological, physicochemical and organoleptic characteristics such as the total viable counts (TVC), total volatile basic-nitrogen (TVB-N), pH value and color parameters L* were determined to appraise pork edible quality. Savitzky-Golay (SG) based on five and eleven smoothing points, Multiple Scattering Correlation (MSC) and first derivative pre-processing methods were employed to eliminate the spectra noise. The support vector machines (SVM) and partial least square regression (PLSR) were applied to establish prediction models using the de-noised spectra. A linear correlation was developed between the VIS/NIR spectroscopy and parameters such as TVC, TVB-N, pH and color parameter L* indexes, which could gain prediction results with Rv of 0.931, 0.844, 0.805 and 0.852, respectively. The results demonstrated that VIS/NIR spectroscopy technique combined with SVM possesses a powerful assessment capability. It can provide a potential tool for detecting pork edible quality rapidly and non-destructively.

Hemodynamic Response Alterations in Sensorimotor Areas as a Function of Barbell Load Levels during Squatting: An fNIRS Study

PubMed Central

Kenville, Rouven; Maudrich, Tom; Carius, Daniel; Ragert, Patrick

2017-01-01

Functional near-infrared spectroscopy (fNIRS) serves as a promising tool to examine hemodynamic response alterations in a sports-scientific context. The present study aimed to investigate how brain activity within the human motor system changes its processing in dependency of different barbell load conditions while executing a barbell squat (BS). Additionally, we used different fNIRS probe configurations to identify and subsequently eliminate potential exercise induced systemic confounders such as increases in extracerebral blood flow. Ten healthy, male participants were enrolled in a crossover design. Participants performed a BS task with random barbell load levels (0% 1RM (1 repetition maximum), 20% 1RM and 40% 1RM for a BS) during fNIRS recordings. Initially, we observed global hemodynamic response alterations within and outside the human motor system. However, short distance channel regression of fNIRS data revealed a focalized hemodynamic response alteration within bilateral superior parietal lobe (SPL) for oxygenated hemoglobin (HbO2) and not for deoxygenated hemoglobin (HHb) when comparing different load levels. These findings indicate that the previously observed load/force-brain relationship for simple and isolated movements is also present in complex multi-joint movements such as the BS. Altogether, our results show the feasibility of fNIRS to investigate brain processing in a sports-related context. We suggest for future studies to incorporate short distance channel regression of fNIRS data to reduce the likelihood of false-positive hemodynamic response alterations during complex whole movements. PMID:28555098

Evaluation of the Brain Activity Using the Functional Near-Infared Spectroscopy while Having Stimulated by Pleasant and Unpleasant Music

NASA Astrophysics Data System (ADS)

Asano, Hirotoshi; Hiroshige, Satoru; Ide, Hideto

We propose the psychological research and physiological measurements. We used oxyHb as physiological measurements in order to evaluate the emotion of “pleasant-unpleasant”. Concretely, we evaluated the difference in the emotion of “pleasant-unpleasant” from oxyHb of the frontal lobe. The experiment showed that a relation between psychological amount and ⊿oxyHb. Based on the result, we presumed the psychological amount using the multiple regression analysis. As a result, it turned out that we can evaluate the emotion of “pleasant-unpleasant” by fNIRS.

Systematization method for distinguishing plastic groups by using NIR spectroscopy.

PubMed

Kaihara, Mikio; Satoh, Minami; Satoh, Minoru

2007-07-01

A systematic classification method for polymers is not yet available in case of using near infrared spectra (NIR). That is why we have been searching for a systematic method. Because raw NIR spectra usually have few obvious peaks, NIR spectra have been pretreated by 2nd derivation for taking well modulated spectra. After the pretreatment, we applied classification and regression trees (CART) to the discrimination between the spectra and the species of polymers. As a result, we obtained a relatively simple classification tree. Judging from the obtained splitting conditions and the classified polymers, we concluded that obtained knowledge on the chemical function groups estimated by the important wavelength regions is not always applicable to this classification tree. However, we clarified the splitting rules for polymer species from the NIR spectral point of view.

Firmness prediction in Prunus persica 'Calrico' peaches by visible/short-wave near infrared spectroscopy and acoustic measurements using optimised linear and non-linear chemometric models.

PubMed

Lafuente, Victoria; Herrera, Luis J; Pérez, María del Mar; Val, Jesús; Negueruela, Ignacio

2015-08-15

In this work, near infrared spectroscopy (NIR) and an acoustic measure (AWETA) (two non-destructive methods) were applied in Prunus persica fruit 'Calrico' (n = 260) to predict Magness-Taylor (MT) firmness. Separate and combined use of these measures was evaluated and compared using partial least squares (PLS) and least squares support vector machine (LS-SVM) regression methods. Also, a mutual-information-based variable selection method, seeking to find the most significant variables to produce optimal accuracy of the regression models, was applied to a joint set of variables (NIR wavelengths and AWETA measure). The newly proposed combined NIR-AWETA model gave good values of the determination coefficient (R(2)) for PLS and LS-SVM methods (0.77 and 0.78, respectively), improving the reliability of MT firmness prediction in comparison with separate NIR and AWETA predictions. The three variables selected by the variable selection method (AWETA measure plus NIR wavelengths 675 and 697 nm) achieved R(2) values 0.76 and 0.77, PLS and LS-SVM. These results indicated that the proposed mutual-information-based variable selection algorithm was a powerful tool for the selection of the most relevant variables. © 2014 Society of Chemical Industry.

A rapid method for detection of fumonisins B1 and B2 in corn meal using Fourier transform near infrared (FT-NIR) spectroscopy implemented with integrating sphere.

PubMed

Gaspardo, B; Del Zotto, S; Torelli, E; Cividino, S R; Firrao, G; Della Riccia, G; Stefanon, B

2012-12-01

Fourier transform near infrared (FT-NIR) spectroscopy is an analytical procedure generally used to detect organic compounds in food. In this work the ability to predict fumonisin B(1)+B(2) contents in corn meal using an FT-NIR spectrophotometer, equipped with an integration sphere, was assessed. A total of 143 corn meal samples were collected in Friuli Venezia Giulia Region (Italy) and used to define a 15 principal components regression model, applying partial least square regression algorithm with full cross validation as internal validation. External validation was performed to 25 unknown samples. Coefficients of correlation, root mean square error and standard error of calibration were 0.964, 0.630 and 0.632, respectively and the external validation confirmed a fair potential of the model in predicting FB(1)+FB(2) concentration. Results suggest that FT-NIR analysis is a suitable method to detect FB(1)+FB(2) in corn meal and to discriminate safe meals from those contaminated. Copyright © 2012 Elsevier Ltd. All rights reserved.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).

PubMed

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS. Copyright © 2013 Elsevier B.V. All rights reserved.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

NASA Astrophysics Data System (ADS)

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P.; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

Multi-parameters monitoring during traditional Chinese medicine concentration process with near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Liu, Ronghua; Sun, Qiaofeng; Hu, Tian; Li, Lian; Nie, Lei; Wang, Jiayue; Zhou, Wanhui; Zang, Hengchang

2018-03-01

As a powerful process analytical technology (PAT) tool, near infrared (NIR) spectroscopy has been widely used in real-time monitoring. In this study, NIR spectroscopy was applied to monitor multi-parameters of traditional Chinese medicine (TCM) Shenzhiling oral liquid during the concentration process to guarantee the quality of products. Five lab scale batches were employed to construct quantitative models to determine five chemical ingredients and physical change (samples density) during concentration process. The paeoniflorin, albiflorin, liquiritin and samples density were modeled by partial least square regression (PLSR), while the content of the glycyrrhizic acid and cinnamic acid were modeled by support vector machine regression (SVMR). Standard normal variate (SNV) and/or Savitzkye-Golay (SG) smoothing with derivative methods were adopted for spectra pretreatment. Variable selection methods including correlation coefficient (CC), competitive adaptive reweighted sampling (CARS) and interval partial least squares regression (iPLS) were performed for optimizing the models. The results indicated that NIR spectroscopy was an effective tool to successfully monitoring the concentration process of Shenzhiling oral liquid.

Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area

NASA Astrophysics Data System (ADS)

Pleniou, Magdalini; Koutsias, Nikos

2013-05-01

The aim of our study was to explore the spectral properties of fire-scorched (burned) and non fire-scorched (vegetation) areas, as well as areas with different burn/vegetation ratios, using a multisource multiresolution satellite data set. A case study was undertaken following a very destructive wildfire that occurred in Parnitha, Greece, July 2007, for which we acquired satellite images from LANDSAT, ASTER, and IKONOS. Additionally, we created spatially degraded satellite data over a range of coarser resolutions using resampling techniques. The panchromatic (1 m) and multispectral component (4 m) of IKONOS were merged using the Gram-Schmidt spectral sharpening method. This very high-resolution imagery served as the basis to estimate the cover percentage of burned areas, bare land and vegetation at pixel level, by applying the maximum likelihood classification algorithm. Finally, multiple linear regression models were fit to estimate each land-cover fraction as a function of surface reflectance values of the original and the spatially degraded satellite images. The main findings of our research were: (a) the Near Infrared (NIR) and Short-wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas; (b) when the bi-spectral space consists only of NIR and SWIR, then the NIR ground reflectance value plays a more significant role in estimating the percent of burned areas, and the SWIR appears to be more important in estimating the percent of vegetation; and (c) semi-burned areas comprising 45-55% burned area and 45-55% vegetation are spectrally closer to burned areas in the NIR channel, whereas those areas are spectrally closer to vegetation in the SWIR channel. These findings, at least partially, are attributed to the fact that: (i) completely burned pixels present low variance in the NIR and high variance in the SWIR, whereas the opposite is observed in completely vegetated areas where higher variance is observed in the NIR and lower variance in the SWIR, and (ii) bare land modifies the spectral signal of burned areas more than the spectral signal of vegetated areas in the NIR, while the opposite is observed in SWIR region of the spectrum where the bare land modifies the spectral signal of vegetation more than the burned areas because the bare land and the vegetation are spectrally more similar in the NIR, and the bare land and burned areas are spectrally more similar in the SWIR.

A Brief Review on the Use of Functional Near-Infrared Spectroscopy (fNIRS) for Language Imaging Studies in Human Newborns and Adults

ERIC Educational Resources Information Center

Quaresima, Valentina; Bisconti, Silvia; Ferrari, Marco

2012-01-01

Upon stimulation, real time maps of cortical hemodynamic responses can be obtained by non-invasive functional near-infrared spectroscopy (fNIRS) which measures changes in oxygenated and deoxygenated hemoglobin after positioning multiple sources and detectors over the human scalp. The current commercially available transportable fNIRS systems have…

Tunable multiple emissions in manganese-concentrated sulfide through simultaneous tailoring of Mn-site coordination and Mn-Mn pair geometry

NASA Astrophysics Data System (ADS)

Chen, Zitao; Song, Enhai; Ye, Shi; Zhang, Qinyuan

2017-12-01

In contrast to generally single-band visible emission feature from Mn2+, simultaneous visible (VIS) and near-infrared (NIR) multiple emissions are demonstrated in Mn2+ concentrated sulfide (MnS) by only involving a single crystallographic site. Upon varying the Mn2+-site coordination and/or Mn-Mn pairs geometry in different structural MnS, the multiple emissions from divalent manganese can be easily tuned from 575 to 720 nm (VIS) or from 880 to 900 or 1380 nm (NIR), respectively. The excitation spectroscopy and the luminescent decay, together with crystal structural analyses, are employed to investigate the electronic transition and the excited state dynamics of these Mn2+ concentrated systems. It is found that the VIS and NIR emissions can be ascribed to the isolated Mn2+ ion and exchange coupled Mn-Mn pair center, respectively. The effect of crystal field and bridging geometry, as well as temperature on the exchange coupled Mn2+ pairs NIR emissive center, is also investigated in detail. This work not only provides keen insights into the de-excitation pathway of Mn2+-concentrated material, but also offers the possibilities of designing a novel NIR emitting source for various photonic applications.

Near-infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Parching Process of Traditional Chinese Medicine Based on Two Kinds of Chemical Indicators.

PubMed

Li, Kaiyue; Wang, Weiying; Liu, Yanping; Jiang, Su; Huang, Guo; Ye, Liming

2017-01-01

The active ingredients and thus pharmacological efficacy of traditional Chinese medicine (TCM) at different degrees of parching process vary greatly. Near-infrared spectroscopy (NIR) was used to develop a new method for rapid online analysis of TCM parching process, using two kinds of chemical indicators (5-(hydroxymethyl) furfural [5-HMF] content and 420 nm absorbance) as reference values which were obviously observed and changed in most TCM parching process. Three representative TCMs, Areca ( Areca catechu L.), Malt ( Hordeum Vulgare L.), and Hawthorn ( Crataegus pinnatifida Bge.), were used in this study. With partial least squares regression, calibration models of NIR were generated based on two kinds of reference values, i.e. 5-HMF contents measured by high-performance liquid chromatography (HPLC) and 420 nm absorbance measured by ultraviolet-visible spectroscopy (UV/Vis), respectively. In the optimized models for 5-HMF, the root mean square errors of prediction (RMSEP) for Areca, Malt, and Hawthorn was 0.0192, 0.0301, and 0.2600 and correlation coefficients ( R cal ) were 99.86%, 99.88%, and 99.88%, respectively. Moreover, in the optimized models using 420 nm absorbance as reference values, the RMSEP for Areca, Malt, and Hawthorn was 0.0229, 0.0096, and 0.0409 and R cal were 99.69%, 99.81%, and 99.62%, respectively. NIR models with 5-HMF content and 420 nm absorbance as reference values can rapidly and effectively identify three kinds of TCM in different parching processes. This method has great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online analysis and quality control in TCM industrial manufacturing process. Near-infrared spectroscopy.(NIR) was used to develop a new method for online analysis of traditional Chinese medicine.(TCM) parching processCalibration and validation models of Areca, Malt, and Hawthorn were generated by partial least squares regression using 5.(hydroxymethyl) furfural contents and 420.nm absorbance as reference values, respectively, which were main indicator components during parching process of most TCMThe established NIR models of three TCMs had low root mean square errors of prediction and high correlation coefficientsThe NIR method has great promise for use in TCM industrial manufacturing processes for rapid online analysis and quality control. Abbreviations used: NIR: Near-infrared Spectroscopy; TCM: Traditional Chinese medicine; Areca: Areca catechu L.; Hawthorn: Crataegus pinnatifida Bge.; Malt: Hordeum vulgare L.; 5-HMF: 5-(hydroxymethyl) furfural; PLS: Partial least squares; D: Dimension faction; SLS: Straight line subtraction, MSC: Multiplicative scatter correction; VN: Vector normalization; RMSECV: Root mean square errors of cross-validation; RMSEP: Root mean square errors of validation; R cal : Correlation coefficients; RPD: Residual predictive deviation; PAT: Process analytical technology; FDA: Food and Drug Administration; ICH: International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use.

Optical system for tablet variety discrimination using visible/near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Shao, Yongni; He, Yong; Hu, Xingyue

2007-12-01

An optical system based on visible/near-infrared spectroscopy (Vis/NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325 and 1075 nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400 nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.

Near Infrared Spectrometry of Clinically Significant Fatty Acids Using Multicomponent Regression

NASA Astrophysics Data System (ADS)

Kalinin, A. V.; Krasheninnikov, V. N.; Sviridov, A. P.; Titov, V. N.

2016-11-01

We have developed methods for determining the content of clinically important fatty acids (FAs), primarily saturated palmitic acid, monounsaturated oleic acid, and the sum of polyenoic fatty acids (eicosapentaenoic + docosahexaenoic), in oily media (food products and supplements, fish oils) using different types of near infrared (NIR) spectrometers: Fourier-transform, linear photodiode array, and Raman. Based on a calibration method (regression) by means of projections to latent structures, using standard samples of oil and fat mixtures, we have confirmed the feasibility of reliable and selective quantitative analysis of the above-indicated fatty acids. As a result of comparing the calibration models for Fourier-transform spectrometers in different parts of the NIR range (based on different overtones and combinations of fatty acid absorption), we have provided a basis for selection of the spectral range for a portable linear InGaAs-photodiode array spectrometer. In testing the calibrations of a linear InGaAs-photodiode array spectrometer which is a prototype for a portable instrument, for palmitic and oleic acids and also the sum of the polyenoic fatty acids we have achieved a multiple correlation coefficient of 0.89, 0.85, and 0.96 and a standard error of 0.53%, 1.43%, and 0.39% respectively. We have confirmed the feasibility of using Raman spectra to determine the content of the above-indicated fatty acids in media where water is present.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

PubMed

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

VIS-NIR spectroscopy as a process analytical technology for compositional characterization of film biopolymers and correlation with their mechanical properties.

PubMed

Barbin, Douglas Fernandes; Valous, Nektarios A; Dias, Adriana Passos; Camisa, Jaqueline; Hirooka, Elisa Yoko; Yamashita, Fabio

2015-11-01

There is an increasing interest in the use of polysaccharides and proteins for the production of biodegradable films. Visible and near-infrared (VIS-NIR) spectroscopy is a reliable analytical tool for objective analyses of biological sample attributes. The objective is to investigate the potential of VIS-NIR spectroscopy as a process analytical technology for compositional characterization of biodegradable materials and correlation to their mechanical properties. Biofilms were produced by single-screw extrusion with different combinations of polybutylene adipate-co-terephthalate, whole oat flour, glycerol, magnesium stearate, and citric acid. Spectral data were recorded in the range of 400-2498nm at 2nm intervals. Partial least square regression was used to investigate the correlation between spectral information and mechanical properties. Results show that spectral information is influenced by the major constituent components, as they are clustered according to polybutylene adipate-co-terephthalate content. Results for regression models using the spectral information as predictor of tensile properties achieved satisfactory results, with coefficients of prediction (R(2)C) of 0.83, 0.88 and 0.92 (calibration models) for elongation, tensile strength, and Young's modulus, respectively. Results corroborate the correlation of NIR spectra with tensile properties, showing that NIR spectroscopy has potential as a rapid analytical technology for non-destructive assessment of the mechanical properties of the films. Copyright © 2015 Elsevier B.V. All rights reserved.

Development of NIRS method for quality control of drug combination artesunate–azithromycin for the treatment of severe malaria

PubMed Central

Boyer, Chantal; Gaudin, Karen; Kauss, Tina; Gaubert, Alexandra; Boudis, Abdelhakim; Verschelden, Justine; Franc, Mickaël; Roussille, Julie; Boucher, Jacques; Olliaro, Piero; White, Nicholas J.; Millet, Pascal; Dubost, Jean-Pierre

2012-01-01

Near infrared spectroscopy (NIRS) methods were developed for the determination of analytical content of an antimalarial-antibiotic (artesunate and azithromycin) co-formulation in hard gelatin capsule (HGC). The NIRS consists of pre-processing treatment of spectra (raw spectra and first-derivation of two spectral zones), a unique principal component analysis model to ensure the specificity and then two partial least-squares regression models for the determination content of each active pharmaceutical ingredient. The NIRS methods were developed and validated with no reference method, since the manufacturing process of HGC is basically mixed excipients with active pharmaceutical ingredients. The accuracy profiles showed β-expectation tolerance limits within the acceptance limits (±5%). The analytical control approach performed by reversed phase (HPLC) required two different methods involving two different preparation and chromatographic methods. NIRS offers advantages in terms of lower costs of equipment and procedures, time saving, environmentally friendly. PMID:22579599

Optimisation of near-infrared reflectance model in measuring protein and amylose content of rice flour.

PubMed

Xie, L H; Tang, S Q; Chen, N; Luo, J; Jiao, G A; Shao, G N; Wei, X J; Hu, P S

2014-01-01

Near-infrared reflectance spectroscopy (NIRS) has been used to predict the cooking quality parameters of rice, such as the protein (PC) and amylose content (AC). Using brown and milled flours from 519 rice samples representing a wide range of grain qualities, this study was to compare the calibration models generated by different mathematical, preprocessing treatments, and combinations of different regression algorithm. A modified partial least squares model (MPLS) with the mathematic treatment "2, 8, 8, 2" (2nd order derivative computed based on 8 data points, and 8 and 2 data points in the 1st and 2nd smoothing, respectively) and inverse multiplicative scattering correction preprocessing treatment was identified as the best model for simultaneously measurement of PC and AC in brown flours. MPLS/"2, 8, 8, 2"/detrend preprocessing was identified as the best model for milled flours. The results indicated that NIRS could be useful in estimation of PC and AC of breeding lines in early generations of the breeding programs, and for the purposes of quality control in the food industry. Copyright © 2013 Elsevier Ltd. All rights reserved.

Near infrared spectroscopy for prediction of antioxidant compounds in the honey.

PubMed

Escuredo, Olga; Seijo, M Carmen; Salvador, Javier; González-Martín, M Inmaculada

2013-12-15

The selection of antioxidant variables in honey is first time considered applying the near infrared (NIR) spectroscopic technique. A total of 60 honey samples were used to develop the calibration models using the modified partial least squares (MPLS) regression method and 15 samples were used for external validation. Calibration models on honey matrix for the estimation of phenols, flavonoids, vitamin C, antioxidant capacity (DPPH), oxidation index and copper using near infrared (NIR) spectroscopy has been satisfactorily obtained. These models were optimised by cross-validation, and the best model was evaluated according to multiple correlation coefficient (RSQ), standard error of cross-validation (SECV), ratio performance deviation (RPD) and root mean standard error (RMSE) in the prediction set. The result of these statistics suggested that the equations developed could be used for rapid determination of antioxidant compounds in honey. This work shows that near infrared spectroscopy can be considered as rapid tool for the nondestructive measurement of antioxidant constitutes as phenols, flavonoids, vitamin C and copper and also the antioxidant capacity in the honey. Copyright © 2013 Elsevier Ltd. All rights reserved.

Application of NIRS coupled with PLS regression as a rapid, non-destructive alternative method for quantification of KBA in Boswellia sacra

NASA Astrophysics Data System (ADS)

Al-Harrasi, Ahmed; Rehman, Najeeb Ur; Mabood, Fazal; Albroumi, Muhammaed; Ali, Liaqat; Hussain, Javid; Hussain, Hidayat; Csuk, René; Khan, Abdul Latif; Alam, Tanveer; Alameri, Saif

2017-09-01

In the present study, for the first time, NIR spectroscopy coupled with PLS regression as a rapid and alternative method was developed to quantify the amount of Keto-β-Boswellic Acid (KBA) in different plant parts of Boswellia sacra and the resin exudates of the trunk. NIR spectroscopy was used for the measurement of KBA standards and B. sacra samples in absorption mode in the wavelength range from 700-2500 nm. PLS regression model was built from the obtained spectral data using 70% of KBA standards (training set) in the range from 0.1 ppm to 100 ppm. The PLS regression model obtained was having R-square value of 98% with 0.99 corelationship value and having good prediction with RMSEP value 3.2 and correlation of 0.99. It was then used to quantify the amount of KBA in the samples of B. sacra. The results indicated that the MeOH extract of resin has the highest concentration of KBA (0.6%) followed by essential oil (0.1%). However, no KBA was found in the aqueous extract. The MeOH extract of the resin was subjected to column chromatography to get various sub-fractions at different polarity of organic solvents. The sub-fraction at 4% MeOH/CHCl3 (4.1% of KBA) was found to contain the highest percentage of KBA followed by another sub-fraction at 2% MeOH/CHCl3 (2.2% of KBA). The present results also indicated that KBA is only present in the gum-resin of the trunk and not in all parts of the plant. These results were further confirmed through HPLC analysis and therefore it is concluded that NIRS coupled with PLS regression is a rapid and alternate method for quantification of KBA in Boswellia sacra. It is non-destructive, rapid, sensitive and uses simple methods of sample preparation.

The natural abundance of 13C with different agricultural management by NIRS with fibre optic probe technology.

PubMed

Fuentes, Mariela; González-Martín, Inmaculada; Hernández-Hierro, Jose Miguel; Hidalgo, Claudia; Govaerts, Bram; Etchevers, Jorge; Sayre, Ken D; Dendooven, Luc

2009-06-30

In the present study the natural abundance of (13)C is quantified in agricultural soils in Mexico which have been submitted to different agronomic practices, zero and conventional tillage, retention of crop residues (with and without) and rotation of crops (wheat and maize) for 17 years, which have influenced the physical, chemical and biological characteristics of the soil. The natural abundance of C13 is quantified by near infrared spectra (NIRS) with a remote reflectance fibre optic probe, applying the probe directly to the soil samples. Discriminate partial least squares analysis of the near infrared spectra allowed to classify soils with and without residues, regardless of the type of tillage or rotation systems used with a prediction rate of 90% in the internal validation and 94% in the external validation. The NIRS calibration model using a modified partial least squares regression allowed to determine the delta(13)C in soils with or without residues, with multiple correlation coefficients 0.81 and standard error prediction 0.5 per thousand in soils with residues and 0.92 and 0.2 per thousand in soils without residues. The ratio performance deviation for the quantification of delta(13)C in soil was 2.5 in soil with residues and 3.8 without residues. This indicated that the model was adequate to determine the delta(13)C of unknown soils in the -16.2 per thousand to -20.4 per thousand range. The development of the NIR calibration permits analytic determinations of the values of delta(13)C in unknown agricultural soils in less time, employing a non-destructive method, by the application of the fibre optic probe of remote reflectance to the soil sample.

Dynamic Filtering Improves Attentional State Prediction with fNIRS

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

The Detection and Quantification of Adulteration in Ground Roasted Asian Palm Civet Coffee Using Near-Infrared Spectroscopy in Tandem with Chemometrics

NASA Astrophysics Data System (ADS)

Suhandy, D.; Yulia, M.; Ogawa, Y.; Kondo, N.

2018-05-01

In the present research, an evaluation of using near infrared (NIR) spectroscopy in tandem with full spectrum partial least squares (FS-PLS) regression for quantification of degree of adulteration in civet coffee was conducted. A number of 126 ground roasted coffee samples with degree of adulteration 0-51% were prepared. Spectral data were acquired using a NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement in the range of 1300-2500 nm. The samples were divided into two groups calibration sample set (84 samples) and prediction sample set (42 samples). The calibration model was developed on original spectra using FS-PLS regression with full-cross validation method. The calibration model exhibited the determination coefficient R2=0.96 for calibration and R2=0.92 for validation. The prediction resulted in low root mean square error of prediction (RMSEP) (4.67%) and high ratio prediction to deviation (RPD) (3.75). In conclusion, the degree of adulteration in civet coffee have been quantified successfully by using NIR spectroscopy and FS-PLS regression in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

PubMed

Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine

2016-01-01

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ . SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber.

PubMed

Juliano da Silva, Carlos; Pasquini, Celio

2015-01-21

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Characterization of near infrared spectral variance in the authentication of skim and nonfat dry milk powder collection using ANOVA-PCA, Pooled-ANOVA, and partial least squares regression

USDA-ARS?s Scientific Manuscript database

Forty-one samples of skim milk powder (SMP) and non-fat dry milk (NFDM) from 8 suppliers, 13 production sites, and 3 processing temperatures were analyzed by NIR diffuse reflectance spectrometry over a period of three days. NIR reflectance spectra (1700-2500 nm) were converted to pseudo-absorbance ...

Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques.

PubMed

Douglas, R K; Nawar, S; Alamar, M C; Mouazen, A M; Coulon, F

2018-03-01

Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C 10 and C 35 . Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R 2 ) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg -1 , and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R 2 =0.63, RMSEP=107.54mgkg -1 and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of essential oil content of oregano by hand-held and Fourier transform NIR spectroscopy.

PubMed

Camps, Cédric; Gérard, Marianne; Quennoz, Mélanie; Brabant, Cécile; Oberson, Carine; Simonnet, Xavier

2014-05-01

In the framework of a breeding programme, the analysis of hundreds of oregano samples to determine their essential oil content (EOC) is time-consuming and expensive in terms of labour. Therefore developing a new method that is rapid, accurate and less expensive to use would be an asset to breeders. The aim of the present study was to develop a method based on near-inrared (NIR) spectroscopy to determine the EOC of oregano dried powder. Two spectroscopic approaches were compared, the first using a hand-held NIR device and the second a Fourier transform (FT) NIR spectrometer. Hand-held NIR (1000-1800 nm) measurements and partial least squares regression allowed the determination of EOC with R² and SEP values of 0.58 and 0.81 mL per 100 g dry matter (DM) respectively. Measurements with FT-NIR (1000-2500 nm) allowed the determination of EOC with R² and SEP values of 0.91 and 0.68 mL per 100 g DM respectively. RPD, RER and RPIQ values for the model implemented with FT-NIR data were satisfactory for screening application, while those obtained with hand-held NIR data were below the level required to consider the model as enough accurate for screening application. The FT-NIR approach allowed the development of an accurate model for EOC prediction. Although the hand-held NIR approach is promising, it needs additional development before it can be used in practice. © 2013 Society of Chemical Industry.

Rapid identification of oil-contaminated soils using visible near-infrared diffuse reflectance spectroscopy.

PubMed

Chakraborty, Somsubhra; Weindorf, David C; Morgan, Cristine L S; Ge, Yufeng; Galbraith, John M; Li, Bin; Kahlon, Charanjit S

2010-01-01

In the United States, petroleum extraction, refinement, and transportation present countless opportunities for spillage mishaps. A method for rapid field appraisal and mapping of petroleum hydrocarbon-contaminated soils for environmental cleanup purposes would be useful. Visible near-infrared (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy (DRS) is a rapid, nondestructive, proximal-sensing technique that has proven adept at quantifying soil properties in situ. The objective of this study was to determine the prediction accuracy of VisNIR DRS in quantifying petroleum hydrocarbons in contaminated soils. Forty-six soil samples (including both contaminated and reference samples) were collected from six different parishes in Louisiana. Each soil sample was scanned using VisNIR DRS at three combinations of moisture content and pretreatment: (i) field-moist intact aggregates, (ii) air-dried intact aggregates, (iii) and air-dried ground soil (sieved through a 2-mm sieve). The VisNIR spectra of soil samples were used to predict total petroleum hydrocarbon (TPH) content in the soil using partial least squares (PLS) regression and boosted regression tree (BRT) models. Each model was validated with 30% of the samples that were randomly selected and not used in the calibration model. The field-moist intact scan proved best for predicting TPH content with a validation r2 of 0.64 and relative percent difference (RPD) of 1.70. Because VisNIR DRS was promising for rapidly predicting soil petroleum hydrocarbon content, future research is warranted to evaluate the methodology for identifying petroleum contaminated soils.

Near and mid infrared spectroscopy and multivariate data analysis in studies of oxidation of edible oils.

PubMed

Wójcicki, Krzysztof; Khmelinskii, Igor; Sikorski, Marek; Sikorska, Ewa

2015-11-15

Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). Copyright © 2015 Elsevier Ltd. All rights reserved.

Determination of Protein Content by NIR Spectroscopy in Protein Powder Mix Products.

PubMed

Ingle, Prashant D; Christian, Roney; Purohit, Piyush; Zarraga, Veronica; Handley, Erica; Freel, Keith; Abdo, Saleem

2016-01-01

Protein is a principal component in commonly used dietary supplements and health food products. The analysis of these products, within the consumer package form, is of critical importance for the purpose of ensuring quality and supporting label claims. A rapid test method was developed using near-infrared (NIR) spectroscopy as a compliment to current protein determination by the Dumas combustion method. The NIR method was found to be a rapid, low-cost, and green (no use of chemicals and reagents) complimentary technique. The protein powder samples analyzed in this study were in the range of 22-90% protein. The samples were prepared as mixtures of soy protein, whey protein, and silicon dioxide ingredients, which are common in commercially sold protein powder drink-mix products in the market. A NIR regression model was developed with 17 samples within the constituent range and was validated with 20 independent samples of known protein levels (85-88%). The results show that the NIR method is capable of predicting the protein content with a bias of ±2% and a maximum bias of 3% between NIR and the external Dumas method.

Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis.

PubMed

Kamruzzaman, Mohammed; Sun, Da-Wen; ElMasry, Gamal; Allen, Paul

2013-01-15

Many studies have been carried out in developing non-destructive technologies for predicting meat adulteration, but there is still no endeavor for non-destructive detection and quantification of adulteration in minced lamb meat. The main goal of this study was to develop and optimize a rapid analytical technique based on near-infrared (NIR) hyperspectral imaging to detect the level of adulteration in minced lamb. Initial investigation was carried out using principal component analysis (PCA) to identify the most potential adulterate in minced lamb. Minced lamb meat samples were then adulterated with minced pork in the range 2-40% (w/w) at approximately 2% increments. Spectral data were used to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range (910-1700 nm) with a coefficient of determination (R(2)(cv)) of 0.99 and root-mean-square errors estimated by cross validation (RMSECV) of 1.37%. Four important wavelengths (940, 1067, 1144 and 1217 nm) were selected using weighted regression coefficients (Bw) and a multiple linear regression (MLR) model was then established using these important wavelengths to predict adulteration. The MLR model resulted in a coefficient of determination (R(2)(cv)) of 0.98 and RMSECV of 1.45%. The developed MLR model was then applied to each pixel in the image to obtain prediction maps to visualize the distribution of adulteration of the tested samples. The results demonstrated that the laborious and time-consuming tradition analytical techniques could be replaced by spectral data in order to provide rapid, low cost and non-destructive testing technique for adulterate detection in minced lamb meat. Copyright © 2012 Elsevier B.V. All rights reserved.

Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools.

PubMed

Martelo-Vidal, M J; Vázquez, M

2014-09-01

Spectral analysis is a quick and non-destructive method to analyse wine. In this work, trans-resveratrol, oenin, malvin, catechin, epicatechin, quercetin and syringic acid were determined in commercial red wines from DO Rías Baixas and DO Ribeira Sacra (Spain) by UV-VIS-NIR spectroscopy. Calibration models were developed using principal component regression (PCR) or partial least squares (PLS) regression. HPLC was used as reference method. The results showed that reliable PLS models were obtained to quantify all polyphenols for Rías Baixas wines. For Ribeira Sacra, feasible models were obtained to determine quercetin, epicatechin, oenin and syringic acid. PCR calibration models showed worst reliable of prediction than PLS models. For red wines from mencía grapes, feasible models were obtained for catechin and oenin, regardless the geographical origin. The results obtained demonstrate that UV-VIS-NIR spectroscopy can be used to determine individual polyphenolic compounds in red wines. Copyright © 2014 Elsevier Ltd. All rights reserved.

Novel equations to estimate lean body mass in maintenance hemodialysis patients.

PubMed

Noori, Nazanin; Kovesdy, Csaba P; Bross, Rachelle; Lee, Martin; Oreopoulos, Antigone; Benner, Deborah; Mehrotra, Rajnish; Kopple, Joel D; Kalantar-Zadeh, Kamyar

2011-01-01

Lean body mass (LBM) is an important nutritional measure representing muscle mass and somatic protein in hemodialysis patients, for whom we developed and tested equations to estimate LBM. A study of diagnostic test accuracy. The development cohort included 118 hemodialysis patients with LBM measured using dual-energy x-ray absorptiometry (DEXA) and near-infrared (NIR) interactance. The validation cohort included 612 additional hemodialysis patients with LBM measured using a portable NIR interactance technique during hemodialysis. 3-month averaged serum concentrations of creatinine, albumin, and prealbumin; normalized protein nitrogen appearance; midarm muscle circumference (MAMC); handgrip strength; and subjective global assessment of nutrition. LBM measured using DEXA in the development cohort and NIR interactance in validation cohorts. In the development cohort, DEXA and NIR interactance correlated strongly (r = 0.94, P < 0.001). DEXA-measured LBM correlated with serum creatinine level, MAMC, and handgrip strength, but not with other nutritional markers. Three regression equations to estimate DEXA-measured LBM were developed based on each of these 3 surrogates and sex, height, weight, and age (and urea reduction ratio for the serum creatinine regression). In the validation cohort, the validity of the equations was tested against the NIR interactance-measured LBM. The equation estimates correlated well with NIR interactance-measured LBM (R² ≥ 0.88), although in higher LBM ranges, they tended to underestimate it. Median (95% confidence interval) differences and interquartile range for differences between equation estimates and NIR interactance-measured LBM were 3.4 (-3.2 to 12.0) and 3.0 (1.1-5.1) kg for serum creatinine and 4.0 (-2.6 to 13.6) and 3.7 (1.3-6.0) kg for MAMC, respectively. DEXA measurements were obtained on a nondialysis day, whereas NIR interactance was performed during hemodialysis treatment, with the likelihood of confounding by volume status variations. Compared with reference measures of LBM, equations using serum creatinine level, MAMC, or handgrip strength and demographic variables can estimate LBM accurately in long-term hemodialysis patients. Copyright © 2010 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Novel Equations to Estimate Lean Body Mass in Maintenance Hemodialysis Patients

PubMed Central

Noori, Nazanin; Kovesdy, Csaba P; Bross, Rachelle; Lee, Martin; Oreopoulos, Antigone; Benner, Deborah; Mehrotra, Rajnish; Kopple, Joel D; Kalantar-Zadeh, Kamyar

2010-01-01

Background Lean body mass (LBM) is an important nutritional measure representing muscle mass and somatic protein in hemodialysis patients, in whom we developed and tested equations to estimate LBM. Study Design A study of diagnostic test accuracy. Setting and Participants The development cohort included 118 hemodialysis patients, with LBM measured using dual-energy -X-ray absorptiometry (DEXA) and near-infrared (NIR) interactance. The validation cohort included 612 additional hemodialysis patients with LBM measured using portable NIR interactance technique during hemodialysis. Index Tests 3-month averaged serum concentrations of creatinine, albumin and prealbumin, normalized protein-nitrogen-appearance, mid-arm muscle circumference (MAMC), handgrip strength, and subjective global assessment of nutrition. Reference Test LBM measured via DEXA in the development cohort and via NIR interactance in validation cohorts. Results In the development cohort, DEXA and NIR interactance were strongly correlated (r=0.94, p<0.001). DEXA-measured LBM correlated with serum creatinine, MAMC, handgrip strength but not with other nutritional markers. Three regression equations to estimate DEXA-measured LBM were developed based on each of these three surrogates and gender, height, weight, and age (and urea reduction ratio for the serum creatinine regression). In the validation cohort, the validity of the equations were tested against the NIR interactance measured LBM. The equation estimates correlated well with NIR interactance measured LBM (R221 ≥0.88), although in higher LBM ranges they tended to underestimate it. Median differences between equation estimates and NIR interactance-measured LBM were 3.4 (25th–75th percentile, −3.2 to 12.0) and 3.0 (25th–75th percentile, 1.1–5.1) kg for serum creatinine and 4.0 (25th–75th percentile, −2.6 to 13.6) and 3.7 (25th–75th percentile, 1.3–6.0) kg for MAMC. Limitations DEXA measurements were performed on a non-dialysis day whereas NIR interactance was obtained during the hemodialysis treatment, with likelihood of confounding by volume status variations. Conclusions Comparing to reference measures of LBM, equations using serum creatinine, MAMC, or handgrip strength and demographic variables can accurately estimate LBM in long-term hemodialysis patients. PMID:21184920

Dynamic filtering improves attentional state prediction with fNIRS

PubMed Central

Harrivel, Angela R.; Weissman, Daniel H.; Noll, Douglas C.; Huppert, Theodore; Peltier, Scott J.

2016-01-01

Brain activity can predict a person’s level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise – thereby increasing such state prediction accuracy – remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% ± 6% versus 72% ± 15%). PMID:27231602

Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine.

PubMed

Giovenzana, Valentina; Civelli, Raffaele; Beghi, Roberto; Oberti, Roberto; Guidetti, Riccardo

2015-11-01

The aim of this work was to test a simplified optical prototype for a rapid estimation of the ripening parameters of white grape for Franciacorta wine directly in field. Spectral acquisition based on reflectance at four wavelengths (630, 690, 750 and 850 nm) was proposed. The integration of a simple processing algorithm in the microcontroller software would allow to visualize real time values of spectral reflectance. Non-destructive analyses were carried out on 95 grape bunches for a total of 475 berries. Samplings were performed weekly during the last ripening stages. Optical measurements were carried out both using the simplified system and a portable commercial vis/NIR spectrophotometer, as reference instrument for performance comparison. Chemometric analyses were performed in order to extract the maximum useful information from optical data. Principal component analysis (PCA) was performed for a preliminary evaluation of the data. Correlations between the optical data matrix and ripening parameters (total soluble solids content, SSC; titratable acidity, TA) were carried out using partial least square (PLS) regression for spectra and using multiple linear regression (MLR) for data from the simplified device. Classification analysis were also performed with the aim of discriminate ripe and unripe samples. PCA, MLR and classification analyses show the effectiveness of the simplified system in separating samples among different sampling dates and in discriminating ripe from unripe samples. Finally, simple equations for SSC and TA prediction were calculated. Copyright © 2015 Elsevier B.V. All rights reserved.

Determination of double bond conversion in dental resins by near infrared spectroscopy.

PubMed

Stansbury, J W; Dickens, S H

2001-01-01

This study determined the validity and practicality of near infrared (NIR) spectroscopic techniques for measurement of conversion in dental resins. Conversion measurements by NIR and mid-IR were compared using two techniques: (1) The conversion of 3mm thick photopolymerized Bis-GMA/TEGDMA resin specimens was determined by transmission NIR. Specimens were then ground and reanalyzed in KBr pellet form by mid-IR. (2) As further verification, thin resin films were photocured and analyzed by mid-IR. Multiple thin films were then compressed into a thick pellet for examination by NIR. Conversion values obtained by NIR and mid-IR techniques did not differ significantly. A correction for changing specimen thickness due to polymerization shrinkage was applied to NIR conversion measurements since an internal standard reference peak was not employed. Sensitivity of the NIR technique was superior to those based on the mid-IR. The nondestructive analysis of conversion in dental resins by NIR offers advantages of convenience, practical specimen dimensions and precision compared with standard mid-IR analytical procedures. Because glass is virtually transparent in the NIR spectrum, this technique has excellent potential for use with filled dental resins as well.

Validation of brain-derived signals in near-infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging.

PubMed

Moriguchi, Yoshiya; Noda, Takamasa; Nakayashiki, Kosei; Takata, Yohei; Setoyama, Shiori; Kawasaki, Shingo; Kunisato, Yoshihiko; Mishima, Kazuo; Nakagome, Kazuyuki; Hanakawa, Takashi

2017-10-01

Near-infrared spectroscopy (NIRS) is a convenient and safe brain-mapping tool. However, its inevitable confounding with hemodynamic responses outside the brain, especially in the frontotemporal head, has questioned its validity. Some researchers attempted to validate NIRS signals through concurrent measurements with functional magnetic resonance imaging (fMRI), but, counterintuitively, NIRS signals rarely correlate with local fMRI signals in NIRS channels, although both mapping techniques should measure the same hemoglobin concentration. Here, we tested a novel hypothesis that different voxels within the scalp and the brain tissues might have substantially different hemoglobin absorption rates of near-infrared light, which might differentially contribute to NIRS signals across channels. Therefore, we newly applied a multivariate approach, a partial least squares regression, to explain NIRS signals with multivoxel information from fMRI within the brain and soft tissues in the head. We concurrently obtained fMRI and NIRS signals in 9 healthy human subjects engaging in an n-back task. The multivariate fMRI model was quite successfully able to predict the NIRS signals by cross-validation (interclass correlation coefficient = ∼0.85). This result confirmed that fMRI and NIRS surely measure the same hemoglobin concentration. Additional application of Monte-Carlo permutation tests confirmed that the model surely reflects temporal and spatial hemodynamic information, not random noise. After this thorough validation, we calculated the ratios of the contributions of the brain and soft-tissue hemodynamics to the NIRS signals, and found that the contribution ratios were quite different across different NIRS channels in reality, presumably because of the structural complexity of the frontotemporal regions. Hum Brain Mapp 38:5274-5291, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Intelligent MEMS spectral sensor for NIR applications (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kantojärvi, Uula; Antila, Jarkko E.; Mäkynen, Jussi; Suhonen, Janne

2017-05-01

Near Infrared (NIR) spectrometers have been widely used in many material inspection applications, but mainly in central laboratories. The role of miniaturization, robustness of spectrometer and portability are really crucial when field inspection tools should be developed. We present an advanced spectral sensor based on a tunable Microelectromechanical (MEMS) Fabry-Perot Interferometer which will meet these requirements. We describe the wireless device design, operation principle and easy-to-use algorithms to adapt the sensor to number of applications. Multiple devices can be operated simultaneously and seamlessly through cloud connectivity. We also present some practical NIR applications carried out with truly portable NIR device.

A validated near-infrared spectroscopic method for methanol detection in biodiesel

NASA Astrophysics Data System (ADS)

Paul, Andrea; Bräuer, Bastian; Nieuwenkamp, Gerard; Ent, Hugo; Bremser, Wolfram

2016-06-01

Biodiesel quality control is a relevant issue as biodiesel properties influence diesel engine performance and integrity. Within the European metrology research program (EMRP) ENG09 project ‘Metrology for Biofuels’, an on-line/at-site suitable near-infrared spectroscopy (NIRS) method has been developed in parallel with an improved EN14110 headspace gas chromatography (GC) analysis method for methanol in biodiesel. Both methods have been optimized for a methanol content of 0.2 mass% as this represents the maximum limit of methanol content in FAME according to EN 14214:2009. The NIRS method is based on a mobile NIR spectrometer equipped with a fiber-optic coupled probe. Due to the high volatility of methanol, a tailored air-tight adaptor was constructed to prevent methanol evaporation during measurement. The methanol content of biodiesel was determined from evaluation of NIRS spectra by partial least squares regression (PLS). Both GC analysis and NIRS exhibited a significant dependence on biodiesel feedstock. The NIRS method is applicable to a content range of 0.1% (m/m) to 0.4% (m/m) of methanol with uncertainties at around 6% relative for the different feedstocks. A direct comparison of headspace GC and NIRS for samples of FAMEs yielded that the results of both methods are fully compatible within their stated uncertainties.

Efficient room-temperature near-infrared detection with solution-processed networked single wall carbon nanotube field effect transistors.

PubMed

Hwang, Ihn; Jung, Hee June; Cho, Sung Hwan; Jo, Seong Soon; Choi, Yeon Sik; Sung, Ji Ho; Choi, Jae Ho; Jo, Moon Ho; Park, Cheolmin

2014-02-26

Efficient room temperature NIR detection with sufficient current gain is made with a solution-processed networked SWNT FET. The high performance NIR-FET with significantly enhanced photocurrent by more than two orders of magnitude compared to dark current in the depleted state is attributed to multiple Schottky barriers in the network, each of which absorb NIR and effectively separate photocarriers to corresponding electrodes. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterisation of PDO olive oil Chianti Classico by non-selective (UV-visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques.

PubMed

Casale, M; Oliveri, P; Casolino, C; Sinelli, N; Zunin, P; Armanino, C; Forina, M; Lanteri, S

2012-01-27

An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico. Copyright © 2011 Elsevier B.V. All rights reserved.

[On-site evaluation of raw milk qualities by portable Vis/NIR transmittance technique].

PubMed

Wang, Jia-Hua; Zhang, Xiao-Wei; Wang, Jun; Han, Dong-Hai

2014-10-01

To ensure the material safety of dairy products, visible (Vis)/near infrared (NIR) spectroscopy combined with che- mometrics methods was used to develop models for fat, protein, dry matter (DM) and lactose on-site evaluation. A total of 88 raw milk samples were collected from individual livestocks in different years. The spectral of raw milk were measured by a porta- ble Vis/NIR spectrometer with diffused transmittance accessory. To remove the scatter effect and baseline drift, the diffused transmittance spectra were preprocessed by 2nd order derivative with Savitsky-Golay (polynomial order 2, data point 25). Changeable size moving window partial least squares (CSMWPLS) and genetic algorithms partial least squares (GAPLS) meth- ods were suggested to select informative regions for PLS calibration. The PLS and multiple linear regression (MLR) methods were used to develop models for predicting quality index of raw milk. The prediction performance of CSMWPLS models were similar to GAPLS models for fat, protein, DM and lactose evaluation, the root mean standard errors of prediction (RMSEP) were 0.115 6/0.103 3, 0.096 2/0.113 7, 0.201 3/0.123 7 and 0.077 4/0.066 8, and the relative standard deviations of prediction (RPD) were 8.99/10.06, 3.53/2.99, 5.76/9.38 and 1.81/2.10, respectively. Meanwhile, the MLR models were also cal- ibrated with 8, 10, 9 and 7 variables for fat, protein, DM and lactose, respectively. The prediction performance of MLR models was better than or close to PLS models. The MLR models to predict fat, protein, DM and lactose yielded the RMSEP of 0.107 0, 0.093 0, 0.136 0 and 0.065 8, and the RPD of 9.72, 3.66, 8.53 and 2.13, respectively. The results demonstrated the usefulness of Vis/NIR spectra combined with multivariate calibration methods as an objective and rapid method for the quality evaluation of complicated raw milks. And the results obtained also highlight the potential of portable Vis/NIR instruments for on-site assessing quality indexes of raw milk.

[Determination of acidity and vitamin C in apples using portable NIR analyzer].

PubMed

Yang, Fan; Li, Ya-Ting; Gu, Xuan; Ma, Jiang; Fan, Xing; Wang, Xiao-Xuan; Zhang, Zhuo-Yong

2011-09-01

Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (R(c)) obtained by the acidity model was 0.999 4, and for prediction samples (R(p)) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, R(c) was 0.989 1, R(p) was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.

Near infrared photoimmunotherapy for lung metastases

PubMed Central

Sato, Kazuhide; Nagaya, Tadanobu; Mitsunaga, Makoto; Choyke, Peter L.; Kobayashi, Hisataka

2015-01-01

Lung metastases are a leading cause of cancer related deaths; nonetheless current treatments are limited. Near infrared photoimmunotherapy (NIR-PIT) is a new cancer treatment that combines the specificity of intravenously injected antibodies that target tumors with the toxicity induced by photosensitizers activated by NIR-light. Herein, we demonstrate the efficacy of NIR-PIT in a mouse model of lung metastases. Experiments were conducted with a HER2, luciferase and GFP expressing cell line (3T3/HER2-luc-GFP). An antibody-photosensitizer conjugate (APC) consisting of trastuzumab and a phthalocyanine dye, IRDye-700DX, was synthesized. In vitro NIR-PIT-induced cytotoxicity was light dose dependent. With 3D culture, repeated NIR-PIT could eradicate entire spheroids. In vivo anti-tumor effects of NIR-PIT included significant reductions in both tumor volume (p = 0.0141 vs. APC) and bioluminescence image (BLI) (p = 0.0086 vs. APC) in the flank model, and prolonged survival (p < 0.0001). BLI demonstrated a significant reduction in lung metastases volume (p = 0.0117 vs. APC). Multiple NIR-PIT doses significantly prolonged survival in the lung metastases model (p < 0.0001). These results suggested that NIR-PIT is a potential new therapy for the local control of lung metastases. PMID:26021765

Analysis of task-evoked systemic interference in fNIRS measurements: insights from fMRI.

PubMed

Erdoğan, Sinem B; Yücel, Meryem A; Akın, Ata

2014-02-15

Functional near infrared spectroscopy (fNIRS) is a promising method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of the task related neuronal activation. In this study, we use the anatomical resolution of functional magnetic resonance imaging (fMRI) to extract scalp and brain vascular signals separately and construct an optically weighted spatial average of the fMRI blood oxygen level-dependent (BOLD) signal for characterizing the scalp signal contribution to fNIRS measurements. We introduce an extended superficial signal regression (ESSR) method for canceling physiology-based systemic interference where the effects of cerebral and superficial systemic interference are treated separately. We apply and validate our method on the optically weighted BOLD signals, which are obtained by projecting the fMRI image onto optical measurement space by use of the optical forward problem. The performance of ESSR method in removing physiological artifacts is compared to i) a global signal regression (GSR) method and ii) a superficial signal regression (SSR) method. The retrieved signals from each method are compared with the neural signals that represent the 'ground truth' brain activation cleaned from cerebral systemic fluctuations. We report significant improvements in the recovery of task induced neural activation with the ESSR method when compared to the other two methods as reflected in the Pearson R(2) coefficient and mean square error (MSE) metrics (two tailed paired t-tests, p<0.05). The signal quality is enhanced most when ESSR method is applied with higher spatial localization, lower inter-trial variability, a clear canonical waveform and higher contrast-to-noise (CNR) improvement (60%). Our findings suggest that, during a cognitive task i) superficial scalp signal contribution to fNIRS signals varies significantly among different regions on the forehead and ii) using an average scalp measurement together with a local measure of superficial hemodynamics better accounts for the systemic interference inherent in the brain as well as superficial scalp tissue. We conclude that maximizing the overlap between the optical pathlength of superficial and deeper penetration measurements is of crucial importance for accurate recovery of the evoked hemodynamic response in fNIRS recordings. © 2013 Elsevier Inc. All rights reserved.

Study on feasibility of determination of glucosamine content of fermentation process using a micro NIR spectrometer.

PubMed

Sun, Zhongyu; Li, Can; Li, Lian; Nie, Lei; Dong, Qin; Li, Danyang; Gao, Lingling; Zang, Hengchang

2018-08-05

N-acetyl-d-glucosamine (GlcNAc) is a microbial fermentation product, and NIR spectroscopy is an effective process analytical technology (PAT) tool in detecting the key quality attribute: the GlcNAc content. Meanwhile, the design of NIR spectrometers is under the trend of miniaturization, portability and low-cost nowadays. The aim of this study was to explore a portable micro NIR spectrometer with the fermentation process. First, FT-NIR spectrometer and Micro-NIR 1700 spectrometer were compared with simulated fermentation process solutions. The R c 2 , R p 2 , RMSECV and RMSEP of the optimal FT-NIR and Micro-NIR 1700 models were 0.999, 0.999, 3.226 g/L, 1.388 g/L and 0.999, 0.999, 1.821 g/L, 0.967 g/L. Passing-Bablok regression method and paired t-test results showed there were no significant differences between the two instruments. Then the Micro-NIR 1700 was selected for the practical fermentation process, 135 samples from 10 batches were collected. Spectral pretreatment methods and variables selection methods (BiPLS, FiPLS, MWPLS and CARS-PLS) for PLS modeling were discussed. The R c 2 , R p 2 , RMSECV and RMSEP of the optimal GlcNAc content PLS model of the practical fermentation process were 0.994, 0.995, 2.792 g/L and 1.946 g/L. The results have a positive reference for application of the Micro-NIR spectrometer. To some extent, it could provide theoretical supports in guiding the microbial fermentation or the further assessment of bioprocess. Copyright © 2018. Published by Elsevier B.V.

A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application.

PubMed

Ferrari, Marco; Quaresima, Valentina

2012-11-01

This review is aimed at celebrating the upcoming 20th anniversary of the birth of human functional near-infrared spectroscopy (fNIRS). After the discovery in 1992 that the functional activation of the human cerebral cortex (due to oxygenation and hemodynamic changes) can be explored by NIRS, human functional brain mapping research has gained a new dimension. fNIRS or optical topography, or near-infrared imaging or diffuse optical imaging is used mainly to detect simultaneous changes in optical properties of the human cortex from multiple measurement sites and displays the results in the form of a map or image over a specific area. In order to place current fNIRS research in its proper context, this paper presents a brief historical overview of the events that have shaped the present status of fNIRS. In particular, technological progresses of fNIRS are highlighted (i.e., from single-site to multi-site functional cortical measurements (images)), introduction of the commercial multi-channel systems, recent commercial wireless instrumentation and more advanced prototypes. Copyright © 2012 Elsevier Inc. All rights reserved.

A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults.

PubMed

Quaresima, Valentina; Bisconti, Silvia; Ferrari, Marco

2012-05-01

Upon stimulation, real time maps of cortical hemodynamic responses can be obtained by non-invasive functional near-infrared spectroscopy (fNIRS) which measures changes in oxygenated and deoxygenated hemoglobin after positioning multiple sources and detectors over the human scalp. The current commercially available transportable fNIRS systems have a time resolution of 1-10 Hz, a depth sensitivity of about 1.5 cm, and a spatial resolution of about 1cm. The goal of this brief review is to report infants, children and adults fNIRS language studies. Since 1998, 60 studies have been published on cortical activation in the brain's classic language areas in children/adults as well as newborns using fNIRS instrumentations of different complexity. In addition, the basic principles of fNIRS including features, strengths, advantages, and limitations are summarized in terms that can be understood even by non specialists. Future prospects of fNIRS in the field of language processing imaging are highlighted. Copyright © 2011 Elsevier Inc. All rights reserved.

Determination of Landslide and Driftwood Potentials by Fixed-wing UAV-Borne RGB and NIR images: A Case Study of Shenmu Area in Taiwan

NASA Astrophysics Data System (ADS)

Chen, Su-Chin; Hsiao, Yu-Shen; Chung, Ta-Hsien

2015-04-01

This study is aimed at determining the landslide and driftwood potentials at Shenmu area in Taiwan by Unmanned Aerial Vehicle (UAV). High-resolution orthomosaics and digital surface models (DSMs) are both obtained from several UAV practical surveys by using a red-green-blue(RGB) camera and a near-infrared(NIR) one, respectively. Couples of artificial aerial survey targets are used for ground control in photogrammtry. The algorithm for this study is based on Logistic regression. 8 main factors, which are elevations, terrain slopes, terrain aspects, terrain reliefs, terrain roughness, distances to roads, distances to rivers, land utilizations, are taken into consideration in our Logistic regression model. The related results from UAV are compared with those from traditional photogrammetry. Overall, the study is focusing on monitoring the distribution of the areas with high-risk landslide and driftwood potentials in Shenmu area by Fixed-wing UAV-Borne RGB and NIR images. We also further analyze the relationship between forests, landslides, disaster potentials and upper river areas.

In-line multipoint near-infrared spectroscopy for moisture content quantification during freeze-drying.

PubMed

Kauppinen, Ari; Toiviainen, Maunu; Korhonen, Ossi; Aaltonen, Jaakko; Järvinen, Kristiina; Paaso, Janne; Juuti, Mikko; Ketolainen, Jarkko

2013-02-19

During the past decade, near-infrared (NIR) spectroscopy has been applied for in-line moisture content quantification during a freeze-drying process. However, NIR has been used as a single-vial technique and thus is not representative of the entire batch. This has been considered as one of the main barriers for NIR spectroscopy becoming widely used in process analytical technology (PAT) for freeze-drying. Clearly it would be essential to monitor samples that reliably represent the whole batch. The present study evaluated multipoint NIR spectroscopy for in-line moisture content quantification during a freeze-drying process. Aqueous sucrose solutions were used as model formulations. NIR data was calibrated to predict the moisture content using partial least-squares (PLS) regression with Karl Fischer titration being used as a reference method. PLS calibrations resulted in root-mean-square error of prediction (RMSEP) values lower than 0.13%. Three noncontact, diffuse reflectance NIR probe heads were positioned on the freeze-dryer shelf to measure the moisture content in a noninvasive manner, through the side of the glass vials. The results showed that the detection of unequal sublimation rates within a freeze-dryer shelf was possible with the multipoint NIR system in use. Furthermore, in-line moisture content quantification was reliable especially toward the end of the process. These findings indicate that the use of multipoint NIR spectroscopy can achieve representative quantification of moisture content and hence a drying end point determination to a desired residual moisture level.

Near infrared spectrometric technique for testing fruit quality: optimisation of regression models using genetic algorithms

NASA Astrophysics Data System (ADS)

Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.

2016-02-01

Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.

Simple aerosol correction technique based on the spectral relationships of the aerosol multiple-scattering reflectances for atmospheric correction over the oceans.

PubMed

Ahn, Jae-Hyun; Park, Young-Je; Kim, Wonkook; Lee, Boram

2016-12-26

An estimation of the aerosol multiple-scattering reflectance is an important part of the atmospheric correction procedure in satellite ocean color data processing. Most commonly, the utilization of two near-infrared (NIR) bands to estimate the aerosol optical properties has been adopted for the estimation of the effects of aerosols. Previously, the operational Geostationary Color Ocean Imager (GOCI) atmospheric correction scheme relies on a single-scattering reflectance ratio (SSE), which was developed for the processing of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data to determine the appropriate aerosol models and their aerosol optical thicknesses. The scheme computes reflectance contributions (weighting factor) of candidate aerosol models in a single scattering domain then spectrally extrapolates the single-scattering aerosol reflectance from NIR to visible (VIS) bands using the SSE. However, it directly applies the weight value to all wavelengths in a multiple-scattering domain although the multiple-scattering aerosol reflectance has a non-linear relationship with the single-scattering reflectance and inter-band relationship of multiple scattering aerosol reflectances is non-linear. To avoid these issues, we propose an alternative scheme for estimating the aerosol reflectance that uses the spectral relationships in the aerosol multiple-scattering reflectance between different wavelengths (called SRAMS). The process directly calculates the multiple-scattering reflectance contributions in NIR with no residual errors for selected aerosol models. Then it spectrally extrapolates the reflectance contribution from NIR to visible bands for each selected model using the SRAMS. To assess the performance of the algorithm regarding the errors in the water reflectance at the surface or remote-sensing reflectance retrieval, we compared the SRAMS atmospheric correction results with the SSE atmospheric correction using both simulations and in situ match-ups with the GOCI data. From simulations, the mean errors for bands from 412 to 555 nm were 5.2% for the SRAMS scheme and 11.5% for SSE scheme in case-I waters. From in situ match-ups, 16.5% for the SRAMS scheme and 17.6% scheme for the SSE scheme in both case-I and case-II waters. Although we applied the SRAMS algorithm to the GOCI, it can be applied to other ocean color sensors which have two NIR wavelengths.

Cerebrovascular pattern improved by ozone autohemotherapy: an entropy-based study on multiple sclerosis patients.

PubMed

Molinari, Filippo; Rimini, Daniele; Liboni, William; Acharya, U Rajendra; Franzini, Marianno; Pandolfi, Sergio; Ricevuti, Giovanni; Vaiano, Francesco; Valdenassi, Luigi; Simonetti, Vincenzo

2017-08-01

Ozone major autohemotherapy is effective in reducing the symptoms of multiple sclerosis (MS) patients, but its effects on brain are still not clear. In this work, we have monitored the changes in the cerebrovascular pattern of MS patients and normal subjects during major ozone autohemotherapy by using near-infrared spectroscopy (NIRS) as functional and vascular technique. NIRS signals are analyzed using a combination of time, time-frequency analysis and nonlinear analysis of intrinsic mode function signals obtained from empirical mode decomposition technique. Our results show that there is an improvement in the cerebrovascular pattern of all subjects indicated by increasing the entropy of the NIRS signals. Hence, we can conclude that the ozone therapy increases the brain metabolism and helps to recover from the lower activity levels which is predominant in MS patients.

Modeling of temperature-induced near-infrared and low-field time-domain nuclear magnetic resonance spectral variation: chemometric prediction of limonene and water content in spray-dried delivery systems.

PubMed

Andrade, Letícia; Farhat, Imad A; Aeberhardt, Kasia; Bro, Rasmus; Engelsen, Søren Balling

2009-02-01

The influence of temperature on near-infrared (NIR) and nuclear magnetic resonance (NMR) spectroscopy complicates the industrial applications of both spectroscopic methods. The focus of this study is to analyze and model the effect of temperature variation on NIR spectra and NMR relaxation data. Different multivariate methods were tested for constructing robust prediction models based on NIR and NMR data acquired at various temperatures. Data were acquired on model spray-dried limonene systems at five temperatures in the range from 20 degrees C to 60 degrees C and partial least squares (PLS) regression models were computed for limonene and water predictions. The predictive ability of the models computed on the NIR spectra (acquired at various temperatures) improved significantly when data were preprocessed using extended inverted signal correction (EISC). The average PLS regression prediction error was reduced to 0.2%, corresponding to 1.9% and 3.4% of the full range of limonene and water reference values, respectively. The removal of variation induced by temperature prior to calibration, by direct orthogonalization (DO), slightly enhanced the predictive ability of the models based on NMR data. Bilinear PLS models, with implicit inclusion of the temperature, enabled limonene and water predictions by NMR with an error of 0.3% (corresponding to 2.8% and 7.0% of the full range of limonene and water). For NMR, and in contrast to the NIR results, modeling the data using multi-way N-PLS improved the models' performance. N-PLS models, in which temperature was included as an extra variable, enabled more accurate prediction, especially for limonene (prediction error was reduced to 0.2%). Overall, this study proved that it is possible to develop models for limonene and water content prediction based on NIR and NMR data, independent of the measurement temperature.

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

PubMed Central

Xiao, Li; Wei, Hui; Himmel, Michael E.; Jameel, Hasan; Kelley, Stephen S.

2014-01-01

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass. PMID:25147552

Rapid quantification of multi-components in alcohol precipitation liquid of Codonopsis Radix using near infrared spectroscopy (NIRS).

PubMed

Luo, Yu; Li, Wen-Long; Huang, Wen-Hua; Liu, Xue-Hua; Song, Yan-Gang; Qu, Hai-Bin

2017-05-01

A near infrared spectroscopy (NIRS) approach was established for quality control of the alcohol precipitation liquid in the manufacture of Codonopsis Radix. By applying NIRS with multivariate analysis, it was possible to build variation into the calibration sample set, and the Plackett-Burman design, Box-Behnken design, and a concentrating-diluting method were used to obtain the sample set covered with sufficient fluctuation of process parameters and extended concentration information. NIR data were calibrated to predict the four quality indicators using partial least squares regression (PLSR). In the four calibration models, the root mean squares errors of prediction (RMSEPs) were 1.22 μg/ml, 10.5 μg/ml, 1.43 μg/ml, and 0.433% for lobetyolin, total flavonoids, pigments, and total solid contents, respectively. The results indicated that multi-components quantification of the alcohol precipitation liquid of Codonopsis Radix could be achieved with an NIRS-based method, which offers a useful tool for real-time release testing (RTRT) of intermediates in the manufacture of Codonopsis Radix.

Quantitative Determination of Fluorine Content in Blends of Polylactide (PLA)–Talc Using Near Infrared Spectroscopy

PubMed Central

Tamburini, Elena; Tagliati, Chiara; Bonato, Tiziano; Costa, Stefania; Scapoli, Chiara; Pedrini, Paola

2016-01-01

Near-infrared spectroscopy (NIRS) has been widely used for quantitative and/or qualitative determination of a wide range of matrices. The objective of this study was to develop a NIRS method for the quantitative determination of fluorine content in polylactide (PLA)-talc blends. A blending profile was obtained by mixing different amounts of PLA granules and talc powder. The calibration model was built correlating wet chemical data (alkali digestion method) and NIR spectra. Using FT (Fourier Transform)-NIR technique, a Partial Least Squares (PLS) regression model was set-up, in a concentration interval of 0 ppm of pure PLA to 800 ppm of pure talc. Fluorine content prediction (R2cal = 0.9498; standard error of calibration, SEC = 34.77; standard error of cross-validation, SECV = 46.94) was then externally validated by means of a further 15 independent samples (R2EX.V = 0.8955; root mean standard error of prediction, RMSEP = 61.08). A positive relationship between an inorganic component as fluorine and NIR signal has been evidenced, and used to obtain quantitative analytical information from the spectra. PMID:27490548

High-throughput prediction of tablet weight and trimethoprim content of compound sulfamethoxazole tablets for controlling the uniformity of dosage units by NIR.

