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.

[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral Regression Discriminant Analysis].

PubMed

Yu, Shuang; Liu, Guo-hai; Xia, Rong-sheng; Jiang, Hui

2016-01-01

In order to achieve the rapid monitoring of process state of solid state fermentation (SSF), this study attempted to qualitative identification of process state of SSF of feed protein by use of Fourier transform near infrared (FT-NIR) spectroscopy analysis technique. Even more specifically, the FT-NIR spectroscopy combined with Adaboost-SRDA-NN integrated learning algorithm as an ideal analysis tool was used to accurately and rapidly monitor chemical and physical changes in SSF of feed protein without the need for chemical analysis. Firstly, the raw spectra of all the 140 fermentation samples obtained were collected by use of Fourier transform near infrared spectrometer (Antaris II), and the raw spectra obtained were preprocessed by use of standard normal variate transformation (SNV) spectral preprocessing algorithm. Thereafter, the characteristic information of the preprocessed spectra was extracted by use of spectral regression discriminant analysis (SRDA). Finally, nearest neighbors (NN) algorithm as a basic classifier was selected and building state recognition model to identify different fermentation samples in the validation set. Experimental results showed as follows: the SRDA-NN model revealed its superior performance by compared with other two different NN models, which were developed by use of the feature information form principal component analysis (PCA) and linear discriminant analysis (LDA), and the correct recognition rate of SRDA-NN model achieved 94.28% in the validation set. In this work, in order to further improve the recognition accuracy of the final model, Adaboost-SRDA-NN ensemble learning algorithm was proposed by integrated the Adaboost and SRDA-NN methods, and the presented algorithm was used to construct the online monitoring model of process state of SSF of feed protein. Experimental results showed as follows: the prediction performance of SRDA-NN model has been further enhanced by use of Adaboost lifting algorithm, and the correct recognition rate of the Adaboost-SRDA-NN model achieved 100% in the validation set. The overall results demonstrate that SRDA algorithm can effectively achieve the spectral feature information extraction to the spectral dimension reduction in model calibration process of qualitative analysis of NIR spectroscopy. In addition, the Adaboost lifting algorithm can improve the classification accuracy of the final model. The results obtained in this work can provide research foundation for developing online monitoring instruments for the monitoring of SSF process.

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

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

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.

Selection of the NIR region for a regression model of the ethanol concentration in fermentation process by an online NIR and mid-IR dual-region spectrometer and 2D heterospectral correlation spectroscopy.

PubMed

Nishii, Takashi; Genkawa, Takuma; Watari, Masahiro; Ozaki, Yukihiro

2012-01-01

A new selection procedure of an informative near-infrared (NIR) region for regression model building is proposed that uses an online NIR/mid-infrared (mid-IR) dual-region spectrometer in conjunction with two-dimensional (2D) NIR/mid-IR heterospectral correlation spectroscopy. In this procedure, both NIR and mid-IR spectra of a liquid sample are acquired sequentially during a reaction process using the NIR/mid-IR dual-region spectrometer; the 2D NIR/mid-IR heterospectral correlation spectrum is subsequently calculated from the obtained spectral data set. From the calculated 2D spectrum, a NIR region is selected that includes bands of high positive correlation intensity with mid-IR bands assigned to the analyte, and used for the construction of a regression model. To evaluate the performance of this procedure, a partial least-squares (PLS) regression model of the ethanol concentration in a fermentation process was constructed. During fermentation, NIR/mid-IR spectra in the 10000 - 1200 cm(-1) region were acquired every 3 min, and a 2D NIR/mid-IR heterospectral correlation spectrum was calculated to investigate the correlation intensity between the NIR and mid-IR bands. NIR regions that include bands at 4343, 4416, 5778, 5904, and 5955 cm(-1), which result from the combinations and overtones of the C-H group of ethanol, were selected for use in the PLS regression models, by taking the correlation intensity of a mid-IR band at 2985 cm(-1) arising from the CH(3) asymmetric stretching vibration mode of ethanol as a reference. The predicted results indicate that the ethanol concentrations calculated from the PLS regression models fit well to those obtained by high-performance liquid chromatography. Thus, it can be concluded that the selection procedure using the NIR/mid-IR dual-region spectrometer combined with 2D NIR/mid-IR heterospectral correlation spectroscopy is a powerful method for the construction of a reliable regression model.

[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.

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.

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

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

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.

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.

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.

`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

Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

NASA Astrophysics Data System (ADS)

Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

2018-01-01

The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

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.

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.

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

NASA Astrophysics Data System (ADS)

Shanmugam, Palanisamy; He, Xianqiang; Singh, Rakesh Kumar; Varunan, Theenathayalan

2018-05-01

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

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.

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.

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.

Spectroscopic diagnosis of laryngeal carcinoma using near-infrared Raman spectroscopy and random recursive partitioning ensemble techniques.

PubMed

Teh, Seng Khoon; Zheng, Wei; Lau, David P; Huang, Zhiwei

2009-06-01

In this work, we evaluated the diagnostic ability of near-infrared (NIR) Raman spectroscopy associated with the ensemble recursive partitioning algorithm based on random forests for identifying cancer from normal tissue in the larynx. A rapid-acquisition NIR Raman system was utilized for tissue Raman measurements at 785 nm excitation, and 50 human laryngeal tissue specimens (20 normal; 30 malignant tumors) were used for NIR Raman studies. The random forests method was introduced to develop effective diagnostic algorithms for classification of Raman spectra of different laryngeal tissues. High-quality Raman spectra in the range of 800-1800 cm(-1) can be acquired from laryngeal tissue within 5 seconds. Raman spectra differed significantly between normal and malignant laryngeal tissues. Classification results obtained from the random forests algorithm on tissue Raman spectra yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification. The random forests technique also provided variables importance that facilitates correlation of significant Raman spectral features with cancer transformation. This study shows that NIR Raman spectroscopy in conjunction with random forests algorithm has a great potential for the rapid diagnosis and detection of malignant tumors in the larynx.

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.

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.

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.

Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering.

PubMed

Jahani, Sahar; Setarehdan, Seyed K; Boas, David A; Yücel, Meryem A

2018-01-01

Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important challenge in realizing the full potential of NIRS for real-life applications. Various motion correction algorithms have been used to alleviate the effect of motion artifacts on the estimation of the hemodynamic response function. While smoothing methods, such as wavelet filtering, are excellent in removing motion-induced sharp spikes, the baseline shifts in the signal remain after this type of filtering. Methods, such as spline interpolation, on the other hand, can properly correct baseline shifts; however, they leave residual high-frequency spikes. We propose a hybrid method that takes advantage of different correction algorithms. This method first identifies the baseline shifts and corrects them using a spline interpolation method or targeted principal component analysis. The remaining spikes, on the other hand, are corrected by smoothing methods: Savitzky-Golay (SG) filtering or robust locally weighted regression and smoothing. We have compared our new approach with the existing correction algorithms in terms of hemodynamic response function estimation using the following metrics: mean-squared error, peak-to-peak error ([Formula: see text]), Pearson's correlation ([Formula: see text]), and the area under the receiver operator characteristic curve. We found that spline-SG hybrid method provides reasonable improvements in all these metrics with a relatively short computational time. The dataset and the code used in this study are made available online for the use of all interested researchers.

Simultaneous signal reconstruction from both superficial and deep tissue for fNIRS using depth-selective filtering method

NASA Astrophysics Data System (ADS)

Fujii, M.

2017-07-01

Two variations of a depth-selective back-projection filter for functional near-infrared spectroscopy (fNIRS) systems are introduced. The filter comprises a depth-selective algorithm that uses inverse problems applied to an optically diffusive multilayer medium. In this study, simultaneous signal reconstruction of both superficial and deep tissue from fNIRS experiments of the human forehead using a prototype of a CW-NIRS system is demonstrated.

Modeling soil organic matter (SOM) from satellite data using VISNIR-SWIR spectroscopy and PLS regression with step-down variable selection algorithm: case study of Campos Amazonicos National Park savanna enclave, Brazil

NASA Astrophysics Data System (ADS)

Rosero-Vlasova, O.; Borini Alves, D.; Vlassova, L.; Perez-Cabello, F.; Montorio Lloveria, R.

2017-10-01

Deforestation in Amazon basin due, among other factors, to frequent wildfires demands continuous post-fire monitoring of soil and vegetation. Thus, the study posed two objectives: (1) evaluate the capacity of Visible - Near InfraRed - ShortWave InfraRed (VIS-NIR-SWIR) spectroscopy to estimate soil organic matter (SOM) in fire-affected soils, and (2) assess the feasibility of SOM mapping from satellite images. For this purpose, 30 soil samples (surface layer) were collected in 2016 in areas of grass and riparian vegetation of Campos Amazonicos National Park, Brazil, repeatedly affected by wildfires. Standard laboratory procedures were applied to determine SOM. Reflectance spectra of soils were obtained in controlled laboratory conditions using Fieldspec4 spectroradiometer (spectral range 350nm- 2500nm). Measured spectra were resampled to simulate reflectances for Landsat-8, Sentinel-2 and EnMap spectral bands, used as predictors in SOM models developed using Partial Least Squares regression and step-down variable selection algorithm (PLSR-SD). The best fit was achieved with models based on reflectances simulated for EnMap bands (R2=0.93; R2cv=0.82 and NMSE=0.07; NMSEcv=0.19). The model uses only 8 out of 244 predictors (bands) chosen by the step-down variable selection algorithm. The least reliable estimates (R2=0.55 and R2cv=0.40 and NMSE=0.43; NMSEcv=0.60) resulted from Landsat model, while Sentinel-2 model showed R2=0.68 and R2cv=0.63; NMSE=0.31 and NMSEcv=0.38. The results confirm high potential of VIS-NIR-SWIR spectroscopy for SOM estimation. Application of step-down produces sparser and better-fit models. Finally, SOM can be estimated with an acceptable accuracy (NMSE 0.35) from EnMap and Sentinel-2 data enabling mapping and analysis of impacts of repeated wildfires on soils in the study area.

Evaluating bio-optical models to determine chlorophyll a from hyper spectral data in the turbid coastal waters of South Carolina

NASA Astrophysics Data System (ADS)

Hames, J. B.; Ali, K.

2013-12-01

Millions of people visit the beaches of South Carolina every year and the increasing utilization of the coastal waters is leading to the deterioration of water quality and the marine ecosystem. Ecological stress on these environments is reflected by the increase in the frequency and severity of Harmful Algal Blooms (HABs). This was evident during recent summer seasons particularly in the shallow nearshore waters of Long Bay, South Carolina, an open coast embayment on the South Atlantic Bight. These aspects threaten human and marine life. The early detection of HABs in the coastal waters requires more efficient and accurate monitoring tools. Remote sensing provides synoptic view of the entire Long Bay waters at high temporal coverage and allows resource managers to effectively map and monitor algal bloom development, near real time. Various remote sensing (RS) algorithms have been developed but were mostly calibrated to low resolution global data and or other specific sites. In the summer of 2013, a suite of measurements and water samples were collected from 15 locations along the nearshore waters of Long Bay using the Grice Laboratory R/V. In this study, we evaluate the efficiency of 10 bio-optical blue-green and NIR-red based RS models applied to GER 1500 hyper spectral reflectance data to predict chlorophyll a, a proxy for phytoplankton density, in the Long Bay waters of SC. Efficiency of the algorithms performance in the study site were tested through a least squares regression and residual analysis. Results show that among the selected suite of algorithms the blue green models by Darecki and Stramski (2004) produced R2 of 0.68 with RMSE=0.39μg/l, Oc4v4 model by O'Reilly et al. (2000) gave R2 of 0.62 with RMSE=0.73ug/l, and the Oc2v4 also by O'Reilly et al (2000) gave R2 of 0.69 with RMSE=0.65. Among the NIR-red models, Moses et al (2009) two-band algorithm produced R2 of 0.75 and RMSE=1.79, and the three-band version generated R2 of 0.81 and RMSE=2.25ug/l. This suggests that the global RS models have the potential to monitor water quality parameters in the region but may require calibration for higher accuracy in Long Bay, SC.

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®.

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.

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.

An image analysis system for near-infrared (NIR) fluorescence lymph imaging

NASA Astrophysics Data System (ADS)

Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Rasmussen, John C.; Sevick-Muraca, Eva M.

2011-03-01

Quantitative analysis of lymphatic function is crucial for understanding the lymphatic system and diagnosing the associated diseases. Recently, a near-infrared (NIR) fluorescence imaging system is developed for real-time imaging lymphatic propulsion by intradermal injection of microdose of a NIR fluorophore distal to the lymphatics of interest. However, the previous analysis software3, 4 is underdeveloped, requiring extensive time and effort to analyze a NIR image sequence. In this paper, we develop a number of image processing techniques to automate the data analysis workflow, including an object tracking algorithm to stabilize the subject and remove the motion artifacts, an image representation named flow map to characterize lymphatic flow more reliably, and an automatic algorithm to compute lymph velocity and frequency of propulsion. By integrating all these techniques to a system, the analysis workflow significantly reduces the amount of required user interaction and improves the reliability of the measurement.

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.

Spectral relationships for atmospheric correction. I. Validation of red and near infra-red marine reflectance relationships.

PubMed

Goyens, C; Jamet, C; Ruddick, K G

2013-09-09

The present study provides an extensive overview of red and near infra-red (NIR) spectral relationships found in the literature and used to constrain red or NIR-modeling schemes in current atmospheric correction (AC) algorithms with the aim to improve water-leaving reflectance retrievals, ρw(λ), in turbid waters. However, most of these spectral relationships have been developed with restricted datasets and, subsequently, may not be globally valid, explaining the need of an accurate validation exercise. Spectral relationships are validated here with turbid in situ data for ρw(λ). Functions estimating ρw(λ) in the red were only valid for moderately turbid waters (ρw(λNIR) < 3.10(-3)). In contrast, bounding equations used to limit ρw(667) retrievals according to the water signal at 555 nm, appeared to be valid for all turbidity ranges presented in the in situ dataset. In the NIR region of the spectrum, the constant NIR reflectance ratio suggested by Ruddick et al. (2006) (Limnol. Oceanogr. 51, 1167-1179), was valid for moderately to very turbid waters (ρw(λNIR) < 10(-2)) while the polynomial function, initially developed by Wang et al. (2012) (Opt. Express 20, 741-753) with remote sensing reflectances over the Western Pacific, was also valid for extremely turbid waters (ρw(λNIR) > 10(-2)). The results of this study suggest to use the red bounding equations and the polynomial NIR function to constrain red or NIR-modeling schemes in AC processes with the aim to improve ρw(λ) retrievals where current AC algorithms fail.

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

PubMed Central

Barker, Jeffrey W.; Rosso, Andrea L.; Sparto, Patrick J.; Huppert, Theodore J.

2016-01-01

Abstract. Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods. PMID:27226974

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Neonatal hemodynamic response to visual cortex activity: high-density near-infrared spectroscopy study

NASA Astrophysics Data System (ADS)

Liao, Steve M.; Gregg, Nick M.; White, Brian R.; Zeff, Benjamin W.; Bjerkaas, Katelin A.; Inder, Terrie E.; Culver, Joseph P.

2010-03-01

The neurodevelopmental outcome of neonatal intensive care unit (NICU) infants is a major clinical concern with many infants displaying neurobehavioral deficits in childhood. Functional neuroimaging may provide early recognition of neural deficits in high-risk infants. Near-infrared spectroscopy (NIRS) has the advantage of providing functional neuroimaging in infants at the bedside. However, limitations in traditional NIRS have included contamination from superficial vascular dynamics in the scalp. Furthermore, controversy exists over the nature of normal vascular, responses in infants. To address these issues, we extend the use of novel high-density NIRS arrays with multiple source-detector distances and a superficial signal regression technique to infants. Evaluations of healthy term-born infants within the first three days of life are performed without sedation using a visual stimulus. We find that the regression technique significantly improves brain activation signal quality. Furthermore, in six out of eight infants, both oxy- and total hemoglobin increases while deoxyhemoglobin decreases, suggesting that, at term, the neurovascular coupling in the visual cortex is similar to that found in healthy adults. These results demonstrate the feasibility of using high-density NIRS arrays in infants to improve signal quality through superficial signal regression, and provide a foundation for further development of high-density NIRS as a clinical tool.

Diagnostic potential of Raman spectroscopy in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Wong Kee Song, Louis-Michel; Molckovsky, Andrea; Wang, Kenneth K.; Burgart, Lawrence J.; Dolenko, Brion; Somorjai, Rajmund L.; Wilson, Brian C.

2005-04-01

Patients with Barrett's esophagus (BE) undergo periodic endoscopic surveillance with random biopsies in an effort to detect dysplastic or early cancerous lesions. Surveillance may be enhanced by near-infrared Raman spectroscopy (NIRS), which has the potential to identify endoscopically-occult dysplastic lesions within the Barrett's segment and allow for targeted biopsies. The aim of this study was to assess the diagnostic performance of NIRS for identifying dysplastic lesions in BE in vivo. Raman spectra (Pexc=70 mW; t=5 s) were collected from Barrett's mucosa at endoscopy using a custom-built NIRS system (λexc=785 nm) equipped with a filtered fiber-optic probe. Each probed site was biopsied for matching histological diagnosis as assessed by an expert pathologist. Diagnostic algorithms were developed using genetic algorithm-based feature selection and linear discriminant analysis, and classification was performed on all spectra with a bootstrap-based cross-validation scheme. The analysis comprised 192 samples (112 non-dysplastic, 54 low-grade dysplasia and 26 high-grade dysplasia/early adenocarcinoma) from 65 patients. Compared with histology, NIRS differentiated dysplastic from non-dysplastic Barrett's samples with 86% sensitivity, 88% specificity and 87% accuracy. NIRS identified 'high-risk' lesions (high-grade dysplasia/early adenocarcinoma) with 88% sensitivity, 89% specificity and 89% accuracy. In the present study, NIRS classified Barrett's epithelia with high and clinically-useful diagnostic accuracy.

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy.

PubMed

Huppert, Theodore J

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementation in other fields, particularly for functional magnetic resonance imaging, the specific application of these methods in fNIRS research differs in several key ways related to the sources of noise and artifacts unique to fNIRS. In this brief communication, we discuss the application of linear regression models in fNIRS and the modifications needed to generalize these models in order to deal with structured (colored) noise due to systemic physiology and noise heteroscedasticity due to motion artifacts. The objective of this work is to present an overview of these noise properties in the context of the linear model as it applies to fNIRS data. This work is aimed at explaining these mathematical issues to the general fNIRS experimental researcher but is not intended to be a complete mathematical treatment of these concepts.

Exploring the use of optical flow for the study of functional NIRS signals

NASA Astrophysics Data System (ADS)

Fernandez Rojas, Raul; Huang, Xu; Ou, Keng-Liang; Hernandez-Juarez, Jesus

2017-03-01

Near infrared spectroscopy (NIRS) is an optical imaging technique that allows real-time measurements of Oxy and Deoxy-hemoglobin concentrations in human body tissue. In functional NIRS (fNIRS), this technique is used to study cortical activation in response to changes in neural activity. However, analysis of activation regions using NIRS is a challenging task in the field of medical image analysis and despite existing solutions, no homogeneous analysis method has yet been determined. For that reason, the aim of our present study is to report the use of an optical flow method for the analysis of cortical activation using near-infrared spectroscopy signals. We used real fNIRS data recorded from a noxious stimulation experiment as base of our implementation. To compute the optical flow algorithm, we first arrange NIRS signals (Oxy-hemoglobin) following our 24 channels (12 channels per hemisphere) head-probe configuration to create image-like samples. We then used two consecutive fNIRS samples per hemisphere as input frames for the optical flow algorithm, making one computation per hemisphere. The output from these two computations is the velocity field representing cortical activation from each hemisphere. The experimental results showed that the radial structure of flow vectors exhibited the origin of cortical activity, the development of stimulation as expansion or contraction of such flow vectors, and the flow of activation patterns may suggest prediction in cortical activity. The present study demonstrates that optical flow provides a power tool for the analysis of NIRS signals. Finally, we suggested a novel idea to identify pain status in nonverbal patients by using optical flow motion vectors; however, this idea will be study further in our future research.

Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean ( Phaseolus vulgaris L.).

PubMed

Hacisalihoglu, Gokhan; Larbi, Bismark; Settles, A Mark

2010-01-27

The objective of this study was to explore the potential of near-infrared reflectance (NIR) spectroscopy to determine individual seed composition in common bean ( Phaseolus vulgaris L.). NIR spectra and analytical measurements of seed weight, protein, and starch were collected from 267 individual bean seeds representing 91 diverse genotypes. Partial least-squares (PLS) regression models were developed with 61 bean accessions randomly assigned to a calibration data set and 30 accessions assigned to an external validation set. Protein gave the most accurate PLS regression, with the external validation set having a standard error of prediction (SEP) = 1.6%. PLS regressions for seed weight and starch had sufficient accuracy for seed sorting applications, with SEP = 41.2 mg and 4.9%, respectively. Seed color had a clear effect on the NIR spectra, with black beans having a distinct spectral type. Seed coat color did not impact the accuracy of PLS predictions. This research demonstrates that NIR is a promising technique for simultaneous sorting of multiple seed traits in single bean seeds with no sample preparation.

Remote Sensing of Cloud Properties using Ground-based Measurements of Zenith Radiance

NASA Technical Reports Server (NTRS)

Chiu, J. Christine; Marshak, Alexander; Knyazikhin, Yuri; Wiscombe, Warren J.; Barker, Howard W.; Barnard, James C.; Luo, Yi

2006-01-01

An extensive verification of cloud property retrievals has been conducted for two algorithms using zenith radiances measured by the Atmospheric Radiation Measurement (ARM) Program ground-based passive two-channel (673 and 870 nm) Narrow Field-Of-View Radiometer. The underlying principle of these algorithms is that clouds have nearly identical optical properties at these wavelengths, but corresponding spectral surface reflectances (for vegetated surfaces) differ significantly. The first algorithm, the RED vs. NIR, works for a fully three-dimensional cloud situation. It retrieves not only cloud optical depth, but also an effective radiative cloud fraction. Importantly, due to one-second time resolution of radiance measurements, we are able, for the first time, to capture detailed changes in cloud structure at the natural time scale of cloud evolution. The cloud optical depths tau retrieved by this algorithm are comparable to those inferred from both downward fluxes in overcast situations and microwave brightness temperatures for broken clouds. Moreover, it can retrieve tau for thin patchy clouds, where flux and microwave observations fail to detect them. The second algorithm, referred to as COUPLED, couples zenith radiances with simultaneous fluxes to infer 2. In general, the COUPLED and RED vs. NIR algorithms retrieve consistent values of tau. However, the COUPLED algorithm is more sensitive to the accuracies of measured radiance, flux, and surface reflectance than the RED vs. NIR algorithm. This is especially true for thick overcast clouds where it may substantially overestimate z.

Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models.

PubMed

Chen, Hui; Tan, Chao; Lin, Zan

2018-08-05

The wool content in textiles is a key quality index and the corresponding quantitative analysis takes an important position due to common adulterations in both raw and finished textiles. Conventional methods are maybe complicated, destructive, time-consuming, environment-unfriendly. Developing a quick, easy-to-use and green alternative method is interesting. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and several partial least squares (PLS)-based algorithms and elastic component regression (ECR) algorithms for measuring wool content in textile. A total of 108 cloth samples with wool content ranging from 0% to 100% (w/w) were collected and all the compositions are really existent in the market. The dataset was divided equally into the training and test sets for developing and validating calibration models. When using local PLS, the original spectrum axis was split into 20 sub-intervals. No obvious difference of performance can be seen for the local PLS models. The ECR model is comparable or superior to the other models due its flexibility, i.e., being transition state from PCR to PLS. It seems that ECR combined with NIR technique may be a potential method for determining wool content in textile products. In addition, it might have regulatory advantages to avoid time-consuming and environmental-unfriendly chemical analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

Validation of a New NIRS Method for Measuring Muscle Oxygenation During Rhythmic Handgrip Exercise

NASA Technical Reports Server (NTRS)

Hagan, R. Donald; Soller, Babs R.; Soyemi, Olusola; Landry, Michelle; Shear, Michael; Wu, Jacqueline

2006-01-01

Near infrared spectroscopy (NIRS) is commonly used to measure muscle oxygenation during exercise and recovery. Current NIRS algorithms do not account for variation in water content and optical pathlength during exercise. The current effort attempts to validate a newly developed NIRS algorithm during rhythmic handgrip exercise and recovery. Six female subjects, aver age 28 +/- 6 yrs, participated in the study. A venous catheter was placed in the retrograde direction in the antecubital space. A NIRS sensor with 30 mm source-detector separation was placed on the flexor digitorum profundus. Subjects performed two 5-min bouts of rhythmic handgrip exercise (2 s contraction/1 s relaxation) at 15% and 30% of maximal voluntary contraction. Venous blood was sampled before each bout, during the last minute of exercise, and after 5 minutes of recovery. Venous oxygen saturation (SvO2) was measured with a I-stat CG-4+ cartridge. Spectra were collected between 700-900 nm. A modified Beer's Law formula was used to calculate the absolute concentration of oxyhemoglobin (HbO2), deoxyhemoglobin (Hb) and water, as well as effective pathlength for each spectrum. Muscle oxygen saturation (SmO2) was calculated from the HbO2 and Hb results. The correlation between SvO2 and SmO2 was determined. Optical pathlength and water varied significantly during each exercise bout, with pathlength increasing approximately 20% and water increasing about 2%. R2 between blood and muscle SO2 was found to be 0.74, the figure shows the relationship over SvO2 values between 22% and 82%. The NIRS measurement was, on average, 6% lower than the blood measurement. It was concluded that pathlength changes during exercise because muscle contraction causes variation in optical scattering. Water concentration also changes, but only slightly. A new NIRS algorithm which accounts for exercise-induced variation in water and pathlength provided an accurate assessment of muscle oxygen saturation before, during and after exercise.

