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.

Classification of plum spirit drinks by synchronous fluorescence spectroscopy.

PubMed

Sádecká, J; Jakubíková, M; Májek, P; Kleinová, A

2016-04-01

Synchronous fluorescence spectroscopy was used in combination with principal component analysis (PCA) and linear discriminant analysis (LDA) for the differentiation of plum spirits according to their geographical origin. A total of 14 Czech, 12 Hungarian and 18 Slovak plum spirit samples were used. The samples were divided in two categories: colorless (22 samples) and colored (22 samples). Synchronous fluorescence spectra (SFS) obtained at a wavelength difference of 60 nm provided the best results. Considering the PCA-LDA applied to the SFS of all samples, Czech, Hungarian and Slovak colorless samples were properly classified in both the calibration and prediction sets. 100% of correct classification was also obtained for Czech and Hungarian colored samples. However, one group of Slovak colored samples was classified as belonging to the Hungarian group in the calibration set. Thus, the total correct classifications obtained were 94% and 100% for the calibration and prediction steps, respectively. The results were compared with those obtained using near-infrared (NIR) spectroscopy. Applying PCA-LDA to NIR spectra (5500-6000 cm(-1)), the total correct classifications were 91% and 92% for the calibration and prediction steps, respectively, which were slightly lower than those obtained using SFS. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Discrimination of donkey meat by NIR and chemometrics].

PubMed

Niu, Xiao-Ying; Shao, Li-Min; Dong, Fang; Zhao, Zhi-Lei; Zhu, Yan

2014-10-01

Donkey meat samples (n = 167) from different parts of donkey body (neck, costalia, rump, and tendon), beef (n = 47), pork (n = 51) and mutton (n = 32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classification models in the spectra range of 4,000~12,500 cm(-1). The accuracies of classification models constructed by Mahalanobis distances analysis, soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM), respectively combined with pretreatment of Savitzky-Golay smooth (5, 15 and 25 points) and derivative (first and second), multiplicative scatter correction and standard normal variate, were compared. The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs, and correctly classified 100% of calibration set and 98. 96% of prediction set. For minced samples of 7 mm diameter the optimal result was attained by LS-SVM with the first 5 PCs from original spectra as inputs, which gained an accuracy of 100% for calibration and 97.53% for prediction. For minced diameter of 5 mm SIMCA model with the first 8 PCs from original spectra as inputs correctly classified 100% of calibration and prediction. And for minced diameter of 3 mm Mahalanobis distances analysis and SIMCA models both achieved 100% accuracy for calibration and prediction respectively with the first 7 and 9 PCs from original spectra as inputs. And in these models, donkey meat samples were all correctly classified with 100% either in calibration or prediction. The results show that it is feasible that NIR with chemometrics methods is used to discriminate donkey meat from the else meat.

Canopy reflectance related to marsh dieback onset and progression in Coastal Louisiana

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, A.

2006-01-01

In this study, we extended previous work linking leaf spectral changes, dieback onset, and progression of Spartina alterniflora marshes to changes in site-specific canopy reflectance spectra. First, we obtained canopy reflectance spectra (approximately 20 m ground resolution) from the marsh sites occupied during the leaf spectral analyses and from additional sites exhibiting visual signs of dieback. Subsequently, the canopy spectra were analyzed at two spectral scales: the first scale corresponded to whole-spectra sensors, such as the NASA Earth Observing-1 (EO-1) Hyperion, and the second scale corresponded to broadband spectral sensors, such as the EO-1 Advanced Land Imager and the Landsat Enhanced Thematic Mapper. In the whole-spectra analysis, spectral indicators were generated from the whole canopy spectra (about 400 nm to 1,000 nm) by extracting typical dead and healthy marsh spectra, and subsequently using them to determine the percent composition of all canopy reflectance spectra. Percent compositions were then used to classify canopy spectra at each field site into groups exhibiting similar levels of dieback progression ranging from relatively healthy to completely dead. In the broadband reflectance analysis, blue, green, red, red-edge, and near infrared (NIR) spectral bands and NIR/green and NIR/red transforms were extracted from the canopy spectra. Spectral band and band transform indicators of marsh dieback and progression were generated by relating them to marsh status indicators derived from classifications of the 35 mm slides collected at the same time as the canopy reflectance recordings. The whole spectra and broadband spectral indicators were both able to distinguish (a) healthy marsh, (b) live marsh impacted by dieback, and (c) dead marsh, and they both provided some discrimination of dieback progression. Whole-spectra resolution sensors like the EO-1 Hyperion, however, offered an enhanced ability to categorize dieback progression. ?? 2006 American Society for Photogrammetry and Remote Sensing.

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.

Determination of the Characteristics and Classification of Near-Infrared Spectra of Patchouli Oil (Pogostemon Cablin Benth.) from Different Origin

NASA Astrophysics Data System (ADS)

Diego, M. C. R.; Purwanto, Y. A.; Sutrisno; Budiastra, I. W.

2018-05-01

Research related to the non-destructive method of near-infrared (NIR) spectroscopy in aromatic oil is still in development in Indonesia. The objectives of the study were to determine the characteristics of the near-infrared spectra of patchouli oil and classify it based on its origin. The samples were selected from seven different places in Indonesia (Bogor and Garut from West Java, Aceh, and Jambi from Sumatra and Konawe, Masamba and Kolaka from Sulawesi Island). The spectral data of patchouli oil was obtained by FT-NIR spectrometer at the wavelength of 1000-2500 nm, and after that, the samples were subjected to composition analysis using Gas Chromatography-Mass Spectrometry. The transmittance and absorbance spectra were analyzed and then principal component analysis (PCA) was carried out. Discriminant analysis (DA) of the principal component was developed to classify patchouli oil based on its origin. The result shows that the data of both spectra (transmittance and absorbance spectra) by the PC analysis give a similar result for discriminating the seven types of patchouli oil due to their distribution and behavior. The DA of the three principal component in both data processed spectra could classify patchouli oil accurately. This result exposed that NIR spectroscopy can be successfully used as a correct method to classify patchouli oil based on its origin.

[Rapid identification of potato cultivars using NIR-excited fluorescence and Raman spectroscopy].

PubMed

Dai, Fen; Bergholt, Mads Sylvest; Benjamin, Arnold Julian Vinoj; Hong, Tian-Sheng; Zhiwei, Huang

2014-03-01

Potato is one of the most important food in the world. Rapid and noninvasive identification of potato cultivars plays a important role in the better use of varieties. In this study, The identification ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy and NIR fluorescence spectroscopy, for invasive detection of potato cultivars was evaluated. A rapid NIR Raman spectroscopy system was applied to measure the composite Raman and NIR fluorescence spectroscopy of 3 different species of potatoes (98 samples in total) under 785 nm laser light excitation. Then pure Raman and NIR fluorescence spectroscopy were abstracted from the composite spectroscopy, respectively. At last, the partial least squares-discriminant analysis (PLS-DA) was utilized to analyze and classify Raman spectra of 3 different types of potatoes. All the samples were divided into two sets at random: the calibration set (74samples) and prediction set (24 samples), the model was validated using a leave-one-out, cross-validation method. The results showed that both the NIR-excited fluorescence spectra and pure Raman spectra could be used to identify three cultivars of potatoes. The fluorescence spectrum could distinguish the Favorita variety well (sensitivity: 1, specificity: 0.86 and accuracy: 0.92), but the result for Diamant (sensitivity: 0.75, specificity: 0.75 and accuracy: 0. 75) and Granola (sensitivity: 0.16, specificity: 0.89 and accuracy: 0.71) cultivars identification were a bit poorer. We demonstrated that Raman spectroscopy uncovered the main biochemical compositions contained in potato species, and provided a better classification sensitivity, specificity and accuracy (sensitivity: 1, specificity: 1 and accuracy: 1 for all 3 potato cultivars identification) among the three types of potatoes as compared to fluorescence spectroscopy.

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

PubMed Central

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

2017-01-01

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

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.

Type II Supernova Light Curves and Spectra from the CfA

NASA Astrophysics Data System (ADS)

Hicken, Malcolm; Friedman, Andrew S.; Blondin, Stephane; Challis, Peter; Berlind, Perry; Calkins, Mike; Esquerdo, Gil; Matheson, Thomas; Modjaz, Maryam; Rest, Armin; Kirshner, Robert P.

2017-11-01

We present multiband photometry of 60 spectroscopically confirmed supernovae (SNe): 39 SNe II/IIP, 19 IIn, 1 IIb, and 1 that was originally classified as a IIn but later as a Ibn. Of these, 46 have only optical photometry, 6 have only near-infrared (NIR) photometry, and 8 have both optical and NIR. The median redshift of the sample is 0.016. We also present 195 optical spectra for 48 of the 60 SN. There are 26 optical and 2 NIR light curves of SNe II/IIP with redshifts z> 0.01, some of which may give rise to useful distances for cosmological applications. All photometry was obtained between 2000 and 2011 at the Fred Lawrence Whipple Observatory (FLWO), via the 1.2 m and 1.3 m PAIRITEL telescopes for the optical and NIR, respectively. Each SN was observed in a subset of the u\\prime {UBVRIr}\\prime I\\prime {{JHK}}s bands. There are a total of 2932 optical and 816 NIR light curve points. Optical spectra were obtained using the FLWO 1.5 m Tillinghast telescope with the FAST spectrograph and the MMT Telescope with the Blue Channel Spectrograph. Our photometry is in reasonable agreement with select samples from the literature: two-thirds of our star sequences have average V offsets within ±0.02 mag and roughly three-quarters of our light curves have average differences within ±0.04 mag. The data from this work and the literature will provide insight into SN II explosions, help with developing methods for photometric SN classification, and contribute to their use as cosmological distance indicators.

Spectrophotometric versus NIR-MIR assessments of cowpea pods for discriminating the impact of freezing.

PubMed

Machado, Nelson; Domínguez-Perles, Raúl; Ramos, Ana; Rosa, Eduardo As; Barros, Ana Irna

2017-10-01

Freezing represents an important storage method for vegetal foodstuffs, such as cowpea pods, and thus the impact of this process on the chemical composition of these matrices arises as a prominent issue. In this sense, the phytochemical contents in frozen cowpea pods (i.e. at 6 and 9 months) have been compared with fresh cowpea pods material, with the samples being concomitantly assessed by Fourier-transform infrared spectroscopy (FTIR), both mid-infrared (MIR) and near infrared (NIR), aiming to evaluate the potential of these techniques as a rapid tool for the traceability of these matrices. A decrease in phytochemical contents during freezing was observed, allowing the classification of samples according to the freezing period based on such variations. Also, MIR and NIR allowed discrimination of samples: the use of the first derivative demonstrated a better performance for this purpose, whereas the use of the normalized spectra gave the best correlations between the spectra and specific contents. In both cases, NIR displayed the best performance. Freezing of cowpea pods leads to a decrease of phytochemical contents, which can be monitored by FTIR spectroscopy, both within the MIR and NIR ranges, whereas the use of this technique, in tandem with chemometrics, constitutes a suitable methodology for the traceability of these matrices. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis.

PubMed

Li, Tao; Su, Chen

2018-06-02

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and effectiveness of clinical application of Rhodiola. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs.

PubMed

Huang, Y; Andueza, D; de Oliveira, L; Zawadzki, F; Prache, S

2015-11-01

Since consumers are showing increased interest in the origin and method of production of their food, it is important to be able to authenticate dietary history of animals by rapid and robust methods used in the ruminant products. Promising breakthroughs have been made in the use of spectroscopic methods on fat to discriminate pasture-fed and concentrate-fed lambs. However, questions remained on their discriminatory ability in more complex feeding conditions, such as concentrate-finishing after pasture-feeding. We compared the ability of visible reflectance spectroscopy (Vis RS, wavelength range: 400 to 700 nm) with that of visible-near-infrared reflectance spectroscopy (Vis-NIR RS, wavelength range: 400 to 2500 nm) to differentiate between carcasses of lambs reared with three feeding regimes, using partial least square discriminant analysis (PLS-DA) as a classification method. The sample set comprised perirenal fat of Romane male lambs fattened at pasture (P, n = 69), stall-fattened indoors on commercial concentrate and straw (S, n = 55) and finished indoors with concentrate and straw for 28 days after pasture-feeding (PS, n = 65). The overall correct classification rate was better for Vis-NIR RS than for Vis RS (99.0% v. 95.1%, P < 0.05). Vis-NIR RS allowed a correct classification rate of 98.6%, 100.0% and 98.5% for P, S and PS lambs, respectively, whereas Vis RS allowed a correct classification rate of 98.6%, 94.5% and 92.3% for P, S and PS lambs, respectively. This study suggests the likely implication of molecules absorbing light in the non-visible part of the Vis-NIR spectra (possibly fatty acids), together with carotenoid and haem pigments, in the discrimination of the three feeding regimes.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.

PubMed

Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-03-08

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds

PubMed Central

Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-01-01

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400–1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides’ spectra of every seed), and mixture datasets (two sides’ spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner. PMID:29517991

Authentication of the botanical origin of honey by near-infrared spectroscopy.

PubMed

Ruoff, Kaspar; Luginbühl, Werner; Bogdanov, Stefan; Bosset, Jacques Olivier; Estermann, Barbara; Ziolko, Thomas; Amado, Renato

2006-09-06

Fourier transform near-infrared spectroscopy (FT-NIR) was evaluated for the authentication of eight unifloral and polyfloral honey types (n = 364 samples) previously classified using traditional methods such as chemical, pollen, and sensory analysis. Chemometric evaluation of the spectra was carried out by applying principal component analysis and linear discriminant analysis. The corresponding error rates were calculated according to Bayes' theorem. NIR spectroscopy enabled a reliable discrimination of acacia, chestnut, and fir honeydew honey from the other unifloral and polyfloral honey types studied. The error rates ranged from <0.1 to 6.3% depending on the honey type. NIR proved also to be useful for the classification of blossom and honeydew honeys. The results demonstrate that near-infrared spectrometry is a valuable, rapid, and nondestructive tool for the authentication of the above-mentioned honeys, but not for all varieties studied.

Automatic classification of spectral units in the Aristarchus plateau

NASA Astrophysics Data System (ADS)

Erard, S.; Le Mouelic, S.; Langevin, Y.

1999-09-01

A reduction scheme has been recently proposed for the NIR images of Clementine (Le Mouelic et al, JGR 1999). This reduction has been used to build an integrated UVvis-NIR image cube of the Aristarchus region, from which compositional and maturity variations can be studied (Pinet et al, LPSC 1999). We will present an analysis of this image cube, providing a classification in spectral types and spectral units. The image cube is processed with Gmode analysis using three different data sets: Normalized spectra provide a classification based mainly on spectral slope variations (ie. maturity and volcanic glasses). This analysis discriminates between craters plus ejecta, mare basalts, and DMD. Olivine-rich areas and Aristarchus central peak are also recognized. Continuum-removed spectra provide a classification more related to compositional variations, which correctly identifies olivine and pyroxenes-rich areas (in Aristarchus, Krieger, Schiaparelli\\ldots). A third analysis uses spectral parameters related to maturity and Fe composition (reflectance, 1 mu m band depth, and spectral slope) rather than intensities. It provides the most spatially consistent picture, but fails in detecting Vallis Schroeteri and DMDs. A supplementary unit, younger and rich in pyroxene, is found on Aristarchus south rim. In conclusion, Gmode analysis can discriminate between different spectral types already identified with more classic methods (PCA, linear mixing\\ldots). No previous assumption is made on the data structure, such as endmembers number and nature, or linear relationship between input variables. The variability of the spectral types is intrinsically accounted for, so that the level of analysis is always restricted to meaningful limits. A complete classification should integrate several analyses based on different sets of parameters. Gmode is therefore a powerful light toll to perform first look analysis of spectral imaging data. This research has been partly founded by the French Programme National de Planetologie.