PubMed

Dong, Yanhong; Li, Juan; Zhong, Xiaoxiao; Cao, Liya; Luo, Yang; Fan, Qi

2016-04-15

This paper establishes a novel method to simultaneously predict the tablet weight (TW) and trimethoprim (TMP) content of compound sulfamethoxazole tablets (SMZCO) by near infrared (NIR) spectroscopy with partial least squares (PLS) regression for controlling the uniformity of dosage units (UODU). The NIR spectra for 257 samples were measured using the optimized parameter values and pretreated using the optimized chemometric techniques. After the outliers were ignored, two PLS models for predicting TW and TMP content were respectively established by using the selected spectral sub-ranges and the reference values. The TW model reaches the correlation coefficient of calibration (R(c)) 0.9543 and the TMP content model has the R(c) 0.9205. The experimental results indicate that this strategy expands the NIR application in controlling UODU, especially in the high-throughput and rapid analysis of TWs and contents of the compound pharmaceutical tablets, and may be an important complement to the common NIR on-line analytical method for pharmaceutical tablets. Copyright © 2016 Elsevier B.V. All rights reserved.

Intraoperative Identification of a Normal Pituitary Gland and an Adenoma Using Near-Infrared Fluorescence Imaging and Low-Dose Indocyanine Green.

PubMed

Verstegen, Marco J T; Tummers, Quirijn R J G; Schutte, Pieter J; Pereira, Alberto M; van Furth, Wouter R; van de Velde, Cornelis J H; Malessy, Martijn J A; Vahrmeijer, Alexander L

2016-09-01

The intraoperative distinction between normal and abnormal pituitary tissue is crucial during pituitary adenoma surgery to obtain a complete tumor resection while preserving endocrine function. Near-infrared (NIR) fluorescence imaging is a technique to intraoperatively visualize tumors by using indocyanine green (ICG), a contrast agent allowing visualization of differences in tissue vascularization. Although NIR fluorescence imaging has been described in pituitary surgery, it has, in contrast to other surgical areas, never become widely used. To evaluate NIR fluorescence imaging in pituitary surgery, both qualitatively and quantitatively, and to assess the additional value of resecting adenoma tissue under NIR fluorescence guidance. We included 10 patients planned to undergo transnasal transsphenoidal selective adenomectomy. Patients received multiple intravenous administrations of 5 mg ICG, up to a maximum of 15 mg per patient. Endoscopic NIR fluorescence imaging was performed at multiple points in time. The NIR fluorescent signal in both the adenoma and pituitary gland was obtained, and the fluorescence contrast ratio was assessed. Four patients had Cushing disease, 1 had acromegaly, and 1 had a prolactinoma. Four patients had a nonfunctioning macroadenoma. In 9 of 10 patients with a histologically proven pituitary adenoma, the normal pituitary gland showed a stronger fluorescent signal than the adenoma. A fluorescence contrast ratio of normal pituitary gland to adenoma of 1.5 ± 0.2 was obtained. In 2 patients; adenoma resection was actually performed under NIR fluorescence guidance instead of under white light. NIR fluorescence imaging can easily and safely be implemented in pituitary surgery. The timing of ICG administration is important for optimal results and warrants further study. It appears that injection of ICG can best be postponed until some part of the normal pituitary gland is identified. Subsequent repeated low-dose ICG administrations improved the distinction between adenoma and gland.

Handling of uncertainty due to interference fringe in FT-NIR transmittance spectroscopy - Performance comparison of interference elimination techniques using glucose-water system

NASA Astrophysics Data System (ADS)

Beganović, Anel; Beć, Krzysztof B.; Henn, Raphael; Huck, Christian W.

2018-05-01

The applicability of two elimination techniques for interferences occurring in measurements with cells of short pathlength using Fourier transform near-infrared (FT-NIR) spectroscopy was evaluated. Due to the growing interest in the field of vibrational spectroscopy in aqueous biological fluids (e.g. glucose in blood), aqueous solutions of D-(+)-glucose were prepared and split into a calibration set and an independent validation set. All samples were measured with two FT-NIR spectrometers at various spectral resolutions. Moving average smoothing (MAS) and fast Fourier transform filter (FFT filter) were applied to the interference affected FT-NIR spectra in order to eliminate the interference pattern. After data pre-treatment, partial least squares regression (PLSR) models using different NIR regions were constructed using untreated (interference affected) spectra and spectra treated with MAS and FFT filter. The prediction of the independent validation set revealed information about the performance of the utilized interference elimination techniques, as well as the different NIR regions. The results showed that the combination band of water at approx. 5200 cm-1 is of great importance since its performance was superior to the one of the so-called first overtone of water at approx. 6800 cm-1. Furthermore, this work demonstrated that MAS and FFT filter are fast and easy-to-use techniques for the elimination of interference fringes in FT-NIR transmittance spectroscopy.

Multiple complexes of nitrogen assimilatory enzymes in spinach chloroplasts: possible mechanisms for the regulation of enzyme function.

PubMed

Kimata-Ariga, Yoko; Hase, Toshiharu

2014-01-01

Assimilation of nitrogen is an essential biological process for plant growth and productivity. Here we show that three chloroplast enzymes involved in nitrogen assimilation, glutamate synthase (GOGAT), nitrite reductase (NiR) and glutamine synthetase (GS), separately assemble into distinct protein complexes in spinach chloroplasts, as analyzed by western blots under blue native electrophoresis (BN-PAGE). GOGAT and NiR were present not only as monomers, but also as novel complexes with a discrete size (730 kDa) and multiple sizes (>120 kDa), respectively, in the stromal fraction of chloroplasts. These complexes showed the same mobility as each monomer on two-dimensional (2D) SDS-PAGE after BN-PAGE. The 730 kDa complex containing GOGAT dissociated into monomers, and multiple complexes of NiR reversibly converted into monomers, in response to the changes in the pH of the stromal solvent. On the other hand, the bands detected by anti-GS antibody were present not only in stroma as a conventional decameric holoenzyme complex of 420 kDa, but also in thylakoids as a novel complex of 560 kDa. The polypeptide in the 560 kDa complex showed slower mobility than that of the 420 kDa complex on the 2D SDS-PAGE, implying the assembly of distinct GS isoforms or a post-translational modification of the same GS protein. The function of these multiple complexes was evaluated by in-gel GS activity under native conditions and by the binding ability of NiR and GOGAT with their physiological electron donor, ferredoxin. The results indicate that these multiplicities in size and localization of the three nitrogen assimilatory enzymes may be involved in the physiological regulation of their enzyme function, in a similar way as recently described cases of carbon assimilatory enzymes.

Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market.

PubMed

Hernández-Hierro, J M; García-Villanova, R J; González-Martín, I

2008-08-01

The potential of the near infrared spectroscopy (NIRS) technique for the analysis of red paprika for aflatoxin B(1), ochratoxin A and total aflatoxins is explored. As a reference, the results from a chromatographic method with fluorescence detection (HPLC-FD) following an immunoaffinity cleanup (IAC) were employed. For the NIRS measurement, a remote reflectance fibre-optic probe was applied directly onto the samples of paprika. There was no need for pre-treatment or manipulation of the sample. The modified partial least squares (MPLS) algorithm was employed as a regression method. The multiple correlation coefficients (RSQ) and the prediction corrected standard errors (SEP(C)) were respectively 0.955 and 0.2 microg kg(-1), 0.853 and 2.3 microg kg(-1), 0.938 and 0.3 microg kg(-1) for aflatoxin B(1), ochratoxin A and total aflatoxins, respectively. The capacity for prediction of the developed model measured as ratio performance deviation (RPD) for aflatoxin B(1) (5.2), ochratoxin A (2.8) and total aflatoxins (4.4) indicate that NIRS technique using a fibre-optic probe offers an alternative for the determination of these three parameters in paprika, with an advantageously lower cost and higher speed as compared with the chemical method. Content of aflatoxin B(1) and total aflatoxins are the parameters currently employed by the food regulations to limit the levels of the four aflatoxins in many foodstuffs. In addition, aflatoxin B(1) itself is an excellent indicator for aflatoxins' contamination since it is always the most abundant and toxic.

The study on the near infrared spectrum technology of sauce component analysis

NASA Astrophysics Data System (ADS)

Li, Shangyu; Zhang, Jun; Chen, Xingdan; Liang, Jingqiu; Wang, Ce

2006-01-01

The author, Shangyu Li, engages in supervising and inspecting the quality of products. In soy sauce manufacturing, quality control of intermediate and final products by many components such as total nitrogen, saltless soluble solids, nitrogen of amino acids and total acid is demanded. Wet chemistry analytical methods need much labor and time for these analyses. In order to compensate for this problem, we used near infrared spectroscopy technology to measure the chemical-composition of soy sauce. In the course of the work, a certain amount of soy sauce was collected and was analyzed by wet chemistry analytical methods. The soy sauce was scanned by two kinds of the spectrometer, the Fourier Transform near infrared spectrometer (FT-NIR spectrometer) and the filter near infrared spectroscopy analyzer. The near infrared spectroscopy of soy sauce was calibrated with the components of wet chemistry methods by partial least squares regression and stepwise multiple linear regression. The contents of saltless soluble solids, total nitrogen, total acid and nitrogen of amino acids were predicted by cross validation. The results are compared with the wet chemistry analytical methods. The correlation coefficient and root-mean-square error of prediction (RMSEP) in the better prediction run were found to be 0.961 and 0.206 for total nitrogen, 0.913 and 1.215 for saltless soluble solids, 0.855 and 0.199 nitrogen of amino acids, 0.966 and 0.231 for total acid, respectively. The results presented here demonstrate that the NIR spectroscopy technology is promising for fast and reliable determination of major components of soy sauce.

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model.

PubMed

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Kim, Moon S; Chao, Kuanglin; Qin, Jianwei; Fu, Xiaping; Baek, Insuck; Cho, Byoung-Kwan

2016-05-01

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of informative bands in the short-wavelength NIR region for non-invasive blood glucose measurement.

PubMed

Uwadaira, Yasuhiro; Ikehata, Akifumi; Momose, Akiko; Miura, Masayo

2016-07-01

The "glucose-linked wavelength" in the short-wavelength near-infrared (NIR) region, in which the light intensity reflected from the hand palm exhibits a good correlation to the blood glucose value, was investigated. We performed 391 2-h carbohydrate tolerance tests (CTTs) using 34 participants and a glucose-linked wavelength was successfully observed in almost every CTT; however, this wavelength varied between CTTs even for the same person. The large resulting data set revealed the distribution of the informative wavelength. The blood glucose values were efficiently estimated by a simple linear regression with clinically acceptable accuracies. The result suggested the potential for constructing a personalized low-invasive blood glucose sensor using short-wavelength NIR spectroscopy.

Multimodal Imaging of Disease-Associated Pigmentary Changes in Retinitis Pigmentosa

PubMed Central

Schuerch, Kaspar; Marsiglia, Marcela; Lee, Winston; Tsang, Stephen H.; Sparrow, Janet R.

2016-01-01

Purpose Using multiple imaging modalities we evaluated the changes in photoreceptor cells and RPE that are associated with bone spicule-shaped melanin pigmentation in retinitis pigmentosa (RP). Methods In a cohort of 60 RP patients, short-wavelength autofluorescence (SW-AF), near-infrared (NIR)-AF, NIR-reflectance (NIR-R), spectral domain optical coherence tomography (SD-OCT) and color fundus images were studied. Results Central AF rings were visible in both SW-AF and NIR-AF images. Bone spicule pigmentation was non-reflective in NIR-R, hypoautofluorescent with SW-AF and NIR-AF imaging and presented as intraretinal hyperreflective foci in SD-OCT images. In areas beyond the AF ring outer border, the photoreceptor ellipsoid zone (EZ) band was absent in SD-OCT scans and the visibility of choroidal vessels in SW-AF, NIR-AF and NIR-R images was indicative of reduced RPE pigmentation. Choroidal visibility was most pronounced in the zone approaching peripheral areas of bone spicule pigmentation; here RPE/Bruch’s membrane thinning became apparent in SD-OCT scans. Conclusions These findings are consistent with a process by which RPE cells vacate their monolayer and migrate into inner retina in response to photoreceptor cell degeneration. The remaining RPE spread, undergo thinning and consequently become less pigmented. An explanation for the absence of NIR-AF melanin signal in relation to bone spicule pigmentation is not forthcoming. PMID:28005673

A novel CXCR4-targeted near-infrared (NIR) fluorescent probe (Peptide R-NIR750) specifically detects CXCR4 expressing tumors.

PubMed

Santagata, Sara; Portella, Luigi; Napolitano, Maria; Greco, Adelaide; D'Alterio, Crescenzo; Barone, Maria Vittoria; Luciano, Antonio; Gramanzini, Matteo; Auletta, Luigi; Arra, Claudio; Zannetti, Antonella; Scala, Stefania

2017-05-31

C-X-C chemokine receptor 4 (CXCR4) is over-expressed in multiple human cancers and correlates with tumor aggressiveness, poor prognosis and increased risk for distant metastases. Imaging agents for CXCR4 are thus highly desirable. We developed a novel CXCR4-targeted near-infrared (NIR) fluorescent probe (Peptide R-NIR750) conjugating the new developed CXCR4 peptidic antagonist Peptide R with the NIR fluorescent dye VivoTag-S750. Specific CXCR4 binding was obtained in cells overexpressing human CXCR4 (B16-hCXCR4 and human melanoma cells PES43), but not in CXCR4 low expressing cells (FB-1). Ex vivo evaluation demonstrated that PepR-NIR750 specifically detects B16-hCXCR4-derived subcutaneous tumors and lung metastases. Fluorescence Molecular Tomography (FMT) in vivo imaging was performed on mice carrying subcutaneous CHO and CHO-CXCR4 tumors. PepR-NIR750 accumulates only in CXCR4-positive expressing subcutaneous tumors. Additionally, an intense NIR fluorescence signal was detected in PES43-derived lung metastases of nude mice injected with PepR-NIR750 versus mice injected with VivoTag-S750. With a therapeutic intent, mice bearing PES43-derived lung metastases were treated with Peptide R. A the dramatic reduction in PES43-derived lung metastases was detected through a decrease of the PepR-NIR750 signal. PepR-NIR750 is a specific probe for non-invasive detection of human high CXCR4-expressing tumors and metastatic lesion and thus a valuable tool for cancer molecular imaging.

Quantitative analysis of multi-component gas mixture based on AOTF-NIR spectroscopy

NASA Astrophysics Data System (ADS)

Hao, Huimin; Zhang, Yong; Liu, Junhua

2007-12-01

Near Infrared (NIR) spectroscopy analysis technology has attracted many eyes and has wide application in many domains in recent years because of its remarkable advantages. But the NIR spectrometer can only be used for liquid and solid analysis by now. In this paper, a new quantitative analysis method of gas mixture by using new generation NIR spectrometer is explored. To collect the NIR spectra of gas mixtures, a vacuumable gas cell was designed and assembled to Luminar 5030-731 Acousto-Optic Tunable Filter (AOTF)-NIR spectrometer. Standard gas samples of methane (CH 4), ethane (C IIH 6) and propane (C 3H 8) are diluted with super pure nitrogen via precision volumetric gas flow controllers to obtain gas mixture samples of different concentrations dynamically. The gas mixtures were injected into the gas cell and the spectra of wavelength between 1100nm-2300nm were collected. The feature components extracted from gas mixture spectra by using Partial Least Squares (PLS) were used as the inputs of the Support Vector Regress Machine (SVR) to establish the quantitative analysis model. The effectiveness of the model is tested by the samples of predicting set. The prediction Root Mean Square Error (RMSE) of CH 4, C IIH 6 and C 3H 8 is respectively 1.27%, 0.89%, and 1.20% when the concentrations of component gas are over 0.5%. It shows that the AOTF-NIR spectrometer with gas cell can be used for gas mixture analysis. PLS combining with SVR has a good performance in NIR spectroscopy analysis. This paper provides the bases for extending the application of NIR spectroscopy analysis to gas detection.

Light shift from ultraviolet to near infrared light: Cerenkov luminescence with gold nanocluster - near infrared (AuNc-NIR) conjugates

NASA Astrophysics Data System (ADS)

Yoo, Su Woong; Mun, Hyoyoung; Oh, Gyungseok; Ryu, Youngjae; Kim, Min-Gon; Chung, Euiheon

2015-03-01

Cerenkov luminescence (CL) is generated when a charged particle moves faster than the speed of light in dielectric media. Recently CL imaging becomes an emerging technique with the use of radioisotopes. However, due to relatively weak blue light production and massive tissue attenuation, CL has not been applied widely. Therefore, we attempted to shift the CL emission to more near infrared (NIR) spectrum for better tissue penetration by using Cerenkov Radiation Energy Transfer (CRET). Gold nanoclusters were conjugated with NIR dye molecules (AuNc-IR820 and AuNc-ICG) to be activated with ultraviolet light. We found optimal conjugate concentrations of AuNc-NIR conjugates by spectroscopy system to generate maximal photon emission. When exposed by ultraviolet light, the emission of NIR light from the conjugates were verified. In quantitative analysis, AuNc-NIR conjugates emit brighter light signal than pure AuNc. This result implies that NIR fluorescent dyes (both IR820 and ICG) can be excited by the emission from AuNc. Following the above baseline experiment, we mixed F-18 fluorodeoxyglucose (F-18 FDG) radioisotope to the AuNc- NIR conjugates, to confirm NIR emission induced from Cerenkov radiation. Long pass filter was used to block Cerenkov luminescence and to collect the emission from AuNc-NIR conjugates. Instead of one long exposure imaging with CCD, we used multiple frame scheme to eliminate gamma radiation strike in each frame prior to combination. In summary, we obtained NIR emission light from AuNc-NIR conjugated dyes that is induced from CL. We plan to perform in vivo small animal imaging with these conjugates to assess better tissue penetration.

Localization of pulmonary nodules using navigation bronchoscope and a near-infrared fluorescence thoracoscope.

PubMed

Anayama, Takashi; Qiu, Jimmy; Chan, Harley; Nakajima, Takahiro; Weersink, Robert; Daly, Michael; McConnell, Judy; Waddell, Thomas; Keshavjee, Shaf; Jaffray, David; Irish, Jonathan C; Hirohashi, Kentaro; Wada, Hironobu; Orihashi, Kazumasa; Yasufuku, Kazuhiro

2015-01-01

Video-assisted thoracoscopic wedge resection of multiple small, non-visible, and nonpalpable pulmonary nodules is a clinical challenge. We propose an ultra-minimally invasive technique for localization of pulmonary nodules using the electromagnetic navigation bronchoscope (ENB)-guided transbronchial indocyanine green (ICG) injection and intraoperative fluorescence detection with a near-infrared (NIR) fluorescence thoracoscope. Fluorescence properties of ICG topically injected into the lung parenchyma were determined using a resected porcine lung. The combination of ENB-guided ICG injection and NIR fluorescence detection was tested using a live porcine model. An electromagnetic sensor integrated flexible bronchoscope was geometrically registered to the three-dimensional chest computed tomographic image data by way of a real-time electromagnetic tracking system. The ICG mixed with iopamidol was injected into the pulmonary nodules by ENB guidance; ICG fluorescence was visualized by a near-infrared (NIR) thoracoscope. The ICG existing under 24-mm depth of inflated lung was detectable by the NIR fluorescence thoracoscope. The size of the fluorescence spot made by 0.1 mL of ICG was 10.4 ± 2.2 mm. An ICG or iopamidol spot remained at the injected point of the lung for more than 6 hours in vivo. The ICG fluorescence spot injected into the pulmonary nodule with ENB guidance was identified at the pulmonary nodule with the NIR thoracoscope. The ENB-guided transbronchial ICG injection and intraoperative NIR thoracoscopic detection is a feasible method to localize multiple pulmonary nodules. Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Near-infrared laser-induced fluorescence detection in capillary electrophoresis.

PubMed

McWhorter, S; Soper, S A

2000-04-01

As capillary electrophoresis continues to focus on miniaturization, either through reducing column dimensions or situating entire electrophoresis systems on planar chips, advances in detection become necessary to meet the challenges posed by these electrophoresis platforms. The challenges result from the fact that miniaturization requires smaller load volumes, demanding highly sensitive detection. In addition, many times multiple targets must be analyzed simultaneously (multiplexed applications), further complicating detection. Near-infrared (NIR) fluorescence offers an attractive alternative to visible fluorescence for critical applications in capillary electrophoresis due to the impressive limits of detection that can be generated, in part resulting from the low background levels that are observed in the NIR. Advances in instrumentation and fluorogenic labels appropriate for NIR monitoring have led to a growing number of examples of the use of NIR fluorescence in capillary electrophoresis. In this review, we will cover instrumental components used to construct ultrasensitive NIR fluorescence detectors, including light sources and photon transducers. In addition, we will discuss various types of labeling dyes appropriate for NIR fluorescence and finally, we will present several applications that have used NIR fluorescence in capillary electrophoresis, especially for DNA sequencing and fragment analysis.

Heterologous expression of the Aspergillus nidulans regulatory gene nirA in Fusarium oxysporum.

PubMed

Daboussi, M J; Langin, T; Deschamps, F; Brygoo, Y; Scazzocchio, C; Burger, G

1991-12-20

We have isolated strains of Fusarium oxysporum carrying mutations conferring a phenotype characteristic of a loss of function in the regulatory gene of nitrate assimilation (nirA in Aspergillus nidulans, nit-4 in Neurospora crassa). One of these nir- mutants was successfully transformed with a plasmid containing the nirA gene of A. nidulans. The nitrate reductase of the transformants is still inducible, although the maximum activity is lower than in the wild type. Single and multiple integration events were found, as well as a strict correlation between the presence of the nirA gene and the Nir+ phenotype of the F. oxysporum transformants. We also investigated how the A. nidulans structural gene (niaD) is regulated in F. oxysporum. Enzyme assays and Northern experiments show that the niaD gene is subject to nitrate induction and that it responds to nitrogen metabolite repression in a F. oxysporum genetic background. This indicates that both the mechanisms of specific induction, mediated by a gene product isofunctional to nirA, and nitrogen metabolite repression, presumably mediated by a gene product isofunctional to the homologous gene of A. nidulans, are operative in F. oxysporum.

Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters

NASA Astrophysics Data System (ADS)

de Santana, Felipe Bachion; de Souza, André Marcelo; Poppi, Ronei Jesus

2018-02-01

This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.

On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)

USGS Publications Warehouse

Ji, Lel; Zhang, Li; Wylie, Bruce K.; Rover, Jennifer R.

2011-01-01

The spectral vegetation index (ρNIR – ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI), and normalized burn ratio (NBR), etc. After reviewing each term’s definition, associated sensors, and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3 µm, 1.55–1.75 µm, or 2.05–2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term’s popularity, and the “rule of priority” in scientific nomenclature, NDWI, NDII, and NBR, each corresponding to the three SWIR regions, are more preferable terms.

Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial

PubMed Central

Aasted, Christopher M.; Yücel, Meryem A.; Cooper, Robert J.; Dubb, Jay; Tsuzuki, Daisuke; Becerra, Lino; Petkov, Mike P.; Borsook, David; Dan, Ippeita; Boas, David A.

2015-01-01

Abstract. Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that is used to noninvasively measure cerebral hemoglobin concentration changes induced by brain activation. Using structural guidance in fNIRS research enhances interpretation of results and facilitates making comparisons between studies. AtlasViewer is an open-source software package we have developed that incorporates multiple spatial registration tools to enable structural guidance in the interpretation of fNIRS studies. We introduce the reader to the layout of the AtlasViewer graphical user interface, the folder structure, and user files required in the creation of fNIRS probes containing sources and detectors registered to desired locations on the head, evaluating probe fabrication error and intersubject probe placement variability, and different procedures for estimating measurement sensitivity to different brain regions as well as image reconstruction performance. Further, we detail how AtlasViewer provides a generic head atlas for guiding interpretation of fNIRS results, but also permits users to provide subject-specific head anatomies to interpret their results. We anticipate that AtlasViewer will be a valuable tool in improving the anatomical interpretation of fNIRS studies. PMID:26157991

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR).

PubMed

Genisheva, Z; Quintelas, C; Mesquita, D P; Ferreira, E C; Oliveira, J M; Amaral, A L

2018-04-25

This work aims to explore the potential of near infrared (NIR) spectroscopy to quantify volatile compounds in Vinho Verde wines, commonly determined by gas chromatography. For this purpose, 105 Vinho Verde wine samples were analyzed using Fourier transform near infrared (FT-NIR) transmission spectroscopy in the range of 5435 cm -1 to 6357 cm -1 . Boxplot and principal components analysis (PCA) were performed for clusters identification and outliers removal. A partial least square (PLS) regression was then applied to develop the calibration models, by a new iterative approach. The predictive ability of the models was confirmed by an external validation procedure with an independent sample set. The obtained results could be considered as quite good with coefficients of determination (R 2 ) varying from 0.94 to 0.97. The current methodology, using NIR spectroscopy and chemometrics, can be seen as a promising rapid tool to determine volatile compounds in Vinho Verde wines. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predicting soil quality indices with near infrared analysis in a wildfire chronosequence.

PubMed

Cécillon, Lauric; Cassagne, Nathalie; Czarnes, Sonia; Gros, Raphaël; Vennetier, Michel; Brun, Jean-Jacques

2009-01-15

We investigated the power of near infrared (NIR) analysis for the quantitative assessment of soil quality in a wildfire chronosequence. The effect of wildfire disturbance and soil engineering activity of earthworms on soil organic matter quality was first assessed with principal component analysis of NIR spectra. Three soil quality indices were further calculated using an adaptation of the method proposed by Velasquez et al. [Velasquez, E., Lavelle, P., Andrade, M. GISQ, a multifunctional indicator of soil quality. Soil Biol Biochem 2007; 39: 3066-3080.], each one addressing an ecosystem service provided by soils: organic matter storage, nutrient supply and biological activity. Partial least squares regression models were developed to test the predicting ability of NIR analysis for these soil quality indices. All models reached coefficients of determination above 0.90 and ratios of performance to deviation above 2.8. This finding provides new opportunities for the monitoring of soil quality, using NIR scanning of soil samples.

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

PubMed

Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme

2015-01-01

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy.

PubMed

Jiang, B; Huang, Y D

2008-05-26

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39-14.14mgcm(-2). The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R(2)), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20s without sample destruction.

Matrix Effects in Quantitative Assessment of Pharmaceutical Tablets Using Transmission Raman and Near-Infrared (NIR) Spectroscopy.

PubMed

Sparén, Anders; Hartman, Madeleine; Fransson, Magnus; Johansson, Jonas; Svensson, Olof

2015-05-01

Raman spectroscopy can be an alternative to near-infrared spectroscopy (NIR) for nondestructive quantitative analysis of solid pharmaceutical formulations. Compared with NIR spectra, Raman spectra have much better selectivity, but subsampling was always an issue for quantitative assessment. Raman spectroscopy in transmission mode has reduced this issue, since a large volume of the sample is measured in transmission mode. The sample matrix, such as particle size of the drug substance in a tablet, may affect the Raman signal. In this work, matrix effects in transmission NIR and Raman spectroscopy were systematically investigated for a solid pharmaceutical formulation. Tablets were manufactured according to an experimental design, varying the factors particle size of the drug substance (DS), particle size of the filler, compression force, and content of drug substance. All factors were varied at two levels plus a center point, except the drug substance content, which was varied at five levels. Six tablets from each experimental point were measured with transmission NIR and Raman spectroscopy, and their concentration of DS was determined for a third of those tablets. Principal component analysis of NIR and Raman spectra showed that the drug substance content and particle size, the particle size of the filler, and the compression force affected both NIR and Raman spectra. For quantitative assessment, orthogonal partial least squares regression was applied. All factors varied in the experimental design influenced the prediction of the DS content to some extent, both for NIR and Raman spectroscopy, the particle size of the filler having the largest effect. When all matrix variations were included in the multivariate calibrations, however, good predictions of all types of tablets were obtained, both for NIR and Raman spectroscopy. The prediction error using transmission Raman spectroscopy was about 30% lower than that obtained with transmission NIR spectroscopy.

Near infrared radiation rescues mitochondrial dysfunction in cortical neurons after oxygen-glucose deprivation.

PubMed

Yu, Zhanyang; Liu, Ning; Zhao, Jianhua; Li, Yadan; McCarthy, Thomas J; Tedford, Clark E; Lo, Eng H; Wang, Xiaoying

2015-04-01

Near infrared radiation (NIR) is known to penetrate and affect biological systems in multiple ways. Recently, a series of experimental studies suggested that low intensity NIR may protect neuronal cells against a wide range of insults that mimic diseases such as stroke, brain trauma and neurodegeneration. However, the potential molecular mechanisms of neuroprotection with NIR remain poorly defined. In this study, we tested the hypothesis that low intensity NIR may attenuate hypoxia/ischemia-induced mitochondrial dysfunction in neurons. Primary cortical mouse neuronal cultures were subjected to 4 h oxygen-glucose deprivation followed by reoxygenation for 2 h, neurons were then treated with a 2 min exposure to 810-nm NIR. Mitochondrial function markers including MTT reduction and mitochondria membrane potential were measured at 2 h after treatment. Neurotoxicity was quantified 20 h later. Our results showed that 4 h oxygen-glucose deprivation plus 20 h reoxygenation caused 33.8 ± 3.4 % of neuron death, while NIR exposure significantly reduced neuronal death to 23.6 ± 2.9 %. MTT reduction rate was reduced to 75.9 ± 2.7 % by oxygen-glucose deprivation compared to normoxic controls, but NIR exposure significantly rescued MTT reduction to 87.6 ± 4.5 %. Furthermore, after oxygen-glucose deprivation, mitochondria membrane potential was reduced to 48.9 ± 4.39 % of normoxic control, while NIR exposure significantly ameliorated this reduction to 89.6 ± 13.9 % of normoxic control. Finally, NIR significantly rescued OGD-induced ATP production decline at 20 min after NIR. These findings suggest that low intensity NIR can protect neurons against oxygen-glucose deprivation by rescuing mitochondrial function and restoring neuronal energetics.

Near infrared radiation rescues mitochondrial dysfunction in cortical neurons after oxygen-glucose deprivation

PubMed Central

Yu, Zhanyang; Liu, Ning; Zhao, Jianhua; Li, Yadan; McCarthy, Thomas J.; Tedford, Clark E.; Lo, Eng H.; Wang, Xiaoying

2014-01-01

Near infrared radiation (NIR) is known to penetrate and affect biological systems in multiple ways. Recently, a series of experimental studies suggested that low intensity NIR may protect neuronal cells against a wide range of insults that mimic diseases such as stroke, brain trauma and neuro-degeneration. However, the potential molecular mechanisms of neuroprotection with NIR remain poorly defined. In this study, we tested the hypothesis that low intensity NIR may attenuate hypoxia/ischemia-induced mitochondrial dysfunction in neurons. Primary cortical mouse neuronal cultures were subjected to 4 h oxygen-glucose deprivation followed by reoxygenation for 2 h, neurons were then treated with a 2 min exposure to 810-nm NIR. Mitochondrial function markers including MTT reduction and mitochondria membrane potential were measured at 2 h after treatment. Neurotoxicity was quantified 20 h later. Our results showed that 4 h oxygen-glucose deprivation plus 20 h reoxygenation caused 33.8±3.4 % of neuron death, while NIR exposure significantly reduced neuronal death to 23.6±2.9 %. MTT reduction rate was reduced to 75.9±2.7 % by oxygen-glucose deprivation compared to normoxic controls, but NIR exposure significantly rescued MTT reduction to 87.6±4.5 %. Furthermore, after oxygen-glucose deprivation, mitochondria membrane potential was reduced to 48.9±4.39 % of normoxic control, while NIR exposure significantly ameliorated this reduction to 89.6±13.9 % of normoxic control. Finally, NIR significantly rescued OGD-induced ATP production decline at 20 min after NIR. These findings suggest that low intensity NIR can protect neurons against oxygen-glucose deprivation by rescuing mitochondrial function and restoring neuronal energetics. PMID:24599760

Phylogenetically diverse denitrifying and ammonia-oxidizing bacteria in corals Alcyonium gracillimum and Tubastraea coccinea.

PubMed

Yang, Shan; Sun, Wei; Zhang, Fengli; Li, Zhiyong

2013-10-01

To date, the association of coral-bacteria and the ecological roles of bacterial symbionts in corals remain largely unknown. In particular, little is known about the community components of bacterial symbionts of corals involved in the process of denitrification and ammonia oxidation. In this study, the nitrite reductase (nirS and nirK) and ammonia monooxygenase subunit A (amoA) genes were used as functional markers. Diverse bacteria with the potential to be active as denitrifiers and ammonia-oxidizing bacteria (AOB) were found in two East China Sea corals: stony coral Alcyonium gracillimum and soft coral Tubastraea coccinea. The 16S rRNA gene library analysis demonstrated different communities of bacterial symbionts in these two corals of the same location. Nitrite reductase nirK gene was found only in T. coccinea, while both nirK and nirS genes were detected in A. gracillimum, which might be the result of the presence of different bacterial symbionts in these two corals. AOB rather than ammonia-oxidizing archaea were detected in both corals, suggesting that AOB might play an important role in the ammonia oxidation process of the corals. This study indicates that the coral bacterial symbionts with the potential for nitrite reduction and ammonia oxidation might have multiple ecological roles in the coral holobiont, which promotes our understanding of bacteria-mediated nitrogen cycling in corals. To our knowledge, this study is the first assessment of the community structure and phylogenetic diversity of denitrifying bacteria and AOB in corals based on nirK, nirS, and amoA gene library analysis.

Determination of Spatially Resolved Tablet Density and Hardness Using Near-Infrared Chemical Imaging (NIR-CI).

PubMed

Talwar, Sameer; Roopwani, Rahul; Anderson, Carl A; Buckner, Ira S; Drennen, James K

2017-08-01

Near-infrared chemical imaging (NIR-CI) combines spectroscopy with digital imaging, enabling spatially resolved analysis and characterization of pharmaceutical samples. Hardness and relative density are critical quality attributes (CQA) that affect tablet performance. Intra-sample density or hardness variability can reveal deficiencies in formulation design or the tableting process. This study was designed to develop NIR-CI methods to predict spatially resolved tablet density and hardness. The method was implemented using a two-step procedure. First, NIR-CI was used to develop a relative density/solid fraction (SF) prediction method for pure microcrystalline cellulose (MCC) compacts only. A partial least squares (PLS) model for predicting SF was generated by regressing the spectra of certain representative pixels selected from each image against the compact SF. Pixel selection was accomplished with a threshold based on the Euclidean distance from the median tablet spectrum. Second, micro-indentation was performed on the calibration compacts to obtain hardness values. A univariate model was developed by relating the empirical hardness values to the NIR-CI predicted SF at the micro-indented pixel locations: this model generated spatially resolved hardness predictions for the entire tablet surface.

[Near-infrared reflectance spectroscopy predicts protein, moisture and ash in beans].

PubMed

Gao, Huiyu; Wang, Guodong; Men, Jianhua; Wang, Zhu

2017-05-01

To explore the potential of near-infrared reflectance( NIR)spectroscopy to determine macronutrient contents in beans. NIR spectra and analytical measurements of protein, moisture and ash were collected from 70 kinds of beans. Reference methods were used to analyze all the ground beans samples. NIR spectra on intact and ground beans samples were registered. Partial least-squares( PLS)regression models were developed with principal components analysis( PCA) to assign 49 bean accessions to a calibration data set and 21 accessions to an external validation set. For intact beans, the relative predictive determinant( RPD) values for protein and ash( 3. 67 and 3. 97, respectively) were good for screening. RPD value for moisture was only 1. 39, which was not recommended. For ground beans, the RPD values for protein, moisture and ash( 6. 63, 5. 25 and 3. 57, respectively) were good enough for screening. The protein, moisture and ash levels for intact and ground beans were all significantly correlated( P < 0. 001) between the NIR and reference method and there was no statistically significant difference in the mean with these three traits. This research demonstrates that NIR is a promising technique for simultaneous sorting ofmultiple traits in beans with no or easy sample preparation.

MULTIMODAL IMAGING OF DISEASE-ASSOCIATED PIGMENTARY CHANGES IN RETINITIS PIGMENTOSA.

PubMed

Schuerch, Kaspar; Marsiglia, Marcela; Lee, Winston; Tsang, Stephen H; Sparrow, Janet R

2016-12-01

Using multiple imaging modalities, we evaluated the changes in photoreceptor cells and retinal pigment epithelium (RPE) that are associated with bone spicule-shaped melanin pigmentation in retinitis pigmentosa. In a cohort of 60 patients with retinitis pigmentosa, short-wavelength autofluorescence, near-infrared autofluorescence (NIR-AF), NIR reflectance, spectral domain optical coherence tomography, and color fundus images were studied. Central AF rings were visible in both short-wavelength autofluorescence and NIR-AF images. Bone spicule pigmentation was nonreflective in NIR reflectance, hypoautofluorescent with short-wavelength autofluorescence and NIR-AF imaging, and presented as intraretinal hyperreflective foci in spectral domain optical coherence tomography images. In areas beyond the AF ring outer border, the photoreceptor ellipsoid zone band was absent in spectral domain optical coherence tomography and the visibility of choroidal vessels in short-wavelength autofluorescence, NIR-AF, and NIR reflectance images was indicative of reduced RPE pigmentation. Choroidal visibility was most pronounced in the zone approaching peripheral areas of bone spicule pigmentation; here RPE/Bruch membrane thinning became apparent in spectral domain optical coherence tomography. These findings are consistent with a process by which RPE cells vacate their monolayer and migrate into inner retina in response to photoreceptor cell degeneration. The remaining RPE spread undergo thinning and consequently become less pigmented. An explanation for the absence of NIR-AF melanin signal in relation to bone spicule pigmentation is not forthcoming.

Multivariate analysis of nystatin and metronidazole in a semi-solid matrix by means of diffuse reflectance NIR spectroscopy and PLS regression.

PubMed

Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A

2006-01-23

A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.

Improvements of the Vis-NIRS Model in the Prediction of Soil Organic Matter Content Using Spectral Pretreatments, Sample Selection, and Wavelength Optimization

NASA Astrophysics Data System (ADS)

Lin, Z. D.; Wang, Y. B.; Wang, R. J.; Wang, L. S.; Lu, C. P.; Zhang, Z. Y.; Song, L. T.; Liu, Y.

2017-07-01

A total of 130 topsoil samples collected from Guoyang County, Anhui Province, China, were used to establish a Vis-NIR model for the prediction of organic matter content (OMC) in lime concretion black soils. Different spectral pretreatments were applied for minimizing the irrelevant and useless information of the spectra and increasing the spectra correlation with the measured values. Subsequently, the Kennard-Stone (KS) method and sample set partitioning based on joint x-y distances (SPXY) were used to select the training set. Successive projection algorithm (SPA) and genetic algorithm (GA) were then applied for wavelength optimization. Finally, the principal component regression (PCR) model was constructed, in which the optimal number of principal components was determined using the leave-one-out cross validation technique. The results show that the combination of the Savitzky-Golay (SG) filter for smoothing and multiplicative scatter correction (MSC) can eliminate the effect of noise and baseline drift; the SPXY method is preferable to KS in the sample selection; both the SPA and the GA can significantly reduce the number of wavelength variables and favorably increase the accuracy, especially GA, which greatly improved the prediction accuracy of soil OMC with Rcc, RMSEP, and RPD up to 0.9316, 0.2142, and 2.3195, respectively.

Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy

PubMed Central

Liu, Ning; Cui, Xu; Bryant, Daniel M.; Glover, Gary H.; Reiss, Allan L.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson’s correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research. PMID:25798327

Fluorescence Imaging In Vivo at Wavelengths beyond 1500 nm.

PubMed

Diao, Shuo; Blackburn, Jeffrey L; Hong, Guosong; Antaris, Alexander L; Chang, Junlei; Wu, Justin Z; Zhang, Bo; Cheng, Kai; Kuo, Calvin J; Dai, Hongjie

2015-12-01

Compared to imaging in the visible and near-infrared regions below 900 nm, imaging in the second near-infrared window (NIR-II, 1000-1700 nm) is a promising method for deep-tissue high-resolution optical imaging in vivo mainly owing to the reduced scattering of photons traversing through biological tissues. Herein, semiconducting single-walled carbon nanotubes with large diameters were used for in vivo fluorescence imaging in the long-wavelength NIR region (1500-1700 nm, NIR-IIb). With this imaging agent, 3-4 μm wide capillary blood vessels at a depth of about 3 mm could be resolved. Meanwhile, the blood-flow speeds in multiple individual vessels could be mapped simultaneously. Furthermore, NIR-IIb tumor imaging of a live mouse was explored. NIR-IIb imaging can be generalized to a wide range of fluorophores emitting at up to 1700 nm for high-performance in vivo optical imaging. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Thermal consequences of colour and near-infrared reflectance.

PubMed

Stuart-Fox, Devi; Newton, Elizabeth; Clusella-Trullas, Susana

2017-07-05

The importance of colour for temperature regulation in animals remains controversial. Colour can affect an animal's temperature because all else being equal, dark surfaces absorb more solar energy than do light surfaces, and that energy is converted into heat. However, in reality, the relationship between colour and thermoregulation is complex and varied because it depends on environmental conditions and the physical properties, behaviour and physiology of the animal. Furthermore, the thermal effects of colour depend as much on absorptance of near-infrared ((NIR), 700-2500 nm) as visible (300-700 nm) wavelengths of direct sunlight; yet the NIR is very rarely considered or measured. The few available data on NIR reflectance in animals indicate that the visible reflectance is often a poor predictor of NIR reflectance. Adaptive variation in animal coloration (visible reflectance) reflects a compromise between multiple competing functions such as camouflage, signalling and thermoregulation. By contrast, adaptive variation in NIR reflectance should primarily reflect thermoregulatory requirements because animal visual systems are generally insensitive to NIR wavelengths. Here, we assess evidence and identify key research questions regarding the thermoregulatory function of animal coloration, and specifically consider evidence for adaptive variation in NIR reflectance.This article is part of the themed issue 'Animal coloration: production, perception, function and application'. © 2017 The Author(s).

Quantitative analysis of binary polymorphs mixtures of fusidic acid by diffuse reflectance FTIR spectroscopy, diffuse reflectance FT-NIR spectroscopy, Raman spectroscopy and multivariate calibration.

PubMed

Guo, Canyong; Luo, Xuefang; Zhou, Xiaohua; Shi, Beijia; Wang, Juanjuan; Zhao, Jinqi; Zhang, Xiaoxia

2017-06-05

Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multivariate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs. Copyright © 2017 Elsevier B.V. All rights reserved.

[Rapid determination of the components in ternary blended edible oil using near infrared transmission spectroscopy].

PubMed

Liu, Fu-Li; Chen, Hua-Cai

2009-08-01

The FT-NIR transmission spectra of ternary blended edible oil samples were collected over 10 000-4 200 cm(-1). After being pretreated with different methods, the calibration models of quantitative analysis of soybean oil, peanut oil and corn oil contents in ternary blended edible oil were established using partial least square (PLS) regression. The accuracy and precision of the models for the predicted sample set were examined to make sure of the practicability of the models. After being pretreated with first derivative and multiplicative signal correction (FD+MSC), the optimal soybean oil NIR model was built over 5 450.1-4 597.7 cm(-1). The best prediction model for peanut oil was established between 7 521.3 and 6 098.1 cm(-1) after using first derivative with straight line subtraction (FD+SLS) preprocess method. The best pretreated method and the best spectrum range for corn oil content model were first derivative (FD) and 9 993.7-7 498.2 cm(-1), respectively. The best correlation coefficients (R2) of the three prediction models were 99.89%, 99.88% and 99.76%, respectively. The RMSEP of the soybean oil content model was 1.09%, while the peanut oil prediction model's RMSEP was 1.17%, and 1.48% for the corn oil prediction model. The values of the t-test were between 0.007 9 and 0.371 9, and all values of the relative standard deviation (RSD) were less than 1.50%. The results showed that NIR could be an ideal tool for fast determination of the soybean oil, peanut oil and corn oil contents in ternary blended edible oil.

Chemoinformetrical evaluation of dissolution property of indomethacin tablets by near-infrared spectroscopy.

PubMed

Otsuka, Makoto; Tanabe, Hideaki; Osaki, Kazuo; Otsuka, Kuniko; Ozaki, Yukihiro

2007-04-01

The purpose of this study was to use near-infrared spectrometry (NIR) with chemoinformetrics to predict the change of dissolution properties in indomethacin (IMC) tablets during the manufacturing process. A comparative evaluation of the dissolution properties of the tablets was performed by the diffused reflectance (DRNIR) and transmittance (TNIR) NIR spectroscopic methods. Various kinds of IMC tablets (200 mg) were obtained from a powder (20 mg of IMC, 18 mg of microcrystalline cellulose, 160 mg of lactose, and 2 mg of magnesium stearate) under various compression pressures (60-398 MPa). Dissolution tests were performed in phosphate buffer, and the time required for 75% dissolution (T75) and mean dissolution time (MDT) were calculated. DRNIR and TNIR spectra were recorded, and the both NIR spectra used to establish a calibration model for predicting the dissolution properties by principal component regression analysis (PCR). The T75 and MDT increased as the compression pressure increased, since tablet porosity decreased with increasing pressure. Intensity of the DRNIR spectra of the compressed tablets decreased as the compression pressure increased. However, the intensity of TNIR spectra increased along with the pressure. The calibration models used to evaluate the dissolution properties of tablets were established by using PCR based on both DRNIR and TNIR spectra of the tablets. The multiple correlation coefficients of the relationship between the actual and predictive T75 by the DRNIR and TNIR methods were 0.831 and 0.962, respectively. It is possible to predict the dissolution properties of pharmaceutical preparations using both DRNIR and TNIR chemoinformetric methods. The TNIR method was more accurate for predictions of the dissolution behavior of tablets than the DRNIR method. (c) 2007 Wiley-Liss, Inc.

Nondestructive detection of total viable count changes of chilled pork in high oxygen storage condition based on hyperspectral technology

NASA Astrophysics Data System (ADS)

Zheng, Xiaochun; Peng, Yankun; Li, Yongyu; Chao, Kuanglin; Qin, Jianwei

2017-05-01

The plate count method is commonly used to detect the total viable count (TVC) of bacteria in pork, which is timeconsuming and destructive. It has also been used to study the changes of the TVC in pork under different storage conditions. In recent years, many scholars have explored the non-destructive methods on detecting TVC by using visible near infrared (VIS/NIR) technology and hyperspectral technology. The TVC in chilled pork was monitored under high oxygen condition in this study by using hyperspectral technology in order to evaluate the changes of total bacterial count during storage, and then evaluate advantages and disadvantages of the storage condition. The VIS/NIR hyperspectral images of samples stored in high oxygen condition was acquired by a hyperspectral system in range of 400 1100nm. The actual reference value of total bacteria was measured by standard plate count method, and the results were obtained in 48 hours. The reflection spectra of the samples are extracted and used for the establishment of prediction model for TVC. The spectral preprocessing methods of standard normal variate transformation (SNV), multiple scatter correction (MSC) and derivation was conducted to the original reflectance spectra of samples. Partial least squares regression (PLSR) of TVC was performed and optimized to be the prediction model. The results show that the near infrared hyperspectral technology based on 400-1100nm combined with PLSR model can describe the growth pattern of the total bacteria count of the chilled pork under the condition of high oxygen very vividly and rapidly. The results obtained in this study demonstrate that the nondestructive method of TVC based on NIR hyperspectral has great potential in monitoring of edible safety in processing and storage of meat.

Near-infrared imaging of secondary caries lesions around composite restorations at wavelengths from 1300-1700-nm.

PubMed

Simon, Jacob C; A Lucas, Seth; Lee, Robert C; Darling, Cynthia L; Staninec, Michal; Vaderhobli, Ram; Pelzner, Roger; Fried, Daniel

2016-04-01

Current clinical methods for diagnosing secondary caries are unreliable for identifying the early stages of decay around restorative materials. The objective of this study was to access the integrity of restoration margins in natural teeth using near-infrared (NIR) reflectance and transillumination images at wavelengths between 1300 and 1700-nm and to determine the optimal NIR wavelengths for discriminating composite materials from dental hard tissues. Twelve composite margins (n=12) consisting of class I, II and V restorations were chosen from ten extracted teeth. The samples were imaged in vitro using NIR transillumination and reflectance, polarization sensitive optical coherence tomography (PS-OCT) and a high-magnification digital microscope. Samples were serially sectioned into 200-μm slices for histological analysis using polarized light microscopy (PLM) and transverse microradiography (TMR). Two independent examiners evaluated the presence of demineralization at the sample margin using visible detection with 10× magnification and NIR images presented digitally. Composite restorations were placed in sixteen sound teeth (n=16) and imaged at multiple NIR wavelengths ranging from λ=1300 to 1700-nm using NIR transillumination. The image contrast was calculated between the composite and sound tooth structure. Intensity changes in NIR images at wavelengths ranging from 1300 to 1700-nm correlate with increased mineral loss measured using TMR. NIR reflectance and transillumination at wavelengths coincident with increased water absorption yielded significantly higher (P<0.001) contrast between sound enamel and adjacent demineralized enamel. In addition, NIR reflectance exhibited significantly higher (P<0.01) contrast between sound enamel and adjacent composite restorations than visible reflectance. This study shows that NIR imaging is well suited for the rapid screening of secondary caries lesions. Copyright © 2016 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Near-infrared Imaging of Secondary Caries Lesions around Composite Restorations at Wavelengths from 1300–1700-nm

PubMed Central

Simon, Jacob C.; Lucas, Seth; Lee, Robert; Darling, Cynthia L.; Staninec, Michal; Vanderhobli, Ram; Pelzner, Roger; Fried, Daniel

2016-01-01

Background and Objectives Current clinical methods for diagnosing secondary caries are unreliable for identifying the early stages of decay around restorative materials. The objective of this study was to access the integrity of restoration margins in natural teeth using near-infrared (NIR) reflectance and transillumination images at wavelengths between 1300–1700-nm and to determine the optimal NIR wavelengths for discriminating composite materials from dental hard tissues. Materials and Methods Twelve composite margins (n=12) consisting of class I, II & V restorations were chosen from ten extracted teeth. The samples were imaged in vitro using NIR transillumination and reflectance, polarization sensitive optical coherence tomography (PS-OCT) and a high-magnification digital microscope. Samples were serially sectioned into 200–μm slices for histological analysis using polarized light microscopy (PLM) and transverse microradiography (TMR). Two independent examiners evaluated the presence of demineralization at the sample margin using visible detection with 10× magnification and NIR images presented digitally. Composite restorations were placed in sixteen sound teeth (n=16) and imaged at multiple NIR wavelengths ranging from λ=1300–1700-nm using NIR transillumination. The image contrast was calculated between the composite and sound tooth structure. Results Intensity changes in NIR images at wavelengths ranging from 1300–1700-nm correlate with increased mineral loss measured using TMR. NIR reflectance and transillumination at wavelengths coincident with increased water absorption yielded significantly higher (P<0.001) contrast between sound enamel and adjacent demineralized enamel. In addition, NIR reflectance exhibited significantly higher (P<0.01) contrast between sound enamel and adjacent composite restorations than visible reflectance. Significance This study shows that NIR imaging is well suited for the rapid screening of secondary caries lesions. PMID:26876234

Estimation of Sensory Analysis Cupping Test Arabica Coffee Using NIR Spectroscopy

NASA Astrophysics Data System (ADS)

Safrizal; Sutrisno; Lilik, P. E. N.; Ahmad, U.; Samsudin

2018-05-01

Flavors have become the most important coffee quality parameters now day, many coffee consuming countries require certain taste scores for the coffee to be ordered, the currently used cupping method of appraisal is the method designed by The Specialty Coffee Association Of America (SCAA), from several previous studies was found that Near-Infrared Spectroscopy (NIRS) can be used to detect chemical composition of certain materials including those associated with flavor so it is possible also to be applied to coffee powder. The aim of this research is to get correlation between NIRS spectrum with cupping scoring by tester, then look at the possibility of testing coffee taste sensors using NIRS spectrum. The coffee samples were taken from various places, altitudes and postharvest handling methods, then the samples were prepared following the SCAA protocol, for sensory analysis was done in two ways, with the expert tester and with the NIRS test. The calibration between both found that Without pretreatment using PLS get RMSE cross validation 6.14, using Multiplicative Scatter Correction spectra obtained RMSE cross validation 5.43, the best RMSE cross-validation was 1.73 achieved by de-trending correction, NIRS can be used to predict the score of cupping.

Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2005-11-01

A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.

Non-destructive and rapid prediction of moisture content in red pepper (Capsicum annuum L.) powder using near-infrared spectroscopy and a partial least squares regression model

USDA-ARS?s Scientific Manuscript database

Purpose: The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated in...

Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS).

PubMed

de Paz, José Miguel; Visconti, Fernando; Chiaravalle, Mara; Quiñones, Ana

2016-05-01

Early diagnosis of specific chloride toxicity in persimmon trees requires the reliable and fast determination of the leaf chloride content, which is usually performed by means of a cumbersome, expensive and time-consuming wet analysis. A methodology has been developed in this study as an alternative to determine chloride in persimmon leaves using near-infrared spectroscopy (NIRS) in combination with multivariate calibration techniques. Based on a training dataset of 134 samples, a predictive model was developed from their NIR spectral data. For modelling, the partial least squares regression (PLSR) method was used. The best model was obtained with the first derivative of the apparent absorbance and using just 10 latent components. In the subsequent external validation carried out with 35 external data this model reached r(2) = 0.93, RMSE = 0.16% and RPD = 3.6, with standard error of 0.026% and bias of -0.05%. From these results, the model based on NIR spectral readings can be used for speeding up the laboratory determination of chloride in persimmon leaves with only a modest loss of precision. The intermolecular interaction between chloride ions and the peptide bonds in leaf proteins through hydrogen bonding, i.e. N-H···Cl, explains the ability for chloride determinations on the basis of NIR spectra.

In-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz based on qualitative and quantitative uses of near-infrared spectroscopy.

PubMed

Wu, Yongjiang; Jin, Ye; Ding, Haiying; Luan, Lianjun; Chen, Yong; Liu, Xuesong

2011-09-01

The application of near-infrared (NIR) spectroscopy for in-line monitoring of extraction process of scutellarein from Erigeron breviscapus (vant.) Hand-Mazz was investigated. For NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm pathlength flow cell were utilized to collect spectra in real-time. High performance liquid chromatography (HPLC) was used as a reference method to determine scutellarein in extract solution. Partial least squares regression (PLSR) calibration model of Savitzky-Golay smoothing NIR spectra in the 5450-10,000 cm(-1) region gave satisfactory predictive results for scutellarein. The results showed that the correlation coefficients of calibration and cross validation were 0.9967 and 0.9811, respectively, and the root mean square error of calibration and cross validation were 0.044 and 0.105, respectively. Furthermore, both the moving block standard deviation (MBSD) method and conformity test were used to identify the end point of extraction process, providing real-time data and instant feedback about the extraction course. The results obtained in this study indicated that the NIR spectroscopy technique provides an efficient and environmentally friendly approach for fast determination of scutellarein and end point control of extraction process. Copyright © 2011 Elsevier B.V. All rights reserved.

Online monitoring of P(3HB) produced from used cooking oil with near-infrared spectroscopy.

PubMed

Cruz, Madalena V; Sarraguça, Mafalda Cruz; Freitas, Filomena; Lopes, João Almeida; Reis, Maria A M

2015-01-20

Online monitoring process for the production of polyhydroxyalkanoates (PHA), using cooking oil (UCO) as the sole carbon source and Cupriavidus necator, was developed. A batch reactor was operated and hydroxybutyrate homopolymer was obtained. The biomass reached a maximum concentration of 11.6±1.7gL(-1) with a polymer content of 63±10.7% (w/w). The yield of product on substrate was 0.77±0.04gg(-1). Near-infrared (NIR) spectroscopy was used for online monitoring of the fermentation, using a transflectance probe. Partial least squares regression was applied to relate NIR spectra with biomass, UCO and PHA concentrations in the broth. The NIR predictions were compared with values obtained by offline reference methods. Prediction errors to these parameters were 1.18, 2.37 and 1.58gL(-1) for biomass, UCO and PHA, respectively, which indicate the suitability of the NIR spectroscopy method for online monitoring and as a method to assist bioreactor control. Copyright © 2014 Elsevier B.V. All rights reserved.

Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS).

PubMed

Fluckiger, Miriam; Brown, Malcolm R; Ward, Louise R; Moltschaniwskyj, Natalie A

2011-06-15

Near infrared reflectance spectroscopy (NIRS) was used to predict glycogen concentrations in the foot muscle of cultured abalone. NIR spectra of live, shucked and freeze-dried abalones were modelled against chemically measured glycogen data (range: 0.77-40.9% of dry weight (DW)) using partial least squares (PLS) regression. The calibration models were then used to predict glycogen concentrations of test abalone samples and model robustness was assessed from coefficient of determination of the validation (R2(val)) and standard error of prediction (SEP) values. The model for freeze-dried abalone gave the best prediction (R2(val) 0.97, SEP=1.71), making it suitable for quantifying glycogen. Models for live and shucked abalones had R2(val) of 0.86 and 0.90, and SEP of 3.46 and 3.07 respectively, making them suitable for producing estimations of glycogen concentration. As glycogen is a taste-active component associated with palatability in abalone, this study demonstrated the potential of NIRS as a rapid method to monitor the factors associated with abalone quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Disease quantification in dermatology: in vivo near-infrared spectroscopy measures correlate strongly with the clinical assessment of psoriasis severity

NASA Astrophysics Data System (ADS)

Greve, Tanja Maria; Kamp, Søren; Jemec, Gregor B. E.

2013-03-01

Accurate documentation of disease severity is a prerequisite for clinical research and the practice of evidence-based medicine. The quantification of skin diseases such as psoriasis currently relies heavily on clinical scores. Although these clinical scoring methods are well established and very useful in quantifying disease severity, they require an extensive clinical experience and carry a risk of subjectivity. We explore the opportunity to use in vivo near-infrared (NIR) spectra as an objective and noninvasive method for local disease severity assessment in 31 psoriasis patients in whom selected plaques were scored clinically. A partial least squares (PLS) regression model was used to analyze and predict the severity scores on the NIR spectra of psoriatic and uninvolved skin. The correlation between predicted and clinically assigned scores was R=0.94 (RMSE=0.96), suggesting that in vivo NIR provides accurate clinical quantification of psoriatic plaques. Hence, NIR may be a practical solution to clinical severity assessment of psoriasis, providing a continuous, linear, numerical value of severity.

Detection and quantification of adulteration in sandalwood oil through near infrared spectroscopy.

PubMed

Kuriakose, Saji; Thankappan, Xavier; Joe, Hubert; Venkataraman, Venkateswaran

2010-10-01

The confirmation of authenticity of essential oils and the detection of adulteration are problems of increasing importance in the perfumes, pharmaceutical, flavor and fragrance industries. This is especially true for 'value added' products like sandalwood oil. A methodical study is conducted here to demonstrate the potential use of Near Infrared (NIR) spectroscopy along with multivariate calibration models like principal component regression (PCR) and partial least square regression (PLSR) as rapid analytical techniques for the qualitative and quantitative determination of adulterants in sandalwood oil. After suitable pre-processing of the NIR raw spectral data, the models are built-up by cross-validation. The lowest Root Mean Square Error of Cross-Validation and Calibration (RMSECV and RMSEC % v/v) are used as a decision supporting system to fix the optimal number of factors. The coefficient of determination (R(2)) and the Root Mean Square Error of Prediction (RMSEP % v/v) in the prediction sets are used as the evaluation parameters (R(2) = 0.9999 and RMSEP = 0.01355). The overall result leads to the conclusion that NIR spectroscopy with chemometric techniques could be successfully used as a rapid, simple, instant and non-destructive method for the detection of adulterants, even 1% of the low-grade oils, in the high quality form of sandalwood oil.

Prediction of pH of cola beverage using Vis/NIR spectroscopy and least squares-support vector machine

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-02-01

Visible and near infrared (Vis/NIR) transmission spectroscopy and chemometric methods were utilized to predict the pH values of cola beverages. Five varieties of cola were prepared and 225 samples (45 samples for each variety) were selected for the calibration set, while 75 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay and standard normal variate (SNV) followed by first-derivative were used as the pre-processing methods. Partial least squares (PLS) analysis was employed to extract the principal components (PCs) which were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. Then LS-SVM with radial basis function (RBF) kernel function and a two-step grid search technique were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias were 0.961, 0.040 and 0.012 for PLS, while 0.975, 0.031 and 4.697x10 -3 for LS-SVM, respectively. Both methods obtained a satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be applied as an alternative way for the prediction of pH of cola beverages.

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Identifying and quantifying main components of physiological noise in functional near infrared spectroscopy on the prefrontal cortex.

PubMed

Kirilina, Evgeniya; Yu, Na; Jelzow, Alexander; Wabnitz, Heidrun; Jacobs, Arthur M; Tachtsidis, Ilias

2013-01-01

Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. The goal of the present study was to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data. To achieve this goal we combined concurrent time-domain fNIRS and peripheral physiology recordings with wavelet coherence analysis (WCA). Depth selectivity was achieved by analyzing moments of photon time-of-flight distributions provided by time-domain fNIRS. Simultaneously, mean arterial blood pressure (MAP), heart rate (HR), and skin blood flow (SBF) on the forehead were recorded. WCA was employed to quantify the impact of physiological processes on fNIRS signals separately for different time scales. We identified three main processes contributing to physiological noise in fNIRS signals on the forehead. The first process with the period of about 3 s is induced by respiration. The second process is highly correlated with time lagged MAP and HR fluctuations with a period of about 10 s often referred as Mayer waves. The third process is local regulation of the facial SBF time locked to the task-evoked fNIRS signals. All processes affect oxygenated haemoglobin concentration more strongly than that of deoxygenated haemoglobin. Based on these results we developed a set of physiological regressors, which were used for physiological de-noising of fNIRS signals. Our results demonstrate that proposed de-noising method can significantly improve the sensitivity of fNIRS to cerebral signals.

Near Infrared Spectroscopy for On-line Monitoring of Alkali- Free Cloth/Phenolic Resin Prepreg During Manufacture

PubMed Central

Jiang, Bo; Huang, Yu Dong

2007-01-01

A NIR method was developed for the on-line monitoring of alkali-free cloth/phenolic resin prepreg during its manufacturing process. First, the sizing content of the alkali-free cloth was analyzed, and then the resin, soluble resin and volatiles content of the prepreg was analyzed simultaneously using the FT-NIR spectrometer. Partial least square (PLS) regression was used to develop the calibration models, which for the sizing content was preprocessed by 1stDER +MSC, for the volatile content by 1stDER +VN, for the soluble resin content by 1stDER +MSC and for the resin content by the VN spectral data preprocessing method. RMSEP of the prediction model for the sizing content was 0.732 %, for the resin content it was 0.605, for the soluble resin content it was 0.101 and for volatiles content it was 0.127. The results of the paired t-test revealed that there was no significant difference between the NIR method and the standard method. The NIR spectroscopy method could be used to predict the resin, soluble resin and the volatiles content of the prepreg simultaneously, as well as sizing content of alkali-free cloth. The processing parameters of the prepreg during manufacture could be adjusted quickly with the help of the NIR analysis results. The results indicated that the NIR spectroscopy method was sufficiently accurate and effective for the on-line monitoring of alkali-free cloth/phenolic resin prepreg.

Real-time soil sensing based on fiber optics and spectroscopy

NASA Astrophysics Data System (ADS)

Li, Minzan

2005-08-01

Using NIR spectroscopic techniques, correlation analysis and regression analysis for soil parameter estimation was conducted with raw soil samples collected in a cornfield and a forage field. Soil parameters analyzed were soil moisture, soil organic matter, nitrate nitrogen, soil electrical conductivity and pH. Results showed that all soil parameters could be evaluated by NIR spectral reflectance. For soil moisture, a linear regression model was available at low moisture contents below 30 % db, while an exponential model can be used in a wide range of moisture content up to 100 % db. Nitrate nitrogen estimation required a multi-spectral exponential model and electrical conductivity could be evaluated by a single spectral regression. According to the result above mentioned, a real time soil sensor system based on fiber optics and spectroscopy was developed. The sensor system was composed of a soil subsoiler with four optical fiber probes, a spectrometer, and a control unit. Two optical fiber probes were used for illumination and the other two optical fiber probes for collecting soil reflectance from visible to NIR wavebands at depths around 30 cm. The spectrometer was used to obtain the spectra of reflected lights. The control unit consisted of a data logging device, a personal computer, and a pulse generator. The experiment showed that clear photo-spectral reflectance was obtained from the underground soil. The soil reflectance was equal to that obtained by the desktop spectrophotometer in laboratory tests. Using the spectral reflectance, the soil parameters, such as soil moisture, pH, EC and SOM, were evaluated.

Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Yufei, Liu; Liming, Ye; Chunyan, Li; Quanwei, Yang; Weiying, Wang; Yun, Jing; Minxiang, Zhang; Peng, Li

2018-01-01

Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).

Intercalibration of Two Polar Satellite Instruments Without Simultaneous Nadir Observations

NASA Astrophysics Data System (ADS)

Manninen, Terhikki; Riihela, Aku; Schaaf, Crystal; Key, Jeffrey; Lattanzio, Alessio

2016-08-01

A new intercalibration method for two polar satellite instruments is presented. It is based on statistical fitting of two data sets covering the same area during the same period, but not simultaneously. Deming regression with iterative weights is used. The accuracy of the method was better than about 0.5 % for the MODIS vs. MODIS and AVHRR vs. AVHRR test data sets. The intercalibration of AVHRR vs. MODIS red and NIR channels is carried out and showed a difference of reflectance values of 2% (red) and 6 % (NIR). The red channel intercalibration has slightly higher accuracy for all cases studied.

Comparison of three chemometrics methods for near-infrared spectra of glucose in the whole blood

NASA Astrophysics Data System (ADS)

Zhang, Hongyan; Ding, Dong; Li, Xin; Chen, Yu; Tang, Yuguo

2005-01-01

Principal Component Regression (PCR), Partial Least Square (PLS) and Artificial Neural Networks (ANN) methods are used in the analysis for the near infrared (NIR) spectra of glucose in the whole blood. The calibration model is built up in the spectrum band where there are the glucose has much more spectral absorption than the water, fat, and protein with these methods and the correlation coefficients of the model are showed in this paper. Comparing these results, a suitable method to analyze the glucose NIR spectrum in the whole blood is found.