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.

Variables selection methods in near-infrared spectroscopy.

PubMed

Xiaobo, Zou; Jiewen, Zhao; Povey, Malcolm J W; Holmes, Mel; Hanpin, Mao

2010-05-14

Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), "Univariate" and "Sequential" selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (iPLS), windows PLS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given. Copyright 2010 Elsevier B.V. All rights reserved.

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.

A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children

NASA Astrophysics Data System (ADS)

Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita

2017-02-01

In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang

2006-01-01

Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.

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.

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.

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.

PubMed

Moore, Timothy S; Dowell, Mark D; Bradt, Shane; Verdu, Antonio Ruiz

2014-03-05

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll- a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll- a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll- a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll- a algorithms-the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands-with RMS error of 0.416 and 0.437 for each in log chlorophyll- a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll- a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll- a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

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.

Wound size measurement of lower extremity ulcers using segmentation algorithms

NASA Astrophysics Data System (ADS)

Dadkhah, Arash; Pang, Xing; Solis, Elizabeth; Fang, Ruogu; Godavarty, Anuradha

2016-03-01

Lower extremity ulcers are one of the most common complications that not only affect many people around the world but also have huge impact on economy since a large amount of resources are spent for treatment and prevention of the diseases. Clinical studies have shown that reduction in the wound size of 40% within 4 weeks is an acceptable progress in the healing process. Quantification of the wound size plays a crucial role in assessing the extent of healing and determining the treatment process. To date, wound healing is visually inspected and the wound size is measured from surface images. The extent of wound healing internally may vary from the surface. A near-infrared (NIR) optical imaging approach has been developed for non-contact imaging of wounds internally and differentiating healing from non-healing wounds. Herein, quantitative wound size measurements from NIR and white light images are estimated using a graph cuts and region growing image segmentation algorithms. The extent of the wound healing from NIR imaging of lower extremity ulcers in diabetic subjects are quantified and compared across NIR and white light images. NIR imaging and wound size measurements can play a significant role in potentially predicting the extent of internal healing, thus allowing better treatment plans when implemented for periodic imaging in future.

Discrimination methods of biological contamination on fresh-cut lettuce based on VNIR and NIR hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms on fresh-cut lettuce. The optimal wavebands that detect worm on fresh-cut lettuce for each type of HSI were investigated using the one-way...

Nondestructive determination of the modulus of elasticity of Fraxinus mandschurica using near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Yu, Huiling; Liang, Hao; Lin, Xue; Zhang, Yizhuo

2018-04-01

A nondestructive methodology is proposed to determine the modulus of elasticity (MOE) of Fraxinus mandschurica samples by using near-infrared (NIR) spectroscopy. The test data consisted of 150 NIR absorption spectra of the wood samples obtained using an NIR spectrometer, with the wavelength range of 900 to 1900 nm. To eliminate the high-frequency noise and the systematic variations on the baseline, Savitzky-Golay convolution combined with standard normal variate and detrending transformation was applied as data pretreated methods. The uninformative variable elimination (UVE), improved by the evolutionary Monte Carlo (EMC) algorithm and successive projections algorithm (SPA) selected three characteristic variables from full 117 variables. The predictive ability of the models was evaluated concerning the root-mean-square error of prediction (RMSEP) and coefficient of determination (Rp2) in the prediction set. In comparison with the predicted results of all the models established in the experiments, UVE-EMC-SPA-LS-SVM presented the best results with the smallest RMSEP of 0.652 and the highest Rp2 of 0.887. Thus, it is feasible to determine the MOE of F. mandschurica using NIR spectroscopy accurately.

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.

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters

PubMed Central

Moore, Timothy S.; Dowell, Mark D.; Bradt, Shane; Verdu, Antonio Ruiz

2014-01-01

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie. PMID:24839311

Near-infrared face recognition utilizing open CV software

NASA Astrophysics Data System (ADS)

Sellami, Louiza; Ngo, Hau; Fowler, Chris J.; Kearney, Liam M.

2014-06-01

Commercially available hardware, freely available algorithms, and authors' developed software are synergized successfully to detect and recognize subjects in an environment without visible light. This project integrates three major components: an illumination device operating in near infrared (NIR) spectrum, a NIR capable camera and a software algorithm capable of performing image manipulation, facial detection and recognition. Focusing our efforts in the near infrared spectrum allows the low budget system to operate covertly while still allowing for accurate face recognition. In doing so a valuable function has been developed which presents potential benefits in future civilian and military security and surveillance operations.

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.

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.

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.

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

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

PubMed

de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Héctor Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino

2018-05-01

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions. Published by Elsevier B.V.

[Measurement of soil organic matter and available K based on SPA-LS-SVM].

PubMed

Zhang, Hai-Liang; Liu, Xue-Mei; He, Yong

2014-05-01

Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LSSVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0. 860 2 and 2. 98 for OM and 0. 730 5 and 15. 78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325 approximately 1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.

Performance of regional oxygen saturation monitoring by near-infrared spectroscopy (NIRS) in pediatric inter-hospital transports with special reference to air ambulance transports: a methodological study.

PubMed

Hamrin, Tova Hannegård; Radell, Peter J; Fläring, Urban; Berner, Jonas; Eksborg, Staffan

2017-12-28

The aim of the present study was to evaluate the performance of regional oxygen saturation (rSO 2 ) monitoring with near infrared spectroscopy (NIRS) during pediatric inter-hospital transports and to optimize processing of the electronically stored data. Cerebral (rSO 2 -C) and abdominal (rSO 2 -A) NIRS sensors were used during transport in air ambulance and connecting ground ambulance. Data were electronically stored by the monitor during transport, extracted and analyzed off-line after the transport. After removal of all zero and floor effect values, the Savitzky-Golay algorithm of data smoothing was applied on the NIRS-signal. The second order of smoothing polynomial was used and the optimal number of neighboring points for the smoothing procedure was evaluated. NIRS-data from 38 pediatric patients was examined. Reliability, defined as measurements without values of 0 or 15%, was acceptable during transport (> 90% of all measurements). There were, however, individual patients with < 90% reliable measurements during transport, while no patient was found to have < 90% reliable measurements in hospital. Satisfactory noise reduction of the signal, without distortion of the underlying information, was achieved when 20-50 neighbors ("window-size") were used. The use of NIRS for measuring rSO 2 in clinical studies during pediatric transport in ground and air-ambulance is feasible but hampered by unreliable values and signal interference. By applying the Savitzky-Golay algorithm, the signal-to-noise ratio was improved and enabled better post-hoc signal evaluation.

Validation of ALFIA: a platform for quantifying near-infrared fluorescent images of lymphatic propulsion in humans

NASA Astrophysics Data System (ADS)

Rasmussen, John C.; Bautista, Merrick; Tan, I.-Chih; Adams, Kristen E.; Aldrich, Melissa; Marshall, Milton V.; Fife, Caroline E.; Maus, Erik A.; Smith, Latisha A.; Zhang, Jingdan; Xiang, Xiaoyan; Zhou, Shaohua Kevin; Sevick-Muraca, Eva M.

2011-02-01

Recently, we demonstrated near-infrared (NIR) fluorescence imaging for quantifying real-time lymphatic propulsion in humans following intradermal injections of microdose amounts of indocyanine green. However computational methods for image analysis are underdeveloped, hindering the translation and clinical adaptation of NIR fluorescent lymphatic imaging. In our initial work we used ImageJ and custom MatLab programs to manually identify lymphatic vessels and individual propulsion events using the temporal transit of the fluorescent dye. In addition, we extracted the apparent velocities of contractile propagation and time periods between propulsion events. Extensive time and effort were required to analyze the 6-8 gigabytes of NIR fluorescent images obtained for each subject. To alleviate this bottleneck, we commenced development of ALFIA, an integrated software platform which will permit automated, near real-time analysis of lymphatic function using NIR fluorescent imaging. However, prior to automation, the base algorithms calculating the apparent velocity and period must be validated to verify that they produce results consistent with the proof-of-concept programs. To do this, both methods were used to analyze NIR fluorescent images of two subjects and the number of propulsive events identified, the average apparent velocities, and the average periods for each subject were compared. Paired Student's t-tests indicate that the differences between their average results are not significant. With the base algorithms validated, further development and automation of ALFIA can be realized, significantly reducing the amount of user interaction required, and potentially enabling the near real-time, clinical evaluation of NIR fluorescent lymphatic imaging.

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.

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.

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

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.

Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece

NASA Astrophysics Data System (ADS)

Tsakiridis, Nikolaos L.; Tziolas, Nikolaos; Dimitrakos, Agathoklis; Galanis, Georgios; Ntonou, Eleftheria; Tsirika, Anastasia; Terzopoulou, Evangelia; Kalopesa, Eleni; Zalidis, George C.

2017-09-01

Soil Spectral Libraries facilitate agricultural production taking into account the principles of a low-input sustainable agriculture and provide more valuable knowledge to environmental policy makers, enabling improved decision making and effective management of natural resources in the region. In this paper, a comparison in the predictive performance of two state of the art algorithms, one linear (Partial Least Squares Regression) and one non-linear (Cubist), employed in soil spectroscopy is conducted. The comparison was carried out in a regional Soil Spectral Library developed in the Eastern Macedonia and Thrace region of Northern Greece, comprised of roughly 450 Entisol soil samples from soil horizons A (0-30 cm) and B (30-60 cm). The soil spectra were acquired in the visible - Near Infrared Red region (vis- NIR, 350nm-2500nm) using a standard protocol in the laboratory. Three soil properties, which are essential for agriculture, were analyzed and taken into account for the comparison. These were the Organic Matter, the Clay content and the concentration of nitrate-N. Additionally, three different spectral pre-processing techniques were utilized, namely the continuum removal, the absorbance transformation, and the first derivative. Following the removal of outliers using the Mahalanobis distance in the first 5 principal components of the spectra (accounting for 99.8% of the variance), a five-fold cross-validation experiment was considered for all 12 datasets. Statistical comparisons were conducted on the results, which indicate that the Cubist algorithm outperforms PLSR, while the most informative transformation is the first derivative.

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.

[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.

Typing SNP based on the near-infrared spectroscopy and artificial neural network

NASA Astrophysics Data System (ADS)

Ren, Li; Wang, Wei-Peng; Gao, Yu-Zhen; Yu, Xiao-Wei; Xie, Hong-Ping

2009-07-01

Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G > A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg-Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.

Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering

PubMed Central

Zhang, Xian; Noah, Jack Adam; Hirsch, Joy

2016-01-01

Abstract. Global systemic effects not specific to a task can be prominent in functional near-infrared spectroscopy (fNIRS) signals and the separation of task-specific fNIRS signals and global nonspecific effects is challenging due to waveform correlations. We describe a principal component spatial filter algorithm for separation of the global and local effects. The effectiveness of the approach is demonstrated using fNIRS signals acquired during a right finger-thumb tapping task where the response patterns are well established. Both the temporal waveforms and the spatial pattern consistencies between oxyhemoglobin and deoxyhemoglobin signals are significantly improved, consistent with the basic physiological basis of fNIRS signals and the expected pattern of activity associated with the task. PMID:26866047

Near-infrared spectroscopy determined cerebral oxygenation with eliminated skin blood flow in young males.

PubMed

Hirasawa, Ai; Kaneko, Takahito; Tanaka, Naoki; Funane, Tsukasa; Kiguchi, Masashi; Sørensen, Henrik; Secher, Niels H; Ogoh, Shigehiko

2016-04-01

We estimated cerebral oxygenation during handgrip exercise and a cognitive task using an algorithm that eliminates the influence of skin blood flow (SkBF) on the near-infrared spectroscopy (NIRS) signal. The algorithm involves a subtraction method to develop a correction factor for each subject. For twelve male volunteers (age 21 ± 1 yrs) +80 mmHg pressure was applied over the left temporal artery for 30 s by a custom-made headband cuff to calculate an individual correction factor. From the NIRS-determined ipsilateral cerebral oxyhemoglobin concentration (O2Hb) at two source-detector distances (15 and 30 mm) with the algorithm using the individual correction factor, we expressed cerebral oxygenation without influence from scalp and scull blood flow. Validity of the estimated cerebral oxygenation was verified during cerebral neural activation (handgrip exercise and cognitive task). With the use of both source-detector distances, handgrip exercise and a cognitive task increased O2Hb (P < 0.01) but O2Hb was reduced when SkBF became eliminated by pressure on the temporal artery for 5 s. However, when the estimation of cerebral oxygenation was based on the algorithm developed when pressure was applied to the temporal artery, estimated O2Hb was not affected by elimination of SkBF during handgrip exercise (P = 0.666) or the cognitive task (P = 0.105). These findings suggest that the algorithm with the individual correction factor allows for evaluation of changes in an accurate cerebral oxygenation without influence of extracranial blood flow by NIRS applied to the forehead.

Estimating Particulate Inorganic Carbon Concentrations of the Global Ocean From Ocean Color Measurements Using a Reflectance Difference Approach

NASA Astrophysics Data System (ADS)

Mitchell, C.; Hu, C.; Bowler, B.; Drapeau, D.; Balch, W. M.

2017-11-01

A new algorithm for estimating particulate inorganic carbon (PIC) concentrations from ocean color measurements is presented. PIC plays an important role in the global carbon cycle through the oceanic carbonate pump, therefore accurate estimations of PIC concentrations from satellite remote sensing are crucial for observing changes on a global scale. An extensive global data set was created from field and satellite observations for investigating the relationship between PIC concentrations and differences in the remote sensing reflectance (Rrs) at green, red, and near-infrared (NIR) wavebands. Three color indices were defined: two as the relative height of Rrs(667) above a baseline running between Rrs(547) and an Rrs in the NIR (either 748 or 869 nm), and one as the difference between Rrs(547) and Rrs(667). All three color indices were found to explain over 90% of the variance in field-measured PIC. But, due to the lack of availability of Rrs(NIR) in the standard ocean color data products, most of the further analysis presented here was done using the color index determined from only two bands. The new two-band color index algorithm was found to retrieve PIC concentrations more accurately than the current standard algorithm used in generating global PIC data products. Application of the new algorithm to satellite imagery showed patterns on the global scale as revealed from field measurements. The new algorithm was more resistant to atmospheric correction errors and residual errors in sun glint corrections, as seen by a reduction in the speckling and patchiness in the satellite-derived PIC images.

Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Virtanen, Jaakko; Noponen, Tommi; Kotilahti, Kalle; Virtanen, Juha; Ilmoniemi, Risto J.

2011-08-01

In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.

Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.

PubMed

Zhang, Shen; Zheng, Yanchun; Wang, Daifa; Wang, Ling; Ma, Jianai; Zhang, Jing; Xu, Weihao; Li, Deyu; Zhang, Dan

2017-08-10

Motor imagery is one of the most investigated paradigms in the field of brain-computer interfaces (BCIs). The present study explored the feasibility of applying a common spatial pattern (CSP)-based algorithm for a functional near-infrared spectroscopy (fNIRS)-based motor imagery BCI. Ten participants performed kinesthetic imagery of their left- and right-hand movements while 20-channel fNIRS signals were recorded over the motor cortex. The CSP method was implemented to obtain the spatial filters specific for both imagery tasks. The mean, slope, and variance of the CSP filtered signals were taken as features for BCI classification. Results showed that the CSP-based algorithm outperformed two representative channel-wise methods for classifying the two imagery statuses using either data from all channels or averaged data from imagery responsive channels only (oxygenated hemoglobin: CSP-based: 75.3±13.1%; all-channel: 52.3±5.3%; averaged: 64.8±13.2%; deoxygenated hemoglobin: CSP-based: 72.3±13.0%; all-channel: 48.8±8.2%; averaged: 63.3±13.3%). Furthermore, the effectiveness of the CSP method was also observed for the motor execution data to a lesser extent. A partial correlation analysis revealed significant independent contributions from all three types of features, including the often-ignored variance feature. To our knowledge, this is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs. Copyright © 2017 Elsevier B.V. All rights reserved.

Atmospheric correction using near-infrared bands for satellite ocean color data processing in the turbid western Pacific region.

PubMed

Wang, Menghua; Shi, Wei; Jiang, Lide

2012-01-16

A regional near-infrared (NIR) ocean normalized water-leaving radiance (nL(w)(λ)) model is proposed for atmospheric correction for ocean color data processing in the western Pacific region, including the Bohai Sea, Yellow Sea, and East China Sea. Our motivation for this work is to derive ocean color products in the highly turbid western Pacific region using the Geostationary Ocean Color Imager (GOCI) onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS). GOCI has eight spectral bands from 412 to 865 nm but does not have shortwave infrared (SWIR) bands that are needed for satellite ocean color remote sensing in the turbid ocean region. Based on a regional empirical relationship between the NIR nL(w)(λ) and diffuse attenuation coefficient at 490 nm (K(d)(490)), which is derived from the long-term measurements with the Moderate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, an iterative scheme with the NIR-based atmospheric correction algorithm has been developed. Results from MODIS-Aqua measurements show that ocean color products in the region derived from the new proposed NIR-corrected atmospheric correction algorithm match well with those from the SWIR atmospheric correction algorithm. Thus, the proposed new atmospheric correction method provides an alternative for ocean color data processing for GOCI (and other ocean color satellite sensors without SWIR bands) in the turbid ocean regions of the Bohai Sea, Yellow Sea, and East China Sea, although the SWIR-based atmospheric correction approach is still much preferred. The proposed atmospheric correction methodology can also be applied to other turbid coastal regions.

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.

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.

Near-infrared hyperspectral imaging of atherosclerotic plaque in WHHLMI rabbit artery

NASA Astrophysics Data System (ADS)

Ishii, Katsunori; Kitayabu, Akiko; Omiya, Kota; Honda, Norihiro; Awazu, Kunio

2013-03-01

Hyperspectral imaging (HSI) of rabbit atherosclerotic plaque in near-infrared (NIR) range from 1150 to 2400 nm was demonstrated. A method to identify vulnerable plaques that are likely to cause acute coronary events has been required. The object of this study is identifying vulnerable plaques by NIR-HSI for an angioscopic application. In this study, we observed the hyperspectral images of the atherosclerotic plaque in WHHLMI rabbit (atherosclerotic rabbit) artery under simulated angioscopic conditions by NIR-HSI. NIR-HSI system was constructed by a NIR super continuum light and a mercury-cadmium-telluride camera. Spectral absorbance values (log (1/R) data) were obtained in the wavelength range from 1150 to 2400 nm at 10 nm intervals. The hyperspectral images were constructed with spectral angle mapper algorithm. As a result, the detections of atherosclerotic plaque under angioscopic observation conditions were achieved especially in the wavelength around 1200 nm, which corresponds to the second overtone of CH stretching vibration mode. The NIR-HSI was considered to serve as an angioscopic diagnosis technique to identify vulnerable plaques without clamping and saline injection.

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

Feasibility of using a miniature NIR spectrometer to measure volumic mass during alcoholic fermentation.

PubMed

Fernández-Novales, Juan; López, María-Isabel; González-Caballero, Virginia; Ramírez, Pilar; Sánchez, María-Teresa

2011-06-01

Volumic mass-a key component of must quality control tests during alcoholic fermentation-is of great interest to the winemaking industry. Transmitance near-infrared (NIR) spectra of 124 must samples over the range of 200-1,100-nm were obtained using a miniature spectrometer. The performance of this instrument to predict volumic mass was evaluated using partial least squares (PLS) regression and multiple linear regression (MLR). The validation statistics coefficient of determination (r(2)) and the standard error of prediction (SEP) were r(2) = 0.98, n = 31 and r(2) = 0.96, n = 31, and SEP = 5.85 and 7.49 g/dm(3) for PLS and MLR equations developed to fit reference data for volumic mass and spectral data. Comparison of results from MLR and PLS demonstrates that a MLR model with six significant wavelengths (P < 0.05) fit volumic mass data to transmittance (1/T) data slightly worse than a more sophisticated PLS model using the full scanning range. The results suggest that NIR spectroscopy is a suitable technique for predicting volumic mass during alcoholic fermentation, and that a low-cost NIR instrument can be used for this purpose.

Validating the absolute reliability of a fat free mass estimate equation in hemodialysis patients using near-infrared spectroscopy.

PubMed

Kono, Kenichi; Nishida, Yusuke; Moriyama, Yoshihumi; Taoka, Masahiro; Sato, Takashi

2015-06-01

The assessment of nutritional states using fat free mass (FFM) measured with near-infrared spectroscopy (NIRS) is clinically useful. This measurement should incorporate the patient's post-dialysis weight ("dry weight"), in order to exclude the effects of any change in water mass. We therefore used NIRS to investigate the regression, independent variables, and absolute reliability of FFM in dry weight. The study included 47 outpatients from the hemodialysis unit. Body weight was measured before dialysis, and FFM was measured using NIRS before and after dialysis treatment. Multiple regression analysis was used to estimate the FFM in dry weight as the dependent variable. The measured FFM before dialysis treatment (Mw-FFM), and the difference between measured and dry weight (Mw-Dw) were independent variables. We performed Bland-Altman analysis to detect errors between the statistically estimated FFM and the measured FFM after dialysis treatment. The multiple regression equation to estimate the FFM in dry weight was: Dw-FFM = 0.038 + (0.984 × Mw-FFM) + (-0.571 × [Mw-Dw]); R(2)  = 0.99). There was no systematic bias between the estimated and the measured values of FFM in dry weight. Using NIRS, FFM in dry weight can be calculated by an equation including FFM in measured weight and the difference between the measured weight and the dry weight. © 2015 The Authors. Therapeutic Apheresis and Dialysis © 2015 International Society for Apheresis.

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.

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

NASA Technical Reports Server (NTRS)

Harrivel, Angela; Hearn, Tristan

2012-01-01

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in realtime. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

NASA Technical Reports Server (NTRS)

Harrivel, Angela; Hearn, Tristan A.

2012-01-01

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in real-time. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors.

Near-infrared hyperspectral imaging of atherosclerotic tissue phantom

NASA Astrophysics Data System (ADS)

Ishii, K.; Nagao, R.; Kitayabu, A.; Awazu, K.

2013-06-01

A method to identify vulnerable plaques that are likely to cause acute coronary events has been required. The object of this study is identifying vulnerable plaques by hyperspectral imaging in near-infrared range (NIR-HSI) for an angioscopic application. In this study, NIR-HSI of atherosclerotic tissue phantoms was demonstrated under simulated angioscopic conditions. NIR-HSI system was constructed by a NIR super continuum light and a mercury-cadmium-telluride camera. Spectral absorbance values were obtained in the wavelength range from 1150 to 2400 nm at 10 nm intervals. The hyperspectral images were constructed with spectral angle mapper algorithm. As a result, detections of the lipid area in the atherosclerotic tissue phantom under angioscopic observation conditions were achieved especially in the wavelength around 1200 nm, which corresponds to the second overtone of CH stretching vibration mode.

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.

Rapid monitoring of grape withering using visible near-infrared spectroscopy.

PubMed

Beghi, Roberto; Giovenzana, Valentina; Marai, Simone; Guidetti, Riccardo

2015-12-01

Wineries need new practical and quick instruments, non-destructive and able to quantitatively evaluate during withering the parameters that impact product quality. The aim of the work was to test an optical portable system (visible near-infrared (NIR) spectrophotometer) in a wavelength range of 400-1000 nm for the prediction of quality parameters of grape berries during withering. A total of 300 red grape samples (Vitis vinifera L., Corvina cultivar) harvested in vintage year 2012 from the Valpolicella area (Verona, Italy) were analyzed. Qualitative (principal component analysis, PCA) and quantitative (partial least squares regression algorithm, PLS) evaluations were performed on grape spectra. PCA showed a clear sample grouping for the different withering stages. PLS models gave encouraging predictive capabilities for soluble solids content (R(2) val  = 0.62 and ratio performance deviation, RPD = 1.87) and firmness (R(2) val  = 0.56 and RPD = 1.79). The work demonstrated the applicability of visible NIR spectroscopy as a rapid technique for the analysis of grape quality directly in barns, during withering. The sector could be provided with simple and inexpensive optical systems that could be used to monitor the withering degree of grape for better management of the wine production process. © 2014 Society of Chemical Industry.