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.

Classification of broiler breast filets according to deboning time using near infrared spectroscopy and multivariate analysis

USDA-ARS?s Scientific Manuscript database

Chicken breast filets were deboned and NIR spectra were collected after 2, 4, and 24 hours. The deboning was performed on pairs of filets to minimize differences due only to the meat and not the deboning time (i.e. right at 2 hours, left at 24; right at 2, left at 4; right at 4, left at 24 hrs). The...

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.

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.

ASTM clustering for improving coal analysis by near-infrared spectroscopy.

PubMed

Andrés, J M; Bona, M T

2006-11-15

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.

Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.

PubMed

Li, Zhaohua; Wang, Yuduo; Quan, Wenxiang; Wu, Tongning; Lv, Bin

2015-02-15

Based on near-infrared spectroscopy (NIRS), recent converging evidence has been observed that patients with schizophrenia exhibit abnormal functional activities in the prefrontal cortex during a verbal fluency task (VFT). Therefore, some studies have attempted to employ NIRS measurements to differentiate schizophrenia patients from healthy controls with different classification methods. However, no systematic evaluation was conducted to compare their respective classification performances on the same study population. In this study, we evaluated the classification performance of four classification methods (including linear discriminant analysis, k-nearest neighbors, Gaussian process classifier, and support vector machines) on an NIRS-aided schizophrenia diagnosis. We recruited a large sample of 120 schizophrenia patients and 120 healthy controls and measured the hemoglobin response in the prefrontal cortex during the VFT using a multichannel NIRS system. Features for classification were extracted from three types of NIRS data in each channel. We subsequently performed a principal component analysis (PCA) for feature selection prior to comparison of the different classification methods. We achieved a maximum accuracy of 85.83% and an overall mean accuracy of 83.37% using a PCA-based feature selection on oxygenated hemoglobin signals and support vector machine classifier. This is the first comprehensive evaluation of different classification methods for the diagnosis of schizophrenia based on different types of NIRS signals. Our results suggested that, using the appropriate classification method, NIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Copyright © 2014 Elsevier B.V. All rights reserved.

Analysis of Flavonoid in Medicinal Plant Extract Using Infrared Spectroscopy and Chemometrics

PubMed Central

Retnaningtyas, Yuni; Nuri; Lukman, Hilmia

2016-01-01

Infrared (IR) spectroscopy combined with chemometrics has been developed for simple analysis of flavonoid in the medicinal plant extract. Flavonoid was extracted from medicinal plant leaves by ultrasonication and maceration. IR spectra of selected medicinal plant extract were correlated with flavonoid content using chemometrics. The chemometric method used for calibration analysis was Partial Last Square (PLS) and the methods used for classification analysis were Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogies (SIMCA), and Support Vector Machines (SVM). In this study, the calibration of NIR model that showed best calibration with R 2 and RMSEC value was 0.9916499 and 2.1521897, respectively, while the accuracy of all classification models (LDA, SIMCA, and SVM) was 100%. R 2 and RMSEC of calibration of FTIR model were 0.8653689 and 8.8958149, respectively, while the accuracy of LDA, SIMCA, and SVM was 86.0%, 91.2%, and 77.3%, respectively. PLS and LDA of NIR models were further used to predict unknown flavonoid content in commercial samples. Using these models, the significance of flavonoid content that has been measured by NIR and UV-Vis spectrophotometry was evaluated with paired samples t-test. The flavonoid content that has been measured with both methods gave no significant difference. PMID:27529051

NIR remission spectroscopy of turbid media

NASA Astrophysics Data System (ADS)

Krauter, P.; Foschum, F.; Kienle, A.

2013-06-01

We present a method for the determination of absorption spectra in VIS and NIR spectra of turbid media without the need for calibration. Measurements of the absorption spectra of a phantom and butter are presented.

Estimation of Anthocyanin Content of Berries by NIR Method

NASA Astrophysics Data System (ADS)

Zsivanovits, G.; Ludneva, D.; Iliev, A.

2010-01-01

Anthocyanin contents of fruits were estimated by VIS spectrophotometer and compared with spectra measured by NIR spectrophotometer (600-1100 nm step 10 nm). The aim was to find a relationship between NIR method and traditional spectrophotometric method. The testing protocol, using NIR, is easier, faster and non-destructive. NIR spectra were prepared in pairs, reflectance and transmittance. A modular spectrocomputer, realized on the basis of a monochromator and peripherals Bentham Instruments Ltd (GB) and a photometric camera created at Canning Research Institute, were used. An important feature of this camera is the possibility offered for a simultaneous measurement of both transmittance and reflectance with geometry patterns T0/180 and R0/45. The collected spectra were analyzed by CAMO Unscrambler 9.1 software, with PCA, PLS, PCR methods. Based on the analyzed spectra quality and quantity sensitive calibrations were prepared. The results showed that the NIR method allows measuring of the total anthocyanin content in fresh berry fruits or processed products without destroying them.

Estimation of Anthocyanin Content of Berries by NIR Method

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

Zsivanovits, G.; Ludneva, D.; Iliev, A.

2010-01-21

Anthocyanin contents of fruits were estimated by VIS spectrophotometer and compared with spectra measured by NIR spectrophotometer (600-1100 nm step 10 nm). The aim was to find a relationship between NIR method and traditional spectrophotometric method. The testing protocol, using NIR, is easier, faster and non-destructive. NIR spectra were prepared in pairs, reflectance and transmittance. A modular spectrocomputer, realized on the basis of a monochromator and peripherals Bentham Instruments Ltd (GB) and a photometric camera created at Canning Research Institute, were used. An important feature of this camera is the possibility offered for a simultaneous measurement of both transmittance andmore » reflectance with geometry patterns T0/180 and R0/45. The collected spectra were analyzed by CAMO Unscrambler 9.1 software, with PCA, PLS, PCR methods. Based on the analyzed spectra quality and quantity sensitive calibrations were prepared. The results showed that the NIR method allows measuring of the total anthocyanin content in fresh berry fruits or processed products without destroying them.« less

Detection of Tetracycline in Milk using NIR Spectroscopy and Partial Least Squares

NASA Astrophysics Data System (ADS)

Wu, Nan; Xu, Chenshan; Yang, Renjie; Ji, Xinning; Liu, Xinyuan; Yang, Fan; Zeng, Ming

2018-02-01

The feasibility of measuring tetracycline in milk was investigated by near infrared (NIR) spectroscopic technique combined with partial least squares (PLS) method. The NIR transmittance spectra of 40 pure milk samples and 40 tetracycline adulterated milk samples with different concentrations (from 0.005 to 40 mg/L) were obtained. The pure milk and tetracycline adulterated milk samples were properly assigned to the categories with 100% accuracy in the calibration set, and the rate of correct classification of 96.3% was obtained in the prediction set. For the quantitation of tetracycline in adulterated milk, the root mean squares errors for calibration and prediction models were 0.61 mg/L and 4.22 mg/L, respectively. The PLS model had good fitting effect in calibration set, however its predictive ability was limited, especially for low tetracycline concentration samples. Totally, this approach can be considered as a promising tool for discrimination of tetracycline adulterated milk, as a supplement to high performance liquid chromatography.

Spectra-structure correlations of saturated and unsaturated medium-chain fatty acids. Near-infrared and anharmonic DFT study of hexanoic acid and sorbic acid

NASA Astrophysics Data System (ADS)

Grabska, Justyna; Beć, Krzysztof B.; Ishigaki, Mika; Wójcik, Marek J.; Ozaki, Yukihiro

2017-10-01

Quantum chemical reproduction of entire NIR spectra is a new trend, enabled by contemporary advances in the anharmonic approaches. At the same time, recent increase of the importance of NIR spectroscopy of biological samples raises high demand for gaining deeper understanding of NIR spectra of biomolecules, i.e. fatty acids. In this work we investigate saturated and unsaturated medium-chain fatty acids, hexanoic acid and sorbic acid, in the near-infrared region. By employing fully anharmonic density functional theory (DFT) calculations we reproduce the experimental NIR spectra of these systems, including the highly specific spectral features corresponding to the dimerization of fatty acids. Broad range of concentration levels from 5 · 10- 4 M in CCl4 to pure samples are investigated. The major role of cyclic dimers can be evidenced for the vast majority of these samples. A highly specific NIR feature of fatty acids, the elevation of spectral baseline around 6500-4000 cm- 1, is being explained by the contributions of combination bands resulting from the vibrations of hydrogen-bonded OH groups in the cyclic dimers. Based on the high agreement between the calculated and experimental NIR spectra, a detailed NIR band assignments are proposed for hexanoic acid and sorbic acid. Subsequently, the correlations between the structure and NIR spectra are elucidated, emphasizing the regions in which clear and universal traces of specific bands corresponding to saturated and unsaturated alkyl chains can be established, thus demonstrating the wavenumber regions highly valuable for structural identifications.

Spectra-structure correlations of saturated and unsaturated medium-chain fatty acids. Near-infrared and anharmonic DFT study of hexanoic acid and sorbic acid.

PubMed

Grabska, Justyna; Beć, Krzysztof B; Ishigaki, Mika; Wójcik, Marek J; Ozaki, Yukihiro

2017-10-05

Quantum chemical reproduction of entire NIR spectra is a new trend, enabled by contemporary advances in the anharmonic approaches. At the same time, recent increase of the importance of NIR spectroscopy of biological samples raises high demand for gaining deeper understanding of NIR spectra of biomolecules, i.e. fatty acids. In this work we investigate saturated and unsaturated medium-chain fatty acids, hexanoic acid and sorbic acid, in the near-infrared region. By employing fully anharmonic density functional theory (DFT) calculations we reproduce the experimental NIR spectra of these systems, including the highly specific spectral features corresponding to the dimerization of fatty acids. Broad range of concentration levels from 5·10 -4 M in CCl 4 to pure samples are investigated. The major role of cyclic dimers can be evidenced for the vast majority of these samples. A highly specific NIR feature of fatty acids, the elevation of spectral baseline around 6500-4000cm -1 , is being explained by the contributions of combination bands resulting from the vibrations of hydrogen-bonded OH groups in the cyclic dimers. Based on the high agreement between the calculated and experimental NIR spectra, a detailed NIR band assignments are proposed for hexanoic acid and sorbic acid. Subsequently, the correlations between the structure and NIR spectra are elucidated, emphasizing the regions in which clear and universal traces of specific bands corresponding to saturated and unsaturated alkyl chains can be established, thus demonstrating the wavenumber regions highly valuable for structural identifications. Copyright © 2017 Elsevier B.V. All rights reserved.

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

PubMed

Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P

2015-01-01

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

[Comparative research on the NIR and MIR micro-imaging of two similar plastic materials].

PubMed

Wang, Dong; Ma, Zhi-Hong; Zhao, Liu; Pan, Li-Gang; Li, Xiao-Ting; Wang, Ji-Hua

2011-09-01

The NIR/MIR micro-imaging can supply not only the information of spectra, but also the information of spacial distribution of the sample, which is superior to the traditional NIR/MIR spectroscopy analysis. In the present paper, polyethylene and parafilm, with similar appearances, were regarded as the research objects, of which the NIR/MIR micro-imaging was collected. Chemical imaging (CI) and compare correlation imaging were carried out for the two materials respectively to discuss the imaging methods of the two materials. The result indicated that the differentiation of the CI values of the two materials in the NIR/MIR CI for material II was 0.004 8 and 0.254 8 respectively, while those in the NIR/MIR CI for material I were 0.002 6 and 0.326 5, respectively. Clear CI was acquired, and the two materials could be differentiated. The result of the compare correlation imagings indicated that the compare correlation imagings, in which the NIR/MIR spectra of the two materials were regarded as reference spectra respectively, can differentiate the two materials remarkably with clear imagings. In the compare correlation imagings of MIR micro-imaging, the difference of the correlation coefficients between the two materials' MIR spectra and the reference spectrum was more than 0.12, which showed a better imaging result; while a tiny difference of the correlation coefficients between the two materials' NIR spectra and the reference spectrum could be employed to show a clear imaging result for NIR compare correlation imaging so as to differentiate the two materials. This thesis, to some extent, can supply the reference to not only the rapid discrimination of the safety of the packaging material for agri-food, but also the imaging methods for NIR/MIR micro-imaging to differentiate the different materials.

Visible and near infrared spectroscopy as an authentication tool: Preliminary investigation of the prediction of the ageing time of beef steaks.

PubMed

Moran, Lara; Andres, Sonia; Allen, Paul; Moloney, Aidan P

2018-08-01

Visible-near infrared spectroscopy (Vis-NIRS) has been suggested to have potential for authentication of food products. The aim of the present preliminary study was to assess if this technology can be used to authenticate the ageing time (3, 7, 14 and 21 days post mortem) of beef steaks from three different muscles (M. Longissimus thoracis, M. Gluteus medius and M. Semitendinosus). Various mathematical pre-treatments were applied to the spectra to correct scattering and overlapping effects, and then partial least squares-discrimination analysis (PLS-DA) procedures applied. The best models were specific for each muscle, and the ability of prediction of ageing time was validated using full (leave-one-out) cross-validation, whereas authentication performance was evaluated using the parameters of sensitivity, specificity and overall correct classification. The results indicate that overall correct classification ranging from 94.2 to 100% was achieved, depending on the muscle. In conclusion, Vis-NIRS technology seems a valid tool for the authentication of ageing time of beef steaks. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

In-line near-infrared (NIR) and Raman spectroscopy coupled with principal component analysis (PCA) for in situ evaluation of the transesterification reaction.

PubMed

Fontalvo-Gómez, Miriam; Colucci, José A; Velez, Natasha; Romañach, Rodolfo J

2013-10-01

Biodiesel was synthesized from different commercially available oils while in-line Raman and near-infrared (NIR) spectra were obtained simultaneously, and the spectral changes that occurred during the reaction were evaluated with principal component analysis (PCA). Raman and NIR spectra were acquired every 30 s with fiber optic probes inserted into the reaction vessel. The reaction was performed at 60-70 °C using magnetic stirring. The time of reaction was 90 min, and during this time, 180 Raman and NIR spectra were collected. NIR spectra were collected using a transflectance probe and an optical path length of 1 mm at 8 cm(-1) spectral resolution and averaging 32 scans; for Raman spectra a 3 s exposure time and three accumulations were adequate for the analysis. Raman spectroscopy showed the ester conversion as evidenced by the displacement of the C=O band from 1747 to 1744 cm(-1) and the decrease in the intensity of the 1000-1050 cm(-1) band and the 1405 cm(-1) band as methanol was consumed in the reaction. NIR spectra also showed the decrease in methanol concentration with the band in the 4750-5000 cm(-1) region; this signal is present in the spectra of the transesterification reaction but not in the neat oils. The variations in the intensity of the methanol band were a main factor in the in-line monitoring of the transesterification reaction using Raman and NIR spectroscopy. The score plot of the first principal component showed the progress of the reaction. The final product was analyzed using (1)H nuclear magnetic resonance ((1)H NMR) spectroscopy and using mid-infrared spectroscopy, confirming the conversion of the oils to biodiesel.

Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies

NASA Astrophysics Data System (ADS)

John, C. M.; Kavya, N.

2014-11-01

Aquatic macrophytes (AM) can serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various AM were investigated to determine whether species could be discriminated by remote sensing. In this study the spectral readings of different AM communities identified were done using the ASD Fieldspec® Hand Held spectro-radiometer in the wavelength range of 325-1075 nm. The collected specific reflectance spectra were applied to space borne multi-spectral remote sensing data from Worldview-2, acquired on 26th March 2011. The dimensionality reduction of the spectro-radiometric data was done using the technique principal components analysis (PCA). Out of the different PCA axes generated, 93.472 % variance of the spectra was explained by the first axis. The spectral derivative analysis was done to identify the wavelength where the greatest difference in reflectance is shown. The identified wavelengths are 510, 690, 720, 756, 806, 885, 907 and 923 nm. The output of PCA and derivative analysis were applied to Worldview-2 satellite data for spectral subsetting. The unsupervised classification was used to effectively classify the AM species using the different spectral subsets. The accuracy assessment of the results of the unsupervised classification and their comparison were done. The overall accuracy of the result of unsupervised classification using the band combinations Red-Edge, Green, Coastal blue & Red-edge, Yellow, Blue is 100%. The band combinations NIR-1, Green, Coastal blue & NIR-1, Yellow, Blue yielded an accuracy of 82.35 %. The existing vegetation indices and new hyper-spectral indices for the different type of AM communities were computed. Overall, results of this study suggest that high spectral and spatial resolution images provide useful information for natural resource managers especially with regard to the location identification and distribution mapping of macrophyte species and their communities.

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.

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.

FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud.

PubMed

Márquez, Cristina; López, M Isabel; Ruisánchez, Itziar; Callao, M Pilar

2016-12-01

Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96-100% and 88-100% for the mid- and high-level data fusion strategies, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Parametric models of reflectance spectra for dyed fabrics

NASA Astrophysics Data System (ADS)

Aiken, Daniel C.; Ramsey, Scott; Mayo, Troy; Lambrakos, Samuel G.; Peak, Joseph

2016-05-01

This study examines parametric modeling of NIR reflectivity spectra for dyed fabrics, which provides for both their inverse and direct modeling. The dye considered for prototype analysis is triarylamine dye. The fabrics considered are camouflage textiles characterized by color variations. The results of this study provide validation of the constructed parametric models, within reasonable error tolerances for practical applications, including NIR spectral characteristics in camouflage textiles, for purposes of simulating NIR spectra corresponding to various dye concentrations in host fabrics, and potentially to mixtures of dyes.

Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

PubMed Central

Gupta, Rishabh; Falk, Tiago H.

2017-01-01

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs. PMID:29181021

[Determination of wine original regions using information fusion of NIR and MIR spectroscopy].

PubMed

Xiang, Ling-Li; Li, Meng-Hua; Li, Jing-Mingz; Li, Jun-Hui; Zhang, Lu-Da; Zhao, Long-Lian

2014-10-01

Geographical origins of wine grapes are significant factors affecting wine quality and wine prices. Tasters' evaluation is a good method but has some limitations. It is important to discriminate different wine original regions quickly and accurately. The present paper proposed a method to determine wine original regions based on Bayesian information fusion that fused near-infrared (NIR) transmission spectra information and mid-infrared (MIR) ATR spectra information of wines. This method improved the determination results by expanding the sources of analysis information. NIR spectra and MIR spectra of 153 wine samples from four different regions of grape growing were collected by near-infrared and mid-infrared Fourier transform spe trometer separately. These four different regions are Huailai, Yantai, Gansu and Changli, which areall typical geographical originals for Chinese wines. NIR and MIR discriminant models for wine regions were established using partial least squares discriminant analysis (PLS-DA) based on NIR spectra and MIR spectra separately. In PLS-DA, the regions of wine samples are presented in group of binary code. There are four wine regions in this paper, thereby using four nodes standing for categorical variables. The output nodes values for each sample in NIR and MIR models were normalized first. These values stand for the probabilities of each sample belonging to each category. They seemed as the input to the Bayesian discriminant formula as a priori probability value. The probabilities were substituteed into the Bayesian formula to get posterior probabilities, by which we can judge the new class characteristics of these samples. Considering the stability of PLS-DA models, all the wine samples were divided into calibration sets and validation sets randomly for ten times. The results of NIR and MIR discriminant models of four wine regions were as follows: the average accuracy rates of calibration sets were 78.21% (NIR) and 82.57% (MIR), and the average accuracy rates of validation sets were 82.50% (NIR) and 81.98% (MIR). After using the method proposed in this paper, the accuracy rates of calibration and validation changed to 87.11% and 90.87% separately, which all achieved better results of determination than individual spectroscopy. These results suggest that Bayesian information fusion of NIR and MIR spectra is feasible for fast identification of wine original regions.

Relationships between NIR spectra and sensory attributes of Thai commercial fish sauces.

PubMed

Ritthiruangdej, Pitiporn; Suwonsichon, Thongchai

2007-07-01

Twenty Thai commercial fish sauces were characterized by sensory descriptive analysis and near-infrared (NIR) spectroscopy. The main objectives were i) to investigate the relationships between sensory attributes and NIR spectra of samples and ii) to characterize the sensory characteristics of fish sauces based on NIR data. A generic descriptive analysis with 12 trained panels was used to characterize the sensory attributes. These attributes consisted of 15 descriptors: brown color, 5 aromatics (sweet, caramelized, fermented, fishy, and musty), 4 tastes (sweet, salty, bitter, and umami), 3 aftertastes (sweet, salty and bitter) and 2 flavors (caramelized and fishy). The results showed that Thai fish sauce samples exhibited significant differences in all of sensory attribute values (p < 0.05). NIR transflectance spectra were obtained from 1100 to 2500 nm. Prior to investigation of the relationships between sensory attributes and NIR spectra, principal component analysis (PCA) was applied to reduce the dimensionality of the spectral data from 622 wavelengths to two uncorrelated components (NIR1 and NIR2) which explained 92 and 7% of the total variation, respectively. NIR1 was highly correlated with the wavelength regions of 1100 - 1544, 1774 - 2062, 2092 - 2308, and 2358 - 2440 nm, while NIR2 was highly correlated with the wavelength regions of 1742 - 1764, 2066 - 2088, and 2312 - 2354 nm. Subsequently, the relationships among these two components and all sensory attributes were also investigated by PCA. The results showed that the first three principal components (PCs) named as fishy flavor component (PC1), sweet component (PC2) and bitterness component (PC3), respectively, explained a total of 66.86% of the variation. NIR1 was mainly correlated to the sensory attributes of fishy aromatic, fishy flavor and sweet aftertaste on PC1. In addition, the PCA using only the factor loadings of NIR1 and NIR2 could be used to classify samples into three groups which showed high, medium and low degrees of fishy aromatic, fishy flavor and sweet aftertaste.

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.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

PubMed

Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

2018-01-01

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2015-12-01

Objective. The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach. Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results. VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance. These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.

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.

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

NASA Astrophysics Data System (ADS)

Shinzawa, Hideyuki; Mizukado, Junji

2018-03-01

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

[Traceability of Wine Varieties Using Near Infrared Spectroscopy Combined with Cyclic Voltammetry].

PubMed

Li, Meng-hua; Li, Jing-ming; Li, Jun-hui; Zhang, Lu-da; Zhao, Long-lian

2015-06-01

To achieve the traceability of wine varieties, a method was proposed to fuse Near-infrared (NIR) spectra and cyclic voltammograms (CV) which contain different information using D-S evidence theory. NIR spectra and CV curves of three different varieties of wines (cabernet sauvignon, merlot, cabernet gernischt) which come from seven different geographical origins were collected separately. The discriminant models were built using PLS-DA method. Based on this, D-S evidence theory was then applied to achieve the integration of the two kinds of discrimination results. After integrated by D-S evidence theory, the accuracy rate of cross-validation is 95.69% and validation set is 94.12% for wine variety identification. When only considering the wine that come from Yantai, the accuracy rate of cross-validation is 99.46% and validation set is 100%. All the traceability models after fusion achieved better results on classification than individual method. These results suggest that the proposed method combining electrochemical information with spectral information using the D-S evidence combination formula is benefit to the improvement of model discrimination effect, and is a promising tool for discriminating different kinds of wines.

Do NIR spectra collected from laboratory-reared mosquitoes differ from those collected from wild mosquitoes?

USDA-ARS?s Scientific Manuscript database

Do near infrared spectra from lab-reared mosquitoes differ from spectra from wild mosquitoes? Near infrared spectroscopy (NIRS) can classify the age of lab-reared mosquitoes as younger or older than seven days with accuracy greater than 80%. Hence, it has been proposed in several studies as a comple...

Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine.

PubMed

da Silva, Neirivaldo Cavalcante; Honorato, Ricardo Saldanha; Pimentel, Maria Fernanda; Garrigues, Salvador; Cervera, Maria Luisa; de la Guardia, Miguel

2015-09-01

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2% w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples. © 2015 American Academy of Forensic Sciences.

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

PubMed Central

Rana, Mohit; Prasad, Vinod A.; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

2016-01-01

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability.

PubMed

An, Dong; Cui, Yongjin; Liu, Xu; Jia, Shiqiang; Zheng, Shuyun; Che, Xiaoping; Liu, Zhe; Zhang, Xiaodong; Zhu, Dehai; Li, Shaoming

2016-01-01

The effects of varieties, producing areas, ears, and ear positions of maize on near-infrared (NIR) spectra were investigated to determine the factors causing the differences in NIR fingerprints of maize varieties. A total of 130 inbred lines were grown in two regions in China, and 12,350 kernel samples were analyzed through NIR spectroscopy. Spectral differences among varieties, producing areas, ears, and ear positions were determined and compared on the basis of pretreated spectra. The bands at 1300-1470, 1768-1949, 2010-2064, and 2235-2311 nm were mainly affected by the producing area. Band selection and principal component analysis were applied to improve the influence of variety on NIR spectra by processing the pretreated spectra. The degrees of the influence of varieties, producing areas, ears, and ear positions were calculated, and the percentages of the influence of varieties, producing areas, ears, and ear positions were 45.40%, 42.66%, 8.22%, and 3.72%, respectively. Therefore, genetic differences among maize inbred lines are the main factors accounted for NIR spectral differences. Producing area is a secondary factor. These results could provide a reference for researchers who authenticate varieties, perform geographical origin traceabilities, and conduct maize seed breeding.

Age determination of bottled Chinese rice wine by VIS-NIR spectroscopy

NASA Astrophysics Data System (ADS)

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

2006-10-01

The feasibility of non-invasive visible and near infrared (VIS-NIR) spectroscopy for determining wine age (1, 2, 3, 4, and 5 years) of Chinese rice wine was investigated. Samples of Chinese rice wine were analyzed in 600 mL square brown glass bottles with side length of approximately 64 mm at room temperature. VIS-NIR spectra of 100 bottled Chinese rice wine samples were collected in transmission mode in the wavelength range of 350-1200 nm by a fiber spectrometer system. Discriminant models were developed based on discriminant analysis (DA) together with raw, first and second derivative spectra. The concentration of alcoholic degree, total acid, and °Brix was determined to validate the NIR results. The calibration result for raw spectra was better than that for first and second derivative spectra. The percentage of samples correctly classified for raw spectra was 98%. For 1-, 2-, and 3-year-old sample groups, the sample were all correctly classified, and for 4- and 5-year-old sample groups, the percentage of samples correctly classified was 92.9%, respectively. In validation analysis, the percentage of samples correctly classified was 100%. The results demonstrated that VIS-NIR spectroscopic technique could be used as a non-invasive, rapid and reliable method for predicting wine age of bottled Chinese rice wine.

Analysis of Peanut Seed Oil by NIR

USDA-ARS?s Scientific Manuscript database

Near infrared reflectance spectra (NIRS) were collected from Arachis hypogaea seed samples and used in predictive models to rapidly identify varieties with high oleic acid. The method was developed for shelled peanut seeds with intact testa. Spectra were evaluated initially by principal component an...

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.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

PubMed

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques can be useful for rapid distinguishing the healthy wheat samples from those damaged by sunn pest in the maintenance and processing centers. Copyright © 2018 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.

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

PubMed

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

2015-07-05

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

Transmission near-infrared (NIR) and photon time-of-flight (PTOF) spectroscopy in a comparative analysis of pharmaceuticals.

PubMed

Kamran, Faisal; Abildgaard, Otto H A; Sparén, Anders; Svensson, Olof; Johansson, Jonas; Andersson-Engels, Stefan; Andersen, Peter E; Khoptyar, Dmitry

2015-03-01

We present a comprehensive study of the application of photon time-of-flight spectroscopy (PTOFS) in the wavelength range 1050-1350 nm as a spectroscopic technique for the evaluation of the chemical composition and structural properties of pharmaceutical tablets. PTOFS is compared to transmission near-infrared spectroscopy (NIRS). In contrast to transmission NIRS, PTOFS is capable of directly and independently determining the absorption and reduced scattering coefficients of the medium. Chemometric models were built on the evaluated absorption spectra for predicting tablet drug concentration. Results are compared to corresponding predictions built on transmission NIRS measurements. The predictive ability of PTOFS and transmission NIRS is comparable when models are based on uniformly distributed tablet sets. For non-uniform distribution of tablets based on particle sizes, the prediction ability of PTOFS is better than that of transmission NIRS. Analysis of reduced scattering spectra shows that PTOFS is able to characterize tablet microstructure and manufacturing process parameters. In contrast to the chemometric pseudo-variables provided by transmission NIRS, PTOFS provides physically meaningful quantities such as scattering strength and slope of particle size. The ability of PTOFS to quantify the reduced scattering spectra, together with its robustness in predicting drug content, makes it suitable for such evaluations in the pharmaceutical industry.

Identification, classification, and discrimination of agave syrups from natural sweeteners by infrared spectroscopy and HPAEC-PAD.