Signal Processing in Functional Near-Infrared Spectroscopy (fNIRS): Methodological Differences Lead to Different Statistical Results.

PubMed

Pfeifer, Mischa D; Scholkmann, Felix; Labruyère, Rob

2017-01-01

Even though research in the field of functional near-infrared spectroscopy (fNIRS) has been performed for more than 20 years, consensus on signal processing methods is still lacking. A significant knowledge gap exists between established researchers and those entering the field. One major issue regularly observed in publications from researchers new to the field is the failure to consider possible signal contamination by hemodynamic changes unrelated to neurovascular coupling (i.e., scalp blood flow and systemic blood flow). This might be due to the fact that these researchers use the signal processing methods provided by the manufacturers of their measurement device without an advanced understanding of the performed steps. The aim of the present study was to investigate how different signal processing approaches (including and excluding approaches that partially correct for the possible signal contamination) affect the results of a typical functional neuroimaging study performed with fNIRS. In particular, we evaluated one standard signal processing method provided by a commercial company and compared it to three customized approaches. We thereby investigated the influence of the chosen method on the statistical outcome of a clinical data set (task-evoked motor cortex activity). No short-channels were used in the present study and therefore two types of multi-channel corrections based on multiple long-channels were applied. The choice of the signal processing method had a considerable influence on the outcome of the study. While methods that ignored the contamination of the fNIRS signals by task-evoked physiological noise yielded several significant hemodynamic responses over the whole head, the statistical significance of these findings disappeared when accounting for part of the contamination using a multi-channel regression. We conclude that adopting signal processing methods that correct for physiological confounding effects might yield more realistic results in cases where multi-distance measurements are not possible. Furthermore, we recommend using manufacturers' standard signal processing methods only in case the user has an advanced understanding of every signal processing step performed.

Combining feature extraction and classification for fNIRS BCIs by regularized least squares optimization.

PubMed

Heger, Dominic; Herff, Christian; Schultz, Tanja

2014-01-01

In this paper, we show that multiple operations of the typical pattern recognition chain of an fNIRS-based BCI, including feature extraction and classification, can be unified by solving a convex optimization problem. We formulate a regularized least squares problem that learns a single affine transformation of raw HbO(2) and HbR signals. We show that this transformation can achieve competitive results in an fNIRS BCI classification task, as it significantly improves recognition of different levels of workload over previously published results on a publicly available n-back data set. Furthermore, we visualize the learned models and analyze their spatio-temporal characteristics.

Diffuse Optical Tomography for Brain Imaging: Continuous Wave Instrumentation and Linear Analysis Methods

NASA Astrophysics Data System (ADS)

Giacometti, Paolo; Diamond, Solomon G.

Diffuse optical tomography (DOT) is a functional brain imaging technique that measures cerebral blood oxygenation and blood volume changes. This technique is particularly useful in human neuroimaging measurements because of the coupling between neural and hemodynamic activity in the brain. DOT is a multichannel imaging extension of near-infrared spectroscopy (NIRS). NIRS uses laser sources and light detectors on the scalp to obtain noninvasive hemodynamic measurements from spectroscopic analysis of the remitted light. This review explains how NIRS data analysis is performed using a combination of the modified Beer-Lambert law (MBLL) and the diffusion approximation to the radiative transport equation (RTE). Laser diodes, photodiode detectors, and optical terminals that contact the scalp are the main components in most NIRS systems. Placing multiple sources and detectors over the surface of the scalp allows for tomographic reconstructions that extend the individual measurements of NIRS into DOT. Mathematically arranging the DOT measurements into a linear system of equations that can be inverted provides a way to obtain tomographic reconstructions of hemodynamics in the brain.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Thaumarchaeal amoA and nirK Gene Abundance Patterns Reveal Spatiotemporal Dynamics of Ammonia-oxidizing Archaeal Populations in Monterey Bay, CA

NASA Astrophysics Data System (ADS)

Tolar, B. B.; Reji, L.; Smith, J. M.; Chavez, F.; Francis, C.

2016-12-01

Thaumarchaeaota are among the most abundant microorganisms on the planet, and are significant players in the global nitrogen cycle. All cultivated members of the phylum are capable of performing the first and rate-limiting step of nitrification - the aerobic oxidation of ammonia to nitrite. In marine environments, ammonia-oxidizing archaea (AOA) have been found to greatly outnumber their bacterial counterparts. However, much about their ecology remains largely unknown. Monterey Bay, a non-estuarine embayment on the central California coast, is an ideal site for studying the dynamics of natural thaumarchaeal assemblages, given the highly dynamic nature of the Bay waters with seasonal upwelling episodes and the associated steep gradients in environmental variables. In the present study, we examined thaumarchaeal population dynamics in the upper Monterey Bay water column (0-500 m) using multiple molecular markers. Following high-resolution spatiotemporal sampling (i.e., up to 10 depths sampled monthly over a period of 2 years) at two stations in the Bay, we quantified thaumarchaeal functional genes - the ammonia monooxygenase (amoA) gene and its `shallow' and `deep' marine ecotypes, and variants of the marine nitrite reductase (nirK) gene. The abundances of both genes were regressed against environmental variables to gain insights into factors shaping their spatiotemporal dynamics in the Bay. Gene abundances at both stations varied with depth and season, with winter months generally having several orders of magnitude greater abundances. Statistical analyses point to differential controls on the gene abundances, with depth and temperature potentially being the major environmental determinants of thaumarchaeal population size. Our results also highlight the importance of employing multiple marker genes to gain a more highly resolved picture of thaumarchaeal population dynamics in complex environmental systems such as the coastal ocean.

NIR technology for on-line determination of superficial a(w) and moisture content during the drying process of fermented sausages.

PubMed

Collell, Carles; Gou, Pere; Arnau, Jacint; Muñoz, Israel; Comaposada, Josep

2012-12-01

Three different NIR equipment were evaluated based on their ability to predict superficial water activity (a(w)) and moisture content in two types of fermented sausages (with and without moulds on surface), using partial least squares (PLS) regression models. The instruments differed mainly in wavelength range, resolution and measurement configuration. The most accurate equipment was used in a new experiment to achieve robust models in sausages with different salt contents and submitted to different drying conditions. The models developed showed determination coefficients (R(2)(P)) values of 0.990, 0.910 and 0.984, and RMSEP values of 1.560%, 0.220% and 0.007% for moisture, salt and a(w) respectively. It was demonstrated that NIR spectroscopy could be a suitable non-destructive method for on-line monitoring and control of the drying process in fermented sausages. Copyright © 2012 Elsevier Ltd. All rights reserved.

Motor learning and modulation of prefrontal cortex: an fNIRS assessment

NASA Astrophysics Data System (ADS)

Ono, Yumie; Noah, Jack Adam; Zhang, Xian; Nomoto, Yasunori; Suzuki, Tatsuya; Shimada, Sotaro; Tachibana, Atsumichi; Bronner, Shaw; Hirsch, Joy

2015-12-01

Objective. Prefrontal hemodynamic responses are observed during performance of motor tasks. Using a dance video game (DVG), a complex motor task that requires temporally accurate footsteps with given visual and auditory cues, we investigated whether 20 h of DVG training modified hemodynamic responses of the prefrontal cortex in six healthy young adults. Approach. Fronto-temporal activity during actual DVG play was measured using functional near-infrared spectroscopy (fNIRS) pre- and post-training. To evaluate the training-induced changes in the time-courses of fNIRS signals, we employed a regression analysis using the task-specific template fNIRS signals that were generated from alternate well-trained and/or novice DVG players. The HRF was also separately incorporated as a template to construct an alternate regression model. Change in coefficients for template functions at pre- and post- training were determined and compared among different models. Main results. Training significantly increased the motor performance using the number of temporally accurate steps in the DVG as criteria. The mean oxygenated hemoglobin (ΔoxyHb) waveform changed from an activation above baseline pattern to that of a below baseline pattern. Participants showed significantly decreased coefficients for regressors of the ΔoxyHb response of novice players and HRF. The model using ΔoxyHb responses from both well-trained and novice players of DVG as templates showed the best fit for the ΔoxyHb responses of the participants at both pre- and post-training when analyzed with Akaike information criteria. Significance. These results suggest that the coefficients for the template ΔoxyHb responses of the novice players are sensitive indicators of motor learning during the initial stage of training and thus clinically useful to determine the improvement in motor performance when patients are engaged in a specific rehabilitation program.

Exploring effective multiplicity in multichannel functional near-infrared spectroscopy using eigenvalues of correlation matrices

PubMed Central

Uga, Minako; Dan, Ippeita; Dan, Haruka; Kyutoku, Yasushi; Taguchi, Y-h; Watanabe, Eiju

2015-01-01

Abstract. Recent advances in multichannel functional near-infrared spectroscopy (fNIRS) allow wide coverage of cortical areas while entailing the necessity to control family-wise errors (FWEs) due to increased multiplicity. Conventionally, the Bonferroni method has been used to control FWE. While Type I errors (false positives) can be strictly controlled, the application of a large number of channel settings may inflate the chance of Type II errors (false negatives). The Bonferroni-based methods are especially stringent in controlling Type I errors of the most activated channel with the smallest p value. To maintain a balance between Types I and II errors, effective multiplicity (Meff) derived from the eigenvalues of correlation matrices is a method that has been introduced in genetic studies. Thus, we explored its feasibility in multichannel fNIRS studies. Applying the Meff method to three kinds of experimental data with different activation profiles, we performed resampling simulations and found that Meff was controlled at 10 to 15 in a 44-channel setting. Consequently, the number of significantly activated channels remained almost constant regardless of the number of measured channels. We demonstrated that the Meff approach can be an effective alternative to Bonferroni-based methods for multichannel fNIRS studies. PMID:26157982

Physiological Aging Influence on Brain Hemodynamic Activity during Task-Switching: A fNIRS Study.

PubMed

Vasta, Roberta; Cutini, Simone; Cerasa, Antonio; Gramigna, Vera; Olivadese, Giuseppe; Arabia, Gennarina; Quattrone, Aldo

2017-01-01

Task-switching (TS) paradigm is a well-known validated tool useful for exploring the neural substrates of cognitive control, in particular the activity of the lateral and medial prefrontal cortex. This work is aimed at investigating how physiological aging influences hemodynamic response during the execution of a color-shape TS paradigm. A multi-channel near infrared spectroscopy (fNIRS) was used to measure hemodynamic activity in 27 young (30.00 ± 7.90 years) and 11 elderly participants (57.18 ± 9.29 years) healthy volunteers (55% male, age range: (19-69) years) during the execution of a TS paradigm. Two holders were placed symmetrically over the left/right hemispheres to record cortical activity [oxy-(HbO) and deoxy-hemoglobin (HbR) concentration] of the dorso-lateral prefrontal cortex (DLPFC), the dorsal premotor cortex (PMC), and the dorso-medial part of the superior frontal gyrus (sFG). TS paradigm requires participants to repeat the same task over a variable number of trials, and then to switch to a different task during the trial sequence. A two-sample t -test was carried out to detect differences in cortical responses between groups. Multiple linear regression analysis was used to evaluate the impact of age on the prefrontal neural activity. Elderly participants were significantly slower than young participants in both color- ( p < 0.01, t = -3.67) and shape-single tasks ( p = 0.026, t = -2.54) as well as switching ( p = 0.026, t = -2.41) and repetition trials ( p = 0.012, t = -2.80). Differences in cortical activation between groups were revealed for HbO mean concentration of switching task in the PMC ( p = 0.048, t = 2.94). In the whole group, significant increases of behavioral performance were detected in switching trials, which positively correlated with aging. Multivariate regression analysis revealed that the HbO mean concentration of switching task in the PMC ( p = 0.01, β = -0.321) and of shape single-task in the sFG ( p = 0.003, β = 0.342) were the best predictors of age effects. Our findings demonstrated that TS might be a reliable instrument to gather a measure of cognitive resources in older people. Moreover, the fNIRS-related brain activity extracted from frontoparietal cortex might become a useful indicator of aging effects.

Brain activation changes during locomotion in middle-aged to older adults with multiple sclerosis.

PubMed

Hernandez, Manuel E; Holtzer, Roee; Chaparro, Gioella; Jean, Kharine; Balto, Julia M; Sandroff, Brian M; Izzetoglu, Meltem; Motl, Robert W

2016-11-15

Mobility and cognitive impairments are common in persons with multiple sclerosis (MS), and are expected to worsen with increasing age. However, no studies, to date, in part due to limitations of conventional neuroimaging methods, have examined changes in brain activation patterns during active locomotion in older patients with MS. This study used functional Near Infrared Spectroscopy (fNIRS) to evaluate real-time neural activation differences in the pre-frontal cortex (PFC) between middle-aged to older adults with MS and healthy controls during single (Normal Walk; NW) and dual-task (Walking While Talking; WWT) locomotion tasks. Eight middle-aged to older adults with MS and eight healthy controls underwent fNIRS recording while performing the NW and WWT tasks with an fNIRS cap consisting of 16 optodes positioned over the forehead. The MS group had greater elevations in PFC oxygenation levels during WWT compared to NW than healthy controls. There was no walking performance difference between groups during locomotion. These findings suggest that middle-aged to older individuals with MS might be able to achieve similar levels of performance through the use of increased brain activation. This study is the first to investigate brain activation changes during the performance of simple and divided-attention locomotion tasks in MS using fNIRS. Copyright © 2016 Elsevier B.V. All rights reserved.

Enhanced Cellular Ablation by Attenuating Hypoxia Status and Reprogramming Tumor-Associated Macrophages via NIR Light-Responsive Upconversion Nanocrystals.

PubMed

Ai, Xiangzhao; Hu, Ming; Wang, Zhimin; Lyu, Linna; Zhang, Wenmin; Li, Juan; Yang, Huanghao; Lin, Jun; Xing, Bengang

2018-04-18

Near-infrared (NIR) light-mediated photodynamic therapy (PDT), especially based on lanthanide-doped upconversion nanocrystals (UCNs), have been extensively investigated as a promising strategy for effective cellular ablation owing to their unique optical properties to convert NIR light excitation into multiple short-wavelength emissions. Despite the deep tissue penetration of NIR light in living systems, the therapeutic efficiency is greatly restricted by insufficient oxygen supply in hypoxic tumor microenvironment. Moreover, the coexistent tumor-associated macrophages (TAMs) play critical roles in tumor recurrence during the post-PDT period. Herein, we developed a unique photosensitizer-loaded UCNs nanoconjugate (PUN) by integrating manganese dioxide (MnO 2 ) nanosheets and hyaluronic acid (HA) biopolymer to improve NIR light-mediated PDT efficacy through attenuating hypoxia status and synergistically reprogramming TAMs populations. After the reaction with overproduced H 2 O 2 in acidic tumor microenvironment, the MnO 2 nanosheets were degraded for the production of massive oxygen to greatly enhance the oxygen-dependent PDT efficiency upon 808 nm NIR light irradiation. More importantly, the bioinspired polymer HA could effectively reprogram the polarization of pro-tumor M2-type TAMs to anti-tumor M1-type macrophages to prevent tumor relapse after PDT treatment. Such promising results provided the great opportunities to achieve enhanced cellular ablation upon NIR light-mediated PDT treatment by attenuating hypoxic tumor microenvironment, and thus facilitated the rational design of new generations of nanoplatforms toward immunotherapy to inhibit tumor recurrence during post-PDT period.

Digital soil classification and elemental mapping using imaging Vis-NIR spectroscopy: How to explicitly quantify stagnic properties of a Luvisol under Norway spruce

NASA Astrophysics Data System (ADS)

Kriegs, Stefanie; Buddenbaum, Henning; Rogge, Derek; Steffens, Markus

2015-04-01

Laboratory imaging Vis-NIR spectroscopy of soil profiles is a novel technique in soil science that can determine quantity and quality of various chemical soil properties with a hitherto unreached spatial resolution in undisturbed soil profiles. We have applied this technique to soil cores in order to get quantitative proof of redoximorphic processes under two different tree species and to proof tree-soil interactions at microscale. Due to the imaging capabilities of Vis-NIR spectroscopy a spatially explicit understanding of soil processes and properties can be achieved. Spatial heterogeneity of the soil profile can be taken into account. We took six 30 cm long rectangular soil columns of adjacent Luvisols derived from quaternary aeolian sediments (Loess) in a forest soil near Freising/Bavaria using stainless steel boxes (100×100×300 mm). Three profiles were sampled under Norway spruce and three under European beech. A hyperspectral camera (VNIR, 400-1000 nm in 160 spectral bands) with spatial resolution of 63×63 µm² per pixel was used for data acquisition. Reference samples were taken at representative spots and analysed for organic carbon (OC) quantity and quality with a CN elemental analyser and for iron oxides (Fe) content using dithionite extraction followed by ICP-OES measurement. We compared two supervised classification algorithms, Spectral Angle Mapper and Maximum Likelihood, using different sets of training areas and spectral libraries. As established in chemometrics we used multivariate analysis such as partial least-squares regression (PLSR) in addition to multivariate adaptive regression splines (MARS) to correlate chemical data with Vis-NIR spectra. As a result elemental mapping of Fe and OC within the soil core at high spatial resolution has been achieved. The regression model was validated by a new set of reference samples for chemical analysis. Digital soil classification easily visualizes soil properties within the soil profiles. By combining both techniques, detailed soil maps, elemental balances and a deeper understanding of soil forming processes at the microscale become feasible for complete soil profiles.

An Improved Weighted Partial Least Squares Method Coupled with Near Infrared Spectroscopy for Rapid Determination of Multiple Components and Anti-Oxidant Activity of Pu-Erh Tea.

PubMed

Liu, Ze; Xie, Hua-Lin; Chen, Lin; Huang, Jian-Hua

2018-05-02

Background: Pu-erh tea is a unique microbially fermented tea, which distinctive chemical constituents and activities are worthy of systematic study. Near infrared spectroscopy (NIR) coupled with suitable chemometrics approaches can rapidly and accurately quantitatively analyze multiple compounds in samples. Methods: In this study, an improved weighted partial least squares (PLS) algorithm combined with near infrared spectroscopy (NIR) was used to construct a fast calibration model for determining four main components, i.e., tea polyphenols, tea polysaccharide, total flavonoids, theanine content, and further determine the total antioxidant capacity of pu-erh tea. Results: The final correlation coefficients R square for tea polyphenols, tea polysaccharide, total flavonoids content, theanine content, and total antioxidant capacity were 0.8288, 0.8403, 0.8415, 0.8537 and 0.8682, respectively. Conclusions : The current study provided a comprehensive study of four main ingredients and activity of pu-erh tea, and demonstrated that NIR spectroscopy technology coupled with multivariate calibration analysis could be successfully applied to pu-erh tea quality assessment.

`VIS/NIR mapping of TOC and extent of organic soils in the Nørre Å valley

NASA Astrophysics Data System (ADS)

Knadel, M.; Greve, M. H.; Thomsen, A.

2009-04-01

Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The system also acquired soil electrical conductivity (EC) for two soil depths: shallow EC-SH (0- 31 cm) and deep conductivity EC-DP (0- 91 cm). The conductivity was recorded together with GPS coordinates and spectral data for further construction of the calibration models. Two maps of organic soils in the Nørre Å valley (Central Jutland) were generated: (i) based on a conventional 25 m grid with 162 sampling points and laboratory analysis of TOC, (ii) based on in-situ VIS/NIR measurements supported by chemometrics. Before regression analysis, spectral information was compressed by calculating principal components. The outliers were determined by a mahalanobis distance equation and removed. Clustering using a fuzzy c- means algorithm was conducted. Within each cluster a location with the minimal spatial variability was selected. A map of 15 representative sample locations was proposed. The interpolation of the spectra into a single spectrum was performed using a Gaussian kernel weighting function. Spectra obtained near a sampled location were averaged. The collected spectra were correlated to TOC of the 15 representative samples using multivariate regression techniques (Unscrambler 9.7; Camo ASA, Oslo, Norway). Two types of calibrations were performed: using only spectra and using spectra together with the auxiliary data (EC-SH and EC-DP). These calibration equations were computed using PLS regression, segmented cross-validation method on centred data (using the raw spectral data, log 1/R). Six different spectra pre-treatments were conducted: (1) only spectra, (2) Savitsky-Golay smoothing over 11 wavelength points and transformation to a (3) 1'st and (4) 2'nd Savitzky and Golay derivative algorithm with a derivative interval of 21 wavelength points, (5) with or (6) without smoothing. The best treatment was considered to be the one with the lowest Root Mean Square Error of Prediction (RMSEP), the highest r2 between the VIS/NIR-predicted and measured values in the calibration model and the lowest mean deviation of predicted TOC values. The best calibration model was obtained with the mathematical pre-treatment's including smoothing, calculating the 2'nd derivative and outlier removal. The two TOC maps were compared after interpolation using kriging. They showed a similar pattern in the TOC distribution. Despite the unfavourable field conditions the VIS/NIR system performed well in both low and high TOC areas. Water content in places exceeding field capacity in the lower parts of the investigated field did not seriously degrade measurements. The present study represents the first attempt to apply the mobile Veris VIS/NIR system to the mapping of TOC of peat soils in Denmark. The result from this study show that a mobile VIS/NIR system can be applied to cost effective TOC mapping of mineral and organic soils with highly varying water content. Key words: VIS/NIR spectroscopy, organic soils, TOC

Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR spectroscopy.

PubMed

Wedding, Brett B; White, Ronald D; Grauf, Steve; Wright, Carole; Tilse, Bonnie; Hofman, Peter; Gadek, Paul A

2011-01-30

The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra-seasonal variation and orchard conditions. It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4-34.2%. The results of the study indicate the potential of FT-NIRS in diffuse reflectance mode to non-invasively predict %DM of whole 'Hass' avocado fruit. When the FT-NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados. 2010 Society of Chemical Industry.

Diagnosis of colorectal cancer by near-infrared optical fiber spectroscopy and random forest

NASA Astrophysics Data System (ADS)

Chen, Hui; Lin, Zan; Wu, Hegang; Wang, Li; Wu, Tong; Tan, Chao

2015-01-01

Near-infrared (NIR) spectroscopy has such advantages as being noninvasive, fast, relatively inexpensive, and no risk of ionizing radiation. Differences in the NIR signals can reflect many physiological changes, which are in turn associated with such factors as vascularization, cellularity, oxygen consumption, or remodeling. NIR spectral differences between colorectal cancer and healthy tissues were investigated. A Fourier transform NIR spectroscopy instrument equipped with a fiber-optic probe was used to mimic in situ clinical measurements. A total of 186 spectra were collected and then underwent the preprocessing of standard normalize variate (SNV) for removing unwanted background variances. All the specimen and spots used for spectral collection were confirmed staining and examination by an experienced pathologist so as to ensure the representative of the pathology. Principal component analysis (PCA) was used to uncover the possible clustering. Several methods including random forest (RF), partial least squares-discriminant analysis (PLSDA), K-nearest neighbor and classification and regression tree (CART) were used to extract spectral features and to construct the diagnostic models. By comparison, it reveals that, even if no obvious difference of misclassified ratio (MCR) was observed between these models, RF is preferable since it is quicker, more convenient and insensitive to over-fitting. The results indicate that NIR spectroscopy coupled with RF model can serve as a potential tool for discriminating the colorectal cancer tissues from normal ones.

A Near Infrared Spectroscopy (NIRS) and Chemometric Approach to Improve Apple Fruit Quality Management: A Case Study on the Cultivars "Cripps Pink" and "Braeburn".

PubMed

Eisenstecken, Daniela; Panarese, Alessia; Robatscher, Peter; Huck, Christian W; Zanella, Angelo; Oberhuber, Michael

2015-07-24

The potential of near infrared spectroscopy (NIRS) in the wavelength range of 1000-2500 nm for predicting quality parameters such as total soluble solids (TSS), acidity (TA), firmness, and individual sugars (glucose, fructose, sucrose, and xylose) for two cultivars of apples ("Braeburn" and "Cripps Pink") was studied during the pre- and post-storage periods. Simultaneously, a qualitative investigation on the capability of NIRS to discriminate varieties, harvest dates, storage periods and fruit inhomogeneity was carried out. In order to generate a sample set with high variability within the most relevant apple quality traits, three different harvest time points in combination with five different storage periods were chosen, and the evolution of important quality parameters was followed both with NIRS and wet chemical methods. By applying a principal component analysis (PCA) a differentiation between the two cultivars, freshly harvested vs. long-term stored apples and, notably, between the sun-exposed vs. shaded side of apples could be found. For the determination of quality parameters effective prediction models for titratable acid (TA) and individual sugars such as fructose, glucose and sucrose by using partial least square (PLS) regression have been developed. Our results complement earlier reports, highlighting the versatility of NIRS as a fast, non-invasive method for quantitative and qualitative studies on apples.

The critical role of NIR spectroscopy and statistical process control (SPC) strategy towards captopril tablets (25 mg) manufacturing process understanding: a case study.

PubMed

Curtivo, Cátia Panizzon Dal; Funghi, Nathália Bitencourt; Tavares, Guilherme Diniz; Barbosa, Sávio Fujita; Löbenberg, Raimar; Bou-Chacra, Nádia Araci

2015-05-01

In this work, near-infrared spectroscopy (NIRS) method was used to evaluate the uniformity of dosage units of three captopril 25 mg tablets commercial batches. The performance of the calibration method was assessed by determination of Q value (0.9986), standard error of estimation (C-set SEE = 1.956), standard error of prediction (V-set SEP = 2.076) as well as the consistency (106.1%). These results indicated the adequacy of the selected model. The method validation revealed the agreement of the reference high pressure liquid chromatography (HPLC) and NIRS methods. The process evaluation using the NIRS method showed that the variability was due to common causes and delivered predictable results consistently. Cp and Cpk values were, respectively, 2.05 and 1.80. These results revealed a non-centered process in relation to the average target (100% w/w), in the specified range (85-115%). The probability of failure was 21:100 million tablets of captopril. The NIRS in combination with the method of multivariate calibration, partial least squares (PLS) regression, allowed the development of methodology for the uniformity of dosage units evaluation of captopril tablets 25 mg. The statistical process control strategy associated with NIRS method as PAT played a critical role in understanding of the sources and degree of variation and its impact on the process. This approach led towards a better process understanding and provided the sound scientific basis for its continuous improvement.

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-07-15

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm-partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)-are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis. Copyright © 2011 Elsevier B.V. All rights reserved.

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

PubMed Central

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. PMID:28045443

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data.

PubMed

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.

Optical spectroscopic characterization of human meniscus biomechanical properties

NASA Astrophysics Data System (ADS)

Ala-Myllymäki, Juho; Danso, Elvis K.; Honkanen, Juuso T. J.; Korhonen, Rami K.; Töyräs, Juha; Afara, Isaac O.

2017-12-01

This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development (83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eɛ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery.

Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopy for rapid quality assessment of Chinese medicine preparation Honghua Oil.

PubMed

Wu, Yan-Wen; Sun, Su-Qin; Zhou, Qun; Leung, Hei-Wun

2008-02-13

Honghua Oil (HHO), a traditional Chinese medicine (TCM) oil preparation, is a mixture of several plant essential oils. In this text, the extended ranges of Fourier transform mid-infrared (FT-MIR) and near infrared (FT-NIR) were recorded for 48 commercially available HHOs of different batches from nine manufacturers. The qualitative and quantitative analysis of three marker components, alpha-pinene, methyl salicylate and eugenol, in different HHO products were performed rapidly by the two vibrational spectroscopic methods, i.e. MIR with horizontal attenuated total reflection (HATR) accessory and NIR with direct sampling technique, followed by partial least squares (PLS) regression treatment of the set of spectra obtained. The results indicated that it was successful to identify alpha-pinene, methyl salicylate and eugenol in all of the samples by simple inspection of the MIR-HATR spectra. Both PLS models established with MIR-HATR and NIR spectral data using gas chromatography (GC) peak areas as calibration reference showed a good linear correlation for each of all three target substances in HHO samples. The above spectroscopic techniques may be the promising methods for the rapid quality assessment/quality control (QA/QC) of TCM oil preparations.

Development of a near-infrared spectroscopy method (NIRS) for fast analysis of total, indolic, aliphatic and individual glucosinolates in new bred open pollinating genotypes of broccoli (Brassica oleracea convar. botrytis var. italica).

PubMed

Sahamishirazi, Samira; Zikeli, Sabine; Fleck, Michael; Claupein, Wilhelm; Graeff-Hoenninger, Simone

2017-10-01

This study describes the development of near-infrared spectroscopy (NIRS) calibration to determine individual and total glucosinolates (GSLs) content of 12 new-bred open-pollinating genotypes of broccoli (Brassica oleracea convar. botrytis var. italica). Six individual GSLs were identified using high-performance-liquid chromatography (HPLC). The NIRS calibration was established based on modified partial least squares regression with reference values of HPLC. The calibration was analyzed using coefficient of determination in prediction (R 2 ) and ratio of preference of determination (RPD). Large variation occurred in the calibrations, R 2 and RPD due to the variability of the samples. Derived calibrations for total-GSLs, aliphatic-GSLs, glucoraphanin and 4-methoxyglucobrassicin were quantitative with a high accuracy (RPD=1.36, 1.65, 1.63, 1.11) while, for indole-GSLs, glucosinigrin, glucoiberin, glucobrassicin and 1-methoxyglucobrassicin were more qualitative (RPD=0.95, 0.62, 0.67, 0.81, 0.56). Overall, the results indicated NIRS has a good potential to determine different GSLs in a large sample pool of broccoli quantitatively and qualitatively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of ethanol in bottled Chinese rice wine by NIR spectroscopy

NASA Astrophysics Data System (ADS)

Ying, Yibin; Yu, Haiyan; Pan, Xingxiang; Lin, Tao

2006-10-01

To evaluate the applicability of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining ethanol concentration of Chinese rice wine in square brown glass bottle, transmission spectra of 100 bottled Chinese rice wine samples were collected in the spectral range of 350-1200 nm. Statistical equations were established between the reference data and VIS-NIR spectra by partial least squares (PLS) regression method. Performance of three kinds of mathematical treatment of spectra (original spectra, first derivative spectra and second derivative spectra) were also discussed. The PLS models of original spectra turned out better results, with higher correlation coefficient in calibration (R cal) of 0.89, lower root mean standard error of calibration (RMSEC) of 0.165, and lower root mean standard error of cross validation (RMSECV) of 0.179. Using original spectra, PLS models for ethanol concentration prediction were developed. The R cal and the correlation coefficient in validation (R val) were 0.928 and 0.875, respectively; and the RMSEC and the root mean standard error of validation (RMSEP) were 0.135 (%, v v -1) and 0.177 (%, v v -1), respectively. The results demonstrated that VIS-NIR spectroscopy could be used to predict ethanol concentration in bottled Chinese rice wine.

Rapid, simultaneous and non-destructive assessment of the moisture, water activity, firmness and SO2 content of the intact sulphured-dried apricots using FT-NIRS and chemometrics.

PubMed

Özdemir, İbrahim Sani; Öztürk, Bülent; Çelik, Belgin; Sarıtepe, Yüksel; Aksoy, Hatice

2018-08-15

The potential of using FT-NIR spectroscopy for the rapid and non-destructive measurement of the moisture, water activity, firmness and SO 2 content of the intact sulphured-dried apricots (SDA) was investigated for the first time in the literature. The partial least squares regression (PLS-R) models constructed using FT-NIR spectra were very successful in predicting the moisture content (R 2 p = 0.986, RMSEP = 1.22%, RPD = 9.15) and water activity (R 2 p = 0.987, RMSEP = 0.016, RPD = 9.37) of SDAs. Satisfactory results were also obtained for the models developed for the prediction of the firmness (R 2 p = 0.845, RMSEP = 0.445, RPD = 2.55) and SO 2 content (R 2 p = 0.804, RMSEP = 349 mg kg -1 , RPD = 2.27). These results clearly demonstrate that the major quality parameters of SDA can be simultaneously measured in a short time by FT-NIR spectroscopy without any need for the sample preparation or skilled laboratory personnel. Copyright © 2018 Elsevier B.V. All rights reserved.

Design of a simple non-destructive detection system using P-wave lasers for determining the soluble solids content of apples.

PubMed

Hua, Shih-Hao; Chen, Chao-Pin; Han, Pin

2017-08-01

The simple and nondestructive detection system studied in this work uses a near-infrared (NIR) detector and parallel-polarized (P-wave) NIR lasers to determine the soluble solids content (SSC) of apples. The P-wave NIR laser in this system is incident into the apple's pulp at the Brewster angle to minimize the interference caused by interfacial reflections. After the apple has been illuminated by four P-wave NIR lasers that correspond to the specified wavelengths of the SSC chemical bonds (880, 940, 980, and 1064 nm), the prediction of correlation (rp2) and the root-mean-square error for prediction (RMSEP) of the SSC are determined via partial least square regression analysis of the reflectance. Our results indicate that the use of P-wave lasers at the Brewster angle (as the angle of incidence) and the above specified wavelengths for the prediction set measurement of the SSC of apples obtained an rp2 of 0.88 and an RMSEP of 0.47°Brix. These rp2 are 6% higher, and the RMSEPs are 9% lower, than those obtained using non-polarized lasers.

Field applications of stand-off sensing using visible/NIR multivariate optical computing

NASA Astrophysics Data System (ADS)

Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.

2001-02-01

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

Early tumor detection afforded by in vivo imaging of near-infrared II fluorescence.

PubMed

Tao, Zhimin; Dang, Xiangnan; Huang, Xing; Muzumdar, Mandar D; Xu, Eric S; Bardhan, Neelkanth Manoj; Song, Haiqin; Qi, Ruogu; Yu, Yingjie; Li, Ting; Wei, Wei; Wyckoff, Jeffrey; Birrer, Michael J; Belcher, Angela M; Ghoroghchian, P Peter

2017-07-01

Cell-intrinsic reporters such as luciferase (LUC) and red fluorescent protein (RFP) have been commonly utilized in preclinical studies to image tumor growth and to monitor therapeutic responses. While extrinsic reporters that emit near infrared I (NIR-I: 650-950 nm) or near-infrared II (NIR-II: 1000-1700 nm) optical signals have enabled minimization of tissue autofluorescence and light scattering, it has remained unclear as to whether their use has afforded more accurate tumor imaging in small animals. Here, we developed a novel optical imaging construct comprised of rare earth lanthanide nanoparticles coated with biodegradable diblock copolymers and doped with organic fluorophores, generating NIR-I and NIR-II emissive bands upon optical excitation. Simultaneous injection of multiple spectrally-unique nanoparticles into mice bearing tumor implants established via intraperitoneal dissemination of LUC + /RFP + OVCAR-8 ovarian cancer cells enabled direct comparisons of imaging with extrinsic vs. intrinsic reporters, NIR-II vs. NIR-I signals, as well as targeted vs. untargeted exogenous contrast agents in the same animal and over time. We discovered that in vivo optical imaging at NIR-II wavelengths facilitates more accurate detection of smaller and earlier tumor deposits, offering enhanced sensitivity, improved spatial contrast, and increased depths of tissue penetration as compared to imaging with visible or NIR-I fluorescent agents. Our work further highlights the hitherto underappreciated enhancements in tumor accumulation that may be achieved with intraperitoneal as opposed to intravenous administration of nanoparticles. Lastly, we found discrepancies in the fidelity of tumor uptake that could be obtained by utilizing small molecules for in vivo as opposed to in vitro targeting of nanoparticles to disseminated tumors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Near infrared spectroscopic imaging assessment of cartilage composition: Validation with mid infrared imaging spectroscopy.

PubMed

Palukuru, Uday P; Hanifi, Arash; McGoverin, Cushla M; Devlin, Sean; Lelkes, Peter I; Pleshko, Nancy

2016-07-05

Disease or injury to articular cartilage results in loss of extracellular matrix components which can lead to the development of osteoarthritis (OA). To better understand the process of disease development, there is a need for evaluation of changes in cartilage composition without the requirement of extensive sample preparation. Near infrared (NIR) spectroscopy is a chemical investigative technique based on molecular vibrations that is increasingly used as an assessment tool for studying cartilage composition. However, the assignment of specific molecular vibrations to absorbance bands in the NIR spectrum of cartilage, which arise from overtones and combinations of primary absorbances in the mid infrared (MIR) spectral region, has been challenging. In contrast, MIR spectroscopic assessment of cartilage is well-established, with many studies validating the assignment of specific bands present in MIR spectra to specific molecular vibrations. In the current study, NIR imaging spectroscopic data were obtained for compositional analysis of tissues that served as an in vitro model of OA. MIR spectroscopic data obtained from the identical tissue regions were used as the gold-standard for collagen and proteoglycan (PG) content. MIR spectroscopy in transmittance mode typically requires a much shorter pathlength through the sample (≤10 microns thick) compared to NIR spectroscopy (millimeters). Thus, this study first addressed the linearity of small absorbance bands in the MIR region with increasing tissue thickness, suitable for obtaining a signal in both the MIR and NIR regions. It was found that the linearity of specific, small MIR absorbance bands attributable to the collagen and PG components of cartilage (at 1336 and 856 cm(-1), respectively) are maintained through a thickness of 60 μm, which was also suitable for NIR data collection. MIR and NIR spectral data were then collected from 60 μm thick samples of cartilage degraded with chondroitinase ABC as a model of OA. Partial least squares (PLS) regression using NIR spectra as input predicted the MIR-determined compositional parameters of PG/collagen within 6% of actual values. These results indicate that NIR spectral data can be used to assess molecular changes that occur with cartilage degradation, and further, the data provide a foundation for future clinical studies where NIR fiber optic probes can be used to assess the progression of cartilage degradation. Copyright © 2016 Elsevier B.V. All rights reserved.

Dental optical tomography with upconversion nanoparticles—a feasibility study

PubMed Central

Long, Feixiao; Intes, Xavier

2017-01-01

Abstract. Upconversion nanoparticles (UCNPs) have the unique ability to emit multiple colors upon excitation by near-infrared (NIR) light. Herein, we investigate the potential use of UCNPs as contrast agents for dental optical tomography, with a focus on monitoring the status of fillings after dental restoration. The potential of performing tomographic imaging using UCNP emission of visible or NIR light is established. This in silico and ex vivo study paves the way toward employing UCNPs as theranostic agents for dental applications. PMID:28586852

Dental optical tomography with upconversion nanoparticles—a feasibility study

NASA Astrophysics Data System (ADS)

Long, Feixiao; Intes, Xavier

2017-06-01

Upconversion nanoparticles (UCNPs) have the unique ability to emit multiple colors upon excitation by near-infrared (NIR) light. Herein, we investigate the potential use of UCNPs as contrast agents for dental optical tomography, with a focus on monitoring the status of fillings after dental restoration. The potential of performing tomographic imaging using UCNP emission of visible or NIR light is established. This in silico and ex vivo study paves the way toward employing UCNPs as theranostic agents for dental applications.

Dental optical tomography with upconversion nanoparticles-a feasibility study.

PubMed

Long, Feixiao; Intes, Xavier

2017-06-01

Upconversion nanoparticles (UCNPs) have the unique ability to emit multiple colors upon excitation by near-infrared (NIR) light. Herein, we investigate the potential use of UCNPs as contrast agents for dental optical tomography, with a focus on monitoring the status of fillings after dental restoration. The potential of performing tomographic imaging using UCNP emission of visible or NIR light is established. This in silico and ex vivo study paves the way toward employing UCNPs as theranostic agents for dental applications.

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Liming, Ye; Yufei, Liu; Chunyan, Li; Han, Chen

2017-05-01

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography

NASA Astrophysics Data System (ADS)

Jermyn, Michael; Ghadyani, Hamid; Mastanduno, Michael A.; Turner, Wes; Davis, Scott C.; Dehghani, Hamid; Pogue, Brian W.

2013-08-01

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.

NIR-triggered high-efficient photodynamic and chemo-cascade therapy using caspase-3 responsive functionalized upconversion nanoparticles.

PubMed

Zhao, Na; Wu, Baoyan; Hu, Xianglong; Xing, Da

2017-10-01

Stimuli-responsive nanoparticles with multiple therapeutic/diagnostic functions are highly desirable for effective tumor treatment. Herein novel caspase-3 responsive functionalized upconversion nanoparticles (CFUNs) were fabricated with three-in-one functional integration: near-infrared (NIR) triggered photodynamic damage along with caspase-3 activation, subsequent caspase-3 responsive drug release, and cascade chemotherapeutic activation. CFUNs were formulated from the self-assembly of caspase-3 responsive doxorubicin (DOX) prodrug tethered with DEVD peptide (DEVD-DOX), upconversion nanoparticles (UCNP), a photosensitizer (pyropheophorbide-a methyl ester, MPPa), and tumor-targeting cRGD-PEG-DSPE to afford multifunctional CFUNs, MPPa/UCNP-DEVD-DOX/cRGD. Upon cellular uptake and NIR irradiation, the visible light emission of UCNP could excite MPPa to produce reactive oxygen species for photodynamic therapy (PDT) along with the activation of caspase-3, which further cleaved DEVD peptide to release DOX within tumor cells, thus accomplishing NIR-triggered PDT and cascade chemotherapy. CFUNs presented silent therapeutic potency and negligible cytotoxicity in the dark, whereas in vitro and in vivo experiments demonstrated the NIR-triggered cascade therapeutic activation and tumor inhibition due to consecutive PDT and chemotherapy. Current NIR-activated cascade tumor therapy with two distinct mechanisms is significantly favorable to overcome multidrug resistance and tumor heterogeneity for persistent tumor treatment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rheo-optical near-infrared (NIR) spectroscopy study of partially miscible polymer blend of polymethyl methacrylate (PMMA) and polyethylene glycol (PEG)

NASA Astrophysics Data System (ADS)

Shinzawa, Hideyuki; Mizukado, Junji

2018-03-01

Tensile deformations of a partially miscible blend of polymethyl methacrylate (PMMA) and polyethylene glycol (PEG) is studied by a rheo-optical characterization near-infrared (NIR) technique to probe deformation behavior during tensile deformation. Sets of NIR spectra of the polymer samples were collected by using an acousto-optic tunable filter (AOTF) NIR spectrometer coupled with a tensile testing machine as an excitation device. While deformations of the samples were readily captured as strain-dependent NIR spectra, the entire feature of the spectra was overwhelmed with the baseline fluctuation induced by the decrease in the sample thickness and subsequent change in the light scattering. Several pretreatment techniques, including multiplicative scatter collection (MSC) and null-space projection, are subjected to the NIR spectra prior to the determination of the sequential order of the spectral intensity changes by two-dimensional (2D) correlation analysis. The comparison of the MSC and null-space projection provided an interesting insight into the system, especially deformation-induced variation of light scattering observed during the tensile testing of the polymer sample. In addition, the sequential order determined with the 2D correlation spectra revealed that orientation of a specific part of PMMA chain occurs before that of the others because of the interaction between Cdbnd O group of PMMA and terminal sbnd OH group of PEG.

Development of a NIR-based blend uniformity method for a drug product containing multiple structurally similar actives by using the quality by design principles.

PubMed

Lin, Yiqing; Li, Weiyong; Xu, Jin; Boulas, Pierre

2015-07-05

The aim of this study is to develop an at-line near infrared (NIR) method for the rapid and simultaneous determination of four structurally similar active pharmaceutical ingredients (APIs) in powder blends intended for the manufacturing of tablets. Two of the four APIs in the formula are present in relatively small amounts, one at 0.95% and the other at 0.57%. Such small amounts in addition to the similarity in structures add significant complexity to the blend uniformity analysis. The NIR method is developed using spectra from six laboratory-created calibration samples augmented by a small set of spectra from a large-scale blending sample. Applying the quality by design (QbD) principles, the calibration design included concentration variations of the four APIs and a main excipient, microcrystalline cellulose. A bench-top FT-NIR instrument was used to acquire the spectra. The obtained NIR spectra were analyzed by applying principal component analysis (PCA) before calibration model development. Score patterns from the PCA were analyzed to reveal relationship between latent variables and concentration variations of the APIs. In calibration model development, both PLS-1 and PLS-2 models were created and evaluated for their effectiveness in predicting API concentrations in the blending samples. The final NIR method shows satisfactory specificity and accuracy. Copyright © 2015 Elsevier B.V. All rights reserved.

Neuroendocrine Tumor-Targeted Upconversion Nanoparticle-Based Micelles for Simultaneous NIR-Controlled Combination Chemotherapy and Photodynamic Therapy, and Fluorescence Imaging.

PubMed

Chen, Guojun; Jaskula-Sztul, Renata; Esquibel, Corinne R; Lou, Irene; Zheng, Qifeng; Dammalapati, Ajitha; Harrison, April; Eliceiri, Kevin W; Tang, Weiping; Chen, Herbert; Gong, Shaoqin

2017-02-23

Although neuroendocrine tumors (NETs) are slow growing, they are frequently metastatic at the time of discovery and no longer amenable to curative surgery, emphasizing the need for the development of other treatments. In this study, multifunctional upconversion nanoparticle (UCNP)-based theranostic micelles are developed for NET-targeted and near-infrared (NIR)-controlled combination chemotherapy and photodynamic therapy (PDT), and bioimaging. The theranostic micelle is formed by individual UCNP functionalized with light-sensitive amphiphilic block copolymers poly(4,5-dimethoxy-2-nitrobenzyl methacrylate)-polyethylene glycol (PNBMA-PEG) and Rose Bengal (RB) photosensitizers. A hydrophobic anticancer drug, AB3, is loaded into the micelles. The NIR-activated UCNPs emit multiple luminescence bands, including UV, 540 nm, and 650 nm. The UV peaks overlap with the absorption peak of photocleavable hydrophobic PNBMA segments, triggering a rapid drug release due to the NIR-induced hydrophobic-to-hydrophilic transition of the micelle core and thus enabling NIR-controlled chemotherapy. RB molecules are activated via luminescence resonance energy transfer to generate 1 O 2 for NIR-induced PDT. Meanwhile, the 650 nm emission allows for efficient fluorescence imaging. KE108, a true pansomatostatin nonapeptide, as an NET-targeting ligand, drastically increases the tumoral uptake of the micelles. Intravenously injected AB3-loaded UCNP-based micelles conjugated with RB and KE108-enabling NET-targeted combination chemotherapy and PDT-induce the best antitumor efficacy.

In-line moisture monitoring in fluidized bed granulation using a novel multi-resonance microwave sensor.

PubMed

Peters, Johanna; Bartscher, Kathrin; Döscher, Claas; Taute, Wolfgang; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg

2017-08-01

Microwave resonance technology (MRT) is known as a process analytical technology (PAT) tool for moisture measurements in fluid-bed granulation. It offers a great potential for wet granulation processes even where the suitability of near-infrared (NIR) spectroscopy is limited, e.g. colored granules, large variations in bulk density. However, previous sensor systems operating around a single resonance frequency showed limitations above approx. 7.5% granule moisture. This paper describes the application of a novel sensor working with four resonance frequencies. In-line data of all four resonance frequencies were collected and further processed. Based on calculation of density-independent microwave moisture values multiple linear regression (MLR) models using Karl-Fischer titration (KF) as well as loss on drying (LOD) as reference methods were build. Rapid, reliable in-process moisture control (RMSEP≤0.5%) even at higher moisture contents was achieved. Copyright © 2017 Elsevier B.V. All rights reserved.

Neuroimaging-Aided Prediction of the Effect of Methylphenidate in Children with Attention-Deficit Hyperactivity Disorder: A Randomized Controlled Trial.

PubMed

Ishii-Takahashi, Ayaka; Takizawa, Ryu; Nishimura, Yukika; Kawakubo, Yuki; Hamada, Kasumi; Okuhata, Shiho; Kawasaki, Shingo; Kuwabara, Hitoshi; Shimada, Takafumi; Todokoro, Ayako; Igarashi, Takashi; Watanabe, Kei-Ichiro; Yamasue, Hidenori; Kato, Nobumasa; Kasai, Kiyoto; Kano, Yukiko

2015-11-01

Although methylphenidate hydrochloride (MPH) is a first-line treatment for children with attention-deficit hyperactivity disorder (ADHD), the non-response rate is 30%. Our aim was to develop a supplementary neuroimaging biomarker for predicting the clinical effect of continuous MPH administration by using near-infrared spectroscopy (NIRS). After baseline assessment, we performed a double-blind, placebo-controlled, crossover trial with a single dose of MPH, followed by a prospective 4-to-8-week open trial with continuous MPH administration, and an ancillary 1-year follow-up. Twenty-two drug-naïve and eight previously treated children with ADHD (NAÏVE and NON-NAÏVE) were compared with 20 healthy controls (HCs) who underwent multiple NIRS measurements without intervention. We tested whether NIRS signals at the baseline assessment or ΔNIRS (single dose of MPH minus baseline assessment) predict the Clinical Global Impressions-Severity (CGI-S) score after 4-to-8-week or 1-year MPH administration. The secondary outcomes were the effect of MPH on NIRS signals after single-dose, 4-to-8-week, and 1-year administration. ΔNIRS significantly predicted CGI-S after 4-to-8-week MPH administration. The leave-one-out classification algorithm had 81% accuracy using the NIRS signal. ΔNIRS also significantly predicted CGI-S scores after 1 year of MPH administration. For secondary analyses, NAÏVE exhibited significantly lower prefrontal activation than HCs at the baseline assessment, whereas NON-NAÏVE and HCs showed similar activation. A single dose of MPH significantly increased activation compared with the placebo in NAÏVE. After 4-to-8-week administration, and even after MPH washout following 1-year administration, NAÏVE demonstrated normalized prefrontal activation. Supplementary NIRS measurements may serve as an objective biomarker for clinical decisions and monitoring concerning continuous MPH treatment in children with ADHD.

Shortwave infrared fluorescence imaging with the clinically approved near-infrared dye indocyanine green.

PubMed

Carr, Jessica A; Franke, Daniel; Caram, Justin R; Perkinson, Collin F; Saif, Mari; Askoxylakis, Vasileios; Datta, Meenal; Fukumura, Dai; Jain, Rakesh K; Bawendi, Moungi G; Bruns, Oliver T

2018-04-24

Fluorescence imaging is a method of real-time molecular tracking in vivo that has enabled many clinical technologies. Imaging in the shortwave IR (SWIR; 1,000-2,000 nm) promises higher contrast, sensitivity, and penetration depths compared with conventional visible and near-IR (NIR) fluorescence imaging. However, adoption of SWIR imaging in clinical settings has been limited, partially due to the absence of US Food and Drug Administration (FDA)-approved fluorophores with peak emission in the SWIR. Here, we show that commercially available NIR dyes, including the FDA-approved contrast agent indocyanine green (ICG), exhibit optical properties suitable for in vivo SWIR fluorescence imaging. Even though their emission spectra peak in the NIR, these dyes outperform commercial SWIR fluorophores and can be imaged in the SWIR, even beyond 1,500 nm. We show real-time fluorescence imaging using ICG at clinically relevant doses, including intravital microscopy, noninvasive imaging in blood and lymph vessels, and imaging of hepatobiliary clearance, and show increased contrast compared with NIR fluorescence imaging. Furthermore, we show tumor-targeted SWIR imaging with IRDye 800CW-labeled trastuzumab, an NIR dye being tested in multiple clinical trials. Our findings suggest that high-contrast SWIR fluorescence imaging can be implemented alongside existing imaging modalities by switching the detection of conventional NIR fluorescence systems from silicon-based NIR cameras to emerging indium gallium arsenide-based SWIR cameras. Using ICG in particular opens the possibility of translating SWIR fluorescence imaging to human clinical applications. Indeed, our findings suggest that emerging SWIR-fluorescent in vivo contrast agents should be benchmarked against the SWIR emission of ICG in blood.

Simulation and performance evaluation of fiber optic sensor for detection of hepatic malignancies in human liver tissues

NASA Astrophysics Data System (ADS)

Sharma, Anuj K.; Gupta, Jyoti; Basu, Rikmantra

2018-01-01

A fiber optic sensor is proposed for the identification of healthy and cancerous liver tissues through determination of their corresponding refractive index values. Existing experimental results describing variation of complex refractive index of liver tissues in near infrared (NIR) spectral region are considered for theoretical calculations. The intensity interrogation method with chalcogenide fiber is considered. The sensor's performance is closely analyzed in terms of its sensitivity at multiple operating wavelengths falling in NIR region. Operating at shorter NIR wavelengths leads to greater sensitivity. The effect of design parameters (sensing region length and fiber core diameter), different launching conditions, and fiber glass materials on sensor's performance is examined. The proposed sensor has the potential to provide high sensitivity of liver tissue detection.

Utilization of functional near infrared spectroscopy for non-invasive evaluation

NASA Astrophysics Data System (ADS)

Halim, A. A. A.; Laili, M. H.; Aziz, N. A.; Laili, A. R.; Salikin, M. S.; Rusop, M.

2016-07-01

The goal of this brief review is to report the techniques of functional near infrared spectroscopy for non-invasive evaluation in human study. The development of functional near infrared spectroscopy (fNIRS) technologies has advanced quantification signal using multiple wavelength and detector to solve the propagation of light inside the tissues including the absorption, scattering coefficient and to define the light penetration into tissues multilayers. There are a lot of studies that demonstrate signal from fNIRS which can be used to evaluate the changes of oxygenation level and measure the limitation of muscle performance in human brain and muscle tissues. Comprehensive reviews of diffuse reflectance based on beer lambert law theory were presented in this paper. The principle and development of fNIRS instrumentation is reported in detail.

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

PubMed

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

Process analytical technology in continuous manufacturing of a commercial pharmaceutical product.

PubMed

Vargas, Jenny M; Nielsen, Sarah; Cárdenas, Vanessa; Gonzalez, Anthony; Aymat, Efrain Y; Almodovar, Elvin; Classe, Gustavo; Colón, Yleana; Sanchez, Eric; Romañach, Rodolfo J

2018-03-01

The implementation of process analytical technology and continuous manufacturing at an FDA approved commercial manufacturing site is described. In this direct compaction process the blends produced were monitored with a Near Infrared (NIR) spectroscopic calibration model developed with partial least squares (PLS) regression. The authors understand that this is the first study where the continuous manufacturing (CM) equipment was used as a gravimetric reference method for the calibration model. A principal component analysis (PCA) model was also developed to identify the powder blend, and determine whether it was similar to the calibration blends. An air diagnostic test was developed to assure that powder was present within the interface when the NIR spectra were obtained. The air diagnostic test as well the PCA and PLS calibration model were integrated into an industrial software platform that collects the real time NIR spectra and applies the calibration models. The PCA test successfully detected an equipment malfunction. Variographic analysis was also performed to estimate the sampling analytical errors that affect the results from the NIR spectroscopic method during commercial production. The system was used to monitor and control a 28 h continuous manufacturing run, where the average drug concentration determined by the NIR method was 101.17% of label claim with a standard deviation of 2.17%, based on 12,633 spectra collected. The average drug concentration for the tablets produced from these blends was 100.86% of label claim with a standard deviation of 0.4%, for 500 tablets analyzed by Fourier Transform Near Infrared (FT-NIR) transmission spectroscopy. The excellent agreement between the mean drug concentration values in the blends and tablets produced provides further evidence of the suitability of the validation strategy that was followed. Copyright © 2018 Elsevier B.V. All rights reserved.

NIR spectroscopy for the quality control of Moringa oleifera (Lam.) leaf powders: Prediction of minerals, protein and moisture contents.

PubMed

Rébufa, Catherine; Pany, Inès; Bombarda, Isabelle

2018-09-30

A rapid methodology was developed to simultaneously predict water content and activity values (a w ) of Moringa oleifera leaf powders (MOLP) using near infrared (NIR) signatures and experimental sorption isotherms. NIR spectra of MOLP samples (n = 181) were recorded. A Partial Least Square Regression model (PLS2) was obtained with low standard errors of prediction (SEP of 1.8% and 0.07 for water content and a w respectively). Experimental sorption isotherms obtained at 20, 30 and 40 °C showed similar profiles. This result is particularly important to use MOLP in food industry. In fact, a temperature variation of the drying process will not affect their available water content (self-life). Nutrient contents based on protein and selected minerals (Ca, Fe, K) were also predicted from PLS1 models. Protein contents were well predicted (SEP of 2.3%). This methodology allowed for an improvement in MOLP safety, quality control and traceability. Published by Elsevier Ltd.

A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves.

PubMed

Zhang, Junjie; Li, Shuqi; Lin, Mengfei; Yang, Endian; Chen, Xiaoyang

2018-05-01

The drumstick tree has traditionally been used as foodstuff and fodder in several countries. Due to its high nutritional value and good biomass production, interest in this plant has increased in recent years. It has therefore become important to rapidly and accurately evaluate drumstick quality. In this study, we addressed the optimization of Near-infrared spectroscopy (NIRS) to analyze crude protein, crude fat, crude fiber, iron (Fe), and potassium (K) in a variety of drumstick accessions (N = 111) representing different populations, cultivation programs, and climates. Partial least-squares regression with internal cross-validation was used to evaluate the models and identify possible spectral outliers. The calibration statistics for these fodder-related chemical components suggest that NIRS can predict these parameters in a wide range of drumstick types with high accuracy. The NIRS calibration models developed in this study will be useful in predicting drumstick forage quality for these five quality parameters.

Rapid monitoring of the fermentation process for Korean traditional rice wine 'Makgeolli' using FT-NIR spectroscopy

NASA Astrophysics Data System (ADS)

Kim, Dae-Yong; Cho, Byoung-Kwan

2015-11-01

The quality parameters of the Korean traditional rice wine "Makgeolli" were monitored using Fourier transform near-infrared (FT-NIR) spectroscopy with multivariate statistical analysis (MSA) during fermentation. Alcohol, reducing sugar, and titratable acid were the parameters assessed to determine the quality index of fermentation substrates and products. The acquired spectra were analyzed with partial least squares regression (PLSR). The best prediction model for alcohol was obtained with maximum normalization, showing a coefficient of determination (Rp2) of 0.973 and a standard error of prediction (SEP) of 0.760%. In addition, the best prediction model for reducing sugar was obtained with no data preprocessing, with a Rp2 value of 0.945 and a SEP of 1.233%. The prediction of titratable acidity was best with mean normalization, showing a Rp2 value of 0.882 and a SEP of 0.045%. These results demonstrate that FT-NIR spectroscopy can be used for rapid measurements of quality parameters during Makgeolli fermentation.

Fast and simultaneous prediction of animal feed nutritive values using near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Samadi; Wajizah, S.; Munawar, A. A.

2018-02-01

Feed plays an important factor in animal production. The purpose of this study is to apply NIRS method in determining feed values. NIRS spectra data were acquired for feed samples in wavelength range of 1000 - 2500 nm with 32 scans and 0.2 nm wavelength. Spectral data were corrected by de-trending (DT) and standard normal variate (SNV) methods. Prediction of in vitro dry matter digestibility (IVDMD) and in vitro organic matter digestibility (IVOMD) were established as model by using principal component regression (PCR) and validated using leave one out cross validation (LOOCV). Prediction performance was quantified using coefficient correlation (r) and residual predictive deviation (RPD) index. The results showed that IVDMD and IVOMD can be predicted by using SNV spectra data with r and RPD index: 0.93 and 2.78 for IVDMD ; 0.90 and 2.35 for IVOMD respectively. In conclusion, NIRS technique appears feasible to predict animal feed nutritive values.

NIR detection of honey adulteration reveals differences in water spectral pattern.

PubMed

Bázár, György; Romvári, Róbert; Szabó, András; Somogyi, Tamás; Éles, Viktória; Tsenkova, Roumiana

2016-03-01

High fructose corn syrup (HFCS) was mixed with four artisanal Robinia honeys at various ratios (0-40%) and near infrared (NIR) spectra were recorded with a fiber optic immersion probe. Levels of HFCS adulteration could be detected accurately using leave-one-honey-out cross-validation (RMSECV=1.48; R(2)CV=0.987), partial least squares regression and the 1300-1800nm spectral interval containing absorption bands related to both water and carbohydrates. Aquaphotomics-based evaluations showed that unifloral honeys contained more highly organized water than the industrial sugar syrup, supposedly because of the greater variety of molecules dissolved in the multi-component honeys. Adulteration with HFCS caused a gradual reduction of water molecular structures, especially water trimers, which facilitate interaction with other molecules. Quick, non-destructive NIR spectroscopy combined with aquaphotomics could be used to describe water molecular structures in honey and to detect a rather common form of adulteration. Copyright © 2015 Elsevier Ltd. All rights reserved.

Near-Infrared Spectroscopy as an Analytical Process Technology for the On-Line Quantification of Water Precipitation Processes during Danhong Injection.

PubMed

Liu, Xuesong; Wu, Chunyan; Geng, Shu; Jin, Ye; Luan, Lianjun; Chen, Yong; Wu, Yongjiang

2015-01-01

This paper used near-infrared (NIR) spectroscopy for the on-line quantitative monitoring of water precipitation during Danhong injection. For these NIR measurements, two fiber optic probes designed to transmit NIR radiation through a 2 mm flow cell were used to collect spectra in real-time. Partial least squares regression (PLSR) was developed as the preferred chemometrics quantitative analysis of the critical intermediate qualities: the danshensu (DSS, (R)-3, 4-dihydroxyphenyllactic acid), protocatechuic aldehyde (PA), rosmarinic acid (RA), and salvianolic acid B (SAB) concentrations. Optimized PLSR models were successfully built and used for on-line detecting of the concentrations of DSS, PA, RA, and SAB of water precipitation during Danhong injection. Besides, the information of DSS, PA, RA, and SAB concentrations would be instantly fed back to site technical personnel for control and adjustment timely. The verification experiments determined that the predicted values agreed with the actual homologic value.

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

PubMed Central

Unni, Anirudh; Ihme, Klas; Jipp, Meike; Rieger, Jochem W.

2017-01-01

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing. PMID:28424602

Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

PubMed

Unni, Anirudh; Ihme, Klas; Jipp, Meike; Rieger, Jochem W

2017-01-01

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver's cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous 'n' speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

Near-infrared reflectance spectroscopy (NIRS) for rapid determination of ginsenoside Rg1 and Re in Chinese patent medicine Naosaitong pill

NASA Astrophysics Data System (ADS)

Zhang, Wei; Qu, Zhengyi; Wang, Yingping; Yao, Chunlin; Bai, Xueyuan; Bian, Shuai; Zhao, Bing

2015-03-01

Ginsenosides in plant samples have been extensively studied because protopanaxadiol saponins are ubiquitous in Chinese patent medicines, in which they can be used in promoting human health as the main active ingredients. A method for rapid determination of two ginsenosides (Rg1 and Re) in Naosaitong (NST) samples using near-infrared reflectance spectroscopy (NIRS) is studied to determine the contents of ginsenoside Rg1 and Re in this work. Partial least square (PLS) regression was used for building the calibration models, and the effects of spectral preprocessing and variable selection on the models are investigated for optimization of the models. A total of 93 samples were scanned by NIRS, and also by high performance liquid chromatography coupled to a diode array detector to determine the contents of ginsenoside Rg1 and Re. The calibration models for Rg1 and Re had high values of the coefficient of determination (R2) (0.9766 and 0.9764) and low root mean square error of cross validation (RMSECV) (0.0136 and 0.0104), and the values of the standard error of prediction set (SEP) are 0.00764 and 0.0103, which indicate a good correlation between reference values and NIRS predicted values. The overall results show that NIRS could be applied for the rapid determination of the contents of ginsenosides in Ginseng byproducts for pharmaceuticals that develop high-quality Chinese patent medicines.

Spatially selective depleting tumor-associated negative regulatory T-(Treg) cells with near infrared photoimmunotherapy (NIR-PIT): A new cancer immunotherapy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Kobayashi, Hisataka

2017-02-01

Near infrared photoimmunotherapy (NIR-PIT) is a new type of molecularly-targeted photo-therapy based on conjugating a near infrared silica-phthalocyanine dye, IR700, to a monoclonal antibody (MAb) targeting target-specific cell-surface molecules. When exposed to NIR light, the conjugate rapidly induces a highly-selective cell death only in receptor-positive, MAb-IR700-bound cells. Current immunotherapies for cancer seek to modulate the balance among different immune cell populations, thereby promoting anti-tumor immune responses. However, because these are systemic therapies, they often cause treatment-limiting autoimmune adverse effects. It would be ideal to manipulate the balance between suppressor and effector cells within the tumor without disturbing homeostasis elsewhere in the body. CD4+CD25+Foxp3+ regulatory T cells (Tregs) are well-known immune-suppressor cells that play a key role in tumor immuno-evasion and have been the target of systemic immunotherapies. We used CD25-targeted NIR-PIT to selectively deplete Tregs, thus activating CD8+ T and NK cells and restoring local anti-tumor immunity. This not only resulted in regression of the treated tumor but also induced responses in separate untreated tumors of the same cell-line derivation. We conclude that CD25-targeted NIR-PIT causes spatially selective depletion of Tregs, thereby providing an alternative approach to cancer immunotherapy that can treat not only local tumors but also distant metastatic tumors.

On-line monitoring the extract process of Fu-fang Shuanghua oral solution using near infrared spectroscopy and different PLS algorithms

NASA Astrophysics Data System (ADS)

Kang, Qian; Ru, Qingguo; Liu, Yan; Xu, Lingyan; Liu, Jia; Wang, Yifei; Zhang, Yewen; Li, Hui; Zhang, Qing; Wu, Qing

2016-01-01

An on-line near infrared (NIR) spectroscopy monitoring method with an appropriate multivariate calibration method was developed for the extraction process of Fu-fang Shuanghua oral solution (FSOS). On-line NIR spectra were collected through two fiber optic probes, which were designed to transmit NIR radiation by a 2 mm flange. Partial least squares (PLS), interval PLS (iPLS) and synergy interval PLS (siPLS) algorithms were used comparatively for building the calibration regression models. During the extraction process, the feasibility of NIR spectroscopy was employed to determine the concentrations of chlorogenic acid (CA) content, total phenolic acids contents (TPC), total flavonoids contents (TFC) and soluble solid contents (SSC). High performance liquid chromatography (HPLC), ultraviolet spectrophotometric method (UV) and loss on drying methods were employed as reference methods. Experiment results showed that the performance of siPLS model is the best compared with PLS and iPLS. The calibration models for AC, TPC, TFC and SSC had high values of determination coefficients of (R2) (0.9948, 0.9992, 0.9950 and 0.9832) and low root mean square error of cross validation (RMSECV) (0.0113, 0.0341, 0.1787 and 1.2158), which indicate a good correlation between reference values and NIR predicted values. The overall results show that the on line detection method could be feasible in real application and would be of great value for monitoring the mixed decoction process of FSOS and other Chinese patent medicines.

Development and validation of NIR-chemometric methods for chemical and pharmaceutical characterization of meloxicam tablets.