Evaluation of a reflectance model used in the SeaWiFS ocean color algorithm: implications for chlorophyll concentration retrievals

NASA Astrophysics Data System (ADS)

Yan, Banghua; Stamnes, Knut; Toratani, Mitsuhiro; Li, Wei; Stamnes, Jakob J.

2002-10-01

For the atmospheric correction of ocean-color imagery obtained over Case I waters with the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) instrument the method currently used to relax the black-pixel assumption in the near infrared (NIR) relies on (1) an approximate model for the nadir NIR remote-sensing reflectance and (2) an assumption that the water-leaving radiance is isotropic over the upward hemisphere. Radiance simulations based on a comprehensive radiative-transfer model for the coupled atmosphere-ocean system and measurements of the nadir remote-sensing reflectance at 670 nm compiled in the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) database are used to assess the validity of this method. The results show that (1) it is important to improve the flexibility of the reflectance model to provide more realistic predictions of the nadir NIR water-leaving reflectance for different ocean regions and (2) the isotropic assumption should be avoided in the retrieval of ocean color, if the chlorophyll concentration is larger than approximately 6, 10, and 40 mg m-3 when the aerosol optical depth is approximately 0.05, 0.1, and 0.3, respectively. Finally, we extend our scope to Case II ocean waters to gain insight and enhance our understanding of the NIR aspects of ocean color. The results show that the isotropic assumption is invalid in a wider range than in Case I waters owing to the enhanced water-leaving reflectance resulting from oceanic sediments in the NIR wavelengths.

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.

Parallel programming of gradient-based iterative image reconstruction schemes for optical tomography.

PubMed

Hielscher, Andreas H; Bartel, Sebastian

2004-02-01

Optical tomography (OT) is a fast developing novel imaging modality that uses near-infrared (NIR) light to obtain cross-sectional views of optical properties inside the human body. A major challenge remains the time-consuming, computational-intensive image reconstruction problem that converts NIR transmission measurements into cross-sectional images. To increase the speed of iterative image reconstruction schemes that are commonly applied for OT, we have developed and implemented several parallel algorithms on a cluster of workstations. Static process distribution as well as dynamic load balancing schemes suitable for heterogeneous clusters and varying machine performances are introduced and tested. The resulting algorithms are shown to accelerate the reconstruction process to various degrees, substantially reducing the computation times for clinically relevant problems.

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.

Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm.

PubMed

Insausti, Matías; Gomes, Adriano A; Cruz, Fernanda V; Pistonesi, Marcelo F; Araujo, Mario C U; Galvão, Roberto K H; Pereira, Claudete F; Band, Beatriz S F

2012-08-15

This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes. Copyright © 2012 Elsevier B.V. All rights reserved.

Determination of fat and total protein content in milk using conventional digital imaging.

PubMed

Kucheryavskiy, Sergey; Melenteva, Anastasiia; Bogomolov, Andrey

2014-04-01

The applicability of conventional digital imaging to quantitative determination of fat and total protein in cow's milk, based on the phenomenon of light scatter, has been proved. A new algorithm for extracting features from digital images of milk samples has been developed. The algorithm takes into account spatial distribution of light, diffusely transmitted through a sample. The proposed method has been tested on two sample sets prepared from industrial raw milk standards, with variable fat and protein content. Partial Least-Squares (PLS) regression on the features calculated from images of monochromatically illuminated milk samples resulted in models with high prediction performance when analysed the sets separately (best models with cross-validated R(2)=0.974 for protein and R(2)=0.973 for fat content). However when analysed the sets jointly with the obtained results were significantly worse (best models with cross-validated R(2)=0.890 for fat content and R(2)=0.720 for protein content). The results have been compared with previously published Vis/SW-NIR spectroscopic study of similar samples. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

PubMed

Navarro, Pedro J; Pérez, Fernando; Weiss, Julia; Egea-Cortines, Marcos

2016-05-05

Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

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

Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

PubMed

Villar, Alberto; Vadillo, Julen; Santos, Jose I; Gorritxategi, Eneko; Mabe, Jon; Arnaiz, Aitor; Fernández, Luis A

2017-04-15

Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Prediction of SOC content by Vis-NIR spectroscopy at European scale using a modified local PLS algorithm

NASA Astrophysics Data System (ADS)

Nocita, M.; Stevens, A.; Toth, G.; van Wesemael, B.; Montanarella, L.

2012-12-01

In the context of global environmental change, the estimation of carbon fluxes between soils and the atmosphere has been the object of a growing number of studies. This has been motivated notably by the possibility to sequester CO2 into soils by increasing the soil organic carbon (SOC) stocks and by the role of SOC in maintaining soil quality. Spatial variability of SOC masks its slow accumulation or depletion, and the sampling density required to detect a change in SOC content is often very high and thus very expensive and labour intensive. Visible near infrared diffuse reflectance spectroscopy (Vis-NIR DRS) has been shown to be a fast, cheap and efficient tool for the prediction of SOC at fine scales. However, when applied to regional or country scales, Vis-NIR DRS did not provide sufficient accuracy as an alternative to standard laboratory soil analysis for SOC monitoring. Under the framework of Land Use/Cover Area Frame Statistical Survey (LUCAS) project of the European Commission's Joint Research Centre (JRC), about 20,000 samples were collected all over European Union. Soil samples were analyzed for several physical and chemical parameters, and scanned with a Vis-NIR spectrometer in the same laboratory. The scope of our research was to predict SOC content at European scale using LUCAS spectral library. We implemented a modified local partial least square regression (l-PLS) including, in addition to spectral distance, other potentially useful covariates (geography, texture, etc.) to select for each unknown sample a group of predicting neighbours. The dataset was split in mineral soils under cropland, mineral soils under grassland, mineral soils under woodland, and organic soils due to the extremely diverse spectral response of the four classes. Four every class training (70%) and test (30%) sets were created to calibrate and validate the SOC prediction models. The results showed very good prediction ability for mineral soils under cropland and mineral soils under grassland, with a root mean square error (RMSE) of 3.6 and 7.2 g C kg-1 respectively, while mineral soils under woodland and organic soils predictions were less accurate (RMSE of 11.9 and 51.1 g C kg-1). The RMSE was lower (except for organic soils) when sand content was used as covariate in the selection of the l-PLS predicting neighbours. The obtained results proved that: (i) Although the enormous spatial variability of European soils, the developed modified l-PLS algorithm was able to produce stable calibrations and accurate predictions. (ii) It is essential to invest in spectral libraries built according to sampling strategies, based on soil types, and a standardized laboratory protocol. (iii) Vis-NIR DRS spectroscopy is a powerful and cost effective tool to predict SOC content at regional/continental scales, and should be converted from a pure research discipline into a reference operational method decreasing the uncertainties of SOC monitoring and terrestrial ecosystems carbon fluxes at all scales.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2015-01-01

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Calculation of local optical properties in highly scattering media using a-priori structural information for application to simultaneous NIR-MR breast examination

NASA Astrophysics Data System (ADS)

Ntziachristos, Vasilis; Yodh, Arjun G.; Schnall, Mitchell D.; Ma, XuHui; Chance, Britton

1998-12-01

A single photon counting NIR imager designed to work simultaneously with an MRI scanner for concurrent NIR-MR mammography has recently been developed. The combination of imaging modalities aims in effectively investigating the competence of optical imaging as a stand along modality and as an MRI add-on in order to increase the sensitivity and specificity of the mammoraphic examination. In this work we focus on the second aim. We present the methodology developed to employ the MR anatomical information in order to simplify the forward problem and accurately calculate local tissue optical properties, by fitting the NIR data to this model. Derivation of local optical properties due to intrinsic or extrinsic may identify the existence of malignant and benign breast tissue NIR signatures. We have evaluated the performance of the solver with experimental measurements, also presented here, from models with known absorption perturbations. The average quantification error of absolute absorption of local lesions has been found to be less than 10% in simple models and algorithm convergence is always ensured.

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.

Theoretical and experimental study on near infrared time-resolved optical diffuse tomography

NASA Astrophysics Data System (ADS)

Zhao, Huijuan; Gao, Feng; Tanikawa, Yukari; Yamada, Yukio

2006-08-01

Parts of the works of our group in the past five years on near infrared time-resolved (TR) optical tomography are summarized in this paper. The image reconstruction algorithm is based on Newton Raphson scheme with a datatype R generated from modified Generalized Pulse Spectrum Technique. Firstly, the algorithm is evaluated with simulated data from a 2-D model and the datatype R is compared with other popularly used datatypes. In this second part of the paper, the in vitro and in vivo NIR DOT imaging on a chicken leg and a human forearm, respectively are presented for evaluating both the image reconstruction algorithm and the TR measurement system. The third part of this paper is about the differential pathlength factor of human head while monitoring head activity with NIRS and applying the modified Lambert-Beer law. Benefiting from the TR system, the measured DPF maps of the three import areas of human head are presented in this paper.

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.

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

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.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy

NASA Astrophysics Data System (ADS)

Tewari, Jagdish C.; Dixit, Vivechana; Cho, Byoung-Kwan; Malik, Kamal A.

2008-12-01

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1100 to 2500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient ( R2). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R2 value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Characterization of Particle Backscattering of Global Highly Turbid Waters From VIIRS Ocean Color Observations

NASA Astrophysics Data System (ADS)

Shi, Wei; Wang, Menghua

2017-11-01

Normalized water-leaving radiance spectra nLw(λ) at the near-infrared (NIR) from five years of observations (2012-2016) with the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) are used to derive the particle backscattering coefficients bbp(λ) for global highly turbid coastal and inland waters. Based on the fact that the absorption coefficient of sea water aw(λ) is generally much larger than those of the other constituents aiop(λ) at the NIR wavelengths in coastal and inland waters, an NIR-based bbp(λ) algorithm for turbid coastal and inland waters has been developed and used in this study. This algorithm can be safely used for highly turbid waters with nLw(745) and nLw(862) < ˜6 and ˜4 mW cm-2 μm-1 sr-1, respectively. Seasonal and interannual variations of bbp(λ) in China's east coastal region, the Amazon River Estuary, the La Plata River Estuary, the Meghna River Estuary, the Atchafalaya River Estuary, and Lake Taihu are characterized and quantified. The coefficient bbp(λ) can reach over ˜3-4 m-1 in the Amazon River Estuary and China's east coastal region. The Amazon River Estuary is identified as the most turbid region in the global ocean in terms of bbp(λ) magnitude. bbp(λ) spectra in these five highly turbid regions are also seasonal-dependent and regional-dependent. In the highly turbid waters of China's east coastal region and the Amazon River Estuary, bbp(λ) generally increases in wavelength from 410 to 862 nm, while it decreases in the La Plata River Estuary and Atchafalaya River Estuary. This is attributed to the different particle size distributions in these waters. The geophysical implication of the bbp(λ) spectral curvatures for different waters is discussed. To improve global bbp(λ) for both open oceans and coastal turbid waters, a new combined NIR-based and Quasi-Analytical Algorithm (QAA)-based bbp(λ) algorithm is proposed and demonstrated.

Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy.

PubMed

Tewari, Jagdish C; Dixit, Vivechana; Cho, Byoung-Kwan; Malik, Kamal A

2008-12-01

The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1,100 to 2,500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient (R(2)). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R(2) value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.

Airborne laser systems for atmospheric sounding in the near infrared

NASA Astrophysics Data System (ADS)

Sabatini, Roberto; Richardson, Mark A.; Jia, Huamin; Zammit-Mangion, David

2012-06-01

This paper presents new techniques for atmospheric sounding using Near Infrared (NIR) laser sources, direct detection electro-optics and passive infrared imaging systems. These techniques allow a direct determination of atmospheric extinction and, through the adoption of suitable inversion algorithms, the indirect measurement of some important natural and man-made atmospheric constituents, including Carbon Dioxide (CO2). The proposed techniques are suitable for remote sensing missions performed by using aircraft, satellites, Unmanned Aerial Vehicles (UAV), parachute/gliding vehicles, Roving Surface Vehicles (RSV), or Permanent Surface Installations (PSI). The various techniques proposed offer relative advantages in different scenarios. All are based on measurements of the laser energy/power incident on target surfaces of known geometric and reflective characteristics, by means of infrared detectors and/or infrared cameras calibrated for radiance. Experimental results are presented relative to ground and flight trials performed with laser systems operating in the near infrared (NIR) at λ = 1064 nm and λ = 1550 nm. This includes ground tests performed with 10 Hz and 20 KHz PRF NIR laser systems in a variety of atmospheric conditions, and flight trials performed with a 10 Hz airborne NIR laser system installed on a TORNADO aircraft, flying up to altitudes of 22,000 ft above ground level. Future activities are planned to validate the atmospheric retrieval algorithms developed for CO2 column density measurements, with emphasis on aircraft related emissions at airports and other high air-traffic density environments.

Shedding light to sleep studies

NASA Astrophysics Data System (ADS)

Dieffenderfer, James; Krystal, Andrew; Bozkurt, Alper

2017-08-01

This paper presents our efforts in the development of a small wireless, flexible bandage sized near-infrared spectroscopy (NIRS) system for sleep analysis. The current size of the system is 2.8 cm × 1.7 cm × 0.6 cm. It is capable of performing NIRS with 660nm, 940nm and 850nm wavelengths for up to 11 hours continuously. The device is placed on the forehead to measure from the prefrontal cortex and the raw data is continuously streamed over Bluetooth to a nearby data aggregator such as a smartphone for post processing and cloud connection. In this study, we performed traditional polysomnography simultaneously with NIRS to evaluate agreement with traditional measures of sleep and to provide labelled data for future work involving learning algorithms. Ultimately, we expect a machine learning algorithm to be able to generate characterization of sleep states comparable to traditional methods based on this biophotonics data. The system also includes an inertial measurement unit and the features that can be extracted from the presented system include sleep posture, heart rate, respiratory rate, relative change in oxy and deoxy hemoglobin concentrations and tissue oxygenation and cerebral arterial oxygen extracted from these. Preliminary proof of concept results are promising and demonstrate the capability to measure heart rate, respiratory rate and slow-wave-sleep stages. This system serves as a prototype to evaluate the potential of a small bandage-size continuous-wave NIRS device to be a useful means of studying sleep.

Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

PubMed Central

Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe

2016-01-01

Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325

Feasibility of FT–Raman spectroscopy for rapid screening for DON toxin in ground wheat and barley.

PubMed

Liu, Y; Delwiche, S R; Dong, Y

2009-10-01

Rapid detection of deoxynivalenol (DON) in cereal-based food and feed has long been the goal of regulators and manufacturers. As non-destructive approaches, infrared (IR) and near-infrared (NIR) spectroscopic techniques have been used for the prediction and classification of contaminated single-kernel and ground grain without any DON extraction steps. These methods, however, are hindered by the intense and broad spectral bands attributed to naturally occurring moisture. Raman spectroscopy could be an alternative to IR and NIR due to its insensitivity to water and fewer overlapped bands. This study explored the feasibility of the Raman technique for rapid and non-destructive screening of DON-contaminated wheat and barley meal. The advantages of this technique include the use of a 1064-nm NIR excitation laser that reduces interference from fluorescence of biological compounds in wheat and barley, the use of a simple intensity-intensity algorithm at two unique frequencies, plus the technique's ease of sample preparation. The results indicate that the simple algorithm, as well as principal component analysis applied to the Raman spectra, can be used to classify low from high DON grain.

Anatomical and Functional Images of in vitro and in vivo Tissues by NIR Time-domain Diffuse Optical Tomography

NASA Astrophysics Data System (ADS)

Zhao, Huijuan; Gao, Feng; Tanikawa, Yukari; Homma, Kazuhiro; Onodera, Yoichi; Yamada, Yukio

Near infra-red (NIR) diffuse optical tomography (DOT) has gained much attention and it will be clinically applied to imaging breast, neonatal head, and the hemodynamics of the brain because of its noninvasiveness and deep penetration in biological tissue. Prior to achieving the imaging of infant brain using DOT, the developed methodologies need to be experimentally justified by imaging some real organs with simpler structures. Here we report our results of an in vitro chicken leg and an in vivo exercising human forearm from the data measured by a multi-channel time-resolved NIR system. Tomographic images were reconstructed by a two-dimensional image reconstruction algorithm based on a modified generalized pulse spectrum technique for simultaneous reconstruction of the µa and µs´. The absolute µa- and µs´-images revealed the inner structures of the chicken leg and the forearm, where the bones were clearly distinguished from the muscle. The Δµa-images showed the blood volume changes during the forearm exercise, proving that the system and the image reconstruction algorithm could potentially be used for imaging not only the anatomic structure but also the hemodynamics in neonatal heads.

Discrimination methods for biological contaminants in fresh-cut lettuce based on VNIR and NIR hyperspectral imaging

NASA Astrophysics Data System (ADS)

Mo, Changyeun; Kim, Giyoung; Kim, Moon S.; Lim, Jongguk; Lee, Seung Hyun; Lee, Hong-Seok; Cho, Byoung-Kwan

2017-09-01

The rapid detection of biological contaminants such as worms in fresh-cut vegetables is necessary to improve the efficiency of visual inspections carried out by workers. Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms in fresh-cut lettuce. The optimal wavebands that can detect worms in fresh-cut lettuce were investigated for each type of HSI using one-way ANOVA. Worm-detection imaging algorithms for VNIR and NIR imaging exhibited prediction accuracies of 97.00% (RI547/945) and 100.0% (RI1064/1176, SI1064-1176, RSI-I(1064-1173)/1064, and RSI-II(1064-1176)/(1064+1176)), respectively. The two HSI techniques revealed that spectral images with a pixel size of 1 × 1 mm or 2 × 2 mm had the best classification accuracy for worms. The results demonstrate that hyperspectral reflectance imaging techniques have the potential to detect worms in fresh-cut lettuce. Future research relating to this work will focus on a real-time sorting system for lettuce that can simultaneously detect various defects such as browning, worms, and slugs.

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.

The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor

NASA Astrophysics Data System (ADS)

Loyola, Diego G.; Gimeno García, Sebastián; Lutz, Ronny; Argyrouli, Athina; Romahn, Fabian; Spurr, Robert J. D.; Pedergnana, Mattia; Doicu, Adrian; Molina García, Víctor; Schüssler, Olena

2018-01-01

This paper presents the operational cloud retrieval algorithms for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel-5 Precursor (S5P) mission scheduled for launch in 2017. Two algorithms working in tandem are used for retrieving cloud properties: OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks). OCRA retrieves the cloud fraction using TROPOMI measurements in the ultraviolet (UV) and visible (VIS) spectral regions, and ROCINN retrieves the cloud top height (pressure) and optical thickness (albedo) using TROPOMI measurements in and around the oxygen A-band in the near infrared (NIR). Cloud parameters from TROPOMI/S5P will be used not only for enhancing the accuracy of trace gas retrievals but also for extending the satellite data record of cloud information derived from oxygen A-band measurements, a record initiated with the Global Ozone Monitoring Experiment (GOME) on board the second European Remote-Sensing Satellite (ERS-2) over 20 years ago. The OCRA and ROCINN algorithms are integrated in the S5P operational processor UPAS (Universal Processor for UV/VIS/NIR Atmospheric Spectrometers), and we present here UPAS cloud results using the Ozone Monitoring Instrument (OMI) and GOME-2 measurements. In addition, we examine anticipated challenges for the TROPOMI/S5P cloud retrieval algorithms, and we discuss the future validation needs for OCRA and ROCINN.

A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Li, Li-Na; Ma, Chang-Ming; Chang, Ming; Zhang, Ren-Cheng

2017-12-01

A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard-Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.

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.

Determination of main components and anaerobic rumen digestibility of aquatic plants in vitro using near-infrared-reflectance spectroscopy.

PubMed

Yue, Zheng-Bo; Zhang, Meng-Lin; Sheng, Guo-Ping; Liu, Rong-Hua; Long, Ying; Xiang, Bing-Ren; Wang, Jin; Yu, Han-Qing

2010-04-01

A near-infrared-reflectance (NIR) spectroscopy-based method is established to determine the main components of aquatic plants as well as their anaerobic rumen biodegradability. The developed method is more rapid and accurate compared to the conventional chemical analysis and biodegradability tests. Moisture, volatile solid, Klason lignin and ash in entire aquatic plants could be accurately predicted using this method with coefficient of determination (r(2)) values of 0.952, 0.916, 0.939 and 0.950, respectively. In addition, the anaerobic rumen biodegradability of aquatic plants, represented as biogas and methane yields, could also be predicted well. The algorithm of continuous wavelet transform for the NIR spectral data pretreatment is able to greatly enhance the robustness and predictive ability of the NIR spectral analysis. These results indicate that NIR spectroscopy could be used to predict the main components of aquatic plants and their anaerobic biodegradability. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Hyperspectral near infrared spectroscopy assessment of the brain during hypoperfusion

NASA Astrophysics Data System (ADS)

Nguyen, Thu N.; Wu, Wen; Woldemichael, Ermias; Toronov, Vladislav; Lin, Steve

2018-02-01

Two-thirds of out-of-hospital cardiac arrest patients, who survive to hospital admission, die in the hospital from neurological injuries related to cerebral hypoperfusion. Hyperspectral near infrared spectroscopy (hNIRS) is a noninvasive technique that measures the major chromophores in the brain, such as oxygenated hemoglobin, deoxygenated hemoglobin and cytochrome C oxidase ([CCO]), an intracellular marker of oxygen consumption. We have demonstrated that hNIRS is feasible and can detect changes in cerebral oxygenation and metabolism in patients undergoing transcatheter aortic valve insertion (TAVI) - a procedure that temporarily induces sudden hypotension and hypoperfusion that mimics cardiac arrest. Using multi-distance hNIRS, we found that while measured regional oxygen saturation (rSO2) changes resulted mainly from the extra-cerebral tissues, [CCO] changes during cardiac arrests occurred mainly in the brains of patients. We also applied the hNIRS algorithm based on the "2-layer model" to the data to measure cerebral oxygen saturation and [CCO] in patients during the procedure.

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.

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.

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.

Discrimination of premalignant lesions and cancer tissues from normal gastric tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Luo, Shuwen; Chen, Changshui; Mao, Hua; Jin, Shaoqin

2013-06-01

The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm-1. Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level.

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.

Speech-evoked activation in adult temporal cortex measured using functional near-infrared spectroscopy (fNIRS): Are the measurements reliable?

PubMed

Wiggins, Ian M; Anderson, Carly A; Kitterick, Pádraig T; Hartley, Douglas E H

2016-09-01

Functional near-infrared spectroscopy (fNIRS) is a silent, non-invasive neuroimaging technique that is potentially well suited to auditory research. However, the reliability of auditory-evoked activation measured using fNIRS is largely unknown. The present study investigated the test-retest reliability of speech-evoked fNIRS responses in normally-hearing adults. Seventeen participants underwent fNIRS imaging in two sessions separated by three months. In a block design, participants were presented with auditory speech, visual speech (silent speechreading), and audiovisual speech conditions. Optode arrays were placed bilaterally over the temporal lobes, targeting auditory brain regions. A range of established metrics was used to quantify the reproducibility of cortical activation patterns, as well as the amplitude and time course of the haemodynamic response within predefined regions of interest. The use of a signal processing algorithm designed to reduce the influence of systemic physiological signals was found to be crucial to achieving reliable detection of significant activation at the group level. For auditory speech (with or without visual cues), reliability was good to excellent at the group level, but highly variable among individuals. Temporal-lobe activation in response to visual speech was less reliable, especially in the right hemisphere. Consistent with previous reports, fNIRS reliability was improved by averaging across a small number of channels overlying a cortical region of interest. Overall, the present results confirm that fNIRS can measure speech-evoked auditory responses in adults that are highly reliable at the group level, and indicate that signal processing to reduce physiological noise may substantially improve the reliability of fNIRS measurements. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Improving the Multi-Wavelength Capability of Chandra Large Programs

NASA Astrophysics Data System (ADS)

Pacucci, Fabio

2017-09-01

In order to fully exploit the joint Chandra/JWST/HST ventures to detect faint sources, we urgently need an advanced matching algorithm between optical/NIR and X-ray catalogs/images. This will be of paramount importance in bridging the gap between upcoming optical/NIR facilities (JWST) and later X-ray ones (Athena, Lynx). We propose to develop an advanced and automated tool to improve the identification of Chandra X-ray counterparts detected in deep optical/NIR fields based on T-PHOT, a software widely used in the community. The developed code will include more than 20 years in advancements of X-ray data analysis and will be released to the public. Finally, we will release an updated catalog of X-ray sources in the CANDELS regions: a leap forward in our endeavor of charting the Universe.