PubMed

Mellado-Mojica, Erika; López, Mercedes G

2015-01-15

Agave syrups are gaining popularity as new natural sweeteners. Identification, classification and discrimination by infrared spectroscopy coupled to chemometrics (NIR-MIR-SIMCA-PCA) and HPAEC-PAD of agave syrups from natural sweeteners were achieved. MIR-SIMCA-PCA allowed us to classify the natural sweeteners according to their natural source. Natural syrups exhibited differences in the MIR spectra region 1500-900 cm(-1). The agave syrups displayed strong absorption in the MIR spectra region 1061-1,063 cm(-1), in agreement with their high fructose content. Additionally, MIR-SIMCA-PCA allowed us to differentiate among syrups from different Agave species (Agavetequilana and Agavesalmiana). Thin-layer chromatography and HPAEC-PAD revealed glucose, fructose, and sucrose as the principal carbohydrates in all of the syrups. Oligosaccharide profiles showed that A. tequilana syrups are mainly composed of fructose (>60%) and fructooligosaccharides, while A. salmiana syrups contain more sucrose (28-32%). We conclude that MIR-SIMCA-PCA and HPAEC-PAD can be used to unequivocally identify and classified agave syrups. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Near-infrared reflectance spectra of mixtures of kaolin-group minerals: Use in clay mineral studies

USGS Publications Warehouse

Crowley, James K.; Vergo, Norma

1988-01-01

Near-infrared (NIR) reflectance spectra for mixtures of ordered kaolinite and ordered dickite have been found to simulate the spectral response of disordered kaolinite. The amount of octahedral vacancy disorder in nine disordered kaolinite samples was estimated by comparing the sample spectra to the spectra of reference mixtures. The resulting estimates are consistent with previously published estimates of vacancy disorder for similar kaolin minerals that were modeled from calculated X-ray diffraction patterns. The ordered kaolinite and dickite samples used in the reference mixtures were carefully selected to avoid undesirable particle size effects that could bias the spectral results.NIR spectra were also recorded for laboratory mixtures of ordered kaolinite and halloysite to assess whether the spectra could be potentially useful for determining mineral proportions in natural physical mixtures of these two clays. Although the kaolinite-halloysite proportions could only be roughly estimated from the mixture spectra, the halloysite component was evident even when halloysite was present in only minor amounts. A similar approach using NIR spectra for laboratory mixtures may have applications in other studies of natural clay mixtures.

NIR technique in the classification of cotton leaf grade

USDA-ARS?s Scientific Manuscript database

Near infrared (NIR) spectroscopy, a useful technique due to the speed, ease of use, and adaptability to on-line or off-line implementation, has been applied to perform the qualitative classification and quantitative prediction of cotton quality characteristics, including trash index. One term to as...

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.

21 CFR 882.1935 - Near Infrared (NIR) Brain Hematoma Detector.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Near Infrared (NIR) Brain Hematoma Detector. 882... Infrared (NIR) Brain Hematoma Detector. (a) Identification. A Near Infrared (NIR) Brain Hematoma Detector... evaluate suspected brain hematomas. (b) Classification. Class II (special controls). The special controls...

21 CFR 882.1935 - Near Infrared (NIR) Brain Hematoma Detector.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Near Infrared (NIR) Brain Hematoma Detector. 882... Infrared (NIR) Brain Hematoma Detector. (a) Identification. A Near Infrared (NIR) Brain Hematoma Detector... evaluate suspected brain hematomas. (b) Classification. Class II (special controls). The special controls...

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Near-infrared-excited confocal Raman spectroscopy advances in vivo diagnosis of cervical precancer.

PubMed

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J H; Ilancheran, Arunachalam; Huang, Zhiwei

2013-06-01

Raman spectroscopy is a unique optical technique that can probe the changes of vibrational modes of biomolecules associated with tissue premalignant transformation. This study evaluates the clinical utility of confocal Raman spectroscopy over near-infrared (NIR) autofluorescence (AF) spectroscopy and composite NIR AF/Raman spectroscopy for improving early diagnosis of cervical precancer in vivo at colposcopy. A rapid NIR Raman system coupled with a ball-lens fiber-optic confocal Raman probe was utilized for in vivo NIR AF/Raman spectral measurements of the cervix. A total of 1240 in vivo Raman spectra [normal (n=993), dysplasia (n=247)] were acquired from 84 cervical patients. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with a leave-one-patient-out, cross-validation method were used to extract the diagnostic information associated with distinctive spectroscopic modalities. The diagnostic ability of confocal Raman spectroscopy was evaluated using the PCA-LDA model developed from the significant principal components (PCs) [i.e., PC4, 0.0023%; PC5, 0.00095%; PC8, 0.00022%, (p<0.05)], representing the primary tissue Raman features (e.g., 854, 937, 1095, 1253, 1311, 1445, and 1654 cm(-1)). Confocal Raman spectroscopy coupled with PCA-LDA modeling yielded the diagnostic accuracy of 84.1% (a sensitivity of 81.0% and a specificity of 87.1%) for in vivo discrimination of dysplastic cervix. The receiver operating characteristic curves further confirmed that the best classification was achieved using confocal Raman spectroscopy compared to the composite NIR AF/Raman spectroscopy or NIR AF spectroscopy alone. This study illustrates that confocal Raman spectroscopy has great potential to improve early diagnosis of cervical precancer in vivo during clinical colposcopy.

NIR techniques create added values for the pellet and biofuel industry.

PubMed

Lestander, Torbjörn A; Johnsson, Bo; Grothage, Morgan

2009-02-01

A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values.

NIR spectroscopic sensing for point-of-need freshness assessment of meat, fish, vegetables and fruits

NASA Astrophysics Data System (ADS)

Lee, Seoho; Noh, Tae Gyoon; Choi, Jun Hoe; Han, Jeongsu; Ha, Joo Young; Lee, Ji Young; Park, Yongjong

2017-05-01

Foodborne illness represents a significant health burden worldwide. While monitoring the freshness of food before consumption could significantly improve the current predicament, there is a lack of a simple system that one can use to accurately assess the freshness of their food. Currently, the most common practice for food quality determination is by visual or odor inspection which lacks objectivity, accuracy and precision. Near infrared (NIR) spectroscopic techniques can help address this problem by providing rapid and non-destructive means to estimate the freshness state of various foods based on the changes to their characteristic spectra in the NIR region. Recent advancements in the development of portable NIR spectrometers are also enabling the realization of this technique at the point-of-need. In this study, we have evaluated the feasibility of using NIR spectroscopy at the point-of-need to estimate the freshness of various foods including: beef sirloin, beef eyeround, pork sirloin, bass, salmon, corvina, tomato and watermelon. Using a commercial portable NIR spectrometer, we periodically scanned and collected NIR spectra from the food items that were stored at 4°C inside a refrigerator for up to 30 days. For these food items, we show that the NIR spectra can be classified by the foods' aging day as well as by the levels of chemical/microbial indicators (i.e., thiobarbituric acid, volatile basic nitrogen and bacteria levels) with high accuracy, which represents high prospects of NIR spectroscopy for point-of-need freshness assessment of meat, fish, vegetables and fruits.

Prediction of Caffeine Content in Java Preanger Coffee Beans by NIR Spectroscopy Using PLS and MLR Method

NASA Astrophysics Data System (ADS)

Budiastra, I. W.; Sutrisno; Widyotomo, S.; Ayu, P. C.

2018-05-01

Caffeine is one of important components in coffee that contributes to the coffee beverages flavor. Caffeine concentration in coffee bean is usually determined by chemical method which is time consuming and destructive method. A nondestructive method using NIR spectroscopy was successfully applied to determine the caffeine concentration of Arabica gayo coffee bean. In this study, NIR Spectroscopy was assessed to determine the caffeine concentration of java preanger coffee bean. A hundred samples, each consist of 96 g coffee beans were prepared for reflectance and chemical measurement. Reflectance of the sample was measured by FT-NIR spectrometer in the wavelength of 1000-2500 nm (10000-4000 cm-1) followed by determination of caffeine content using LCMS method. Calibration of NIR spectra and the caffeine content was carried out using PLS and MLR methods. Several spectra data processing was conducted to increase the accuracy of prediction. The result of the study showed that caffeine content could be determined by PLS model using 7 factors and spectra data processing of combination of the first derivative and MSC of spectra absorbance (r = 0.946; CV = 1.54 %; RPD = 2.28). A lower accuracy was obtained by MLR model consisted of three caffeine and other four absorption wavelengths (r = 0.683; CV = 3.31%; RPD = 1.18).

Near-infrared spectra of the Martian surface: Reading between the lines

NASA Technical Reports Server (NTRS)

Crisp, D.; Bell, J. F., III

1993-01-01

Moderate-resolution near-infrared (NIR) spectra of Mars have been widely used in studies of the Martian surface because many candidate surface materials have distinctive absorption features at these wavelengths. Recent advances in NIR detector technology and instrumentation have also encouraged studies in this spectral region. The use of moderate spectral resolution has often been justified for NIR surface observations because the spectral features produced by most surface materials are relatively broad, and easily discriminated at this resolution. In spite of this, NIR spectra of Mars are usually very difficult to interpret quantitatively. One problem is that NIR surface absorption features are often only a few percent deep, requiring observations with great signal-to-noise ratios. A more significant problem is that gases in the Martian atmosphere contribute numerous absorption features at these wavelengths. Ground-based observers must also contend with variable absorption by several gases in the Earth's atmosphere (H2O, CO2, O3, N2O, CH4, O2). The strong CO2 bands near 1.4, 1.6, 2.0, 2.7, 4.3, and 4.8 micrometers largely preclude the analysis of surface spectral features at these wavelengths. Martian atmospheric water vapor also contributes significant absorption near 1.33, 1.88, and 2.7 micrometers, but water vapor in the Earth's atmosphere poses a much larger problem to ground-based studies of these spectral regions. The third most important NIR absorber in the Martian atmosphere is CO. This gas absorbs most strongly in the relatively-transparent spectral windows near 4.6 and 2.3 micrometers. It also produces 1-10 percent absorption in the solar spectrum at these NIR wavelengths. This solar CO absorption cannot be adequately removed by dividing the Martian spectrum by that of a star, as is commonly done to calibrate ground-based spectroscopic observations, because most stars do not have identical amounts of CO absorption in their spectra. Here, we describe tow effective methods for eliminating contamination of Martian surface spectra by absorption in the solar, terrestrial, and Martian atmospheres. Both methods involve the use of very-high-resolution spectra that completely resolve the narrow atmospheric absorption lines.

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

PubMed

Heger, Dominic; Herff, Christian; Schultz, Tanja

2014-01-01

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

[A Study of the Relationship Among Genetic Distances, NIR Spectra Distances, and NIR-Based Identification Model Performance of the Seeds of Maize Iinbred Lines].

PubMed

Liu, Xu; Jia, Shi-qiang; Wang, Chun-ying; Liu, Zhe; Gu, Jian-cheng; Zhai, Wei; Li, Shao-ming; Zhang, Xiao-dong; Zhu, De-hai; Huang, Hua-jun; An, Dong

2015-09-01

This paper explored the relationship among genetic distances, NIR spectra distances and NIR-based identification model performance of the seeds of maize inbred lines. Using 3 groups (total 15 pairs) of maize inbred lines whose genetic distaches are different as experimental materials, we calculates the genetic distance between these seeds with SSR markers and uses Euclidean distance between distributed center points of maize NIR spectrum in the PCA space as the distances of NIR spectrum. BPR method is used to build identification model of inbred lines and the identification accuracy is used as a measure of model identification performance. The results showed that, the correlation of genetic distance and spectra distancesis 0.9868, and it has a correlation of 0.9110 with the identification accuracy, which is highly correlated. This means near-Infrared spectrum of seedscan reflect genetic relationship of maize inbred lines. The smaller the genetic distance, the smaller the distance of spectrum, the poorer ability of model to identify. In practical application, near infrared spectrum analysis technology has the potential to be used to analyze maize inbred genetic relations, contributing much to genetic breeding, identification of species, purity sorting and so on. What's more, when creating a NIR-based identification model, the impact of the maize inbred lines which have closer genetic relationship should be fully considered.

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.

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.

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.

Classification of jet fuel properties by near-infrared spectroscopy using fuzzy rule-building expert systems and support vector machines.

PubMed

Xu, Zhanfeng; Bunker, Christopher E; Harrington, Peter de B

2010-11-01

Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.

PESSTO: survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects

NASA Astrophysics Data System (ADS)

Smartt, S. J.; Valenti, S.; Fraser, M.; Inserra, C.; Young, D. R.; Sullivan, M.; Pastorello, A.; Benetti, S.; Gal-Yam, A.; Knapic, C.; Molinaro, M.; Smareglia, R.; Smith, K. W.; Taubenberger, S.; Yaron, O.; Anderson, J. P.; Ashall, C.; Balland, C.; Baltay, C.; Barbarino, C.; Bauer, F. E.; Baumont, S.; Bersier, D.; Blagorodnova, N.; Bongard, S.; Botticella, M. T.; Bufano, F.; Bulla, M.; Cappellaro, E.; Campbell, H.; Cellier-Holzem, F.; Chen, T.-W.; Childress, M. J.; Clocchiatti, A.; Contreras, C.; Dall'Ora, M.; Danziger, J.; de Jaeger, T.; De Cia, A.; Della Valle, M.; Dennefeld, M.; Elias-Rosa, N.; Elman, N.; Feindt, U.; Fleury, M.; Gall, E.; Gonzalez-Gaitan, S.; Galbany, L.; Morales Garoffolo, A.; Greggio, L.; Guillou, L. L.; Hachinger, S.; Hadjiyska, E.; Hage, P. E.; Hillebrandt, W.; Hodgkin, S.; Hsiao, E. Y.; James, P. A.; Jerkstrand, A.; Kangas, T.; Kankare, E.; Kotak, R.; Kromer, M.; Kuncarayakti, H.; Leloudas, G.; Lundqvist, P.; Lyman, J. D.; Hook, I. M.; Maguire, K.; Manulis, I.; Margheim, S. J.; Mattila, S.; Maund, J. R.; Mazzali, P. A.; McCrum, M.; McKinnon, R.; Moreno-Raya, M. E.; Nicholl, M.; Nugent, P.; Pain, R.; Pignata, G.; Phillips, M. M.; Polshaw, J.; Pumo, M. L.; Rabinowitz, D.; Reilly, E.; Romero-Cañizales, C.; Scalzo, R.; Schmidt, B.; Schulze, S.; Sim, S.; Sollerman, J.; Taddia, F.; Tartaglia, L.; Terreran, G.; Tomasella, L.; Turatto, M.; Walker, E.; Walton, N. A.; Wyrzykowski, L.; Yuan, F.; Zampieri, L.

2015-07-01

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January - April and August - December inclusive, and typically has allocations of 10 nights per month. Aims: We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). Methods: PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345-9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935-2.53 μm and resolutions 23-33 Å) and imaging with broadband JHKs filters. Results: This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012-2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ~15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHKs imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. Conclusions: Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere, Chile, as part of programme 188.D-3003 (PESSTO). http://www.pessto.org

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.

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.

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.

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.