PubMed

Tomuta, Ioan; Iovanov, Rares; Bodoki, Ede; Vonica, Loredana

2014-04-01

Near-Infrared (NIR) spectroscopy is an important component of a Process Analytical Technology (PAT) toolbox and is a key technology for enabling the rapid analysis of pharmaceutical tablets. The aim of this research work was to develop and validate NIR-chemometric methods not only for the determination of active pharmaceutical ingredients content but also pharmaceutical properties (crushing strength, disintegration time) of meloxicam tablets. The development of the method for active content assay was performed on samples corresponding to 80%, 90%, 100%, 110% and 120% of meloxicam content and the development of the methods for pharmaceutical characterization was performed on samples prepared at seven different compression forces (ranging from 7 to 45 kN) using NIR transmission spectra of intact tablets and PLS as a regression method. The results show that the developed methods have good trueness, precision and accuracy and are appropriate for direct active content assay in tablets (ranging from 12 to 18 mg/tablet) and also for predicting crushing strength and disintegration time of intact meloxicam tablets. The comparative data show that the proposed methods are in good agreement with the reference methods currently used for the characterization of meloxicam tablets (HPLC-UV methods for the assay and European Pharmacopeia methods for determining the crushing strength and disintegration time). The results show the possibility to predict both chemical properties (active content) and physical/pharmaceutical properties (crushing strength and disintegration time) directly, without any sample preparation, from the same NIR transmission spectrum of meloxicam tablets.

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms.

PubMed

Gonzalez Viejo, Claudia; Fuentes, Sigfredo; Torrico, Damir; Howell, Kate; Dunshea, Frank R

2018-01-01

Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. The ANN method was able to create more accurate models (R 2  = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type. The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2014-02-01

Objective. Near-infrared spectroscopy (NIRS) has recently gained attention as a modality for brain-computer interfaces (BCIs), which may serve as an alternative access pathway for individuals with severe motor impairments. For NIRS-BCIs to be used as a real communication pathway, reliable online operation must be achieved. Yet, only a limited number of studies have been conducted online to date. These few studies were carried out under a synchronous paradigm and did not accommodate an unconstrained resting state, precluding their practical clinical implication. Furthermore, the potentially discriminative power of spatiotemporal characteristics of activation has yet to be considered in an online NIRS system. Approach. In this study, we developed and evaluated an online system-paced NIRS-BCI which was driven by a mental arithmetic activation task and accommodated an unconstrained rest state. With a dual-wavelength, frequency domain near-infrared spectrometer, measurements were acquired over nine sites of the prefrontal cortex, while ten able-bodied participants selected letters from an on-screen scanning keyboard via intentionally controlled brain activity (using mental arithmetic). Participants were provided dynamic NIR topograms as continuous visual feedback of their brain activity as well as binary feedback of the BCI's decision (i.e. if the letter was selected or not). To classify the hemodynamic activity, temporal features extracted from the NIRS signals and spatiotemporal features extracted from the dynamic NIR topograms were used in a majority vote combination of multiple linear classifiers. Main results. An overall online classification accuracy of 77.4 ± 10.5% was achieved across all participants. The binary feedback was found to be very useful during BCI use, while not all participants found value in the continuous feedback provided. Significance. These results demonstrate that mental arithmetic is a potent mental task for driving an online system-paced NIRS-BCI. BCI feedback that reflects the classifier's decision has the potential to improve user performance. The proposed system can provide a framework for future online NIRS-BCI development and testing.

Multispectral imaging with vertical silicon nanowires

PubMed Central

Park, Hyunsung; Crozier, Kenneth B.

2013-01-01

Multispectral imaging is a powerful tool that extends the capabilities of the human eye. However, multispectral imaging systems generally are expensive and bulky, and multiple exposures are needed. Here, we report the demonstration of a compact multispectral imaging system that uses vertical silicon nanowires to realize a filter array. Multiple filter functions covering visible to near-infrared (NIR) wavelengths are simultaneously defined in a single lithography step using a single material (silicon). Nanowires are then etched and embedded into polydimethylsiloxane (PDMS), thereby realizing a device with eight filter functions. By attaching it to a monochrome silicon image sensor, we successfully realize an all-silicon multispectral imaging system. We demonstrate visible and NIR imaging. We show that the latter is highly sensitive to vegetation and furthermore enables imaging through objects opaque to the eye. PMID:23955156

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

Detection of reduced interhemispheric cortical communication during task execution in multiple sclerosis patients using functional near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Jimenez, Jon J.; Yang, Runze; Nathoo, Nabeela; Varshney, Vishal P.; Golestani, Ali-Mohammad; Goodyear, Bradley G.; Metz, Luanne M.; Dunn, Jeff F.

2014-07-01

Multiple sclerosis (MS) impairs brain activity through demyelination and loss of axons. Increased brain activity is accompanied by increases in microvascular hemoglobin oxygen saturation (oxygenation) and total hemoglobin, which can be measured using functional near-infrared spectroscopy (fNIRS). Due to the potentially reduced size and integrity of the white matter tracts within the corpus callosum, it may be expected that MS patients have reduced functional communication between the left and right sides of the brain; this could potentially be an indicator of disease progression. To assess interhemispheric communication in MS, we used fNIRS during a unilateral motor task and the resting state. The magnitude of the change in hemoglobin parameters in the motor cortex was significantly reduced in MS patients during the motor task relative to healthy control subjects. There was also a significant decrease in interhemispheric communication between the motor cortices (expressed as coherence) in MS patients compared to controls during the motor task, but not during the resting state. fNIRS assessment of interhemispheric coherence during task execution may be a useful marker in disorders with white matter damage or axonal loss, including MS.

Flume experiments elucidate relationships between microbial genetics, nitrogen species and hydraulics in controlling nitrous oxide production in the hyporheic zone

NASA Astrophysics Data System (ADS)

Quick, A. M.; Farrell, T. B.; Reeder, W. J.; Feris, K. P.; Tonina, D.; Benner, S. G.

2014-12-01

The hyporheic zone is a potentially important producer of nitrous oxide, a powerful greenhouse gas. The location and magnitude of nitrous oxide generation within the hyporheic zone involves complex interactions between multiple nitrogen species, redox conditions, microbial communities, and hydraulics. To better understand nitrous oxide generation and emissions from streams, we conducted large-scale flume experiments in which we monitored pore waters along hyporheic flow paths within stream dune structures. Measured dissolved oxygen, ammonia, nitrate, nitrite, and dissolved nitrous oxide showed distinct spatial relationships reflecting redox changes along flow paths. Denitrifying genes (nosZ, nirS, and nirK), determined using qPCR, were spatially associated with abundances of nitrogen species. Using residence times along a flow path, clear trends in oxygen conditions, genes encoding for microbial catalysis, and nitrogen species were observed. Hotspots of targeted genes correlated with hotspots for conversion of nitrogen species, including nitrous oxide production and conversion to dinitrogen. Trends were apparent regardless of dune size, allowing for the possibility to apply observed relationships to multiple streambed morphologies. Relating streambed morphology and loading of nitrogen species allows for prediction of nitrous oxide production in the hyporheic zone.

Estimation of PM2.5 and PM10 using ground-based AOD measurements during KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Koo, J. H.; Kim, J.; Kim, S.; Go, S.; Lee, S.; Lee, H.; Mok, J.; Hong, J.; Lee, J.; Eck, T. F.; Holben, B. N.

2017-12-01

During the KORUS-AQ campaign (2 May - 12 June, 2016), aerosol optical depth (AOD) was obtained at multiple channels using various ground-based instruments at Yonsei University, Seoul: AERONET sunphotometer, SKYNET skyradiometer, Brewer spectrophotometer, and multi-filter rotating shadowband radiometer (MFRSR). At the same location, planetary boundary layer (PBL) height and vertical profile of backscattering coefficients also can be obtained based on the celiometer measurements. Using celiometer products and various AODs, we try to estimate the amount of particular matter (PM2.5 and PM10) and validate with in-situ surface PM2.5 and PM10 measurements from AIRKOREA network. Direct comparison between PM2.5 and AOD reveals that the ultraviolet(UV) channel AOD has better correlations, due to the higher sensitivity of short wavelength to the fine-mode particle. In contrast, PM10 shows the highest correlation with the near-infrared(NIR) AOD. Next, we extract the boundary-layer portion of AOD using either PBL height or vertical profile of backscattering coefficients to compare with PM2.5 and PM10. Both results enhance the correlation, but consideration of weighting factor calculated from backscattering coefficients shows larger contribution to the correlation increase. Finally, we performed the multiple linear regression to estimate PM2.5 and PM10 using AODs. Consideration of meteorology (temperature, wind speed, and relative humidity) can enhance the correlation and also O3 and NO2 consideration highly contributes to the high correlation. This finding implies the importance to consider the ambient condition of secondary aerosol formation related to the PM2.5 variation. Multiple regression model finally finds the correlation 0.7-0.8, and diminishes the wavelength-dependent correlation patterns.

Use of Vis/NIRS for the determination of sugar content of cola soft drinks based on chemometric methods

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-03-01

Three different chemometric methods were performed for the determination of sugar content of cola soft drinks using visible and near infrared spectroscopy (Vis/NIRS). Four varieties of colas were prepared and 180 samples (45 samples for each variety) were selected for the calibration set, while 60 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay, standard normal variate (SNV) and Savitzky-Golay first derivative transformation were applied for the pre-processing of spectral data. The first eleven principal components (PCs) extracted by partial least squares (PLS) analysis were employed as the inputs of BP neural network (BPNN) and least squares-support vector machine (LS-SVM) model. Then the BPNN model with the optimal structural parameters and LS-SVM model with radial basis function (RBF) kernel were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.971, 1.259 and -0.335 for PLS, 0.986, 0.763, and -0.042 for BPNN, while 0.978, 0.995 and -0.227 for LS-SVM, respectively. All the three methods supplied a high and satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be utilized as a high precision way for the determination of sugar content of cola soft drinks.

Neurofeedback-induced facilitation of the supplementary motor area affects postural stability.

PubMed

Fujimoto, Hiroaki; Mihara, Masahito; Hattori, Noriaki; Hatakenaka, Megumi; Yagura, Hajime; Kawano, Teiji; Miyai, Ichiro; Mochizuki, Hideki

2017-10-01

Near-infrared spectroscopy-mediated neurofeedback (NIRS-NFB) is a promising therapeutic intervention for patients with neurological diseases. Studies have shown that NIRS-NFB can facilitate task-related cortical activation and induce task-specific behavioral changes. These findings indicate that the effect of neuromodulation depends on local cortical function. However, when the target cortical region has multiple functions, our understanding of the effects is less clear. This is true in the supplementary motor area (SMA), which is involved both in postural control and upper-limb movement. To address this issue, we investigated the facilitatory effect of NIRS SMA neurofeedback on cortical activity and behavior, without any specific task. Twenty healthy individuals participated in real and sham neurofeedback. Balance and hand dexterity were assessed before and after each NIRS-NFB session. We found a significant interaction between assessment periods (pre/post) and condition (real/sham) with respect to balance as assessed by the center of the pressure path length but not for hand dexterity as assessed by the 9-hole peg test. SMA activity only increased during real neurofeedback. Our findings indicate that NIRS-NFB itself has the potential to modulate focal cortical activation, and we suggest that it be considered a therapy to facilitate the SMA for patients with postural impairment.

Pocket-size near-infrared spectrometer for narcotic materials identification

NASA Astrophysics Data System (ADS)

Pederson, Christopher G.; Friedrich, Donald M.; Hsiung, Chang; von Gunten, Marc; O'Brien, Nada A.; Ramaker, Henk-Jan; van Sprang, Eric; Dreischor, Menno

2014-05-01

While significant progress has been made towards the miniaturization of Raman, mid-infrared (IR), and near-infrared (NIR) spectrometers for homeland security and law enforcement applications, there remains continued interest in pushing the technology envelope for smaller, lower cost, and easier to use analyzers. In this paper, we report on the use of the MicroNIR Spectrometer, an ultra-compact, handheld near infrared (NIR) spectrometer, the, that weighs less than 60 grams and measures < 50mm in diameter for the classification of 140 different substances most of which are controlled substances (such as cocaine, heroin, oxycodone, diazepam), as well as synthetic cathinones (also known as bath salts), and synthetic cannabinoids. A library of the materials was created from a master MicroNIR spectrometer. A set of 25 unknown samples were then identified with three other MicroNIRs showing: 1) the ability to correctly identify the unknown with a very low rate of misidentification, and 2) the ability to use the same library with multiple instruments. In addition, we have shown that through the use of innovative chemometric algorithms, we were able to identify the individual compounds that make up an unknown mixture based on the spectral library of the individual compounds only. The small size of the spectrometer is enabled through the use of high-performance linear variable filter (LVF) technology.

Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn

NASA Astrophysics Data System (ADS)

Jiang, Hao; Lu, Jiangang

2018-05-01

Corn starch is an important material which has been traditionally used in the fields of food and chemical industry. In order to enhance the rapidness and reliability of the determination for starch content in corn, a methodology is proposed in this work, using an optimal CC-PLSR-RBFNN calibration model and near-infrared (NIR) spectroscopy. The proposed model was developed based on the optimal selection of crucial parameters and the combination of correlation coefficient method (CC), partial least squares regression (PLSR) and radial basis function neural network (RBFNN). To test the performance of the model, a standard NIR spectroscopy data set was introduced, containing spectral information and chemical reference measurements of 80 corn samples. For comparison, several other models based on the identical data set were also briefly discussed. In this process, the root mean square error of prediction (RMSEP) and coefficient of determination (Rp2) in the prediction set were used to make evaluations. As a result, the proposed model presented the best predictive performance with the smallest RMSEP (0.0497%) and the highest Rp2 (0.9968). Therefore, the proposed method combining NIR spectroscopy with the optimal CC-PLSR-RBFNN model can be helpful to determine starch content in corn.

Study on for soluble solids contents measurement of grape juice beverage based on Vis/NIRS and chemomtrics

NASA Astrophysics Data System (ADS)

Wu, Di; He, Yong

2007-11-01

The aim of this study is to investigate the potential of the visible and near infrared spectroscopy (Vis/NIRS) technique for non-destructive measurement of soluble solids contents (SSC) in grape juice beverage. 380 samples were studied in this paper. Smoothing way of Savitzky-Golay and standard normal variate were applied for the pre-processing of spectral data. Least-squares support vector machines (LS-SVM) with RBF kernel function was applied to developing the SSC prediction model based on the Vis/NIRS absorbance data. The determination coefficient for prediction (Rp2) of the results predicted by LS-SVM model was 0. 962 and root mean square error (RMSEP) was 0. 434137. It is concluded that Vis/NIRS technique can quantify the SSC of grape juice beverage fast and non-destructively.. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BP-NN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SSC of grape juice beverage. In this study, the generation ability of LS-SVM, PLS and BP-NN models were also investigated. It is concluded that LS-SVM regression method is a promising technique for chemometrics in quantitative prediction.

A review on the applications of portable near-infrared spectrometers in the agro-food industry.

PubMed

dos Santos, Cláudia A Teixeira; Lopo, Miguel; Páscoa, Ricardo N M J; Lopes, João A

2013-11-01

Industry has created the need for a cost-effective and nondestructive quality-control analysis system. This requirement has increased interest in near-infrared (NIR) spectroscopy, leading to the development and marketing of handheld devices that enable new applications that can be implemented in situ. Portable NIR spectrometers are powerful instruments offering several advantages for nondestructive, online, or in situ analysis: small size, low cost, robustness, simplicity of analysis, sample user interface, portability, and ergonomic design. Several studies of on-site NIR applications are presented: characterization of internal and external parameters of fruits and vegetables; conservation state and fat content of meat and fish; distinguishing among and quality evaluation of beverages and dairy products; protein content of cereals; evaluation of grape ripeness in vineyards; and soil analysis. Chemometrics is an essential part of NIR spectroscopy manipulation because wavelength-dependent scattering effects, instrumental noise, ambient effects, and other sources of variability may complicate the spectra. As a consequence, it is difficult to assign specific absorption bands to specific functional groups. To achieve useful and meaningful results, multivariate statistical techniques (essentially involving regression techniques coupled with spectral preprocessing) are therefore required to extract the information hidden in the spectra. This work reviews the evolution of the use of portable near-infrared spectrometers in the agro-food industry.

Predictive evaluation of pharmaceutical properties of direct compression tablets containing theophylline anhydrate during storage at high humidity by near-infrared spectroscopy.

PubMed

Otsuka, Yuta; Yamamoto, Masahiro; Tanaka, Hideji; Otsuka, Makoto

2015-01-01

Theophylline anhydrate (TA) in tablet formulation is transformed into monohydrate (TH) at high humidity and the phase transformation affected dissolution behavior. Near-infrared spectroscopic (NIR) method is applied to predict the change of pharmaceutical properties of TA tablets during storage at high humidity. The tablet formulation containing TA, lactose, crystalline cellulose and magnesium stearate was compressed at 4.8 kN. Pharmaceutical properties of TA tables were measured by NIR, X-ray diffraction analysis, dissolution test and tablet hardness. TA tablet was almost 100% transformed into TH after 24 hours at RH 96%. The pharmaceutical properties of TA tablets, such as tablet hardness, 20 min dissolution amount (D20) and increase of tablet weight (TW), changed with the degree of hydration. Calibration models for TW, tablet hardness and D20 to predict the pharmaceutical properties at high-humidity conditions were developed on the basis of the NIR spectra by partial least squares regression analysis. The relationships between predicted and actual measured values for TW, tablet hardness and D20 had straight lines, respectively. From the results of NIR-chemometrics, it was confirmed that these predicted models had high accuracy to monitor the tablet properties during storage at high humidity.

The rapid measurement of soil carbon stock using near-infrared technology

NASA Astrophysics Data System (ADS)

Kusumo, B. H.; Sukartono; Bustan

2018-03-01

As a soil pool stores carbon (C) three times higher than an atmospheric pool, the depletion of C stock in the soil will significantly increase the concentration of CO2 in the atmosphere, causing global warming. However, the monitoring or measurement of soil C stock using conventional procedures is time-consuming and expensive. So it requires a rapid and non-destructive technique that is simple and does not need chemical substances. This research is aimed at testing whether near-infrared (NIR) technology is able to rapidly measure C stock in the soil. Soil samples were collected from an agricultural land at the sub-district of Kayangan, North Lombok, Indonesia. The coordinates of the samples were recorded. Parts of the samples were analyzed using conventional procedure (Walkley and Black) and some other parts were scanned using near-infrared spectroscopy (NIRS) for soil spectral collection. Partial Least Square Regression (PLSR) was used to develop models from soil C data measured by conventional analysis and from spectral data scanned by NIRS. The best model was moderately successful to measure soil C stock in the study area in North Lombok. This indicates that the NIR technology can be further used to monitor the change of soil C stock in the soil.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm

NASA Astrophysics Data System (ADS)

Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan

2012-11-01

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV = 0.0776, Rc = 0.9777, RMSEP = 0.0963, and Rp = 0.9686 for pH model; RMSECV = 1.3544% w/w, Rc = 0.8871, RMSEP = 1.4946% w/w, and Rp = 0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.

Mapping The Temporal and Spatial Variability of Soil Moisture Content Using Proximal Soil Sensing

NASA Astrophysics Data System (ADS)

Virgawati, S.; Mawardi, M.; Sutiarso, L.; Shibusawa, S.; Segah, H.; Kodaira, M.

2018-05-01

In studies related to soil optical properties, it has been proven that visual and NIR soil spectral response can predict soil moisture content (SMC) using proper data analysis techniques. SMC is one of the most important soil properties influencing most physical, chemical, and biological soil processes. The problem is how to provide reliable, fast and inexpensive information of SMC in the subsurface from numerous soil samples and repeated measurement. The use of spectroscopy technology has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to develop calibration models based on laboratory Vis-NIR spectroscopy to estimate the SMC at four different growth stages of the soybean crop in Yogyakarta Province. An ASD Field-spectrophotoradiometer was used to measure the reflectance of soil samples. The partial least square regression (PLSR) was performed to establish the relationship between the SMC with Vis-NIR soil reflectance spectra. The selected calibration model was used to predict the new samples of SMC. The temporal and spatial variability of SMC was performed in digital maps. The results revealed that the calibration model was excellent for SMC prediction. Vis-NIR spectroscopy was a reliable tool for the prediction of SMC.

Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy.

PubMed

García-Alvarez, M; Ceresuela, S; Huidobro, J F; Hermida, M; Rodríguez-Otero, J L

2002-01-30

NIR transflectance spectroscopy was used to determine polarimetric parameters (direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides) and sucrose in honey. In total, 156 honey samples were collected during 1992 (45 samples), 1995 (56 samples), and 1996 (55 samples). Samples were analyzed by NIR spectroscopy and polarimetric methods. Calibration (118 samples) and validation (38 samples) sets were made up; honeys from the three years were included in both sets. Calibrations were performed by modified partial least-squares regression and scatter correction by standard normal variation and detrend methods. For direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides, good statistics (bias, SEV, and R(2)) were obtained for the validation set, and no statistically (p = 0.05) significant differences were found between instrumental and polarimetric methods for these parameters. Statistical data for sucrose were not as good as those of the other parameters. Therefore, NIR spectroscopy is not an effective method for quantitative analysis of sucrose in these honey samples. However, NIR spectroscopy may be an acceptable method for semiquantitative evaluation of sucrose for honeys, such as those in our study, containing up to 3% of sucrose. Further work is necessary to validate the uncertainty at higher levels.

Neuroendocrine Tumor-Targeted Upconversion Nanoparticle-Based Micelles for Simultaneous NIR-Controlled Combination Chemotherapy and Photodynamic Therapy, and Fluorescence Imaging

PubMed Central

Chen, Guojun; Jaskula-Sztul, Renata; Esquibel, Corinne R.; Lou, Irene; Zheng, Qifeng; Dammalapati, Ajitha; Harrison, April; Eliceiri, Kevin W.; Tang, Weiping

2017-01-01

Although neuroendocrine tumors (NETs) are slow growing, they are frequently metastatic at the time of discovery and no longer amenable to curative surgery, emphasizing the need for the development of other treatments. In this study, multifunctional upconversion nanoparticle (UCNP)-based theranostic micelles are developed for NET-targeted and near-infrared (NIR)-controlled combination chemotherapy and photodynamic therapy (PDT), and bioimaging. The theranostic micelle is formed by individual UCNP functionalized with light-sensitive amphiphilic block copolymers poly(4,5-dimethoxy-2-nitrobenzyl methacrylate)-polyethylene glycol (PNBMA-PEG) and Rose Bengal (RB) photosensitizers. A hydrophobic anticancer drug, AB3, is loaded into the micelles. The NIR-activated UCNPs emit multiple luminescence bands, including UV, 540 nm, and 650 nm. The UV peaks overlap with the absorption peak of photocleavable hydrophobic PNBMA segments, triggering a rapid drug release due to the NIR-induced hydrophobic-to-hydrophilic transition of the micelle core and thus enabling NIR-controlled chemotherapy. RB molecules are activated via luminescence resonance energy transfer to generate 1O2 for NIR-induced PDT. Meanwhile, the 650 nm emission allows for efficient fluorescence imaging. KE108, a true pansomatostatin nonapeptide, as an NET-targeting ligand, drastically increases the tumoral uptake of the micelles. Intravenously injected AB3-loaded UCNP-based micelles conjugated with RB and KE108—enabling NET-targeted combination chemotherapy and PDT—induce the best antitumor efficacy. PMID:28989337

MOEMS FPI sensors for NIR-MIR microspectrometer applications

NASA Astrophysics Data System (ADS)

Akujärvi, A.; Guo, B.; Mannila, R.; Rissanen, A.

2016-03-01

This paper presents near- and mid- infrared (NIR-MIR) wavelength range optical MEMS Fabry-Perot interferometers (FPIs) developed for automotive and multi-gas sensing applications. MEMS FPI platform for NIR-range consist of LPCVD (low-pressure chemical vapour) deposited polySi-SiN λ/4-thin film Bragg reflectors, with the air gap formed by sacrificial SiO2 etching in HF vapour. Characterization results for the NIR MFPI devices for λ = 1.5 - 2.0 μm show resolution of 15 nm at the optimization wavelength of 1750 nm. We also present a MIR-range MEMS FPI for λ = 2.5 - 3.5 μm, which utilizes silicon and air in within the Bragg reflector structure to provide a high contrast for improved resolution. Characterization results show a FWHM (Full Width Half Maximum) of 20 nm in comparison to the 50 nm resolution provided by earlier MEMS FPIs realized for hydrocarbon sensing with conventional CVD-thin film materials. The improved resolution and the extended operation region shows potential to enable simultaneous sensing of CO2 and multiple hydrocarbons.

At-line determination of pharmaceuticals small molecule's blending end point using chemometric modeling combined with Fourier transform near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tewari, Jagdish; Strong, Richard; Boulas, Pierre

2017-02-01

This article summarizes the development and validation of a Fourier transform near infrared spectroscopy (FT-NIR) method for the rapid at-line prediction of active pharmaceutical ingredient (API) in a powder blend to optimize small molecule formulations. The method was used to determine the blend uniformity end-point for a pharmaceutical solid dosage formulation containing a range of API concentrations. A set of calibration spectra from samples with concentrations ranging from 1% to 15% of API (w/w) were collected at-line from 4000 to 12,500 cm- 1. The ability of the FT-NIR method to predict API concentration in the blend samples was validated against a reference high performance liquid chromatography (HPLC) method. The prediction efficiency of four different types of multivariate data modeling methods such as partial least-squares 1 (PLS1), partial least-squares 2 (PLS2), principal component regression (PCR) and artificial neural network (ANN), were compared using relevant multivariate figures of merit. The prediction ability of the regression models were cross validated against results generated with the reference HPLC method. PLS1 and ANN showed excellent and superior prediction abilities when compared to PLS2 and PCR. Based upon these results and because of its decreased complexity compared to ANN, PLS1 was selected as the best chemometric method to predict blend uniformity at-line. The FT-NIR measurement and the associated chemometric analysis were implemented in the production environment for rapid at-line determination of the end-point of the small molecule blending operation. FIGURE 1: Correlation coefficient vs Rank plot FIGURE 2: FT-NIR spectra of different steps of Blend and final blend FIGURE 3: Predictions ability of PCR FIGURE 4: Blend uniformity predication ability of PLS2 FIGURE 5: Prediction efficiency of blend uniformity using ANN FIGURE 6: Comparison of prediction efficiency of chemometric models TABLE 1: Order of Addition for Blending Steps

Nondestructive prediction of the drug content of an aspirin suppository by near-infrared spectroscopy.

PubMed

Otsuka, Eri; Abe, Hiroyuki; Aburada, Masaki; Otsuka, Makoto

2010-07-01

A suppository dosage form has a rapid effect on therapeutics, because it dissolves in the rectum, is absorbed in the bloodstream, and passes the hepatic metabolism. However, the dosage form is unstable, because a suppository is made in a semisolid form, and so it is not easy to mix the bulk drug powder in the base. This article describes a nondestructive method of determining the drug content of suppositories using near-infrared spectrometry (NIR) combined with chemometrics. Suppositories (aspirin content: 1.8, 2.7, 4.5, 7.3, and 9.1%, w/w) were produced by mixing an aspirin bulk powder with hard fat at 50 degrees C and pouring the melt mixture into a plastic mold (2.25 mL). NIR spectra of 12 calibration and 12 validation sample sets were recorded 5 times. A total of 60 spectral data were used as a calibration set to establish a calibration model to predict drug content with a partial least-squares (PLS) regression analysis. NIR data of the suppository samples were divided into two wave number ranges, 4000-12500 cm(-1) (LR), and 5900-6300 cm(-1) (SR). Calibration models for the aspirin content of the suppositories were calculated based on LR and SR ranges of second-derivative NIR spectra using PLS. The models for LR and SR consisted of five and one principal components (PC), respectively. The plots of predicted values against actual values gave a straight line with regression coefficient constants of 0.9531 and 0.9749, respectively. The mean bias and mean accuracy of the calibration models were calculated based on the SR of variation data sets, and were lower than those of LR, respectively. Limiting the wave number of spectral data sets is useful to help understand the calibration model because of noise cancellation and to measure objective functions.

Nondestructive detection of zebra chip disease in potatoes using near-infrared spectroscopy

USDA-ARS?s Scientific Manuscript database

Near-Infrared (NIR) spectroscopy in the wavelength region from 900 nm to 2600 nm was evaluated as the basis for a rapid, non-destructive method for the detection of Zebra Chip disease in potatoes and the measurement of sugar concentrations in affected tubers. Using stepwise regression in conjunction...

Prediction of brain tissue temperature using near-infrared spectroscopy.

PubMed

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-04-01

Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of [Formula: see text] (animal dataset) and [Formula: see text] (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications.

Fitting and Phenomenology in Type IA Supernova Cosmology: Generalized Likelihood Analyses for Multiple Evolving Populations and Observations of Near-Infrared Lightcurves Including Host Galaxy Properties

NASA Astrophysics Data System (ADS)

Ponder, Kara A.

In the late 1990s, Type Ia supernovae (SNeIa) led to the discovery that the Universe is expanding at an accelerating rate due to dark energy. Since then, many different tracers of acceleration have been used to characterize dark energy, but the source of cosmic acceleration has remained a mystery. To better understand dark energy, future surveys such as the ground-based Large Synoptic Survey Telescope and the space-based Wide-Field Infrared Survey Telescope will collect thousands of SNeIa to use as a primary dark energy probe. These large surveys will be systematics limited, which makes it imperative for our insight regarding systematics to dramatically increase over the next decade for SNeIa to continue to contribute to precision cosmology. I approach this problem by improving statistical methods in the likelihood analysis and collecting near infrared (NIR) SNeIa with their host galaxies to improve the nearby data set and search for additional systematics. Using more statistically robust methods to account for systematics within the likelihood function can increase accuracy in cosmological parameters with a minimal precision loss. Though a sample of at least 10,000 SNeIa is necessary to confirm multiple populations of SNeIa, the bias in cosmology is ˜ 2 sigma with only 2,500 SNeIa. This work focused on an example systematic (host galaxy correlations), but it can be generalized for any systematic that can be represented by a distribution of multiple Gaussians. The SweetSpot survey gathered 114 low-redshift, NIR SNeIa that will act as a crucial anchor sample for the future high redshift surveys. NIR observations are not as affected by dust contamination, which may lead to increased understanding of systematics seen in optical wavelengths. We obtained spatially resolved spectra for 32 SweetSpot host galaxies to test for local host galaxy correlations. For the first time, we probe global host galaxy correlations with NIR brightnesses from the current literature sample of SNeIa with host galaxy data from publicly available catalogs. We find inconclusive evidence that more massive galaxies host SNeIa that are brighter in the NIR than SNeIa hosted in less massive galaxies.

High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil.

PubMed

Paul, Andrea; Wander, Lukas; Becker, Roland; Goedecke, Caroline; Braun, Ulrike

2018-05-12

The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination, analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, we tested a macroscopic dimensioned near-infrared (NIR) process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/ soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 μm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber-optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils, and real-world samples, e.g., fermenter residue, suggests a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pretreatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

NASA Astrophysics Data System (ADS)

Croce, Pierpaolo; Zappasodi, Filippo; Merla, Arcangelo; Chiarelli, Antonio Maria

2017-08-01

Objective. Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Approach. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). Main results. We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. Significance. The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

Rapid evaluation and quantitative analysis of thyme, origano and chamomile essential oils by ATR-IR and NIR spectroscopy

NASA Astrophysics Data System (ADS)

Schulz, Hartwig; Quilitzsch, Rolf; Krüger, Hans

2003-12-01

The essential oils obtained from various chemotypes of thyme, origano and chamomile species were studied by ATR/FT-IR as well as NIR spectroscopy. Application of multivariate statistics (PCA, PLS) in conjunction with analytical reference data leads to very good IR and NIR calibration results. For the main essential oil components (e.g. carvacrol, thymol, γ-terpinene, α-bisabolol and β-farnesene) standard errors are in the range of the applied GC reference method. In most cases the multiple coefficients of determination ( R2) are >0.97. Using the IR fingerprint region (900-1400 cm -1) a qualitative discrimination of the individual chemotypes is possible already by visual judgement without to apply any chemometric algorithms.The described rapid and non-destructive methods can be applied in industry to control very easily purifying, blending and redistillation processes of the mentioned essential oils.

Tantalum Sulfide Nanosheets as a Theranostic Nanoplatform for Computed Tomography Imaging-Guided Combinatorial Chemo-Photothermal Therapy.

PubMed

Liu, Yanlan; Ji, Xiaoyuan; Liu, Jianhua; Tong, Winnie W L; Askhatova, Diana; Shi, Jinjun

2017-10-19

Near-infrared (NIR)-absorbing metal-based nanomaterials have shown tremendous potential for cancer therapy, given their facile and controllable synthesis, efficient photothermal conversion, capability of spatiotemporal-controlled drug delivery, and intrinsic imaging function. Tantalum (Ta) is among the most biocompatible metals and arouses negligible adverse biological responses in either oxidized or reduced forms, and thus Ta-derived nanomaterials represent promising candidates for biomedical applications. However, Ta-based nanomaterials by themselves have not been explored for NIR-mediated photothermal ablation therapy. In this work, we report an innovative Ta-based multifunctional nanoplatform composed of biocompatible tantalum sulfide (TaS 2 ) nanosheets (NSs) for simultaneous NIR hyperthermia, drug delivery, and computed tomography (CT) imaging. The TaS 2 NSs exhibit multiple unique features including (i) efficient NIR light-to-heat conversion with a high photothermal conversion efficiency of 39%. (ii) high drug loading (177% by weight), (iii) controlled drug release triggered by NIR light and moderate acidic pH, (iv) high tumor accumulation via heat-enhanced tumor vascular permeability, (v) complete tumor ablation and negligible side effects, and (vi) comparable CT imaging contrast efficiency to the widely clinically used agent iobitridol. We expect that this multifunctional NS platform can serve as a promising candidate for imaging-guided cancer therapy and selection of cancer patients with high tumor accumulation.

Adaptive optics imaging of geographic atrophy.

PubMed

Gocho, Kiyoko; Sarda, Valérie; Falah, Sabrina; Sahel, José-Alain; Sennlaub, Florian; Benchaboune, Mustapha; Ullern, Martine; Paques, Michel

2013-05-01

To report the findings of en face adaptive optics (AO) near infrared (NIR) reflectance fundus flood imaging in eyes with geographic atrophy (GA). Observational clinical study of AO NIR fundus imaging was performed in 12 eyes of nine patients with GA, and in seven controls using a flood illumination camera operating at 840 nm, in addition to routine clinical examination. To document short term and midterm changes, AO imaging sessions were repeated in four patients (mean interval between sessions 21 days; median follow up 6 months). As compared with scanning laser ophthalmoscope imaging, AO NIR imaging improved the resolution of the changes affecting the RPE. Multiple hyporeflective clumps were seen within and around GA areas. Time-lapse imaging revealed micrometric-scale details of the emergence and progression of areas of atrophy as well as the complex kinetics of some hyporeflective clumps. Such dynamic changes were observed within as well as outside atrophic areas. in eyes affected by GA, AO nir imaging allows high resolution documentation of the extent of RPE damage. this also revealed that a complex, dynamic process of redistribution of hyporeflective clumps throughout the posterior pole precedes and accompanies the emergence and progression of atrophy. therefore, these clumps are probably also a biomarker of rpe damage. AO NIR imaging may, therefore, be of interest to detect the earliest stages, to document the retinal pathology and to monitor the progression oF GA. (ClinicalTrials.gov number, NCT01546181.).

Calibration sets selection strategy for the construction of robust PLS models for prediction of biodiesel/diesel blends physico-chemical properties using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Palou, Anna; Miró, Aira; Blanco, Marcelo; Larraz, Rafael; Gómez, José Francisco; Martínez, Teresa; González, Josep Maria; Alcalà, Manel

2017-06-01

Even when the feasibility of using near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for prediction of physico-chemical properties of biodiesel/diesel blends has been widely demonstrated, inclusion in the calibration sets of the whole variability of diesel samples from diverse production origins still remains as an important challenge when constructing the models. This work presents a useful strategy for the systematic selection of calibration sets of samples of biodiesel/diesel blends from diverse origins, based on a binary code, principal components analysis (PCA) and the Kennard-Stones algorithm. Results show that using this methodology the models can keep their robustness over time. PLS calculations have been done using a specialized chemometric software as well as the software of the NIR instrument installed in plant, and both produced RMSEP under reproducibility values of the reference methods. The models have been proved for on-line simultaneous determination of seven properties: density, cetane index, fatty acid methyl esters (FAME) content, cloud point, boiling point at 95% of recovery, flash point and sulphur.

Rapid determination of major bioactive isoflavonoid compounds during the extraction process of kudzu (Pueraria lobata) by near-infrared transmission spectroscopy.

PubMed

Wang, Pei; Zhang, Hui; Yang, Hailong; Nie, Lei; Zang, Hengchang

2015-02-25

Near-infrared (NIR) spectroscopy has been developed into an indispensable tool for both academic research and industrial quality control in a wide field of applications. The feasibility of NIR spectroscopy to monitor the concentration of puerarin, daidzin, daidzein and total isoflavonoid (TIF) during the extraction process of kudzu (Pueraria lobata) was verified in this work. NIR spectra were collected in transmission mode and pretreated with smoothing and derivative. Partial least square regression (PLSR) was used to establish calibration models. Three different variable selection methods, including correlation coefficient method, interval partial least squares (iPLS), and successive projections algorithm (SPA) were performed and compared with models based on all of the variables. The results showed that the approach was very efficient and environmentally friendly for rapid determination of the four quality indices (QIs) in the kudzu extraction process. This method established may have the potential to be used as a process analytical technological (PAT) tool in the future. Copyright © 2014 Elsevier B.V. All rights reserved.

Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran.

PubMed

Bagchi, Torit Baran; Sharma, Srigopal; Chattopadhyay, Krishnendu

2016-01-15

With the escalating persuasion of economic and nutritional importance of rice grain protein and nutritional components of rice bran (RB), NIRS can be an effective tool for high throughput screening in rice breeding programme. Optimization of NIRS is prerequisite for accurate prediction of grain quality parameters. In the present study, 173 brown rice (BR) and 86 RB samples with a wide range of values were used to compare the calibration models generated by different chemometrics for grain protein (GPC) and amylose content (AC) of BR and proximate compositions (protein, crude oil, moisture, ash and fiber content) of RB. Various modified partial least square (mPLSs) models corresponding with the best mathematical treatments were identified for all components. Another set of 29 genotypes derived from the breeding programme were employed for the external validation of these calibration models. High accuracy of all these calibration and prediction models was ensured through pair t-test and correlation regression analysis between reference and predicted values. Copyright © 2015 Elsevier Ltd. All rights reserved.

Portable visible and near-infrared spectrophotometer for triglyceride measurements.

PubMed

Kobayashi, Takanori; Kato, Yukiko Hakariya; Tsukamoto, Megumi; Ikuta, Kazuyoshi; Sakudo, Akikazu

2009-01-01

An affordable and portable machine is required for the practical use of visible and near-infrared (Vis-NIR) spectroscopy. A portable fruit tester comprising a Vis-NIR spectrophotometer was modified for use in the transmittance mode and employed to quantify triglyceride levels in serum in combination with a chemometric analysis. Transmittance spectra collected in the 600- to 1100-nm region were subjected to a partial least-squares regression analysis and leave-out cross-validation to develop a chemometrics model for predicting triglyceride concentrations in serum. The model yielded a coefficient of determination in cross-validation (R2VAL) of 0.7831 with a standard error of cross-validation (SECV) of 43.68 mg/dl. The detection limit of the model was 148.79 mg/dl. Furthermore, masked samples predicted by the model yielded a coefficient of determination in prediction (R2PRED) of 0.6856 with a standard error of prediction (SEP) and detection limit of 61.54 and 159.38 mg/dl, respectively. The portable Vis-NIR spectrophotometer may prove convenient for the measurement of triglyceride concentrations in serum, although before practical use there remain obstacles, which are discussed.

Assessment of infant formula quality and composition using Vis-NIR, MIR and Raman process analytical technologies.

PubMed

Wang, Xiao; Esquerre, Carlos; Downey, Gerard; Henihan, Lisa; O'Callaghan, Donal; O'Donnell, Colm

2018-06-01

In this study, visible and near-infrared (Vis-NIR), mid-infrared (MIR) and Raman process analytical technologies were investigated for assessment of infant formula quality and compositional parameters namely preheat temperature, storage temperature, storage time, fluorescence of advanced Maillard products and soluble tryptophan (FAST) index, soluble protein, fat and surface free fat (SFF) content. PLS-DA models developed using spectral data with appropriate data pre-treatment and significant variables selected using Martens' uncertainty test had good accuracy for the discrimination of preheat temperature (92.3-100%) and storage temperature (91.7-100%). The best PLS regression models developed yielded values for the ratio of prediction error to deviation (RPD) of 3.6-6.1, 2.1-2.7, 1.7-2.9, 1.6-2.6 and 2.5-3.0 for storage time, FAST index, soluble protein, fat and SFF content prediction respectively. Vis-NIR, MIR and Raman were demonstrated to be potential PAT tools for process control and quality assurance applications in infant formula and dairy ingredient manufacture. Copyright © 2018 Elsevier B.V. All rights reserved.

Photoluminescence enhancement of silicon quantum dot monolayer by plasmonic substrate fabricated by nano-imprint lithography

NASA Astrophysics Data System (ADS)

Yanagawa, Hiroto; Inoue, Asuka; Sugimoto, Hiroshi; Shioi, Masahiko; Fujii, Minoru

2017-12-01

Near-field coupling between a silicon quantum dot (Si-QD) monolayer and a plasmonic substrate fabricated by nano-imprint lithography and having broad multiple resonances in the near-infrared (NIR) window of biological substances was studied by precisely controlling the QDs-substrate distance. A strong enhancement of the NIR photoluminescence (PL) of Si-QDs was observed. Detailed analyses of the PL and PL excitation spectra, the PL decay dynamics, and the reflectance spectra revealed that both the excitation cross-sections and the emission rates are enhanced by the surface plasmon resonances, thanks to the broad multiple resonances of the plasmonic substrate, and that the relative contribution of the two enhancement processes depends strongly on the excitation wavelength. Under excitation by short wavelength photons (405 nm), where enhancement of the excitation cross-section is not expected, the maximum enhancement was obtained when the QDs-substrate distance was around 30 nm. On the other hand, under long wavelength excitation (641 nm), where strong excitation cross-section enhancement is expected, the largest enhancement was obtained when the distance was minimum (around 1 nm). The achievement of efficient excitation of NIR luminescence of Si-QDs by long wavelength photons paves the way for the development of Si-QD-based fluorescence bio-sensing devices with a high bound-to-free ratio.

Short separation channel location impacts the performance of short channel regression in NIRS

PubMed Central

Gagnon, Louis; Cooper, Robert J.; Yücel, Meryem A.; Perdue, Katherine L.; Greve, Douglas N.; Boas, David A.

2011-01-01

Near-Infrared Spectroscopy (NIRS) allows the recovery of cortical oxy-and deoxyhemoglobin changes associated with evoked brain activity. NIRS is a back-reflection measurement making it very sensitive to the superficial layers of the head, i.e. the skin and the skull, where systemic interference occurs. As a result, the NIRS signal is strongly contaminated with systemic interference of superficial origin. A recent approach to overcome this problem has been the use of additional short source-detector separation optodes as regressors. Since these additional measurements are mainly sensitive to superficial layers in adult humans, they can be used to remove the systemic interference present in longer separation measurements, improving the recovery of the cortical hemodynamic response function (HRF). One question that remains to answer is whether or not a short separation measurement is required in close proximity to each long separation NIRS channel. Here, we show that the systemic interference occurring in the superficial layers of the human head is inhomogeneous across the surface of the scalp. As a result, the improvement obtained by using a short separation optode decreases as the relative distance between the short and the long measurement is increased. NIRS data was acquired on 6 human subjects both at rest and during a motor task consisting of finger tapping. The effect of distance between the short and the long channel was first quantified by recovering a synthetic hemodynamic response added over the resting-state data. The effect was also observed in the functional data collected during the finger tapping task. Together, these results suggest that the short separation measurement must be located as close as 1.5 cm from the standard NIRS channel in order to provide an improvement which is of practical use. In this case, the improvement in Contrast-to-Noise Ratio (CNR) compared to a standard General Linear Model (GLM) procedure without using any small separation optode reached 50 % for HbO and 100 % for HbR. Using small separations located farther than 2 cm away resulted in mild or negligible improvements only. PMID:21945793

Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press.

PubMed

De Leersnyder, Fien; Peeters, Elisabeth; Djalabi, Hasna; Vanhoorne, Valérie; Van Snick, Bernd; Hong, Ke; Hammond, Stephen; Liu, Angela Yang; Ziemons, Eric; Vervaet, Chris; De Beer, Thomas

2018-03-20

A calibration model for in-line API quantification based on near infrared (NIR) spectra collection during tableting in the tablet press feed frame was developed and validated. First, the measurement set-up was optimised and the effect of filling degree of the feed frame on the NIR spectra was investigated. Secondly, a predictive API quantification model was developed and validated by calculating the accuracy profile based on the analysis results of validation experiments. Furthermore, based on the data of the accuracy profile, the measurement uncertainty was determined. Finally, the robustness of the API quantification model was evaluated. An NIR probe (SentroPAT FO) was implemented into the feed frame of a rotary tablet press (Modul™ P) to monitor physical mixtures of a model API (sodium saccharine) and excipients with two different API target concentrations: 5 and 20% (w/w). Cutting notches into the paddle wheel fingers did avoid disturbances of the NIR signal caused by the rotating paddle wheel fingers and hence allowed better and more complete feed frame monitoring. The effect of the design of the notched paddle wheel fingers was also investigated and elucidated that straight paddle wheel fingers did cause less variation in NIR signal compared to curved paddle wheel fingers. The filling degree of the feed frame was reflected in the raw NIR spectra. Several different calibration models for the prediction of the API content were developed, based on the use of single spectra or averaged spectra, and using partial least squares (PLS) regression or ratio models. These predictive models were then evaluated and validated by processing physical mixtures with different API concentrations not used in the calibration models (validation set). The β-expectation tolerance intervals were calculated for each model and for each of the validated API concentration levels (β was set at 95%). PLS models showed the best predictive performance. For each examined saccharine concentration range (i.e., between 4.5 and 6.5% and between 15 and 25%), at least 95% of future measurements will not deviate more than 15% from the true value. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of a method for the determination of caffeine anhydrate in various designed intact tablets [correction of tables] by near-infrared spectroscopy: a comparison between reflectance and transmittance technique.

PubMed

Ito, Masatomo; Suzuki, Tatsuya; Yada, Shuichi; Kusai, Akira; Nakagami, Hiroaki; Yonemochi, Etsuo; Terada, Katsuhide

2008-08-05

Using near-infrared (NIR) spectroscopy, an assay method which is not affected by such elements of tablet design as thickness, shape, embossing and scored line was developed. Tablets containing caffeine anhydrate were prepared by direct compression at various compression force levels using different shaped punches. NIR spectra were obtained from these intact tablets using the reflectance and transmittance techniques. A reference assay was performed by high-performance liquid chromatography (HPLC). Calibration models were generated by the partial least-squares (PLS) regression. Changes in the tablet thickness, shape, embossing and scored line caused NIR spectral changes in different ways, depending on the technique used. As a result, noticeable errors in drug content prediction occurred using calibration models generated according to the conventional method. On the other hand, when the various tablet design elements which caused the NIR spectral changes were included in the model, the prediction of the drug content in the tablets was scarcely affected by those elements when using either of the techniques. A comparison of these techniques resulted in higher predictability under the tablet design variations using the transmittance technique with preferable linearity and accuracy. This is probably attributed to the transmittance spectra which sensitively reflect the differences in tablet thickness or shape as a result of obtaining information inside the tablets.

Quality evaluation of frozen guava and yellow passion fruit pulps by NIR spectroscopy and chemometrics.

PubMed

Alamar, Priscila D; Caramês, Elem T S; Poppi, Ronei J; Pallone, Juliana A L

2016-07-01

The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp. Copyright © 2016 Elsevier Ltd. All rights reserved.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm.

PubMed

Jiang, Hui; Liu, Guohai; Mei, Congli; Yu, Shuang; Xiao, Xiahong; Ding, Yuhan

2012-11-01

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry. Copyright © 2012 Elsevier B.V. All rights reserved.

[Application of wavelet transform-radial basis function neural network in NIRS for determination of rifampicin and isoniazide tablets].

PubMed

Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong

2008-06-01

A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Load-unloading response of intact and artificially degraded articular cartilage correlated with near infrared (NIR) absorption spectra.

PubMed

Afara, I O; Singh, S; Oloyede, A

2013-04-01

The conventional mechanical properties of articular cartilage, such as compressive stiffness, have been demonstrated to be limited in their capacity to distinguish intact (visually normal) from degraded cartilage samples. In this paper, we explore the correlation between a new mechanical parameter, namely the reswelling of articular cartilage following unloading from a given compressive load, and the near infrared (NIR) spectrum. The capacity to distinguish mechanically intact from proteoglycan-depleted tissue relative to the "reswelling" characteristic was first established, and the result was subsequently correlated with the NIR spectral data of the respective tissue samples. To achieve this, normal intact and enzymatically degraded samples were subjected to both NIR probing and mechanical compression based on a load-unload-reswelling protocol. The parameter δr, characteristic of the osmotic "reswelling" of the matrix after unloading to a constant small load in the order of the osmotic pressure of cartilage, was obtained for the different sample types. Multivariate statistics was employed to determine the degree of correlation between δr and the NIR absorption spectrum of relevant specimens using Partial Least Squared (PLS) regression. The results show a strong relationship (R(2)=95.89%, p<0.0001) between the spectral data and δr. This correlation of δr with NIR spectral data suggests the potential for determining the reswelling characteristics non-destructively. It was also observed that δr values bear a significant relationship with the cartilage matrix integrity, indicated by its proteoglycan content, and can therefore differentiate between normal and artificially degraded proteoglycan-depleted cartilage samples. It is therefore argued that the reswelling of cartilage, which is both biochemical (osmotic) and mechanical (hydrostatic pressure) in origin, could be a strong candidate for characterizing the tissue, especially in regions surrounding focal cartilage defects in joints. Copyright © 2012 Elsevier Ltd. All rights reserved.

NIR spectroscopic investigation of m. vastus lateralis in patients with mitochondrial myopathies as detected by respirometric investigation of mitochondrial function in skinned fibers

NASA Astrophysics Data System (ADS)

Gellerich, Frank N.; Mueller, Tobias; Nioka, Shoko; Hertel, Katrin; Schulte-Mattler, Wilhelm J.; Zierz, Stephan; Chance, Britton

1998-01-01

Noninvasive measurement of changes in oxygenation of human skeletal muscle can be done with a dual-wavelength near infrared (NIR) spectrophotometer. This allows a noninvasive investigation of muscle mitochondria. An exercise protocol was developed to study the load dependent changes in oxygenation of m. vastus lateralis of myopathic patients. On a bicycle ergometer exercise was done periodically. One period consisted of 1.5 min exercise followed by 3 min rest. Work load in the first period was 20 W, and was increased by 10 W for each subsequent period until maximal work load was reached. In 12 healthy volunteers we observed oxygenation of muscle during periods of low work load (warm-up effect). During periods of high work load the muscle deoxygenated. The work load at transition from oxygenation to deoxygenation (deoxygenation threshold) in controls was 75 W. In 3 patients with myopathies, in addition to NIR- spectroscopy, function of mitochondria of specimen of m. vastus lateralis was investigated biochemically. Muscle fibers were skinned with saponin and investigated with high resolution respirometry and multiple substrate-inhibitor- titration. Mitochondrial function was impaired in patients who had abnormal findings in NIR spectroscopy.

NIR spectroscopic investigation of m. vastus lateralis in patients with mitochondrial myopathies as detected by respirometric investigation of mitochondrial function in skinned fibers

NASA Astrophysics Data System (ADS)

Gellerich, Frank N.; Mueller, Tobias; Nioka, Shoko; Hertel, Katrin; Schulte-Mattler, Wilhelm J.; Zierz, Stephan; Chance, Britton

1997-12-01

Noninvasive measurement of changes in oxygenation of human skeletal muscle can be done with a dual-wavelength near infrared (NIR) spectrophotometer. This allows a noninvasive investigation of muscle mitochondria. An exercise protocol was developed to study the load dependent changes in oxygenation of m. vastus lateralis of myopathic patients. On a bicycle ergometer exercise was done periodically. One period consisted of 1.5 min exercise followed by 3 min rest. Work load in the first period was 20 W, and was increased by 10 W for each subsequent period until maximal work load was reached. In 12 healthy volunteers we observed oxygenation of muscle during periods of low work load (warm-up effect). During periods of high work load the muscle deoxygenated. The work load at transition from oxygenation to deoxygenation (deoxygenation threshold) in controls was 75 W. In 3 patients with myopathies, in addition to NIR- spectroscopy, function of mitochondria of specimen of m. vastus lateralis was investigated biochemically. Muscle fibers were skinned with saponin and investigated with high resolution respirometry and multiple substrate-inhibitor- titration. Mitochondrial function was impaired in patients who had abnormal findings in NIR spectroscopy.

MODIS polarization performance and anomalous four-cycle polarization phenomenon

NASA Astrophysics Data System (ADS)

Young, James B.; Knight, Ed; Merrow, Cindy

1998-10-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be one of the primary instruments observing the earth on the Earth Observing System (EOS) scheduled for launch in 1999. MODIS polarization performance characterization was required for the 0.4 to 0.6 micrometers (VIS), 0.6 micrometers to 1.0 micrometers (NIR), and 1.0 micrometers to 2.3 micrometers (SWIR) regions. A polarized source assembly (PSA) consisting of a collimator with a rotatable Ahrens polarizer was used to illuminate MODIS with a linearly polarized beam. MODIS signal function having two-cycles per 360 degrees prism rotation signal function was expected. However, some spectral bands had a distinct four-cycle anomalous signal. The expected two-cycle function was present in all regions with the four-cycle anomaly being limited to the NIR region. Fourier analysis was very useful tooling determining the cause of the anomaly. A simplified polarization model of the PSA and MODIS was generated using Mueller matrices-Stokes vector formalism. Parametric modeling illustrated that this anomaly could be produced by energy having multiple passes between PSA Ahrens prism and the MODIS focal plane filters. Furthermore, the model gave NIR four-cycle magnitudes that were consistent with observations. The IVS and SWIR optical trans had birefringent elements that served to scramble the multiple pass anomaly. The model validity was demonstrated with an experimental setup that had partial aperture illumination which eliminated the possibility of multiple passes. The four-cycle response was eliminated while producing the same two-cycle polarization response. Data will be shown to illustrate the four-cycle phenomenon.

Biomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission

USGS Publications Warehouse

Marshall, Michael T.; Thenkabail, Prasad S.

2014-01-01

New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniques: two-band hyperspectral vegetation indices (HVIs), multiple band-HVIs (MB-HVIs) developed from Sequential Search Methods (SSM), and MB-HVIs developed from Principal Component Regression. Overall, the two-band HVIs and MB-HVIs developed from SSMs using first derivative transformed spectra in the visible blue and green and NIR explained more biomass variability and had lower error than the other approaches or transformations; however a better search criterion needs to be developed in order to reflect the true ability of the two-band HVI approach. Short-Wave Infrared 1 (1000 to 1700 nm) proved less effective, but still important in the final models.

Spatiotemporal Built-up Land Density Mapping Using Various Spectral Indices in Landsat-7 ETM+ and Landsat-8 OLI/TIRS (Case Study: Surakarta City)

NASA Astrophysics Data System (ADS)

Risky, Yanuar S.; Aulia, Yogi H.; Widayani, Prima

2017-12-01

Spectral indices variations support for rapid and accurate extracting information such as built-up density. However, the exact determination of spectral waves for built-up density extraction is lacking. This study explains and compares the capabilities of 5 variations of spectral indices in spatiotemporal built-up density mapping using Landsat-7 ETM+ and Landsat-8 OLI/TIRS in Surakarta City on 2002 and 2015. The spectral indices variations used are 3 mid-infrared (MIR) based indices such as the Normalized Difference Built-up Index (NDBI), Urban Index (UI) and Built-up and 2 visible based indices such as VrNIR-BI (visible red) and VgNIR-BI (visible green). Linear regression statistics between ground value samples from Google Earth image in 2002 and 2015 and spectral indices for determining built-up land density. Ground value used amounted to 27 samples for model and 7 samples for accuracy test. The classification of built-up density mapping is divided into 9 classes: unclassified, 0-12.5%, 12.5-25%, 25-37.5%, 37.5-50%, 50-62.5%, 62.5-75%, 75-87.5% and 87.5-100 %. Accuracy of built-up land density mapping in 2002 and 2015 using VrNIR-BI (81,823% and 73.235%), VgNIR-BI (78.934% and 69.028%), NDBI (34.870% and 74.365%), UI (43.273% and 64.398%) and Built-up (59.755% and 72.664%). Based all spectral indices, Surakarta City on 2000-2015 has increased of built-up land density. VgNIR-BI has better capabilities for built-up land density mapping on Landsat-7 ETM + and Landsat-8 OLI/TIRS.

Characterization of Articular Cartilage Recovery and Its Correlation with Optical Response in the Near-Infrared Spectral Range.

PubMed

Afara, Isaac Oluwaseun; Singh, Sanjleena; Moody, Hayley; Zhang, Lihai; Oloyede, Adekunle

2017-07-01

In this study, we examine the capacity of a new parameter, based on the recovery response of articular cartilage, to distinguish between healthy and damaged tissues. We also investigate whether or not this new parameter correlates with the near-infrared (NIR) optical response of articular cartilage. Normal and artificially degenerated (proteoglycan-depleted) bovine cartilage samples were nondestructively probed using NIR spectroscopy. Subsequently they were subjected to a load and unloading protocol, and the recovery response was logged during unloading. The recovery parameter, elastic rebound ( E R ), is based on the strain energy released as the samples underwent instantaneous elastic recovery. Our results reveal positive relationship between the rebound parameter and cartilage proteoglycan content (normal samples: 2.20 ± 0.10 N mm; proteoglycan-depleted samples: 0.50 ± 0.04 N mm for 1 hour of enzymatic treatment and 0.13 ± 0.02 N mm for 4 hours of enzymatic treatment). In addition, multivariate analysis using partial least squares regression was employed to investigate the relationship between E R and NIR spectral data. The results reveal significantly high correlation ( R 2 cal = 98.35% and R 2 val = 79.87%; P < 0.0001), with relatively low error (14%), between the recovery and optical response of cartilage in the combined NIR regions 5,450 to 6,100 cm -1 and 7,500 to 12,500 cm -1 . We conclude that E R can indicate the mechanical condition and state of health of articular cartilage. The correlation of E R with cartilage optical response in the NIR range could facilitate real-time evaluation of the tissue's integrity during arthroscopic surgery and could also provide an important tool for cartilage assessment in tissue engineering and regeneration research.

Determination of NIR informative wavebands for transmission non-invasive blood glucose measurement using a Fourier transform spectrometer

NASA Astrophysics Data System (ADS)

Yang, Wenming; Liao, Ningfang; Cheng, Haobo; Li, Yasheng; Bai, Xueqiong; Deng, Chengyang

2018-03-01

Non-invasive blood glucose measurement using near infrared (NIR) spectroscopy relies on wavebands that provide reliable information about spectral absorption. In this study, we investigated wavebands which are informative for blood glucose in the NIR shortwave band (900˜1450 nm) and the first overtone band (1450˜1700 nm) through a specially designed NIR Fourier transform spectrometer (FTS), which featured a test fixture (where a sample or subject's finger could be placed) and all-reflective optics, except for a Michelson structure. Different concentrations of glucose solution and seven volunteers who had undergone oral glucose tolerance tests (OGTT) were studied to acquire transmission spectra in the shortwave band and the first overtone band. Characteristic peaks of glucose absorption were identified from the spectra of glucose aqueous solution by second-order derivative processing. The wavebands linked to blood glucose were successfully estimated through spectra of the middle fingertip of OGTT participants by a simple linear regression and correlation coefficient. The light intensity difference showed that glucose absorption in the first overtone band was much more prominent than it was in the shortwave band. The results of the SLR model established from seven OGTTs in total on seven participants enabled a positive estimation of the glucose-linked wavelength. It is suggested that wavebands with prominent characteristic peaks, a high correlation coefficient between blood glucose and light intensity difference and a relatively low standard deviation of predicted values will be the most informative wavebands for transmission non-invasive blood glucose measurement methods. This work provides a guidance for waveband selection for the development of non-invasive NIR blood glucose measurement.

Near-infrared chemical imaging (NIR-CI) as a process monitoring solution for a production line of roll compaction and tableting.

PubMed

Khorasani, Milad; Amigo, José M; Sun, Changquan Calvin; Bertelsen, Poul; Rantanen, Jukka

2015-06-01

In the present study the application of near-infrared chemical imaging (NIR-CI) supported by chemometric modeling as non-destructive tool for monitoring and assessing the roller compaction and tableting processes was investigated. Based on preliminary risk-assessment, discussion with experts and current work from the literature the critical process parameter (roll pressure and roll speed) and critical quality attributes (ribbon porosity, granule size, amount of fines, tablet tensile strength) were identified and a design space was established. Five experimental runs with different process settings were carried out which revealed intermediates (ribbons, granules) and final products (tablets) with different properties. Principal component analysis (PCA) based model of NIR images was applied to map the ribbon porosity distribution. The ribbon porosity distribution gained from the PCA based NIR-CI was used to develop predictive models for granule size fractions. Predictive methods with acceptable R(2) values could be used to predict the granule particle size. Partial least squares regression (PLS-R) based model of the NIR-CI was used to map and predict the chemical distribution and content of active compound for both roller compacted ribbons and corresponding tablets. In order to select the optimal process, setting the standard deviation of tablet tensile strength and tablet weight for each tablet batch was considered. Strong linear correlation between tablet tensile strength and amount of fines and granule size was established, respectively. These approaches are considered to have a potentially large impact on quality monitoring and control of continuously operating manufacturing lines, such as roller compaction and tableting processes. Copyright © 2015 Elsevier B.V. All rights reserved.

Rapid on-line detection and grading of wooden breast myopathy in chicken fillets by near-infrared spectroscopy.

PubMed

Wold, Jens Petter; Veiseth-Kent, Eva; Høst, Vibeke; Løvland, Atle

2017-01-01

The main objective of this work was to develop a method for rapid and non-destructive detection and grading of wooden breast (WB) syndrome in chicken breast fillets. Near-infrared (NIR) spectroscopy was chosen as detection method, and an industrial NIR scanner was applied and tested for large scale on-line detection of the syndrome. Two approaches were evaluated for discrimination of WB fillets: 1) Linear discriminant analysis based on NIR spectra only, and 2) a regression model for protein was made based on NIR spectra and the estimated concentrations of protein were used for discrimination. A sample set of 197 fillets was used for training and calibration. A test set was recorded under industrial conditions and contained spectra from 79 fillets. The classification methods obtained 99.5-100% correct classification of the calibration set and 100% correct classification of the test set. The NIR scanner was then installed in a commercial chicken processing plant and could detect incidence rates of WB in large batches of fillets. Examples of incidence are shown for three broiler flocks where a high number of fillets (9063, 6330 and 10483) were effectively measured. Prevalence of WB of 0.1%, 6.6% and 8.5% were estimated for these flocks based on the complete sample volumes. Such an on-line system can be used to alleviate the challenges WB represents to the poultry meat industry. It enables automatic quality sorting of chicken fillets to different product categories. Manual laborious grading can be avoided. Incidences of WB from different farms and flocks can be tracked and information can be used to understand and point out main causes for WB in the chicken production. This knowledge can be used to improve the production procedures and reduce today's extensive occurrence of WB.

Optoelectronic sensor device for monitoring ethanol concentration in winemaking applications

NASA Astrophysics Data System (ADS)

Jiménez-Márquez, F.; Vázquez, J.; Úbeda, J.; Rodríguez-Rey, J.; Sánchez-Rojas, J. L.

2015-05-01

The supervision of key variables such as sugar, alcohol, released CO2 and microbiological evolution in fermenting grape must is of great importance in the winemaking industry. However, the fermentation kinetics is assessed by monitoring the evolution of the density as it varies during a fermentation, since density is an indicator of the total amount of sugars, ethanol and glycerol. Even so, supervising the fermentation process is an awkward and non-comprehensive task, especially in wine cellars where production rates are massive, and enologists usually measure the density of the extracted samples from each fermentation tank manually twice a day. This work aims at the design of a fast, low-cost, portable and reliable optoelectronic sensor for measuring ethanol concentration in fermenting grape must samples. Different sets of model solutions, which contain ethanol, fructose, glucose, glycerol dissolved in water and emulate the grape must composition at different stages of the fermentation, were prepared both for calibration and validation. The absorption characteristics of these model solutions were analyzed by a commercial spectrophotometer in the NIR region, in order to identify key wavelengths from which valuable information regarding the sample composition can be extracted. Finally, a customized optoelectronic prototype based on absorbance measurements at two wavelengths belonging to the NIR region was designed, fabricated and successfully tested. The system, whose optoelectronics is reduced after a thorough analysis to only two LED lamps and their corresponding paired photodiodes operating at 1.2 and 1.3 μm respectively, calculates the ethanol content by a multiple linear regression.

Combined bio-engineering and neurophysiological in vivo technologies allow studying rat brain metabolism and neuronal activities in vivo in real time

NASA Astrophysics Data System (ADS)

Crespi, F.; Donini, M.; Bandera, A.; Congestri, F.; Heidbreder, C.; Rovati, L.

2006-04-01

Franz Joebsis first used near infrared spectroscopy (NIRS) as a tool for the in vivo monitoring of tissue oxygenation. Today, NIRS instruments are more and more used in clinical environments since these systems are now easy to use, sensitive, robust, give rapid analysis and have multiple measuring points. In the present work, optic fibre probes were used as optical head of a CW-NIR instrument adapted for in vivo NIRS measurements in the brain of rodents. This prototype was designed for non-invasive analysis of the two main forms of haemoglobin: oxy-haemoglobin (HbO II) and deoxy-haemoglobin (Hb), chromophores present in biological tissues. In the present experiments it was applied to measure non- invasively HbO II and Hb levels in the rat brain; that are markers of the degree of tissue oxygenation, thus providing an index of blood levels and therefore of brain metabolism. In addition, the same animals set for central NIRS studies, were also surgically prepared for electrophysiological monitoring of cell firing in discrete brain areas. These are raphe dorsalis nucleus, locus coeruleus, ventral tegmental area that are defined as main serotoninergic, noradrenergic and dopaminergic cell containing regions of the CNS and therefore involved in the major cerebral syndromes. Then, following a control recording period, exogenous oxygen (O2, 0.1bar, 2min) or carbon dioxide (CO2 0.1bar, 20min) was inflated orally. The data gathered indicate an original relationship between NIRS analysis of brain metabolism and electrical changes in three major nuclei of CNS involved in neurophysiologic and pathologic activities.