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration.

PubMed

Inácio, Maria Raquel Cavalcanti; de Lima, Kássio Michell Gomes; Lopes, Valquiria Garcia; Pessoa, José Dalton Cruz; de Almeida Teixeira, Gustavo Henrique

2013-02-15

The aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2 g kg(-1), limit of quantification of 20.7 g kg(-1), accuracy estimated as root mean square error of prediction of 4.8 g kg(-1), mean selectivity of 0.79 g kg(-1), sensitivity of 5.04×10(-3) g kg(-1), precision of 27.8 g kg(-1), and signal-to-noise ratio of 1.04×10(-3) g kg(-1). These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit. Copyright © 2012 Elsevier Ltd. 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.

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

PubMed Central

Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

2018-01-01

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets. PMID:29748495

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors.

PubMed

Arsalan, Muhammad; Naqvi, Rizwan Ali; Kim, Dong Seop; Nguyen, Phong Ha; Owais, Muhammad; Park, Kang Ryoung

2018-05-10

The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

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.

Determination of zinc oxide content of mineral medicine calamine using near-infrared spectroscopy based on MIV and BP-ANN algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Chen, Long; Sun, Yangbo; Bai, Yu; Huang, Bisheng; Chen, Keli

2018-03-01

Near-infrared (NIR) spectroscopy has been widely used in the analysis fields of traditional Chinese medicine. It has the advantages of fast analysis, no damage to samples and no pollution. In this research, a fast quantitative model for zinc oxide (ZnO) content in mineral medicine calamine was explored based on NIR spectroscopy. NIR spectra of 57 batches of calamine samples were collected and the first derivative (FD) method was adopted for conducting spectral pretreatment. The content of ZnO in calamine sample was determined using ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy. 57 batches of calamine samples were categorized into calibration and prediction set using the Kennard-Stone (K-S) algorithm. Firstly, in the calibration set, to calculate the correlation coefficient (r) between the absorbance value and the ZnO content of corresponding samples at each wave number. Next, according to the square correlation coefficient (r2) value to obtain the top 50 wave numbers to compose the characteristic spectral bands (4081.8-4096.3, 4188.9-4274.7, 4335.4, 4763.6,4794.4-4802.1, 4809.9, 4817.6-4875.4 cm- 1), which were used to establish the quantitative model of ZnO content using back propagation artificial neural network (BP-ANN) algorithm. Then, the 50 wave numbers were operated by the mean impact value (MIV) algorithm to choose wave numbers whose absolute value of MIV greater than or equal to 25, to obtain the optimal characteristic spectral bands (4875.4-4836.9, 4223.6-4080.9 cm- 1). And then, both internal cross and external validation were used to screen the number of hidden layer nodes of BP-ANN. Finally, the number 4 of hidden layer nodes was chosen as the best. At last, the BP-ANN model was found to enjoy a high accuracy and strong forecasting capacity for analyzing ZnO content in calamine samples ranging within 42.05-69.98%, with relative mean square error of cross validation (RMSECV) of 1.66% and coefficient of determination (R2) of 95.75% in internal cross and relative mean square error of prediction (RMSEP) of 1.98%, R2 of 97.94% and ratio of performance to deviation (RPD) of 6.11 in external validation.

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.

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.

Compensation of spectral artifacts in dual-modality intravascular optical coherence tomography and near-infrared spectroscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Fard, Ali M.; Gardecki, Joseph A.; Ughi, Giovanni J.; Hyun, Chulho; Tearney, Guillermo J.

2016-02-01

Intravascular optical coherence tomography (OCT) is a high-resolution catheter-based imaging method that provides three-dimensional microscopic images of coronary artery in vivo, facilitating coronary artery disease treatment decisions based on detailed morphology. Near-infrared spectroscopy (NIRS) has proven to be a powerful tool for identification of lipid-rich plaques inside the coronary walls. We have recently demonstrated a dual-modality intravascular imaging technology that integrates OCT and NIRS into one imaging catheter using a two-fiber arrangement and a custom-made dual-channel fiber rotary junction. It therefore enables simultaneous acquisition of microstructural and composition information at 100 frames/second for improved diagnosis of coronary lesions. The dual-modality OCT-NIRS system employs a single wavelength-swept light source for both OCT and NIRS modalities. It subsequently uses a high-speed photoreceiver to detect the NIRS spectrum in the time domain. Although use of one light source greatly simplifies the system configuration, such light source exhibits pulse-to-pulse wavelength and intensity variation due to mechanical scanning of the wavelength. This can be in particular problematic for NIRS modality and sacrifices the reliability of the acquired spectra. In order to address this challenge, here we developed a robust data acquisition and processing method that compensates for the spectral variations of the wavelength-swept light source. The proposed method extracts the properties of the light source, i.e., variation period and amplitude from a reference spectrum and subsequently calibrates the NIRS datasets. We have applied this method on datasets obtained from cadaver human coronary arteries using a polygon-scanning (1230-1350nm) OCT system, operating at 100,000 sweeps per second. The results suggest that our algorithm accurately and robustly compensates the spectral variations and visualizes the dual-modality OCT-NIRS images. These findings are therefore crucial for the practical application and clinical translation of dual-modality intravascular OCT-NIRS imaging when the same swept sources are used for both OCT and spectroscopy.

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.

Spectrally resolving and scattering-compensated x-ray luminescence/fluorescence computed tomography

PubMed Central

Cong, Wenxiang; Shen, Haiou; Wang, Ge

2011-01-01

The nanophosphors, or other similar materials, emit near-infrared (NIR) light upon x-ray excitation. They were designed as optical probes for in vivo visualization and analysis of molecular and cellular targets, pathways, and responses. Based on the previous work on x-ray fluorescence computed tomography (XFCT) and x-ray luminescence computed tomography (XLCT), here we propose a spectrally-resolving and scattering-compensated x-ray luminescence/fluorescence computed tomography (SXLCT or SXFCT) approach to quantify a spatial distribution of nanophosphors (other similar materials or chemical elements) within a biological object. In this paper, the x-ray scattering is taken into account in the reconstruction algorithm. The NIR scattering is described in the diffusion approximation model. Then, x-ray excitations are applied with different spectra, and NIR signals are measured in a spectrally resolving fashion. Finally, a linear relationship is established between the nanophosphor distribution and measured NIR data using the finite element method and inverted using the compressive sensing technique. The numerical simulation results demonstrate the feasibility and merits of the proposed approach. PMID:21721815

Temporal hemodynamic classification of two hands tapping using functional near—infrared spectroscopy

PubMed Central

Thanh Hai, Nguyen; Cuong, Ngo Q.; Dang Khoa, Truong Q.; Van Toi, Vo

2013-01-01

In recent decades, a lot of achievements have been obtained in imaging and cognitive neuroscience of human brain. Brain's activities can be shown by a number of different kinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technology for experimental brain purposes. The change of oxygenation changes (oxy-Hb) along task period depending on location of channel on the cortex has been studied: sustained activation in the motor cortex, transient activation during the initial segments in the somatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010). Oxy-Hb concentration at the aforementioned sites in the brain can also be used as a predictive factor allows prediction of subject's investigation behavior with a considerable degree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithm will be described for recognition whether one taps the left hand (LH) or the right hand (RH). Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky–Golay filter for getting more smoothly data. Characteristics of the filtered signals during LH and RH tapping process will be extracted using a polynomial regression (PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin (Oxy-Hb) concentration, will be applied for the recognition models of hand tapping. Support Vector Machines (SVM) will be applied to validate the obtained coefficient data for hand tapping recognition. In addition, for the objective of comparison, Artificial Neural Networks (ANNs) was also applied to recognize hand tapping side with the same principle. Experimental results have been done many trials on three subjects to illustrate the effectiveness of the proposed method. PMID:24032008

Temporal hemodynamic classification of two hands tapping using functional near-infrared spectroscopy.

PubMed

Thanh Hai, Nguyen; Cuong, Ngo Q; Dang Khoa, Truong Q; Van Toi, Vo

2013-01-01

In recent decades, a lot of achievements have been obtained in imaging and cognitive neuroscience of human brain. Brain's activities can be shown by a number of different kinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technology for experimental brain purposes. The change of oxygenation changes (oxy-Hb) along task period depending on location of channel on the cortex has been studied: sustained activation in the motor cortex, transient activation during the initial segments in the somatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010). Oxy-Hb concentration at the aforementioned sites in the brain can also be used as a predictive factor allows prediction of subject's investigation behavior with a considerable degree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithm will be described for recognition whether one taps the left hand (LH) or the right hand (RH). Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky-Golay filter for getting more smoothly data. Characteristics of the filtered signals during LH and RH tapping process will be extracted using a polynomial regression (PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin (Oxy-Hb) concentration, will be applied for the recognition models of hand tapping. Support Vector Machines (SVM) will be applied to validate the obtained coefficient data for hand tapping recognition. In addition, for the objective of comparison, Artificial Neural Networks (ANNs) was also applied to recognize hand tapping side with the same principle. Experimental results have been done many trials on three subjects to illustrate the effectiveness of the proposed method.

Soil organic carbon content assessment in a heterogeneous landscape: comparison of digital soil mapping and visible and near Infrared spectroscopy approaches

NASA Astrophysics Data System (ADS)

Michot, Didier; Fouad, Youssef; Pascal, Pichelin; Viaud, Valérie; Soltani, Inès; Walter, Christian

2017-04-01

This study aims are: i) to assess SOC content distribution according to the global soil map (GSM) project recommendations in a heterogeneous landscape ; ii) to compare the prediction performance of digital soil mapping (DSM) and visible-near infrared (Vis-NIR) spectroscopy approaches. The study area of 140 ha, located at Plancoët, surrounds the unique mineral spring water of Brittany (Western France). It's a hillock characterized by a heterogeneous landscape mosaic with different types of forest, permanent pastures and wetlands along a small coastal river. We acquired two independent datasets: j) 50 points selected using a conditioned Latin hypercube sampling (cLHS); jj) 254 points corresponding to the GSM grid. Soil samples were collected in three layers (0-5, 20-25 and 40-50cm) for both sampling strategies. SOC content was only measured in cLHS soil samples, while Vis-NIR spectra were measured on all the collected samples. For the DSM approach, a machine-learning algorithm (Cubist) was applied on the cLHS calibration data to build rule-based models linking soil carbon content in the different layers with environmental covariates, derived from digital elevation model, geological variables, land use data and existing large scale soil maps. For the spectroscopy approach, we used two calibration datasets: k) the local cLHS ; kk) a subset selected from the regional spectral database of Brittany after a PCA with a hierarchical clustering analysis and spiked by local cLHS spectra. The PLS regression algorithm with "leave-one-out" cross validation was performed for both calibration datasets. SOC contents for the 3 layers of the GSM grid were predicted using the different approaches and were compared with each other. Their prediction performance was evaluated by the following parameters: R2, RMSE and RPD. Both approaches led to satisfactory predictions for SOC content with an advantage for the spectral approach, particularly as regards the pertinence of the variation range.

Determination of total phenolic compounds in compost by infrared spectroscopy.

PubMed

Cascant, M M; Sisouane, M; Tahiri, S; Krati, M El; Cervera, M L; Garrigues, S; de la Guardia, M

2016-06-01

Middle and near infrared (MIR and NIR) were applied to determine the total phenolic compounds (TPC) content in compost samples based on models built by using partial least squares (PLS) regression. The multiplicative scatter correction, standard normal variate and first derivative were employed as spectra pretreatment, and the number of latent variable were optimized by leave-one-out cross-validation. The performance of PLS-ATR-MIR and PLS-DR-NIR models was evaluated according to root mean square error of cross validation and prediction (RMSECV and RMSEP), the coefficient of determination for prediction (Rpred(2)) and residual predictive deviation (RPD) being obtained for this latter values of 5.83 and 8.26 for MIR and NIR, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Near infrared spectral linearisation in quantifying soluble solids content of intact carambola.

PubMed

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-04-12

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques.

Near Infrared Spectral Linearisation in Quantifying Soluble Solids Content of Intact Carambola

PubMed Central

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-01-01

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques. PMID:23584118

Detection of artificially ripened mango using spectrometric analysis

NASA Astrophysics Data System (ADS)

Mithun, B. S.; Mondal, Milton; Vishwakarma, Harsh; Shinde, Sujit; Kimbahune, Sanjay

2017-05-01

Hyperspectral sensing has been proven to be useful to determine the quality of food in general. It has also been used to distinguish naturally and artificially ripened mangoes by analyzing the spectral signature. However the focus has been on improving the accuracy of classification after performing dimensionality reduction, optimum feature selection and using suitable learning algorithm on the complete visible and NIR spectrum range data, namely 350nm to 1050nm. In this paper we focus on, (i) the use of low wavelength resolution and low cost multispectral sensor to reliably identify artificially ripened mango by selectively using the spectral information so that classification accuracy is not hampered at the cost of low resolution spectral data and (ii) use of visible spectrum i.e. 390nm to 700 nm data to accurately discriminate artificially ripened mangoes. Our results show that on a low resolution spectral data, the use of logistic regression produces an accuracy of 98.83% and outperforms other methods like classification tree, random forest significantly. And this is achieved by analyzing only 36 spectral reflectance data points instead of the complete 216 data points available in visual and NIR range. Another interesting experimental observation is that we are able to achieve more than 98% classification accuracy by selecting only 15 irradiance values in the visible spectrum. Even the number of data needs to be collected using hyper-spectral or multi-spectral sensor can be reduced by a factor of 24 for classification with high degree of confidence

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.

Effect of Palagonite Dust Deposition on the Automated Detection of Carbonate Vis/NIR Spectra

NASA Technical Reports Server (NTRS)

Gilmore, Martha S.; Merrill, Matthew D.; Castano, Rebecca; Bornstein, Benjamin; Greenwood, James

2004-01-01

Currently Mars missions can collect more data than can be returned. Future rovers of increased mission lifetime will benefit from onboard autonomous data processing systems to guide the selection, measurement and return of scientifically important data. One approach is to train a neural net to recognize spectral reflectance characteristics of minerals of interest. We have developed a carbonate detector using a neural net algorithm trained on 10,000 synthetic Vis/NIR (350-2500 nm) spectra. The detector was able to correctly identify carbonates in the spectra of 30 carbonate and noncarbonate field samples with 100% success. However, Martian dust coatings strongly affect the spectral characteristics of surface rocks potentially masking the underlying substrate rock. In this experiment, we measure Vis/NIR spectra of calcite coated with different thicknesses of palagonite dust and evaluate the performance of the carbonate detector.

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

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

Cen Haiyan; Bao Yidan; He Yong

2006-10-10

Visible and near-infrared reflectance (visible-NIR) spectroscopy is applied to discriminate different varieties of bayberry juices. The discrimination of visible-NIR spectra from samples is a matter of pattern recognition. By partial least squares (PLS), the spectrum is reduced to certain factors, which are then taken as the input of the backpropagation neural network (BPNN). Through training and prediction, three different varieties of bayberry juice are classified based on the output of the BPNN. In addition, a mathematical model is built and the algorithm is optimized. With proper parameters in the training set,100% accuracy is obtained by the BPNN. Thus it ismore » concluded that the PLS analysis combined with the BPNN is an alternative for pattern recognition based on visible and NIR spectroscopy.« less

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.

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.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

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.

Towards real-time non contact spatial resolved oxygenation monitoring using a multi spectral filter array camera in various light conditions

NASA Astrophysics Data System (ADS)

Bauer, Jacob R.; van Beekum, Karlijn; Klaessens, John; Noordmans, Herke Jan; Boer, Christa; Hardeberg, Jon Y.; Verdaasdonk, Rudolf M.

2018-02-01

Non contact spatial resolved oxygenation measurements remain an open challenge in the biomedical field and non contact patient monitoring. Although point measurements are the clinical standard till this day, regional differences in the oxygenation will improve the quality and safety of care. Recent developments in spectral imaging resulted in spectral filter array cameras (SFA). These provide the means to acquire spatial spectral videos in real-time and allow a spatial approach to spectroscopy. In this study, the performance of a 25 channel near infrared SFA camera was studied to obtain spatial oxygenation maps of hands during an occlusion of the left upper arm in 7 healthy volunteers. For comparison a clinical oxygenation monitoring system, INVOS, was used as a reference. In case of the NIRS SFA camera, oxygenation curves were derived from 2-3 wavelength bands with a custom made fast analysis software using a basic algorithm. Dynamic oxygenation changes were determined with the NIR SFA camera and INVOS system at different regional locations of the occluded versus non-occluded hands and showed to be in good agreement. To increase the signal to noise ratio, algorithm and image acquisition were optimised. The measurement were robust to different illumination conditions with NIR light sources. This study shows that imaging of relative oxygenation changes over larger body areas is potentially possible in real time.

Rapid discrimination and quantification of alkaloids in Corydalis Tuber by near-infrared spectroscopy.

PubMed

Lu, Hai-yan; Wang, Shi-sheng; Cai, Rui; Meng, Yu; Xie, Xin; Zhao, Wei-jie

2012-02-05

With the application of near-infrared spectroscopy (NIRS), a convenient and rapid method for determination of alkaloids in Corydalis Tuber extract and classification for samples from different locations have been developed. Five different samples were collected according to their geographical origin, 2-Der with smoothing point of 17 was applied as the spectral pre-treatment, and the 1st to scaling range algorithm was adjusted to be optimal approach, classification model was constructed over the wavelength range of 4582-4270 cm⁻¹, 5562-4976 cm⁻¹ and 7000-7467 cm⁻¹ with a great recognition rate. For prediction model, partial least squares (PLS) algorithm was utilized referring to HPLC-UV reference method, the optimum models were obtained after adjustment. Pre-processing methods of calibration models were COE for protopine and min-max normalization for palmatine and MSC for tetrahydropalmatine, respectively. The root mean square errors of cross-validation (RMSECV) for protopine, palmatine, tetrahydropalmatine were 0.884, 1.83, 3.23 mg/g. The correlation coefficients (R²) were 99.75, 98.41 and 97.34%. T test was applied, in the model of tetrahydropalmatine; there is no significant difference between NIR prediction and HPLC reference method at 95% confidence interval with t=0.746

Remote sensing of the ocean contributions from ultraviolet to near-infrared using the shortwave infrared bands: simulations.

PubMed

Wang, Menghua

2007-03-20

In the remote sensing of the ocean near-surface properties, it is essential to derive accurate water-leaving radiance spectra through the process of the atmospheric correction. The atmospheric correction algorithm for Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) uses two near-infrared (NIR) bands at 765 and 865 nm (748 and 869 nm for MODIS) for retrieval of aerosol properties with assumption of the black ocean at the NIR wavelengths. Modifications are implemented to account for some of the NIR ocean contributions for the productive but not very turbid waters. For turbid waters in the coastal regions, however, the ocean could have significant contributions in the NIR, leading to significant errors in the satellite-derived ocean water-leaving radiances. For the shortwave infrared (SWIR) wavelengths (approximately > 1000 nm), water has significantly larger absorption than those for the NIR bands. Thus the black ocean assumption at the SWIR bands is generally valid for turbid waters. In addition, for future sensors, it is also useful to include the UV bands to better quantify the ocean organic and inorganic materials, as well as for help in atmospheric correction. Simulations are carried out to evaluate the performance of atmospheric correction for nonabsorbing and weakly absorbing aerosols using the NIR bands and various combinations of the SWIR bands for deriving the water-leaving radiances at the UV (340 nm) and visible wavelengths. Simulations show that atmospheric correction using the SWIR bands can generally produce results comparable to atmospheric correction using the NIR bands. In particular, the water-leaving radiance at the UV band (340 nm) can also be derived accurately. The results from a sensitivity study for the required sensor noise equivalent reflectance, (NE Delta rho), [or the signal-to-noise ratio (SNR)] for the NIR and SWIR bands are provided and discussed.

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...

Online spectral fit tool (OSFT) for analyzing reflectance spectra

NASA Astrophysics Data System (ADS)

Penttilä, A.; Kohout, T.; Muinonen, K.

2015-10-01

We present an algorithm and its implementation for fitting continuum and absorption bands to UV/VIS/NIR reflectance spectra. The implementation is done completely in JavaScript and HTML, and will run in any modern web browser without requiring external libraries to be installed.

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

NASA Astrophysics Data System (ADS)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

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.

Fusion of visible and near-infrared images based on luminance estimation by weighted luminance algorithm

NASA Astrophysics Data System (ADS)

Wang, Zhun; Cheng, Feiyan; Shi, Junsheng; Huang, Xiaoqiao

2018-01-01

In a low-light scene, capturing color images needs to be at a high-gain setting or a long-exposure setting to avoid a visible flash. However, such these setting will lead to color images with serious noise or motion blur. Several methods have been proposed to improve a noise-color image through an invisible near infrared flash image. A novel method is that the luminance component and the chroma component of the improved color image are estimated from different image sources [1]. The luminance component is estimated mainly from the NIR image via a spectral estimation, and the chroma component is estimated from the noise-color image by denoising. However, it is challenging to estimate the luminance component. This novel method to estimate the luminance component needs to generate the learning data pairs, and the processes and algorithm are complex. It is difficult to achieve practical application. In order to reduce the complexity of the luminance estimation, an improved luminance estimation algorithm is presented in this paper, which is to weight the NIR image and the denoised-color image and the weighted coefficients are based on the mean value and standard deviation of both images. Experimental results show that the same fusion effect at aspect of color fidelity and texture quality is achieved, compared the proposed method with the novel method, however, the algorithm is more simple and practical.

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.

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.

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.

Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging.

PubMed

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

2013-11-01

The purpose of this study was to develop and test a hyperspectral imaging system (900-1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner-Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (Rcv=0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. Successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evaluation of Shortwave Infrared Atmospheric Correction for Ocean Color Remote Sensing of Chesapeake Bay

NASA Technical Reports Server (NTRS)

Werdell, P. Jeremy; Franz, Bryan A.; Bailey, Sean W.

2010-01-01

The NASA Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODIS-Aqua) provides a viable data stream for operational water quality monitoring of Chesapeake Bay. Marine geophysical products from MODIS-Aqua depend on the efficacy of the atmospheric correction process, which can be problematic in coastal environments. The operational atmospheric correction algorithm for MODIS-Aqua requires an assumption of negligible near-infrared water-leaving radiance, nL(sub w)(NIR). This assumption progressively degrades with increasing turbidity and, as such, methods exist to account for non-negligible nL(sub w)(NIR) within the atmospheric correction process or to use alternate radiometric bands where the assumption is satisfied, such as those positioned within shortwave infrared (SWIR) region of the spectrum. We evaluated a decade-long time-series of nL(sub w)(lambda) from MODIS-Aqua in Chesapeake Bay derived using NIR and SWIR bands for atmospheric correction. Low signal-to-noise ratios (SNR) for the SWIR bands of MODIS-Aqua added noise errors to the derived radiances, which produced broad, flat frequency distributions of nL(sub w)(lambda) relative to those produced using the NIR bands. The SWIR approach produced an increased number of negative nL(sub w)(lambda) and decreased sample size relative to the NIR approach. Revised vicarious calibration and regional tuning of the scheme to switch between the NIR and SWIR approaches may improve retrievals in Chesapeake Bay, however, poor SNR values for the MODIS-Aqua SWIR bands remain the primary deficiency of the SWIR-based atmospheric correction approach.

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).

An automatic flow system for NIR screening analysis of liquefied petroleum gas with respect to propane content.

PubMed

Dantas, Hebertty V; Barbosa, Mayara F; Nascimento, Elaine C L; Moreira, Pablo N T; Galvão, Roberto K H; Araújo, Mário C U

2013-03-15

This paper proposes a NIR spectrometric method for screening analysis of liquefied petroleum gas (LPG) samples. The proposed method is aimed at discriminating samples with low and high propane content, which can be useful for the adjustment of burn settings in industrial applications. A gas flow system was developed to introduce the LPG sample into a NIR flow cell at constant pressure. In addition, a gas chromatographer was employed to determine the propane content of the sample for reference purposes. The results of a principal component analysis, as well as a classification study using SIMCA (soft independent modeling of class analogies), revealed that the samples can be successfully discriminated with respect to propane content by using the NIR spectrum in the range 8100-8800 cm(-1). In addition, by using SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm), it was found that perfect discrimination can also be achieved by using only two wavenumbers (8215 and 8324 cm(-1)). This finding may be of value for the design of a dedicated, low-cost instrument for routine analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Non-contact finger vein acquisition system using NIR laser

NASA Astrophysics Data System (ADS)

Kim, Jiman; Kong, Hyoun-Joong; Park, Sangyun; Noh, SeungWoo; Lee, Seung-Rae; Kim, Taejeong; Kim, Hee Chan

2009-02-01

Authentication using finger vein pattern has substantial advantage than other biometrics. Because human vein patterns are hidden inside the skin and tissue, it is hard to forge vein structure. But conventional system using NIR LED array has two drawbacks. First, direct contact with LED array raise sanitary problem. Second, because of discreteness of LEDs, non-uniform illumination exists. We propose non-contact finger vein acquisition system using NIR laser and Laser line generator lens. Laser line generator lens makes evenly distributed line laser from focused laser light. Line laser is aimed on the finger longitudinally. NIR camera was used for image acquisition. 200 index finger vein images from 20 candidates are collected. Same finger vein pattern extraction algorithm was used to evaluate two sets of images. Acquired images from proposed non-contact system do not show any non-uniform illumination in contrary with conventional system. Also results of matching are comparable to conventional system. We developed Non-contact finger vein acquisition system. It can prevent potential cross contamination of skin diseases. Also the system can produce uniformly illuminated images unlike conventional system. With the benefit of non-contact, proposed system shows almost equivalent performance compared with conventional system.