Compilation of a near-infrared library for the construction of quantitative models of amoxicillin and potassium clavulanate oral dosage forms

NASA Astrophysics Data System (ADS)

Zou, Wen-bo; Chong, Xiao-meng; Wang, Yan; Hu, Chang-qin

2018-05-01

The accuracy of NIR quantitative models depends on calibration samples with concentration variability. Conventional sample collecting methods have some shortcomings especially the time-consuming which remains a bottleneck in the application of NIR models for Process Analytical Technology (PAT) control. A study was performed to solve the problem of sample selection collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms were used as examples. The aim was to find a normal approach to rapidly construct NIR quantitative models using an NIR spectral library based on the idea of a universal model [2021]. The NIR spectral library of amoxicillin and potassium clavulanate oral dosage forms was defined and consisted of spectra of 377 batches of samples produced by 26 domestic pharmaceutical companies, including tablets, dispersible tablets, chewable tablets, oral suspensions, and granules. The correlation coefficient (rT) was used to indicate the similarities of the spectra. The samples’ calibration sets were selected from a spectral library according to the median rT of the samples to be analyzed. The rT of the samples selected was close to the median rT. The difference in rT of those samples was 1.0% to 1.5%. We concluded that sample selection is not a problem when constructing NIR quantitative models using a spectral library versus conventional methods of determining universal models. The sample spectra with a suitable concentration range in the NIR models were collected quickly. In addition, the models constructed through this method were more easily targeted.

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.

Laboratory spectroscopy of meteorite samples at UV-vis-NIR wavelengths: Analysis and discrimination by principal components analysis

NASA Astrophysics Data System (ADS)

Penttilä, Antti; Martikainen, Julia; Gritsevich, Maria; Muinonen, Karri

2018-02-01

Meteorite samples are measured with the University of Helsinki integrating-sphere UV-vis-NIR spectrometer. The resulting spectra of 30 meteorites are compared with selected spectra from the NASA Planetary Data System meteorite spectra database. The spectral measurements are transformed with the principal component analysis, and it is shown that different meteorite types can be distinguished from the transformed data. The motivation is to improve the link between asteroid spectral observations and meteorite spectral measurements.

Rheo-optical two-dimensional (2D) near-infrared (NIR) correlation spectroscopy for probing strain-induced molecular chain deformation of annealed and quenched Nylon 6 films

NASA Astrophysics Data System (ADS)

Shinzawa, Hideyuki; Mizukado, Junji

2018-04-01

A rheo-optical characterization technique based on the combination of a near-infrared (NIR) spectrometer and a tensile testing machine is presented here. In the rheo-optical NIR spectroscopy, tensile deformations are applied to polymers to induce displacement of ordered or disordered molecular chains. The molecular-level variation of the sample occurring on short time scales is readily captured as a form of strain-dependent NIR spectra by taking an advantage of an acousto-optic tunable filter (AOTF) equipped with the NIR spectrometer. In addition, the utilization of NIR with much less intense absorption makes it possible to measure transmittance spectra of relatively thick samples which are often required for conventional tensile testing. An illustrative example of the rheo-optical technique is given with annealed and quenched Nylon 6 samples to show how this technique can be utilized to derive more penetrating insight even from the seemingly simple polymers. The analysis of the sets of strain-dependent NIR spectra suggests the presence of polymer structures undergoing different variations during the tensile elongation. For instance, the tensile deformation of the semi-crystalline Nylon 6 involves a separate step of elongation of the rubbery amorphous chains and subsequent disintegration of the rigid crystalline structure. Excess amount of crystalline phase in Nylon 6, however, results in the retardation of the elastic deformation mainly achieved by the amorphous structure, which eventually leads to the simultaneous orientation of both amorphous and crystalline structures.

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

Near-infrared fluorescence amplified organic nanoparticles with aggregation-induced emission characteristics for in vivo imaging

NASA Astrophysics Data System (ADS)

Geng, Junlong; Zhu, Zhenshu; Qin, Wei; Ma, Lin; Hu, Yong; Gurzadyan, Gagik G.; Tang, Ben Zhong; Liu, Bin

2013-12-01

Near-infrared (NIR) fluorescence signals are highly desirable to achieve high resolution in biological imaging. To obtain NIR emission with high brightness, fluorescent nanoparticles (NPs) are synthesized by co-encapsulation of 2,3-bis(4-(phenyl(4-(1,2,2-triphenylvinyl)phenylamino)phenyl)fumaronitrile (TPETPAFN), a luminogen with aggregation-induced emission (AIE) characteristics, and a NIR fluorogen of silicon 2,3-naphthalocyanine bis(trihexylsilyloxide) (NIR775) using 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] as the encapsulation matrix. The good spectral overlap between the emission of TPETPAFN and the absorption of NIR775 leads to efficient energy transfer, resulting in a 47-fold enhancement of the NIR775 emission intensity upon excitation of TPETPAFN at 510 nm as compared to that upon direct excitation of NIR775 at 760 nm. The obtained fluorescent NPs show sharp NIR emission with a band width of 20 nm, a large Stokes shift of 275 nm, good photostability and low cytotoxicity. In vivo imaging study reveals that the synthesized NPs are able to provide high fluorescence contrast in live animals. The Förster resonance energy transfer strategy overcomes the intrinsic limitation of broad emission spectra for AIE NPs, which opens new opportunities to synthesize organic NPs with high brightness and narrow emission for potential applications in multiplex sensing and imaging.Near-infrared (NIR) fluorescence signals are highly desirable to achieve high resolution in biological imaging. To obtain NIR emission with high brightness, fluorescent nanoparticles (NPs) are synthesized by co-encapsulation of 2,3-bis(4-(phenyl(4-(1,2,2-triphenylvinyl)phenylamino)phenyl)fumaronitrile (TPETPAFN), a luminogen with aggregation-induced emission (AIE) characteristics, and a NIR fluorogen of silicon 2,3-naphthalocyanine bis(trihexylsilyloxide) (NIR775) using 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] as the encapsulation matrix. The good spectral overlap between the emission of TPETPAFN and the absorption of NIR775 leads to efficient energy transfer, resulting in a 47-fold enhancement of the NIR775 emission intensity upon excitation of TPETPAFN at 510 nm as compared to that upon direct excitation of NIR775 at 760 nm. The obtained fluorescent NPs show sharp NIR emission with a band width of 20 nm, a large Stokes shift of 275 nm, good photostability and low cytotoxicity. In vivo imaging study reveals that the synthesized NPs are able to provide high fluorescence contrast in live animals. The Förster resonance energy transfer strategy overcomes the intrinsic limitation of broad emission spectra for AIE NPs, which opens new opportunities to synthesize organic NPs with high brightness and narrow emission for potential applications in multiplex sensing and imaging. Electronic supplementary information (ESI) available: Characterization of AIE properties of TPETPAFN, UV-vis spectra of NPs, PL spectra comparison upon excitation at the donor and receptor absorbance maxima, ex vivo fluorescence imaging of mice organs. See DOI: 10.1039/c3nr04243j

Influence of wood barrels classified by NIRS on the ellagitannin content/composition and on the organoleptic properties of wine.

PubMed

Michel, Julien; Jourdes, Michael; Le Floch, Alexandra; Giordanengo, Thomas; Mourey, Nicolas; Teissedre, Pierre-Louis

2013-11-20

Ellagitannins are extracted from oak wood during wine aging in oak barrels. This research is based on the NIRS (Oakscan) oak wood classification according to their index polyphenolic (IP) (between 21.07 and 70.15). Their level in wood is very variable (between 5.95 and 32.91 mg/g dry wood) and influenced their concentration in red wine (between 2.30 and 32.56 mg/L after 24 months of aging) and thus their impact on wine organoleptic properties. The results show a good correlation between the NIRS classification and the chemical analysis (HPLC-UV-MS and acidic hydrolysis procedure) and with the wood ellagitannin level, the ellagitannin extraction kinetic, and the ellagitannins evolution in red wine (Cabernet Sauvignon). Moreover, a correlation between the NIRS classification and the increasing intensity of some wood aromas (woody, spicy, vanilla, and smoked/toasted), flavors (bitterness and astringency), and a decreasing intensity of fruitiness was also observed.

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

PubMed

Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu

2012-08-01

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.

Method of predicting mechanical properties of decayed wood

DOEpatents

Kelley, Stephen S.

2003-07-15

A method for determining the mechanical properties of decayed wood that has been exposed to wood decay microorganisms, comprising: a) illuminating a surface of decayed wood that has been exposed to wood decay microorganisms with wavelengths from visible and near infrared (VIS-NIR) spectra; b) analyzing the surface of the decayed wood using a spectrometric method, the method generating a first spectral data of wavelengths in VIS-NIR spectra region; and c) using a multivariate analysis to predict mechanical properties of decayed wood by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of wavelengths in VIS-NIR spectra obtained from a reference decay wood, the second spectral data being correlated with a known mechanical property analytical result obtained from the reference decayed wood.

Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions

NASA Astrophysics Data System (ADS)

Bratchenko, Ivan A.; Artemyev, Dmitry N.; Myakinin, Oleg O.; Khristoforova, Yulia A.; Moryatov, Alexander A.; Kozlov, Sergey V.; Zakharov, Valery P.

2017-02-01

The differentiation of skin melanomas and basal cell carcinomas (BCCs) was demonstrated based on combined analysis of Raman and autofluorescence spectra stimulated by visible and NIR lasers. It was ex vivo tested on 39 melanomas and 40 BCCs. Six spectroscopic criteria utilizing information about alteration of melanin, porphyrins, flavins, lipids, and collagen content in tumor with a comparison to healthy skin were proposed. The measured correlation between the proposed criteria makes it possible to define weakly correlated criteria groups for discriminant analysis and principal components analysis application. It was shown that the accuracy of cancerous tissues classification reaches 97.3% for a combined 6-criteria multimodal algorithm, while the accuracy determined separately for each modality does not exceed 79%. The combined 6-D method is a rapid and reliable tool for malignant skin detection and classification.

NIR detection of pits and pit fragments in fresh cherries (abstract)

USDA-ARS?s Scientific Manuscript database

The feasibility of using near infrared (NIR) diffuse reflectance spectroscopy for the detection of pits and pit fragments in cherries was demonstrated. For detection of whole pits, 300 cherries were obtained locally and pits were removed from half. NIR reflectance spectra were obtained in triplicate...

Nondestructive NIR reflectance spectroscopic method for rapid fatty acid analysis of peanut seeds

USDA-ARS?s Scientific Manuscript database

NIR reflectance spectroscopy was used to analyze the fatty acid concentration present in breeder's peanut seeds samples, rapidly and nondestructively. Absorbance spectra were collected in the wavelength range from 400 nm to 2500 nm using a NIR spectrometer. Fatty acids, oleic, linoleic and palmitic ...

Spectroscopic and Quantum Mechanical Calculation Study of the Effect of Isotopic Substitution on NIR Spectra of Methanol.

PubMed

Grabska, Justyna; Czarnecki, Mirosław A; Beć, Krzysztof B; Ozaki, Yukihiro

2017-10-19

In this work, we studied methanol and its deuterated derivatives (CH 3 OH, CH 3 OD, CD 3 OH, CD 3 OD) by NIR spectroscopy and anharmonic quantum chemical calculations. Vibrational bands corresponding to up to three quanta transitions (first and second overtones, binary and ternary combination modes) were predicted by the use of the VPT2 route. The accuracy of prediction of NIR modes was evaluated through density functional theory (DFT) with selected density functionals and basis sets. On the basis of the theoretical NIR spectra, detailed band assignments for all studied molecules were proposed. It was found that the pattern of bands in NIR spectra of deuterated methanols can be used for identification of isotopically equalized forms. Calculations of NIR spectra of all possible forms of CXXXOX (X = H, D) molecules demonstrated that the isotopic contamination can be identified due to a coexistence of bands specific to OH and OD groups. Also, bands from partially deuterated methyl groups can be distinguished in NIR spectra. Since the VPT2 framework is known to be sensitive to inaccuracy in the case of highly anharmonic modes, we obtained an independent insight by numerical solving of the time-independent Schrödinger equation corresponding to the O-X stretching mode scanned within -0.4 to 2.0 Å over a dense grid of 0.005 Å. This way the energies of vibrational levels of the CX1X2X3OX4 (X = H, D) isotopomers and the corresponding transition frequencies were obtained with high accuracy (<0.1 cm -1 ). The change in normal coordinate influences the reduced mass of the oscillator and thus its frequency. Our results lead to a conclusion that the effect of deuterization of the methyl group introduces a very specific and consistent frequency shift of the first overtone of the O-X stretching mode depending on the substitution of X1, X2, or X3 positions (<2 cm -1 ). However, the pattern of this shift is not reproduced accurately and is also largely overestimated by VPT2 calculations.

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.

Ground-Based VIS/NIR Reflectance Spectra of 25143 Itokawa: What Hayabusa will See and How Ground-Based Data can Augment Analyses

NASA Technical Reports Server (NTRS)

Vilas, Faith; Abell, P. A.; Jarvis, K. S.

2004-01-01

Planning for the arrival of the Hayabusa spacecraft at asteroid 25143 Itokawa includes consideration of the expected spectral information to be obtained using the AMICA and NIRS instruments. The rotationally-resolved spatial coverage the asteroid we have obtained with ground-based telescopic spectrophotometry in the visible and near-infrared can be utilized here to address expected spacecraft data. We use spectrophotometry to simulate the types of data that Hayabusa will receive with the NIRS and AMICA instruments, and will demonstrate them here. The NIRS will cover a wavelength range from 0.85 m, and have a dispersion per element of 250 Angstroms. Thus, we are limited in coverage of the 1.0 micrometer and 2.0 micrometer mafic silicate absorption features. The ground-based reflectance spectra of Itokawa show a large component of olivine in its surface material, and the 2.0 micrometer feature is shallow. Determining the olivine to pyroxene abundance ratio is critically dependent on the attributes of the 1.0- and 2.0 micrometer features. With a cut-off near 2,1 micrometer the longer edge of the 2.0- feature will not be obtained by NIRS. Reflectance spectra obtained using ground-based telescopes can be used to determine the regional composition around space-based spectral observations, and possibly augment the longer wavelength spectral attributes. Similarly, the shorter wavelength end of the 1.0 micrometer absorption feature will be partially lost to the NIRS. The AMICA filters mimic the ECAS filters, and have wavelength coverage overlapping with the NIRS spectral range. We demonstrate how merging photometry from AMICA will extend the spectral coverage of the NIRS. Lessons learned from earlier spacecraft to asteroids should be considered.

Comparison of near infrared spectra within broiler breast fillets deboned at different postmortem time

USDA-ARS?s Scientific Manuscript database

Near infrared (NIR) spectroscopy has been used to predict texture quality of broiler breast fillets. Sampling is an important issue in NIR measurements to obtain accurate results. There are no research papers about sampling of chicken breast fillet for NIR measurement. The objective of this study wa...

Building Blocks of the Terrestrial Planets: Mineralogy of Hungaria Asteroids

NASA Astrophysics Data System (ADS)

Lucas, Michael; Emery, J. P.