Cryogenic and radiation-hard asic for interfacing large format NIR/SWIR detector arrays

NASA Astrophysics Data System (ADS)

Gao, Peng; Dupont, Benoit; Dierickx, Bart; Müller, Eric; Verbruggen, Geert; Gielis, Stijn; Valvekens, Ramses

2017-11-01

For scientific and earth observation space missions, weight and power consumption is usually a critical factor. In order to obtain better vehicle integration, efficiency and controllability for large format NIR/SWIR detector arrays, a prototype ASIC is designed. It performs multiple detector array interfacing, power regulation and data acquisition operations inside the cryogenic chambers. Both operation commands and imaging data are communicated via the SpaceWire interface which will significantly reduce the number of wire goes in and out the cryogenic chamber. This "ASIC" prototype is realized in 0.18um CMOS technology and is designed for radiation hardness.

Multiple View Zenith Angle Observations of Reflectance From Ponderosa Pine Stands

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Lawless, James G. (Technical Monitor)

1994-01-01

Reflectance factors (RF(lambda)) from dense and sparse ponderosa pine (Pinus ponderosa) stands, derived from radiance data collected in the solar principal plane by the Advanced Solid-State Array Spectro-radiometer (ASAS), were examined as a function of view zenith angle (theta(sub v)). RF(lambda) was maximized with theta(sub v) nearest the solar retrodirection, and minimized near the specular direction throughout the ASAS spectral region. The dense stand had much higher RF anisotropy (ma)dmurn RF is minimum RF) in the red region than did the sparse stand (relative differences of 5.3 vs. 2.75, respectively), as a function of theta(sub v), due to the shadow component in the canopy. Anisotropy in the near-infrared (NIR) was more similar between the two stands (2.5 in the dense stand and 2.25 in the sparse stand); the dense stand exhibited a greater hotspot effect than 20 the sparse stand in this spectral region. Two common vegetation transforms, the NIR/red ratio and the normalized difference vegetation index (NDVI), both showed a theta(sub v) dependence for the dense stand. Minimum values occurred near the retrodirection and maximum values occurred near the specular direction. Greater relative differences were noted for the NIR/red ratio (2.1) than for the NDVI (1.3). The sparse stand showed no obvious dependence on theta(sub v) for either transform, except for slightly elevated values toward the specular direction.

'Multi-associations': predisposed to misinterpretation of peripheral tissue oxygenation and circulation in neonates.

PubMed

Pichler, Gerhard; Pocivalnik, Mirjam; Riedl, Regina; Pichler-Stachl, Elisabeth; Morris, Nicholas; Zotter, Heinz; Müller, Wilhelm; Urlesberger, Berndt

2011-08-01

Interpretation of peripheral circulation in ill neonates is crucial but difficult. The aim was to analyse parameters potentially influencing peripheral oxygenation and circulation. In a prospective observational cohort study in 116 cardio-circulatory stable neonates, peripheral muscle near-infrared spectroscopy (NIRS) with venous occlusion was performed. Tissue oxygenation index (TOI), mixed venous oxygenation (SvO(2)), fractional oxygen extraction (FOE), fractional tissue oxygen extraction (FTOE), haemoglobin flow (Hbflow), oxygen delivery (DO(2)), oxygen consumption (VO(2)), and vascular resistance (VR) were assessed. Correlation coefficients between NIRS parameters and demographic parameters (gestational age, birth weight, age, actual weight, diameter of calf, subcutaneous adipose tissue), monitoring parameters (heart rate, arterial oxygen saturation (SaO(2)), mean blood pressure (MAP), core/peripheral temperature, central/peripheral capillary refill time) and laboratory parameters (haemoglobin concentration (Hb-blood), pCO(2)) were calculated. All demographic parameters except for Hbflow and DO(2) correlated with NIRS parameters. Heart rate correlated with TOI, SvO(2), VO(2) and VR. SaO(2) correlated with FOE/FTOE. MAP correlated with Hbflow, DO(2), VO(2) and VR. Core temperature correlated with FTOE. Peripheral temperature correlated with all NIRS parameters except VO(2). Hb-blood correlated with FOE and VR. pCO(2) levels correlated with TOI and SvO(2). The presence of multiple interdependent factors associated with peripheral oxygenation and circulation highlights the difficulty in interpreting NIRS data. Nevertheless, these findings have to be taken into account when analysing peripheral oxygenation and circulation data.

[Determination of calcium and magnesium in tobacco by near-infrared spectroscopy and least squares-support vector machine].

PubMed

Tian, Kuang-da; Qiu, Kai-xian; Li, Zu-hong; Lü, Ya-qiong; Zhang, Qiu-ju; Xiong, Yan-mei; Min, Shun-geng

2014-12-01

The purpose of the present paper is to determine calcium and magnesium in tobacco using NIR combined with least squares-support vector machine (LS-SVM). Five hundred ground and dried tobacco samples from Qujing city, Yunnan province, China, were surveyed by a MATRIX-I spectrometer (Bruker Optics, Bremen, Germany). At the beginning of data processing, outliers of samples were eliminated for stability of the model. The rest 487 samples were divided into several calibration sets and validation sets according to a hybrid modeling strategy. Monte-Carlo cross validation was used to choose the best spectral preprocess method from multiplicative scatter correction (MSC), standard normal variate transformation (SNV), S-G smoothing, 1st derivative, etc., and their combinations. To optimize parameters of LS-SVM model, the multilayer grid search and 10-fold cross validation were applied. The final LS-SVM models with the optimizing parameters were trained by the calibration set and accessed by 287 validation samples picked by Kennard-Stone method. For the quantitative model of calcium in tobacco, Savitzky-Golay FIR smoothing with frame size 21 showed the best performance. The regularization parameter λ of LS-SVM was e16.11, while the bandwidth of the RBF kernel σ2 was e8.42. The determination coefficient for prediction (Rc(2)) was 0.9755 and the determination coefficient for prediction (Rp(2)) was 0.9422, better than the performance of PLS model (Rc(2)=0.9593, Rp(2)=0.9344). For the quantitative analysis of magnesium, SNV made the regression model more precise than other preprocess. The optimized λ was e15.25 and σ2 was e6.32. Rc(2) and Rp(2) were 0.9961 and 0.9301, respectively, better than PLS model (Rc(2)=0.9716, Rp(2)=0.8924). After modeling, the whole progress of NIR scan and data analysis for one sample was within tens of seconds. The overall results show that NIR spectroscopy combined with LS-SVM can be efficiently utilized for rapid and accurate analysis of calcium and magnesium in tobacco.

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions.

PubMed

González-Sáiz, J M; Esteban-Díez, I; Sánchez-Gallardo, C; Pizarro, C

2008-08-01

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting-PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.

ICG laser therapy of acne vulgaris

NASA Astrophysics Data System (ADS)

Tuchin, Valery V.; Altshuler, Gregory B.; Genina, Elina A.; Bashkatov, Alexey N.; Simonenko, Georgy V.; Odoevskaya, Olga D.; Yaroslavsky, Ilya V.

2004-07-01

The near-infrared (NIR) laser radiation due to its high penetration depth is widely used in phototherapy. In application to skin appendages a high selectivity of laser treatment is needed to prevent light action on surrounding tissues. Indocyanine Green (ICG) dye may provide a high selectivity of treatment due to effective ICG uploading by a target and its narrow band of considerable absorption just at the wavelength of the NIR diode laser. The goal of this study is to demonstrate the efficacy of the NIR diode laser phototherapy in combination with topical application of ICG suggested for soft and thermal treatment of acne vulgaris. 28 volunteers with facile or back-located acne were enrolled. Skin sites of subjects were stained by ICG and irradiated by NIR laser-diode light (803 or 809 nm). Untreated, only stained and only light irradiated skin areas served as controls. For soft acne treatment, the low-intensity (803 nm, 10 - 50 mW/cm2, 5-10 min) or the medium-intensity (809 nm, 150 - 190 mW/cm2, 15 min) protocols were used. The single and multiple (up to 8-9) treatments were provided. The individual acne lesions were photothermally treated at 18 W/cm2 (803 nm, 0.5 sec) without skin surface cooling or at 200 W/cm2 (809 nm, 0.5 sec) with cooling. The results of the observations during 1-2 months after the completion of the treatment have shown that only in the case of the multiple-wise treatment a combined action of ICG and NIR irradiation reduces inflammation and improves skin state during a month without any side effects. At high power densities (up to 200 W/cm2) ICG stained acne inflammatory elements were destructed for light exposures of 0.5 sec. Based on the concept that hair follicle, especially sebaceous gland, can be intensively and selectively stained by ICG due to dye diffusion through pilosebaceous canal and its fast uptake by living microorganisms, by vital keratinocytes of epithelium of the canal and sebaceous duct, and by rapidly proliferating sebocytes, new technologies of soft and thermal acne lesions treatment that could be used in clinical treatment of acne were proposed.

Prediction of brain tissue temperature using near-infrared spectroscopy

PubMed Central

Holper, Lisa; Mitra, Subhabrata; Bale, Gemma; Robertson, Nicola; Tachtsidis, Ilias

2017-01-01

Abstract. Broadband near-infrared spectroscopy (NIRS) can provide an endogenous indicator of tissue temperature based on the temperature dependence of the water absorption spectrum. We describe a first evaluation of the calibration and prediction of brain tissue temperature obtained during hypothermia in newborn piglets (animal dataset) and rewarming in newborn infants (human dataset) based on measured body (rectal) temperature. The calibration using partial least squares regression proved to be a reliable method to predict brain tissue temperature with respect to core body temperature in the wavelength interval of 720 to 880 nm with a strong mean predictive power of R2=0.713±0.157 (animal dataset) and R2=0.798±0.087 (human dataset). In addition, we applied regression receiver operating characteristic curves for the first time to evaluate the temperature prediction, which provided an overall mean error bias between NIRS predicted brain temperature and body temperature of 0.436±0.283°C (animal dataset) and 0.162±0.149°C (human dataset). We discuss main methodological aspects, particularly the well-known aspect of over- versus underestimation between brain and body temperature, which is relevant for potential clinical applications. PMID:28630878

Modeling of feed-forward control using the partial least squares regression method in the tablet compression process.

PubMed

Hattori, Yusuke; Otsuka, Makoto

2017-05-30

In the pharmaceutical industry, the implementation of continuous manufacturing has been widely promoted in lieu of the traditional batch manufacturing approach. More specially, in recent years, the innovative concept of feed-forward control has been introduced in relation to process analytical technology. In the present study, we successfully developed a feed-forward control model for the tablet compression process by integrating data obtained from near-infrared (NIR) spectra and the physical properties of granules. In the pharmaceutical industry, batch manufacturing routinely allows for the preparation of granules with the desired properties through the manual control of process parameters. On the other hand, continuous manufacturing demands the automatic determination of these process parameters. Here, we proposed the development of a control model using the partial least squares regression (PLSR) method. The most significant feature of this method is the use of dataset integrating both the NIR spectra and the physical properties of the granules. Using our model, we determined that the properties of products, such as tablet weight and thickness, need to be included as independent variables in the PLSR analysis in order to predict unknown process parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum

NASA Astrophysics Data System (ADS)

Fu, Haiyan; Yin, Qiaobo; Xu, Lu; Wang, Weizheng; Chen, Feng; Yang, Tianming

2017-07-01

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.

Reduced cortical microvascular oxygenation in multiple sclerosis: a blinded, case-controlled study using a novel quantitative near-infrared spectroscopy method

NASA Astrophysics Data System (ADS)

Yang, Runze; Dunn, Jeff F.

2015-11-01

Hypoxia (low oxygen) is associated with many brain disorders as well as inflammation, but the lack of widely available technology has limited our ability to study hypoxia in human brain. Multiple sclerosis (MS) is a poorly understood neurological disease with a significant inflammatory component which may cause hypoxia. We hypothesized that if hypoxia were to occur, there should be reduced microvascular hemoglobin saturation (StO2). In this study, we aimed to determine if reduced StO2 can be detected in MS using frequency domain near-infrared spectroscopy (fdNIRS). We measured fdNIRS data in cortex and assessed disability of 3 clinical isolated syndrome (CIS), 72 MS patients and 12 controls. Control StO2 was 63.5 ± 3% (mean ± SD). In MS patients, 42% of StO2 values were more than 2 × SD lower than the control mean. There was a significant relationship between StO2 and clinical disability. A reduced microvascular StO2 is supportive (although not conclusive) that there may be hypoxic regions in MS brain. This is the first study showing how quantitative NIRS can be used to detect reduced StO2 in patients with MS, opening the door to understanding how microvascular oxygenation impacts neurological conditions.

Retrieval of total suspended matter concentrations from high resolution WorldView-2 imagery: a case study of inland rivers

NASA Astrophysics Data System (ADS)

Shi, Liangliang; Mao, Zhihua; Wang, Zheng

2018-02-01

Satellite imagery has played an important role in monitoring water quality of lakes or coastal waters presently, but scarcely been applied in inland rivers. This paper presents an attempt of feasibility to apply regression model to quantify and map the concentrations of total suspended matter (CTSM) in inland rivers which have a large scale of spatial and a high CTSM dynamic range by using high resolution satellite remote sensing data, WorldView-2. An empirical approach to quantify CTSM by integrated use of high resolution WorldView-2 multispectral data and 21 in situ CTSM measurements. Radiometric correction, geometric and atmospheric correction involved in image processing procedure is carried out for deriving the surface reflectance to correlate the CTSM and satellite data by using single-variable and multivariable regression technique. Results of regression model show that the single near-infrared (NIR) band 8 of WorldView-2 have a relative strong relationship (R2=0.93) with CTSM. Different prediction models were developed on various combinations of WorldView-2 bands, the Akaike Information Criteria approach was used to choose the best model. The model involving band 1, 3, 5, and 8 of WorldView-2 had a best performance, whose R2 reach to 0.92, with SEE of 53.30 g/m3. The spatial distribution maps were produced by using the best multiple regression model. The results of this paper indicated that it is feasible to apply the empirical model by using high resolution satellite imagery to retrieve CTSM of inland rivers in routine monitoring of water quality.

Multicomponent blood lipid analysis by means of near infrared spectroscopy, in geese.

PubMed

Bazar, George; Eles, Viktoria; Kovacs, Zoltan; Romvari, Robert; Szabo, Andras

2016-08-01

This study provides accurate near infrared (NIR) spectroscopic models on some laboratory determined clinicochemical parameters (i.e. total lipid (5.57±1.95 g/l), triglyceride (2.59±1.36 mmol/l), total cholesterol (3.81±0.68 mmol/l), high density lipoprotein (HDL) cholesterol (2.45±0.58 mmol/l)) of blood serum samples of fattened geese. To increase the performance of multivariate chemometrics, samples significantly deviating from the regression models implying laboratory error were excluded from the final calibration datasets. Reference data of excluded samples having outlier spectra in principal component analysis were not marked as false. Samples deviating from the regression models but having non outlier spectra in PCA were identified as having false reference constituent values. Based on the NIR selection methods, 5% of the reference measurement data were rated as doubtful. The achieved models reached R(2) of 0.864, 0.966, 0.850, 0.793, and RMSE of 0.639 g/l, 0.232 mmol/l, 0.210 mmol/l, 0.241 mmol/l for total lipid, triglyceride, total cholesterol and HDL cholesterol, respectively, during independent validation. Classical analytical techniques focus on single constituents and often require chemicals, time-consuming measurements, and experienced technicians. NIR technique provides a quick, cost effective, non-hazardous alternative method for analysis of several constituents based on one single spectrum of each sample, and it also offers the possibility for looking at the laboratory reference data critically. Evaluation of reference data to identify and exclude falsely analyzed samples can provide warning feedback to the reference laboratory, especially in the case of analyses where laboratory methods are not perfectly suited to the subjected material and there is an increased chance of laboratory error. Copyright © 2016 Elsevier B.V. All rights reserved.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS)

NASA Astrophysics Data System (ADS)

Krepper, Gabriela; Romeo, Florencia; Fernandes, David Douglas de Sousa; Diniz, Paulo Henrique Gonçalves Dias; de Araújo, Mário César Ugulino; Di Nezio, María Susana; Pistonesi, Marcelo Fabián; Centurión, María Eugenia

2018-01-01

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12 mg kg- 1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (w w- 1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59 mg kg- 1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS).

PubMed

Krepper, Gabriela; Romeo, Florencia; Fernandes, David Douglas de Sousa; Diniz, Paulo Henrique Gonçalves Dias; de Araújo, Mário César Ugulino; Di Nezio, María Susana; Pistonesi, Marcelo Fabián; Centurión, María Eugenia

2018-01-15

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg -1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww -1 ). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg -1 , REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

Classification of Fusarium-Infected Korean Hulled Barley Using Near-Infrared Reflectance Spectroscopy and Partial Least Squares Discriminant Analysis

PubMed Central

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Yoo, Hyeonchae; Ham, Hyeonheui; Kim, Moon S.

2017-01-01

The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate Fusarium-infected hulled barley. Both normal hulled barley and Fusarium-infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for Fusarium discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the Fusarium-infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the Fusarium-infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method. PMID:28974012

Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy.

PubMed

Dou, Ying; Mi, Hong; Zhao, Lingzhi; Ren, Yuqiu; Ren, Yulin

2006-09-01

The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Jiang, Xunpeng; Zhou, Xingfan; Zhang, Yinqiao; Zhang, Naiqian; Wei, Chongfeng; Mao, Wenhua

2017-10-01

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of <24%, 24-30%, >30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

PubMed Central

Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China. PMID:29410795

Determination of alcohol and extract concentration in beer samples using a combined method of near-infrared (NIR) spectroscopy and refractometry.

PubMed

Castritius, Stefan; Kron, Alexander; Schäfer, Thomas; Rädle, Matthias; Harms, Diedrich

2010-12-22

A new approach of combination of near-infrared (NIR) spectroscopy and refractometry was developed in this work to determine the concentration of alcohol and real extract in various beer samples. A partial least-squares (PLS) regression, as multivariate calibration method, was used to evaluate the correlation between the data of spectroscopy/refractometry and alcohol/extract concentration. This multivariate combination of spectroscopy and refractometry enhanced the precision in the determination of alcohol, compared to single spectroscopy measurements, due to the effect of high extract concentration on the spectral data, especially of nonalcoholic beer samples. For NIR calibration, two mathematical pretreatments (first-order derivation and linear baseline correction) were applied to eliminate light scattering effects. A sample grouping of the refractometry data was also applied to increase the accuracy of the determined concentration. The root mean squared errors of validation (RMSEV) of the validation process concerning alcohol and extract concentration were 0.23 Mas% (method A), 0.12 Mas% (method B), and 0.19 Mas% (method C) and 0.11 Mas% (method A), 0.11 Mas% (method B), and 0.11 Mas% (method C), respectively.

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy.

PubMed

Ribeiro, J S; Ferreira, M M C; Salva, T J G

2011-02-15

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein. Copyright © 2010 Elsevier B.V. All rights reserved.

Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods.

PubMed

Guo, Xiuhan; Cai, Rui; Wang, Shisheng; Tang, Bo; Li, Yueqing; Zhao, Weijie

2018-01-01

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber ( Apostichopus japonicus ) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least-squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictor selection algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by principal component analysis combined with Mahalanobis distance and scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square error of prediction was 0.45, and correlation coefficient was 0.90. The correct classification rates of 100% were obtained in both identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.

Maturation of the cytochrome cd1 nitrite reductase NirS from Pseudomonas aeruginosa requires transient interactions between the three proteins NirS, NirN and NirF

PubMed Central

Nicke, Tristan; Schnitzer, Tobias; Münch, Karin; Adamczack, Julia; Haufschildt, Kristin; Buchmeier, Sabine; Kucklick, Martin; Felgenträger, Undine; Jänsch, Lothar; Riedel, Katharina; Layer, Gunhild

2013-01-01

The periplasmic cytochrome cd1 nitrite reductase NirS occurring in denitrifying bacteria such as the human pathogen Pseudomonas aeruginosa contains the essential tetrapyrrole cofactors haem c and haem d1. Whereas the haem c is incorporated into NirS by the cytochrome c maturation system I, nothing is known about the insertion of the haem d1 into NirS. Here, we show by co-immunoprecipitation that NirS interacts with the potential haem d1 insertion protein NirN in vivo. This NirS–NirN interaction is dependent on the presence of the putative haem d1 biosynthesis enzyme NirF. Further, we show by affinity co-purification that NirS also directly interacts with NirF. Additionally, NirF is shown to be a membrane anchored lipoprotein in P. aeruginosa. Finally, the analysis by UV–visible absorption spectroscopy of the periplasmic protein fractions prepared from the P. aeruginosa WT (wild-type) and a P. aeruginosa ΔnirN mutant shows that the cofactor content of NirS is altered in the absence of NirN. Based on our results, we propose a potential model for the maturation of NirS in which the three proteins NirS, NirN and NirF form a transient, membrane-associated complex in order to achieve the last step of haem d1 biosynthesis and insertion of the cofactor into NirS. PMID:23683062

Optimization measurement of muscle oxygen saturation under isometric studies using FNIRS

NASA Astrophysics Data System (ADS)

Halim, A. A. A.; Laili, M. H.; Salikin, M. S.; Rusop, M.

2018-05-01

Development of functional near infrared spectroscopy (fNIRS) technologies has advanced quantification signal using multiple wavelength and detector to investigate hemodynamic response in human muscle. These non-invasive technologies have been widely used to solve the propagation of light inside the tissues including the absorption, scattering coefficient and to quantify the oxygenation level of haemoglobin and myoglobin in human muscle. The goal of this paper is to optimize the measurement of muscle oxygen saturation during isometric exercise using functional near infrared spectroscopy (fNIRS). The experiment was carried out on 15 sedentary healthy male volunteers. All volunteers are required to perform an isometric exercise at three assessment of muscular fatigue's level on flexor digitalis (FDS) muscle in the human forearm using fNIRS. The slopes of the signals have been highlighted to evaluate the muscle oxygen saturation of regional muscle fatigue. As a result, oxygen saturation slope from 10% exercise showed steeper than the first assessment at 30%-50% of fatigues level. The hemodynamic signal response showed significant value (p=0.04) at all three assessment of muscular fatigue's level which produce a p-value (p<0.05) measured by fNIRS. Thus, this highlighted parameter could be used to estimate fatigue's level of human and could open other possibilities to study muscle performance diagnosis.

[Comparison of Three Spectroscopies for the Determination of Composition of LDPE/PP Blend with Partial Least-Squares].

PubMed

Chen, Ru-huang; Jin, Gang

2015-08-01

This paper presented an application of mid-infrared (MIR), near-infrared (NIR) and Raman spectroscopies for collecting the spectra of 31 kinds of low density polyethylene/polyprolene (LDPE/PP) samples with different proportions. The different pre-processing methods (multiplicative scatter correction, mean centering and Savitzky-Golay first derivative) and spectral region were explored to develop partial least-squares (PLS) model for LDPE, their influence on the accuracy of PLS model also being discussed. Three spectroscopies were compared about the accuracy of quantitative measurement. Consequently, the pre-processing methods and spectral region have a great impact on the accuracy of PLS model, especially the spectra with subtle difference, random noise and baseline variation. After being pre-processed and spectral region selected, the calibration model of MIR, NIR and Raman exhibited R2/RMSEC values of 0.9906/2.941, 0.9973/1.561 and 0.9972/1.598 respectively, which corrsponding to 0.8876/10.15, 0.8493/11.75 and 0.8757/10.67 before any treatment. The results also suggested MIR, NIR and Raman are three strong tools to predict the content of LDPE in LDPE/PP blend. However, NIR and Raman showed higher accuracy after being pre-processed and more suitability to fast quantitative characterization due to their high measuring speed.

Ultrasmall lanthanide-doped nanoparticles as multimodal platforms

NASA Astrophysics Data System (ADS)

Yust, Brian G.; Pedraza, Francisco J.; Sardar, Dhiraj K.

2014-03-01

Recently, there has been a great amount of interest in nanoparticles which are able to provide a platform with high contrast for multiple imaging modalities in order to advance the tools available to biomedical researchers and physicians. However, many nanoparticles do not have ideal properties to provide high contrast in different imaging modes. In order to address this, ultrasmall lanthanide doped oxide and fluoride nanoparticles with strong NIR to NIR upconversion fluorescence and a strong magnetic response for magnetic resonance imaging (MRI) have been developed. Specifically, these nanoparticles incorporate gadolinium, dysprosium, or a combination of both into the nano-crystalline host to achieve the magnetic properties. Thulium, erbium, and neodymium codopants provide the strong NIR absorption and emission lines that allow for deeper tissue imaging since near infrared light is not strongly absorbed or scattered by most tissues within this region. This also leads to better image quality and lower necessary excitation intensities. As a part of the one pot synthesis, these nanoparticles are coated with peg, pmao, or d-glucuronic acid to make them water soluble, biocompatible, and bioconjugable due to the available carboxyl or amine groups. Here, the synthesis, morphological characterization, magnetic response, NIR emission, and the quantum yield will be discussed. Cytotoxicity tested through cell viability at varying concentrations of nanoparticles in growth media will also be discussed.

Association of low non-invasive near-infrared spectroscopic measurements during initial trauma resuscitation with future development of multiple organ dysfunction.

PubMed

Nicks, Bret A; Campons, Kevin M; Bozeman, William P

2015-01-01

Near-infrared spectroscopy (NIRS) non-invasively monitors muscle tissue oxygen saturation (StO2). It may provide a continuous noninvasive measurement to identify occult hypoperfusion, guide resuscitation, and predict the development of multiple organ dysfunction (MOD) after severe trauma. We evaluated the correlation between initial StO2 and the development of MOD in multi-trauma patients. Patients presenting to our urban, academic, Level I Trauma Center/Emergency Department and meeting standardized trauma-team activation criteria were enrolled in this prospective trial. NIRS monitoring was initiated immediately on arrival with collection of StO2 at the thenar eminence and continued up to 24 hours for those admitted to the Trauma Intensive Care Unit (TICU). Standardized resuscitation laboratory measures and clinical evaluation tools were collected. The primary outcome was the association between initial StO2 and the development of MOD within the first 24 hours based on a MOD score of 6 or greater. Descriptive statistical analyses were performed; numeric means, multivariate regression and rank sum comparisons were utilized. Clinicians were blinded from the StO2 values. Over a 14 month period, 78 patients were enrolled. Mean age was 40.9 years (SD 18.2), 84.4% were male, 76.9% had a blunt trauma mechanism and mean injury severity score (ISS) was 18.5 (SD 12.9). Of the 78 patients, 26 (33.3%) developed MOD within the first 24 hours. The MOD patients had mean initial StO2 values of 53.3 (SD 10.3), significantly lower than those of non-MOD patients 61.1 (SD 10.0); P=0.002. The mean ISS among MOD patients was 29.9 (SD 11.5), significantly higher than that of non-MODS patients, 12.1 (SD 9.1) (P<0.0001). The mean shock index (SI) among MOD patients was 0.92 (SD 0.28), also significantly higher than that of non-MODS patients, 0.73 (SD 0.19) (P=0.0007). Lactate values were not significantly different between groups. Non-invasive, continuous StO2 near-infrared spectroscopy values during initial trauma resuscitation correlate with the later development of multiple organ dysfunction in this patient population.

Hexaphyrin as a Potential Theranostic Dye for Photothermal Therapy and 19 F Magnetic Resonance Imaging.

PubMed

Higashino, Tomohiro; Nakatsuji, Hirotaka; Fukuda, Ryosuke; Okamoto, Haruki; Imai, Hirohiko; Matsuda, Tetsuya; Tochio, Hidehito; Shirakawa, Masahiro; Tkachenko, Nikolai V; Hashida, Mitsuru; Murakami, Tatsuya; Imahori, Hiroshi

2017-05-18

Two features of meso-Aryl-substituted expanded porphyrins suggest suitability as theranostic agents. They have excellent absorption in near infrared (NIR) region, and they offer the possibility of introduction of multiple fluorine atoms at structurally equivalent positions. Here, hexaphyrin (hexa) was synthesized from 2,6-bis(trifluoromethyl)-4-formyl benzoate and pyrrole and evaluated as a novel expanded porphyrin with the above features. Under NIR illumination hexa showed intense photothermal and weak photodynamic effects, which were most likely due to its low excited states, close to singlet oxygen. The sustained photothermal effect caused ablation of cancer cells more effectively than the photodynamic effect of indocyanine green (a clinical dye). In addition, hexa showed potential for use in the visualization of tumors by 19 F magnetic resonance imaging (MRI), because of the multiple fluorine atoms. Our results strongly support the utility of expanded porphyrins as theranostic agents in both photothermal therapy and 19 F MRI. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Attosecond transient absorption probing of electronic superpositions of bound states in neon. Detection of quantum beats

DOE PAGES

Beck, Annelise R; Bernhardt, Birgitta; Warrick, Erika R.; ...

2014-11-07

Electronic wavepackets composed of multiple bound excited states of atomic neon lying between 19.6 and 21.5 eV are launched using an isolated attosecond pulse. Individual quantum beats of the wavepacket are detected by perturbing the induced polarization of the medium with a time-delayed few-femtosecond near-infrared (NIR) pulse via coupling the individual states to multiple neighboring levels. All of the initially excited states are monitored simultaneously in the attosecond transient absorption spectrum, revealing Lorentzian to Fano lineshape spectral changes as well as quantum beats. The most prominent beating of the several that were observed was in the spin–orbit split 3d absorptionmore » features, which has a 40 femtosecond period that corresponds to the spin–orbit splitting of 0.1 eV. The few-level models and multilevel calculations confirm that the observed magnitude of oscillation depends strongly on the spectral bandwidth and tuning of the NIR pulse and on the location of possible coupling states.« less

NIR spectrometer using a Schottky photodetector enhanced by grating-based SPR.

PubMed

Chen, Wenjing; Kan, Tetsuo; Ajiki, Yoshiharu; Matsumoto, Kiyoshi; Shimoyama, Isao

2016-10-31

We present a near-infrared (NIR) spectrum measurement method using a Schottky photodetector enhanced by surface plasmon resonance (SPR). An Au grating was fabricated on an n-type silicon wafer to form a Schottky barrier and act as an SPR coupler. The resulting photodetector provides wavelength-selective photodetection depending on the SPR coupling angle. A matrix was pre-calculated to describe this characteristic. The spectrum was obtained from this matrix and the measured photocurrents at various SPR coupling angles. Light with single and multiple wavelengths was tested. Comparative measurements showed that our method is able to detect spectra with a wavelength resolution comparable to that of a commercial spectrometer.

Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

2010-12-01

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

PubMed Central

Wang, Jingzhe; Abulimiti, Aerzuna; Cai, Lianghong

2018-01-01

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity. PMID:29736341

Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS-NIR) spectroscopy, Ebinur Lake Wetland, Northwest China.

PubMed

Wang, Jingzhe; Ding, Jianli; Abulimiti, Aerzuna; Cai, Lianghong

2018-01-01

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS-NIR) spectroscopy. The soil samples ( n  = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0-2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R 2 (0.93), RMSE (4.57 dS m -1 ), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

Profile soil property estimation using a VIS-NIR-EC-force probe

USDA-ARS?s Scientific Manuscript database

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. ...

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

Development of FT-NIR Models for the Simultaneous Estimation of Chlorophyll and Nitrogen Content in Fresh Apple (Malus Domestica) Leaves

PubMed Central

Tamburini, Elena; Ferrari, Giuseppe; Marchetti, Maria Gabriella; Pedrini, Paola; Ferro, Sergio

2015-01-01

Agricultural practices determine the level of food production and, to great extent, the state of the global environment. During the last decades, the indiscriminate recourse to fertilizers as well as the nitrogen losses from land application have been recognized as serious issues of modern agriculture, globally contributing to nitrate pollution. The development of a reliable Near-Infra-Red Spectroscopy (NIRS)-based method, for the simultaneous monitoring of nitrogen and chlorophyll in fresh apple (Malus domestica) leaves, was investigated on a set of 133 samples, with the aim of estimating the nutritional and physiological status of trees, in real time, cheaply and non-destructively. By means of a FT (Fourier Transform)-NIR instrument, Partial Least Squares (PLS) regression models were developed, spanning a concentration range of 0.577%–0.817% for the total Kjeldahl nitrogen (TKN) content (R2 = 0.983; SEC = 0.012; SEP = 0.028), and of 1.534–2.372 mg/g for the total chlorophyll content (R2 = 0.941; SEC = 0.132; SEP = 0.162). Chlorophyll-a and chlorophyll-b contents were also evaluated (R2 = 0.913; SEC = 0.076; SEP = 0.101 and R2 = 0.899; SEC = 0.059; SEP = 0.101, respectively). All calibration models were validated by means of 47 independent samples. The NIR approach allows a rapid evaluation of the nitrogen and chlorophyll contents, and may represent a useful tool for determining nutritional and physiological status of plants, in order to allow a correction of nutrition programs during the season. PMID:25629703

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Soil Organic Carbon Estimation and Mapping Using "on-the-go" VisNIR Spectroscopy

NASA Astrophysics Data System (ADS)

Brown, D. J.; Bricklemyer, R. S.; Christy, C.

2007-12-01

Soil organic carbon (SOC) and other soil properties related to carbon sequestration (eg. soil clay content and mineralogy) vary spatially across landscapes. To cost effectively capture this variability, new technologies, such as Visible and Near Infrared (VisNIR) spectroscopy, have been applied to soils for rapid, accurate, and inexpensive estimation of SOC and other soil properties. For this study, we evaluated an "on the go" VisNIR sensor developed by Veris Technologies, Inc. (Salinas, KS) for mapping SOC, soil clay content and mineralogy. The Veris spectrometer spanned 350 to 2224 nm with 8 nm spectral resolution, and 25 spectra were integrated every 2 seconds resulting in 3 -5 m scanning distances on the ground. The unit was mounted to a mobile sensor platform pulled by a tractor, and scanned soils at an average depth of 10 cm through a quartz-sapphire window. We scanned eight 16.2 ha (40 ac) wheat fields in north central Montana (USA), with 15 m transect intervals. Using random sampling with spatial inhibition, 100 soil samples from 0-10 cm depths were extracted along scanned transects from each field and were analyzed for SOC. Neat, sieved (<2 mm) soil sample materials were also scanned in the lab using an Analytical Spectral Devices (ASD, Boulder, CO, USA) Fieldspec Pro FR spectroradiometer with a spectral range of 350-2500 and spectral resolution of 2-10 nm. The analyzed samples were used to calibrate and validate a number of partial least squares regression (PLSR) VisNIR models to compare on-the-go scanning vs. higher spectral resolution laboratory spectroscopy vs. standard SOC measurement methods.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

Classification and quantification analysis of peach kernel from different origins with near-infrared diffuse reflection spectroscopy

PubMed Central

Liu, Wei; Wang, Zhen-Zhong; Qing, Jian-Ping; Li, Hong-Juan; Xiao, Wei

2014-01-01

Background: Peach kernels which contain kinds of fatty acids play an important role in the regulation of a variety of physiological and biological functions. Objective: To establish an innovative and rapid diffuse reflectance near-infrared spectroscopy (DR-NIR) analysis method along with chemometric techniques for the qualitative and quantitative determination of a peach kernel. Materials and Methods: Peach kernel samples from nine different origins were analyzed with high-performance liquid chromatography (HPLC) as a reference method. DR-NIR is in the spectral range 1100-2300 nm. Principal component analysis (PCA) and partial least squares regression (PLSR) algorithm were applied to obtain prediction models, The Savitzky-Golay derivative and first derivative were adopted for the spectral pre-processing, PCA was applied to classify the varieties of those samples. For the quantitative calibration, the models of linoleic and oleinic acids were established with the PLSR algorithm and the optimal principal component (PC) numbers were selected with leave-one-out (LOO) cross-validation. The established models were evaluated with the root mean square error of deviation (RMSED) and corresponding correlation coefficients (R2). Results: The PCA results of DR-NIR spectra yield clear classification of the two varieties of peach kernel. PLSR had a better predictive ability. The correlation coefficients of the two calibration models were above 0.99, and the RMSED of linoleic and oleinic acids were 1.266% and 1.412%, respectively. Conclusion: The DR-NIR combined with PCA and PLSR algorithm could be used efficiently to identify and quantify peach kernels and also help to solve variety problem. PMID:25422544

Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques.

PubMed

Philip Ye, X; Liu, Lu; Hayes, Douglas; Womac, Alvin; Hong, Kunlun; Sokhansanj, Shahab

2008-10-01

The objectives of this research were to determine the variation of chemical composition across botanical fractions of cornstover, and to probe the potential of Fourier transform near-infrared (FT-NIR) techniques in qualitatively classifying separated cornstover fractions and in quantitatively analyzing chemical compositions of cornstover by developing calibration models to predict chemical compositions of cornstover based on FT-NIR spectra. Large variations of cornstover chemical composition for wide calibration ranges, which is required by a reliable calibration model, were achieved by manually separating the cornstover samples into six botanical fractions, and their chemical compositions were determined by conventional wet chemical analyses, which proved that chemical composition varies significantly among different botanical fractions of cornstover. Different botanic fractions, having total saccharide content in descending order, are husk, sheath, pith, rind, leaf, and node. Based on FT-NIR spectra acquired on the biomass, classification by Soft Independent Modeling of Class Analogy (SIMCA) was employed to conduct qualitative classification of cornstover fractions, and partial least square (PLS) regression was used for quantitative chemical composition analysis. SIMCA was successfully demonstrated in classifying botanical fractions of cornstover. The developed PLS model yielded root mean square error of prediction (RMSEP %w/w) of 0.92, 1.03, 0.17, 0.27, 0.21, 1.12, and 0.57 for glucan, xylan, galactan, arabinan, mannan, lignin, and ash, respectively. The results showed the potential of FT-NIR techniques in combination with multivariate analysis to be utilized by biomass feedstock suppliers, bioethanol manufacturers, and bio-power producers in order to better manage bioenergy feedstocks and enhance bioconversion.

Evaluating preservation methods for identifying Anopheles gambiae s.s. and Anopheles arabiensis complex mosquitoes species using near infra-red spectroscopy.

PubMed

Mayagaya, Valeriana Simon; Ntamatungiro, Alex John; Moore, Sarah Jane; Wirtz, Robert Andrew; Dowell, Floyd Ercell; Maia, Marta Ferreira

2015-01-27

Near-infrared spectroscopy (NIRS) has been successfully used on fresh and RNAlater-preserved members of the Anopheles gambiae complex to identify sibling species and age. No preservation methods other than using RNAlater have been tested to preserve mosquitoes for species identification using NIRS. However, RNAlater is not the most practical preservative for field settings because it is expensive, requires basic laboratory conditions for storage and is not widely available in sub-Saharan Africa. The aim of this study was to test several cheaper and more field-friendly preservation methods for identifying sibling species of the An. gambiae complex using NIRS. In this study we describe the use of NIRS to identify sibling species of preserved An. gambiae s. s. and An. arabiensis. Mosquitoes of each species were placed in sample tubes and preserved using one of the following preservation methods: (i) refrigeration at 4°C, (ii) freezing at -20°C, (iii) drying over a silica-gel desiccant, (iv) submersion in RNAlater at room temperature, (v) submersion in RNAlater at 4°C, and (vi) submersion in RNAlater at -20°C. Mosquitoes were preserved for 1, 4, 10, 32 or 50 weeks before they were scanned. Storage at 4°C was the only preservation method that, up to 32 weeks, did not result in significantly lower predicted values than those obtained from fresh insects. After 50 weeks, however, refrigerated samples did not give meaningful results. When storing for 50 weeks, desiccating samples over silica gel was the best preservation method, with a partial least squares regression cross-validation of >80%. Predictive data values were analyzed using a generalized linear model. NIRS can be used to identify species of desiccated Anopheles gambiae s.s. and Anopheles arabiensis for up to 50 weeks of storage with more than 80% accuracy.

Near infrared and Raman spectroscopy as Process Analytical Technology tools for the manufacturing of silicone-based drug reservoirs.

PubMed

Mantanus, J; Rozet, E; Van Butsele, K; De Bleye, C; Ceccato, A; Evrard, B; Hubert, Ph; Ziémons, E

2011-08-05

Using near infrared (NIR) and Raman spectroscopy as PAT tools, 3 critical quality attributes of a silicone-based drug reservoir were studied. First, the Active Pharmaceutical Ingredient (API) homogeneity in the reservoir was evaluated using Raman spectroscopy (mapping): the API distribution within the industrial drug reservoirs was found to be homogeneous while API aggregates were detected in laboratory scale samples manufactured with a non optimal mixing process. Second, the crosslinking process of the reservoirs was monitored at different temperatures with NIR spectroscopy. Conformity tests and Principal Component Analysis (PCA) were performed on the collected data to find out the relation between the temperature and the time necessary to reach the crosslinking endpoints. An agreement was found between the conformity test results and the PCA results. Compared to the conformity test method, PCA had the advantage to discriminate the heating effect from the crosslinking effect occurring together during the monitored process. Therefore the 2 approaches were found to be complementary. Third, based on the HPLC reference method, a NIR model able to quantify the API in the drug reservoir was developed and thoroughly validated. Partial Least Squares (PLS) regression on the calibration set was performed to build prediction models of which the ability to quantify accurately was tested with the external validation set. The 1.2% Root Mean Squared Error of Prediction (RMSEP) of the NIR model indicated the global accuracy of the model. The accuracy profile based on tolerance intervals was used to generate a complete validation report. The 95% tolerance interval calculated on the validation results indicated that each future result will have a relative error below ±5% with a probability of at least 95%. In conclusion, 3 critical quality attributes of silicone-based drug reservoirs were quickly and efficiently evaluated by NIR and Raman spectroscopy. Copyright © 2011 Elsevier B.V. All rights reserved.

Rapid on-line detection and grading of wooden breast myopathy in chicken fillets by near-infrared spectroscopy

PubMed Central

Veiseth-Kent, Eva; Høst, Vibeke; Løvland, Atle

2017-01-01

The main objective of this work was to develop a method for rapid and non-destructive detection and grading of wooden breast (WB) syndrome in chicken breast fillets. Near-infrared (NIR) spectroscopy was chosen as detection method, and an industrial NIR scanner was applied and tested for large scale on-line detection of the syndrome. Two approaches were evaluated for discrimination of WB fillets: 1) Linear discriminant analysis based on NIR spectra only, and 2) a regression model for protein was made based on NIR spectra and the estimated concentrations of protein were used for discrimination. A sample set of 197 fillets was used for training and calibration. A test set was recorded under industrial conditions and contained spectra from 79 fillets. The classification methods obtained 99.5–100% correct classification of the calibration set and 100% correct classification of the test set. The NIR scanner was then installed in a commercial chicken processing plant and could detect incidence rates of WB in large batches of fillets. Examples of incidence are shown for three broiler flocks where a high number of fillets (9063, 6330 and 10483) were effectively measured. Prevalence of WB of 0.1%, 6.6% and 8.5% were estimated for these flocks based on the complete sample volumes. Such an on-line system can be used to alleviate the challenges WB represents to the poultry meat industry. It enables automatic quality sorting of chicken fillets to different product categories. Manual laborious grading can be avoided. Incidences of WB from different farms and flocks can be tracked and information can be used to understand and point out main causes for WB in the chicken production. This knowledge can be used to improve the production procedures and reduce today’s extensive occurrence of WB. PMID:28278170

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS2regression method

NASA Astrophysics Data System (ADS)

Yulia, M.; Suhandy, D.

2018-03-01

NIR spectra obtained from spectral data acquisition system contains both chemical information of samples as well as physical information of the samples, such as particle size and bulk density. Several methods have been established for developing calibration models that can compensate for sample physical information variations. One common approach is to include physical information variation in the calibration model both explicitly and implicitly. The objective of this study was to evaluate the feasibility of using explicit method to compensate the influence of different particle size of coffee powder in NIR calibration model performance. A number of 220 coffee powder samples with two different types of coffee (civet and non-civet) and two different particle sizes (212 and 500 µm) were prepared. Spectral data was acquired using NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement. A discrimination method based on PLS-DA was conducted and the influence of different particle size on the performance of PLS-DA was investigated. In explicit method, we add directly the particle size as predicted variable results in an X block containing only the NIR spectra and a Y block containing the particle size and type of coffee. The explicit inclusion of the particle size into the calibration model is expected to improve the accuracy of type of coffee determination. The result shows that using explicit method the quality of the developed calibration model for type of coffee determination is a little bit superior with coefficient of determination (R2) = 0.99 and root mean square error of cross-validation (RMSECV) = 0.041. The performance of the PLS2 calibration model for type of coffee determination with particle size compensation was quite good and able to predict the type of coffee in two different particle sizes with relatively high R2 pred values. The prediction also resulted in low bias and RMSEP values.

Spectral pattern of urinary water as a biomarker of estrus in the giant panda

NASA Astrophysics Data System (ADS)

Kinoshita, Kodzue; Miyazaki, Mari; Morita, Hiroyuki; Vassileva, Maria; Tang, Chunxiang; Li, Desheng; Ishikawa, Osamu; Kusunoki, Hiroshi; Tsenkova, Roumiana

2012-11-01

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection.

Spectral pattern of urinary water as a biomarker of estrus in the giant panda.

PubMed

Kinoshita, Kodzue; Miyazaki, Mari; Morita, Hiroyuki; Vassileva, Maria; Tang, Chunxiang; Li, Desheng; Ishikawa, Osamu; Kusunoki, Hiroshi; Tsenkova, Roumiana

2012-01-01

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection.

Spectral pattern of urinary water as a biomarker of estrus in the giant panda

PubMed Central

Kinoshita, Kodzue; Miyazaki, Mari; Morita, Hiroyuki; Vassileva, Maria; Tang, Chunxiang; Li, Desheng; Ishikawa, Osamu; Kusunoki, Hiroshi; Tsenkova, Roumiana

2012-01-01

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection. PMID:23181188

Cytochrome cd1-containing nitrite reductase encoding gene nirS as a new functional biomarker for detection of anaerobic ammonium oxidizing (Anammox) bacteria.

PubMed

Li, Meng; Ford, Tim; Li, Xiaoyan; Gu, Ji-Dong

2011-04-15

A newly designed primer set (AnnirS), together with a previously published primer set (ScnirS), was used to detect anammox bacterial nirS genes from sediments collected from three marine environments. Phylogenetic analysis demonstrated that all retrieved sequences were clearly different from typical denitrifiers' nirS, but do group together with the known anammox bacterial nirS. Sequences targeted by ScnirS are closely related to Scalindua nirS genes recovered from the Peruvian oxygen minimum zone (OMZ), whereas sequences targeted by AnnirS are more closely affiliated with the nirS of Candidatus 'Kuenenia stuttgartiensis' and even form a new phylogenetic nirS clade, which might be related to other genera of the anammox bacteria. Analysis demonstrated that retrieved sequences had higher sequence identities (>60%) with known anammox bacterial nirS genes than with denitrifiers' nirS, on both nucleotide and amino acid levels. Compared to the 16S rRNA and hydrazine oxidoreductase (hzo) genes, the anammox bacterial nirS not only showed consistent phylogenetic relationships but also demonstrated more reliable quantification of anammox bacteria because of the single copy of the nirS gene in the anammox bacterial genome and the specificity of PCR primers for different genera of anammox bacteria, thus providing a suitable functional biomarker for investigation of anammox bacteria.

Estimation of soil profile physical and chemical properties using a VIS-NIR-EC-force probe

USDA-ARS?s Scientific Manuscript database

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. ...

Potential of a newly developed high-speed near-infrared (NIR) camera (Compovision) in polymer industrial analyses: monitoring crystallinity and crystal evolution of polylactic acid (PLA) and concentration of PLA in PLA/Poly-(R)-3-hydroxybutyrate (PHB) blends.

PubMed

Ishikawa, Daitaro; Nishii, Takashi; Mizuno, Fumiaki; Sato, Harumi; Kazarian, Sergei G; Ozaki, Yukihiro

2013-12-01

This study was carried out to evaluate a new high-speed hyperspectral near-infrared (NIR) camera named Compovision. Quantitative analyses of the crystallinity and crystal evolution of biodegradable polymer, polylactic acid (PLA), and its concentration in PLA/poly-(R)-3-hydroxybutyrate (PHB) blends were investigated using near-infrared (NIR) imaging. This NIR camera can measure two-dimensional NIR spectral data in the 1000-2350 nm region obtaining images with wide field of view of 150 × 250 mm(2) (approximately 100  000 pixels) at high speeds (in less than 5 s). PLA with differing crystallinities between 0 and 50% blended samples with PHB in ratios of 80/20, 60/40, 40/60, 20/80, and pure films of 100% PLA and PHB were prepared. Compovision was used to collect respective NIR spectra in the 1000-2350 nm region and investigate the crystallinity of PLA and its concentration in the blends. The partial least squares (PLS) regression models for the crystallinity of PLA were developed using absorbance, second derivative, and standard normal variate (SNV) spectra from the most informative region of the spectra, between 1600 and 2000 nm. The predicted results of PLS models achieved using the absorbance and second derivative spectra were fairly good with a root mean square error (RMSE) of less than 6.1% and a determination of coefficient (R(2)) of more than 0.88 for PLS factor 1. The results obtained using the SNV spectra yielded the best prediction with the smallest RMSE of 2.93% and the highest R(2) of 0.976. Moreover, PLS models developed for estimating the concentration of PLA in the blend polymers using SNV spectra gave good predicted results where the RMSE was 4.94% and R(2) was 0.98. The SNV-based models provided the best-predicted results, since it can reduce the effects of the spectral changes induced by the inhomogeneity and the thickness of the samples. Wide area crystal evolution of PLA on a plate where a temperature slope of 70-105 °C had occurred was also monitored using NIR imaging. An SNV-based image gave an obvious contrast of the crystallinity around the crystal growth area according to slight temperature change. Moreover, it clarified the inhomogeneity of crystal evolution over the significant wide area. These results have proved that the newly developed hyperspectral NIR camera, Compovision, can be successfully used to study polymers for industrial processes, such as monitoring the crystallinity of PLA and the different composition of PLA/PHB blends.

Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces

NASA Astrophysics Data System (ADS)

Hwang, Han-Jeong; Lim, Jeong-Hwan; Kim, Do-Won; Im, Chang-Hwan

2014-07-01

A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promising neuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focused on enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated the performances of a variety of mental task combinations in order to determine the mental task pairs that are best suited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses while seven participants performed eight different mental tasks. Classification accuracies were then estimated for all possible pairs of the eight mental tasks (C=28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: "mental multiplication," "mental rotation," and "right-hand motor imagery." Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations.

Mitochondria-Targeting Magnetic Composite Nanoparticles for Enhanced Phototherapy of Cancer.

PubMed

Guo, Ranran; Peng, Haibao; Tian, Ye; Shen, Shun; Yang, Wuli

2016-09-01

Photothermal therapy (PTT) and photodynamic therapy (PDT) are promising cancer treatment modalities in current days while the high laser power density demand and low tumor accumulation are key obstacles that have greatly restricted their development. Here, magnetic composite nanoparticles for dual-modal PTT and PDT which have realized enhanced cancer therapeutic effect by mitochondria-targeting are reported. Integrating PTT agent and photosensitizer together, the composite nanoparticles are able to generate heat and reactive oxygen species (ROS) simultaneously upon near infrared (NIR) laser irradiation. After surface modification of targeting ligands, the composite nanoparticles can be selectively delivered to the mitochondria, which amplify the cancer cell apoptosis induced by hyperthermia and the cytotoxic ROS. In this way, better photo therapeutic effects and much higher cytotoxicity are achieved by utilizing the composite nanoparticles than that treated with the same nanoparticles missing mitochondrial targeting unit at a low laser power density. Guided by NIR fluorescence imaging and magnetic resonance imaging, then these results are confirmed in a humanized orthotropic lung cancer model. The composite nanoparticles demonstrate high tumor accumulation and excellent tumor regression with minimal side effect upon NIR laser exposure. Therefore, the mitochondria-targeting composite nanoparticles are expected to be an effective phototherapeutic platform in oncotherapy. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Non-invasive prediction of hematocrit levels by portable visible and near-infrared spectrophotometer.

PubMed

Sakudo, Akikazu; Kato, Yukiko Hakariya; Kuratsune, Hirohiko; Ikuta, Kazuyoshi

2009-10-01

After blood donation, in some individuals having polycythemia, dehydration causes anemia. Although the hematocrit (Ht) level is closely related to anemia, the current method of measuring Ht is performed after blood drawing. Furthermore, the monitoring of Ht levels contributes to a healthy life. Therefore, a non-invasive test for Ht is warranted for the safe donation of blood and good quality of life. A non-invasive procedure for the prediction of hematocrit levels was developed on the basis of a chemometric analysis of visible and near-infrared (Vis-NIR) spectra of the thumbs using portable spectrophotometer. Transmittance spectra in the 600- to 1100-nm region from thumbs of Japanese volunteers were subjected to a partial least squares regression (PLSR) analysis and leave-out cross-validation to develop chemometric models for predicting Ht levels. Ht levels of masked samples predicted by this model from Vis-NIR spectra provided a coefficient of determination in prediction of 0.6349 with a standard error of prediction of 3.704% and a detection limit in prediction of 17.14%, indicating that the model is applicable for normal and abnormal value in Ht level. These results suggest portable Vis-NIR spectrophotometer to have potential for the non-invasive measurement of Ht levels with a combination of PLSR analysis.

iHWG-μNIR: a miniaturised near-infrared gas sensor based on substrate-integrated hollow waveguides coupled to a micro-NIR-spectrophotometer.

PubMed

Rohwedder, J J R; Pasquini, C; Fortes, P R; Raimundo, I M; Wilk, A; Mizaikoff, B

2014-07-21

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

Principal and independent component analysis of concomitant functional near infrared spectroscopy and magnetic resonance imaging data

NASA Astrophysics Data System (ADS)

Schelkanova, Irina; Toronov, Vladislav

2011-07-01

Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by using various signal processing methods, including blind signal separation techniques. In particular, principal component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired at the same wavelength and at multiple sites on the human or animal heads during functional activation. These signal processing procedures resulted in a number of principal or independent components that could be attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the original signals were quite diverse, we found very few different components which corresponded to fMRI signals at different locations in the brain and to different physiological chromophores.

Selective Removal of Natural Occlusal Caries by Coupling Near-infrared Imaging with a CO2 Laser

PubMed Central

Tao, You-Chen; Fried, Daniel

2011-01-01

Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. Last year we successfully demonstrated that near-IR images can be used to guide a CO2 laser ablation system for the selective removal of artificial caries lesions on smooth surfaces. The objective of this study was to test the hypothesis that two-dimensional near-infrared images of natural occlusal caries can be used to guide a CO2 laser for selective removal. Two-dimensional NIR images were acquired at 1310-nm of extracted human molar teeth with occlusal caries. Polarization sensitive optical coherence tomography (PS-OCT) was also used to acquire depth-resolved images of the lesion areas. An imaging processing module was developed to analyze the NIR imaging output and generate optical maps that were used to guide a CO2 laser to selectively remove the lesions at a uniform depth. Post-ablation NIR images were acquired to verify caries removal. Based on the analysis of the NIR images, caries lesions were selectively removed with a CO2 laser while sound tissues were conserved. However, the removal rate varied markedly with the severity of decay and multiple passes were required for caries removal. These initial results are promising but indicate that the selective removal of natural caries is more challenging than the selective removal of artificial lesions due to varying tooth geometry, the highly variable organic/mineral ratio in natural lesions and more complicated lesion structure. PMID:21909225

Selective removal of natural occlusal caries by coupling near-infrared imaging with a CO II laser

NASA Astrophysics Data System (ADS)

Tao, You-Chen; Fried, Daniel

2008-02-01

Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. Last year we successfully demonstrated that near-IR images can be used to guide a CO2 laser ablation system for the selective removal of artificial caries lesions on smooth surfaces. The objective of this study was to test the hypothesis that two-dimensional near-infrared images of natural occlusal caries can be used to guide a CO2 laser for selective removal. Two-dimensional NIR images were acquired at 1310-nm of extracted human molar teeth with occlusal caries. Polarization sensitive optical coherence tomography (PS-OCT) was also used to acquire depth-resolved images of the lesion areas. An imaging processing module was developed to analyze the NIR imaging output and generate optical maps that were used to guide a CO2 laser to selectively remove the lesions at a uniform depth. Post-ablation NIR images were acquired to verify caries removal. Based on the analysis of the NIR images, caries lesions were selectively removed with a CO2 laser while sound tissues were conserved. However, the removal rate varied markedly with the severity of decay and multiple passes were required for caries removal. These initial results are promising but indicate that the selective removal of natural caries is more challenging than the selective removal of artificial lesions due to varying tooth geometry, the highly variable organic/mineral ratio in natural lesions and more complicated lesion structure.

Selective Removal of Natural Occlusal Caries by Coupling Near-infrared Imaging with a CO(2) Laser.

PubMed

Tao, You-Chen; Fried, Daniel

2008-03-01

Laser removal of dental hard tissue can be combined with optical, spectral or acoustic feedback systems to selectively ablate dental caries and restorative materials. Near-infrared (NIR) imaging has considerable potential for the optical discrimination of sound and demineralized tissue. Last year we successfully demonstrated that near-IR images can be used to guide a CO(2) laser ablation system for the selective removal of artificial caries lesions on smooth surfaces. The objective of this study was to test the hypothesis that two-dimensional near-infrared images of natural occlusal caries can be used to guide a CO(2) laser for selective removal. Two-dimensional NIR images were acquired at 1310-nm of extracted human molar teeth with occlusal caries. Polarization sensitive optical coherence tomography (PS-OCT) was also used to acquire depth-resolved images of the lesion areas. An imaging processing module was developed to analyze the NIR imaging output and generate optical maps that were used to guide a CO(2) laser to selectively remove the lesions at a uniform depth. Post-ablation NIR images were acquired to verify caries removal. Based on the analysis of the NIR images, caries lesions were selectively removed with a CO(2) laser while sound tissues were conserved. However, the removal rate varied markedly with the severity of decay and multiple passes were required for caries removal. These initial results are promising but indicate that the selective removal of natural caries is more challenging than the selective removal of artificial lesions due to varying tooth geometry, the highly variable organic/mineral ratio in natural lesions and more complicated lesion structure.

Using Hyperspectral Remote Sensing Models to Determine Phytoplankton Density in the Coastal Waters of Long Bay, South Carolina

NASA Astrophysics Data System (ADS)

Harrington, J. E.; Ali, K.

2013-12-01

The southeast coastal region is one of the fastest growing regions in the United States and the increasing utilization of open water bodies has led to the deterioration of water quality and aquatic ecology, placing the future of these resources at risk. In coastal zones, a key index that can be used to assess the stress on the environment is the water quality. The shallow nearshore waters of Long Bay, South Carolina (SC) are heavily influenced by multiple biogeochemical constituents or color producing agents (CPAs) such as, phytoplankton, suspend matter, and dissolved organic carbon. The interaction of the various chemical, biological and physical components gives rise to the optical complexity observed in the coastal waters producing turbid waters. Ecological stress on these environments is reflected by the increase in the frequency and severity of Harmful Algal Blooms (HABs), a prime agent of water quality deterioration, including foul odors and tastes, deoxygenation of bottom waters (hypoxia), toxicity, fish kills, and food web alterations. These are of great concern for human health and are detrimental to the marine life. Therefore, efficient monitoring tools are required for early detection and forecasting purposes as well as to understand the state of the conditions and better protect, manage and address the question of how various natural and anthropogenic factors affect the health of these environments. This study assesses the efficiency remote sensing as a potential tool for accurate and timely detection of HABs, as well as for providing high spatial and temporal resolution information regarding the biogeodynamics in coastal water bodies. Existing blue-green and NIR-red based remote sensing algorithms are applied to the reflectance data obtained using ASD spectroradiometer to predict the amount of chlorophyll, an independent of other associated CPAs in the Long Bay waters. The pigment is the primary light harvesting pigment in all phytoplankton and is used as an index for the estimation of phytoplankton density. Efficiency of the algorithms were evaluated through a least squares regression and residual analysis. Results show that for prediction models of chlorophyll a concentrations, the Oc4v4 by Reilly et al (2000), two -band blue-green empirical algorithm yielded coefficients of determination as high as 0.64 with RMSE=0.29μg/l for an aggregated dataset (n=62, P<0.05). The NIR-red -based two-band algorithm by Dekker et al. (1993) and Gitelson et al. (2000) gave the best chlorophyll a prediction model, with R2 =0.79, RMSE=0.19μg/l. The results illustrate the potential of remote sensing in accounting for the chlorophyll a variability in the turbid waters of Long Bay, SC.

Prediction of canned black bean texture (Phaseolus vulgaris L.) from intact dry seeds using visible/near infrared spectroscopy and hyperspectral imaging data.

PubMed

Mendoza, Fernando A; Cichy, Karen A; Sprague, Christy; Goffnett, Amanda; Lu, Renfu; Kelly, James D

2018-01-01

Texture is a major quality parameter for the acceptability of canned whole beans. Prior knowledge of this quality trait before processing would be useful to guide variety development by bean breeders and optimize handling protocols by processors. The objective of this study was to evaluate and compare the predictive power of visible and near infrared reflectance spectroscopy (visible/NIRS, 400-2498 nm) and hyperspectral imaging (HYPERS, 400-1000 nm) techniques for predicting texture of canned black beans from intact dry seeds. Black beans were grown in Michigan (USA) over three field seasons. The samples exhibited phenotypic variability for canned bean texture due to genetic variability and processing practice. Spectral preprocessing methods (i.e. smoothing, first and second derivatives, continuous wavelet transform, and two-band ratios), coupled with a feature selection method, were tested for optimizing the prediction accuracy in both techniques based on partial least squares regression (PLSR) models. Visible/NIRS and HYPERS were effective in predicting texture of canned beans using intact dry seeds, as indicated by their correlation coefficients for prediction (R pred ) and standard errors of prediction (SEP). Visible/NIRS was superior (R pred = 0.546-0.923, SEP = 7.5-1.9 kg 100 g -1 ) to HYPERS (R pred = 0.401-0.883, SEP = 7.6-2.4 kg 100 g -1 ), which is likely due to the wider wavelength range collected in visible/NIRS. However, a significant improvement was reached in both techniques when the two-band ratios preprocessing method was applied to the data, reducing SEP by at least 10.4% and 16.2% for visible/NIRS and HYPERS, respectively. Moreover, results from using the combination of the three-season data sets based on the two-band ratios showed that visible/NIRS (R pred = 0.886, SEP = 4.0 kg 100 g -1 ) and HYPERS (R pred = 0.844, SEP = 4.6 kg 100 g -1 ) models were consistently successful in predicting texture over a wide range of measurements. Visible/NIRS and HYPERS have great potential for predicting the texture of canned beans; the robustness of the models is impacted by genotypic diversity, planting year and phenotypic variability for canned bean texture used for model building, and hence, robust models can be built based on data sets with high phenotypic diversity in textural properties, and periodically maintained and updated with new data. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

All-near-infrared multiphoton microscopy interrogates intact tissues at deeper imaging depths than conventional single- and two-photon near-infrared excitation microscopes

PubMed Central

Sarder, Pinaki; Yazdanfar, Siavash; Akers, Walter J.; Tang, Rui; Sudlow, Gail P.; Egbulefu, Christopher

2013-01-01

Abstract. The era of molecular medicine has ushered in the development of microscopic methods that can report molecular processes in thick tissues with high spatial resolution. A commonality in deep-tissue microscopy is the use of near-infrared (NIR) lasers with single- or multiphoton excitations. However, the relationship between different NIR excitation microscopic techniques and the imaging depths in tissue has not been established. We compared such depth limits for three NIR excitation techniques: NIR single-photon confocal microscopy (NIR SPCM), NIR multiphoton excitation with visible detection (NIR/VIS MPM), and all-NIR multiphoton excitation with NIR detection (NIR/NIR MPM). Homologous cyanine dyes provided the fluorescence. Intact kidneys were harvested after administration of kidney-clearing cyanine dyes in mice. NIR SPCM and NIR/VIS MPM achieved similar maximum imaging depth of ∼100  μm. The NIR/NIR MPM enabled greater than fivefold imaging depth (>500  μm) using the harvested kidneys. Although the NIR/NIR MPM used 1550-nm excitation where water absorption is relatively high, cell viability and histology studies demonstrate that the laser did not induce photothermal damage at the low laser powers used for the kidney imaging. This study provides guidance on the imaging depth capabilities of NIR excitation-based microscopic techniques and reveals the potential to multiplex information using these platforms. PMID:24150231

Multichannel optical mapping: investigation of depth information

NASA Astrophysics Data System (ADS)

Sase, Ichiro; Eda, Hideo; Seiyama, Akitoshi; Tanabe, Hiroki C.; Takatsuki, Akira; Yanagida, Toshio

2001-06-01

Near infrared (NIR) light has become a powerful tool for non-invasive imaging of human brain activity. Many systems have been developed to capture the changes in regional brain blood flow and hemoglobin oxygenation, which occur in the human cortex in response to neural activity. We have developed a multi-channel reflectance imaging system, which can be used as a `mapping device' and also as a `multi-channel spectrophotometer'. In the present study, we visualized changes in the hemodynamics of the human occipital region in multiple ways. (1) Stimulating left and right primary visual cortex independently by showing sector shaped checkerboards sequentially over the contralateral visual field, resulted in corresponding changes in the hemodynamics observed by `mapping' measurement. (2) Simultaneous measurement of functional-MRI and NIR (changes in total hemoglobin) during visual stimulation showed good spatial and temporal correlation with each other. (3) Placing multiple channels densely over the occipital region demonstrated spatial patterns more precisely, and depth information was also acquired by placing each pair of illumination and detection fibers at various distances. These results indicate that optical method can provide data for 3D analysis of human brain functions.

Multifunctional magnetic-optical nanoparticle probes for simultaneous detection, separation, and thermal ablation of multiple pathogens.

PubMed

Wang, Chungang; Irudayaraj, Joseph

2010-01-01

Multifunctional nanoparticles possessing magnetization and near-infrared (NIR) absorption have warranted interest due to their significant applications in magnetic resonance imaging, diagnosis, bioseparation, target delivery, and NIR photothermal ablation. Herein, the site-selective assembly of magnetic nanoparticles onto the ends or ends and sides of gold nanorods with different aspect ratios (ARs) to create multifunctional nanorods decorated with varying numbers of magnetic particles is described for the first time. The resulting hybrid nanoparticles are designated as Fe(3)O(4)-Au(rod)-Fe(3)O(4) nanodumbbells and Fe(3)O(4)-Au(rod) necklacelike constructs with tunable optical and magnetic properties, respectively. These hybrid nanomaterials can be used for multiplex detection and separation because of their tunable magnetic and plasmonic functionality. More specifically, Fe(3)O(4)-Au(rod) necklacelike probes of different ARs are utilized for simultaneous optical detection based on their plasmon properties, magnetic separation, and photokilling of multiple pathogens from a single sample at one time. The combined functionalities of the synthesized probes will open up many exciting opportunities in dual imaging for targeted delivery and photothermal therapy.