Continuous wave optical spectroscopic system for use in magnetic resonance imaging scanners for the measurement of changes in hemoglobin oxygenation states in humans

NASA Astrophysics Data System (ADS)

Hulvershorn, Justin; Bloy, Luke; Leigh, John S.; Elliott, Mark A.

2003-09-01

A continuous wave near infrared three-wavelength laser diode spectroscopic (NIRS) system designed for use in magnetic resonance imaging (MRI) scanners is described. This system measures in vivo changes in the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (Hb) in humans. An algorithm is implemented to map changes in light intensity to changes in the concentrations of Hb and HbO. The system's signal to noise ratio is 3.4×103 per wavelength on an intralipid phantom with 10 Hz resolution. To demonstrate the system's performance in vivo, data taken on the human forearm during arterial occlusion, as well as data taken on the forehead during extended breath holds, are presented. The results show that the instrument is an extremely sensitive detector of hemodynamic changes in human tissue at high temporal resolution. NIRS directly measures changes in the concentrations of hemoglobin species. For this reason, NIRS will be useful in determining the sources of MRI signal changes in the body due to hemodynamic causes, while the precise anatomic information provided by MRI will aid in localizing NIRS contrast and improving the accuracy of models of light transport through tissue.

Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein.

PubMed

Warth, Benedikt; Rajkai, György; Mandenius, Carl-Fredrik

2010-05-03

Software sensors for monitoring and on-line estimation of critical bioprocess variables have mainly been used with standard bioreactor sensors, such as electrodes and gas analyzers, where algorithms in the software model have generated the desired state variables. In this article we propose that other on-line instruments, such as NIR probes and on-line HPLC, should be used to make more reliable and flexible software sensors. Five software sensor architectures were compared and evaluated: (1) biomass concentration from an on-line NIR probe, (2) biomass concentration from titrant addition, (3) specific growth rate from titrant addition, (4) specific growth rate from the NIR probe, and (5) specific substrate uptake rate and by-product rate from on-line HPLC and NIR probe signals. The software sensors were demonstrated on an Escherichia coli cultivation expressing a recombinant protein, green fluorescent protein (GFP), but the results could be extrapolated to other production organisms and product proteins. We conclude that well-maintained on-line instrumentation (hardware sensors) can increase the potential of software sensors. This would also strongly support the intentions with process analytical technology and quality-by-design concepts. 2010 Elsevier B.V. All rights reserved.

A portable, multi-channel fNIRS system for prefrontal cortex: Preliminary study on neurofeedback and imagery tasks (Conference Presentation)

NASA Astrophysics Data System (ADS)

Paik, Seung-ho; Kim, Beop-Min

2016-03-01

fNIRS is a neuroimaging technique which uses near-infrared light source in the 700-1000 nm range and enables to detect hemodynamic changes (i.e., oxygenated hemoglobin, deoxygenated hemoglobin, blood volume) as a response to various brain processes. In this study, we developed a new, portable, prefrontal fNIRS system which has 12 light sources, 15 detectors and 108 channels with a sampling rate of 2 Hz. The wavelengths of light source are 780nm and 850nm. ATxmega128A1, 8bit of Micro controller unit (MCU) with 200~4095 resolution along with MatLab data acquisition algorithm was utilized. We performed a simple left and right finger movement imagery tasks which produced statistically significant changes of oxyhemoglobin concentrations in the dorsolateral prefrontal cortex (dlPFC) areas. We observed that the accuracy of the imagery tasks can be improved by carrying out neurofeedback training, during which a real-time feedback signal is provided to a participating subject. The effects of the neurofeedback training was later visually verified using the 3D NIRfast imaging. Our portable fNIRS system may be useful in non-constraint environment for various clinical diagnoses.

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.

Unlocking interpretation in near infrared multivariate calibrations by orthogonal partial least squares.

PubMed

Stenlund, Hans; Johansson, Erik; Gottfries, Johan; Trygg, Johan

2009-01-01

Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood's anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands "process understanding" in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.

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.

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.

Near-infrared reflectance models for the rapid prediction of quality of brewing raw materials.

PubMed

Marte, Luisa; Belloni, Paolo; Genorini, Emiliano; Sileoni, Valeria; Perretti, Giuseppe; Montanari, Luigi; Marconi, Ombretta

2009-01-28

Calibration models for quickly and reliably predicting moisture content and total nitrogen, both "as is" and "dry matter" on malt, as well as moisture content and total lipids, both "as is" and "dry matter", on maize by means of near-infrared (NIR) spectroscopy were developed. The FT-NIR spectra recorded on the finely ground cereals were correlated to the analytical data by means of the multivariate PLS algorithm. In particular, these models were developed on the raw materials, which are used by the main Italian brewing industries. Validation was carried out both by means of cross-validation and test set validation. Regression coefficients (R(2)) were higher than 97 for both malt and maize moisture content and higher than 85 and 88 for malt total nitrogen and maize total lipids, respectively. The RMSE values (both RMSECV and RMSEP) were lower than 0.1% m/m for both malt and maize moisture contents, whereas they ranged from 0.024 to 0.042% m/m for malt total nitrogen and from 0.042 to 0.055% m/m for maize total lipids. Repeatability was tested by taking into account more than one sample for each calibration and compared, when possible, to those of the standard methods. Repeatability (r(95)) ranged from 0.060 to 0.158% m/m and from 0.020 to 0.055% m/m for malt moisture and total nitrogen contents, respectively, and from 0.094 to 0.160% m/m and from 0.076 to 0.208% m/m for maize moisture and total lipids contents, respectively.

Determination of Hemicellulose, Cellulose and Lignin in Moso Bamboo by Near Infrared Spectroscopy

PubMed Central

Li, Xiaoli; Sun, Chanjun; Zhou, Binxiong; He, Yong

2015-01-01

The contents of hemicellulose, cellulose and lignin are important for moso bamboo processing in biomass energy industry. The feasibility of using near infrared (NIR) spectroscopy for rapid determination of hemicellulose, cellulose and lignin was investigated in this study. Initially, the linear relationship between bamboo components and their NIR spectroscopy was established. Subsequently, successive projections algorithm (SPA) was used to detect characteristic wavelengths for establishing the convenient models. For hemicellulose, cellulose and lignin, 22, 22 and 20 characteristic wavelengths were obtained, respectively. Nonlinear determination models were subsequently built by an artificial neural network (ANN) and a least-squares support vector machine (LS-SVM) based on characteristic wavelengths. The LS-SVM models for predicting hemicellulose, cellulose and lignin all obtained excellent results with high determination coefficients of 0.921, 0.909 and 0.892 respectively. These results demonstrated that NIR spectroscopy combined with SPA-LS-SVM is a useful, nondestructive tool for the determinations of hemicellulose, cellulose and lignin in moso bamboo. PMID:26601657

Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

NASA Astrophysics Data System (ADS)

Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.

2012-07-01

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

Thermostatic system of sensor in NIR spectrometer based on PID control

NASA Astrophysics Data System (ADS)

Wang, Zhihong; Qiao, Liwei; Ji, Xufei

2016-11-01

Aiming at the shortcomings of the primary sensor thermostatic control system in the near infrared (NIR) spectrometer, a novel thermostatic control system based on proportional-integral-derivative (PID) control technology was developed to improve the detection precision of the NIR spectrometer. There were five parts including bridge amplifier circuit, analog-digital conversion (ADC) circuit, microcontroller, digital-analog conversion (DAC) circuit and drive circuit in the system. The five parts formed a closed-loop control system based on PID algorithm that was used to control the error between the temperature calculated by the sampling data of ADC and the designed temperature to ensure the stability of the spectrometer's sensor. The experimental results show that, when the operating temperature of sensor is -11°, compared with the original system, the temperature control precision of the new control system is improved from ±0.64° to ±0.04° and the spectrum signal to noise ratio (SNR) is improved from 4891 to 5967.

Normalized-Difference Snow Index (NDSI)

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.

2010-01-01

The Normalized-Difference Snow Index (NDSI) has a long history. 'The use of ratioing visible (VIS) and near-infrared (NIR) or short-wave infrared (SWIR) channels to separate snow and clouds was documented in the literature beginning in the mid-1970s. A considerable amount of work on this subject was conducted at, and published by, the Air Force Geophysics Laboratory (AFGL). The objective of the AFGL work was to discriminate snow cover from cloud cover using an automated algorithm to improve global cloud analyses. Later, automated methods that relied on the VIS/NIR ratio were refined substantially using satellite data In this section we provide a brief history of the use of the NDSI for mapping snow cover.

On-line monitoring of extraction process of Flos Lonicerae Japonicae using near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

NASA Astrophysics Data System (ADS)

Yang, Yue; Wang, Lei; Wu, Yongjiang; Liu, Xuesong; Bi, Yuan; Xiao, Wei; Chen, Yong

2017-07-01

There is a growing need for the effective on-line process monitoring during the manufacture of traditional Chinese medicine to ensure quality consistency. In this study, the potential of near infrared (NIR) spectroscopy technique to monitor the extraction process of Flos Lonicerae Japonicae was investigated. A new algorithm of synergy interval PLS with genetic algorithm (Si-GA-PLS) was proposed for modeling. Four different PLS models, namely Full-PLS, Si-PLS, GA-PLS, and Si-GA-PLS, were established, and their performances in predicting two quality parameters (viz. total acid and soluble solid contents) were compared. In conclusion, Si-GA-PLS model got the best results due to the combination of superiority of Si-PLS and GA. For Si-GA-PLS, the determination coefficient (Rp2) and root-mean-square error for the prediction set (RMSEP) were 0.9561 and 147.6544 μg/ml for total acid, 0.9062 and 0.1078% for soluble solid contents, correspondingly. The overall results demonstrated that the NIR spectroscopy technique combined with Si-GA-PLS calibration is a reliable and non-destructive alternative method for on-line monitoring of the extraction process of TCM on the production scale.

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.

New equations improve NIR prediction of body fat among high school wrestlers.

PubMed

Oppliger, R A; Clark, R R; Nielsen, D H

2000-09-01

Methodologic study to derive prediction equations for percent body fat (%BF). To develop valid regression equations using NIR to assess body composition among high school wrestlers. Clinicians need a portable, fast, and simple field method for assessing body composition among wrestlers. Near-infrared photospectrometry (NIR) meets these criteria, but its efficacy has been challenged. Subjects were 150 high school wrestlers from 2 Midwestern states with mean +/- SD age of 16.3 +/- 1.1 yrs, weight of 69.5 +/- 11.7 kg, and height of 174.4 +/- 7.0 cm. Relative body fatness (%BF) determined from hydrostatic weighing was the criterion measure, and NIR optical density (OD) measurements at multiple sites, plus height, weight, and body mass index (BMI) were the predictor variables. Four equations were developed with multiple R2s that varied from .530 to .693, root mean squared errors varied from 2.8% BF to 3.4% BF, and prediction errors varied from 2.9% BF to 3.1% BF. The best equation used OD measurements at the biceps, triceps, and thigh sites, BMI, and age. The root mean squared error and prediction error for all 4 equations were equal to or smaller than for a skinfold equation commonly used with wrestlers. The results substantiate the validity of NIR for predicting % BF among high school wrestlers. Cross-validation of these equations is warranted.

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.

Simulation of a fast diffuse optical tomography system based on radiative transfer equation

NASA Astrophysics Data System (ADS)

Motevalli, S. M.; Payani, A.

2016-12-01

Studies show that near-infrared (NIR) light (light with wavelength between 700nm and 1300nm) undergoes two interactions, absorption and scattering, when it penetrates a tissue. Since scattering is the predominant interaction, the calculation of light distribution in the tissue and the image reconstruction of absorption and scattering coefficients are very complicated. Some analytical and numerical methods, such as radiative transport equation and Monte Carlo method, have been used for the simulation of light penetration in tissue. Recently, some investigators in the world have tried to develop a diffuse optical tomography system. In these systems, NIR light penetrates the tissue and passes through the tissue. Then, light exiting the tissue is measured by NIR detectors placed around the tissue. These data are collected from all the detectors and transferred to the computational parts (including hardware and software), which make a cross-sectional image of the tissue after performing some computational processes. In this paper, the results of the simulation of an optical diffuse tomography system are presented. This simulation involves two stages: a) Simulation of the forward problem (or light penetration in the tissue), which is performed by solving the diffusion approximation equation in the stationary state using FEM. b) Simulation of the inverse problem (or image reconstruction), which is performed by the optimization algorithm called Broyden quasi-Newton. This method of image reconstruction is faster compared to the other Newton-based optimization algorithms, such as the Levenberg-Marquardt one.

Novel, near-infrared spectroscopic, label-free, techniques to assess bone abnormalities such as Paget's disease, osteoporosis and bone fractures

NASA Astrophysics Data System (ADS)

Sordillo, Diana C.; Sordillo, Laura A.; Shi, Lingyan; Budansky, Yury; Sordillo, Peter P.; Alfano, Robert R.

2015-02-01

Near- infrared (NIR) light with wavelengths from 650 nm to 950 nm (known as the first NIR window) has conventionally been used as a non-invasive technique that can reach deeper penetration depths through media than light at shorter wavelengths. Recently, several novel, NIR, label-free, techniques have been developed to assess Paget's disease of bone, osteoporosis and bone microfractures. We designed a Bone Optical Analyzer (BOA) which utilizes the first window to measure changes of Hb and HbO2. Paget's disease is marked by an increase in vascularization in bones, and this device can enable easy diagnosis and more frequent monitoring of the patient's condition, without exposing him to a high cumulative dose of radiation. We have also used inverse imaging algorithms to reconstruct 2D and 3D maps of the bone's structure. This device could be used to assess diseases such as osteoporosis. Using 800 nm femtosecond excitation with two-photon (2P) microscopy, we acquired 2PM images of the periosteum and spatial frequency spectra (based on emission of collagen) from the periosteal regions. This technique can provide information on the structure of the periosteum and can detect abnormalities which may be an indication of disease. Most recently, we showed that longer NIR wavelengths in the second and third NIR windows (1100 nm-1350 nm, 1600 nm-1870 nm), could be used to image bone microfractures. Use of NIR light could allow for repeated studies in patients with diseases such as Paget's and osteoporosis quickly and non-invasively, and could impact the current management for these diseases.

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.

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.

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.

Real-time state estimation in a flight simulator using fNIRS.

PubMed

Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

2015-01-01

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

PubMed Central

Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan

2015-01-01

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines. PMID:26345990

Development and application of the near-infrared and white-light thoracoscope system for minimally invasive lung cancer surgery

NASA Astrophysics Data System (ADS)

Mao, Yamin; Wang, Kun; He, Kunshan; Ye, Jinzuo; Yang, Fan; Zhou, Jian; Li, Hao; Chen, Xiuyuan; Wang, Jun; Chi, Chongwei; Tian, Jie

2017-06-01

In minimally invasive surgery, the white-light thoracoscope as a standard imaging tool is facing challenges of the low contrast between important anatomical or pathological regions and surrounding tissues. Recently, the near-infrared (NIR) fluorescence imaging shows superior advantages over the conventional white-light observation, which inspires researchers to develop imaging systems to improve overall outcomes of endoscopic imaging. We developed an NIR and white-light dual-channel thoracoscope system, which achieved high-fluorescent signal acquisition efficiency and the simultaneously optimal visualization of the NIR and color dual-channel signals. The system was designed to have fast and accurate image registration and high signal-to-background ratio by optimizing both software algorithms and optical hardware components for better performance in the NIR spectrum band. The system evaluation demonstrated that the minimally detectable concentration of indocyanine green (ICG) was 0.01 μM, and the spatial resolution was 35 μm. The in vivo feasibility of our system was verified by the preclinical experiments using six porcine models with the intravenous injection of ICG. Furthermore, the system was successfully applied for guiding the minimally invasive segmentectomy in three lung cancer patients, which revealed that our system held great promise for the clinical translation in lung cancer surgeries.

Simultaneous measurement of cerebral and muscle tissue parameters during cardiac arrest and cardiopulmonary resuscitation

NASA Astrophysics Data System (ADS)

Nosrati, Reyhaneh; Ramadeen, Andrew; Hu, Xudong; Woldemichael, Ermias; Kim, Siwook; Dorian, Paul; Toronov, Vladislav

2015-03-01

In this series of animal experiments on resuscitation after cardiac arrest we had a unique opportunity to measure hyperspectral near-infrared spectroscopy (hNIRS) parameters directly on the brain dura, or on the brain through the intact pig skull, and simultaneously the muscle hNIRS parameters. Simultaneously the arterial blood pressure and carotid and femoral blood flow were recorded in real time using invasive sensors. We used a novel hyperspectral signalprocessing algorithm to extract time-dependent concentrations of water, hemoglobin, and redox state of cytochrome c oxidase during cardiac arrest and resuscitation. In addition in order to assess the validity of the non-invasive brain measurements the obtained results from the open brain was compared to the results acquired through the skull. The comparison of hNIRS data acquired on brain surface and through the adult pig skull shows that in both cases the hemoglobin and the redox state cytochrome c oxidase changed in similar ways in similar situations and in agreement with blood pressure and flow changes. The comparison of simultaneously measured brain and muscle changes showed expected differences. Overall the results show feasibility of transcranial hNIRS measurements cerebral parameters including the redox state of cytochrome oxidase in human cardiac arrest patients.

Detection algorithm for cracks on the surface of tomatoes using Multispectral Vis/NIR Reflectance Imagery

USDA-ARS?s Scientific Manuscript database

Tomatoes, an important agricultural product in fresh-cut markets, are sometimes a source of foodborne illness, mainly Salmonella spp. Growth cracks on tomatoes can be a pathway for bacteria, so its detection prior to consumption is important for public health. In this study, multispectral Visible/Ne...

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%.

A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments.

PubMed

Pinti, Paola; Merla, Arcangelo; Aichelburg, Clarisse; Lind, Frida; Power, Sarah; Swingler, Elizabeth; Hamilton, Antonia; Gilbert, Sam; Burgess, Paul W; Tachtsidis, Ilias

2017-07-15

Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that "brain-first" rather than "behaviour-first" analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Algorithm Updates for the Fourth SeaWiFS Data Reprocessing

NASA Technical Reports Server (NTRS)

Hooker, Stanford, B. (Editor); Firestone, Elaine R. (Editor); Patt, Frederick S.; Barnes, Robert A.; Eplee, Robert E., Jr.; Franz, Bryan A.; Robinson, Wayne D.; Feldman, Gene Carl; Bailey, Sean W.

2003-01-01

The efforts to improve the data quality for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data products have continued, following the third reprocessing of the global data set in May 2000. Analyses have been ongoing to address all aspects of the processing algorithms, particularly the calibration methodologies, atmospheric correction, and data flagging and masking. All proposed changes were subjected to rigorous testing, evaluation and validation. The results of these activities culminated in the fourth reprocessing, which was completed in July 2002. The algorithm changes, which were implemented for this reprocessing, are described in the chapters of this volume. Chapter 1 presents an overview of the activities leading up to the fourth reprocessing, and summarizes the effects of the changes. Chapter 2 describes the modifications to the on-orbit calibration, specifically the focal plane temperature correction and the temporal dependence. Chapter 3 describes the changes to the vicarious calibration, including the stray light correction to the Marine Optical Buoy (MOBY) data and improved data screening procedures. Chapter 4 describes improvements to the near-infrared (NIR) band correction algorithm. Chapter 5 describes changes to the atmospheric correction and the oceanic property retrieval algorithms, including out-of-band corrections, NIR noise reduction, and handling of unusual conditions. Chapter 6 describes various changes to the flags and masks, to increase the number of valid retrievals, improve the detection of the flag conditions, and add new flags. Chapter 7 describes modifications to the level-la and level-3 algorithms, to improve the navigation accuracy, correct certain types of spacecraft time anomalies, and correct a binning logic error. Chapter 8 describes the algorithm used to generate the SeaWiFS photosynthetically available radiation (PAR) product. Chapter 9 describes a coupled ocean-atmosphere model, which is used in one of the changes described in Chapter 4. Finally, Chapter 10 describes a comparison of results from the third and fourth reprocessings along the US. Northeast coast.

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

PubMed

Cui, Zaixu; Gong, Gaolang

2018-06-02

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations. Copyright © 2018 Elsevier Inc. All rights reserved.

Atmospheric correction over coastal waters using multilayer neural networks

NASA Astrophysics Data System (ADS)

Fan, Y.; Li, W.; Charles, G.; Jamet, C.; Zibordi, G.; Schroeder, T.; Stamnes, K. H.

2017-12-01

Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water-leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) machine learning method. We use a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and train a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The SeaDAS NIR algorithm, the SeaDAS NIR/SWIR algorithm, and the MODIS version of the Case 2 regional water - CoastColour (C2RCC) algorithm are included in the comparison with AERONET-OC measurements. The results show that the MLNN algorithm significantly improves retrieval of normalized Lw in blue bands (412 nm and 443 nm) and yields minor improvements in green and red bands. These results indicate that the MLNN algorithm is suitable for application in turbid coastal waters. Application of the MLNN algorithm to MODIS Aqua images in several coastal areas also shows that it is robust and resilient to contamination due to sunglint or adjacency effects of land and cloud edges. The MLNN algorithm is very fast once the neural network has been properly trained and is therefore suitable for operational use. A significant advantage of the MLNN algorithm is that it does not need SWIR bands, which implies significant cost reduction for dedicated OC missions. A recent effort has been made to extend the MLNN AC algorithm to extreme atmospheric conditions (i.e. heavy polluted continental aerosols) over coastal areas by including additional aerosol and ocean models to generate the training dataset. Preliminary tests show very good results. Results of applying the extended MLNN algorithm to VIIRS images over the Yellow Sea and East China Sea areas with extreme atmospheric and marine conditions will be provided.

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.

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.

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.

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.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert M.

2013-01-01

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.

Modelling and validation of diffuse reflectance of the adult human head for fNIRS: scalp sub-layers definition

NASA Astrophysics Data System (ADS)

Herrera-Vega, Javier; Montero-Hernández, Samuel; Tachtsidis, Ilias; Treviño-Palacios, Carlos G.; Orihuela-Espina, Felipe

2017-11-01

Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 108 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy- and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 +/- 0.004, 0.108 +/- 0.018 and 0.235+/-0.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.

Determination of phytoplankton abundances (Chlorophyll-a) in the optically complex inland water - The Baltic Sea.

PubMed

Zhang, Daoxi; Lavender, Samantha; Muller, Jan-Peter; Walton, David; Karlson, Bengt; Kronsell, Johan

2017-12-01

A novel approach, termed Summed Positive Peaks (SPP), is proposed for determining phytoplankton abundances (Chlorophyll-a or Chl-a) and surface phytoplankton bloom extent in the optically complex Baltic Sea. The SPP approach is established on the basis of a baseline subtraction method using Rayleigh corrected top-of-atmosphere data from the Medium Resolution Imaging Spectrometer (MERIS) measurements. It calculates the reflectance differences between phytoplankton related signals observed in the MERIS red and near infrared (NIR) bands, such as sun-induced chlorophyll fluorescence (SICF) and the backscattering at 709nm, and considers the summation of the positive line heights for estimating Chl-a concentrations. The SPP algorithm is calibrated against near coincident in situ data collected from three types of phytoplankton dominant waters encountered in the Baltic Sea during 2010 (N=379). The validation results show that the algorithm is capable of retrieving Chl-a concentrations ranging from 0.5 to 3mgm -3 , with an RMSE of 0.24mgm -3 (R 2 =0.69, N=264). Additionally, the comparison results with several Chl-a algorithms demonstrates the robustness of the SPP approach and its sensitivity to low to medium biomass waters. Based on the red and NIR reflectance features, a flagging method is also proposed to distinguish intensive surface phytoplankton blooms from the background water. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting extractives content of Eucalyptus bosistoana F. Muell. Heartwood from stem cores by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Li, Yanjie; Altaner, Clemens

2018-06-01

Time and resource are the restricting factors for the wider use of chemical information of wood in tree breeding programs. NIR offers an advantage over wet-chemical analysis in these aspects and is starting to be used for tree breeding. This work describes the development of a NIR-based assessment of extractive content in heartwood of E. bosistoana, which does not require milling and conditioning of the samples. This was achieved by applying the signal processing algorithms (external parameter orthogonalisation (EPO) and significance multivariate correlation (sMC)) to spectra obtained from solid wood cores, which were able to correct for moisture content, grain direction and sample form. The accuracy of extractive content predictions was further improved by variable selection, resulting in a root mean square error of 1.27%. Considering the range of extractive content in E. bosistoana heartwood of 1.3 to 15.0%, the developed NIR calibration has the potential to be used in an E. bosistoana breeding program or to assess the special variation in extractive content throughout a stem.

Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies

NASA Astrophysics Data System (ADS)

Zhao, Yang; Dai, Rui-Na; Xiao, Xiang; Zhang, Zong; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-02-01

Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed "hyperscanning." Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

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.

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures.

PubMed

Sikirzhytski, Vitali; Sikirzhytskaya, Aliaksandra; Lednev, Igor K

2012-10-10

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Data Pre-Processing Method to Remove Interference of Gas Bubbles and Cell Clusters During Anaerobic and Aerobic Yeast Fermentations in a Stirred Tank Bioreactor

NASA Astrophysics Data System (ADS)

Princz, S.; Wenzel, U.; Miller, R.; Hessling, M.

2014-11-01

One aerobic and four anaerobic batch fermentations of the yeast Saccharomyces cerevisiae were conducted in a stirred bioreactor and monitored inline by NIR spectroscopy and a transflectance dip probe. From the acquired NIR spectra, chemometric partial least squares regression (PLSR) models for predicting biomass, glucose and ethanol were constructed. The spectra were directly measured in the fermentation broth and successfully inspected for adulteration using our novel data pre-processing method. These adulterations manifested as strong fluctuations in the shape and offset of the absorption spectra. They resulted from cells, cell clusters, or gas bubbles intercepting the optical path of the dip probe. In the proposed data pre-processing method, adulterated signals are removed by passing the time-scanned non-averaged spectra through two filter algorithms with a 5% quantile cutoff. The filtered spectra containing meaningful data are then averaged. A second step checks whether the whole time scan is analyzable. If true, the average is calculated and used to prepare the PLSR models. This new method distinctly improved the prediction results. To dissociate possible correlations between analyte concentrations, such as glucose and ethanol, the feeding analytes were alternately supplied at different concentrations (spiking) at the end of the four anaerobic fermentations. This procedure yielded low-error (anaerobic) PLSR models for predicting analyte concentrations of 0.31 g/l for biomass, 3.41 g/l for glucose, and 2.17 g/l for ethanol. The maximum concentrations were 14 g/l biomass, 167 g/l glucose, and 80 g/l ethanol. Data from the aerobic fermentation, carried out under high agitation and high aeration, were incorporated to realize combined PLSR models, which have not been previously reported to our knowledge.

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.

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.

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

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.

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.

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.

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.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

PubMed

da Costa, Gean Bezerra; Fernandes, David Douglas Sousa; Gomes, Adriano A; de Almeida, Valber Elias; Veras, Germano

2016-04-01

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantifying seasonal variation of leaf area index using near-infrared digital camera in a rice paddy

NASA Astrophysics Data System (ADS)

Hwang, Y.; Ryu, Y.; Kim, J.

2017-12-01

Digital camera has been widely used to quantify leaf area index (LAI). Numerous simple and automatic methods have been proposed to improve the digital camera based LAI estimates. However, most studies in rice paddy relied on arbitrary thresholds or complex radiative transfer models to make binary images. Moreover, only a few study reported continuous, automatic observation of LAI over the season in rice paddy. The objective of this study is to quantify seasonal variations of LAI using raw near-infrared (NIR) images coupled with a histogram shape-based algorithm in a rice paddy. As vegetation highly reflects the NIR light, we installed NIR digital camera 1.8 m above the ground surface and acquired unsaturated raw format images at one-hour intervals between 15 to 80 º solar zenith angles over the entire growing season in 2016 (from May to September). We applied a sub-pixel classification combined with light scattering correction method. Finally, to confirm the accuracy of the quantified LAI, we also conducted direct (destructive sampling) and indirect (LAI-2200) manual observations of LAI once per ten days on average. Preliminary results show that NIR derived LAI agreed well with in-situ observations but divergence tended to appear once rice canopy is fully developed. The continuous monitoring of LAI in rice paddy will help to understand carbon and water fluxes better and evaluate satellite based LAI products.

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

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

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Feasibility of field portable near infrared (NIR) spectroscopy to determine cyanide concentrations in soil

NASA Astrophysics Data System (ADS)

Sut, Magdalena; Fischer, Thomas; Repmann, Frank; Raab, Thomas

2013-04-01

In Germany, at more than 1000 sites, soil is polluted with an anthropogenic contaminant in form of iron-cyanide complexes. These contaminations are caused by former Manufactured Gas Plants (MGPs), where electricity for lighting was produced in the process of coal gasification. The production of manufactured gas was restrained in 1950, which caused cessation of MGPs. Our study describes the application of Polychromix Handheld Field Portable Near-Infrared (NIR) Analyzer to predict the cyanide concentrations in soil. In recent times, when the soil remediation is of major importance, there is a need to develop rapid and non-destructive methods for contaminant determination in the field. In situ analysis enables determination of 'hot spots', is cheap and time saving in comparison to laboratory methods. This paper presents a novel usage of NIR spectroscopy, where a calibration model was developed, using multivariate calibration algorithms, in order to determine NIR spectral response to the cyanide concentration in soil samples. As a control, the contaminant concentration was determined using conventional Flow Injection Analysis (FIA). The experiments revealed that portable near-infrared spectrometers could be a reliable device for identification of contamination 'hot spots', where cyanide concentration are higher than 2400 mg kg-1 in the field and >1750 mg kg-1 after sample preparation in the laboratory, but cannot replace traditional laboratory analyses due to high limits of detection.

Real-Time State Estimation in a Flight Simulator Using fNIRS

PubMed Central

Gateau, Thibault; Durantin, Gautier; Lancelot, Francois; Scannella, Sebastien; Dehais, Frederic

2015-01-01

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development. PMID:25816347

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

NASA Astrophysics Data System (ADS)

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

2018-06-01

Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

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.

Design of a Mechanical-Tunable Filter Spectrometer for Noninvasive Glucose Measurement

NASA Astrophysics Data System (ADS)

Saptari, Vidi; Youcef-Toumi, Kamal

2004-05-01

The development of an accurate and reliable noninvasive near-infrared (NIR) glucose sensor hinges on the success in addressing the sensitivity and the specificity problems associated with the weak glucose signals and the overlapping NIR spectra. Spectroscopic hardware parameters most relevant to noninvasive blood glucose measurement are discussed, which include the optical throughput, integration time, spectral range, and the spectral resolution. We propose a unique spectroscopic system using a continuously rotating interference filter, which produces a signal-to-noise ratio of the order of 10^5 and is estimated to be the minimum required for successful in vivo glucose sensing. Using a classical least-squares algorithm and a spectral range between 2180 and 2312 nm, we extracted clinically relevant glucose concentrations in multicomponent solutions containing bovine serum albumin, triacetin, lactate, and urea.

Qualitative analysis of pure and adulterated canola oil via SIMCA

NASA Astrophysics Data System (ADS)

Basri, Katrul Nadia; Khir, Mohd Fared Abdul; Rani, Rozina Abdul; Sharif, Zaiton; Rusop, M.; Zoolfakar, Ahmad Sabirin

2018-05-01

This paper demonstrates the utilization of near infrared (NIR) spectroscopy to classify pure and adulterated sample of canola oil. Soft Independent Modeling Class Analogies (SIMCA) algorithm was implemented to discriminate the samples to its classes. Spectral data obtained was divided using Kennard Stone algorithm into training and validation dataset by a fixed ratio of 7:3. The model accuracy obtained based on the model built is 0.99 whereas the sensitivity and precision are 0.92 and 1.00. The result showed the classification model is robust to perform qualitative analysis of canola oil for future application.

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.

2018-01-01

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory 'predictor' variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes.

Abstracting GIS Layers from Hyperspectral Imagery

DTIC Science & Technology

2009-03-01

Difference Vegetative Index ( NDVI ) 2-20 2.2.10 Separating Trees from Grass . . . . . . . . . . . 2-22 2.3 Spatial Analysis...2-18 2.10. Example of the Normalized Difference Vegetation Index ( NDVI ) applied to a hyperspectral image. . . . . . . . . . . . . . . . . . 2-20...3.5. Example of applying NDVI to a SOM. . . . . . . . . . . . . . . 3-8 3.6. Visualization of the NIR scatter tree ID algorithm. . . . . . . . 3-9 ix

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor.

PubMed

Qu, Fangfang; Ren, Dong; Wang, Jihua; Zhang, Zhong; Lu, Na; Meng, Lei

2016-01-11

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools

NASA Astrophysics Data System (ADS)

Ouyang, Qin; Chen, Quansheng; Zhao, Jiewen

2016-02-01

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp = 0.9180 and RMSEP = 2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine.

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

PubMed

Pogue, Brian W; Song, Xiaomei; Tosteson, Tor D; McBride, Troy O; Jiang, Shudong; Paulsen, Keith D

2002-07-01

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

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.

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

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.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

[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.

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.

Classification of diesel pool refinery streams through near infrared spectroscopy and support vector machines using C-SVC and ν-SVC.

PubMed

Alves, Julio Cesar L; Henriques, Claudete B; Poppi, Ronei J

2014-01-03

The use of near infrared (NIR) spectroscopy combined with chemometric methods have been widely used in petroleum and petrochemical industry and provides suitable methods for process control and quality control. The algorithm support vector machines (SVM) has demonstrated to be a powerful chemometric tool for development of classification models due to its ability to nonlinear modeling and with high generalization capability and these characteristics can be especially important for treating near infrared (NIR) spectroscopy data of complex mixtures such as petroleum refinery streams. In this work, a study on the performance of the support vector machines algorithm for classification was carried out, using C-SVC and ν-SVC, applied to near infrared (NIR) spectroscopy data of different types of streams that make up the diesel pool in a petroleum refinery: light gas oil, heavy gas oil, hydrotreated diesel, kerosene, heavy naphtha and external diesel. In addition to these six streams, the diesel final blend produced in the refinery was added to complete the data set. C-SVC and ν-SVC classification models with 2, 4, 6 and 7 classes were developed for comparison between its results and also for comparison with the soft independent modeling of class analogy (SIMCA) models results. It is demonstrated the superior performance of SVC models especially using ν-SVC for development of classification models for 6 and 7 classes leading to an improvement of sensitivity on validation sample sets of 24% and 15%, respectively, when compared to SIMCA models, providing better identification of chemical compositions of different diesel pool refinery streams. Copyright © 2013 Elsevier B.V. All rights reserved.

Evaluating motion processing algorithms for use with functional near-infrared spectroscopy data from young children.

PubMed

Delgado Reyes, Lourdes M; Bohache, Kevin; Wijeakumar, Sobanawartiny; Spencer, John P

2018-04-01

Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal components analysis (PCA), correlation-based signal improvement (CBSI), wavelet filtering, and spline interpolation. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Brigadoi et al. compared motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Given that fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. This study addresses that problem by evaluating motion correction algorithms implemented in HomER2. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response. Results showed that targeted PCA (tPCA), spline, and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using quantitative metrics. The CBSI method corrected many of the artifacts present in our data; however, this approach produced sometimes unstable HRFs. The targeted PCA and spline methods proved to be the most robust, performing well across all comparison metrics. When compared head to head, tPCA consistently outperformed spline. We conclude, therefore, that tPCA is an effective technique for correcting motion artifacts in fNIRS data from young children.

New techniques for laser beam atmospheric extinction measurements from manned and unmanned aerospace vehicles

NASA Astrophysics Data System (ADS)

Sabatini, Roberto; Richardson, Mark

2013-03-01

Novel techniques for laser beam atmospheric extinction measurements, suitable for several air and space platform applications, are presented in this paper. Extinction measurements are essential to support the engineering development and the operational employment of a variety of aerospace electro-optical sensor systems, allowing calculation of the range performance attainable with such systems in current and likely future applications. Such applications include ranging, weaponry, Earth remote sensing and possible planetary exploration missions performed by satellites and unmanned flight vehicles. Unlike traditional LIDAR methods, the proposed techniques are based on measurements of the laser energy (intensity and spatial distribution) incident on target surfaces of known geometric and reflective characteristics, by means of infrared detectors and/or infrared cameras calibrated for radiance. Various laser sources can be employed with wavelengths from the visible to the far infrared portions of the spectrum, allowing for data correlation and extended sensitivity. Errors affecting measurements performed using the proposed methods are discussed in the paper and algorithms are proposed that allow a direct determination of the atmospheric transmittance and spatial characteristics of the laser spot. These algorithms take into account a variety of linear and non-linear propagation effects. Finally, results are presented relative to some experimental activities performed to validate the proposed techniques. Particularly, data are presented relative to both ground and flight trials performed with laser systems operating in the near infrared (NIR) at λ= 1064 nm and λ= 1550 nm. This includes ground tests performed with 10 Hz and 20 KHz PRF NIR laser systems in a large variety of atmospheric conditions, and flight trials performed with a 10 Hz airborne NIR laser system installed on a TORNADO aircraft, flying up to altitudes of 22,000 ft.

Novel atmospheric extinction measurement techniques for aerospace laser system applications

NASA Astrophysics Data System (ADS)

Sabatini, Roberto; Richardson, Mark

2013-01-01

Novel techniques for laser beam atmospheric extinction measurements, suitable for manned and unmanned aerospace vehicle applications, are presented in this paper. Extinction measurements are essential to support the engineering development and the operational employment of a variety of aerospace electro-optical sensor systems, allowing calculation of the range performance attainable with such systems in current and likely future applications. Such applications include ranging, weaponry, Earth remote sensing and possible planetary exploration missions performed by satellites and unmanned flight vehicles. Unlike traditional LIDAR methods, the proposed techniques are based on measurements of the laser energy (intensity and spatial distribution) incident on target surfaces of known geometric and reflective characteristics, by means of infrared detectors and/or infrared cameras calibrated for radiance. Various laser sources can be employed with wavelengths from the visible to the far infrared portions of the spectrum, allowing for data correlation and extended sensitivity. Errors affecting measurements performed using the proposed methods are discussed in the paper and algorithms are proposed that allow a direct determination of the atmospheric transmittance and spatial characteristics of the laser spot. These algorithms take into account a variety of linear and non-linear propagation effects. Finally, results are presented relative to some experimental activities performed to validate the proposed techniques. Particularly, data are presented relative to both ground and flight trials performed with laser systems operating in the near infrared (NIR) at λ = 1064 nm and λ = 1550 nm. This includes ground tests performed with 10 Hz and 20 kHz PRF NIR laser systems in a large variety of atmospheric conditions, and flight trials performed with a 10 Hz airborne NIR laser system installed on a TORNADO aircraft, flying up to altitudes of 22,000 ft.

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.

Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms

DOE PAGES

Peng, Yi; Nguy-Robertson, Anthony; Arkebauer, Timothy; ...

2017-03-02

Here, canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in threemore » irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.« less

Assessment of canopy chlorophyll content retrieval in maize and soybean: Implications of hysteresis on the development of generic algorithms

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

Peng, Yi; Nguy-Robertson, Anthony; Arkebauer, Timothy

Here, canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in threemore » irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2.« less

An algorithm to correct 2D near-infrared fluorescence signals using 3D intravascular ultrasound architectural information

NASA Astrophysics Data System (ADS)

Mallas, Georgios; Brooks, Dana H.; Rosenthal, Amir; Vinegoni, Claudio; Calfon, Marcella A.; Razansky, R. Nika; Jaffer, Farouc A.; Ntziachristos, Vasilis

2011-03-01

Intravascular Near-Infrared Fluorescence (NIRF) imaging is a promising imaging modality to image vessel biology and high-risk plaques in vivo. We have developed a NIRF fiber optic catheter and have presented the ability to image atherosclerotic plaques in vivo, using appropriate NIR fluorescent probes. Our catheter consists of a 100/140 μm core/clad diameter housed in polyethylene tubing, emitting NIR laser light at a 90 degree angle compared to the fiber's axis. The system utilizes a rotational and a translational motor for true 2D imaging and operates in conjunction with a coaxial intravascular ultrasound (IVUS) device. IVUS datasets provide 3D images of the internal structure of arteries and are used in our system for anatomical mapping. Using the IVUS images, we are building an accurate hybrid fluorescence-IVUS data inversion scheme that takes into account photon propagation through the blood filled lumen. This hybrid imaging approach can then correct for the non-linear dependence of light intensity on the distance of the fluorescence region from the fiber tip, leading to quantitative imaging. The experimental and algorithmic developments will be presented and the effectiveness of the algorithm showcased with experimental results in both saline and blood-like preparations. The combined structural and molecular information obtained from these two imaging modalities are positioned to enable the accurate diagnosis of biologically high-risk atherosclerotic plaques in the coronary arteries that are responsible for heart attacks.

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.

[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.

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.

Binocular Multispectral Adaptive Imaging System (BMAIS)

DTIC Science & Technology

2010-07-26

system for pilots that adaptively integrates shortwave infrared (SWIR), visible, near ‐IR (NIR), off‐head thermal, and computer symbology/imagery into...respective areas. BMAIS is a binocular helmet mounted imaging system that features dual shortwave infrared (SWIR) cameras, embedded image processors and...algorithms and fusion of other sensor sites such as forward looking infrared (FLIR) and other aircraft subsystems. BMAIS is attached to the helmet

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data

PubMed Central

Ahn, Sangtae; Nguyen, Thien; Jang, Hyojung; Kim, Jae G.; Jun, Sung C.

2016-01-01

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions. PMID:27242483

Using FT-NIR spectroscopy technique to determine arginine content in fermented Cordyceps sinensis mycelium.

PubMed

Xie, Chuanqi; Xu, Ning; Shao, Yongni; He, Yong

2015-01-01

This research investigated the feasibility of using Fourier transform near-infrared (FT-NIR) spectral technique for determining arginine content in fermented Cordyceps sinensis (C. sinensis) mycelium. Three different models were carried out to predict the arginine content. Wavenumber selection methods such as competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the most important wavenumbers and reduce the high dimensionality of the raw spectral data. Only a few wavenumbers were selected by CARS and CARS-SPA as the optimal wavenumbers, respectively. Among the prediction models, CARS-least squares-support vector machine (CARS-LS-SVM) model performed best with the highest values of the coefficient of determination of prediction (Rp(2)=0.8370) and residual predictive deviation (RPD=2.4741), the lowest value of root mean square error of prediction (RMSEP=0.0841). Moreover, the number of the input variables was forty-five, which only accounts for 2.04% of that of the full wavenumbers. The results showed that FT-NIR spectral technique has the potential to be an objective and non-destructive method to detect arginine content in fermented C. sinensis mycelium. Copyright © 2015 Elsevier B.V. All rights reserved.

RATIR Follow-up of LIGO/Virgo Gravitational Wave Events

NASA Astrophysics Data System (ADS)

Golkhou, V. Zach; Butler, Nathaniel R.; Strausbaugh, Robert; Troja, Eleonora; Kutyrev, Alexander; Lee, William H.; Román-Zúñiga, Carlos G.; Watson, Alan M.

2018-04-01

We have recently witnessed the first multi-messenger detection of colliding neutron stars through gravitational waves (GWs) and electromagnetic (EM) waves (GW 170817) thanks to the joint efforts of LIGO/Virgo and Space/Ground-based telescopes. In this paper, we report on the RATIR follow-up observation strategies and show the results for the trigger G194575. This trigger is not of astrophysical interest; however, it is of great interest to the robust design of a follow-up engine to explore large sky-error regions. We discuss the development of an image-subtraction pipeline for the six-color, optical/NIR imaging camera RATIR. Considering a two-band (i and r) campaign in the fall of 2015, we find that the requirement of simultaneous detection in both bands leads to a factor ∼10 reduction in false alarm rate, which can be further reduced using additional bands. We also show that the performance of our proposed algorithm is robust to fluctuating observing conditions, maintaining a low false alarm rate with a modest decrease in system efficiency that can be overcome utilizing repeat visits. Expanding our pipeline to search for either optical or NIR detections (three or more bands), considering separately the optical riZ and NIR YJH bands, should result in a false alarm rate ≈1% and an efficiency ≈90%. RATIR’s simultaneous optical/NIR observations are expected to yield about one candidate transient in the vast 100 deg2 LIGO error region for prioritized follow-up with larger aperture telescopes.

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.

Gastric cancer target detection using near-infrared hyperspectral imaging with chemometrics

NASA Astrophysics Data System (ADS)

Yi, Weisong; Zhang, Jian; Jiang, Houmin; Zhang, Niya

2014-09-01

Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.

Contributions to "k"-Means Clustering and Regression via Classification Algorithms

ERIC Educational Resources Information Center

Salman, Raied

2012-01-01

The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…

Limb-Nadir Matching for Tropospheric NO2: A New Algorithm in the SCIAMACHY Operational Level 2 Processor

NASA Astrophysics Data System (ADS)

Meringer, Markus; Gretschany, Sergei; Lichtenberg, Gunter; Hilboll, Andreas; Richter, Andreas; Burrows, John P.

2015-11-01

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric ChartographY) aboard ESA's environmental satellite ENVISAT observed the Earth's atmosphere in limb, nadir, and solar/lunar occultation geometries covering the UV-Visible to NIR spectral range. Limb and nadir geometries were the main operation modes for the retrieval of scientific data. The new version 6 of ESA's level 2 processor now provides for the first time an operational algorithm to combine measurements of these two geometries in order to generate new products. As a first instance the retrieval of tropospheric NO2 has been implemented based on IUP-Bremen's reference algorithm. We will detail the single processing steps performed by the operational limb-nadir matching algorithm and report the results of comparisons with the scientific tropospheric NO2 products of IUP and the Tropospheric Emission Monitoring Internet Service (TEMIS).

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

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.

Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging.

PubMed

Moscetti, Roberto; Sturm, Barbara; Crichton, Stuart Oj; Amjad, Waseem; Massantini, Riccardo

2018-05-01

The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ 1 ]-R[λ 2 ] and R[λ 1 ]:R[λ 2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration.

PubMed

Song, Xiaolei; Xiong, Xiaoyun; Bai, Jing

2007-01-01

Fluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are far from meeting the real-time imaging demands. In this work, a fast preiteration algorithm based on the generalized inverse matrix is proposed. This method needs only one step of matrix-vector multiplication online, by pushing the iteration process to be executed offline. In the preiteration process, the second-order iterative format is employed to exponentially accelerate the convergence. Simulations based on an analytical diffusion model show that the distribution of fluorescent yield can be well estimated by this algorithm and the reconstructed speed is remarkably increased.

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.

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

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.

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

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.

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

NASA Technical Reports Server (NTRS)

Harrivel, Angela; Hearn, Tristan

2013-01-01

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Algorithm For Solution Of Subset-Regression Problems

NASA Technical Reports Server (NTRS)

Verhaegen, Michel

1991-01-01

Reliable and flexible algorithm for solution of subset-regression problem performs QR decomposition with new column-pivoting strategy, enables selection of subset directly from originally defined regression parameters. This feature, in combination with number of extensions, makes algorithm very flexible for use in analysis of subset-regression problems in which parameters have physical meanings. Also extended to enable joint processing of columns contaminated by noise with those free of noise, without using scaling techniques.

[Characteristic wavelengths selection of soluble solids content of pear based on NIR spectral and LS-SVM].

PubMed

Fan, Shu-xiang; Huang, Wen-qian; Li, Jiang-bo; Zhao, Chun-jiang; Zhang, Bao-hua

2014-08-01

To improve the precision and robustness of the NIR model of the soluble solid content (SSC) on pear. The total number of 160 pears was for the calibration (n=120) and prediction (n=40). Different spectral pretreatment methods, including standard normal variate (SNV) and multiplicative scatter correction (MSC) were used before further analysis. A combination of genetic algorithm (GA) and successive projections algorithm (SPA) was proposed to select most effective wavelengths after uninformative variable elimination (UVE) from original spectra, SNV pretreated spectra and MSC pretreated spectra respectively. The selected variables were used as the inputs of least squares-support vector machine (LS-SVM) model to build models for de- termining the SSC of pear. The results indicated that LS-SVM model built using SNVE-UVE-GA-SPA on 30 characteristic wavelengths selected from full-spectrum which had 3112 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.956, 0.271 for SSC. The model is reliable and the predicted result is effective. The method can meet the requirement of quick measuring SSC of pear and might be important for the development of portable instruments and online monitoring.

Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems.

PubMed

Dworak, Volker; Selbeck, Joern; Dammer, Karl-Heinz; Hoffmann, Matthias; Zarezadeh, Ali Akbar; Bobda, Christophe

2013-01-24

The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed.

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools.

PubMed

Ouyang, Qin; Chen, Quansheng; Zhao, Jiewen

2016-02-05

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp=0.9180 and RMSEP=2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems

PubMed Central

Dworak, Volker; Selbeck, Joern; Dammer, Karl-Heinz; Hoffmann, Matthias; Zarezadeh, Ali Akbar; Bobda, Christophe

2013-01-01

The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed. PMID:23348037

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.

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.

Specificity of Atmospheric Correction of Satellite Data on Ocean Color in the Far East

NASA Astrophysics Data System (ADS)

Aleksanin, A. I.; Kachur, V. A.

2017-12-01

Calculation errors in ocean-brightness coefficients in the Far Eastern are analyzed for two atmospheric correction algorithms (NIR and MUMM). The daylight measurements in different water types show that the main error component is systematic and has a simple dependence on the magnitudes of the coefficients. The causes of the error behavior are considered. The most probable explanation for the large errors in ocean-color parameters in the Far East is a high concentration of continental aerosol absorbing light. A comparison between satellite and in situ measurements at AERONET stations in the United States and South Korea has been made. It is shown the errors in these two regions differ by up to 10 times upon close water turbidity and relatively high aerosol optical-depth computation precision in the case of using the NIR correction of the atmospheric effect.

Near-infrared autofluorescence spectroscopy of in vivo soft tissue sarcomas

PubMed Central

Nguyen, John Quan; Gowani, Zain; O'Connor, Maggie; Pence, Isaac; Nguyen, The-Quyen; Holt, Ginger; Mahadevan-Jansen, Anita

2016-01-01

Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated via surgical resection. Inadequate resection can lead to local recurrence and decreased survival rates. In this study, we investigate the hypothesis that near-infrared (NIR) autofluorescence can be utilized for tumor margin analysis by differentiating STS from the surrounding normal tissue. Intraoperative in vivo measurements were acquired from 30 patients undergoing STS resection and were characterized to differentiate between normal tissue and STS. Overall, normal muscle and fat were observed to have the highest and lowest autofluorescence intensities, respectively, with STS falling in between. With the exclusion of well-differentiated liposarcomas, the algorithm's accuracy for classifying muscle, fat, and STS was 93%, 92%, and 88%, respectively. These findings suggest that NIR autofluorescence spectroscopy has potential as a rapid and nondestructive surgical guidance tool that can inform surgeons of suspicious margins in need of immediate re-excision. PMID:26625035

[Near infrared spectroscopy study on water content in turbine oil].

PubMed

Chen, Bin; Liu, Ge; Zhang, Xian-Ming

2013-11-01

Near infrared (NIR) spectroscopy combined with successive projections algorithm (SPA) was investigated for determination of water content in turbine oil. Through the 57 samples of different water content in turbine oil scanned applying near infrared (NIR) spectroscopy, with the water content in the turbine oil of 0-0.156%, different pretreatment methods such as the original spectra, first derivative spectra and differential polynomial least squares fitting algorithm Savitzky-Golay (SG), and successive projections algorithm (SPA) were applied for the extraction of effective wavelengths, the correlation coefficient (R) and root mean square error (RMSE) were used as the model evaluation indices, accordingly water content in turbine oil was investigated. The results indicated that the original spectra with different water content in turbine oil were pretreated by the performance of first derivative + SG pretreatments, then the selected effective wavelengths were used as the inputs of least square support vector machine (LS-SVM). A total of 16 variables selected by SPA were employed to construct the model of SPA and least square support vector machine (SPA-LS-SVM). There is 9 as The correlation coefficient was 0.975 9 and the root of mean square error of validation set was 2.655 8 x 10(-3) using the model, and it is feasible to determine the water content in oil using near infrared spectroscopy and SPA-LS-SVM, and an excellent prediction precision was obtained. This study supplied a new and alternative approach to the further application of near infrared spectroscopy in on-line monitoring of contamination such as water content in oil.

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 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.

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.

Fingerprint recognition of alien invasive weeds based on the texture character and machine learning

NASA Astrophysics Data System (ADS)

Yu, Jia-Jia; Li, Xiao-Li; He, Yong; Xu, Zheng-Hao

2008-11-01

Multi-spectral imaging technique based on texture analysis and machine learning was proposed to discriminate alien invasive weeds with similar outline but different categories. The objectives of this study were to investigate the feasibility of using Multi-spectral imaging, especially the near-infrared (NIR) channel (800 nm+/-10 nm) to find the weeds' fingerprints, and validate the performance with specific eigenvalues by co-occurrence matrix. Veronica polita Pries, Veronica persica Poir, longtube ground ivy, Laminum amplexicaule Linn. were selected in this study, which perform different effect in field, and are alien invasive species in China. 307 weed leaves' images were randomly selected for the calibration set, while the remaining 207 samples for the prediction set. All images were pretreated by Wallis filter to adjust the noise by uneven lighting. Gray level co-occurrence matrix was applied to extract the texture character, which shows density, randomness correlation, contrast and homogeneity of texture with different algorithms. Three channels (green channel by 550 nm+/-10 nm, red channel by 650 nm+/-10 nm and NIR channel by 800 nm+/-10 nm) were respectively calculated to get the eigenvalues.Least-squares support vector machines (LS-SVM) was applied to discriminate the categories of weeds by the eigenvalues from co-occurrence matrix. Finally, recognition ratio of 83.35% by NIR channel was obtained, better than the results by green channel (76.67%) and red channel (69.46%). The prediction results of 81.35% indicated that the selected eigenvalues reflected the main characteristics of weeds' fingerprint based on multi-spectral (especially by NIR channel) and LS-SVM model.

A Markov random field based approach to the identification of meat and bone meal in feed by near-infrared spectroscopic imaging.

PubMed

Jiang, Xunpeng; Yang, Zengling; Han, Lujia

2014-07-01

Contaminated meat and bone meal (MBM) in animal feedstuff has been the source of bovine spongiform encephalopathy (BSE) disease in cattle, leading to a ban in its use, so methods for its detection are essential. In this study, five pure feed and five pure MBM samples were used to prepare two sets of sample arrangements: set A for investigating the discrimination of individual feed/MBM particles and set B for larger numbers of overlapping particles. The two sets were used to test a Markov random field (MRF)-based approach. A Fourier transform infrared (FT-IR) imaging system was used for data acquisition. The spatial resolution of the near-infrared (NIR) spectroscopic image was 25 μm × 25 μm. Each spectrum was the average of 16 scans across the wavenumber range 7,000-4,000 cm(-1), at intervals of 8 cm(-1). This study introduces an innovative approach to analyzing NIR spectroscopic images: an MRF-based approach has been developed using the iterated conditional mode (ICM) algorithm, integrating initial labeling-derived results from support vector machine discriminant analysis (SVMDA) and observation data derived from the results of principal component analysis (PCA). The results showed that MBM covered by feed could be successfully recognized with an overall accuracy of 86.59% and a Kappa coefficient of 0.68. Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR spectroscopic images. This new approach enhances the identification of MBM using NIR spectroscopic imaging.

Monte Carlo study for physiological interference reduction in near-infrared spectroscopy based on empirical mode decomposition

NASA Astrophysics Data System (ADS)

Zhang, Yan; Sun, JinWei; Rolfe, Peter

2010-12-01

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source-detector pair.

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

A new optical method coupling light polarization and Vis-NIR spectroscopy to improve the measured absorbance signal's quality of soil samples.

NASA Astrophysics Data System (ADS)

Gobrecht, Alexia; Bendoula, Ryad; Roger, Jean-Michel; Bellon-Maurel, Véronique

2014-05-01

Visible - Near-infrared spectroscopy (Vis-NIRS) is now commonly used to measure different physical and chemical parameters of soils, including carbon content. However, prediction model accuracy is insufficient for Vis-NIRS to replace routine laboratory analysis. One of the biggest issues this technique is facing up to is light scattering due to soil particles. It causes departure in the assumed linear relationship between the Absorbance spectrum and the concentration of the chemicals of interest as stated by Beer-Lambert's Law, which underpins the calibration models. Therefore it becomes essential to improve the metrological quality of the measured signal in order to optimize calibration as light/matter interactions are at the basis of the resulting linear modeling. Optics can help to mitigate scattering effect on the signal. We put forward a new optical setup coupling linearly polarized light with a Vis-NIR spectrometer to free the measured spectra from multi-scattering effect. The corrected measured spectrum was then used to compute an Absorbance spectrum of the sample, using Dahm's Equation in the frame of the Representative Layer Theory. This method has been previously tested and validated on liquid (milk+ dye) and powdered (sand + dye) samples showing scattering (and absorbing) properties. The obtained Absorbance was a very good approximation of the Beer-Lambert's law absorbance. Here, we tested the method on a set of 54 soil samples to predict Soil Organic Carbon content. In order to assess the signal quality improvement by this method, we built and compared calibration models using Partial Least Square (PLS) algorithm. The prediction model built from new Absorbance spectrum outperformed the model built with the classical Absorbance traditionally obtained with Vis-NIR diffuse reflectance. This study is a good illustration of the high influence of signal quality on prediction model's performances.

Monte Carlo modeling of light propagation in the human head for applications in sinus imaging

PubMed Central

Cerussi, Albert E.; Mishra, Nikhil; You, Joon; Bhandarkar, Naveen; Wong, Brian

2015-01-01

Abstract. Sinus blockages are a common reason for physician visits, affecting one out of seven people in the United States, and often require medical treatment. Diagnosis in the primary care setting is challenging because symptom criteria (via detailed clinical history) plus objective imaging [computed tomography (CT) or endoscopy] are recommended. Unfortunately, neither option is routinely available in primary care. We previously demonstrated that low-cost near-infrared (NIR) transillumination correlates with the bulk findings of sinus opacity measured by CT. We have upgraded the technology, but questions of source optimization, anatomical influence, and detection limits remain. In order to begin addressing these questions, we have modeled NIR light propagation inside a three-dimensional adult human head constructed via CT images using a mesh-based Monte Carlo algorithm (MMCLAB). In this application, the sinus itself, which when healthy is a void region (e.g., nonscattering), is the region of interest. We characterize the changes in detected intensity due to clear (i.e., healthy) versus blocked sinuses and the effect of illumination patterns. We ran simulations for two clinical cases and compared simulations with measurements. The simulations presented herein serve as a proof of concept that this approach could be used to understand contrast mechanisms and limitations of NIR sinus imaging. PMID:25781310

Error propagation of partial least squares for parameters optimization in NIR modeling.

PubMed

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Error propagation of partial least squares for parameters optimization in NIR modeling

NASA Astrophysics Data System (ADS)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Monte Carlo modeling of light propagation in the human head for applications in sinus imaging

NASA Astrophysics Data System (ADS)

Cerussi, Albert E.; Mishra, Nikhil; You, Joon; Bhandarkar, Naveen; Wong, Brian

2015-03-01

Sinus blockages are a common reason for physician visits, affecting one out of seven people in the United States, and often require medical treatment. Diagnosis in the primary care setting is challenging because symptom criteria (via detailed clinical history) plus objective imaging [computed tomography (CT) or endoscopy] are recommended. Unfortunately, neither option is routinely available in primary care. We previously demonstrated that low-cost near-infrared (NIR) transillumination correlates with the bulk findings of sinus opacity measured by CT. We have upgraded the technology, but questions of source optimization, anatomical influence, and detection limits remain. In order to begin addressing these questions, we have modeled NIR light propagation inside a three-dimensional adult human head constructed via CT images using a mesh-based Monte Carlo algorithm (MMCLAB). In this application, the sinus itself, which when healthy is a void region (e.g., nonscattering), is the region of interest. We characterize the changes in detected intensity due to clear (i.e., healthy) versus blocked sinuses and the effect of illumination patterns. We ran simulations for two clinical cases and compared simulations with measurements. The simulations presented herein serve as a proof of concept that this approach could be used to understand contrast mechanisms and limitations of NIR sinus imaging.

Artificial Neural Network and application in calibration transfer of AOTF-based NIR spectrometer

NASA Astrophysics Data System (ADS)

Wang, Wenbo; Jiang, Chengzhi; Xu, Kexin; Wang, Bin

2002-09-01

Chemometrics is widely applied to develop models for quantitative prediction of unknown samples in Near-infrared (NIR) spectroscopy. However, calibrated models generally fail when new instruments are introduced or replacement of the instrument parts occurs. Therefore, calibration transfer becomes necessary to avoid the costly, time-consuming recalibration of models. Piecewise Direct Standardization (PDS) has been proven to be a reference method for standardization. In this paper, Artificial Neural Network (ANN) is employed as an alternative to transfer spectra between instruments. Two Acousto-optic Tunable Filter NIR spectrometers are employed in the experiment. Spectra of glucose solution are collected on the spectrometers through transflectance mode. A Back propagation Network with two layers is employed to simulate the function between instruments piecewisely. Standardization subset is selected by Kennard and Stone (K-S) algorithm in the first two score space of Principal Component Analysis (PCA) of spectra matrix. In current experiment, it is noted that obvious nonlinearity exists between instruments and attempts are made to correct such nonlinear effect. Prediction results before and after successful calibration transfer are compared. Successful transfer can be achieved by adapting window size and training parameters. Final results reveal that ANN is effective in correcting the nonlinear instrumental difference and a only 1.5~2 times larger prediction error is expected after successful transfer.

Optical mechanisms for detection of lipid-rich atherosclerotic plaques by near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Hull, Edward L.; Gardner, Craig M.; Muller, James E.; Muller, Vianna J.; Salvato, Christopher V.; Lisauskas, Jennifer B.; Caplan, Jay D.

2008-02-01

InfraReDx has developed a spectroscopic cardiac catheter system capable of acquiring near-infrared (NIR) reflectance spectra from coronary arteries in vivo for identification of lipid-rich plaques of interest (LRP). The spectral data are analyzed with a chemometric model, producing a hyperspectral image (a chemogram) used to identify LRP in the interrogated region. In this paper, we describe a FT-IR microscopy system for measurement of the NIR scattering and absorption properties of healthy and diseased regions of human coronary arteries in small volumes (~10 μl). Scattering and absorption coefficients are obtained from sequential 140 um x 140 um regions of interest across the face of 500-micron thick, saline-irrigated fresh coronary artery sections. A customized FTIR microscope, measurement protocol, and inversion algorithm are used for optical property determination, and the system is calibrated using measurements of tissue-simulating phantoms having well-characterized optical properties. Tissue optical properties are co-registered with brightfield transmission images as well as with stained histologic thin sections (H&E, Movat Pentachrome, and Oil Red O) acquired from an immediately-adjacent section. The ultimate goal of these experiments is to establish a mechanistic link between the multivariate model predictions displayed on the InfraReDx chemogram and the light-tissue interactions that govern the measured NIR reflectance spectra.

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.

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

NASA Astrophysics Data System (ADS)

Pan, Bingyue

2018-02-01

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UV-visible spectroscopic data.

PubMed

de Oliveira, Rodrigo Rocha; de Lima, Kássio Michell Gomes; Tauler, Romà; de Juan, Anna

2014-07-01

This study describes two applications of a variant of the multivariate curve resolution alternating least squares (MCR-ALS) method with a correlation constraint. The first application describes the use of MCR-ALS for the determination of biodiesel concentrations in biodiesel blends using near infrared (NIR) spectroscopic data. In the second application, the proposed method allowed the determination of the synthetic antioxidant N,N'-Di-sec-butyl-p-phenylenediamine (PDA) present in biodiesel mixtures from different vegetable sources using UV-visible spectroscopy. Well established multivariate regression algorithm, partial least squares (PLS), were calculated for comparison of the quantification performance in the models developed in both applications. The correlation constraint has been adapted to handle the presence of batch-to-batch matrix effects due to ageing effects, which might occur when different groups of samples were used to build a calibration model in the first application. Different data set configurations and diverse modes of application of the correlation constraint are explored and guidelines are given to cope with different type of analytical problems, such as the correction of matrix effects among biodiesel samples, where MCR-ALS outperformed PLS reducing the relative error of prediction RE (%) from 9.82% to 4.85% in the first application, or the determination of minor compound with overlapped weak spectroscopic signals, where MCR-ALS gave higher (RE (%)=3.16%) for prediction of PDA compared to PLS (RE (%)=1.99%), but with the advantage of recovering the related pure spectral profile of analytes and interferences. The obtained results show the potential of the MCR-ALS method with correlation constraint to be adapted to diverse data set configurations and analytical problems related to the determination of biodiesel mixtures and added compounds therein. Copyright © 2014 Elsevier B.V. All rights reserved.

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Estimation of the ICBM/2 Organic Matter Simulation Model parameters for biogas digestate mineralisaion in soil using Near Infrared Data.

NASA Astrophysics Data System (ADS)

Cabassi, Giovanni; Cavalli, Daniele; Borrelli, Lamberto; Degano, Luigi; Marino Gallina, Pietro

2014-05-01

The use of simulation models to study the turnover of soil organic matter (SOM) can support experimental data interpretation and the optimization of manure management. Icbm/2 (Katter, 2001) is a SOM simulation model that describes the turnover of SOM with three pools : one for old humified SOM (CO) and two for added manure, CL ( labile "young" C) and CS (stable "young" C). C outflows from CL and CR to be humified (h) and lost as CO2-C (1-h). All pools decay with firs-order kinetics with parameter kYL, kYR and kO (fig. 1).With this model of SOM turnover, during manure decomposition into the soil, only the evolved CO2 can be easily measured. Near infrared spectroscopy has been proved to be a useful technique for soil C evaluation. Since different soil C pools are expected to have different chemical composition, it was proven that NIR can be used as a cheap technique to develop calibration models to estimate the amount of C belonging to different pools). The aim of this work was compare the calibration of ICBM/2 using C respiration data or optimal NIR prediction of CO and CL pools. A total of six laboratory treatments were established using the same soil corresponding to the application of five fertilisers and a control treatment: 1) control without N fertilisation; 2) ammonium sulphate; 3) anaerobically digested dairy cow slurry (Digested slurry); 4-5) the liquid (Liquid fraction) and solid (Solid fraction) fractions after mechanical separation of Digested slurry; and 6) anaerobically stored dairy cow slurry (Stored slurry). The "nursery" method was used with 12 sampling dates. NIR analysis were performed on the air dried grounded soils. Spectra were collected using an FT-NIR Spectrometer. Parameters calibration was done separately for each soil using the downhill simplex method. For each manure, a C partitioning factor (Fi) was optimised. In each optimization step respiration measured data or NIR estimates CL and CO were used as imput for minimisation objective function. At the end the algorithm found those parameters that gave the lowest averaged RMSE between errors in the estimation of respired C. The model parameter extimations obtained using C respiration data and NIR predictions were comparable indicating a general ability of the NIR method to estimate model parameters together with a good prediction of C mineralisation.

BCI Control of Heuristic Search Algorithms

PubMed Central

Cavazza, Marc; Aranyi, Gabor; Charles, Fred

2017-01-01

The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems. PMID:28197092

BCI Control of Heuristic Search Algorithms.

PubMed

Cavazza, Marc; Aranyi, Gabor; Charles, Fred

2017-01-01

The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users' mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems.

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

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

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.

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...

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.

Data preprocessing methods of FT-NIR spectral data for the classification cooking oil

NASA Astrophysics Data System (ADS)

Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli

2014-12-01

This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

Toward a functional near-infrared spectroscopy-based monitoring of pain assessment for nonverbal patients

NASA Astrophysics Data System (ADS)

Fernandez Rojas, Raul; Huang, Xu; Ou, Keng-Liang

2017-10-01

Pain diagnosis for nonverbal patients represents a challenge in clinical settings. Neuroimaging methods, such as functional magnetic resonance imaging and functional near-infrared spectroscopy (fNIRS), have shown promising results to assess neuronal function in response to nociception and pain. Recent studies suggest that neuroimaging in conjunction with machine learning models can be used to predict different cognitive tasks. The aim of this study is to expand previous studies by exploring the classification of fNIRS signals (oxyhaemoglobin) according to temperature level (cold and hot) and corresponding pain intensity (low and high) using machine learning models. Toward this aim, we used the quantitative sensory testing to determine pain threshold and pain tolerance to cold and heat in 18 healthy subjects (three females), mean age±standard deviation (31.9±5.5). The classification model is based on the bag-of-words approach, a histogram representation used in document classification based on the frequencies of extracted words and adapted for time series; two learning algorithms were used separately, K-nearest neighbor (K-NN) and support vector machines (SVM). A comparison between two sets of fNIRS channels was also made in the classification task, all 24 channels and 8 channels from the somatosensory region defined as our region of interest (RoI). The results showed that K-NN obtained slightly better results (92.08%) than SVM (91.25%) using the 24 channels; however, the performance slightly dropped using only channels from the RoI with K-NN (91.53%) and SVM (90.83%). These results indicate potential applications of fNIRS in the development of a physiologically based diagnosis of human pain that would benefit vulnerable patients who cannot self-report pain.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery

PubMed Central

Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

2014-01-01

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale. PMID:24691435

Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery.

PubMed

Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

2014-01-01

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale.

Near-Infrared Spectroscopic Study of Supernova Ejecta and Supernova Dust in Cassiopeia A

NASA Astrophysics Data System (ADS)

Lee, Yong-Hyun; Koo, Bon-Chul; Moon, Dae-Sik; Lee, Jae-Joon; Burton, Michael G.

2016-06-01

We have carried out near-infrared (NIR) spectroscopic observations of the Cassiopeia A supernova (SN) remnant. We obtained medium-resolution, JHK (0.95 - 2.46 µm) spectra around the main ejecta shell. Using a clump-finding algorithm, we identified 63 'knots' in the two-dimensional dispersed images, and derived their spectroscopic properties. We first present the result of spectral classification of the knots using Principal Component (PC) Analysis. We found that the NIR spectral characteristics of the knots can be mostly (85%) represented by three PCs composed of different sets of emission lines: (1) recombination lines of H and He together with [N I] lines, (2) forbidden lines of Si, P, and S lines, and (3) forbidden Fe lines. The distribution of the knots in the PC planes matches well with the above spectral groups, and we classified the knots into the three corresponding groups, i.e., He-rich, S-rich, and Fe-rich knots. The kinematic and chemical properties of the former two groups match well with those of Quasi-Stationary Flocculi and Fast-Moving Knots known from optical studies. The Fe-rich knots show intermediate characteristics between the former two groups, and we suggest that they are the SN ejecta material from the innermost layer of the progenitor. We also present the results of extinction measurements using the flux ratios between the two NIR [Fe II] lines at 1.257 and 1.644 µm. We have found a clear correlation between the NIR extinction and the radial velocity of ejecta knots, indicating the presence of a large amount of SN dust inside and around the main ejecta shell. In a southern part of the ejecta shell, by analyzing the NIR extinction together with far-infrared thermal dust emission, we show that there are warm (˜100 K) and cool (˜40 K) SN dust components and that the former needs to be silicate grains while the latter, which is responsible for the observed NIR extinction, could be either small (.0.01 µm) Fe or large (&0.1 µm) Si grains. We suggest that the warm and cool dust components represent grain species produced in diffuse SN ejecta and in dense ejecta clumps, respectively

Design of an optimum computer vision-based automatic abalone (Haliotis discus hannai) grading algorithm.

PubMed

Lee, Donggil; Lee, Kyounghoon; Kim, Seonghun; Yang, Yongsu

2015-04-01

An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively. © 2015 Institute of Food Technologists®

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.

Prediction of dynamical systems by symbolic regression

NASA Astrophysics Data System (ADS)

Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.

2016-07-01

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.

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.

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.

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.

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.

A Wearable EEG-HEG-HRV Multimodal System With Simultaneous Monitoring of tES for Mental Health Management.

PubMed

Ha, Unsoo; Lee, Yongsu; Kim, Hyunki; Roh, Taehwan; Bae, Joonsung; Kim, Changhyeon; Yoo, Hoi-Jun

2015-12-01

A multimodal mental management system in the shape of the wearable headband and earplugs is proposed to monitor electroencephalography (EEG), hemoencephalography (HEG) and heart rate variability (HRV) for accurate mental health monitoring. It enables simultaneous transcranial electrical stimulation (tES) together with real-time monitoring. The total weight of the proposed system is less than 200 g. The multi-loop low-noise amplifier (MLLNA) achieves over 130 dB CMRR for EEG sensing and the capacitive correlated-double sampling transimpedance amplifier (CCTIA) has low-noise characteristics for HEG and HRV sensing. Measured three-physiology domains such as neural, vascular and autonomic domain signals are combined with canonical correlation analysis (CCA) and temporal kernel canonical correlation analysis (tkCCA) algorithm to find the neural-vascular-autonomic coupling. It supports highly accurate classification with the 19% maximum improvement with multimodal monitoring. For the multi-channel stimulation functionality, after-effects maximization monitoring and sympathetic nerve disorder monitoring, the stimulator is designed as reconfigurable. The 3.37 × 2.25 mm(2) chip has 2-channel EEG sensor front-end, 2-channel NIRS sensor front-end, NIRS current driver to drive dual-wavelength VCSEL and 6-b DAC current source for tES mode. It dissipates 24 mW with 2 mA stimulation current and 5 mA NIRS driver current.