2013-10-01

Deciphering the mineralogy of the Hungaria asteroids has the potential to place constraints on the material from which the terrestrial planets accreted. Among asteroids with semi-major axes interior to the main-belt (e.g., Hungarias, Mars-crossers, and near-Earth asteroids), only the Hungarias are located in relatively stable orbital space. Hungaria asteroids have likely resided in this orbital space since the planets completed their migration to their current orbits. The accretion and igneous differentiation of primitive asteroids appears to be a function of chronology and heliocentric distance. However, differentiated bodies that originated in the terrestrial planet region were either accreted or scattered out of this region early in solar system history. Thus, the Hungaria asteroids represent the closest reservoir of in situ material to the terrestrial planet region from early in solar system history. We present VISNIR 0.45-2.45 µm) and NIR spectra 0.65-2.45 µm) spectra of 24 Hungaria group (objects in similar orbital space) asteroids. Our NIR data (17 objects) were acquired using the InfraRed Telescope Facility and was supplemented with available visible data. Spectra of seven objects were obtained from the MIT-UH-IRTF survey. We distinguish our sample between Hungaria family (presumed fragments of parent 434 Hungaria; 2 objects) and Hungaria background (group minus family 22 objects) asteroids using proper orbital elements. The classification of each asteroid is determined using the taxonomy of Bus-DeMeo. We find that S- and S-subtypes are prevalent among the Hungaria background population (17/22). Spectral band parameters measurements (i.e., Band I and Band II centers and depths, and Band Area Ratio) indicate that eight of these S-types are analogous with undifferentiated ordinary chondrites (SIV “boot” of S-subtypes plot). Mafic silicate mineral abundances and compositions derived for these SIV asteroids mainly correlate with L chondrites. However, one object is an SIII subtype (possible ureilite analog), while two asteroids are SVI subtypes (possible primitive achondrite analog). Family member 6447 Terrycole is a Xe-type, consistent with the taxonomic classification of the parent 434 Hungaria.

NIR measurements of glucose in synthetic biological solutions using high-throughput angle-tuned filter spectrometer

NASA Astrophysics Data System (ADS)

Saptari, Vidi A.; Youcef-Toumi, Kamal; Zhang, John

2004-06-01

A noninvasive blood glucose monitoring device will provide an invaluable tool in the diagnosis and treatment of diabetes. Near infrared (NIR) absorption spectroscopy is one of the most promising optical techniques for in vivo blood glucose sensing to date. Successful realization of such a technology hinges on solving two main problems. First, instrument sensitivity needs to be improved in order to resolve the weak NIR spectral variations due to glucose physiological changes in the blood. Second, interfering signals due to other blood components and tissue changes need to be sufficiently eliminated or compensated for. A simple, low-cost, high-throughput, filter spectrometer optimized for long-wave NIR measurements of biological fluids is developed. The instrument provides noise spectra with a typical rms value of 7 μAU between 2180 nm and 2310 nm with only 5 seconds of data measurement or averaging. Using such an instrument, spectra of aquaeous, synthetic biological solutions containing varying levels of glucose, BSA, triacetin, lactate and urea are obtained. Glucose spectra are isolated, despite the overlapping spectra. Glucose concentrations are predicted with excellent accuracy (SEP<=8.2 mg/dL) using the simple classical least-squares (CLS) and the connonly used partial least-squares (PLS) multivariate techniques.

A Moderate Resolution NIR Spectral Library of Weak-Lined T Tauri Stars

NASA Astrophysics Data System (ADS)

Cooper, Rachel; Covey, K. R.

2013-01-01

We present a spectral library of high-quality moderate resolution (R ~ 3500) NIR spectra for 44 weak-lined T Tauri Stars (WTTS) in the Taurus-Auriga Molecular Cloud. These spectra, obtained with the TripleSpec spectrograph on the Astrophysical Research Consortium (ARC) 3.5 meter telescope, provide full coverage of the J, H, and K near-infrared bands in a single epoch. Analyzing these spectra, along with those of dwarf and giant spectral type standards from the SpeX Spectral Library, we have identified several elemental and molecular absorption lines that vary in strength with respect to each star's spectral type and luminosity class. Calibrating each of these features as a spectral type indicator, we provide a detailed characterization for each of the WTTSs in our sample, identifying each star's NIR spectral type and line-of-sight extinction, estimated both from the shape of the overall continuum and from the fluxes of the Paschen beta and Brackett gamma emission lines. In addition to improving our understanding of the properties of these WTTSs, this well characterized spectral library will be a valuable resource for analyses of the NIR continuum veiling and line emission present in the spectra of accreting classical T Tauri stars. This research was made possible by NSF Grant AST-1004107.

FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng.

PubMed

Li, Yun; Zhang, Jin-Yu; Wang, Yuan-Zhong

2018-01-01

Three data fusion strategies (low-llevel, mid-llevel, and high-llevel) combined with a multivariate classification algorithm (random forest, RF) were applied to authenticate the geographical origins of Panax notoginseng collected from five regions of Yunnan province in China. In low-level fusion, the original data from two spectra (Fourier transform mid-IR spectrum and near-IR spectrum) were directly concatenated into a new matrix, which then was applied for the classification. Mid-level fusion was the strategy that inputted variables extracted from the spectral data into an RF classification model. The extracted variables were processed by iterate variable selection of the RF model and principal component analysis. The use of high-level fusion combined the decision making of each spectroscopic technique and resulted in an ensemble decision. The results showed that the mid-level and high-level data fusion take advantage of the information synergy from two spectroscopic techniques and had better classification performance than that of independent decision making. High-level data fusion is the most effective strategy since the classification results are better than those of the other fusion strategies: accuracy rates ranged between 93% and 96% for the low-level data fusion, between 95% and 98% for the mid-level data fusion, and between 98% and 100% for the high-level data fusion. In conclusion, the high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng. Graphical abstract The analytical steps of Fourier transform mid-IR and near-IR spectral data fusion for the geographical traceability of Panax notoginseng.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

Effect of hormonal variation on in vivo high wavenumber Raman spectra improves cervical precancer detection

NASA Astrophysics Data System (ADS)

Duraipandian, Shiyamala; Zheng, Wei; Ng, Joseph; Low, Jeffrey J. H.; Ilancheran, A.; Huang, Zhiwei

2012-03-01

Raman spectroscopy is a unique analytical probe for molecular vibration and is capable of providing specific spectroscopic fingerprints of molecular compositions and structures of biological tissues. The aim of this study is to improve the classification accuracy of cervical precancer by characterizing the variations in the normal high wavenumber (HW - 2800-3700cm-1) Raman spectra arising from the menopausal status of the cervix. A rapidacquisition near-infrared (NIR) Raman spectroscopic system was used for in vivo tissue Raman measurements at 785 nm excitation. Individual HW Raman spectrum was measured with a 5s exposure time from both normal and precancer tissue sites of 15 patients recruited. The acquired Raman spectra were stratified based on the menopausal status of the cervix before the data analysis. Significant differences were noticed in Raman intensities of prominent band at 2924 cm-1 (CH3 stretching of proteins) and the broad water Raman band (in the 3100-3700 cm-1 range) with a peak at 3390 cm-1 in normal and dysplasia cervical tissue sites. Multivariate diagnostic decision algorithm based on principal component analysis (PCA) and linear discriminant analysis (LDA) was utilized to successfully differentiate the normal and precancer cervical tissue sites. By considering the variations in the Raman spectra of normal cervix due to the hormonal or menopausal status of women, the diagnostic accuracy was improved from 71 to 91%. By incorporating these variations prior to tissue classification, we can significantly improve the accuracy of cervical precancer detection using HW Raman spectroscopy.

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.

Near infrared Raman spectra of Rhizoma dioscoreae

NASA Astrophysics Data System (ADS)

Lin, Wenshuo; Chen, Rong; Chen, Guannan; Feng, Sangyuan; Li, Yongzeng; Huang, Zufang; Li, Yongsen

2008-03-01

A novel and compact near-infrared (NIR) Raman system is developed using 785-nm diode laser, volume-phase technology holographic system, and NIR intensified charge-coupled device (CCD). Raman spectra and first derivative spectra of Rhizoma Dioscoreae are obtained. Raman spectra of Rhizoma Dioscoreae showed three strong characteristic peaks at 477.4cm -1, 863.9cm -1, and 936.0cm -1. The major ingredients are protein, amino acid, starch, polysaccharides and so on, matched with the known basic biochemical composition of Rhizoma Dioscoreae. In the first derivative spectra of Rhizoma Dioscoreae, distinguishing characteristic peaks appeared at 467.674cm -1, 484.603cm -1, 870.37cm -1, 943.368cm -1. Contrasted with Rhizoma Dioscoreae Raman spectra, in 600cm -1 to 800cm -1, 1000cm -1 to 1400cm -1 regions, changes in Rhizoma Dioscoreae Raman first derivative spectra are represented more clearly than Rhizoma Dioscoreae Raman spectra. So Rhizoma Dioscoreae raman first derivative spectra can be an accurate supplementary analysis method to Rhizoma Dioscoreae Raman spectra.

Post-maximum Near-infrared Spectra of SN 2014J: A Search for Interaction Signatures

NASA Astrophysics Data System (ADS)

Sand, D. J.; Hsiao, E. Y.; Banerjee, D. P. K.; Marion, G. H.; Diamond, T. R.; Joshi, V.; Parrent, J. T.; Phillips, M. M.; Stritzinger, M. D.; Venkataraman, V.

2016-05-01

We present near-infrared (NIR) spectroscopic and photometric observations of the nearby Type Ia SN 2014J. The 17 NIR spectra span epochs from +15.3 to +92.5 days after B-band maximum light, while the {{JHK}}s photometry include epochs from -10 to +71 days. These data are used to constrain the progenitor system of SN 2014J utilizing the Paβ line, following recent suggestions that this phase period and the NIR in particular are excellent for constraining the amount of swept-up hydrogen-rich material associated with a non-degenerate companion star. We find no evidence for Paβ emission lines in our post-maximum spectra, with a rough hydrogen mass limit of ≲ 0.1 M ⊙, which is consistent with previous limits in SN 2014J from late-time optical spectra of the Hα line. Nonetheless, the growing data set of high-quality NIR spectra holds the promise of very useful hydrogen constraints. Based on observations obtained at the Gemini Observatory under program GN-2014A-Q-8 (PI: Sand). Gemini is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina), and Ministério da Ciência, Tecnologia e Inovação (Brazil).

A Comparison of Analytical and Data Preprocessing Methods for Spectral Fingerprinting

PubMed Central

LUTHRIA, DEVANAND L.; MUKHOPADHYAY, SUDARSAN; LIN, LONG-ZE; HARNLY, JAMES M.

2013-01-01

Spectral fingerprinting, as a method of discriminating between plant cultivars and growing treatments for a common set of broccoli samples, was compared for six analytical instruments. Spectra were acquired for finely powdered solid samples using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (NIR) spectrometry. Spectra were also acquired for unfractionated aqueous methanol extracts of the powders using molecular absorption in the ultraviolet (UV) and visible (VIS) regions and mass spectrometry with negative (MS−) and positive (MS+) ionization. The spectra were analyzed using nested one-way analysis of variance (ANOVA) and principal component analysis (PCA) to statistically evaluate the quality of discrimination. All six methods showed statistically significant differences between the cultivars and treatments. The significance of the statistical tests was improved by the judicious selection of spectral regions (IR and NIR), masses (MS+ and MS−), and derivatives (IR, NIR, UV, and VIS). PMID:21352644

Acquisition of reproducible transmission near-infrared (NIR) spectra of solid samples with inconsistent shapes by irradiation with isotropically diffused radiation using polytetrafluoroethylene (PTFE) beads.

PubMed

Lee, Jinah; Duy, Pham Khac; Yoon, Jihye; Chung, Hoeil

2014-06-21

A bead-incorporated transmission scheme (BITS) has been demonstrated for collecting reproducible transmission near-infrared (NIR) spectra of samples with inconsistent shapes. Isotropically diffused NIR radiation was applied around a sample and the surrounding radiation was allowed to interact homogeneously with the sample for transmission measurement. Samples were packed in 1.40 mm polytetrafluoroethylene (PTFE) beads, ideal diffusers without NIR absorption, and then transmission spectra were collected by illuminating the sample-containing beads using NIR radiation. When collimated radiation was directly applied, a small portion of the non-fully diffused radiation (NFDR) propagated through the void space of the packing and eventually degraded the reproducibility. Pre-diffused radiation was introduced by placing an additional PTFE disk in front of the packing to diminish NFDR, which produced more reproducible spectral features. The proposed scheme was evaluated by analyzing two different solid samples: density determination for individual polyethylene (PE) pellets and identification of mining locality for tourmalines. Because spectral collection was reproducible, the use of the spectrum acquired from one PE pellet was sufficient to accurately determine the density of nine other pellets with different shapes. The differentiation of tourmalines, which are even more dissimilar in appearance, according to their mining locality was also feasible with the help of the scheme.

Hemoglobin spectra affect measurement of tissue oxygen saturation

NASA Astrophysics Data System (ADS)

Ostojic, Daniel; Kleiser, Stefan; Nasseri, Nassim; Isler, Helene; Scholkmann, Felix; Karen, Tanja; Wolf, Martin

2018-02-01

Tissue oxygen saturation (StO2) is a valuable clinical parameter e.g. for intensive care applications or monitoring during surgery. Studies showed that near-infrared spectroscopy (NIRS) based tissue oximeters of different brands give systematically different readings of StO2. Usually these readings are linearly correlated and therefore StO2 readings from one instrument can easily be converted to those of another instrument. However, it is interesting to understand why there is this difference. One reason may be that different brands employ different spectra of hemoglobin. The aim here was to investigate how these different absorption spectra of hemoglobin affect the StO2 readings. Therefore, we performed changes in StO2 in a phantom experiment with real human hemoglobin at three different concentrations (26.5, 45 and 70 μM): desaturation by yeast consuming the oxygen and re-saturation by bubbling oxygen gas. The partial pressure of O2 in the liquid changed from at least 10 kPa to 0 kPa and ISS OxiplexTS, a frequency-domain NIRS instrument, was used to monitor changes of StO2. When we employed two different absorption spectra for hemoglobin, StO2 values were comparable in the normal physiological range. However, particularly at high and low StO2 values, a difference of >6% between these two spectra were noticed. Such a difference of >6% is substantial and relevant for medical applications. This may partly explain why different brands of NIRS instruments provide different StO2 readings. The hemoglobin spectra are therefore a factor to be considered for future developments and applications of NIRS oximeters.

Cosmic Infrared Background From Population III Stars and Its Effect on Spectra of High-z Gamma-Ray Bursts

NASA Technical Reports Server (NTRS)

Kashlinsky, A.

2005-01-01

We discuss the contribution of Population III stars to the near-IR (NIR) cosmic infrared background (CIB) and its effect on spectra of high-z, high-energy gamma-ray bursts (GRBs) and other sources. It is shown that if Population III is composed of massive stars, the claimed NIR CIB excess will be reproduced if only approx. 4% plus or minus 2% of all baryons went through these stars. Regardless of the precise amount of the NIR CIB due to them, they likely left enough photons to provide a large optical depth for high-energy photons from distant GRBs. Observations of such GRBs are expected following the planned launch of NASA's GLAST mission. Detecting such damping in the spectra of high-z GRBs will then provide important information on the emissions from the Population III epoch, and the location of this cutoff may serve as an indicator of the GRBs' redshifts. We also point out the difficulty of unambiguously detecting the CIB part originating from Population III in spectra of low-z blazars.