Hemodynamic signals in fNIRS.

PubMed

Hoshi, Y

Near-infrared spectroscopy (NIRS) was originally designed for clinical monitoring of tissue oxygenation, and it has also been developed into a useful tool in neuroimaging studies, with the so-called functional NIRS (fNIRS). With NIRS, cerebral activation is detected by measuring the cerebral hemoglobin (Hb), where however, the precise correlation between NIRS signal and neural activity remains to be fully understood. This can in part be attributed to the situation that NIRS signals are inherently subject to contamination by signals arising from extracerebral tissue. In recent years, several approaches have been investigated to distinguish between NIRS signals originating in cerebral tissue and signals originating in extracerebral tissue. Selective measurements of cerebral Hb will enable a further evolution of fNIRS. This chapter is divided into six sections: first a summary of the basic theory of NIRS, NIRS signals arising in the activated areas, correlations between NIRS signals and fMRI signals, correlations between NIRS signals and neural activities, and the influence of a variety of extracerebral tissue on NIRS signals and approaches to this issue are reviewed. Finally, future prospects of fNIRS are described. © 2016 Elsevier B.V. All rights reserved.

Integrating proximal soil sensing techniques and terrain indexes to generate 3D maps of soil restrictive layers in the Palouse region, Washington, USA

NASA Astrophysics Data System (ADS)

Poggio, Matteo; Brown, David J.; Gasch, Caley K.; Brooks, Erin S.; Yourek, Matt A.

2015-04-01

In the Palouse region of eastern Washington and northern Idaho (USA), spatially discontinuous restrictive layers impede rooting growth and water infiltration. Consequently, accurate maps showing the depth and spatial extent of these restrictive layers are essential for watershed hydrologic modeling appropriate for precision agriculture. In this presentation, we report on the use of a Visible and Near-Infrared (VisNIR) penetrometer fore optic to construct detailed maps of three wheat fields in the Palouse region. The VisNIR penetrometer was used to deliver in situ soil reflectance to an Analytical Spectral Devices (ASD, Boulder, CO, USA) spectrometer and simultaneously acquire insertion force. With a hydraulic push-type soil coring systems for insertion (e.g. Giddings), we collected soil spectra and insertion force data along 41m x 41m grid points (2 fields) and 50m x 50m grid points (1 field) to ≈80cm depth, in addition to interrogation points at 36 representative instrumented locations per field. At each of the 36 instrumented locations, two soil cores were extracted for laboratory determination of clay content and bulk density. We developed calibration models of soil clay content and bulk density with spectra and insertion force collected in situ, using partial least squares regression 2 (PLSR2). Applying spline functions, we delineated clay and bulk density profiles at each points (grid and 24 locations). The soil profiles were then used as inputs in a regression-kriging model with terrain indexes and ECa data (derived from an EM38 field survey, Geonics, Mississauga, Ontario, Canada) as covariates to generate 3D soil maps. Preliminary results show that the VisNIR penetrometer can capture the spatial patterns of restrictive layers. Work is ongoing to evaluate the prediction accuracy of penetrometer-derived 3D clay content and restriction layer maps.

Analysis of near infrared spectra for age-grading of wild populations of Anopheles gambiae.

PubMed

Krajacich, Benjamin J; Meyers, Jacob I; Alout, Haoues; Dabiré, Roch K; Dowell, Floyd E; Foy, Brian D

2017-11-07

Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. The use of near-infrared spectroscopy (NIRS) for age-grading has been demonstrated previously on laboratory and semi-field mosquitoes, but to date has not been utilized on wild-caught mosquitoes whose age is externally validated via parity status or parasite infection stage. In this study, we developed regression and classification models using NIRS on datasets of wild An. gambiae (s.l.) reared from larvae collected from the field in Burkina Faso, and two laboratory strains. We compared the accuracy of these models for predicting the ages of wild-caught mosquitoes that had been scored for their parity status as well as for positivity for Plasmodium sporozoites. Regression models utilizing variable selection increased predictive accuracy over the more common full-spectrum partial least squares (PLS) approach for cross-validation of the datasets, validation, and independent test sets. Models produced from datasets that included the greatest range of mosquito samples (i.e. different sampling locations and times) had the highest predictive accuracy on independent testing sets, though overall accuracy on these samples was low. For classification, we found that intramodel accuracy ranged between 73.5-97.0% for grouping of mosquitoes into "early" and "late" age classes, with the highest prediction accuracy found in laboratory colonized mosquitoes. However, this accuracy was decreased on test sets, with the highest classification of an independent set of wild-caught larvae reared to set ages being 69.6%. Variation in NIRS data, likely from dietary, genetic, and other factors limits the accuracy of this technique with wild-caught mosquitoes. Alternative algorithms may help improve prediction accuracy, but care should be taken to either maximize variety in models or minimize confounders.

Comparison of Benchtop Fourier-Transform (FT) and Portable Grating Scanning Spectrometers for Determination of Total Soluble Solid Contents in Single Grape Berry (Vitis vinifera L.) and Calibration Transfer.

PubMed

Xiao, Hui; Sun, Ke; Sun, Ye; Wei, Kangli; Tu, Kang; Pan, Leiqing

2017-11-22

Near-infrared (NIR) spectroscopy was applied for the determination of total soluble solid contents (SSC) of single Ruby Seedless grape berries using both benchtop Fourier transform (VECTOR 22/N) and portable grating scanning (SupNIR-1500) spectrometers in this study. The results showed that the best SSC prediction was obtained by VECTOR 22/N in the range of 12,000 to 4000 cm -1 (833-2500 nm) for Ruby Seedless with determination coefficient of prediction (R p ²) of 0.918, root mean squares error of prediction (RMSEP) of 0.758% based on least squares support vector machine (LS-SVM). Calibration transfer was conducted on the same spectral range of two instruments (1000-1800 nm) based on the LS-SVM model. By conducting Kennard-Stone (KS) to divide sample sets, selecting the optimal number of standardization samples and applying Passing-Bablok regression to choose the optimal instrument as the master instrument, a modified calibration transfer method between two spectrometers was developed. When 45 samples were selected for the standardization set, the linear interpolation-piecewise direct standardization (linear interpolation-PDS) performed well for calibration transfer with R p ² of 0.857 and RMSEP of 1.099% in the spectral region of 1000-1800 nm. And it was proved that re-calculating the standardization samples into master model could improve the performance of calibration transfer in this study. This work indicated that NIR could be used as a rapid and non-destructive method for SSC prediction, and provided a feasibility to solve the transfer difficulty between totally different NIR spectrometers.

Microbial Abundances Predict Methane and Nitrous Oxide Fluxes from a Windrow Composting System

PubMed Central

Li, Shuqing; Song, Lina; Gao, Xiang; Jin, Yaguo; Liu, Shuwei; Shen, Qirong; Zou, Jianwen

2017-01-01

Manure composting is a significant source of atmospheric methane (CH4) and nitrous oxide (N2O) that are two potent greenhouse gases. The CH4 and N2O fluxes are mediated by methanogens and methanotrophs, nitrifying and denitrifying bacteria in composting manure, respectively, while these specific bacterial functional groups may interplay in CH4 and N2O emissions during manure composting. To test the hypothesis that bacterial functional gene abundances regulate greenhouse gas fluxes in windrow composting systems, CH4 and N2O fluxes were simultaneously measured using the chamber method, and molecular techniques were used to quantify the abundances of CH4-related functional genes (mcrA and pmoA genes) and N2O-related functional genes (amoA, narG, nirK, nirS, norB, and nosZ genes). The results indicate that changes in interacting physicochemical parameters in the pile shaped the dynamics of bacterial functional gene abundances. The CH4 and N2O fluxes were correlated with abundances of specific compositional genes in bacterial community. The stepwise regression statistics selected pile temperature, mcrA and NH4+ together as the best predictors for CH4 fluxes, and the model integrating nirK, nosZ with pmoA gene abundances can almost fully explain the dynamics of N2O fluxes over windrow composting. The simulated models were tested against measurements in paddy rice cropping systems, indicating that the models can also be applicable to predicting the response of CH4 and N2O fluxes to elevated atmospheric CO2 concentration and rising temperature. Microbial abundances could be included as indicators in the current carbon and nitrogen biogeochemical models. PMID:28373862

Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Near Infrared (FT-NIR) spectroscopy.

PubMed

De Girolamo, A; Lippolis, V; Nordkvist, E; Visconti, A

2009-06-01

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 microm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 microg kg(-1)) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 microg kg(-1)). Coefficients of determination (r(2)) indicated an "approximate to good" level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r(2) = 0.71-0.83), and a "good" discrimination between low and high DON contents in the PLS validation models (r(2) = 0.58-0.63). A "limited to good" practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 microg kg(-1) DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission.

Use of chemometrics to compare NIR and HPLC for the simultaneous determination of drug levels in fixed-dose combination tablets employed in tuberculosis treatment.

PubMed

Teixeira, Kelly Sivocy Sampaio; da Cruz Fonseca, Said Gonçalves; de Moura, Luís Carlos Brigido; de Moura, Mario Luís Ribeiro; Borges, Márcia Herminia Pinheiro; Barbosa, Euzébio Guimaraes; De Lima E Moura, Túlio Flávio Accioly

2018-02-05

The World Health Organization recommends that TB treatment be administered using combination therapy. The methodologies for quantifying simultaneously associated drugs are highly complex, being costly, extremely time consuming and producing chemical residues harmful to the environment. The need to seek alternative techniques that minimize these drawbacks is widely discussed in the pharmaceutical industry. Therefore, the objective of this study was to develop and validate a multivariate calibration model in association with the near infrared spectroscopy technique (NIR) for the simultaneous determination of rifampicin, isoniazid, pyrazinamide and ethambutol. These models allow the quality control of these medicines to be optimized using simple, fast, low-cost techniques that produce no chemical waste. In the NIR - PLS method, spectra readings were acquired in the 10,000-4000cm -1 range using an infrared spectrophotometer (IRPrestige - 21 - Shimadzu) with a resolution of 4cm -1 , 20 sweeps, under controlled temperature and humidity. For construction of the model, the central composite experimental design was employed on the program Statistica 13 (StatSoft Inc.). All spectra were treated by computational tools for multivariate analysis using partial least squares regression (PLS) on the software program Pirouette 3.11 (Infometrix, Inc.). Variable selections were performed by the QSAR modeling program. The models developed by NIR in association with multivariate analysis provided good prediction of the APIs for the external samples and were therefore validated. For the tablets, however, the slightly different quantitative compositions of excipients compared to the mixtures prepared for building the models led to results that were not statistically similar, despite having prediction errors considered acceptable in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Fiber-Content Measurement of Wool-Cashmere Blends Using Near-Infrared Spectroscopy.

PubMed

Zhou, Jinfeng; Wang, Rongwu; Wu, Xiongying; Xu, Bugao

2017-10-01

Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool-cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool-cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere-wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool-cashmere blend and have potential to be used for tackling the cashmere adulteration problem.

Robust new NIRS coupled with multivariate methods for the detection and quantification of tallow adulteration in clarified butter samples.

PubMed

Mabood, Fazal; Abbas, Ghulam; Jabeen, Farah; Naureen, Zakira; Al-Harrasi, Ahmed; Hamaed, Ahmad M; Hussain, Javid; Al-Nabhani, Mahmood; Al Shukaili, Maryam S; Khan, Alamgir; Manzoor, Suryyia

2018-03-01

Cows' butterfat may be adulterated with animal fat materials like tallow which causes increased serum cholesterol and triglycerides levels upon consumption. There is no reliable technique to detect and quantify tallow adulteration in butter samples in a feasible way. In this study a highly sensitive near-infrared (NIR) spectroscopy combined with chemometric methods was developed to detect as well as quantify the level of tallow adulterant in clarified butter samples. For this investigation the pure clarified butter samples were intentionally adulterated with tallow at the following percentage levels: 1%, 3%, 5%, 7%, 9%, 11%, 13%, 15%, 17% and 20% (wt/wt). Altogether 99 clarified butter samples were used including nine pure samples (un-adulterated clarified butter) and 90 clarified butter samples adulterated with tallow. Each sample was analysed by using NIR spectroscopy in the reflection mode in the range 10,000-4000 cm -1 , at 2 cm -1 resolution and using the transflectance sample accessory which provided a total path length of 0.5 mm. Chemometric models including principal components analysis (PCA), partial least-squares discriminant analysis (PLSDA), and partial least-squares regressions (PLSR) were applied for statistical treatment of the obtained NIR spectral data. The PLSDA model was employed to differentiate pure butter samples from those adulterated with tallow. The employed model was then externally cross-validated by using a test set which included 30% of the total butter samples. The excellent performance of the model was proved by the low RMSEP value of 1.537% and the high correlation factor of 0.95. This newly developed method is robust, non-destructive, highly sensitive, and economical with very minor sample preparation and good ability to quantify less than 1.5% of tallow adulteration in clarified butter samples.

A validation method for near-infrared spectroscopy based tissue oximeters for cerebral and somatic tissue oxygen saturation measurements.

PubMed

Benni, Paul B; MacLeod, David; Ikeda, Keita; Lin, Hung-Mo

2018-04-01

We describe the validation methodology for the NIRS based FORE-SIGHT ELITE ® (CAS Medical Systems, Inc., Branford, CT, USA) tissue oximeter for cerebral and somatic tissue oxygen saturation (StO 2 ) measurements for adult subjects submitted to the United States Food and Drug Administration (FDA) to obtain clearance for clinical use. This validation methodology evolved from a history of NIRS validations in the literature and FDA recommended use of Deming regression and bootstrapping statistical validation methods. For cerebral validation, forehead cerebral StO 2 measurements were compared to a weighted 70:30 reference (REF CX B ) of co-oximeter internal jugular venous and arterial blood saturation of healthy adult subjects during a controlled hypoxia sequence, with a sensor placed on the forehead. For somatic validation, somatic StO 2 measurements were compared to a weighted 70:30 reference (REF CX S ) of co-oximetry central venous and arterial saturation values following a similar protocol, with sensors place on the flank, quadriceps muscle, and calf muscle. With informed consent, 25 subjects successfully completed the cerebral validation study. The bias and precision (1 SD) of cerebral StO 2 compared to REF CX B was -0.14 ± 3.07%. With informed consent, 24 subjects successfully completed the somatic validation study. The bias and precision of somatic StO 2 compared to REF CX S was 0.04 ± 4.22% from the average of flank, quadriceps, and calf StO 2 measurements to best represent the global whole body REF CX S . The NIRS validation methods presented potentially provide a reliable means to test NIRS monitors and qualify them for clinical use.

Extra-luminal detection of assumed colonic tumor site by near-infrared laparoscopy.

PubMed

Zako, Tamotsu; Ito, Masaaki; Hyodo, Hiroshi; Yoshimoto, Miya; Watanabe, Masayuki; Takemura, Hiroshi; Kishimoto, Hidehiro; Kaneko, Kazuhiro; Soga, Kohei; Maeda, Mizuo

2016-09-01

Localization of colorectal tumors during laparoscopic surgery is generally performed by tattooing into the submucosal layer of the colon. However, faint and diffuse tattoos may lead to difficulties in recognizing cancer sites, resulting in inappropriate resection of the colon. We previously demonstrated that yttrium oxide nanoparticles doped with the rare earth ions (ytterbium and erbium) (YNP) showed strong near-infrared (NIR) emission under NIR excitation (1550 nm emission with 980 nm excitation). NIR light can penetrate deep tissues. In this study, we developed an NIR laparoscopy imaging system and demonstrated its use for accurate resection of the colon in swine. The NIR laparoscopy system consisted of an NIR laparoscope, NIR excitation laser diode, and an NIR camera. Endo-clips coated with YNP (NIR clip), silicon rubber including YNP (NIR silicon mass), and YNP solution (NIR ink) were prepared as test NIR markers. We used a swine model to detect an assumed colon cancer site using NIR laparoscopy, followed by laparoscopic resection. The NIR markers were fixed at an assumed cancer site within the colon by endoscopy. An NIR laparoscope was then introduced into the abdominal cavity through a laparoscopy port. NIR emission from the markers in the swine colon was successfully recognized using the NIR laparoscopy imaging system. The position of the markers in the colon could be identified. Accurate resection of the colon was performed successfully by laparoscopic surgery under NIR fluorescence guidance. The presence of the NIR markers within the extirpated colon was confirmed, indicating resection of the appropriate site. NIR laparoscopic surgery is useful for colorectal cancer site recognition and accurate resection using laparoscopic surgery.

Application of laser Raman spectroscopy in concentration measurements of multiple analytes in human body fluids

NASA Astrophysics Data System (ADS)

Qu, Jianan Y.; Suria, David; Wilson, Brian C.

1998-05-01

The primary goal of these studies was to demonstrate that NIR Raman spectroscopy is feasible as a rapid and reagentless analytic method for clinical diagnostics. Raman spectra were collected on human serum and urine samples using a 785 nm excitation laser and a single-stage holographic spectrometer. A partial east squares method was used to predict the analyte concentrations of interest. The actual concentrations were determined by a standard clinical chemistry. The prediction accuracy of total protein, albumin, triglyceride and glucose in human sera ranged from 1.5 percent to 5 percent which is greatly acceptable for clinical diagnostics. The concentration measurements of acetaminophen, ethanol and codeine inhuman urine have demonstrated the potential of NIR Raman technology in screening of therapeutic drugs and substances of abuse.

Greater contribution of cerebral than extracerebral hemodynamics to near-infrared spectroscopy signals for functional activation and resting-state connectivity in infants.

PubMed

Funane, Tsukasa; Homae, Fumitaka; Watanabe, Hama; Kiguchi, Masashi; Taga, Gentaro

2014-10-01

While near-infrared spectroscopy (NIRS) has been increasingly applied to neuroimaging and functional connectivity studies in infants, it has not been quantitatively examined as to what extent the deep tissue (such as cerebral tissue) as opposed to shallow tissue (such as scalp), contributes to NIRS signals measured in infants. A method for separating the effects of deep- and shallow-tissue layers was applied to data of nine sleeping three-month-old infants who had been exposed to 3-s speech sounds or silence (i.e., resting state) and whose hemodynamic changes over their bilateral temporal cortices had been measured by using an NIRS system with multiple source-detector (S-D) distances. The deep-layer contribution was found to be large during resting [67% at S-D 20 mm, 78% at S-D 30 mm for oxygenated hemoglobin (oxy-Hb)] as well as during the speech condition (72% at S-D 20 mm, 82% at S-D 30 mm for oxy-Hb). A left-right connectivity analysis showed that correlation coefficients between left and right channels did not differ between original- and deep-layer signals under no-stimulus conditions and that of original- and deep-layer signals were larger than those of the shallow layer. These results suggest that NIRS signals obtained in infants with appropriate S-D distances largely reflected cerebral hemodynamic changes.

Greater contribution of cerebral than extracerebral hemodynamics to near-infrared spectroscopy signals for functional activation and resting-state connectivity in infants

PubMed Central

Funane, Tsukasa; Homae, Fumitaka; Watanabe, Hama; Kiguchi, Masashi; Taga, Gentaro

2014-01-01

Abstract. While near-infrared spectroscopy (NIRS) has been increasingly applied to neuroimaging and functional connectivity studies in infants, it has not been quantitatively examined as to what extent the deep tissue (such as cerebral tissue) as opposed to shallow tissue (such as scalp), contributes to NIRS signals measured in infants. A method for separating the effects of deep- and shallow-tissue layers was applied to data of nine sleeping three-month-old infants who had been exposed to 3-s speech sounds or silence (i.e., resting state) and whose hemodynamic changes over their bilateral temporal cortices had been measured by using an NIRS system with multiple source-detector (S-D) distances. The deep-layer contribution was found to be large during resting [67% at S-D 20 mm, 78% at S-D 30 mm for oxygenated hemoglobin (oxy-Hb)] as well as during the speech condition (72% at S-D 20 mm, 82% at S-D 30 mm for oxy-Hb). A left-right connectivity analysis showed that correlation coefficients between left and right channels did not differ between original- and deep-layer signals under no-stimulus conditions and that of original- and deep-layer signals were larger than those of the shallow layer. These results suggest that NIRS signals obtained in infants with appropriate S-D distances largely reflected cerebral hemodynamic changes. PMID:26157977

Combining Lactic Acid Spray with Near-Infrared Radiation Heating To Inactivate Salmonella enterica Serovar Enteritidis on Almond and Pine Nut Kernels

PubMed Central

Ha, Jae-Won

2015-01-01

The aim of this study was to investigate the efficacy of near-infrared radiation (NIR) heating combined with lactic acid (LA) sprays for inactivating Salmonella enterica serovar Enteritidis on almond and pine nut kernels and to elucidate the mechanisms of the lethal effect of the NIR-LA combined treatment. Also, the effect of the combination treatment on product quality was determined. Separately prepared S. Enteritidis phage type (PT) 30 and non-PT 30 S. Enteritidis cocktails were inoculated onto almond and pine nut kernels, respectively, followed by treatments with NIR or 2% LA spray alone, NIR with distilled water spray (NIR-DW), and NIR with 2% LA spray (NIR-LA). Although surface temperatures of nuts treated with NIR were higher than those subjected to NIR-DW or NIR-LA treatment, more S. Enteritidis survived after NIR treatment alone. The effectiveness of NIR-DW and NIR-LA was similar, but significantly more sublethally injured cells were recovered from NIR-DW-treated samples. We confirmed that the enhanced bactericidal effect of the NIR-LA combination may not be attributable to cell membrane damage per se. NIR heat treatment might allow S. Enteritidis cells to become permeable to applied LA solution. The NIR-LA treatment (5 min) did not significantly (P > 0.05) cause changes in the lipid peroxidation parameters, total phenolic contents, color values, moisture contents, and sensory attributes of nut kernels. Given the results of the present study, NIR-LA treatment may be a potential intervention for controlling food-borne pathogens on nut kernel products. PMID:25911473

Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration.

PubMed

Wood, Clive; Alwati, Abdolati; Halsey, Sheelagh; Gough, Tim; Brown, Elaine; Kelly, Adrian; Paradkar, Anant

2016-09-10

The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen-nicotinamide (IBU-NIC) and 1:1 carbamazepine-nicotinamide (CBZ-NIC) has been evaluated. A partial least squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving pharmaceutical co-crystals. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions.

PubMed

Ono, Daiki; Bamba, Takeshi; Oku, Yuichi; Yonetani, Tsutomu; Fukusaki, Eiichiro

2011-09-01

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture. Copyright © 2011 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Polarized near-infrared autofluorescence imaging combined with near-infrared diffuse reflectance imaging for improving colonic cancer detection.

PubMed

Shao, Xiaozhuo; Zheng, Wei; Huang, Zhiwei

2010-11-08

We evaluate the diagnostic feasibility of the integrated polarized near-infrared (NIR) autofluorescence (AF) and NIR diffuse reflectance (DR) imaging technique developed for colonic cancer detection. A total of 48 paired colonic tissue specimens (normal vs. cancer) were measured using the integrated NIR DR (850-1100 nm) and NIR AF imaging at the 785 nm laser excitation. The results showed that NIR AF intensities of cancer tissues are significantly lower than those of normal tissues (p<0.001, paired 2-sided Student's t-test, n=48). NIR AF imaging under polarization conditions gives a higher diagnostic accuracy (of ~92-94%) compared to non-polarized NIR AF imaging or NIR DR imaging. Further, the ratio imaging of NIR DR to NIR AF with polarization provides the best diagnostic accuracy (of ~96%) among the NIR AF and NIR DR imaging techniques. This work suggests that the integrated NIR AF/DR imaging under polarization condition has the potential to improve the early diagnosis and detection of malignant lesions in the colon.

Targeting and destroying tumor vasculature with a near-infrared laser-activated "nanobomb" for efficient tumor ablation.

PubMed

Gao, Wen; Li, Shuangshuang; Liu, Zhenhua; Sun, Yuhui; Cao, Wenhua; Tong, Lili; Cui, Guanwei; Tang, Bo

2017-09-01

Attacking the supportive vasculature network of a tumor offers an important new avenue for cancer therapy. Herein, a near-infrared (NIR) laser-activated "nanobomb" was developed as a noninvasive and targeted physical therapeutic strategy to effectively disrupt tumor neovasculature in an accurate and expeditious manner. This "nanobomb" was rationally fabricated via the encapsulation of vinyl azide (VA) into c(RGDfE) peptide-functionalized, hollow copper sulfide (HCuS) nanoparticles. The resulting RGD@HCuS(VA) was selectively internalized into integrin α v β 3 -expressing tumor vasculature endothelial cells and dramatically increased the photoacoustic signals from the tumor neovasculature, achieving a maximum signal-to-noise ratio at 4 h post-injection. Upon NIR irradiation, the local temperature increase triggered VA to release N 2 bubbles rapidly. Subsequently, these N 2 bubbles could instantly explode to destroy the neovasculature and further induce necrosis of the surrounding tumor cells. A single-dose injection of RGD@HCuS(VA) led to complete tumor regression after laser irradiation, with no tumor regrowth for 30 days. More importantly, high-resolution photoacoustic angiography, combined with excellent biodegradability, facilitated the precise destruction of tumor neovasculature by RGD@HCuS(VA) without damaging normal tissues. These results demonstrate the great potential of this "nanobomb" for clinical translation to treat cancer patients with NIR laser-accessible orthotopic tumors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Potential of Near-Infrared Chemical Imaging as Process Analytical Technology Tool for Continuous Freeze-Drying.

PubMed

Brouckaert, Davinia; De Meyer, Laurens; Vanbillemont, Brecht; Van Bockstal, Pieter-Jan; Lammens, Joris; Mortier, Séverine; Corver, Jos; Vervaet, Chris; Nopens, Ingmar; De Beer, Thomas

2018-04-03

Near-infrared chemical imaging (NIR-CI) is an emerging tool for process monitoring because it combines the chemical selectivity of vibrational spectroscopy with spatial information. Whereas traditional near-infrared spectroscopy is an attractive technique for water content determination and solid-state investigation of lyophilized products, chemical imaging opens up possibilities for assessing the homogeneity of these critical quality attributes (CQAs) throughout the entire product. In this contribution, we aim to evaluate NIR-CI as a process analytical technology (PAT) tool for at-line inspection of continuously freeze-dried pharmaceutical unit doses based on spin freezing. The chemical images of freeze-dried mannitol samples were resolved via multivariate curve resolution, allowing us to visualize the distribution of mannitol solid forms throughout the entire cake. Second, a mannitol-sucrose formulation was lyophilized with variable drying times for inducing changes in water content. Analyzing the corresponding chemical images via principal component analysis, vial-to-vial variations as well as within-vial inhomogeneity in water content could be detected. Furthermore, a partial least-squares regression model was constructed for quantifying the water content in each pixel of the chemical images. It was hence concluded that NIR-CI is inherently a most promising PAT tool for continuously monitoring freeze-dried samples. Although some practicalities are still to be solved, this analytical technique could be applied in-line for CQA evaluation and for detecting the drying end point.

Neurofeedback as a nonpharmacological treatment for adults with attention-deficit/hyperactivity disorder (ADHD): study protocol for a randomized controlled trial.

PubMed

Mayer, Kerstin; Wyckoff, Sarah Nicole; Fallgatter, Andreas J; Ehlis, Ann-Christine; Strehl, Ute

2015-04-18

Neurofeedback has been applied effectively in various areas, especially in the treatment of children with attention-deficit/hyperactivity disorder (ADHD). This study protocol is designed to investigate the effect of slow cortical potential (SCP) feedback and a new form of neurofeedback using near-infrared spectroscopy (NIRS) on symptomatology and neurophysiological parameters in an adult ADHD population. A comparison of SCP and NIRS feedback therapy methods has not been previously conducted and may yield valuable findings about alternative treatments for adult ADHD. The outcome of both neurofeedback techniques will be assessed over 30 treatment sessions and after a 6-month follow-up period, and then will be compared to a nonspecific biofeedback treatment. Furthermore, to investigate if treatment effects in this proof-of-principle study can be predicted by specific neurophysiological baseline parameters, regression models will be applied. Finally, a comparison with healthy controls will be conducted to evaluate deviant pretraining neurophysiological parameters, stability of assessment measures, and treatment outcome. To date, an investigation and comparison of SCP and NIRS feedback training to an active control has not been conducted; therefore, we hope to gain valuable insights in effects and differences of these types of treatment for ADHD in adults. This study is registered with the German Registry of Clinical Trials: DRKS00006767 , date of registration: 8 October 2014.

Determination of the Mineral Composition and Toxic Element Contents of Propolis by Near Infrared Spectroscopy

PubMed Central

González-Martín, M. Inmaculada; Escuredo, Olga; Revilla, Isabel; Vivar-Quintana, Ana M.; Coello, M. Carmen; Palacios Riocerezo, Carlos; Wells Moncada, Guillermo

2015-01-01

The potential of near infrared spectroscopy (NIR) with remote reflectance fiber-optic probes for determining the mineral composition of propolis was evaluated. This technology allows direct measurements without prior sample treatment. Ninety one samples of propolis were collected in Chile (Bio-Bio region) and Spain (Castilla-León and Galicia regions). The minerals measured were aluminum, calcium, iron, potassium, magnesium, phosphorus, and some potentially toxic trace elements such as zinc, chromium, nickel, copper and lead. The modified partial least squares (MPLS) regression method was used to develop the NIR calibration model. The determination coefficient (R2) and root mean square error of prediction (RMSEP) obtained for aluminum (0.79, 53), calcium (0.83, 94), iron (0.69, 134) potassium (0.95, 117), magnesium (0.70, 99), phosphorus (0.94, 24) zinc (0.87, 10) chromium (0.48, 0.6) nickel (0.52, 0.7) copper (0.64, 0.9) and lead (0.70, 2) in ppm. The results demonstrated that the capacity for prediction can be considered good for wide ranges of potassium, phosphorus and zinc concentrations, and acceptable for aluminum, calcium, magnesium, iron and lead. This indicated that the NIR method is comparable to chemical methods. The method is of interest in the rapid prediction of potentially toxic elements in propolis before consumption. PMID:26540058

Predicting Soil Salinity with Vis–NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization

PubMed Central

Liu, Ya; Pan, Xianzhang; Wang, Changkun; Li, Yanli; Shi, Rongjie

2015-01-01

Robust models for predicting soil salinity that use visible and near-infrared (vis–NIR) reflectance spectroscopy are needed to better quantify soil salinity in agricultural fields. Currently available models are not sufficiently robust for variable soil moisture contents. Thus, we used external parameter orthogonalization (EPO), which effectively projects spectra onto the subspace orthogonal to unwanted variation, to remove the variations caused by an external factor, e.g., the influences of soil moisture on spectral reflectance. In this study, 570 spectra between 380 and 2400 nm were obtained from soils with various soil moisture contents and salt concentrations in the laboratory; 3 soil types × 10 salt concentrations × 19 soil moisture levels were used. To examine the effectiveness of EPO, we compared the partial least squares regression (PLSR) results established from spectra with and without EPO correction. The EPO method effectively removed the effects of moisture, and the accuracy and robustness of the soil salt contents (SSCs) prediction model, which was built using the EPO-corrected spectra under various soil moisture conditions, were significantly improved relative to the spectra without EPO correction. This study contributes to the removal of soil moisture effects from soil salinity estimations when using vis–NIR reflectance spectroscopy and can assist others in quantifying soil salinity in the future. PMID:26468645

Quick method (FT-NIR) for the determination of oil and major fatty acids content in whole achenes of milk thistle (Silybum marianum (L.) Gaertn.).

PubMed

Koláčková, Pavla; Růžičková, Gabriela; Gregor, Tomáš; Šišperová, Eliška

2015-08-30

Calibration models for the Fourier transform-near infrared (FT-NIR) instrument were developed for quick and non-destructive determination of oil and fatty acids in whole achenes of milk thistle. Samples with a range of oil and fatty acid levels were collected and their transmittance spectra were obtained by the FT-NIR instrument. Based on these spectra and data gained by the means of the reference method - Soxhlet extraction and gas chromatography (GC) - calibration models were created by means of partial least square (PLS) regression analysis. Precision and accuracy of the calibration models was verified via the cross-validation of validation samples whose spectra were not part of the calibration model and also according to the root mean square error of prediction (RMSEP), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV) and the validation coefficient of determination (R(2) ). R(2) for whole seeds were 0.96, 0.96, 0.83 and 0.67 and the RMSEP values were 0.76, 1.68, 1.24, 0.54 for oil, linoleic (C18:2), oleic (C18:1) and palmitic (C16:0) acids, respectively. The calibration models are appropriate for the non-destructive determination of oil and fatty acids levels in whole seeds of milk thistle. © 2014 Society of Chemical Industry.

Turbo-Satori: a neurofeedback and brain-computer interface toolbox for real-time functional near-infrared spectroscopy.

PubMed

Lührs, Michael; Goebel, Rainer

2017-10-01

Turbo-Satori is a neurofeedback and brain-computer interface (BCI) toolbox for real-time functional near-infrared spectroscopy (fNIRS). It incorporates multiple pipelines from real-time preprocessing and analysis to neurofeedback and BCI applications. The toolbox is designed with a focus in usability, enabling a fast setup and execution of real-time experiments. Turbo-Satori uses an incremental recursive least-squares procedure for real-time general linear model calculation and support vector machine classifiers for advanced BCI applications. It communicates directly with common NIRx fNIRS hardware and was tested extensively ensuring that the calculations can be performed in real time without a significant change in calculation times for all sampling intervals during ongoing experiments of up to 6 h of recording. Enabling immediate access to advanced processing features also allows the use of this toolbox for students and nonexperts in the field of fNIRS data acquisition and processing. Flexible network interfaces allow third party stimulus applications to access the processed data and calculated statistics in real time so that this information can be easily incorporated in neurofeedback or BCI presentations.

Shed a light of wireless technology on portable mobile design of NIRS

NASA Astrophysics Data System (ADS)

Sun, Yunlong; Li, Ting

2016-03-01

Mobile internet is growing rapidly driven by high-tech companies including the popular Apple and Google. The wireless mini-NIRS is believed to deserve a great spread future, while there is sparse report on wireless NIRS device and even for the reported wireless NIRS, its wireless design is scarcely presented. Here we focused on the wireless design of NIRS devices. The widely-used wireless communication standards and wireless communication typical solutions were employed into our NIRS design and then compared on communication efficiency, distance, error rate, low-cost, power consumption, and stabilities, based on the requirements of NIRS applications. The properly-performed wireless communication methods matched with the characteristics of NIRS are picked out. Finally, we realized one recommended wireless communication in our NIRS, developed a test platform on wireless NIRS and tested the full properties on wireless communication. This study elaborated the wireless communication methods specified for NIRS and suggested one implementation with one example fully illustrated, which support the future mobile design on NIRS devices.

STAR FORMATION ACTIVITY IN THE GALACTIC H II REGION Sh2-297

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mallick, K. K.; Ojha, D. K.; Dewangan, L. K.

We present a multiwavelength study of the Galactic H II region Sh2-297, located in the Canis Major OB1 complex. Optical spectroscopic observations are used to constrain the spectral type of ionizing star HD 53623 as B0V. The classical nature of this H II region is affirmed by the low values of electron density and emission measure, which are calculated to be 756 cm{sup -3} and 9.15 Multiplication-Sign 10{sup 5} cm{sup -6} pc using the radio continuum observations at 610 and 1280 MHz, and Very Large Array archival data at 1420 MHz. To understand local star formation, we identified the youngmore » stellar object (YSO) candidates in a region of area {approx}7.'5 Multiplication-Sign 7.'5 centered on Sh2-297 using grism slitless spectroscopy (to identify the H{alpha} emission line stars), and near infrared (NIR) observations. NIR YSO candidates are further classified into various evolutionary stages using color-color and color-magnitude (CM) diagrams, giving 50 red sources (H - K > 0.6) and 26 Class II-like sources. The mass and age range of the YSOs are estimated to be {approx}0.1-2 M {sub Sun} and 0.5-2 Myr using optical (V/V-I) and NIR (J/J-H) CM diagrams. The mean age of the YSOs is found to be {approx}1 Myr, which is of the order of dynamical age of 1.07 Myr of the H II region. Using the estimated range of visual extinction (1.1-25 mag) from literature and NIR data for the region, spectral energy distribution models have been implemented for selected YSOs which show masses and ages to be consistent with estimated values. The spatial distribution of YSOs shows an evolutionary sequence, suggesting triggered star formation in the region. The star formation seems to have propagated from the ionizing star toward the cold dark cloud LDN1657A located west of Sh2-297.« less

Protein Network of the Pseudomonas aeruginosa Denitrification Apparatus

PubMed Central

Borrero-de Acuña, José Manuel; Rohde, Manfred; Wissing, Josef; Jänsch, Lothar; Schobert, Max; Molinari, Gabriella; Timmis, Kenneth N.

2016-01-01

ABSTRACT Oxidative phosphorylation using multiple-component, membrane-associated protein complexes is the most effective way for a cell to generate energy. Here, we systematically investigated the multiple protein-protein interactions of the denitrification apparatus of the pathogenic bacterium Pseudomonas aeruginosa. During denitrification, nitrate (Nar), nitrite (Nir), nitric oxide (Nor), and nitrous oxide (Nos) reductases catalyze the reaction cascade of NO3− → NO2− → NO → N2O → N2. Genetic experiments suggested that the nitric oxide reductase NorBC and the regulatory protein NosR are the nucleus of the denitrification protein network. We utilized membrane interactomics in combination with electron microscopy colocalization studies to elucidate the corresponding protein-protein interactions. The integral membrane proteins NorC, NorB, and NosR form the core assembly platform that binds the nitrate reductase NarGHI and the periplasmic nitrite reductase NirS via its maturation factor NirF. The periplasmic nitrous oxide reductase NosZ is linked via NosR. The nitrate transporter NarK2, the nitrate regulatory system NarXL, various nitrite reductase maturation proteins, NirEJMNQ, and the Nos assembly lipoproteins NosFL were also found to be attached. A number of proteins associated with energy generation, including electron-donating dehydrogenases, the complete ATP synthase, almost all enzymes of the tricarboxylic acid (TCA) cycle, and the Sec system of protein transport, among many other proteins, were found to interact with the denitrification proteins. This deduced nitrate respirasome is presumably only one part of an extensive cytoplasmic membrane-anchored protein network connecting cytoplasmic, inner membrane, and periplasmic proteins to mediate key activities occurring at the barrier/interface between the cytoplasm and the external environment. IMPORTANCE The processes of cellular energy generation are catalyzed by large multiprotein enzyme complexes. The molecular basis for the interaction of these complexes is poorly understood. We employed membrane interactomics and electron microscopy to determine the protein-protein interactions involved. The well-investigated enzyme complexes of denitrification of the pathogenic bacterium Pseudomonas aeruginosa served as a model. Denitrification is one essential step of the universal N cycle and provides the bacterium with an effective alternative to oxygen respiration. This process allows the bacterium to form biofilms, which create low-oxygen habitats and which are a key in the infection mechanism. Our results provide new insights into the molecular basis of respiration, as well as opening a new window into the infection strategies of this pathogen. PMID:26903416

Indocyanine green fluorescence in second near-infrared (NIR-II) window

PubMed Central

Bhavane, Rohan; Ghaghada, Ketan B.; Vasudevan, Sanjeev A.; Kaay, Alexander; Annapragada, Ananth

2017-01-01

Indocyanine green (ICG), a FDA approved near infrared (NIR) fluorescent agent, is used in the clinic for a variety of applications including lymphangiography, intra-operative lymph node identification, tumor imaging, superficial vascular imaging, and marking ischemic tissues. These applications operate in the so-called “NIR-I” window (700–900 nm). Recently, imaging in the “NIR-II” window (1000–1700 nm) has attracted attention since, at longer wavelengths, photon absorption, and scattering effects by tissue components are reduced, making it possible to image deeper into the underlying tissue. Agents for NIR-II imaging are, however, still in pre-clinical development. In this study, we investigated ICG as a NIR-II dye. The absorbance and NIR-II fluorescence emission of ICG were measured in different media (PBS, plasma and ethanol) for a range of ICG concentrations. In vitro and in vivo testing were performed using a custom-built spectral NIR assembly to facilitate simultaneous imaging in NIR-I and NIR-II window. In vitro studies using ICG were performed using capillary tubes (as a simulation of blood vessels) embedded in Intralipid solution and tissue phantoms to evaluate depth of tissue penetration in NIR-I and NIR-II window. In vivo imaging using ICG was performed in nude mice to evaluate vascular visualization in the hind limb in the NIR-I and II windows. Contrast-to-noise ratios (CNR) were calculated for comparison of image quality in NIR-I and NIR-II window. ICG exhibited significant fluorescence emission in the NIR-II window and this emission (similar to the absorption profile) is substantially affected by the environment of the ICG molecules. In vivo imaging further confirmed the utility of ICG as a fluorescent dye in the NIR-II domain, with the CNR values being ~2 times those in the NIR-I window. The availability of an FDA approved imaging agent could accelerate the clinical translation of NIR-II imaging technology. PMID:29121078

Rapid determination of total protein and wet gluten in commercial wheat flour using siSVR-NIR.

PubMed

Chen, Jia; Zhu, Shipin; Zhao, Guohua

2017-04-15

The determination of total protein and wet gluten is of critical importance when screening commercial flour for desired processing suitability. To this end, a near-infrared spectroscopy (NIR) method with support vector regression was developed in the present study. The effects of spectral preprocessing and the synergy interval on model performance were investigated. The results showed that the models from raw spectra were not acceptable, but they were substantially improved by properly applying spectral preprocessing methods. Meanwhile, the synergy interval was validated with a good ability to improve the performance of models based on the whole spectrum. The coefficient of determination (R 2 ), the root mean square error of prediction (RMSEP) and the standard deviation ratio (SDR) of the best models for total protein (wet gluten) were 0.906 (0.850), 0.425 (1.024) and 3.065 (2.482), respectively. These two best models have similar and lower relative errors (approximately 8.8%), which indicates their feasibility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment.

PubMed

Bagnasco, Lucia; Cosulich, M Elisabetta; Speranza, Giovanna; Medini, Luca; Oliveri, Paolo; Lanteri, Silvia

2014-08-15

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial - not so significantly as to justify the coupled use of the two analytical techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of slash-and-burn land management on soil spectral properties estimated with VIS-NIR-SWIR spectroscopy

NASA Astrophysics Data System (ADS)

Rosero-Vlasova, Olga Alexandra; Vlassova, Lidia; Rosero Tufiño, Pedro; Pérez-Cabello, Fernando; Montorio Llovería, Raquel

2017-04-01

Slash-and-burn land management is typical for low-income tropical countries, such as Ecuador. It involves conversion of forest into areas used for agriculture. At first trees are cut and the wood debris is burnt. After initial clearing, biomass burning is performed after each production cycle. Usually, cultivation cycles are followed by the fallow period. In the medium and long term, these practices have negative effect on soil fertility and there is the need for clearing more forest for agricultural use. This is one of the reasons for continuing deforestation with the consequent loss of biodiversity. Changes in physico-chemical properties due to periodic burning are accompanied by changes in soil spectral properties and can be determined using VIS-NIR-SWIR spectroscopy, which can be a cost-effective alternative for traditional methods of soil analysis. The purpose of the study is to assess the viability of VIS-NIR-SWIR spectroscopy for characterization of soils from land areas under slash-and-burn management system. Eighteen samples from soil surface layer were collected from two corn fields in the province of Los Rios, Ecuador, in September 2015. One of the areas has experienced six slash-and-burn cycles, while in the other the samples were collected at the end of the first corn cultivation cycle. Spectral measurements of sieved and air-dried samples were performed in the laboratory of the University of Zaragoza using ASD Fieldspec®4 spectroradiometer (350-2500nm spectral range) and ASD Illuminator Lamp as a light source. Statistically significant differences were observed between soil spectra of the samples from two soil groups. Reflectance of repeatedly burnt soils was 20% higher (mean value for the entire spectrum) for 65% of the samples, being especially important in VIS (>45%) and NIR ( 35%), probably due to the lower organic matter (OM) content. OM models built using Partial least Squares Regression demonstrated high predictive capacity (R2>0.8). Thus, the study confirms VIS-NIR-SWIR soil spectroscopy can be used as a tool for monitoring changes in soils in areas of slash-and-burn land management systems.

A humanized antibody for imaging immune checkpoint ligand PD-L1 expression in tumors

PubMed Central

Gabrielson, Matthew; Lisok, Ala; Wharram, Bryan; Sysa-Shah, Polina; Azad, Babak Behnam; Pomper, Martin G.; Nimmagadda, Sridhar

2016-01-01

Antibodies targeting the PD-1/PD-L1 immune checkpoint lead to tumor regression and improved survival in several cancers. PD-L1 expression in tumors may be predictive of response to checkpoint blockade therapy. Because tissue samples might not always be available to guide therapy, we developed and evaluated a humanized antibody for non-invasive imaging of PD-L1 expression in tumors. Radiolabeled [111In]PD-L1-mAb and near-infrared dye conjugated NIR-PD-L1-mAb imaging agents were developed using the mouse and human cross-reactive PD-L1 antibody MPDL3280A. We tested specificity of [111In]PD-L1-mAb and NIR-PD-L1-mAb in cell lines and in tumors with varying levels of PD-L1 expression. We performed SPECT/CT imaging, biodistribution and blocking studies in NSG mice bearing tumors with constitutive PD-L1 expression (CHO-PDL1) and in controls (CHO). Results were confirmed in triple negative breast cancer (TNBC) (MDAMB231 and SUM149) and non-small cell lung cancer (NSCLC) (H2444 and H1155) xenografts with varying levels of PD-L1 expression. There was specific binding of [111In]PD-L1-mAb and NIR-PD-L1-mAb to tumor cells in vitro, correlating with PD-L1 expression levels. In mice bearing subcutaneous and orthotopic tumors, there was specific and persistent high accumulation of signal intensity in PD-L1 positive tumors (CHO-PDL1, MDAMB231, H2444) but not in controls. These results demonstrate that [111In]PD-L1-mAb and NIR-PD-L1-mAb can detect graded levels of PD-L1 expression in human tumor xenografts in vivo. As a humanized antibody, these findings suggest clinical translation of radiolabeled versions of MPDL3280A for imaging. Specificity of NIR-PD-L1-mAb indicates the potential for optical imaging of PD-L1 expression in tumors in relevant pre-clinical as well as clinical settings. PMID:26848870

Reflectance spectroscopy: a tool for predicting the risk of iron chlorosis in soils

NASA Astrophysics Data System (ADS)

Cañasveras, J. C.; Barrón, V.; Del Campillo, M. C.; Viscarra Rossel, R. A.

2012-04-01

Chlorosis due to iron (Fe) deficiency is the most important nutritional problem a plant can have in calcareous soils. The most characteristic symptom of Fe chlorosis is internervial yellowing in the youngest leaves due to a lack of chlorophyll caused by a disorder in Fe nutrition. Fe chlorosis is related with calcium carbonate equivalent (CCE), clay content and Fe extracted with oxalate (Feo). The conventional technique for determining these properties and others, based on laboratory analysis, are time-consuming and costly. Reflectance spectroscopy (RS) is a rapid, non-destructive, less expensive alternative tool that can be used to enhance or replace conventional methods of soil analysis. The aim of this work was to assess the usefulness of RS for the determination of some properties of Mediterranean soils including clay content, CCE, Feo, cation exchange capacity (CEC), organic matter (OM) and pHw, with emphasis on those with a specially marked influence on the risk of Fe chlorosis. To this end, we used partial least-squares regression (PLS) to construct calibration models, leave-one-out cross-validation and an independent validation set. Our results testify to the usefulness of qualitative soil interpretations based on the variable importance for projection (VIP) as derived by PLS decomposition. The accuracy of predictions in each of the Vis-NIR, MIR and combined spectral regions differed considerably between properties. The R2adj and root mean square error (RMSE) for the external validation predictions were as follows: 0.83 and 37 mg kg-1 for clay content in the Vis-NIR-MIR range; 0.99 and 25 mg kg-1 for CCE, 0.80 and 0.1 mg kg-1 for Feo in the MIR range; 0.93 and 3 cmolc kg-1 for CEC in the Vis-NIR range; 0.87 and 2 mg kg-1 for OM in the Vis-NIR-MIR range, 0.61 and 0.2 for pHw in the MIR range. These results testify to the potential of RS in the Vis, NIR and MIR ranges for efficient soil analysis, the acquisition of soil information and the assessment of the risk of Fe chlorosis in soils.

Multisensor on-the-go mapping of readily dispersible clay, particle size and soil organic matter

NASA Astrophysics Data System (ADS)

Debaene, Guillaume; Niedźwiecki, Jacek; Papierowska, Ewa

2016-04-01

Particle size fractions affect strongly the physical and chemical properties of soil. Readily dispersible clay (RDC) is the part of the clay fraction in soils that is easily or potentially dispersible in water when small amounts of mechanical energy are applied to soil. The amount of RDC in the soil is of significant importance for agriculture and environment because clay dispersion is a cause of poor soil stability in water which in turn contributes to soil erodibility, mud flows, and cementation. To obtain a detailed map of soil texture, many samples are needed. Moreover, RDC determination is time consuming. The use of a mobile visible and near-infrared (VIS-NIR) platform is proposed here to map those soil properties and obtain the first detailed map of RDC at field level. Soil properties prediction was based on calibration model developed with 10 representative samples selected by a fuzzy logic algorithm. Calibration samples were analysed for soil texture (clay, silt and sand), RDC and soil organic carbon (SOC) using conventional wet chemistry analysis. Moreover, the Veris mobile sensor platform is also collecting electrical conductivity (EC) data (deep and shallow), and soil temperature. These auxiliary data were combined with VIS-NIR measurement (data fusion) to improve prediction results. EC maps were also produced to help understanding RDC data. The resulting maps were visually compared with an orthophotography of the field taken at the beginning of the plant growing season. Models were developed with partial least square regression (PLSR) and support vector machine regression (SVMR). There were no significant differences between calibration using PLSR or SVMR. Nevertheless, the best models were obtained with PLSR and standard normal variate (SNV) pretreatment and the fusion with deep EC data (e.g. for RDC and clay content: RMSECV = 0,35% and R2 = 0,71; RMSECV = 0,32% and R2 = 0,73 respectively). The best models were used to predict soil properties from the field spectra collected with the VIS-NIR platform. Maps of soil properties were generated using natural neighbour (NN) interpolation. Calibration results were satisfactory for all soil properties and allowed for the generation of detailed maps. The spatial variability of RDC was in accordance with the field orthophotography. Areas of high RDC content were corresponding to area of bad plant development. Soil texture has been correctly predicted by VIS-NIR spectroscopy (laboratory or on-the-go) before. However, readily dispersible clay (an important parameter for soil stability) has never been investigated before. This study introduces the possibility of using VIS-NIR for predicting readily dispersible clay at field level. The results obtained could be used in preventing soil erosion. Acknowledgement: This research was financed by a National Science Centre grant (NCN - Poland) with decision number UMO-2012/07/B/ST10/04387

Visible-near infrared spectroscopy as a tool to improve mapping of soil properties

NASA Astrophysics Data System (ADS)

Evgrafova, Alevtina; Kühnel, Anna; Bogner, Christina; Haase, Ina; Shibistova, Olga; Guggenberger, Georg; Tananaev, Nikita; Sauheitl, Leopold; Spielvogel, Sandra

2017-04-01

Spectroscopic measurements, which are non-destructive, precise and rapid, can be used to predict soil properties and help estimate the spatial variability of soil properties at the pedon scale. These estimations are required for quantifying soil properties with higher precision, identifying the changes in soil properties and ecosystem response to climate change as well as increasing the estimation accuracy of soil-related models. Our objectives were to (i) predict soil properties for nested samples (n = 296) using the laboratory-based visible-near infrared (vis-NIR) spectra of air-dried (<2 mm) soil samples and values of measured soil properties for gridded samples (n = 174) as calibration and validation sets; (ii) estimate the precision and predictive accuracy of an empirical spectral model using (a) our own spectral library and (b) the global spectral library; (iii) support the global spectral library with obtained vis-NIR spectral data on permafrost-affected soils. The soil samples were collected from three permafrost-affected soil profiles underlain by permafrost at various depths between 23 cm to 57.5 cm below the surface (Cryosols) and one soil profile with no presence of permafrost within the upper 100 cm layer (Cambisol) in order to characterize the spatial distribution and variability of soil properties. The gridded soil samples (n = 174) were collected using an 80 cm wide grid with a mesh size of 10 cm on both axes. In addition, 300 nested soil samples were collected using a grid of 12 cm by 12 cm (25 samples per grid) from a hole of 1 cm in a diameter with a distance from the next sample of 1 cm. Due to a small amount of available soil material (< 1.5 g), 296 nested soil samples were analyzed only using vis-NIR spectroscopy. The air-dried mineral gridded soil samples (n = 174) were sieved through a 2-mm sieve and ground with an agate mortar prior to the elemental analysis. The soil organic carbon and total nitrogen concentrations (in %) were determined using a dry combustion method on the Vario EL cube analyzer (Elementar Analysensysteme GmbH, Germany). Inorganic C was removed from the mineral soil samples with pH values higher than 7 prior to the elemental analysis using the volatilization method (HCl, 6 hours). The pH of soil samples was measured in 0.01 M CaCl2 using a 1:2 soil:solution ratio. However, for soil sample with a high in organic matter content, a 1:10 ratio was applied. We also measured oxalate and dithionite extracted iron, aluminum and manganese oxides and hydroxides using inductively coupled plasma optical emission spectroscopy (Varian Vista MPX ICP-OES, Agilent Technologies, USA). We predicted the above-mentioned soil properties for all nested samples using partial least squares regression, which was performed using R program. We can conclude that vis-NIR spectroscopy can be used effectively in order to describe, estimate and further map the spatial patterns of soil properties using geostatistical methods. This research could also help to improve the global soil spectral library taking into account that only few previous applications of vis-NIR spectroscopy were conducted on permafrost-affected soils of Northern Siberia. Keywords: Visible-near infrared spectroscopy, vis-NIR, permafrost-affected soils, Siberia, partial least squares regression.

Influence of multiple scattering and absorption on the full scattering profile and the isobaric point in tissue

NASA Astrophysics Data System (ADS)

Duadi, Hamootal; Fixler, Dror

2015-05-01

Light reflectance and transmission from soft tissue has been utilized in noninvasive clinical measurement devices such as the photoplethysmograph (PPG) and reflectance pulse oximeter. Incident light on the skin travels into the underlying layers and is in part reflected back to the surface, in part transferred and in part absorbed. Most methods of near infrared (NIR) spectroscopy focus on the volume reflectance from a semi-infinite sample, while very few measure transmission. We have previously shown that examining the full scattering profile (angular distribution of exiting photons) provides more comprehensive information when measuring from a cylindrical tissue. Furthermore, an isobaric point was found which is not dependent on changes in the reduced scattering coefficient. The angle corresponding to this isobaric point depends on the tissue diameter. We investigated the role of multiple scattering and absorption on the full scattering profile of a cylindrical tissue. First, we define the range in which multiple scattering occurs for different tissue diameters. Next, we examine the role of the absorption coefficient in the attenuation of the full scattering profile. We demonstrate that the absorption linearly influences the intensity at each angle of the full scattering profile and, more importantly, the absorption does not change the position of the isobaric point. The findings of this work demonstrate a realistic model for optical tissue measurements such as NIR spectroscopy, PPG, and pulse oximetery.

Detection of diethylene glycol adulteration in propylene glycol--method validation through a multi-instrument collaborative study.

PubMed

Li, Xiang; Arzhantsev, Sergey; Kauffman, John F; Spencer, John A

2011-04-05

Four portable NIR instruments from the same manufacturer that were nominally identical were programmed with a PLS model for the detection of diethylene glycol (DEG) contamination in propylene glycol (PG)-water mixtures. The model was developed on one spectrometer and used on other units after a calibration transfer procedure that used piecewise direct standardization. Although quantitative results were produced, in practice the instrument interface was programmed to report in Pass/Fail mode. The Pass/Fail determinations were made within 10s and were based on a threshold that passed a blank sample with 95% confidence. The detection limit was then established as the concentration at which a sample would fail with 95% confidence. For a 1% DEG threshold one false negative (Type II) and eight false positive (Type I) errors were found in over 500 samples measured. A representative test set produced standard errors of less than 2%. Since the range of diethylene glycol for economically motivated adulteration (EMA) is expected to be above 1%, the sensitivity of field calibrated portable NIR instruments is sufficient to rapidly screen out potentially problematic materials. Following method development, the instruments were shipped to different sites around the country for a collaborative study with a fixed protocol to be carried out by different analysts. NIR spectra of replicate sets of calibration transfer, system suitability and test samples were all processed with the same chemometric model on multiple instruments to determine the overall analytical precision of the method. The combined results collected for all participants were statistically analyzed to determine a limit of detection (2.0% DEG) and limit of quantitation (6.5%) that can be expected for a method distributed to multiple field laboratories. Published by Elsevier B.V.

Macrophage-mediated delivery of light activated nitric oxide prodrugs with spatial, temporal and concentration control† †Electronic supplementary information (ESI) available: Includes detailed experimental details plus 10 additional figures. See DOI: 10.1039/c8sc00015h

PubMed Central

Evans, Michael A.; Huang, Po-Ju; Iwamoto, Yuji; Ibsen, Kelly N.; Chan, Emory M.; Hitomi, Yutaka

2018-01-01

Nitric oxide (NO) holds great promise as a treatment for cancer hypoxia, if its concentration and localization can be precisely controlled. Here, we report a “Trojan Horse” strategy to provide the necessary spatial, temporal, and dosage control of such drug-delivery therapies at targeted tissues. Described is a unique package consisting of (1) a manganese–nitrosyl complex, which is a photoactivated NO-releasing moiety (photoNORM), plus Nd3+-doped upconverting nanoparticles (Nd-UCNPs) incorporated into (2) biodegradable polymer microparticles that are taken up by (3) bone-marrow derived murine macrophages. Both the photoNORM [Mn(NO)dpaqNO2]BPh4(dpaqNO2 = 2-[N,N-bis(pyridin-2-yl-methyl)]-amino-N′-5-nitro-quinolin-8-yl-acetamido) and the Nd-UCNPs are activated by tissue-penetrating near-infrared (NIR) light at ∼800 nm. Thus, simultaneous therapeutic NO delivery and photoluminescence (PL) imaging can be achieved with a NIR diode laser source. The loaded microparticles are non-toxic to their macrophage hosts in the absence of light. The microparticle-carrying macrophages deeply penetrate into NIH-3T3/4T1 tumor spheroid models, and when the infiltrated spheroids are irradiated with NIR light, NO is released in quantifiable amounts while emission from the Nd-UCNPs provides images of microparticle location. Furthermore, varying the intensity of the NIR excitation allows photochemical control over NO release. Low doses reduce levels of hypoxia inducible factor 1 alpha (HIF-1α) in the tumor cells, while high doses are cytotoxic. The use of macrophages to carry microparticles with a NIR photo-activated theranostic payload into a tumor overcomes challenges often faced with therapeutic administration of NO and offers the potential of multiple treatment strategies with a single system. PMID:29780505

The case for a modern multiwavelength, polarization-sensitive LIDAR in orbit around Mars

USGS Publications Warehouse

Brown, Adrian J.; Michaels, Timothy I.; Byrne, Shane; Sun, Wenbo; Titus, Timothy N.; Colaprete, Anthony; Wolff, Michael J.; Videen, Gorden; Grund, Christian J.

2014-01-01

We present the scientific case to build a multiple-wavelength, active, near-infrared (NIR) instrument to measure the reflected intensity and polarization characteristics of backscattered radiation from planetary surfaces and atmospheres. We focus on the ability of such an instrument to enhance, perhaps revolutionize, our understanding of climate, volatiles and astrobiological potential of modern-day Mars.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS.

PubMed

Herff, Christian; Heger, Dominic; Fortmann, Ole; Hennrich, Johannes; Putze, Felix; Schultz, Tanja

2013-01-01

When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online.

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

PubMed Central

Herff, Christian; Heger, Dominic; Fortmann, Ole; Hennrich, Johannes; Putze, Felix; Schultz, Tanja

2014-01-01

When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online. PMID:24474913

Integration of Teaching Processes and Learning Assessment in the Prefrontal Cortex during a Video Game Teaching–learning Task

PubMed Central

Takeuchi, Naoyuki; Mori, Takayuki; Suzukamo, Yoshimi; Izumi, Shin-Ichi

2017-01-01

Human teaching is a social interaction that supports reciprocal and dynamical feedback between the teacher and the student. The prefrontal cortex (PFC) is a region of particular interest due to its demonstrated role in social interaction. In the present study, we evaluated the PFC activity simultaneously in two individuals playing the role of a teacher and student in a video game teaching–learning task. For that, we used two wearable near-infrared spectroscopy (NIRS) devices in order to elucidate the neural mechanisms underlying cognitive interactions between teachers and students. Fifteen teacher–student pairs in total (N = 30) participated in this study. Each teacher was instructed to teach the video game to their student partner, without speaking. The PFC activity was simultaneously evaluated in both participants using a wearable 16-channel NIRS system during the video game teaching–learning task. Two sessions, each including a triplet of a 30-s teaching–learning task, were performed in order to evaluate changes in PFC activity after advancement of teaching–learning state. Changes in the teachers’ left PFC activity between the first and second session positively correlated with those observed in students (r = 0.694, p = 0.004). Moreover, among teachers, multiple regression analysis revealed a correlation between the left PFC activity and the assessment gap between one’s own teaching and the student’s understanding (β = 0.649, p = 0.009). Activity in the left PFC changed synchronously in both teachers and students after advancement of the teaching–learning state. The left PFC of teachers may be involved in integrating information regarding one’s own teaching process and the student’s learning state. The present observations indicate that simultaneous recording and analysis of brain activity data during teacher–student interactions may be useful in the field of educational neuroscience. PMID:28119650

Application of reflectance colorimeter measurements and infrared spectroscopy methods to rapid and nondestructive evaluation of carotenoids content in apricot (Prunus armeniaca L.).

PubMed

Ruiz, David; Reich, Maryse; Bureau, Sylvie; Renard, Catherine M G C; Audergon, Jean-Marc

2008-07-09

The importance of carotenoid content in apricot (Prunus armeniaca L.) is recognized not only because of the color that they impart but also because of their protective activity against human diseases. Current methods to assess carotenoid content are time-consuming, expensive, and destructive. In this work, the application of rapid and nondestructive methods such as colorimeter measurements and infrared spectroscopy has been evaluated for carotenoid determination in apricot. Forty apricot genotypes covering a wide range of peel and flesh colors have been analyzed. Color measurements on the skin and flesh ( L*, a*, b*, hue, chroma, and a*/ b* ratio) as well as Fourier transform near-infrared spectroscopy (FT-NIR) on intact fruits and Fourier transform mid-infrared spectroscopy (FT-MIR) on ground flesh were correlated with the carotenoid content measured by high-performance liquid chromatography. A high variability in color values and carotenoid content was observed. Partial least squares regression analyses between beta-carotene content and provitamin A activity and color measurements showed a high fit in peel, flesh, and edible apricot portion (R(2) ranged from 0.81 to 0.91) and low prediction error. Regression equations were developed for predicting carotenoid content by using color values, which appeared as a simple, rapid, reliable, and nondestructive method. However, FT-NIR and FT-MIR models showed very low R(2) values and very high prediction errors for carotenoid content.

7 CFR 801.7 - Reference methods and tolerances for near-infrared spectroscopy (NIRS) analyzers.

Code of Federal Regulations, 2013 CFR

2013-01-01

..._of_federal_regulations/ibr_locations.html. (b) Tolerances—(1) NIRS wheat protein analyzers. The... Method 992.23. (3) NIRS corn oil, protein, and starch analyzers. The maintenance tolerances for the NIRS... methods and tolerances for near-infrared spectroscopy (NIRS) analyzers. (a) Reference methods. (1) The...

7 CFR 801.7 - Reference methods and tolerances for near-infrared spectroscopy (NIRS) analyzers.

Code of Federal Regulations, 2011 CFR

2011-01-01

..._of_federal_regulations/ibr_locations.html. (b) Tolerances—(1) NIRS wheat protein analyzers. The... Method 992.23. (3) NIRS corn oil, protein, and starch analyzers. The maintenance tolerances for the NIRS... methods and tolerances for near-infrared spectroscopy (NIRS) analyzers. (a) Reference methods. (1) The...

7 CFR 801.7 - Reference methods and tolerances for near-infrared spectroscopy (NIRS) analyzers.

Code of Federal Regulations, 2014 CFR

2014-01-01

..._of_federal_regulations/ibr_locations.html. (b) Tolerances—(1) NIRS wheat protein analyzers. The... Method 992.23. (3) NIRS corn oil, protein, and starch analyzers. The maintenance tolerances for the NIRS... methods and tolerances for near-infrared spectroscopy (NIRS) analyzers. (a) Reference methods. (1) The...

7 CFR 801.7 - Reference methods and tolerances for near-infrared spectroscopy (NIRS) analyzers.

Code of Federal Regulations, 2012 CFR

2012-01-01

..._of_federal_regulations/ibr_locations.html. (b) Tolerances—(1) NIRS wheat protein analyzers. The... Method 992.23. (3) NIRS corn oil, protein, and starch analyzers. The maintenance tolerances for the NIRS... methods and tolerances for near-infrared spectroscopy (NIRS) analyzers. (a) Reference methods. (1) The...

Near infrared fluorescence for image-guided surgery

PubMed Central

2012-01-01

Near infrared (NIR) image-guided surgery holds great promise for improved surgical outcomes. A number of NIR image-guided surgical systems are currently in preclinical and clinical development with a few approved for limited clinical use. In order to wield the full power of NIR image-guided surgery, clinically available tissue and disease specific NIR fluorophores with high signal to background ratio are necessary. In the current review, the status of NIR image-guided surgery is discussed along with the desired chemical and biological properties of NIR fluorophores. Lastly, tissue and disease targeting strategies for NIR fluorophores are reviewed. PMID:23256079

NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tak, Sungho; Jang, Kwang Eun; Jung, Jinwook; Jang, Jaeduck; Jeong, Yong; Ye, Jong Chul

2008-02-01

Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.

Novel water-soluble near-infrared cyanine dyes: synthesis, spectral properties, and use in the preparation of internally quenched fluorescent probes.