Evaluating the freezing impact on the proximate composition of immature cowpea (Vigna unguiculata L.) pods: classical versus spectroscopic approaches.

PubMed

Machado, Nelson; Oppolzer, David; Ramos, Ana; Ferreira, Luis; Rosa, Eduardo As; Rodrigues, Miguel; Domínguez-Perles, Raúl; Barros, Ana Irna

2017-10-01

Freezing represents a common conservation practice regarding vegetal foodstuffs. Since compositional features need to be monitored during storage, the development of rapid monitoring tools suitable for assessing nutritional characteristics arises as a pertinent issue. In this study, cowpea (Vigna unguiculata L.) pods, both fresh and after 6 and 9 months of freezing at -18 °C, were evaluated by high-performance liquid chromatography for their content of protein as well as of essential and nonessential amino acids, while their Fourier transform infrared spectra in the mid infrared (MIR) and near infrared (NIR) ranges were concomitantly registered to assess the feasibility of this approach for the traceability of these frozen matrices. For the NIR interval, the application of the 1st derivative to the spectral data retrieved the best results, while for lower concentrations the application of the Savitzky-Golay algorithm was indispensable to achieve quantification models for the amino acids. MIR is also suitable for this purpose, though being unable to quantify amino acids with concentrations below 0.07 mmol g -1 dry weight, irrespective of the data treatment used. The spectroscopic approach constitutes a methodology suitable for monitoring the impact of freezing on the nutritional properties of cowpea pods, allowing accurate quantification of the protein and amino acid contents, while NIR displayed better performance. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Nondestructive sensing technologies using micro-optical elements for applications in the NIR-MIR spectral regions

NASA Astrophysics Data System (ADS)

Otto, Thomas; Saupe, Ray; Bruch, Reinhard F.; Fritzsch, Uwe; Stock, Volker; Gessner, Thomas; Afanasyeva, Natalia I.

2001-11-01

The field of microtechnology is an important industrial and scientific resource for the 21st century. There is a great interest in spectroscopic sensors in the near and middle infrared (NIR-MIR) wavelength regions (1 - 2.5 micrometers ; 2.5 - 4.5 micrometers ; 4 - 6 micrometers ). The potential for cheap and small devices for nondestructive, remote sensing techniques at a molecular level has stimulated the design and development of more compact analyzer systems. Therefore we will try to build analyzers using micro optical components such as micromirrors and embossed micro gratings optimized for the above mentioned spectral ranges. Potentially, infrared sensors can be used for rapid nondestructive diagnostics of surfaces, liquids, gases, polymers and complex biological systems including proteins, blood, cells and cellular debris as well as body tissue. Furthermore, NIR-MIR microsensing spectroscopy will be utilized to monitor the chemical composition of petrochemical products like gasoline and diesel. In addition, miniature analyzers will be used for rapid measuring of food, in particular oil, starch and meat. In this paper we will present an overview of several new approaches for subsurface and surface sensing technologies based on the integration of optical micro devices, the most promising sensors for biomedical, environmental and industrial applications, data processing and evaluation algorithms for classification of the results. Both scientific and industrial applications will be discussed.

Monitoring cerebral oxygen saturation during cardiopulmonary bypass using near-infrared spectroscopy: the relationships with body temperature and perfusion rate.

PubMed

Teng, Yichao; Ding, Haishu; Gong, Qingcheng; Jia, Zaishen; Huang, Lan

2006-01-01

During cardiopulmonary bypass (CPB) because of weak arterial pulsation, near-IR spectroscopy (NIRS) is almost the only available method to monitor cerebral oxygenation noninvasively. Our group develops a NIRS oximeter to monitor regional cerebral oxygenation especially its oxygen saturation (rScO2). To achieve optimal coupling between the sensor and human brain, the distances between the light source and the detectors on it are properly chosen. The oximeter is calibrated by blood gas analysis, and the results indicate that its algorithm is little influenced by either background absorption or overlying tissue. We used it to measure the rScO2 of 15 patients during CPB. It is shown that rScO2 is negatively correlated with body temperature and positively with perfusion rate. There are two critical stages during CPB when rScO2 might be relatively low: one is the low-perfusion-rate stage, the other is the early rewarming stage. During cooling, the changes of total hemoglobin concentration (C(tHb)) compared with its original value is also monitored. It is shown that C(tHb) decreases to a small extent, which may mainly reflect cerebral vasoconstriction induced by cooling. All these results indicate that NIRS can be used to monitor cerebral oxygenation to protect cerebral tissue during CPB.

Retrospective validation of the laparoscopic ICG SLN mapping in patients with grade 3 endometrial cancer.

PubMed

Papadia, Andrea; Gasparri, Maria Luisa; Radan, Anda P; Stämpfli, Chantal A L; Rau, Tilman T; Mueller, Michael D

2018-04-24

To evaluate the sensitivity, negative predictive value (NPV) and false-negative (FN) rate of the near infrared (NIR) indocyanine green (ICG) sentinel lymph node (SLN) mapping in patients with poorly differentiated endometrial cancer who have undergone a full pelvic and para-aortic lymphadenectomy after SLN mapping. We performed a retrospective analysis of patients with endometrial cancer undergoing a laparoscopic NIR-ICG SLN mapping followed by a systematic pelvic and para-aortic lymphadenectomy. Inclusion criteria were a grade 3 endometrial cancer or a high-risk histology (papillary serous, clear cell carcinoma, carcinosarcoma, and neuroendocrine carcinoma) and a completion pelvic and para-aortic lymphadenectomy to the renal vessels after SLN mapping. Overall and bilateral detection rates, sensitivity, NPV, and FN rates were calculated. From December 2012 until January 2017, 42 patients fulfilled inclusion criteria. Overall and bilateral detection rates were 100 and 90.5%, respectively. Overall, 23.8% of the patients had lymph node metastases. In one patient, despite negative bilateral pelvic SLNs, a metastatic non-SLN-isolated para-aortic metastasis was detected. This NSLN was clinically suspicious and sent to frozen section analysis during the surgery. FN rate, sensitivity, and NPV were 10, 90, and 97.1%, respectively. For the SLN mapping algorithm, FN rate, sensitivity, and NPV were 0, 100, and 100%, respectively. Laparoscopic NIR-ICG SLN mapping in high-risk endometrial cancer patients has acceptable sensitivity, FN rate, and NPV.

Three Dimensional Reconstruction Algorithm for Imaging Pathophysiological Signals Within Breast Tissue Using Near Infrared Light

DTIC Science & Technology

2006-07-01

of water, gelatin (G2625, Sigma Inc.), India ink (for absorption), and titanium dioxide powder (for scatter) (TiO2, Sigma Inc.) is poured into a mold...R. C., Ference, R. J, Refractive index of some mammalian tissue using a fiber optic cladding method. Applied Optics, 1989. 28(12): p. 2297-2303. 3...scans. The NIR system utilizes six optical wavelengths from 660 to 850 nm using intensity modulated diode lasers nominally working at 100 MHz

What’s Wrong with the Murals at the Mogao Grottoes: A Near-Infrared Hyperspectral Imaging Method

PubMed Central

Sun, Meijun; Zhang, Dong; Wang, Zheng; Ren, Jinchang; Chai, Bolong; Sun, Jizhou

2015-01-01

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise. PMID:26394926

Additive Partial Least Squares for efficient modelling of independent variance sources demonstrated on practical case studies.

PubMed

Luoma, Pekka; Natschläger, Thomas; Malli, Birgit; Pawliczek, Marcin; Brandstetter, Markus

2018-05-12

A model recalibration method based on additive Partial Least Squares (PLS) regression is generalized for multi-adjustment scenarios of independent variance sources (referred to as additive PLS - aPLS). aPLS allows for effortless model readjustment under changing measurement conditions and the combination of independent variance sources with the initial model by means of additive modelling. We demonstrate these distinguishing features on two NIR spectroscopic case-studies. In case study 1 aPLS was used as a readjustment method for an emerging offset. The achieved RMS error of prediction (1.91 a.u.) was of similar level as before the offset occurred (2.11 a.u.). In case-study 2 a calibration combining different variance sources was conducted. The achieved performance was of sufficient level with an absolute error being better than 0.8% of the mean concentration, therefore being able to compensate negative effects of two independent variance sources. The presented results show the applicability of the aPLS approach. The main advantages of the method are that the original model stays unadjusted and that the modelling is conducted on concrete changes in the spectra thus supporting efficient (in most cases straightforward) modelling. Additionally, the method is put into context of existing machine learning algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

On the reliable and flexible solution of practical subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm for solving subset regression problems is described. The algorithm performs a QR decomposition with a new column-pivoting strategy, which permits subset selection directly from the originally defined regression parameters. This, in combination with a number of extensions of the new technique, makes the method a very flexible tool for analyzing subset regression problems in which the parameters have a physical meaning.

Computationally efficient algorithm for Gaussian Process regression in case of structured samples

NASA Astrophysics Data System (ADS)

Belyaev, M.; Burnaev, E.; Kapushev, Y.

2016-04-01

Surrogate modeling is widely used in many engineering problems. Data sets often have Cartesian product structure (for instance factorial design of experiments with missing points). In such case the size of the data set can be very large. Therefore, one of the most popular algorithms for approximation-Gaussian Process regression-can be hardly applied due to its computational complexity. In this paper a computationally efficient approach for constructing Gaussian Process regression in case of data sets with Cartesian product structure is presented. Efficiency is achieved by using a special structure of the data set and operations with tensors. Proposed algorithm has low computational as well as memory complexity compared to existing algorithms. In this work we also introduce a regularization procedure allowing to take into account anisotropy of the data set and avoid degeneracy of regression model.

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

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.

Modified retrieval algorithm for three types of precipitation distribution using x-band synthetic aperture radar

NASA Astrophysics Data System (ADS)

Xie, Yanan; Zhou, Mingliang; Pan, Dengke

2017-10-01

The forward-scattering model is introduced to describe the response of normalized radar cross section (NRCS) of precipitation with synthetic aperture radar (SAR). Since the distribution of near-surface rainfall is related to the rate of near-surface rainfall and horizontal distribution factor, a retrieval algorithm called modified regression empirical and model-oriented statistical (M-M) based on the volterra integration theory is proposed. Compared with the model-oriented statistical and volterra integration (MOSVI) algorithm, the biggest difference is that the M-M algorithm is based on the modified regression empirical algorithm rather than the linear regression formula to retrieve the value of near-surface rainfall rate. Half of the empirical parameters are reduced in the weighted integral work and a smaller average relative error is received while the rainfall rate is less than 100 mm/h. Therefore, the algorithm proposed in this paper can obtain high-precision rainfall information.

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

PubMed

Zhang, Hong-guang; Lu, Jian-gang

2016-02-01

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

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.

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.

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.

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.

Time series modeling by a regression approach based on a latent process.

PubMed

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

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.

CCD-camera-based diffuse optical tomography to study ischemic stroke in preclinical rat models

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Niu, Haijing; Liu, Yueming; Su, Jianzhong; Liu, Hanli

2011-02-01

Stroke, due to ischemia or hemorrhage, is the neurological deficit of cerebrovasculature and is the third leading cause of death in the United States. More than 80 percent of stroke patients are ischemic stroke due to blockage of artery in the brain by thrombosis or arterial embolism. Hence, development of an imaging technique to image or monitor the cerebral ischemia and effect of anti-stoke therapy is more than necessary. Near infrared (NIR) optical tomographic technique has a great potential to be utilized as a non-invasive image tool (due to its low cost and portability) to image the embedded abnormal tissue, such as a dysfunctional area caused by ischemia. Moreover, NIR tomographic techniques have been successively demonstrated in the studies of cerebro-vascular hemodynamics and brain injury. As compared to a fiberbased diffuse optical tomographic system, a CCD-camera-based system is more suitable for pre-clinical animal studies due to its simpler setup and lower cost. In this study, we have utilized the CCD-camera-based technique to image the embedded inclusions based on tissue-phantom experimental data. Then, we are able to obtain good reconstructed images by two recently developed algorithms: (1) depth compensation algorithm (DCA) and (2) globally convergent method (GCM). In this study, we will demonstrate the volumetric tomographic reconstructed results taken from tissuephantom; the latter has a great potential to determine and monitor the effect of anti-stroke therapies.

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

Analysis of Sting Balance Calibration Data Using Optimized Regression Models

NASA Technical Reports Server (NTRS)

Ulbrich, N.; Bader, Jon B.

2010-01-01

Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.

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.

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.

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

USGS Publications Warehouse

Olexa, Edward M.; Lawrence, Rick L

2014-01-01

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Anomaly detection in hyperspectral imagery: statistics vs. graph-based algorithms

NASA Astrophysics Data System (ADS)

Berkson, Emily E.; Messinger, David W.

2016-05-01

Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution; however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

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.

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.

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.

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

Documenting Liquefaction Failures Using Satellite Remote Sensing and Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Oommen, T.; Baise, L. G.; Gens, R.; Prakash, A.; Gupta, R. P.

2009-12-01

Historically, earthquake induced liquefaction is known to have caused extensive damage around the world. Therefore, there is a compelling need to characterize and map liquefaction after a seismic event. Currently, after an earthquake event, field-based mapping of liquefaction is sporadic and limited due to inaccessibility, short life of the failures, difficulties in mapping large aerial extents, and lack of resources. We hypothesize that as liquefaction occurs in saturated granular soils due to an increase in pore pressure, the liquefaction related terrain changes should have an associated increase in soil moisture with respect to the surrounding non-liquefied regions. The increase in soil moisture affects the thermal emittance and, hence, change detection using pre- and post-event thermal infrared (TIR) imagery is suitable for identifying areas that have undergone post-earthquake liquefaction. Though change detection using TIR images gives the first indication of areas of liquefaction, the spatial resolution of TIR images is typically coarser than the resolution of corresponding visible, near-infrared (NIR), and shortwave infrared (SWIR) images. We hypothesize that liquefaction induced changes in the soil and associated surface effects cause textural and spectral changes in images acquired in the visible, NIR, and SWIR. Although these changes can be from various factors, a synergistic approach taking advantage of the thermal signature variation due to changing soil moisture condition, together with the spectral information from high resolution visible, NIR, and SWIR bands can help to narrow down the locations of post-event liquefaction for regional documentation. In this study, we analyze the applicability of combining various spectral bands from different satellites (Landsat, Terra-MISR, IRS-1C, and IRS-1D) for documenting liquefaction failures associated with the magnitude 7.6 earthquake that occurred in Bhuj, India, in 2001. We combine the various spectral bands by neighborhood correlation image analysis using an artificial intelligence algorithm called support vector machine to remotely identify and document liquefaction failures across a region; and assess the reliability and accuracy of the thermal remote sensing approach in documenting regional liquefaction failures. Finally, we present the applicability of the satellite data analyzed and appropriateness of a multisensor and multispectral approach for documenting liquefaction related failures.

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.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

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.

The minimal residual QR-factorization algorithm for reliably solving subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm (MRQR). This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem. The advantages of the new procedure are highlighted by a numerical example.

Variable selection based cotton bollworm odor spectroscopic detection

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Gai, Shasha; Luo, Min; Zhao, Bo

2016-10-01

Aiming at rapid automatic pest detection based efficient and targeting pesticide application and shooting the trouble of reflectance spectral signal covered and attenuated by the solid plant, the possibility of near infrared spectroscopy (NIRS) detection on cotton bollworm odor is studied. Three cotton bollworm odor samples and 3 blank air gas samples were prepared. Different concentrations of cotton bollworm odor were prepared by mixing the above gas samples, resulting a calibration group of 62 samples and a validation group of 31 samples. Spectral collection system includes light source, optical fiber, sample chamber, spectrometer. Spectra were pretreated by baseline correction, modeled with partial least squares (PLS), and optimized by genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS). Minor counts differences are found among spectra of different cotton bollworm odor concentrations. PLS model of all the variables was built presenting RMSEV of 14 and RV2 of 0.89, its theory basis is insect volatilizes specific odor, including pheromone and allelochemics, which are used for intra-specific and inter-specific communication and could be detected by NIR spectroscopy. 28 sensitive variables are selected by GA, presenting the model performance of RMSEV of 14 and RV2 of 0.90. Comparably, 8 sensitive variables are selected by CARS, presenting the model performance of RMSEV of 13 and RV2 of 0.92. CARS model employs only 1.5% variables presenting smaller error than that of all variable. Odor gas based NIR technique shows the potential for cotton bollworm detection.

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.

Quantum algorithm for linear regression

NASA Astrophysics Data System (ADS)

Wang, Guoming

2017-07-01

We present a quantum algorithm for fitting a linear regression model to a given data set using the least-squares approach. Differently from previous algorithms which yield a quantum state encoding the optimal parameters, our algorithm outputs these numbers in the classical form. So by running it once, one completely determines the fitted model and then can use it to make predictions on new data at little cost. Moreover, our algorithm works in the standard oracle model, and can handle data sets with nonsparse design matrices. It runs in time poly( log2(N ) ,d ,κ ,1 /ɛ ) , where N is the size of the data set, d is the number of adjustable parameters, κ is the condition number of the design matrix, and ɛ is the desired precision in the output. We also show that the polynomial dependence on d and κ is necessary. Thus, our algorithm cannot be significantly improved. Furthermore, we also give a quantum algorithm that estimates the quality of the least-squares fit (without computing its parameters explicitly). This algorithm runs faster than the one for finding this fit, and can be used to check whether the given data set qualifies for linear regression in the first place.

Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of nonsparse low-rank matrices

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Siopsis, George; Weedbrook, Christian

2018-02-01

With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information

NASA Astrophysics Data System (ADS)

Ouyang, Qin; Liu, Yan; Chen, Quansheng; Zhang, Zhengzhu; Zhao, Jiewen; Guo, Zhiming; Gu, Hang

2017-06-01

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.

Fast 3D NIR systems for facial measurement and lip-reading

NASA Astrophysics Data System (ADS)

Brahm, Anika; Ramm, Roland; Heist, Stefan; Rulff, Christian; Kühmstedt, Peter; Notni, Gunther

2017-05-01

Structured-light projection is a well-established optical method for the non-destructive contactless three-dimensional (3D) measurement of object surfaces. In particular, there is a great demand for accurate and fast 3D scans of human faces or facial regions of interest in medicine, safety, face modeling, games, virtual life, or entertainment. New developments of facial expression detection and machine lip-reading can be used for communication tasks, future machine control, or human-machine interactions. In such cases, 3D information may offer more detailed information than 2D images which can help to increase the power of current facial analysis algorithms. In this contribution, we present new 3D sensor technologies based on three different methods of near-infrared projection technologies in combination with a stereo vision setup of two cameras. We explain the optical principles of an NIR GOBO projector, an array projector and a modified multi-aperture projection method and compare their performance parameters to each other. Further, we show some experimental measurement results of applications where we realized fast, accurate, and irritation-free measurements of human faces.

Fatty acids and fat-soluble vitamins in ewe's milk predicted by near infrared reflectance spectroscopy. Determination of seasonality.

PubMed

Revilla, I; Escuredo, O; González-Martín, M I; Palacios, C

2017-01-01

The aim of the present work was to determine the fatty acid and fat-soluble vitamin composition and the season of ewe's milk production using NIR spectroscopy. 219 ewe's milk samples from different breeds and feeding regimes were taken each month over one year. Fatty acids were analyzed by gas chromatography, and retinol and α-, and γ-tocopherol by liquid chromatography. The results showed that the quantification was more accurate for the milk dried on paper, except for vitamins. Calibration statistical descriptors on milk dried on paper were good for capric, lauric, myristic, palmitoleic, stearic and oleic acids, and acceptable for caprilic, undecanoic, 9c, 11tCLA, ΣCLA, PUFA, ω3, ω6, retinol and α-tocopherol. The equations for the discrimination of seasonality was obtained using the partial least squares discriminant analysis (PLSDA) algorithm. 93% of winter samples and 89% of summer samples were correctly classified using the NIR spectra of milk dried on paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information.

PubMed

Ouyang, Qin; Liu, Yan; Chen, Quansheng; Zhang, Zhengzhu; Zhao, Jiewen; Guo, Zhiming; Gu, Hang

2017-06-05

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Regression Model Optimization for the Analysis of Experimental Data

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2009-01-01

A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.

Discriminating between intentional and unintentional gaze fixation using multimodal-based fuzzy logic algorithm for gaze tracking system with NIR camera sensor

NASA Astrophysics Data System (ADS)

Naqvi, Rizwan Ali; Park, Kang Ryoung

2016-06-01

Gaze tracking systems are widely used in human-computer interfaces, interfaces for the disabled, game interfaces, and for controlling home appliances. Most studies on gaze detection have focused on enhancing its accuracy, whereas few have considered the discrimination of intentional gaze fixation (looking at a target to activate or select it) from unintentional fixation while using gaze detection systems. Previous research methods based on the use of a keyboard or mouse button, eye blinking, and the dwell time of gaze position have various limitations. Therefore, we propose a method for discriminating between intentional and unintentional gaze fixation using a multimodal fuzzy logic algorithm applied to a gaze tracking system with a near-infrared camera sensor. Experimental results show that the proposed method outperforms the conventional method for determining gaze fixation.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Accurate wavelength measurements of a putative standard for near-infrared diffuse reflection spectrometry.

PubMed

Isaksson, Tomas; Yang, Husheng; Kemeny, Gabor J; Jackson, Richard S; Wang, Qian; Alam, M Kathleen; Griffiths, Peter R

2003-02-01

The diffuse reflection (DR) spectrum of a sample consisting of a mixture of rare earth oxides and talc was measured at 2 cm-1 resolution, using five different accessories installed on five different Fourier transform near-infrared (FT-NIR) spectrometers from four manufacturers. Peak positions for 37 peaks were determined using two peak-picking algorithms: center-of-mass and polynomial fitting. The wavenumber of the band center reported by either of these techniques was sensitive to the slope of the baseline, and so the baseline of the spectra was corrected using either a polynomial fit or conversion to the second derivative. Significantly different results were obtained with one combination of spectrometer and accessory than the others. Apparently, the beam path through the interferometer and DR accessory was different for this accessory than for any of the other measurements, causing a severe degradation of the resolution. Spectra measured on this instrument were removed as outliers. For measurements made on FT-NIR spectrometers, it is shown that it is important to check the resolution at which the spectrum has been measured using lines in the vibration-rotation spectrum of atmospheric water vapor and to specify the peak-picking and baseline-correction algorithms that are used to process the measured spectra. The variance between the results given by the four different methods of peak-picking and baseline correction was substantially larger than the variance between the remaining five measurements. Certain bands were found to be more suitable than others for use as wavelength standards. A band at 5943.13 cm-1 (1682.62 nm) was found to be the most stable band between the four methods and the six measurements. A band at 5177.04 cm-1 (1931.61 nm) has the highest precision between different measurements when polynomial baseline correction and polynomial peak-picking algorithms are used.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Remote Sensing of Coastal and Inland Waters

NASA Astrophysics Data System (ADS)

De Keukelaere, L.; Sterckx, S.; Adriaensen, S.; Knaeps, E.

2016-02-01

The new generation of satellites (e.g. Landsat 8, HyspIRI, Sentinel 2 and Sentinel 3 …) contain sensors that enable monitoring at increased spatial and/or spectral resolution. This opens a wide range of new opportunities, amongst others improved observation of coastal and inland waters. Algorithms for the pre-processing of these images and the derivation of Level 2 products for these waters need to take into account the specific nature of these environments, with adjacency effects of the nearby land and complex interactions of the optially active substances with varying degrees of turbidity. Here a new atmospheric correction algorithm, OPERA, is presented which can deal with these highly complex environments and which is sensor generic. OPERA accounts for the contribution of adjacency effects and provides surface reflectances for both land and water targets. OPERA is extended with a level 2 water algorithm providing TSM and turbidity estimates for a wide variety of water types. The algorithm is based on a multi wavelength switching approach using shorter wavelengths in low turbid waters and long NIR and SWIR wavelengths for highly and extremely turbid waters. Results are shown for Landsat-8, Sentinel-2 and MERIS for a variety of scenes, validated with field aeronet and turbidity data.

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.