Electrical and mechanical anharmonicities from NIR-VCD spectra of compounds exhibiting axial and planar chirality: the cases of (S)-2,3-pentadiene and methyl-d(3) (R)- and (S)-[2.2]paracyclophane-4-carboxylate.

PubMed

Abbate, Sergio; Longhi, Giovanna; Gangemi, Fabrizio; Gangemi, Roberto; Superchi, Stefano; Caporusso, Anna Maria; Ruzziconi, Renzo

2011-10-01

The IR and Near infrared (NIR) vibrational circular dichroism (VCD) spectra of molecules endowed with noncentral chirality have been investigated. Data for fundamental, first, and second overtone regions of (S)-2,3-pentadiene, exhibiting axial chirality, and methyl-d(3) (R)- and (S)-[2.2]paracyclophane-4-carboxylate, exhibiting planar chirality have been measured and analyzed. The analysis of NIR and IR VCD spectra was based on the local-mode model and the use of density functional theory (DFT), providing mechanical and electrical anharmonic terms for all CH-bonds. The comparison of experimental and calculated spectra is satisfactory and allows one to monitor fine details in the asymmetric charge distribution in the molecules: these details consist in the harmonic frequencies, in the principal anharmonicity constants, in both the atomic polar and axial tensors and in their first and second derivatives with respect to the CH-stretching coordinates. Copyright © 2011 Wiley-Liss, Inc.

Method of radiometric quality assessment of NIR images acquired with a custom sensor mounted on an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Wierzbicki, Damian; Fryskowska, Anna; Kedzierski, Michal; Wojtkowska, Michalina; Delis, Paulina

2018-01-01

Unmanned aerial vehicles are suited to various photogrammetry and remote sensing missions. Such platforms are equipped with various optoelectronic sensors imaging in the visible and infrared spectral ranges and also thermal sensors. Nowadays, near-infrared (NIR) images acquired from low altitudes are often used for producing orthophoto maps for precision agriculture among other things. One major problem results from the application of low-cost custom and compact NIR cameras with wide-angle lenses introducing vignetting. In numerous cases, such cameras acquire low radiometric quality images depending on the lighting conditions. The paper presents a method of radiometric quality assessment of low-altitude NIR imagery data from a custom sensor. The method utilizes statistical analysis of NIR images. The data used for the analyses were acquired from various altitudes in various weather and lighting conditions. An objective NIR imagery quality index was determined as a result of the research. The results obtained using this index enabled the classification of images into three categories: good, medium, and low radiometric quality. The classification makes it possible to determine the a priori error of the acquired images and assess whether a rerun of the photogrammetric flight is necessary.

Quality analysis, classification, and authentication of liquid foods by near-infrared spectroscopy: A review of recent research developments.

PubMed

Wang, Lu; Sun, Da-Wen; Pu, Hongbin; Cheng, Jun-Hu

2017-05-03

Nowadays, near-infrared spectroscopy (NIR) has become one of the most efficient and advanced techniques for analysis of food products. Many relevant researches have been conducted in this regard. However, no reviews about the applications of NIR for liquid food analysis are reported. Therefore, this review summarizes the recent research developments of NIR technology in the field of liquid foods, focusing on the detection of quality attributes of various liquid foods, including alcoholic beverages (red wines, rice wines, and beer), nonalcoholic beverages (juice, fruit vinegars, coffee beverages, and cola beverages), dairy products (milk and yogurt), and oils (vegetable, camellia, peanut, and virgin olive oils and frying oil). In addition, the classification and authentication detection of adulteration are also covered. It is hoped that the current paper can serve as a reference source for the future liquid food analysis by NIR techniques.

Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi.

PubMed

Lomiwes, D; Reis, M M; Wiklund, E; Young, O A; North, M

2010-12-01

The potential of near infrared (NIR) spectroscopy as an on-line method to quantify glycogen and predict ultimate pH (pH(u)) of pre rigor beef M. longissimus dorsi (LD) was assessed. NIR spectra (538 to 1677 nm) of pre rigor LD from steers, cows and bulls were collected early post mortem and measurements were made for pre rigor glycogen concentration and pH(u). Spectral and measured data were combined to develop models to quantify glycogen and predict the pH(u) of pre rigor LD. NIR spectra and pre rigor predicted values obtained from quantitative models were shown to be poorly correlated against glycogen and pH(u) (r(2)=0.23 and 0.20, respectively). Qualitative models developed to categorize each muscle according to their pH(u) were able to correctly categorize 42% of high pH(u) samples. Optimum qualitative and quantitative models derived from NIR spectra found low correlation between predicted values and reference measurements. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd.. All rights reserved.

Newer views of the Moon: Comparing spectra from Clementine and the Moon Mineralogy Mapper

USGS Publications Warehouse

Kramer, G.Y.; Besse, S.; Nettles, J.; Combe, J.-P.; Clark, R.N.; Pieters, C.M.; Staid, M.; Malaret, E.; Boardman, J.; Green, R.O.; Head, J.W.; McCord, T.B.

2011-01-01

The Moon Mineralogy Mapper (M3) provided the first global hyperspectral data of the lunar surface in 85 bands from 460 to 2980 nm. The Clementine mission provided the first global multispectral maps the lunar surface in 11 spectral bands across the ultraviolet-visible (UV-VIS) and near-infrared (NIR). In an effort to understand how M3 improves our ability to analyze and interpret lunar data, we compare M3 spectra with those from Clementine's UV-VIS and NIR cameras. The Clementine mission provided the first global multispectral maps the lunar surface in 11 spectral bands across the UV-VIS and NIR. We have found that M3 reflectance values are lower across all wavelengths compared with albedos from both of Clementine's UV-VIS and NIR cameras. M3 spectra show the Moon to be redder, that is, have a steeper continuum slope, than indicated by Clementine. The 1 m absorption band depths may be comparable between the instruments, but Clementine data consistently exhibit shallower 2 m band depths than M 3. Absorption band minimums are difficult to compare due to the significantly different spectral resolutions. Copyright 2011 by the American Geophysical Union.

Newer views of the Moon: Comparing spectra from Clementineand the Moon Mineralogy Mapper

USGS Publications Warehouse

Georgiana Y. Kramer,; Sebastian Besse,; Nettles, Jeff; Jean-Philippe Combe,; Clark, Roger N.; Pieters, Carle M.; Matthew Staid,; Joseph Boardman,; Robert Green,; McCord, Thomas B.; Malaret, Erik; Head, James W.

2011-01-01

The Moon Mineralogy Mapper (M3) provided the first global hyperspectral data of the lunar surface in 85 bands from 460 to 2980 nm. The Clementine mission provided the first global multispectral maps the lunar surface in 11 spectral bands across the ultraviolet-visible (UV-VIS) and near-infrared (NIR). In an effort to understand how M3 improves our ability to analyze and interpret lunar data, we compare M3 spectra with those from Clementine's UV-VIS and NIR cameras. The Clementine mission provided the first global multispectral maps the lunar surface in 11 spectral bands across the UV-VIS and NIR. We have found that M3 reflectance values are lower across all wavelengths compared with albedos from both of Clementine's UV-VIS and NIR cameras. M3 spectra show the Moon to be redder, that is, have a steeper continuum slope, than indicated by Clementine. The 1 μm absorption band depths may be comparable between the instruments, but Clementine data consistently exhibit shallower 2 μm band depths than M3. Absorption band minimums are difficult to compare due to the significantly different spectral resolutions.

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near-Infrared Spectroscopy (NIR).

PubMed

Canneddu, Giovanna; Júnior, Luis Carlos Cunha; de Almeida Teixeira, Gustavo Henrique

2016-07-01

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near-infrared (FT-NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA-LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (Rp (2) ) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, Rp (2) = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT-NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though. © 2016 Institute of Food Technologists®

Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces

NASA Astrophysics Data System (ADS)

Hwang, Han-Jeong; Lim, Jeong-Hwan; Kim, Do-Won; Im, Chang-Hwan

2014-07-01

A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promising neuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focused on enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated the performances of a variety of mental task combinations in order to determine the mental task pairs that are best suited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses while seven participants performed eight different mental tasks. Classification accuracies were then estimated for all possible pairs of the eight mental tasks (C=28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: "mental multiplication," "mental rotation," and "right-hand motor imagery." Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations.

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.

Effects of grown origin, genotype, harvest year, and their interactions of wheat kernels on near infrared spectral fingerprints for geographical traceability.

PubMed

Zhao, Haiyan; Guo, Boli; Wei, Yimin; Zhang, Bo

2014-01-01

The effects of origin, genotype, harvest year, and their interactions on wheat near infrared (NIR) spectra were studied to find the reasons for differences in NIR fingerprints of wheat from different geographical origins and the stability of NIR fingerprints among different years. Ten varieties were grown in three regions of China for 2 years. 180 kernel samples were analysed by NIR. The spectra after pre-treatment were analysed by principal component analysis, multi-way analysis of variance, and discriminant partial least-squares. The results showed that origin, genotype, year, and their interactions all had significant effects on wheat NIR fingerprints. The second overtones of N-H and C-H stretching vibrations and a combination of stretch and deformation of C-H group in wheat were mainly influenced by the geographical origin. The wavelength ranges 975-990 nm, 1200 nm, and 1355-1380 nm contained plenty of origin information to build robust discriminant models of wheat geographical origin. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Complementary analysis of tissue homogenates composition obtained by Vis and NIR laser excitations and Raman spectroscopy.

PubMed

Staniszewska-Slezak, Emilia; Malek, Kamilla; Baranska, Malgorzata

2015-08-05

Raman spectroscopy and four excitation lines in the visible (Vis: 488, 532, 633 nm) and near infrared (NIR: 785 nm) were used for biochemical analysis of rat tissue homogenates, i.e. myocardium, brain, liver, lung, intestine, and kidney. The Vis Raman spectra are very similar for some organs (brain/intestines and kidney/liver) and dominated by heme signals when tissues of lung and myocardium were investigated (especially with 532 nm excitation). On the other hand, the NIR Raman spectra are specific for each tissue and more informative than the corresponding ones collected with the Vis excitations. The spectra analyzed without any special pre-processing clearly illustrate different chemical composition of each tissue and give information about main components e.g. lipids or proteins, but also about the content of some specific compounds such as amino acid residues, nucleotides and nucleobases. However, in order to obtain the whole spectral information about tissues complex composition the spectra of Vis and NIR excitations should be collected and analyzed together. A good agreement of data gathered from Raman spectra of the homogenates and those obtained previously from Raman imaging of the tissue cross-sections indicates that the presented here approach can be a method of choice for an investigation of biochemical variation in animal tissues. Moreover, the Raman spectral profile of tissue homogenates is specific enough to be used for an investigation of potential pathological changes the organism undergoes, in particular when supported by the complementary FTIR spectroscopy. Copyright © 2015 Elsevier B.V. All rights reserved.

Spectra-structure correlations in NIR region: Spectroscopic and anharmonic DFT study of n-hexanol, cyclohexanol and phenol

NASA Astrophysics Data System (ADS)

Beć, Krzysztof B.; Grabska, Justyna; Czarnecki, Mirosław A.

2018-05-01

We investigated near-infrared (7500-4000 cm-1) spectra of n-hexanol, cyclohexanol and phenol in CCl4 (0.2 M) by using anharmonic quantum calculations. These molecules represent three major kinds of alcohols; linear and cyclic aliphatic, and aromatic ones. Vibrational second-order perturbation theory (VPT2) was employed to calculate the first overtones and binary combination modes and to reproduce the experimental NIR spectra. The level of conformational flexibility of these three alcohols varies from one stable conformer of phenol through four conformers of cyclohexanol to few hundreds conformers in the case of n-hexanol. To take into account the most relevant conformational population of n-hexanol, a systematic conformational search was performed. Accurate reproduction of the experimental NIR spectra was achieved and detailed spectra-structure correlations were obtained for these three alcohols. VPT2 approach provides less reliable description of highly anharmonic modes, i.e. OH stretching. In the present work this limitation was manifested in erroneous results yielded by VPT2 for 2νOH mode of cyclohexanol. To study the anharmonicity of this mode we solved the corresponding time-independent Schrödinger equation based on a dense-grid probing of the relevant vibrational potential. These results allowed for significant improvement of the agreement between the calculated and experimental 2νOH band of cyclohexanol. Various important biomolecules include similar structural units to the systems investigated here. A detailed knowledge on spectral properties of these three types of alcohols is therefore essential for advancing our understanding of NIR spectroscopy of biomolecules.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

PubMed

Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

2014-03-01

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

The transfer of NIR calibrations for undried grass silage from the laboratory to on-site instruments: comparison of two approaches.

PubMed

Soldado, A; Fearn, T; Martínez-Fernández, A; de la Roza-Delgado, B

2013-02-15

As a first step in a project whose aim is to implement near infrared (NIR) analysis of animal feed on the farm, the present work has examined the possibility of transferring undried grass silage calibrations for dry matter, crude protein, and neutral detergent fiber from a dispersive laboratory NIR instrument (Foss NIRSystem 6500) to a diode array on-site NIR instrument (Zeiss Corona 45 visNIR 1.7). Because the samples are complex and heterogeneous and have high humidity levels it is not easy to establish good calibrations, and it is even more of a challenge to transfer them. By cutting the spectral range to 1100-1650 nm and treating with first or second derivative followed by standard normal variate (SNV) scatter correction, it was possible to obtain very similar spectra from the two instruments. To make the transfer, two approaches were tried. Simply correcting the Corona spectra by subtracting the mean difference spectrum from a transfer set met with only limited success. Making a calibration on the Foss using a calibration set of 503 samples with spectra orthogonalized to the all the difference spectra in the transfer set of 10 samples resulted in a successful transfer for all three calibrations, as judged by performance on two prediction sets of size 22 and 29. Measuring 5 replicate subsamples with the Corona allows it to see a similar surface area to that of 3 replicates in the Foss transport cell, and it is suggested that this is an appropriate level of replication for the Corona. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

NIR-to-NIR Deep Penetrating Nanoplatforms Y2O3:Nd3+/Yb3+@SiO2@Cu2S toward Highly Efficient Photothermal Ablation.