PubMed

Bouteiller, Cédric; Clavé, Guillaume; Bernardin, Aude; Chipon, Bertrand; Massonneau, Marc; Renard, Pierre-Yves; Romieu, Anthony

2007-01-01

In this paper, we describe the synthesis and the photophysical properties of two novel near-infrared (NIR) cyanine dyes (NIR5.5-2 and NIR7.0-2) which are water soluble potential substitutes of the commercially available Cy 5.5 and Cy 7.0 fluorescent labels respectively. For each one of these cyanine dyes, the synthetic strategy relies on the postsynthetic derivatization of a cyanine precursor in order to introduce the key functionalities required for bioconjugation of these NIR fluorophores. For NIR5.5-2, a reactive amino group was acylated with an original trisulfonated linker for water solubility. For NIR7.0-2, a vinylic chlorine atom was derivatized through a SRN1 reaction for the introduction of a monoreactive carboxyl group for labeling purposes. Unexpectedly, when these two fluorophores were closely associated within a peptidic architecture, mutual fluorescence quenching between NIR5.5-2 and NIR7.0-2 was observed both at 705 (NIR5.5-2) and 798 nm (NIR7.0-2). On the basis of this property, a novel internally quenched caspase-3-sensitive NIR fluorescent probe was prepared.

Novel water soluble NIR dyes: does charge matter?

NASA Astrophysics Data System (ADS)

Patonay, Gabor; Henary, Maged; Beckford, Garfield; Daube, Alison

2012-03-01

Near-Infrared (NIR) dyes are used as reporters, probes or markers in the biological and medical field. NIR dyes can be useful for investigating and characterizing biomolecular interactions or imaging which is possible because biological mammalian tissue has a low absorption window in the NIR region. Biomolecules such as proteins are known to bind to NIR dyes. Upon binding NIR dyes often exhibit spectral changes that can be used for characterizing the binding event. Serum albumins may be responsible for in vivo transport of NIR dyes. Studying this binding event can be useful when correlated to in vivo behavior of the NIR dye. The studies presented here use spectroscopic methods to investigate how NIR dyes that may be used in imaging, biological or bioanalytical applications bind to proteins, such as serum albumins. Our research group systematically synthesized several NIR dyes that have varying hydrophobicity, chromophore size and charge. During these investigations we developed novel NIR cyanine fluorophores having varying aqueous solubility and a variety of net charges. The binding properties of the carbocyanines change when charged or hydrophobic moieties are systematically varied. One of the properties we put a special emphasis on is what we call residual hydrophobicity of the NIR dye molecule which is defined as the unmasked (by the charged moieties) hydrophobicity of the molecule. Residual hydrophobicity may be responsible for binding the otherwise highly water soluble NIR dye to hydrophobic pockets of biomolecules. High residual hydrophobicity of a highly water soluble dye can be disadvantageous during biological, medical or similar applications.

Near Infrared Photoimmunotherapy Targeting EGFR Positive Triple Negative Breast Cancer: Optimizing the Conjugate-Light Regimen

PubMed Central

Nagaya, Tadanobu; Sato, Kazuhide; Harada, Toshiko; Nakamura, Yuko; Choyke, Peter L.; Kobayashi, Hisataka

2015-01-01

Aim Triple-negative breast cancer (TNBC) is considered one of the most aggressive subtypes of breast cancer. Near infrared photoimmunotherapy (NIR-PIT) is a cancer treatment that employs an antibody-photosensitizer conjugate (APC) followed by exposure of NIR light for activating selective cytotoxicity on targeted cancer cells and may have application to TNBC. In order to minimize the dose of APC while maximizing the therapeutic effects, dosing of the APC and NIR light need to be optimized. In this study, we investigate in vitro and in vivo efficacy of cetuximab (cet)-IR700 NIR-PIT on two breast cancer models MDAMB231 (TNBC, EGFR moderate) and MDAMB468 (TNBC, EGFR high) cell lines, and demonstrate a method to optimize the dosing APC and NIR light. Method After validating in vitro cell-specific cytotoxicity, NIR-PIT therapeutic effects were investigated in mouse models using cell lines derived from TNBC tumors. Tumor-bearing mice were separated into 4 groups for the following treatments: (1) no treatment (control); (2) 300 μg of cet-IR700 i.v., (APC i.v. only); (3) NIR light exposure only, NIR light was administered at 50 J/cm2 on day 1 and 100 J/cm2 on day 2 (NIR light only); (4) 300 μg of cet-IR700 i.v., NIR light was administered at 50 J/cm2 on day 1 after injection and 100 J/cm2 of light on day 2 after injection (one shot NIR-PIT). To compare different treatment regimens with a fixed dose of APC, we added the following treatments (5) 100 μg of cet-IR700 i.v., NIR light administered at 50 J/cm2 on day 1 and 50 μg of cet-IR700 i.v. immediately after NIR-PIT, then NIR light was administered at 100 J/cm2 on day 2, which were performed two times every week (“two split” NIR-PIT) and (6) 100 μg of cet-IR700 i.v., NIR light was administered at 50 J/cm2 on day 1 and 100 J/cm2 on day 2, which were performed three times per week (“three split” NIR-PIT). Result Both specific binding and NIR-PIT effects were greater with MDAMB468 than MDAMB231 cells in vitro. Tumor accumulation of cet-IR700 in MDAMB468 tumors was significantly higher (p < 0.05) than in MDAMB231 tumors in vivo. Tumor growth and survival of MDAMB231 tumor bearing mice was significantly lower in the NIR-PIT treatment group (p < 0.05). In MDAMB468 bearing mice, tumor growth and survival was significantly improved in the NIR-PIT treatment groups in all treatment regimens (one shot NIR-PIT; p < 0.05, “two split” NIR-PIT; p < 0.01, “three split” NIR-PIT; p < 0.001) compared with control groups. Conclusion NIR-PIT for TNBC was effective regardless of expression of EGFR, however, greater cell killing was shown with higher EGFR expression tumor in vitro. In all treatment regimens, NIR-PIT suppressed tumor growth, resulting in significantly prolonged survival that further improved by splitting the APC dose and using repeated light exposures. PMID:26313651

Near Infrared Photoimmunotherapy Targeting EGFR Positive Triple Negative Breast Cancer: Optimizing the Conjugate-Light Regimen.

PubMed

Nagaya, Tadanobu; Sato, Kazuhide; Harada, Toshiko; Nakamura, Yuko; Choyke, Peter L; Kobayashi, Hisataka

2015-01-01

Triple-negative breast cancer (TNBC) is considered one of the most aggressive subtypes of breast cancer. Near infrared photoimmunotherapy (NIR-PIT) is a cancer treatment that employs an antibody-photosensitizer conjugate (APC) followed by exposure of NIR light for activating selective cytotoxicity on targeted cancer cells and may have application to TNBC. In order to minimize the dose of APC while maximizing the therapeutic effects, dosing of the APC and NIR light need to be optimized. In this study, we investigate in vitro and in vivo efficacy of cetuximab (cet)-IR700 NIR-PIT on two breast cancer models MDAMB231 (TNBC, EGFR moderate) and MDAMB468 (TNBC, EGFR high) cell lines, and demonstrate a method to optimize the dosing APC and NIR light. After validating in vitro cell-specific cytotoxicity, NIR-PIT therapeutic effects were investigated in mouse models using cell lines derived from TNBC tumors. Tumor-bearing mice were separated into 4 groups for the following treatments: (1) no treatment (control); (2) 300 μg of cet-IR700 i.v., (APC i.v. only); (3) NIR light exposure only, NIR light was administered at 50 J/cm2 on day 1 and 100 J/cm2 on day 2 (NIR light only); (4) 300 μg of cet-IR700 i.v., NIR light was administered at 50 J/cm2 on day 1 after injection and 100 J/cm2 of light on day 2 after injection (one shot NIR-PIT). To compare different treatment regimens with a fixed dose of APC, we added the following treatments (5) 100 μg of cet-IR700 i.v., NIR light administered at 50 J/cm2 on day 1 and 50 μg of cet-IR700 i.v. immediately after NIR-PIT, then NIR light was administered at 100 J/cm2 on day 2, which were performed two times every week ("two split" NIR-PIT) and (6) 100 μg of cet-IR700 i.v., NIR light was administered at 50 J/cm2 on day 1 and 100 J/cm2 on day 2, which were performed three times per week ("three split" NIR-PIT). Both specific binding and NIR-PIT effects were greater with MDAMB468 than MDAMB231 cells in vitro. Tumor accumulation of cet-IR700 in MDAMB468 tumors was significantly higher (p < 0.05) than in MDAMB231 tumors in vivo. Tumor growth and survival of MDAMB231 tumor bearing mice was significantly lower in the NIR-PIT treatment group (p < 0.05). In MDAMB468 bearing mice, tumor growth and survival was significantly improved in the NIR-PIT treatment groups in all treatment regimens (one shot NIR-PIT; p < 0.05, "two split" NIR-PIT; p < 0.01, "three split" NIR-PIT; p < 0.001) compared with control groups. NIR-PIT for TNBC was effective regardless of expression of EGFR, however, greater cell killing was shown with higher EGFR expression tumor in vitro. In all treatment regimens, NIR-PIT suppressed tumor growth, resulting in significantly prolonged survival that further improved by splitting the APC dose and using repeated light exposures.

Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

NASA Astrophysics Data System (ADS)

Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

2013-05-01

Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy.

PubMed

Galvez-Sola, Luis; García-Sánchez, Francisco; Pérez-Pérez, Juan G; Gimeno, Vicente; Navarro, Josefa M; Moral, Raul; Martínez-Nicolás, Juan J; Nieves, Manuel

2015-01-01

Sufficient nutrient application is one of the most important factors in producing quality citrus fruits. One of the main guides in planning citrus fertilizer programs is by directly monitoring the plant nutrient content. However, this requires analysis of a large number of leaf samples using expensive and time-consuming chemical techniques. Over the last 5 years, it has been demonstrated that it is possible to quantitatively estimate certain nutritional elements in citrus leaves by using the spectral reflectance values, obtained by using near infrared reflectance spectroscopy (NIRS). This technique is rapid, non-destructive, cost-effective and environmentally friendly. Therefore, the estimation of macro and micronutrients in citrus leaves by this method would be beneficial in identifying the mineral status of the trees. However, to be used effectively NIRS must be evaluated against the standard techniques across different cultivars. In this study, NIRS spectral analysis, and subsequent nutrient estimations for N, K, Ca, Mg, B, Fe, Cu, Mn, and Zn concentration, were performed using 217 leaf samples from different citrus trees species. Partial least square regression and different pre-processing signal treatments were used to generate the best estimation against the current best practice techniques. It was verified a high proficiency in the estimation of N (Rv = 0.99) and Ca (Rv = 0.98) as well as achieving acceptable estimation for K, Mg, Fe, and Zn. However, no successful calibrations were obtained for the estimation of B, Cu, and Mn.

Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy

PubMed Central

Galvez-Sola, Luis; García-Sánchez, Francisco; Pérez-Pérez, Juan G.; Gimeno, Vicente; Navarro, Josefa M.; Moral, Raul; Martínez-Nicolás, Juan J.; Nieves, Manuel

2015-01-01

Sufficient nutrient application is one of the most important factors in producing quality citrus fruits. One of the main guides in planning citrus fertilizer programs is by directly monitoring the plant nutrient content. However, this requires analysis of a large number of leaf samples using expensive and time-consuming chemical techniques. Over the last 5 years, it has been demonstrated that it is possible to quantitatively estimate certain nutritional elements in citrus leaves by using the spectral reflectance values, obtained by using near infrared reflectance spectroscopy (NIRS). This technique is rapid, non-destructive, cost-effective and environmentally friendly. Therefore, the estimation of macro and micronutrients in citrus leaves by this method would be beneficial in identifying the mineral status of the trees. However, to be used effectively NIRS must be evaluated against the standard techniques across different cultivars. In this study, NIRS spectral analysis, and subsequent nutrient estimations for N, K, Ca, Mg, B, Fe, Cu, Mn, and Zn concentration, were performed using 217 leaf samples from different citrus trees species. Partial least square regression and different pre-processing signal treatments were used to generate the best estimation against the current best practice techniques. It was verified a high proficiency in the estimation of N (Rv = 0.99) and Ca (Rv = 0.98) as well as achieving acceptable estimation for K, Mg, Fe, and Zn. However, no successful calibrations were obtained for the estimation of B, Cu, and Mn. PMID:26257767

Rapid discrimination and determination of antibiotics drugs in plastic syringes using near infrared spectroscopy with chemometric analysis: Application to amoxicillin and penicillin.

PubMed

Lê, Laetitia Minh Mai; Eveleigh, Luc; Hasnaoui, Ikram; Prognon, Patrice; Baillet-Guffroy, Arlette; Caudron, Eric

2017-05-10

The aim of this study was to investigate near infrared spectroscopy (NIRS) combined to chemometric analysis to discriminate and quantify three antibiotics by direct measurement in plastic syringes.Solutions of benzylpenicillin (PENI), amoxicillin (AMOX) and amoxicillin/clavulanic acid (AMOX/CLAV) were analyzed at therapeutic concentrations in glass vials and plastic syringes with NIR spectrometer by direct measurement. Chemometric analysis using partial least squares regression and discriminative analysis was conducted to develop qualitative and quantitative calibration models. Discrimination of the three antibiotics was optimal for concentrated solutions with 100% of accuracy. For quantitative analysis, the three antibiotics furnished a linear response (R²>0.9994) for concentrations ranging from 0.05 to 0.2 g/mL for AMOX, 0.1 to 1.0 MUI/mL for PENI and 0.005 to 0.05 g/mL for AMOX/CLAV with excellent repeatability (maximum 1.3%) and intermediate precision (maximum of 3.2%). Based on proposed models, 94.4% of analyzed AMOX syringes, 80.0% of AMOX/CLAV syringes and 85.7% of PENI syringes were compliant with a relative error including the limit of ± 15%.NIRS as rapid, non-invasive and non-destructive analytical method represents a potentially powerful tool to further develop for securing the drug administration circuit of healthcare institutions to ensure that patients receive the correct product at the right dose. Copyright © 2017 Elsevier B.V. All rights reserved.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

NASA Astrophysics Data System (ADS)

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-01

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

Semi-quantitative prediction of a multiple API solid dosage form with a combination of vibrational spectroscopy methods.

PubMed

Hertrampf, A; Sousa, R M; Menezes, J C; Herdling, T

2016-05-30

Quality control (QC) in the pharmaceutical industry is a key activity in ensuring medicines have the required quality, safety and efficacy for their intended use. QC departments at pharmaceutical companies are responsible for all release testing of final products but also all incoming raw materials. Near-infrared spectroscopy (NIRS) and Raman spectroscopy are important techniques for fast and accurate identification and qualification of pharmaceutical samples. Tablets containing two different active pharmaceutical ingredients (API) [bisoprolol, hydrochlorothiazide] in different commercially available dosages were analysed using Raman- and NIR Spectroscopy. The goal was to define multivariate models based on each vibrational spectroscopy to discriminate between different dosages (identity) and predict their dosage (semi-quantitative). Furthermore the combination of spectroscopic techniques was investigated. Therefore, two different multiblock techniques based on PLS have been applied: multiblock PLS (MB-PLS) and sequential-orthogonalised PLS (SO-PLS). NIRS showed better results compared to Raman spectroscopy for both identification and quantitation. The multiblock techniques investigated showed that each spectroscopy contains information not present or captured with the other spectroscopic technique, thus demonstrating that there is a potential benefit in their combined use for both identification and quantitation purposes. Copyright © 2016 Elsevier B.V. All rights reserved.

[Distribution Characteristics of Nitrifiers and Denitrifiers in the River Sediments of Tongling City].

PubMed

Cheng, Jian-hua; Dou, Zhi-yong; Sun, Qing-ye

2016-04-15

Rivers in mining areas were influenced by contaminants such as nitrogen, phosphorus and organic matter due to domestic and agricultural wastewater discharge in addition to pollutants caused by mining activities. In this study, surface sediment samples of rivers in Tongling city were collected to address the effect of season and pollution type on the abundance of nitrifiers and denitrifiers using quantitative polymerase chain reaction (QPCR) technique targeting at the ammonia monooxygenase (amoA) and nitrite reductase (nir) genes. The results showed that the average ahundance of ammonia oxidizing archaea (AGA) (ranging from 1.74 x 10⁵ to 1.45 x 10⁸ copies · g⁻¹) was 4.39 times that of ammonia oxidizing hacteria (AGH) (ranging from 1.39 x 10⁵ to 3.39 x 10⁷ copies · g⁻¹); and the average abundance of nirK gene (ranging from 4.45 x 10⁶ to 1.51 x 10⁸ copies · g) was almost a thirtieth part of nirS gene (ranging from 1.69 x 10⁷ to 8.55 x 10⁹ copies · g⁻¹). The abundance of AOA was higher in spring and autumn, and lower in summer and winter. And sediment AOB abundance was higher in spring and winter than in summer and autumn. Meanwhile, the abundance of nir genes was in the order of spring (nirS )/autumn (nirK) > summer > winter > autumn (nirS )/spring (nirK). Moreover, the abundance of bacterial and archaeal arnoA and nirS genes in sediments influenced by mine pollution was generally higher than that in sediments influenced by agricultural non-point pollution, whereas the abundance of nirK gene showed an opposite trend.

Relative Contribution of nirK- and nirS- Bacterial Denitrifiers as Well as Fungal Denitrifiers to Nitrous Oxide Production from Dairy Manure Compost.

PubMed

Maeda, Koki; Toyoda, Sakae; Philippot, Laurent; Hattori, Shohei; Nakajima, Keiichi; Ito, Yumi; Yoshida, Naohiro

2017-12-19

The relative contribution of fungi, bacteria, and nirS and nirK denirifiers to nitrous oxide (N 2 O) emission with unknown isotopic signature from dairy manure compost was examined by selective inhibition techniques. Chloramphenicol (CHP), cycloheximide (CYH), and diethyl dithiocarbamate (DDTC) were used to suppress the activity of bacteria, fungi, and nirK-possessing denitrifiers, respectively. Produced N 2 O were surveyed to isotopocule analysis, and its 15 N site preference (SP) and δ 18 O values were compared. Bacteria, fungi, nirS, and nirK gene abundances were compared by qPCR. The results showed that N 2 O production was strongly inhibited by CHP addition in surface pile samples (82.2%) as well as in nitrite-amended core samples (98.4%), while CYH addition did not inhibit the N 2 O production. N 2 O with unknown isotopic signature (SP = 15.3-16.2‰), accompanied by δ 18 O (19.0-26.8‰) values which were close to bacterial denitrification, was also suppressed by CHP and DDTC addition (95.3%) indicating that nirK denitrifiers were responsible for this N 2 O production despite being less abundant than nirS denitrifiers. Altogether, our results suggest that bacteria are important for N 2 O production with different SP values both from compost surface and pile core. However, further work is required to decipher whether N 2 O with unknown isotopic signature is mostly due to nirK denitrifiers that are taxonomically different from the SP-characterized strains and therefore have different SP values rather than also being interwoven with the contribution of the NO-detoxifying pathway and/or of co-denitrification.

Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 2. Hydrated gluten.

PubMed

Bruun, Susanne Wrang; Søndergaard, Ib; Jacobsen, Susanne

2007-09-05

Hydrated gluten, treated with various salts, was analyzed by near-infrared (NIR) spectroscopy to assess the ability of this method to reveal protein structure and interaction changes in perturbed food systems. The spectra were pretreated with second-derivative transformation and extended multiplicative signal correction for improving the band resolution and removing physical and quantitative spectral variations. Principal component analysis of the preprocessed spectra showed spectral effects that depended on salt type and concentration. Although both gluten texture and the NIR spectra were little influenced by treatment with salt solutions of low concentrations (0.1-0.2 M), they were significantly and diversely affected by treatment with 1.0 M salt solutions. Compared to hydration in water, hydration in 1.0 M sulfate salts caused spectral effects similar to a drying-out effect, which could be explained by salting-out.

Compressed single pixel imaging in the spatial frequency domain

PubMed Central

Torabzadeh, Mohammad; Park, Il-Yong; Bartels, Randy A.; Durkin, Anthony J.; Tromberg, Bruce J.

2017-01-01

Abstract. We have developed compressed sensing single pixel spatial frequency domain imaging (cs-SFDI) to characterize tissue optical properties over a wide field of view (35  mm×35  mm) using multiple near-infrared (NIR) wavelengths simultaneously. Our approach takes advantage of the relatively sparse spatial content required for mapping tissue optical properties at length scales comparable to the transport scattering length in tissue (ltr∼1  mm) and the high bandwidth available for spectral encoding using a single-element detector. cs-SFDI recovered absorption (μa) and reduced scattering (μs′) coefficients of a tissue phantom at three NIR wavelengths (660, 850, and 940 nm) within 7.6% and 4.3% of absolute values determined using camera-based SFDI, respectively. These results suggest that cs-SFDI can be developed as a multi- and hyperspectral imaging modality for quantitative, dynamic imaging of tissue optical and physiological properties. PMID:28300272

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I + II + III supernatant in human albumin separation

NASA Astrophysics Data System (ADS)

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-01

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I + II + III (FI + II + III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (Rp2), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501 g/L, 0.465 g/L and 5.57 for TP, and 0.969, 0.530 g/L, 0.341 g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI + II + III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS.

Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy.

PubMed

Magalhães, Luís M; Machado, Sandia; Segundo, Marcela A; Lopes, João A; Páscoa, Ricardo N M J

2016-01-15

Spent coffee grounds (SCGs) are a great source of bioactive compounds with interest to pharmaceutical and cosmetic industries. Phenolics and methylxanthines are the main health related compounds present in SCG samples. Content estimation of these compounds in SCGs is of upmost importance in what concerns their profitable use by waste recovery industries. In the present work, near-infrared spectroscopy (NIRS) was proposed as a rapid and non-destructive technique to assess the content of three main phenolics (caffeic acid, (+)-catechin and chlorogenic acid) and three methylxanthines (caffeine, theobromine and theophylline) in SCG samples obtained from different coffee brands and diverse coffee machines. The content of these compounds was determined for 61 SCG samples by HPLC coupled with diode-array detection. Partial least squares (PLS) regression based models were calibrated to correlate diffuse reflectance NIR spectra against the reference data for the six parameters obtained by HPLC. Spectral wavelength selection and number of latent variables were optimized by minimizing the cross-validation error. PLS models showed good linearity with a coefficient of determination for the prediction set (Rp(2)) of 0.95, 0.92, 0.88, 071 and 0.84 for caffeine, caffeic acid, (+)-catechin, chlorogenic acid and theophylline, respectively. The range error ratio (RER) was higher for caffeine (17.8) when compared to other compounds (12.0, 10.1, 7.6 and 9.2, respectively for caffeic acid, (+)-catechin, chlorogenic acid and theophylline). Moreover, the content of caffeine could be used to predict the antioxidant properties of SCG samples (R=0.808, n=61), despite not presenting this property itself. The results obtained confirmed that NIRS is a suitable technique to screen SCG samples unveiling those with high content of bioactive compounds, which are interesting for subsequent extraction procedures. Copyright © 2015 Elsevier B.V. All rights reserved.

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I+II+III supernatant in human albumin separation.

PubMed

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-15

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I+II+III (FI+II+III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (R p 2 ), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501g/L, 0.465g/L and 5.57 for TP, and 0.969, 0.530g/L, 0.341g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI+II+III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS. Copyright © 2016 Elsevier B.V. All rights reserved.

Strategies for multivariate modeling of moisture content in freeze-dried mannitol-containing products by near-infrared spectroscopy.

PubMed

Yip, Wai Lam; Gausemel, Ingvil; Sande, Sverre Arne; Dyrstad, Knut

2012-11-01

Accurate determination of residual moisture content of a freeze-dried (FD) pharmaceutical product is critical for prediction of its quality. Near-infrared (NIR) spectroscopy is a fast and non-invasive method routinely used for quantification of moisture. However, several physicochemical properties of the FD product may interfere with absorption bands related to the water content. A commonly used stabilizer and bulking agent in FD known for variation in physicochemical properties, is mannitol. To minimize this physicochemical interference, different approaches for multivariate correlation between NIR spectra of a FD product containing mannitol and the corresponding moisture content measured by Karl Fischer (KF) titration have been investigated. A novel method, MIPCR (Main and Interactions of Individual Principal Components Regression), was found to have significantly increased predictive ability of moisture content compared to a traditional PLS approach. The philosophy behind the MIPCR is that the interference from a variety of particle and morphology attributes has interactive effects on the water related absorption bands. The transformation of original wavelength variables to orthogonal scores gives a new set of variables (scores) without covariance structure, and the possibility of inclusion of interaction terms in the further modeling. The residual moisture content of the FD product investigated is in the range from 0.7% to 2.6%. The mean errors of cross validated prediction of models developed in the investigated NIR regions were reduced from a range of 24.1-27.6% for traditional PLS method to 15.7-20.5% for the MIPCR method. Improved model quality by application of MIPCR, without the need for inclusion of a large number of calibration samples, might increase the use of NIR in early phase product development, where availability of calibration samples is often limited. Copyright © 2012 Elsevier B.V. All rights reserved.

Near-infrared interactance (NIR): a new non-invasive technique to estimate subcutaneous body fat in newborns.

PubMed

Demarini, S; Donnelly, M M

1994-01-01

Body fat (BF) is rarely determined routinely in infants due to the lack of a simple measuring device. A portable NIR instrument, successfully applied in adults, takes 5 seconds for a measurement and involves no skin manipulation. We designed this study 1) to compare BF estimates by NIR to skinfold thickness (ST) and 2) to assess the relationship of NIR and ST values with standard measures reflecting BF, such as Weight/Length Ratio, Body Mass Index and Ponderal Index. We studied BF in 40 healthy term infants within 12 hours of birth by NIR and ST at 3 standard sites: triceps (TRI), subscapular (SUB) and abdominal (ABD). RESULTS. Significant correlations were found between NIR and ST (R=0.70, 0.58 and 0.64 for SUB, TRI and ABD, respectively); between the sums of the 3 measurements (R= 0.69), and between birthweight and ST (R=0.57) or NIR (0.51), and between Weight/Length Ratio and ST (R=0.55) or NIR (R=0.51). We conclude that NIR measurements correlate well with skinfold measurements and NIR can be measured faster than skinfolds (5 vs 60 seconds). We speculate that NIR could be cost-effective for routine clinical measure of body fat and growth in infants.

UV/vis and NIR light-responsive spiropyran self-assembled monolayers.

PubMed

Ivashenko, Oleksii; van Herpt, Jochem T; Feringa, Ben L; Rudolf, Petra; Browne, Wesley R

2013-04-02

Self-assembled monolayers of a 6-nitro BIPS spiropyran (SP) modified with a disulfide-terminated aliphatic chain were prepared on polycrystalline gold surfaces and characterized by UV/vis absorption, surface-enhanced Raman scattering (SERS), and X-ray photoelectron spectroscopies (XPS). The SAMs obtained are composed of the ring-closed form (i.e., spiropyran) only. Irradiation with UV light results in conversion of the monolayer to the merocyanine form (MC), manifested in the appearance of an N(+) contribution in the N 1s region of the XPS spectrum of the SAMs, the characteristic absorption band of the MC form in the visible region at 555 nm, and the C-O stretching band in the SERS spectrum. Recovery of the initial state of the monolayer was observed both thermally and after irradiation with visible light. Several switching cycles were performed and monitored by SERS spectroscopy, demonstrating the stability of the SAMs during repeated switching between SP and MC states. A key finding in the present study is that ring-opening of the surface-immobilized spiropyrans can be induced by irradiation with continuous wave NIR (785 nm) light as well as by irradiation with UV light. We demonstrate that ring-opening by irradiation at 785 nm proceeds by a two-photon absorption pathway both in the SAMs and in the solid state. Hence, spiropyran SAMs on gold can undergo reversible photochemical switching from the SP to the MC form with both UV and NIR and the reverse reaction induced by irradiation with visible light or heating. Furthermore, the observation of NIR-induced switching with a continuous wave source holds important consequences in the study of photochromic switches on surfaces using SERS and emphasizes the importance of the use of multiple complementary techniques in characterizing photoresponsive SAMs.

EXTINCTION AND POLYCYCLIC AROMATIC HYDROCARBON INTENSITY VARIATIONS ACROSS THE H II REGION IRAS 12063-6259

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stock, D. J.; Peeters, E.; Otaguro, J. N.

The spatial variations in polycyclic aromatic hydrocarbon (PAH) band intensities are normally attributed to the physical conditions of the emitting PAHs, however in recent years it has been suggested that such variations are caused mainly by extinction. To resolve this question, we have obtained near-infrared (NIR), mid-infrared (MIR), and radio observations of the compact H II region IRAS 12063-6259. We use these data to construct multiple independent extinction maps and also to measure the main PAH features (6.2, 7.7, 8.6, and 11.2 {mu}m) in the MIR. Three extinction maps are derived: the first using the NIR hydrogen lines and casemore » B recombination theory; the second combining the NIR data with radio data; and the third making use of the Spitzer/IRS MIR observations to measure the 9.8 {mu}m silicate absorption feature using the Spoon method and PAHFIT (as the depth of this feature can be related to overall extinction). The silicate absorption over the bright, southern component of IRAS 12063-6259 is almost absent while the other methods find significant extinction. While such breakdowns of the relationship between the NIR extinction and the 9.8 {mu}m absorption have been observed in molecular clouds, they have never been observed for H II regions. We then compare the PAH intensity variations in the Spitzer/IRS data after dereddening to those found in the original data. It was found that in most cases, the PAH band intensity variations persist even after dereddening, implying that extinction is not the main cause of the PAH band intensity variations.« less

Measuring temperature induced phase change kinetics in subcutaneous fatty tissues using near infrared spectroscopy, magnetic resonance imaging and optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Sajjadi, Amir Y.; Carp, Stefan A.; Manstein, Dieter

2017-02-01

Monitoring phase transition in adipose tissue and formation of lipid crystals is important in Cryo-procedures such as cryosurgery or Selective Cryolipolysis (SC). In this work, we exploited a Near-Infrared Spectroscopy (NIRS) method to monitor the onset of fat freezing/melting. Concurrent measurements using frequency domain NIRS and MR Spectroscopy during cooling/heating were performed on an in vitro porcine skin sample with a thick subcutaneous fat layer in a human MR scanner. The NIRS probe was placed on the skin measuring the average optical scattering of the fatty layer. Two fiber optic temperature probes were inserted in the area of the MRS and NIRS measurements. To further investigate the microscopic features of the phase-transition, an identical cooling/heating procedure was replicated on the same fat tissue while being imaged by Optical Coherence Tomography. The temperature relationships of optical scattering, MRS peak characteristics and OCT reflection intensity were analyzed to find signatures related to the onset of phase transition. The optical scattering in the fatty tissues decreases during the heating and increases by cooling. However, there is an inflexion in the rate of change of the scattering while the phase transition happens in the fatty layer. The methylene fat peaks on the MR Spectrum are also shown to be broadened during the cooling. OCT intensity displays a sharp increase at the transition temperature. The results from multiple samples show two transition points around 5-10 ˚C (cooling) and 15-20 ˚C (heating) through all three methods, demonstrating that adipose tissue phase change can be monitored non-invasively.

Synthesis and characterization of a glycine-modified heptamethine indocyanine dye for in vivo cancer-targeted near-infrared imaging

PubMed Central

Liu, Tao; Luo, Shenglin; Wang, Yang; Tan, Xu; Qi, Qingrong; Shi, Chunmeng

2014-01-01

Near-infrared (NIR) fluorescent sensors have emerged as promising molecular tools for cancer imaging and detection in living systems. However, cancer NIR fluorescent sensors are very challenging to develop because they are required to exhibit good specificity and low toxicity as an eligible contrast agent. Here, we describe the synthesis of a new heptamethine indocyanine dye (NIR-27) modified with a glycine at the end of each N-alkyl side chain, and its biological characterization for in vivo cancer-targeted NIR imaging. In addition to its high specificity, NIR-27 also shows lower cytotoxicity than indocyanine green, a nonspecific NIR probe widely used in clinic. These characteristics suggest that NIR-27 is a promising prospect as a new NIR fluorescent sensor for sensitive cancer detection. PMID:25246770

Visible and Near-Infrared Spectroscopy Analysis of a Polycyclic Aromatic Hydrocarbon in Soils

PubMed Central

Okparanma, Reuben N.; Mouazen, Abdul M.

2013-01-01

Visible and near-infrared (VisNIR) spectroscopy is becoming recognised by soil scientists as a rapid and cost-effective measurement method for hydrocarbons in petroleum-contaminated soils. This study investigated the potential application of VisNIR spectroscopy (350–2500 nm) for the prediction of phenanthrene, a polycyclic aromatic hydrocarbon (PAH), in soils. A total of 150 diesel-contaminated soil samples were used in the investigation. Partial least-squares (PLS) regression analysis with full cross-validation was used to develop models to predict the PAH compound. Results showed that the PAH compound was predicted well with residual prediction deviation of 2.0–2.32, root-mean-square error of prediction of 0.21–0.25 mg kg−1, and coefficient of determination (r 2) of 0.75–0.83. The mechanism of prediction was attributed to covariation of the PAH with clay and soil organic carbon. Overall, the results demonstrated that the methodology may be used for predicting phenanthrene in soils utilizing the interrelationship between clay and soil organic carbon. PMID:24453798

Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast

PubMed Central

Kandpal, Lalit Mohan; Lee, Hoonsoo; Kim, Moon S.; Mo, Changyeun; Cho, Byoung-Kwan

2013-01-01

Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum R2p of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an R2p of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat. PMID:24084119

Rapid Measurement of Soil Carbon in Rice Paddy Field of Lombok Island Indonesia Using Near Infrared Technology

NASA Astrophysics Data System (ADS)

Kusumo, B. H.; Sukartono, S.; Bustan, B.

2018-02-01

Measuring soil organic carbon (C) using conventional analysis is tedious procedure, time consuming and expensive. It is needed simple procedure which is cheap and saves time. Near infrared technology offers rapid procedure as it works based on the soil spectral reflectance and without any chemicals. The aim of this research is to test whether this technology able to rapidly measure soil organic C in rice paddy field. Soil samples were collected from rice paddy field of Lombok Island Indonesia, and the coordinates of the samples were recorded. Parts of the samples were analysed using conventional analysis (Walkley and Black) and some other parts were scanned using near infrared spectroscopy (NIRS) for soil spectral collection. Partial Least Square Regression (PLSR) Models were developed using data of soil C analysed using conventional analysis and data from soil spectral reflectance. The models were moderately successful to measure soil C in rice paddy field of Lombok Island. This shows that the NIR technology can be further used to monitor the C change in rice paddy soil.

Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy.

PubMed

Mamani-Linares, L W; Gallo, C; Alomar, D

2012-02-01

Visible and near infrared reflectance spectroscopy (VIS-NIRS) was used to discriminate meat and meat juices from three livestock species. In a first trial, samples of Longissimus lumborum muscle, corresponding to beef (31) llamas (21) and horses (27), were homogenised and their spectra collected in reflectance (NIRSystems 6500 scanning monochromator, in the range of 400-2500 nm). In the second trial, samples of meat juice (same muscle) from the same species (20 beef, 19 llama and 19 horse) were scanned in folded transmission (transflectance). Discriminating models (PLS regression) were developed against "dummy" variables, testing different mathematical treatments of the spectra. Best models indentified the species of almost all samples by their meat (reflectance) or meat juice (transflectance) spectra. A few (three of beef and one of llama, for meat samples; one of beef and one of horse, for juice samples) were classified as uncertain. It is concluded that NIRS is an effective tool to recognise meat and meat juice from beef, llama and horses. Copyright © 2011 Elsevier Ltd. All rights reserved.

Qualitative and quantitative detection of honey adulterated with high-fructose corn syrup and maltose syrup by using near-infrared spectroscopy.

PubMed

Li, Shuifang; Zhang, Xin; Shan, Yang; Su, Donglin; Ma, Qiang; Wen, Ruizhi; Li, Jiaojuan

2017-03-01

Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (R p 2 ) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively. Copyright © 2016. Published by Elsevier Ltd.

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

Near-infrared spectrometry allows fast and extensive predictions of functional traits from dry leaves and branches.

PubMed

Costa, Flávia R C; Lang, Carla; Almeida, Danilo R A; Castilho, Carolina V; Poorter, Lourens

2018-05-16

The linking of individual functional traits to ecosystem processes is the basis for making generalizations in ecology, but the measurement of individual values is laborious and time consuming, preventing large-scale trait mapping. Also, in hyper-diverse systems, errors occur because identification is difficult, and species level values ignore intra-specific variation. To allow extensive trait mapping at the individual level, we evaluated the potential of Fourrier-Transformed Near Infra-Red Spectrometry (FT-NIR) to adequately describe 14 traits that are key for plant carbon, water, and nutrient balance. FT-NIR absorption spectra (1,000-2,500 nm) were obtained from dry leaves and branches of 1,324 trees of 432 species from a hyper-diverse Amazonian forest. FT-NIR spectra were related to measured traits for the same plants using partial least squares regressions. A further 80 plants were collected from a different site to evaluate model applicability across sites. Relative prediction error (RMSE rel ) was calculated as the percentage of the trait value range represented by the final model RMSE. The key traits used in most functional trait studies; specific leaf area, leaf dry matter content, wood density and wood dry matter content can be well predicted by the model (R 2  = 0.69-0.78, RMSE rel  = 9-11%), while leaf density, xylem proportion, bark density and bark dry matter content can be moderately well predicted (R 2  = 0.53-0.61, RMSE rel  = 14-17%). Community-weighted means of all traits were well estimated with NIR, as did the shape of the frequency distribution of the community values for the above key traits. The model developed at the core site provided good estimations of the key traits of a different site. An evaluation of the sampling effort indicated that 400 or less individuals may be sufficient for establishing a good local model. We conclude that FT-NIR is an easy, fast and cheap method for the large-scale estimation of individual plant traits that was previously impossible. The ability to use dry intact leaves and branches unlocks the potential for using herbarium material to estimate functional traits; thus advancing our knowledge of community and ecosystem functioning from local to global scales. © 2018 by the Ecological Society of America.

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

PubMed Central

Xu, Shengxiang; Shi, Xuezheng; Wang, Meiyan; Zhao, Yongcun

2016-01-01

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg−1; R2 (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg−1; R2 = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy. PMID:26974821

Folate/NIR 797-Conjugated Albumin Magnetic Nanospheres: Synthesis, Characterisation, and In Vitro and In Vivo Targeting Evaluation

PubMed Central

Liu, Dongfang; Liu, Peidang; Zhang, Dongsheng

2014-01-01

A practical and effective strategy for synthesis of Folate-NIR 797-conjugated Magnetic Albumin Nanospheres (FA-NIR 797-MAN) was developed. For this strategy, Magnetic Albumin Nanospheres (MAN), composed of superparamagnetic iron oxide nanoparticles (SPIONs) and bovine serum albumin (BSA), were covalently conjugated with folic acid (FA) ligands to enhance the targeting capability of the particles to folate receptor (FR) over-expressing tumours. Subsequently, a near-infrared (NIR) fluorescent dye NIR 797 was conjugated with FA-conjugated MAN for in vivo fluorescence imaging. The FA-NIR 797-MAN exhibited low toxicity to a human nasopharyngeal epidermal carcinoma cell line (KB cells). Additionally, in vitro and in vivo evaluation of the dynamic behaviour and targeting ability of FA-NIR 797-MAN to KB tumours validated the highly selective affinity of FA-NIR 797-MAN for FR-positive tumours. In summary, the FA-NIR 797-MAN prepared here exhibited great potential for tumour imaging, since the near-infrared fluorescence contrast agents target cells via FR-mediated endocytosis. The high fluorescence intensity together with the targeting effect makes FA-NIR 797-MAN a promising candidate for imaging, monitoring, and early diagnosis of cancer at the molecular and cellular levels. PMID:25188308

A portable near-infrared fluorescence image overlay device for surgical navigation (Conference Presentation)

NASA Astrophysics Data System (ADS)

McWade, Melanie A.

2016-03-01

A rise in the use of near-infrared (NIR) fluorescent dyes or intrinsic fluorescent markers for surgical guidance and tissue diagnosis has triggered the development of NIR fluorescence imaging systems. Because NIR wavelengths are invisible to the naked eye, instrumentation must allow surgeons to visualize areas of high fluorescence. Current NIR fluorescence imaging systems have limited ease-of-use because they display fluorescent information on remote display monitors that require surgeons to divert attention away from the patient to identify the location of tissue fluorescence. Furthermore, some systems lack simultaneous visible light imaging which provides valuable spatial context to fluorescence images. We have developed a novel, portable NIR fluorescence imaging approach for intraoperative surgical guidance that provides information for surgical navigation within the clinician's line of sight. The system utilizes a NIR CMOS detector to collect excited NIR fluorescence from the surgical field. Tissues with NIR fluorescence are overlaid with visible light to provide information on tissue margins directly on the surgical field. In vitro studies have shown this versatile imaging system can be applied to applications with both extrinsic NIR contrast agents such as indocyanine green and weaker sources of biological fluorescence such as parathyroid gland tissue. This non-invasive, portable NIR fluorescence imaging system overlays an image directly on tissue, potentially allowing surgical decisions to be made quicker and with greater ease-of-use than current NIR fluorescence imaging systems.

nirS-Encoding denitrifier community composition, distribution, and abundance along the coastal wetlands of China.

PubMed

Gao, Juan; Hou, Lijun; Zheng, Yanling; Liu, Min; Yin, Guoyu; Li, Xiaofei; Lin, Xianbiao; Yu, Chendi; Wang, Rong; Jiang, Xiaofen; Sun, Xiuru

2016-10-01

For the past few decades, human activities have intensively increased the reactive nitrogen enrichment in China's coastal wetlands. Although denitrification is a critical pathway of nitrogen removal, the understanding of denitrifier community dynamics driving denitrification remains limited in the coastal wetlands. In this study, the diversity, abundance, and community composition of nirS-encoding denitrifiers were analyzed to reveal their variations in China's coastal wetlands. Diverse nirS sequences were obtained and more than 98 % of them shared considerable phylogenetic similarity with sequences obtained from aquatic systems (marine/estuarine/coastal sediments and hypoxia sea water). Clone library analysis revealed that the distribution and composition of nirS-harboring denitrifiers had a significant latitudinal differentiation, but without a seasonal shift. Canonical correspondence analysis showed that the community structure of nirS-encoding denitrifiers was significantly related to temperature and ammonium concentration. The nirS gene abundance ranged from 4.3 × 10(5) to 3.7 × 10(7) copies g(-1) dry sediment, with a significant spatial heterogeneity. Among all detected environmental factors, temperature was a key factor affecting not only the nirS gene abundance but also the community structure of nirS-type denitrifiers. Overall, this study significantly enhances our understanding of the structure and dynamics of denitrifying communities in the coastal wetlands of China.

NirN Protein from Pseudomonas aeruginosa is a Novel Electron-bifurcating Dehydrogenase Catalyzing the Last Step of Heme d1 Biosynthesis*

PubMed Central

Adamczack, Julia; Hoffmann, Martin; Papke, Ulrich; Haufschildt, Kristin; Nicke, Tristan; Bröring, Martin; Sezer, Murat; Weimar, Rebecca; Kuhlmann, Uwe; Hildebrandt, Peter; Layer, Gunhild

2014-01-01

Heme d1 plays an important role in denitrification as the essential cofactor of the cytochrome cd1 nitrite reductase NirS. At present, the biosynthesis of heme d1 is only partially understood. The last step of heme d1 biosynthesis requires a so far unknown enzyme that catalyzes the introduction of a double bond into one of the propionate side chains of the tetrapyrrole yielding the corresponding acrylate side chain. In this study, we show that a Pseudomonas aeruginosa PAO1 strain lacking the NirN protein does not produce heme d1. Instead, the NirS purified from this strain contains the heme d1 precursor dihydro-heme d1 lacking the acrylic double bond, as indicated by UV-visible absorption spectroscopy and resonance Raman spectroscopy. Furthermore, the dihydro-heme d1 was extracted from purified NirS and characterized by UV-visible absorption spectroscopy and finally identified by high-resolution electrospray ionization mass spectrometry. Moreover, we show that purified NirN from P. aeruginosa binds the dihydro-heme d1 and catalyzes the introduction of the acrylic double bond in vitro. Strikingly, NirN uses an electron bifurcation mechanism for the two-electron oxidation reaction, during which one electron ends up on its heme c cofactor and the second electron reduces the substrate/product from the ferric to the ferrous state. On the basis of our results, we propose novel roles for the proteins NirN and NirF during the biosynthesis of heme d1. PMID:25204657

Molecular characterization of diazotrophic and denitrifying bacteria associated with mangrove roots.

PubMed

Flores-Mireles, Ana L; Winans, Stephen C; Holguin, Gina

2007-11-01

An analysis of the molecular diversity of N(2) fixers and denitrifiers associated with mangrove roots was performed using terminal restriction length polymorphism (T-RFLP) of nifH (N(2) fixation) and nirS and nirK (denitrification), and the compositions and structures of these communities among three sites were compared. The number of operational taxonomic units (OTU) for nifH was higher than that for nirK or nirS at all three sites. Site 3, which had the highest organic matter and sand content in the rhizosphere sediment, as well as the lowest pore water oxygen concentration, had the highest nifH diversity. Principal component analysis of biogeochemical parameters identified soil texture, organic matter content, pore water oxygen concentration, and salinity as the main variables that differentiated the sites. Nonmetric multidimensional scaling (MDS) analyses of the T-RFLP data using the Bray-Curtis coefficient, group analyses, and pairwise comparisons between the sites clearly separated the OTU of site 3 from those of sites 1 and 2. For nirS, there were statistically significant differences in the composition of OTU among the sites, but the variability was less than for nifH. OTU defined on the basis of nirK were highly similar, and the three sites were not clearly separated on the basis of these sequences. The phylogenetic trees of nifH, nirK, and nirS showed that most of the cloned sequences were more similar to sequences from the rhizosphere isolates than to those from known strains or from other environments.

Molecular Characterization of Diazotrophic and Denitrifying Bacteria Associated with Mangrove Roots▿

PubMed Central

Flores-Mireles, Ana L.; Winans, Stephen C.; Holguin, Gina

2007-01-01

An analysis of the molecular diversity of N2 fixers and denitrifiers associated with mangrove roots was performed using terminal restriction length polymorphism (T-RFLP) of nifH (N2 fixation) and nirS and nirK (denitrification), and the compositions and structures of these communities among three sites were compared. The number of operational taxonomic units (OTU) for nifH was higher than that for nirK or nirS at all three sites. Site 3, which had the highest organic matter and sand content in the rhizosphere sediment, as well as the lowest pore water oxygen concentration, had the highest nifH diversity. Principal component analysis of biogeochemical parameters identified soil texture, organic matter content, pore water oxygen concentration, and salinity as the main variables that differentiated the sites. Nonmetric multidimensional scaling (MDS) analyses of the T-RFLP data using the Bray-Curtis coefficient, group analyses, and pairwise comparisons between the sites clearly separated the OTU of site 3 from those of sites 1 and 2. For nirS, there were statistically significant differences in the composition of OTU among the sites, but the variability was less than for nifH. OTU defined on the basis of nirK were highly similar, and the three sites were not clearly separated on the basis of these sequences. The phylogenetic trees of nifH, nirK, and nirS showed that most of the cloned sequences were more similar to sequences from the rhizosphere isolates than to those from known strains or from other environments. PMID:17827324

Redshifted Cherenkov Radiation for in vivo Imaging: Coupling Cherenkov Radiation Energy Transfer to multiple Förster Resonance Energy Transfers

NASA Astrophysics Data System (ADS)

Bernhard, Yann; Collin, Bertrand; Decréau, Richard A.

2017-03-01

Cherenkov Radiation (CR), this blue glow seen in nuclear reactors, is an optical light originating from energetic β-emitter radionuclides. CR emitter 90Y triggers a cascade of energy transfers in the presence of a mixed population of fluorophores (which each other match their respective absorption and emission maxima): Cherenkov Radiation Energy Transfer (CRET) first, followed by multiple Förster Resonance Energy transfers (FRET): CRET ratios were calculated to give a rough estimate of the transfer efficiency. While CR is blue-weighted (300-500 nm), such cascades of Energy Transfers allowed to get a) fluorescence emission up to 710 nm, which is beyond the main CR window and within the near-infrared (NIR) window where biological tissues are most transparent, b) to amplify this emission and boost the radiance on that window: EMT6-tumor bearing mice injected with both a radionuclide and a mixture of fluorophores having a good spectral overlap, were shown to have nearly a two-fold radiance boost (measured on a NIR window centered on the emission wavelength of the last fluorophore in the Energy Transfer cascade) compared to a tumor injected with the radionuclide only. Some CR embarked light source could be converted into a near-infrared radiation, where biological tissues are most transparent.

Redshifted Cherenkov Radiation for in vivo Imaging: Coupling Cherenkov Radiation Energy Transfer to multiple Förster Resonance Energy Transfers.

PubMed

Bernhard, Yann; Collin, Bertrand; Decréau, Richard A

2017-03-24

Cherenkov Radiation (CR), this blue glow seen in nuclear reactors, is an optical light originating from energetic β-emitter radionuclides. CR emitter 90 Y triggers a cascade of energy transfers in the presence of a mixed population of fluorophores (which each other match their respective absorption and emission maxima): Cherenkov Radiation Energy Transfer (CRET) first, followed by multiple Förster Resonance Energy transfers (FRET): CRET ratios were calculated to give a rough estimate of the transfer efficiency. While CR is blue-weighted (300-500 nm), such cascades of Energy Transfers allowed to get a) fluorescence emission up to 710 nm, which is beyond the main CR window and within the near-infrared (NIR) window where biological tissues are most transparent, b) to amplify this emission and boost the radiance on that window: EMT6-tumor bearing mice injected with both a radionuclide and a mixture of fluorophores having a good spectral overlap, were shown to have nearly a two-fold radiance boost (measured on a NIR window centered on the emission wavelength of the last fluorophore in the Energy Transfer cascade) compared to a tumor injected with the radionuclide only. Some CR embarked light source could be converted into a near-infrared radiation, where biological tissues are most transparent.

Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning

PubMed Central

Baker, Joseph M.; Liu, Ning; Cui, Xu; Vrticka, Pascal; Saggar, Manish; Hosseini, S. M. Hadi; Reiss, Allan L.

2016-01-01

Researchers from multiple fields have sought to understand how sex moderates human social behavior. While over 50 years of research has revealed differences in cooperation behavior of males and females, the underlying neural correlates of these sex differences have not been explained. A missing and fundamental element of this puzzle is an understanding of how the sex composition of an interacting dyad influences the brain and behavior during cooperation. Using fNIRS-based hyperscanning in 111 same- and mixed-sex dyads, we identified significant behavioral and neural sex-related differences in association with a computer-based cooperation task. Dyads containing at least one male demonstrated significantly higher behavioral performance than female/female dyads. Individual males and females showed significant activation in the right frontopolar and right inferior prefrontal cortices, although this activation was greater in females compared to males. Female/female dyad’s exhibited significant inter-brain coherence within the right temporal cortex, while significant coherence in male/male dyads occurred in the right inferior prefrontal cortex. Significant coherence was not observed in mixed-sex dyads. Finally, for same-sex dyads only, task-related inter-brain coherence was positively correlated with cooperation task performance. Our results highlight multiple important and previously undetected influences of sex on concurrent neural and behavioral signatures of cooperation. PMID:27270754

Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

PubMed Central

Gupta, Rishabh; Falk, Tiago H.

2017-01-01

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs. PMID:29181021

Emergent macrophytes select for nitrifying and denitrifying microorganisms in constructed wetlands

NASA Astrophysics Data System (ADS)

Trias, Rosalia; Ramió Pujol, Sara; Bañeras, Lluis

2014-05-01

The use of constructed wetlands for wastewater treatment is a reliable low-cost alternative that has been widely developed during the last years. Several processes involving plants, sediments, and microbial communities contribute to nitrogen removal in wetlands. Vegetation plays an important role in this process, not only by nutrient assimilation but also by the stimulation of the plant associated microbiota. Plants supply oxygen at the close proximity of the root surface that may favour ammonia oxidizers. At the same time, exudation of organic compounds potentially speeds-up denitrification in the anoxic environment. The aim of this work was to understand the plant-microbe interactions at the root level in the Empuriabrava free water surface constructed wetland (Spain). The roots of the macrophytes Typha latifolia, Typha angustifolia, Phragmites australis and Bolboschoenus maritimus were sampled at four dates from January to September 2012, covering all the stages of plant growth. Additionally, sediment surrounding vegetation and non-vegetated sediments were sampled. Microbial community structure was analysed by pyrosequencing of bacterial and archaeal 16S rDNA and functional genes (nirK, nirS, nosZ and amoA). Bacterial communities were significantly different in sediments of the vegetated areas compared to the root surface. Plant roots exhibited a higher proportion of proteobacteria whereas Actinobacteria were dominant in sediments. The nitrifiers Nitrosomonas sp. and Nitrosococcus sp. accounted for less than 1% of all sequences. Archaeal communities were dominated by the Miscellaneous Crenarchaeotic Groups C2 and C3 and Methanomicrobia. Higher relative abundances of MCG were found in roots of P. australis, B. maritimus and T. angustifolia. Ammonia oxidizing archaea accounted for less than 0.1% of all sequences but were consistently more abundant in sediment samples compared to roots. NirK and NirS-type bacterial communities showed clearly distinct distribution patterns among plant species, thus indicating different plant-microbe relationships for the two bacterial groups. Our results show that plant roots have implications in multiple steps of the nitrogen cycle and can significantly alter nitrogen removal in wetlands.

Imaging biomarkers to predict response to anti-HER2 (ErbB2) therapy in preclinical models of breast cancer

PubMed Central

Shah, Chirayu; Miller, Todd W.; Wyatt, Shelby K.; McKinley, Eliot T.; Olivares, Maria Graciela; Sanchez, Violeta; Nolting, Donald D.; Buck, Jason R.; Zhao, Ping; Ansari, M. Sib; Baldwin, Ronald M.; Gore, John C.; Schiff, Rachel; Arteaga, Carlos L.; Manning, H. Charles

2010-01-01

Purpose To evaluate non-invasive imaging methods as predictive biomarkers of response to trastuzumab in mouse models of HER2-overexpressing breast cancer. The correlation between tumor regression and molecular imaging of apoptosis, glucose metabolism, and cellular proliferation was evaluated longitudinally in responding and non-responding tumor-bearing cohorts. Experimental Design Mammary tumors from MMTV/HER2 transgenic female mice were transplanted into syngeneic female mice. BT474 human breast carcinoma cell line xenografts were grown in athymic nude mice. Tumor cell apoptosis (NIR700-Annexin-V accumulation), glucose metabolism ([18F]FDG-PET), and proliferation ([18F]FLT-PET) were evaluated throughout a bi-weekly trastuzumab regimen. Imaging metrics were validated by direct measurement of tumor size and immunohistochemical (IHC) analysis of cleaved caspase-3, phosphorylated AKT (p-AKT) and Ki67. Results NIR700-Annexin-V accumulated significantly in trastuzumab-treated MMTV/HER2 and BT474 tumors that ultimately regressed, but not in non-responding or vehicle-treated tumors. Uptake of [18F]FDG was not affected by trastuzumab treatment in MMTV/HER2 or BT474 tumors. [18F]FLT PET imaging predicted trastuzumab response in BT474 tumors but not in MMTV/HER2 tumors, which exhibited modest uptake of [18F]FLT. Close agreement was observed between imaging metrics and IHC analysis. Conclusions Molecular imaging of apoptosis accurately predicts trastuzumab-induced regression of HER2(+) tumors and may warrant clinical exploration to predict early response to neoadjuvant trastuzumab. Trastuzumab does not appear to alter glucose metabolism substantially enough to afford [18F]FDG-PET significant predictive value in this setting. Although promising in one preclinical model, further studies are required to determine the overall value of [18F]FLT-PET as a biomarker of response to trastuzumab in HER2+ breast cancer. PMID:19584166

Pharmaceutical applications using NIR technology in the cloud

NASA Astrophysics Data System (ADS)

Grossmann, Luiz; Borges, Marco A.

2017-05-01

NIR technology has been available for a long time, certainly more than 50 years. Without any doubt, it has found many niche applications, especially in the pharmaceutical, food, agriculture and other industries due to its flexibility. There are a number of advantages over other existing analytical technologies we can list, for example virtually no need for sample preparation; usually NIR does not demand sample destruction and subsequent discard; NIR provides fast results; NIR does not require extensive operator training and carries small operating costs. However, the key point about NIR technology is the fact that it's more related to statistics than chemistry or, in other words, we are more concerned about analyzing and distinguishing features within the data than looking deep into the chemical entities themselves. A simple scan reading in the NIR range usually involves huge inflows of data points. Usually we decompose the signals into hundreds of predictor variables and use complex algorithms to predict classes or quantify specific content. NIR is all about math, especially by converting chemical information into numbers. Easier said than done. A NIR signal is a very complex one. Usually the signal responses are not specific to a particular material, rather, each grouṕs responses add up, thus providing low specificity of a spectral reading. This paper proposes a simple and efficient method to analyze and compare NIR spectra for the purpose of identifying the presence of active pharmaceutical ingredients in finished products using low cost NIR scanning devices connected to the internet cloud.

Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes

PubMed Central

Funane, Tsukasa; Sato, Hiroki; Yahata, Noriaki; Takizawa, Ryu; Nishimura, Yukika; Kinoshita, Akihide; Katura, Takusige; Atsumori, Hirokazu; Fukuda, Masato; Kasai, Kiyoto; Koizumi, Hideaki; Kiguchi, Masashi

2015-01-01

Abstract. It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level–dependent signals was analyzed. A three-way analysis of variance (signal depth×Hb kind×task) indicated that the main effect of fNIRS signal depth on the correlation is significant [F(1,1286)=5.34, p<0.05]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method. PMID:26157983

Clinical application of near-infrared spectroscopy in patients with traumatic brain injury: a review of the progress of the field.

PubMed

Sen, Anish N; Gopinath, Shankar P; Robertson, Claudia S

2016-07-01

Near-infrared spectroscopy (NIRS) is a technique by which the interaction between light in the near-infrared spectrum and matter can be quantitatively measured to provide information about the particular chromophore. Study into the clinical application of NIRS for traumatic brain injury (TBI) began in the 1990s with early reports of the ability to detect intracranial hematomas using NIRS. We highlight the advances in clinical applications of NIRS over the past two decades as they relate to TBI. We discuss recent studies evaluating NIRS techniques for intracranial hematoma detection, followed by the clinical application of NIRS in intracranial pressure and brain oxygenation measurement, and conclude with a summary of potential future uses of NIRS in TBI patient management.

Near infrared photoimmunotherapy in the treatment of disseminated peritoneal ovarian cancer

PubMed Central

Sato, Kazuhide; Hanaoka, Hirofumi; Watanabe, Rira; Nakajima, Takahito; Choyke, Peter L.; Kobayashi, Hisataka

2014-01-01

Near infrared photoimmunotherapy (NIR-PIT) is a new cancer treatment that combines the specificity of intravenously injected antibodies for targeting tumors with the toxicity induced by photosensitizers after exposure to near infrared (NIR) light. Herein, we evaluate the efficacy of NIR-PIT in a mouse model of disseminated peritoneal ovarian cancer. In vitro and in vivo experiments were conducted with a HER2-expressing, luciferase expressing, ovarian cancer cell line (SKOV-luc). An antibody-photosensitizer conjugate (APC) consisting of trastuzumab and a phthalocyanine dye, IRDye-700DX, was synthesized (tra-IR700) and cells or tumors were exposed to near infrared (NIR) light. In vitro PIT cytotoxicity was assessed with dead staining and luciferase activity in freely growing cells and in a 3D spheroid model. In vivo NIR-PIT was performed in mice with tumors implanted in the peritoneum and in the flank and these assessed by tumor volume and/or bioluminescence. In vitro NIR-PIT-induced cytotoxicity was light dose dependent. Repeated light exposures induced complete tumor cell killing in the 3D spheroid model. In vivo the anti-tumor effects of NIR-PIT were confirmed by significant reductions in both tumor volume and luciferase activity in the flank model (NIR-PIT vs control in tumor volume changes at day 10; p=0.0001, NIR-PIT vs control in luciferase activity at day 4; p=0.0237), and the peritoneal model (NIR-PIT vs control in luciferase activity at day 7; p=0.0037). NIR-PIT provided effective cell killing in this HER2 positive model of disseminated peritoneal ovarian cancer. Thus, NIR-PIT is a promising new therapy for the treatment of disseminated peritoneal tumors. PMID:25416790

Cerebral and somatic near-infrared spectroscopy measurements during fluid challenge in cardiac surgery patients: a descriptive pilot study.

PubMed

Fellahi, Jean-Luc; Fischer, Marc-Olivier; Rebet, Olivier; Dalbera, Audrey; Massetti, Massimo; Gérard, Jean-Louis; Hanouz, Jean-Luc

2013-04-01

Little is known about changes in near-infrared spectroscopy (NIRS)-derived cerebral (rSO(2)b) and somatic (rSO(2)s) oxygen saturation during a fluid challenge. The authors tested the hypothesis that they could differ from central venous oxygen saturation (ScvO(2)) and from one site to another. A prospective observational study. A teaching university hospital. Fifty consecutive adult patients. Admission to the intensive care unit after cardiac surgery and investigation before and after a fluid challenge. Simultaneous comparative ScvO(2), rSO(2)b, and rSO(2)s data points were collected from a blood-gas analyzer and the EQUANOX monitor (Nonin Medical, Inc, Plymouth, MN). Correlations were determined by linear regression. Multiple stepwise linear regression models were used to assess independent variables associated with changes in ScvO(2), rSO(2)b, and rSO(2)s. A statistically significant relationship was found between absolute values of ScvO(2) and rSO(2)b (r = 0.42, p < 0.001) but not between absolute values of ScvO(2) and rSO(2)s (r = 0.18, p = 0.066). No relationship was found between percent changes in ScvO(2) and rSO(2)b (r = 0.05, p = 0.715) and between percent changes in ScvO(2) and rSO(2)s (r = 0.02, p = 0.886) after the fluid challenge. Cardiac index contributed to the prediction of changes in ScvO(2) (regression coefficient = -4.09, p = 0.006), whereas the mean arterial pressure contributed to the prediction of changes in rSO(2)b (regression coefficient = -0.05, p = 0.027). rSO(2)b and rSO(2)s cannot be used to provide noninvasive estimation of ScvO(2), and trends in rSO(2)b and rSO(2)s cannot be considered as noninvasive surrogates for the trend in ScvO(2) after cardiac surgery. Different independent variables contribute to the prediction of ScvO(2), rSO(2)b, and rSO(2)s. Copyright © 2013 Elsevier Inc. All rights reserved.

In Situ Measurement of Some Soil Properties in Paddy Soil Using Visible and Near-Infrared Spectroscopy

PubMed Central

Wenjun, Ji; Zhou, Shi; Jingyi, Huang; Shuo, Li

2014-01-01

In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH of paddy soils in Zhejiang province, China. Firstly, the linear partial least squares regression (PLSR) was performed on the in situ spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the non-linear least-square support vector machine (LS-SVM) algorithm was carried out aiming to extract more useful information from the in situ spectra and improve predictions. Results show that in terms of OC, OM, TN, AN and pH, (i) the predictions were worse using in situ spectra compared to laboratory-based spectra with PLSR algorithm (ii) the prediction accuracy using LS-SVM (R2>0.75, RPD>1.90) was obviously improved with in situ vis-NIR spectra compared to PLSR algorithm, and comparable or even better than results generated using laboratory-based spectra with PLSR; (iii) in terms of AP and AK, poor predictions were obtained with in situ spectra (R2<0.5, RPD<1.50) either using PLSR or LS-SVM. The results highlight the use of LS-SVM for in situ vis-NIR spectroscopic estimation of soil properties of paddy soils. PMID:25153132

Quantitative determination and classification of energy drinks using near-infrared spectroscopy.

PubMed

Rácz, Anita; Héberger, Károly; Fodor, Marietta

2016-09-01

Almost a hundred commercially available energy drink samples from Hungary, Slovakia, and Greece were collected for the quantitative determination of their caffeine and sugar content with FT-NIR spectroscopy and high-performance liquid chromatography (HPLC). Calibration models were built with partial least-squares regression (PLSR). An HPLC-UV method was used to measure the reference values for caffeine content, while sugar contents were measured with the Schoorl method. Both the nominal sugar content (as indicated on the cans) and the measured sugar concentration were used as references. Although the Schoorl method has larger error and bias, appropriate models could be developed using both references. The validation of the models was based on sevenfold cross-validation and external validation. FT-NIR analysis is a good candidate to replace the HPLC-UV method, because it is much cheaper than any chromatographic method, while it is also more time-efficient. The combination of FT-NIR with multidimensional chemometric techniques like PLSR can be a good option for the detection of low caffeine concentrations in energy drinks. Moreover, three types of energy drinks that contain (i) taurine, (ii) arginine, and (iii) none of these two components were classified correctly using principal component analysis and linear discriminant analysis. Such classifications are important for the detection of adulterated samples and for quality control, as well. In this case, more than a hundred samples were used for the evaluation. The classification was validated with cross-validation and several randomization tests (X-scrambling). Graphical Abstract The way of energy drinks from cans to appropriate chemometric models.

Rapid Analysis of Deoxynivalenol in Durum Wheat by FT-NIR Spectroscopy

PubMed Central

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-01-01

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50–16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%–90% and 3%–7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation. PMID:25384107

Rapid analysis of deoxynivalenol in durum wheat by FT-NIR spectroscopy.

PubMed

De Girolamo, Annalisa; Cervellieri, Salvatore; Visconti, Angelo; Pascale, Michelangelo

2014-11-06

Fourier-transform-near infrared (FT-NIR) spectroscopy has been used to develop quantitative and classification models for the prediction of deoxynivalenol (DON) levels in durum wheat samples. Partial least-squares (PLS) regression analysis was used to determine DON in wheat samples in the range of <50-16,000 µg/kg DON. The model displayed a large root mean square error of prediction value (1,977 µg/kg) as compared to the EU maximum limit for DON in unprocessed durum wheat (i.e., 1,750 µg/kg), thus making the PLS approach unsuitable for quantitative prediction of DON in durum wheat. Linear discriminant analysis (LDA) was successfully used to differentiate wheat samples based on their DON content. A first approach used LDA to group wheat samples into three classes: A (DON ≤ 1,000 µg/kg), B (1,000 < DON ≤ 2,500 µg/kg), and C (DON > 2,500 µg/kg) (LDA I). A second approach was used to discriminate highly contaminated wheat samples based on three different cut-off limits, namely 1,000 (LDA II), 1,200 (LDA III) and 1,400 µg/kg DON (LDA IV). The overall classification and false compliant rates for the three models were 75%-90% and 3%-7%, respectively, with model LDA IV using a cut-off of 1,400 µg/kg fulfilling the requirement of the European official guidelines for screening methods. These findings confirmed the suitability of FT-NIR to screen a large number of wheat samples for DON contamination and to verify the compliance with EU regulation.

Nutritional evaluation of commercial dry dog foods by near infrared reflectance spectroscopy.

PubMed

Alomar, D; Hodgkinson, S; Abarzúa, D; Fuchslocher, R; Alvarado, C; Rosales, E

2006-06-01

Near infrared reflectance spectroscopy (NIRS) was used to predict the nutritional value of dog foods sold in Chile. Fifty-nine dry foods for adult and growing dogs were collected, ground and scanned across the visible/NIR range and subsequently analysed for dry matter (DM), crude protein (CP), crude fibre (CF), total fat, linoleic acid, gross energy (GE), estimated metabolizable energy (ME) and several amino acids and minerals. Calibration equations were developed by modified partial least squares regression, and tested by cross-validation. Standard error of cross validation (SE(CV)) and coefficient of determination of cross validation (SE(CV)) were used to select best equations. Equations with good predicting accuracy were obtained for DM, CF, CP, GE and fat. Corresponding values for and SE(CV) were 0.96 and 1.7 g/kg, 0.91 and 3.1 g/kg, 0.99 and 5.0 g/kg, 0.93 and 0.26 MJ/kg, 0.89 and 12.4 g/kg. Several amino acids were also well predicted, such as arginine, leucine, isoleucine, phenylalanine-tyrosine (combined), threonine and valine, with values for and SE(CV) (g/kg) of 0.89 and 0.9, 0.94 and 1.3, 0.91 and 0.5, 0.95 and 0.9, 0.91 and 0.5, 0.93 and 0.5. Intermediate values, appropriate for ranking purposes, were obtained for ME, histidine, lysine and methionine-cysteine. Tryptophan, minerals or linoleic acid were not acceptably predicted, irrespective of the mathematical treatment applied. It is concluded that NIR can be successfully used to predict important nutritional characteristics of commercial dog foods.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Relationships between NIR spectra and sensory attributes of Thai commercial fish sauces.

PubMed

Ritthiruangdej, Pitiporn; Suwonsichon, Thongchai

2007-07-01

Twenty Thai commercial fish sauces were characterized by sensory descriptive analysis and near-infrared (NIR) spectroscopy. The main objectives were i) to investigate the relationships between sensory attributes and NIR spectra of samples and ii) to characterize the sensory characteristics of fish sauces based on NIR data. A generic descriptive analysis with 12 trained panels was used to characterize the sensory attributes. These attributes consisted of 15 descriptors: brown color, 5 aromatics (sweet, caramelized, fermented, fishy, and musty), 4 tastes (sweet, salty, bitter, and umami), 3 aftertastes (sweet, salty and bitter) and 2 flavors (caramelized and fishy). The results showed that Thai fish sauce samples exhibited significant differences in all of sensory attribute values (p < 0.05). NIR transflectance spectra were obtained from 1100 to 2500 nm. Prior to investigation of the relationships between sensory attributes and NIR spectra, principal component analysis (PCA) was applied to reduce the dimensionality of the spectral data from 622 wavelengths to two uncorrelated components (NIR1 and NIR2) which explained 92 and 7% of the total variation, respectively. NIR1 was highly correlated with the wavelength regions of 1100 - 1544, 1774 - 2062, 2092 - 2308, and 2358 - 2440 nm, while NIR2 was highly correlated with the wavelength regions of 1742 - 1764, 2066 - 2088, and 2312 - 2354 nm. Subsequently, the relationships among these two components and all sensory attributes were also investigated by PCA. The results showed that the first three principal components (PCs) named as fishy flavor component (PC1), sweet component (PC2) and bitterness component (PC3), respectively, explained a total of 66.86% of the variation. NIR1 was mainly correlated to the sensory attributes of fishy aromatic, fishy flavor and sweet aftertaste on PC1. In addition, the PCA using only the factor loadings of NIR1 and NIR2 could be used to classify samples into three groups which showed high, medium and low degrees of fishy aromatic, fishy flavor and sweet aftertaste.

A photostable near-infrared fluorescent tracker with pH-independent specificity to lysosomes for long time and multicolor imaging.

PubMed

Zhang, Xinfu; Wang, Chao; Han, Zhuo; Xiao, Yi

2014-12-10

A new boron-dipyrromethene-based lysosome tracker, Lyso-NIR, is facilely synthesized. Besides the intensive near-infrared (NIR) fluorescence and high photostability, Lyso-NIR shows the capability to stably localize in lysosomes, which is independent of the local pH. Lyso-NIR does not have the problematic alkalization effect suffered by the commonly used lysotrackers; thus, it shows ignorable cytotoxicity and slightly affects normal physiological functions of lysosomes. The above advantages of Lyso-NIR make it feasible to track lysosomes' dynamic changes in a relatively long time during the full cellular processes such as apoptosis, heavy metal stimulation, and endocytosis, as is demonstrated in this work. Moreover, Lyso-NIR's narrow NIR emission at 740 nm with a full width at half-maximum smaller than 50 nm makes it easy to avoid the crosstalk with the emissions from other common fluorescent probes, which strengthens Lyso-NIR's competitiveness as a standard lysosome tracker for multicolor bioimaging.

Visualization of Pulmonary Clearance Mechanisms via Noninvasive Optical Imaging Validated by Near-Infrared Flow Cytometry

PubMed Central

Zhou, Haiying; Gunsten, Sean P.; Zhegalova, Natalia G.; Bloch, Sharon; Achilefu, Samuel; Holley, J. Christopher; Schweppe, Daniel; Akers, Walter; Brody, Steven L.; Eades, William; Berezin, Mikhail Y.

2016-01-01

In vivo optical imaging with near-infrared (NIR) probes is an established method of diagnostics in preclinical and clinical studies. However, the specificities of these probes are difficult to validate ex vivo due to the lack of NIR flow cytometry. To address this limitation, we modified a flow cytometer to include an additional NIR channel using a 752 nm laser line. The flow cytometry system was tested using NIR microspheres and cell lines labeled with a combination of visible range and NIR fluorescent dyes. The approach was verified in vivo in mice evaluated for immune response in lungs after intratracheal delivery of the NIR contrast agent. Flow cytometry of cells obtained from the lung bronchoalveolar lavage demonstrated that the NIR dye was taken up by pulmonary macrophages as early as four-hours post-injection. This combination of optical imaging with NIR flow cytometry extends the capability of imaging and enables complementation of in vivo imaging with cell-specific studies. PMID:25808737

Variability comparison of simultaneous brain near-infrared spectroscopy (NIRS) and functional MRI (fMRI) during visual stimulation

PubMed Central

Minati, Ludovico; Visani, Elisa; Dowell, Nick G; Medford, Nick; Critchley, Hugo D

2011-01-01

Brain near-infrared spectroscopy (NIRS) is emerging as a potential alternative to functional MRI (fMRI). To date, no study has explicitly compared the two techniques in terms of measurement variability, a key parameter dictating attainable statistical power. Here, NIRS and fMRI were simultaneously recorded during event-related visual stimulation. Inter-subject coefficients of variation (CVs) for peak response amplitude were considerably larger for NIRS than fMRI, but inter-subject CVs for response latency and intra-subject CVs for response amplitude were overall comparable. Our results may represent an optimistic estimate of the CVs of NIRS measurements, as optode positioning was guided by structural MRI, which is normally unavailable. We conclude that fMRI may be preferable to NIRS for group comparisons, but NIRS is equally powerful when comparing conditions within participants. The discrepancy between inter- and intra-subject CVs is likely related to variability in head anatomy and tissue properties which may be better accounted for by emerging NIRS technology. PMID:21780948

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry.

PubMed

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-15

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications. Copyright © 2012 Elsevier B.V. All rights reserved.

Biodegradable Core-shell Dual-Metal-Organic-Frameworks Nanotheranostic Agent for Multiple Imaging Guided Combination Cancer Therapy

PubMed Central

Wang, Dongdong; Zhou, Jiajia; Shi, Ruohong; Wu, Huihui; Chen, Ruhui; Duan, Beichen; Xia, Guoliang; Xu, Pengping; Wang, Hui; Zhou, Shu; Wang, Chengming; Wang, Haibao; Guo, Zhen; Chen, Qianwang

2017-01-01

Metal-organic-frameworks (MOFs) possess high porosity, large surface area, and tunable functionality are promising candidates for synchronous diagnosis and therapy in cancer treatment. Although large number of MOFs has been discovered, conventional MOF-based nanoplatforms are mainly limited to the sole MOF source with sole functionality. In this study, surfactant modified Prussian blue (PB) core coated by compact ZIF-8 shell (core-shell dual-MOFs, CSD-MOFs) has been reported through a versatile stepwise approach. With Prussian blue as core, CSD-MOFs are able to serve as both magnetic resonance imaging (MRI) and fluorescence optical imaging (FOI) agents. We show that CSD-MOFs crystals loading the anticancer drug doxorubicin (DOX) are efficient pH and near-infrared (NIR) dual-stimuli responsive drug delivery vehicles. After the degradation of ZIF-8, simultaneous NIR irradiation to the inner PB MOFs continuously generate heat that kill cancer cells. Their efficacy on HeLa cancer cell lines is higher compared with the respective single treatment modality, achieving synergistic chemo-thermal therapy efficacy. In vivo results indicate that the anti-tumor efficacy of CSD-MOFs@DOX+NIR was 7.16 and 5.07 times enhanced compared to single chemo-therapy and single thermal-therapy respectively. Our strategy opens new possibilities to construct multifunctional theranostic systems through integration of two different MOFs. PMID:29158848

Aqueous CdPbS quantum dots for near-infrared imaging

NASA Astrophysics Data System (ADS)

Au, Giang H. T.; Y Shih, Wan; Tseng, S.-Ja; Shih, Wei-Heng

2012-07-01

Quantum dots (QDs) are semiconducting nanocrystals that have photoluminescent (PL) properties brighter than fluorescent molecules and do not photo-bleach, ideal for in vivo imaging of diseased tissues or monitoring of biological processes. Near-infrared (NIR) fluorescent light within the window of 700-1000 nm, which is separated from the major absorption peaks of hemoglobin and water, has the potential to be detected several millimeters under the surface with minimal interference from tissue autofluorescence. Here we report the synthesis and bioimaging demonstration of a new NIR QDs system, namely, CdPbS, made by an aqueous approach with 3-mercaptopropionic acid (MPA) as the capping molecule. The aqueous-synthesized, MPA-capped CdPbS QDs exhibited an NIR emission in the range of 800-950 nm with xi ≥ 0.3, where xi denotes the initial Pb molar fraction during the synthesis. Optimal PL performance of the CdPbS QDs occurred at xi = 0.7, which was about 4 nm in size as determined by transmission electron microscopy, had a rock salt structure and a quantum yield of 12%. Imaging of CdPbS QDs was tested in membrane staining and transfection studies. Cells transfected with CdPbS QDs were shown to be visible underneath a slab of chicken muscle tissue of up to 0.7 mm in thickness without the use of multiple-photon microscopy.

Pre-Ionization Controlled Laser Plasma Formation for Ignition Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shneider, Mikhail

The presented research explored new physics and ignition schemes based on laser induced plasmas that are fundamentally distinct from past laser ignition research focused on single laser pulses. Specifically, we consider the use of multiple laser pulses where the first pulse provides pre-ionization allowing controlled absorption of the second pulse. In this way, we can form tailored laser plasmas in terms of their ionization fraction, gas temperature (e.g. to achieve elevated temperature of ~2000 K ideally suited for an ignition source), reduced energy loss to shock waves and radiation, and large kernel size (e.g. length ~1-10 cm). The proposed researchmore » included both experimental and modeling efforts, at Colorado State University, Princeton University and University of Tennessee, towards the basic science of the new laser plasma approach with emphasis on tailoring the plasmas to practical propulsion systems. Experimental results (CSU) show that the UV beam produces a pre-ionized volume which assists in breakdown of the NIR beam, leading to reduction in NIR breakdown threshold by factor of >2. Numerical modeling is performed to examine the ionization and breakdown of both beams. The main theoretical and computational parts of the work were done at Princeton University. The modeled breakdown threshold of the NIR, including assist by pre-ionization, is in reasonable agreement with the experimental results.« less

Carbon Nanotubes Reinforced Maleic Anhydride-Modified Xylan-g-Poly(N-isopropylacrylamide) Hydrogel with Multifunctional Properties

PubMed Central

Liu, Xinxin; Song, Tao; Chang, Minmin; Meng, Ling; Wang, Xiaohui; Sun, Runcang; Ren, Junli

2018-01-01

Introducing multifunctional groups and inorganic material imparts xylan-based hydrogels with excellent properties, such as responsiveness to pH, temperature, light, and external magnetic field. In this work, a composite hydrogel was synthesized by introducing acid treated carbon nanotubes (AT-CNTs) into the maleic anhydride modified xylan grafted with poly(N-isopropylacrylamide) (MAX-g-PNIPAM) hydrogels network. It was found that the addition of AT-CNTs affected the MAX-g-PNIPAM hydrogel structure, the swelling ratio and mechanical properties, and imparted the hydrogel with new properties of electrical conductivity and near infrared region (NIR) photothermal conversion. AT-CNTs could reinforce the mechanical properties of MAX-g-PNIPAM hydrogels, being up to 83 kPa for the compressive strength when the amount was 11 wt %, which was eight times than that of PNIPAM hydrogel and four times than that of MAX-g-PNIPAM hydrogel. The electroconductibility was enhanced by the increase of AT-CNTs amounts. Meanwhile, the composite hydrogel also exhibited multiple shape memory and NIR photothermal conversion properties, and water temperature was increased from 26 °C to 56 °C within 8 min under the NIR irradiation. Thus, the AT-CNTs reinforced MAX-g-PNIPAM hydrogel possessed promising multifunctional properties, which offered many potential applications in the fields of biosensors, thermal-arrest technology, and drug-controlled release. PMID:29495611

Carbon Nanotubes Reinforced Maleic Anhydride-Modified Xylan-g-Poly(N-isopropylacrylamide) Hydrogel with Multifunctional Properties.

PubMed

Liu, Xinxin; Song, Tao; Chang, Minmin; Meng, Ling; Wang, Xiaohui; Sun, Runcang; Ren, Junli

2018-02-28

Introducing multifunctional groups and inorganic material imparts xylan-based hydrogels with excellent properties, such as responsiveness to pH, temperature, light, and external magnetic field. In this work, a composite hydrogel was synthesized by introducing acid treated carbon nanotubes (AT-CNTs) into the maleic anhydride modified xylan grafted with poly(N-isopropylacrylamide) (MAX-g-PNIPAM) hydrogels network. It was found that the addition of AT-CNTs affected the MAX-g-PNIPAM hydrogel structure, the swelling ratio and mechanical properties, and imparted the hydrogel with new properties of electrical conductivity and near infrared region (NIR) photothermal conversion. AT-CNTs could reinforce the mechanical properties of MAX-g-PNIPAM hydrogels, being up to 83 kPa for the compressive strength when the amount was 11 wt %, which was eight times than that of PNIPAM hydrogel and four times than that of MAX-g-PNIPAM hydrogel. The electroconductibility was enhanced by the increase of AT-CNTs amounts. Meanwhile, the composite hydrogel also exhibited multiple shape memory and NIR photothermal conversion properties, and water temperature was increased from 26 °C to 56 °C within 8 min under the NIR irradiation. Thus, the AT-CNTs reinforced MAX-g-PNIPAM hydrogel possessed promising multifunctional properties, which offered many potential applications in the fields of biosensors, thermal-arrest technology, and drug-controlled release.

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset.

PubMed

Bulgarelli, Chiara; Blasi, Anna; Arridge, Simon; Powell, Samuel; de Klerk, Carina C J M; Southgate, Victoria; Brigadoi, Sabrina; Penny, William; Tak, Sungho; Hamilton, Antonia

2018-04-12

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies. fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Near infrared radiation protects against oxygen-glucose deprivation-induced neurotoxicity by down-regulating neuronal nitric oxide synthase (nNOS) activity in vitro.

PubMed

Yu, Zhanyang; Li, Zhaoyu; Liu, Ning; Jizhang, Yunneng; McCarthy, Thomas J; Tedford, Clark E; Lo, Eng H; Wang, Xiaoying

2015-06-01

Near infrared radiation (NIR) has been shown to be neuroprotective against neurological diseases including stroke and brain trauma, but the underlying mechanisms remain poorly understood. In the current study we aimed to investigate the hypothesis that NIR may protect neurons by attenuating oxygen-glucose deprivation (OGD)-induced nitric oxide (NO) production and modulating cell survival/death signaling. Primary mouse cortical neurons were subjected to 4 h OGD and NIR was applied at 2 h reoxygenation. OGD significantly increased NO level in primary neurons compared to normal control, which was significantly ameliorated by NIR at 5 and 30 min post-NIR. Neither OGD nor NIR significantly changed neuronal nitric oxide synthase (nNOS) mRNA or total protein levels compared to control groups. However, OGD significantly increased nNOS activity compared to normal control, and this effect was significantly diminished by NIR. Moreover, NIR significantly ameliorated the neuronal death induced by S-Nitroso-N-acetyl-DL-penicillamine (SNAP), a NO donor. Finally, NIR significantly rescued OGD-induced suppression of p-Akt and Bcl-2 expression, and attenuated OGD-induced upregulation of Bax, BAD and caspase-3 activation. These results suggest NIR may protect against OGD at least partially through reducing NO production by down-regulating nNOS activity, and modulating cell survival/death signaling.

Image monitoring of pharmaceutical blending processes and the determination of an end point by using a portable near-infrared imaging device based on a polychromator-type near-infrared spectrometer with a high-speed and high-resolution photo diode array detector.

PubMed

Murayama, Kodai; Ishikawa, Daitaro; Genkawa, Takuma; Sugino, Hiroyuki; Komiyama, Makoto; Ozaki, Yukihiro

2015-03-03

In the present study we have developed a new version (ND-NIRs) of a polychromator-type near-infrared (NIR) spectrometer with a high-resolution photo diode array detector, which we built before (D-NIRs). The new version has four 5 W halogen lamps compared with the three lamps for the older version. The new version also has a condenser lens with a shorter focal point length. The increase in the number of the lamps and the shortening of the focal point of the condenser lens realize high signal-to-noise ratio and high-speed NIR imaging measurement. By using the ND-NIRs we carried out the in-line monitoring of pharmaceutical blending and determined an end point of the blending process. Moreover, to determinate a more accurate end point, a NIR image of the blending sample was acquired by means of a portable NIR imaging device based on ND-NIRs. The imaging result has demonstrated that the mixing time of 8 min is enough for homogeneous mixing. In this way the present study has demonstrated that ND-NIRs and the imaging system based on a ND-NIRs hold considerable promise for process analysis.

Near-infrared fluorescence imaging using organic dye nanoparticles.

PubMed

Yu, Jia; Zhang, Xiujuan; Hao, Xiaojun; Zhang, Xiaohong; Zhou, Mengjiao; Lee, Chun-Sing; Chen, Xianfeng

2014-03-01

Near-infrared (NIR) fluorescence imaging in the 700-1000 nm wavelength range has been very attractive for early detection of cancers. Conventional NIR dyes often suffer from limitation of low brightness due to self-quenching, insufficient photo- and bioenvironmental stability, and small Stokes shift. Herein, we present a strategy of using small-molecule organic dye nanoparticles (ONPs) to encapsulate NIR dyes to enable efficient fluorescence resonance energy transfer to obtain NIR probes with remarkably enhanced performance for in vitro and in vivo imaging. In our design, host ONPs are used as not only carriers to trap and stabilize NIR dyes, but also light-harvesting agent to transfer energy to NIR dyes to enhance their brightness. In comparison with pure NIR dyes, our organic dye nanoparticles possess almost 50-fold increased brightness, large Stokes shifts (∼250 nm) and dramatically enhanced photostability. With surface modification, these NIR-emissive organic nanoparticles have water-dispersity and size- and fluorescence- stability over pH values from 2 to 10 for almost 60 days. With these superior advantages, these NIR-emissive organic nanoparticles can be used for highly efficient folic-acid aided specific targeting in vivo and ex vivo cellular imaging. Finally, during in vivo imaging, the nanoparticles show negligible toxicity. Overall, the results clearly display a potential application of using the NIR-emissive organic nanoparticles for in vitro and in vivo imaging. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cropland Field Monitoring: MMV Page 1 Montana Cropland Enrolled Farm Fields Carbon Sequestration Field Sampling, Measurement, Monitoring, and Verification: Application of Visible-Near Infrared Diffuse Reflectance Spectroscopy (VNIR) and Laser-induced Breakdown Spectroscopy (LIBS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee Spangler; Ross Bricklemyer; David Brown

2012-03-15

There is growing need for rapid, accurate, and inexpensive methods to measure, and verify soil organic carbon (SOC) change for national greenhouse gas accounting and the development of a soil carbon trading market. Laboratory based soil characterization typically requires significant soil processing, which is time and resource intensive. This severely limits application for large-region soil characterization. Thus, development of rapid and accurate methods for characterizing soils are needed to map soil properties for precision agriculture applications, improve regional and global soil carbon (C) stock and flux estimates and efficiently map sub-surface metal contamination, among others. The greatest gains for efficientmore » soil characterization will come from collecting soil data in situ, thus minimizing soil sample transportation, processing, and lab-based measurement costs. Visible and near-infrared diffuse reflectance spectroscopy (VisNIR) and laser-induced breakdown spectroscopy (LIBS) are two complementary, yet fundamentally different spectroscopic techniques that have the potential to meet this need. These sensors have the potential to be mounted on a soil penetrometer and deployed for rapid soil profile characterization at field and landscape scales. Details of sensor interaction, efficient data management, and appropriate statistical analysis techniques for model calibrations are first needed. In situ or on-the-go VisNIR spectroscopy has been proposed as a rapid and inexpensive tool for intensively mapping soil texture and organic carbon (SOC). While lab-based VisNIR has been established as a viable technique for estimating various soil properties, few experiments have compared the predictive accuracy of on-the-go and lab-based VisNIR. Eight north central Montana wheat fields were intensively interrogated using on-the-go and lab-based VisNIR. Lab-based spectral data consistently provided more accurate predictions than on-the-go data. However, neither in situ nor lab-based spectroscopy yielded even semi-quantitative SOC predictions. There was little SOC variability to explain across the eight fields, and on-the-go VisNIR was not able to capture the subtle SOC variability in these Montana soils. With more variation in soil clay content compared to SOC, both lab and on-the-go VisNIR showed better explanatory power. There are several potential explanations for poor on-the-go predictive accuracy: soil heterogeneity, field moisture, consistent sample presentation, and a difference between the spatial support of on-the-go measurements and soil samples collected for laboratory analyses. Though the current configuration of a commercially available on-the-go VisNIR system allows for rapid field scanning, on-the-go soil processing (i.e. drying, crushing, and sieving) could improve soil carbon predictions. Laser-induced breakdown spectroscopy (LIBS) is an emerging elemental analysis technology with the potential to provide rapid, accurate and precise analysis of soil constituents, such as carbon, in situ across landscapes. The research team evaluated the accuracy of LIBS for measuring soil profile carbon in field-moist, intact soil cores simulating conditions that might be encountered by a probe-mounted LIBS instrument measuring soil profile carbon in situ. Over the course of three experiments, more than120 intact soil cores from eight north central Montana wheat fields and the Washington State University (WSU) Cook Agronomy Farm near Pullman, WA were interrogated with LIBS for rapid total carbon (TC), inorganic carbon (IC), and SOC determination. Partial least squares regression models were derived and independently validated at field- and regional scales. Researchers obtained the best LIBS validation predictions for IC followed by TC and SOC. Laser-induced breakdown spectroscopy is fundamentally an elemental analysis technique, yet LIBS PLS2 models appeared to discriminate IC from TC. Regression coefficients from initial models suggested a reliance upon stoichiometric relationships between carbon (247.8 nm) and other elements related to total and inorganic carbon in the soil matrix [Ca (210.2 nm, 211.3 nm, and 220.9 nm), Mg (279.55-280.4 nm, 285.26 nm), and Si (251.6 nm, 288.1 nm)]. Expanding the LIBS spectral range to capture emissions from a broader range of elements related to soil organic matter was explored using two spectrometer systems to improve SOC predictions. Results for increasing the spectral range of LIBS to the full 200-800 nm found modest gains in prediction accuracy for IC, but no gains for predicting TC or SOC. Poor SOC predictions are likely a function of (1) the lack of a consistent/definable molecular composition of SOC, (2) relatively little variation in SOC across field sites, and (3) inorganic carbon constituting the primary form of soil carbon, particularly for Montana soils.« less

MR imaging biomarkers for evaluating therapeutic effects shortly after near infrared photoimmunotherapy

PubMed Central

Nakamura, Yuko; Bernardo, Marcelino; Nagaya, Tadanobu; Sato, Kazuhide; Harada, Toshiko; Choyke, Peter L.; Kobayashi, Hisataka

2016-01-01

Near infrared photoimmunotherapy (NIR-PIT) is a new cancer treatment that combines the specificity of antibodies for targeting tumors with the toxicity induced by photon absorbers after irradiation with NIR light. The purpose of this study was to determine if MR imaging can detect changes in the MR properties of tumor within several hours of NIR-PIT. A431 cells were injected subcutaneously in the right and left dorsi of 12 mice. Six days later, the mice were injected with a photon absorber, IR700, conjugated to panitumumab, an antibody targeting epidermal growth factor receptor. One day later, only right sided tumor was exposed to NIR light (treated tumor). MRI was performed 1 day before and 1-2 hours after NIR-PIT using gadofosveset for six mice and gadopentetate dimeglumine for another six mice. T2 relaxation times, the apparent diffusion coefficient (ADC) for the following combinations of b-values: 0-1000, 200-1000 and 500-1000 s/mm2 and enhancement indices were compared before and after NIR-PIT using a two-sided paired t-test. For treated tumors, T2 relaxation time increased after NIR-PIT (p < 0.01) and all three ADC values decreased after NIR-PIT (p < 0.01). Moreover, the enhancement area under the curve (AUC) using gadofosveset increased after NIR-PIT (p = 0.02). In conclusion, prolongation of T2, reductions in ADC and increased enhancement using gadofosveset are seen within 2 hours of NIR-PIT treatment of tumors. Thus, MRI can be a useful imaging biomarker for detecting early therapeutic changes after NIR-PIT. PMID:26885619

Near Infrared Photoimmunotherapy in the Treatment of Pleural Disseminated NSCLC: Preclinical Experience

PubMed Central

Sato, Kazuhide; Nagaya, Tadanobu; Choyke, Peter L.; Kobayashi, Hisataka

2015-01-01

Pleural metastases are common in patients with advanced thoracic cancers and are a cause of considerable morbidity and mortality yet is difficult to treat. Near Infrared Photoimmunotherapy (NIR-PIT) is a cancer treatment that combines the specificity of intravenously injected antibodies for targeting tumors with the toxicity induced by photosensitizers after exposure to NIR-light. Herein, we evaluate the efficacy of NIR-PIT in a mouse model of pleural disseminated non-small cell lung carcinoma (NSCLC). In vitro and in vivo experiments were conducted with a HER2, luciferase and GFP expressing NSCLC cell line (Calu3-luc-GFP). An antibody-photosensitizer conjugate (APC) consisting of trastuzumab and a phthalocyanine dye, IRDye-700DX, was synthesized. In vitro NIR-PIT cytotoxicity was assessed with dead staining, luciferase activity, and GFP fluorescence intensity. In vivo NIR-PIT was performed in mice with tumors implanted intrathoracic cavity or in the flank, and assessed by tumor volume and/or bioluminescence and fluorescence thoracoscopy. In vitro NIR-PIT-induced cytotoxicity was light dose dependent. In vivo NIR-PIT led significant reductions in both tumor volume (p = 0.002 vs. APC) and luciferase activity (p = 0.0004 vs. APC) in a flank model, and prolonged survival (p < 0.0001). Bioluminescence indicated that NIR-PIT lead to significant reduction in pleural dissemination (1 day after PIT; p = 0.0180). Fluorescence thoracoscopy confirmed the NIR-PIT effect on disseminated pleural disease. In conclusion, NIR-PIT has the ability to effectively treat pleural metastases caused by NSCLC in mice. Thus, NIR-PIT is a promising therapy for pleural disseminated tumors. PMID:25897335

21 CFR 882.1935 - Near Infrared (NIR) Brain Hematoma Detector.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Near Infrared (NIR) Brain Hematoma Detector. 882... Infrared (NIR) Brain Hematoma Detector. (a) Identification. A Near Infrared (NIR) Brain Hematoma Detector... evaluate suspected brain hematomas. (b) Classification. Class II (special controls). The special controls...

21 CFR 882.1935 - Near Infrared (NIR) Brain Hematoma Detector.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Near Infrared (NIR) Brain Hematoma Detector. 882... Infrared (NIR) Brain Hematoma Detector. (a) Identification. A Near Infrared (NIR) Brain Hematoma Detector... evaluate suspected brain hematomas. (b) Classification. Class II (special controls). The special controls...

Spectrally resolved, broadband frequency response characterization of photodetectors using continuous-wave supercontinuum sources

NASA Astrophysics Data System (ADS)

Choudhury, Vishal; Prakash, Roopa; Nagarjun, K. P.; Supradeepa, V. R.

2018-02-01

A simple and powerful method using continuous wave supercontinuum lasers is demonstrated to perform spectrally resolved, broadband frequency response characterization of photodetectors in the NIR Band. In contrast to existing techniques, this method allows for a simple system to achieve the goal, requiring just a standard continuous wave(CW) high-power fiber laser source and an RF spectrum analyzer. From our recent work, we summarize methods to easily convert any high-power fiber laser into a CW supercontinuum. These sources in the time domain exhibit interesting properties all the way down to the femtosecond time scale. This enables measurement of broadband frequency response of photodetectors while the wide optical spectrum of the supercontinuum can be spectrally filtered to obtain this information in a spectrally resolved fashion. The method involves looking at the RF spectrum of the output of a photodetector under test when incident with the supercontinuum. By using prior knowledge of the RF spectrum of the source, the frequency response can be calculated. We utilize two techniques for calibration of the source spectrum, one using a prior measurement and the other relying on a fitted model. Here, we characterize multiple photodetectors from 150MHz bandwidth to >20GHz bandwidth at multiple bands in the NIR region. We utilize a supercontinuum source spanning over 700nm bandwidth from 1300nm to 2000nm. For spectrally resolved measurement, we utilize multiple wavelength bands such as around 1400nm and 1600nm. Interesting behavior was observed in the frequency response of the photodetectors when comparing broadband spectral excitation versus narrower band excitation.

Application of near-infrared image processing in agricultural engineering

NASA Astrophysics Data System (ADS)

Chen, Ming-hong; Zhang, Guo-ping; Xia, Hongxing

2009-07-01

Recently, with development of computer technology, the application field of near-infrared image processing becomes much wider. In this paper the technical characteristic and development of modern NIR imaging and NIR spectroscopy analysis were introduced. It is concluded application and studying of the NIR imaging processing technique in the agricultural engineering in recent years, base on the application principle and developing characteristic of near-infrared image. The NIR imaging would be very useful in the nondestructive external and internal quality inspecting of agricultural products. It is important to detect stored-grain insects by the application of near-infrared spectroscopy. Computer vision detection base on the NIR imaging would be help to manage food logistics. Application of NIR imaging promoted quality management of agricultural products. In the further application research fields of NIR image in the agricultural engineering, Some advices and prospect were put forward.

On children's dyslexia with NIRS

NASA Astrophysics Data System (ADS)

Gan, Zhuo; Li, Chengjun; Gong, Hui; Luo, Qingming; Yao, Bin; Song, Ranran; Wu, Hanrong

2003-12-01

Developmental dyslexia is a kind of prevalent psychologic disease. Some functional imaging technologies, such as FMRI and PET, have been used to study the brain activities of dyslexics. NIRS is a kind of novel technology which is more and more widely being used for study of the cognitive psychology. However, there aren"t reports about the dyslexic research using NIRS to be found until now. This paper introduces a NIRS system of four measuring channels. Brain activities of dyslexic subjects and normal subjects during reading task were studied with the NIRS system. Two groups of subjects, the group of dyslexia and the group of normal, were appointed to perform two reading tasks. At the same time, their cortical activities were measured with the NIRS system. This experimental result indicates that the brain activities of the dyslexic group were significantly higher than the control group in BA 48 and that NIRS can be used for the study of human brain activity.

Design of an FT-NIR spectrometer for online quality analysis of traditional Chinese medicine manufacturing process

NASA Astrophysics Data System (ADS)

Zhu, Ren; Wu, Lan; Wang, Shiming; Ye, Linhua; Ding, Zhihua

2008-03-01

As a fast, non-destructive analysis method, Fourier transform (FT) near-infrared (NIR) spectroscopy is very suitable and effective for online quality analysis of traditional Chinese medicine (TCM) manufacturing process. In this thesis, the theoretics of FT-NIRS was analyzed and an FT-NIR spectrometer with 4 cm -1 resolution in the 12500-5000 cm -1 frequency range was designed. The spectrometer was based on a Michelson interferometer with Bromine tungsten lamp as the NIR light source and InGaAs detector to collect the interference signal. Each element was designed and chosen to provide maximum sensitivity in the NIR spectral region. A fiber-optic flow cell system was used to realize online analysis of traditional Chinese medicine. The performance of the spectrometer was evaluated and the feasibility of using FT-NIR spectrometer to get absorption spectra of traditional Chinese medicine was demonstrated.

Bacterial Phytochromes, Cyanobacteriochromes and Allophycocyanins as a Source of Near-Infrared Fluorescent Probes

PubMed Central

Oliinyk, Olena S.; Chernov, Konstantin G.

2017-01-01

Bacterial photoreceptors absorb light energy and transform it into intracellular signals that regulate metabolism. Bacterial phytochrome photoreceptors (BphPs), some cyanobacteriochromes (CBCRs) and allophycocyanins (APCs) possess the near-infrared (NIR) absorbance spectra that make them promising molecular templates to design NIR fluorescent proteins (FPs) and biosensors for studies in mammalian cells and whole animals. Here, we review structures, photochemical properties and molecular functions of several families of bacterial photoreceptors. We next analyze molecular evolution approaches to develop NIR FPs and biosensors. We then discuss phenotypes of current BphP-based NIR FPs and compare them with FPs derived from CBCRs and APCs. Lastly, we overview imaging applications of NIR FPs in live cells and in vivo. Our review provides guidelines for selection of existing NIR FPs, as well as engineering approaches to develop NIR FPs from the novel natural templates such as CBCRs. PMID:28771184

Synthesis and characterization of near IR fluorescent albumin nanoparticles for optical detection of colon cancer.

PubMed

Cohen, Sarit; Pellach, Michal; Kam, Yossi; Grinberg, Igor; Corem-Salkmon, Enav; Rubinstein, Abraham; Margel, Shlomo

2013-03-01

Near IR (NIR) fluorescent human serum albumin (HSA) nanoparticles hold great promise as contrast agents for tumor diagnosis. HSA nanoparticles are considered to be biocompatible, non-toxic and non-immunogenic. In addition, NIR fluorescence properties of these nanoparticles are important for in vivo tumor diagnostics, with low autofluorescence and relatively deep penetration of NIR irradiation due to low absorption of biomatrices. The present study describes the synthesis of new NIR fluorescent HSA nanoparticles, by entrapment of a NIR fluorescent dye within the HSA nanoparticles, which also significantly increases the photostability of the dye. Tumor-targeting ligands such as peanut agglutinin (PNA) and anti-carcinoembryonic antigen antibodies (anti-CEA) were covalently conjugated to the NIR fluorescent albumin nanoparticles, increasing the potential fluorescent signal in tumors with upregulated corresponding receptors. Specific colon tumor detection by the NIR fluorescent HSA nanoparticles was demonstrated in a chicken embryo model and a rat model. In future work we also plan to encapsulate cancer drugs such as doxorubicin within the NIR fluorescent HSA nanoparticles for both colon cancer imaging and therapy. Copyright © 2012 Elsevier B.V. All rights reserved.

Experimental studies of systematic multiple-energy operation at HIMAC synchrotron

NASA Astrophysics Data System (ADS)

Mizushima, K.; Katagiri, K.; Iwata, Y.; Furukawa, T.; Fujimoto, T.; Sato, S.; Hara, Y.; Shirai, T.; Noda, K.

2014-07-01

Multiple-energy synchrotron operation providing carbon-ion beams with various energies has been used for scanned particle therapy at NIRS. An energy range from 430 to 56 MeV/u and about 200 steps within this range are required to vary the Bragg peak position for effective treatment. The treatment also demands the slow extraction of beam with highly reliable properties, such as spill, position and size, for all energies. We propose an approach to generating multiple-energy operation meeting these requirements within a short time. In this approach, the device settings at most energy steps are determined without manual adjustments by using systematic parameter tuning depending on the beam energy. Experimental verification was carried out at the HIMAC synchrotron, and its results proved that this approach can greatly reduce the adjustment period.

Recovering fNIRS brain signals: physiological interference suppression with independent component analysis

NASA Astrophysics Data System (ADS)

Zhang, Y.; Shi, M.; Sun, J.; Yang, C.; Zhang, Yajuan; Scopesi, F.; Makobore, P.; Chin, C.; Serra, G.; Wickramasinghe, Y. A. B. D.; Rolfe, P.

2015-02-01

Brain activity can be monitored non-invasively by functional near-infrared spectroscopy (fNIRS), which has several advantages in comparison with other methods, such as flexibility, portability, low cost and fewer physical restrictions. However, in practice fNIRS measurements are often contaminated by physiological interference arising from cardiac contraction, breathing and blood pressure fluctuations, thereby severely limiting the utility of the method. Hence, further improvement is necessary to reduce or eliminate such interference in order that the evoked brain activity information can be extracted reliably from fNIRS data. In the present paper, the multi-distance fNIRS probe configuration has been adopted. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Independent component analysis (ICA) is employed for the fNIRS recordings to separate the brain signals and the interference. Least-absolute deviation (LAD) estimator is employed to recover the brain activity signals. We also utilized Monte Carlo simulations based on a five-layer model of the adult human head to evaluate our methodology. The results demonstrate that the ICA algorithm has the potential to separate physiological interference in fNIRS data and the LAD estimator could be a useful criterion to recover the brain activity signals.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

PubMed Central

Giacometti, Paolo; Diamond, Solomon G.

2014-01-01

Abstract. This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R=0.46±0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R=0.43±0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization. PMID:25558462

The impact of using mature compost on nitrous oxide emission and the denitrifier community in the cattle manure composting process.

PubMed

Maeda, Koki; Morioka, Riki; Hanajima, Dai; Osada, Takashi

2010-01-01

The diversity and dynamics of the denitrifying genes (nirS, nirK, and nosZ) encoding nitrite reductase and nitrous oxide (N(2)O) reductase in the dairy cattle manure composting process were investigated. A mixture of dried grass with a cattle manure compost pile and a mature compost-added pile were used, and denaturing gradient gel electrophoresis was used for denitrifier community analysis. The diversity of nirK and nosZ genes significantly changed in the initial stage of composting. These variations might have been induced by the high temperature. The diversity of nirK was constant after the initial variation. On the other hand, the diversity of nosZ changed in the latter half of the process, a change which might have been induced by the accumulation of nitrate and nitrite. The nirS gene fragments could not be detected. The use of mature compost that contains nitrate and nitrite promoted the N(2)O emission and significantly affected the variation of nosZ diversity in the initial stage of composting, but did not affect the variation of nirK diversity. Many Pseudomonas-like nirK and nosZ gene fragments were detected in the stage in which N(2)O was actively emitted.

Comparative NMR and NIRS analysis of oxygen-dependent metabolism in exercising finger flexor muscles.

PubMed

Bendahan, David; Chatel, Benjamin; Jue, Thomas

2017-12-01

Muscle contraction requires the physiology to adapt rapidly to meet the surge in energy demand. To investigate the shift in metabolic control, especially between oxygen and metabolism, researchers often depend on near-infrared spectroscopy (NIRS) to measure noninvasively the tissue O 2 Because NIRS detects the overlapping myoglobin (Mb) and hemoglobin (Hb) signals in muscle, interpreting the data as an index of cellular or vascular O 2 requires deconvoluting the relative contribution. Currently, many in the NIRS field ascribe the signal to Hb. In contrast, 1 H NMR has only detected the Mb signal in contracting muscle, and comparative NIRS and NMR experiments indicate a predominant Mb contribution. The present study has examined the question of the NIRS signal origin by measuring simultaneously the 1 H NMR, 31 P NMR, and NIRS signals in finger flexor muscles during the transition from rest to contraction, recovery, ischemia, and reperfusion. The experiment results confirm a predominant Mb contribution to the NIRS signal from muscle. Given the NMR and NIRS corroborated changes in the intracellular O 2 , the analysis shows that at the onset of muscle contraction, O 2 declines immediately and reaches new steady states as contraction intensity rises. Moreover, lactate formation increases even under quite aerobic condition. Copyright © 2017 the American Physiological Society.

The use of near-infrared spectroscopy in understanding skeletal muscle physiology: recent developments.

PubMed

Ferrari, Marco; Muthalib, Makii; Quaresima, Valentina

2011-11-28

This article provides a snapshot of muscle near-infrared spectroscopy (NIRS) at the end of 2010 summarizing the recent literature, offering the present status and perspectives of the NIRS instrumentation and methods, describing the main NIRS studies on skeletal muscle physiology, posing open questions and outlining future directions. So far, different NIRS techniques (e.g. continuous-wave (CW) and spatially, time- and frequency-resolved spectroscopy) have been used for measuring muscle oxygenation during exercise. In the last four years, approximately 160 muscle NIRS articles have been published on different physiological aspects (primarily muscle oxygenation and haemodynamics) of several upper- and lower-limb muscle groups investigated by using mainly two-channel CW and spatially resolved spectroscopy commercial instruments. Unfortunately, in only 15 of these studies were the advantages of using multi-channel instruments exploited. There are still several open questions in the application of NIRS in muscle studies: (i) whether NIRS can be used in subjects with a large fat layer; (ii) the contribution of myoglobin desaturation to the NIRS signal during exercise; (iii) the effect of scattering changes during exercise; and (iv) the effect of changes in skin perfusion, particularly during prolonged exercise. Recommendations for instrumentation advancements and future muscle NIRS studies are provided.

Nitrite reductase expression is regulated at the post-transcriptional level by the nitrogen source in Nicotiana plumbaginifolia and Arabidopsis thaliana.

PubMed

Crété, P; Caboche, M; Meyer, C

1997-04-01

Higher plant nitrite reductase (NiR) is a monomeric chloroplastic protein catalysing the reduction of nitrite, the product of nitrate reduction, to ammonium. The expression of this enzyme is controlled at the transcriptional level by light and by the nitrogen source. In order to study the post-transcriptional regulation of NiR, Nicotiana plumbaginifolia and Arabidopsis thaliana were transformed with a chimaeric NiR construct containing the tobacco leaf NiR1 coding sequence driven by the CaMV 35S RNA promoter. Transformed plants did not show any phenotypic difference when compared with the wild-type, although they overexpressed NiR activity in the leaves. When these plants were grown in vitro on media containing either nitrate or ammonium as sole nitrogen source, NiR mRNA derived from transgene expression was constitutively expressed, whereas NiR activity and protein level were strongly reduced on ammonium-containing medium. These results suggest that, together with transcriptional control, post-transcriptional regulation by the nitrogen source is operating on NiR expression. This post-transcriptional regulation of tobacco leaf NiR1 expression was observed not only in the closely related species N. plumbaginifolia but also in the more distant species A. thaliana.

Nitrous oxide emission and denitrifier communities in drip-irrigated calcareous soil as affected by chemical and organic fertilizers.

PubMed

Tao, Rui; Wakelin, Steven A; Liang, Yongchao; Hu, Baowei; Chu, Guixin

2018-01-15

The effects of consecutive application of chemical fertilizer with or without organic fertilizer on soil N 2 O emissions and denitrifying community structure in a drip-irrigated field were determined. The four fertilizer treatments were (i) unfertilized, (ii) chemical fertilizer, (iii) 60% chemical fertilizer plus cattle manure, and (iv) 60% chemical fertilizer plus biofertilizer. The treatments with organic amendments (i.e. cattle manure and biofertilizer) reduced cumulative N 2 O emissions by 4.9-9.9%, reduced the N 2 O emission factor by 1.3-42%, and increased denitrifying enzyme activities by 14.3-56.2%. The nirK gene copy numbers were greatest in soil which received only chemical fertilizer. In contrast, nirS- and nosZ-copy numbers were greatest in soil amended with chemical fertilizer plus biofertilizer. Chemical fertilizer application with or without organic fertilizer significantly changed the community structure of nirK-type denitrifiers relative to the unfertilized soil. In comparison, the nirS- and nosZ-type denitrifier genotypes varied in treatments receiving organic fertilizer but not chemical fertilizer alone. The changes in the denitrifier communities were closely associated with soil organic carbon (SOC), NO 3 - , NH 4 + , water holding capacity, and soil pH. Modeling indicated that N 2 O emissions in this soil were primarily associated with the abundance of nirS type denitrifying bacteria, SOC, and NO 3 - . Overall, our findings indicate that (i) the organic fertilizers increased denitrifying enzyme activity, increased denitrifying-bacteria gene copy numbers, but reduced N 2 O emissions, and (ii) nirS- and nosZ-type denitrifiers were more sensitive than nirK-type denitrifiers to the organic fertilizers. Copyright © 2017. Published by Elsevier B.V.

A review of NIR dyes in cancer targeting and imaging.

PubMed

Luo, Shenglin; Zhang, Erlong; Su, Yongping; Cheng, Tianmin; Shi, Chunmeng

2011-10-01

The development of multifunctional agents for simultaneous tumor targeting and near infrared (NIR) fluorescence imaging is expected to have significant impact on future personalized oncology owing to the very low tissue autofluorescence and high tissue penetration depth in the NIR spectrum window. Cancer NIR molecular imaging relies greatly on the development of stable, highly specific and sensitive molecular probes. Organic dyes have shown promising clinical implications as non-targeting agents for optical imaging in which indocyanine green has long been implemented in clinical use. Recently, significant progress has been made on the development of unique NIR dyes with tumor targeting properties. Current ongoing design strategies have overcome some of the limitations of conventional NIR organic dyes, such as poor hydrophilicity and photostability, low quantum yield, insufficient stability in biological system, low detection sensitivity, etc. This potential is further realized with the use of these NIR dyes or NIR dye-encapsulated nanoparticles by conjugation with tumor specific ligands (such as small molecules, peptides, proteins and antibodies) for tumor targeted imaging. Very recently, natively multifunctional NIR dyes that can preferentially accumulate in tumor cells without the need of chemical conjugation to tumor targeting ligands have been developed and these dyes have shown unique optical and pharmaceutical properties for biomedical imaging with superior signal-to-background contrast index. The main focus of this article is to provide a concise overview of newly developed NIR dyes and their potential applications in cancer targeting and imaging. The development of future multifunctional agents by combining targeting, imaging and even therapeutic routes will also be discussed. We believe these newly developed multifunctional NIR dyes will broaden current concept of tumor targeted imaging and hold promise to make an important contribution to the diagnosis and therapeutics for the treatment of cancer. Copyright © 2011 Elsevier Ltd. All rights reserved.

Community structures and activity of denitrifying microbes in a forested catchment in central Japan: survey using nitrite reductase genes

NASA Astrophysics Data System (ADS)

Ohte, N.; Aoki, M.; Katsuyama, C.; Suwa, Y.; Tange, T.

2012-12-01

To elucidate the mechanisms of denitrification processes in the forested catchment, microbial ecological approaches have been applied in an experimental watershed that has previously investigated its hydrological processes. The study catchment is located in the Chiba prefecture in central Japan under the temperate Asian monsoon climate. Potential activities of denitrification of soil samples were measured by incubation experiments under anoxic condition associated with Na15NO3 addition. Existence and variety of microbes having nitrite reductase genes were investigated by PCR amplification, cloning and sequencings of nirK and nirS fragments after DNA extraction. Contrary to our early expectation that the potential denitrification activity was higher at deeper soil horizon with consistent groundwater residence than that in the surface soil, denitrification potential was higher in shallower soil horizons than deeper soils. This suggested that the deficiency of NO3- as a respiratory substrate for denitrifier occurred in deeper soils especially in the summer. However, high denitrification activity and presence of microbes having nirK and nirS in surface soils usually under aerobic condition was explainable by the fact that the majority of denitrifying bacteria have been recognized as a facultative anaerobic bacterium. This also suggests the possibility of that denitrification occurs even in the surface soils if the wet condition is provided by rainwater during and after a storm event. Community structures of microbes having nirK were different between near surface and deeper soil horizons, and ones having nirS was different between saturated zone (under groundwater table) and unsaturated soil horizons. These imply that microbial communities with nisK are sensitive to the concentration of soil organic matters and ones with nirS is sensitive to soil moisture contents.

Near-Infrared 1064 nm Laser Modulates Migratory Dendritic Cells To Augment the Immune Response to Intradermal Influenza Vaccine.

PubMed

Morse, Kaitlyn; Kimizuka, Yoshifumi; Chan, Megan P K; Shibata, Mai; Shimaoka, Yusuke; Takeuchi, Shu; Forbes, Benjamin; Nirschl, Christopher; Li, Binghao; Zeng, Yang; Bronson, Roderick T; Katagiri, Wataru; Shigeta, Ayako; Sîrbulescu, Ruxandra F; Chen, Huabiao; Tan, Rhea Y Y; Tsukada, Kosuke; Brauns, Timothy; Gelfand, Jeffrey; Sluder, Ann; Locascio, Joseph J; Poznansky, Mark C; Anandasabapathy, Niroshana; Kashiwagi, Satoshi

2017-08-15

Brief exposure of skin to near-infrared (NIR) laser light has been shown to augment the immune response to intradermal vaccination and thus act as an immunologic adjuvant. Although evidence indicates that the NIR laser adjuvant has the capacity to activate innate subsets including dendritic cells (DCs) in skin as conventional adjuvants do, the precise immunological mechanism by which the NIR laser adjuvant acts is largely unknown. In this study we sought to identify the cellular target of the NIR laser adjuvant by using an established mouse model of intradermal influenza vaccination and examining the alteration of responses resulting from genetic ablation of specific DC populations. We found that a continuous wave (CW) NIR laser adjuvant broadly modulates migratory DC (migDC) populations, specifically increasing and activating the Lang + and CD11b - Lang - subsets in skin, and that the Ab responses augmented by the CW NIR laser are dependent on DC subsets expressing CCR2 and Langerin. In comparison, a pulsed wave NIR laser adjuvant showed limited effects on the migDC subsets. Our vaccination study demonstrated that the efficacy of the CW NIR laser is significantly better than that of the pulsed wave laser, indicating that the CW NIR laser offers a desirable immunostimulatory microenvironment for migDCs. These results demonstrate the unique ability of the NIR laser adjuvant to selectively target specific migDC populations in skin depending on its parameters, and highlight the importance of optimization of laser parameters for desirable immune protection induced by an NIR laser-adjuvanted vaccine. Copyright © 2017 by The American Association of Immunologists, Inc.

Correlations Among Near-Infrared and Short-Wavelength Autofluorescence and Spectral-Domain Optical Coherence Tomography in Recessive Stargardt Disease

PubMed Central

Duncker, Tobias; Marsiglia, Marcela; Lee, Winston; Zernant, Jana; Tsang, Stephen H.; Allikmets, Rando; Greenstein, Vivienne C.; Sparrow, Janet R.

2014-01-01

Purpose. Short-wavelength (SW) fundus autofluorescence (AF) is considered to originate from lipofuscin in retinal pigment epithelium (RPE) and near-infrared (NIR) AF from melanin. In patients with recessive Stargardt disease (STGD1), we correlated SW-AF and NIR-AF with structural information obtained by spectral-domain optical coherence tomography (SD-OCT). Methods. Twenty-four STGD1 patients (45 eyes; age 8 to 61 years) carrying confirmed disease-associated ABCA4 mutations were studied prospectively. Short-wavelength AF, NIR-AF, and SD-OCT images were acquired. Results. Five phenotypes were identified according to features of the central lesion and extent of fundus change. Central zones of reduced NIR-AF were typically larger than areas of diminished SW-AF and reduced NIR-AF usually approximated areas of ellipsoid zone (EZ) loss identified by SD-OCT (group 1; r, 0.93, P < 0.0001). In patients having a central lesion with overlapping parafoveal rings of increased NIR-AF and SW-AF (group 3), the extent of EZ loss was strongly correlated with the inner diameter of the NIR-AF ring (r, 0.89, P < 0.0001) and the eccentricity of the outer border of the NIR-AF ring was greater than that of the SW-AF ring. Conclusions. Lesion areas were more completely delineated in NIR-AF images than with SW-AF. In most cases, EZ loss was observed only at locations where NIR-AF was reduced or absent, indicating that RPE cell atrophy occurs in advance of photoreceptor cell degeneration. Because SW-AF was often increased within the central area of EZ disruption, degenerating photoreceptor cells may produce lipofuscin at accelerated levels. Consideration is given to mechanisms underlying hyper–NIR-AF in conjunction with increased SW-AF. PMID:25342616

Quantitative interpretations of Visible-NIR reflectance spectra of blood.

PubMed

Serebrennikova, Yulia M; Smith, Jennifer M; Huffman, Debra E; Leparc, German F; García-Rubio, Luis H

2008-10-27

This paper illustrates the implementation of a new theoretical model for rapid quantitative analysis of the Vis-NIR diffuse reflectance spectra of blood cultures. This new model is based on the photon diffusion theory and Mie scattering theory that have been formulated to account for multiple scattering populations and absorptive components. This study stresses the significance of the thorough solution of the scattering and absorption problem in order to accurately resolve for optically relevant parameters of blood culture components. With advantages of being calibration-free and computationally fast, the new model has two basic requirements. First, wavelength-dependent refractive indices of the basic chemical constituents of blood culture components are needed. Second, multi-wavelength measurements or at least the measurements of characteristic wavelengths equal to the degrees of freedom, i.e. number of optically relevant parameters, of blood culture system are required. The blood culture analysis model was tested with a large number of diffuse reflectance spectra of blood culture samples characterized by an extensive range of the relevant parameters.

Attosecond transient absorption of argon atoms in the vacuum ultraviolet region: line energy shifts versus coherent population transfer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cao, Wei; Warrick, Erika R.; Neumark, Daniel M.

Using attosecond transient absorption, the dipole response of an argon atom in the vacuum ultraviolet (VUV) region is studied when an external electromagnetic field is present. An isolated attosecond VUV pulse populates Rydberg states lying 15 eV above the argon ground state. A synchronized few-cycle near infrared (NIR) pulse modifies the oscillating dipoles of argon impulsively, leading to alterations in the VUV absorption spectra. As the NIR pulse is delayed with respect to the VUV pulse, multiple features in the absorption profile emerge simultaneously including line broadening, sideband structure, sub-cycle fast modulations, and 5-10 fs slow modulations. These features indicatemore » the coexistence of two general processes of the light-matter interaction: the energy shift of individual atomic levels and coherent population transfer between atomic eigenstates, revealing coherent superpositions. Finally, an intuitive formula is derived to treat both effects in a unifying framework, allowing one to identify and quantify the two processes in a single absorption spectrogram.« less

Attosecond transient absorption of argon atoms in the vacuum ultraviolet region: line energy shifts versus coherent population transfer

NASA Astrophysics Data System (ADS)

Cao, Wei; Warrick, Erika R.; Neumark, Daniel M.; Leone, Stephen R.

2016-01-01

Using attosecond transient absorption, the dipole response of an argon atom in the vacuum ultraviolet (VUV) region is studied when an external electromagnetic field is present. An isolated attosecond VUV pulse populates Rydberg states lying 15 eV above the argon ground state. A synchronized few-cycle near infrared (NIR) pulse modifies the oscillating dipoles of argon impulsively, leading to alterations in the VUV absorption spectra. As the NIR pulse is delayed with respect to the VUV pulse, multiple features in the absorption profile emerge simultaneously including line broadening, sideband structure, sub-cycle fast modulations, and 5-10 fs slow modulations. These features indicate the coexistence of two general processes of the light-matter interaction: the energy shift of individual atomic levels and coherent population transfer between atomic eigenstates, revealing coherent superpositions. An intuitive formula is derived to treat both effects in a unifying framework, allowing one to identify and quantify the two processes in a single absorption spectrogram.

Attosecond transient absorption of argon atoms in the vacuum ultraviolet region: line energy shifts versus coherent population transfer

DOE PAGES

Cao, Wei; Warrick, Erika R.; Neumark, Daniel M.; ...

2016-01-18

Using attosecond transient absorption, the dipole response of an argon atom in the vacuum ultraviolet (VUV) region is studied when an external electromagnetic field is present. An isolated attosecond VUV pulse populates Rydberg states lying 15 eV above the argon ground state. A synchronized few-cycle near infrared (NIR) pulse modifies the oscillating dipoles of argon impulsively, leading to alterations in the VUV absorption spectra. As the NIR pulse is delayed with respect to the VUV pulse, multiple features in the absorption profile emerge simultaneously including line broadening, sideband structure, sub-cycle fast modulations, and 5-10 fs slow modulations. These features indicatemore » the coexistence of two general processes of the light-matter interaction: the energy shift of individual atomic levels and coherent population transfer between atomic eigenstates, revealing coherent superpositions. Finally, an intuitive formula is derived to treat both effects in a unifying framework, allowing one to identify and quantify the two processes in a single absorption spectrogram.« less

Unravelling the dynamical origin of below- and near-threshold harmonic generation of H 2 + in an intense NIR laser field

DOE PAGES

Heslar, John; Chu, Shih-I.

2016-11-24

Recently, the study of near- and below- threshold regime harmonics as a potential source of intense coherent vacuum-ultraviolet radiation has received considerable attention. However, the dynamical origin of these lower harmonics, particularly for the molecular systems, is less understood and largely unexplored. Here we perform the first fully ab initio and high precision 3D quantum study of the below- and near-threshold harmonic generation of H 2 + molecules in an intense 800-nm near-infrared (NIR) laser field. Furthermore, combining with a synchrosqueezing transform of the quantum time-frequency spectrum and an extended semiclassical analysis, we explore in-depth the roles of various quantummore » trajectories, including short- and long trajectories, multiphoton trajectories, resonance-enhanced trajectories, and multiple rescattering trajectories of the below- and near- threshold harmonic generation processes. Our results shed new light on the dynamical origin of the below- and near-threshold harmonic generation and various quantum trajectories for diatomic molecules for the first time.« less

In-line and real-time process monitoring of a freeze drying process using Raman and NIR spectroscopy as complementary process analytical technology (PAT) tools.

PubMed

De Beer, T R M; Vercruysse, P; Burggraeve, A; Quinten, T; Ouyang, J; Zhang, X; Vervaet, C; Remon, J P; Baeyens, W R G

2009-09-01

The aim of the present study was to examine the complementary properties of Raman and near infrared (NIR) spectroscopy as PAT tools for the fast, noninvasive, nondestructive and in-line process monitoring of a freeze drying process. Therefore, Raman and NIR probes were built in the freeze dryer chamber, allowing simultaneous process monitoring. A 5% (w/v) mannitol solution was used as model for freeze drying. Raman and NIR spectra were continuously collected during freeze drying (one Raman and NIR spectrum/min) and the spectra were analyzed using principal component analysis (PCA) and multivariate curve resolution (MCR). Raman spectroscopy was able to supply information about (i) the mannitol solid state throughout the entire process, (ii) the endpoint of freezing (endpoint of mannitol crystallization), and (iii) several physical and chemical phenomena occurring during the process (onset of ice nucleation, onset of mannitol crystallization). NIR spectroscopy proved to be a more sensitive tool to monitor the critical aspects during drying: (i) endpoint of ice sublimation and (ii) monitoring the release of hydrate water during storage. Furthermore, via NIR spectroscopy some Raman observations were confirmed: start of ice nucleation, end of mannitol crystallization and solid state characteristics of the end product. When Raman and NIR monitoring were performed on the same vial, the Raman signal was saturated during the freezing step caused by reflected NIR light reaching the Raman detector. Therefore, NIR and Raman measurements were done on a different vial. Also the importance of the position of the probes (Raman probe above the vial and NIR probe at the bottom of the sidewall of the vial) in order to obtain all required critical information is outlined. Combining Raman and NIR spectroscopy for the simultaneous monitoring of freeze drying allows monitoring almost all critical freeze drying process aspects. Both techniques do not only complement each other, they also provided mutual confirmation of specific conclusions.

Improving NIR snow pit stratigraphy observations by introducing a controlled NIR light source

NASA Astrophysics Data System (ADS)

Dean, J.; Marshall, H.; Rutter, N.; Karlson, A.

2013-12-01

Near-infrared (NIR) photography in a prepared snow pit measures mm-/grain-scale variations in snow structure, as reflectivity is strongly dependent on microstructure and grain size at the NIR wavelengths. We explore using a controlled NIR light source to maximize signal to noise ratio and provide uniform incident, diffuse light on the snow pit wall. NIR light fired from the flash is diffused across and reflected by an umbrella onto the snow pit; the lens filter transmits NIR light onto the spectrum-modified sensor of the DSLR camera. Lenses are designed to refract visible light properly, not NIR light, so there must be a correction applied for the subsequent NIR bright spot. To avoid interpolation and debayering algorithms automatically performed by programs like Adobe's Photoshop on the images, the raw data are analyzed directly in MATLAB. NIR image data show a doubling of the amount of light collected in the same time for flash over ambient lighting. Transitions across layer boundaries in the flash-lit image are detailed by higher camera intensity values than ambient-lit images. Curves plotted using median intensity at each depth, normalized to the average profile intensity, show a separation between flash- and ambient-lit images in the upper 10-15 cm; the ambient-lit image curve asymptotically approaches the level of the flash-lit image curve below 15cm. We hypothesize that the difference is caused by additional ambient light penetrating the upper 10-15 cm of the snowpack from above and transmitting through the wall of the snow pit. This indicates that combining NIR ambient and flash photography could be a powerful technique for studying penetration depth of radiation as a function of microstructure and grain size. The NIR flash images do not increase the relative contrast at layer boundaries; however, the flash more than doubles the amount of recorded light and controls layer noise as well as layer boundary transition noise.

Low efficiency upconversion nanoparticles for high-resolution coalignment of near-infrared and visible light paths on a light microscope

PubMed Central

Sundaramoorthy, Sriramkumar; Badaracco, Adrian Garcia; Hirsch, Sophia M.; Park, Jun Hong; Davies, Tim; Dumont, Julien; Shirasu-Hiza, Mimi; Kummel, Andrew C.; Canman, Julie C.

2017-01-01

The combination of near infrared (NIR) and visible wavelengths in light microscopy for biological studies is increasingly common. For example, many fields of biology are developing the use of NIR for optogenetics, in which an NIR laser induces a change in gene expression and/or protein function. One major technical barrier in working with both NIR and visible light on an optical microscope is obtaining their precise coalignment at the imaging plane position. Photon upconverting particles (UCPs) can bridge this gap as they are excited by NIR light but emit in the visible range via an anti-Stokes luminescence mechanism. Here, two different UCPs have been identified, high-efficiency micro540-UCPs and lower efficiency nano545-UCPs, that respond to NIR light and emit visible light with high photostability even at very high NIR power densities (>25,000 Suns). Both of these UCPs can be rapidly and reversibly excited by visible and NIR light and emit light at visible wavelengths detectable with standard emission settings used for Green Fluorescent Protein (GFP), a commonly used genetically-encoded fluorophore. However, the high efficiency micro540-UCPs were suboptimal for NIR and visible light coalignment, due to their larger size and spatial broadening from particle-to-particle energy transfer consistent with a long lived excited state and saturated power dependence. In contrast, the lower efficiency nano-UCPs were superior for precise coalignment of the NIR beam with the visible light path (~2 µm versus ~8 µm beam broadening respectively) consistent with limited particle-to-particle energy transfer, superlinear power dependence for emission, and much smaller particle size. Furthermore, the nano-UCPs were superior to a traditional two-camera method for NIR and visible light path alignment in an in vivo Infrared-Laser-Evoked Gene Operator (IR-LEGO) optogenetics assay in the budding yeast S. cerevisiae. In summary, nano-UCPs are powerful new tools for coaligning NIR and visible light paths on a light microscope. PMID:28221018

Multiwavelength Observations of the 2002 Outburst of GX 339-4: Two Patterns of X-Ray-Optical/Near-Infrared Behavior

NASA Astrophysics Data System (ADS)

Homan, Jeroen; Buxton, Michelle; Markoff, Sera; Bailyn, Charles D.; Nespoli, Elisa; Belloni, Tomaso

2005-05-01

We report on quasi-simultaneous Rossi X-Ray Timing Explorer and optical/near-infrared (NIR) observations of the black hole candidate X-ray transient GX 339-4. Our observations were made over a time span of more than 8 months in 2002 and cover the initial rise and transition from a hard to a soft spectral state in X-rays. Two distinct patterns of correlated X-ray-optical/NIR behavior were found. During the hard state, the optical/NIR and X-ray fluxes correlated well, with a NIR versus X-ray flux power-law slope similar to that of the correlation found between X-ray and radio fluxes in previous studies of GX 339-4 and other black hole binaries. As the source went through an intermediate state, the optical/NIR fluxes decreased rapidly, and once it had entered the spectrally soft state, the optical/NIR spectrum of GX 339-4 was much bluer, and the ratio of X-ray to NIR flux was higher by a factor of more than 10 compared to the hard state. In the spectrally soft state, changes in the NIR preceded those in the soft X-rays by more than 2 weeks, indicating a disk origin of the NIR emission and providing a measure of the viscous timescale. A sudden onset of NIR flaring of ~0.5 mag on a timescale of 1 day was also observed during this period. We present spectral energy distributions, including radio data, and discuss possible sources for the optical/NIR emission. We conclude that, in the hard state, this emission probably originates in the optically thin part of a jet and that in none of the X-ray states is X-ray reprocessing the dominant source of optical/NIR emission. Finally, comparing the light curves from the all-sky monitor (ASM) and Proportional Counter Array (PCA) instruments, we find that the X-ray/NIR delay depends critically on the sensitivity of the X-ray detector, with the delay inferred from the PCA (if present at all) being a factor of 3-6 times shorter than the delay inferred from the ASM; this may be important in interpreting previously reported X-ray-optical/NIR lags.

Molecular engineering of a dual emission near-infrared ratiometric fluorophore for the detection of pH at the organism level.

PubMed

Wang, Bo-Lin; Jiang, Chuang; Li, Kun; Liu, Yan-Hong; Xie, Yongmei; Yu, Xiao-Qi

2015-07-07

A near-infrared ratiometric fluorophore (NIR-HBT) was rationally designed and constructed by expanding both the excitation and emission wavelength of the classical ratiometric fluorophore 2-(benzothiazol-2-yl)phenol (HBT) into the near-infrared region. The NIR-HBT was easily synthesized by incorporating the HBT module into the hemicyanine skeleton and showed evident NIR ratiometric fluorophore characteristics. Further application of the new fluorophore for pH detection demonstrated that NIR-HBT possesses superior overall analytical performance and NIR-HBT was successfully applied for detection of acidosis caused by inflammation in living animal tissue, which indicated the potential application value of NIR-HBT in biological imaging and sensing.