PubMed

Zhang, Zhiyu; Suo, Hao; Zhao, Xiaoqi; Sun, Dan; Fan, Li; Guo, Chongfeng

2018-05-02

A difunctional nano-photothermal therapy (PTT) platform with near-infrared excitation to near-infrared emission (NIR-to-NIR) was constructed through core-shell structures Y 2 O 3 :Nd 3+ /Yb 3+ @SiO 2 @Cu 2 S (YRSC), in which the core Y 2 O 3 :Nd 3+ /Yb 3+ and shell Cu 2 S play the role of bioimaging and photothermal conversion function, respectively. The structure and composition of the present PTT agents (PTAs) were characterized by powder X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, and X-ray photoelectron spectra. The NIR emissions of samples in the biological window area were measured by photoluminescence spectra under the excitation of 808 nm laser; further, the penetration depth of NIR emission at different wavelengths in biological tissue was also demonstrated by comparing with visible (vis) emission from Y 2 O 3 :Yb 3+ /Er 3+ @SiO 2 @Cu 2 S and NIR emission from YRSC through different injection depths in pork muscle tissues. The photo-thermal conversion effects were achieved through the outer ultrasmall Cu 2 S nanoparticles simultaneously absorb NIR light emission from the core Y 2 O 3 :Nd 3+/ Yb 3+ and the 808 nm excitation source to generate heat. Further, the heating effect of YRSC nanoparticles was confirmed by thermal imaging and ablation of YRSC to Escherichia coli and human hepatoma (HepG-2) cells. Results indicate that the YRSC has potential applications in PTT and NIR imaging in biological tissue.

Meat mixture detection in Iberian pork sausages.

PubMed

Ortiz-Somovilla, V; España-España, F; De Pedro-Sanz, E J; Gaitán-Jurado, A J

2005-11-01

Five homogenized meat mixture treatments of Iberian (I) and/or Standard (S) pork were set up. Each treatment was analyzed by NIRS as a fresh product (N=75) and as dry-cured sausage (N=75). Spectra acquisition was carried out using DA 7000 equipment (Perten Instruments), obtaining a total of 750 spectra. Several absorption peaks and bands were selected as the most representative for homogenized dry-cured and fresh sausages. Discriminant analysis and mixture prediction equations were carried out based on the spectral data gathered. The best results using discriminant models were for fresh products, with 98.3% (calibration) and 60% (validation) correct classification. For dry-cured sausages 91.7% (calibration) and 80% (validation) of the samples were correctly classified. Models developed using mixture prediction equations showed SECV=4.7, r(2)=0.98 (calibration) and 73.3% of validation set were correctly classified for the fresh product. These values for dry-cured sausages were SECV=5.9, r(2)=0.99 (calibration) and 93.3% correctly classified for validation.

Development and applications of ruggedized VIS/NIR spectrometer system for oilfield wellbores

NASA Astrophysics Data System (ADS)

Fujisawa, Go; Yamate, Tsutomu

2013-12-01

The development and applications of a ruggedized visible to near-infrared (VIS/NIR) spectrometer system capable of measuring fluid spectra in oilfield wellbores are presented. Real-time assessment of formation fluid properties penetrated by an oilfield wellbore is critically important for oilfield operating companies to make informed decisions to optimize the development plan of the well and hydrocarbon reservoir. A ruggedized VIS/NIR spectrometer was designed and built to measure and analyze hydrocarbon spectra reliably under the harsh conditions of the oilfield wellbore environment, including temperature up to 175 °C, pressure up to 170 MPa, and severe mechanical shocks and vibrations. The accuracy of hydrocarbon group composition analysis was compared well with gas chromatography results in the laboratory.

Cartilage analysis by reflection spectroscopy

NASA Astrophysics Data System (ADS)

Laun, T.; Muenzer, M.; Wenzel, U.; Princz, S.; Hessling, M.

2015-07-01

A cartilage bioreactor with analytical functions for cartilage quality monitoring is being developed. For determining cartilage composition, reflection spectroscopy in the visible (VIS) and near infrared (NIR) spectral region is evaluated. Main goal is the determination of the most abundant cartilage compounds water, collagen I and collagen II. Therefore VIS and NIR reflection spectra of different cartilage samples of cow, pig and lamb are recorded. Due to missing analytical instrumentation for identifying the cartilage composition of these samples, typical literature concentration values are used for the development of chemometric models. In spite of these limitations the chemometric models provide good cross correlation results for the prediction of collagen I and II and water concentration based on the visible and the NIR reflection spectra.

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.

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

In-line and real-time process monitoring of a freeze drying process using Raman and NIR spectroscopy as complementary process analytical technology (PAT) tools.

PubMed

De Beer, T R M; Vercruysse, P; Burggraeve, A; Quinten, T; Ouyang, J; Zhang, X; Vervaet, C; Remon, J P; Baeyens, W R G

2009-09-01

The aim of the present study was to examine the complementary properties of Raman and near infrared (NIR) spectroscopy as PAT tools for the fast, noninvasive, nondestructive and in-line process monitoring of a freeze drying process. Therefore, Raman and NIR probes were built in the freeze dryer chamber, allowing simultaneous process monitoring. A 5% (w/v) mannitol solution was used as model for freeze drying. Raman and NIR spectra were continuously collected during freeze drying (one Raman and NIR spectrum/min) and the spectra were analyzed using principal component analysis (PCA) and multivariate curve resolution (MCR). Raman spectroscopy was able to supply information about (i) the mannitol solid state throughout the entire process, (ii) the endpoint of freezing (endpoint of mannitol crystallization), and (iii) several physical and chemical phenomena occurring during the process (onset of ice nucleation, onset of mannitol crystallization). NIR spectroscopy proved to be a more sensitive tool to monitor the critical aspects during drying: (i) endpoint of ice sublimation and (ii) monitoring the release of hydrate water during storage. Furthermore, via NIR spectroscopy some Raman observations were confirmed: start of ice nucleation, end of mannitol crystallization and solid state characteristics of the end product. When Raman and NIR monitoring were performed on the same vial, the Raman signal was saturated during the freezing step caused by reflected NIR light reaching the Raman detector. Therefore, NIR and Raman measurements were done on a different vial. Also the importance of the position of the probes (Raman probe above the vial and NIR probe at the bottom of the sidewall of the vial) in order to obtain all required critical information is outlined. Combining Raman and NIR spectroscopy for the simultaneous monitoring of freeze drying allows monitoring almost all critical freeze drying process aspects. Both techniques do not only complement each other, they also provided mutual confirmation of specific conclusions.

[Fast catalogue of alien invasive weeds by Vis/NIR spectroscopy].

PubMed

Yu, Jia-Jia; Zou, Wei; He, Yong; Xu, Zheng-Hao

2009-11-01

The feasibility of visible and short-wave near-infrared spectroscopy (VIS/WNIR) techniques as means for the nondestructive and fast detection of alien invasive weeds was evaluated. Selected sensitive bands were found validated. In the present study, 3 kinds of alien invasive weeds, Veronica persica, Veronica polita, and Veronica arvensis Linn, and one kind of local weed, Lamiaceae amplexicaule Linn, were employed. The results showed that visible and NIR (Vis/NIR) technology could be introduced in classification of the alien invasive weeds or local weed with the similar outline. Thirty x 4 weeds samples were randomly selected for the calibration set, while the remaining 20 x 4 samples for the prediction set. Smoothing methods of moving average and standard normal variate (SNV) were used to pretreat spectra data. Based on principal components analysis, soft independent models of class analogy (SIMCA) were applied to make the model. Four frontal principal components of each catalogues were applied as the input of SIMCA, and with a significance level of 0.05, recognition ratio of 78.75% was obtained. The average prediction result is 90% except for Veronica polita. According to the modeling power of each spectra data in SIMCA, some possible sensitive bands, 496-521, 589-626 and 789-926 nm, were founded. By using these possible sensitive bands as the inputs of least squares support vector machine (LS-SVM), and setting the result of LS-SVM as the object function value of genetic algorithm (GA), mutational rate, crossover rate and population size were set up as 0.9, 0.5 and 50 respectively. Finally recognition ratio of 95.63% was obtained. The prediction results of 95.63% indicated that the selected wavelengths reflected the main characteristics of the four weeds, which proposed a new way to accelerate the research on cataloguing alien invasive weeds.

Use of a region of the visible and near infrared spectrum to predict mechanical properties of wet wood and standing trees

DOEpatents

Meglen, Robert R.; Kelley, Stephen S.

2003-01-01

In a method for determining the dry mechanical strength for a green wood, the improvement comprising: (a) illuminating a surface of the wood to be determined with a reduced range of wavelengths in the VIS-NIR spectra 400 to 1150 nm, said wood having a green moisture content; (b) analyzing the surface of the wood using a spectrometric method, the method generating a first spectral data of a reduced range of wavelengths in VIS-NIR spectra; and (c) using a multivariate analysis technique to predict the mechanical strength of green wood when dry by comparing the first spectral data with a calibration model, the calibration model comprising a second spectrometric method of spectral data of a reduced range of wavelengths in VIS-NIR spectra obtained from a reference wood having a green moisture content, the second spectral being correlated with a known mechanical strength analytical result obtained from the reference wood when dried and a having a dry moisture content.

Discrimination of Gastrodia elata from Different Geographical Origin for Quality Evaluation Using Newly-Build Near Infrared Spectrum Coupled with Multivariate Analysis.

PubMed

Zuo, Yamin; Deng, Xuehua; Wu, Qing

2018-05-04

Discrimination of Gastrodia elata ( G. elata ) geographical origin is of great importance to pharmaceutical companies and consumers in China. this paper focuses on the feasibility of near infrared spectrum (NIRS) combined multivariate analysis as a rapid and non-destructive method to prove its fit for this purpose. Firstly, 16 batches of G. elata samples from four main-cultivation regions in China were quantified by traditional HPLC method. It showed that samples from different origins could not be efficiently differentiated by the contents of four phenolic compounds in this study. Secondly, the raw near infrared (NIR) spectra of those samples were acquired and two different pattern recognition techniques were used to classify the geographical origins. The results showed that with spectral transformation optimized, discriminant analysis (DA) provided 97% and 99% correct classification for the calibration and validation sets of samples from discriminating of four different main-cultivation regions, and provided 98% and 99% correct classifications for the calibration and validation sets of samples from eight different cities, respectively, which all performed better than the principal component analysis (PCA) method. Thirdly, as phenolic compounds content (PCC) is highly related with the quality of G. elata , synergy interval partial least squares (Si-PLS) was applied to build the PCC prediction model. The coefficient of determination for prediction (R p ²) of the Si-PLS model was 0.9209, and root mean square error for prediction (RMSEP) was 0.338. The two regions (4800 cm −1 ⁻5200 cm −1 , and 5600 cm −1 ⁻6000 cm −1 ) selected by Si-PLS corresponded to the absorptions of aromatic ring in the basic phenolic structure. It can be concluded that NIR spectroscopy combined with PCA, DA and Si-PLS would be a potential tool to provide a reference for the quality control of G. elata.

Near-Infrared (NIR) Spectroscopy of Synthetic Hydroxyapatites and Human Dental Tissues.

PubMed

Kolmas, Joanna; Marek, Dariusz; Kolodziejski, Waclaw

2015-08-01

Near-infrared spectroscopy (NIR) was used to analyze synthetic hydroxyapatite calcined at various temperatures, synthetic carbonated hydroxyapatite, and human hard dental tissues (enamel and dentin). The NIR bands of those materials in the combination, first-overtone, and second-overtone spectral regions were assigned and evaluated for structural characterization. They were attributed to adsorbed and structural water, structural hydroxyl (OH) groups and surface P-OH groups. The NIR spectral features were quantitatively discussed in view of proton solid-state magic-angle spinning nuclear magnetic resonance ((1)H MAS NMR) results. We conclude that the NIR spectra of apatites are useful in the structural characterization of synthetic and biogenic apatites.

`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

Application of near infrared reflectance (NIR) spectroscopy to identify potential PSE meat.

PubMed

Li, Xiao; Feng, Fang; Gao, Runze; Wang, Lu; Qian, Ye; Li, Chunbao; Zhou, Guanghong

2016-07-01

Pale, soft and exudative (PSE) meat is a quality problem that causes a large economic loss to the pork industry. In the present work, near infrared (NIR) quantification and identification methods were used to investigate the feasibility of differentiating potential PSE meat from normal meat. NIR quantification models were developed to estimate meat pH and colour attributes (L*, a*, b*). Promising results were reported for prediction of muscle pH (R(2) CV  = 70.10%, RPDCV = 1.83) and L* (R(2) CV  = 77.18%, RPDCV = 1.91), but it is still hard to promote to practical application at this level. The Factorisation Method applied to NIR spectra could differentiate potential PSE meat from normal meat at 3 h post-mortem. Correlation analysis showed significant relationship between NIR data and LF-NMR T2 components that were indicative of water distribution and mobility in muscle. PSE meat had unconventionally faster energy metabolism than normal meat, which caused greater water mobility. NIR spectra coupled with the Factorisation Method could be a promising technology to identify potential PSE meat. The difference in the intensity of H2 O absorbance peaks between PSE and normal meat might be the basis of this identification method. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

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.

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.

Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus.

PubMed

Monteyne, Tinne; Coopman, Renaat; Kishabongo, Antoine S; Himpe, Jonas; Lapauw, Bruno; Shadid, Samyah; Van Aken, Elisabeth H; Berenson, Darja; Speeckaert, Marijn M; De Beer, Thomas; Delanghe, Joris R

2018-05-11

Glycated keratin allows the monitoring of average tissue glucose exposure over previous weeks. In the present study, we wanted to explore if near-infrared (NIR) spectroscopy could be used as a non-invasive diagnostic tool for assessing glycation in diabetes mellitus. A total of 52 patients with diabetes mellitus and 107 healthy subjects were enrolled in this study. A limited number (n=21) of nails of healthy subjects were glycated in vitro with 0.278 mol/L, 0.556 mol/L and 0.833 mol/L glucose solution to study the effect of glucose on the nail spectrum. Consequently, the nail clippings of the patients were analyzed using a Thermo Fisher Antaris II Near-IR Analyzer Spectrometer and near infrared (NIR) chemical imaging. Spectral classification (patients with diabetes mellitus vs. healthy subjects) was performed using partial least square discriminant analysis (PLS-DA). In vitro glycation resulted in peak sharpening between 4300 and 4400 cm-1 and spectral variations at 5270 cm-1 and between 6600 and 7500 cm-1. Similar regions encountered spectral deviations during analysis of the patients' nails. Optimization of the spectral collection parameters was necessary in order to distinguish a large dataset. Spectra had to be collected at 16 cm-1, 128 scans, region 4000-7500 cm-1. Using standard normal variate, Savitsky-Golay smoothing (7 points) and first derivative preprocessing allowed for the prediction of the test set with 100% correct assignments utilizing a PLS-DA model. Analysis of protein glycation in human fingernail clippings with NIR spectroscopy could be an alternative affordable technique for the diagnosis of diabetes mellitus.

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.

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

Near-infrared spectroscopy of newly developed PEGylated lipids.

PubMed

Bista, Rajan K; Bruch, Reinhard F

2008-11-15

Near-infrared (NIR) spectroscopy has been used to analyze a suite of synthesized PEGylated lipids (1-3) trademarked as QuSomes. The three amphiphiles used in this study, differ in their hydrophobic chain length and contain various units of polyethylene glycol (PEG) head groups. Whilst the spectra of QuSomes show a common pattern, differences in the spectra are observed which enable the lipids to be distinguished. NIR absorption spectra of these new artificial lipids have been recorded in the spectral range of 4800-9000 cm(-1) (approximately 2100-1100 nm) by using a new miniaturized spectrometer based on micro-optical-electro-mechanical systems (MOEMS) technology. Three NIR spectral regions are identified, (a) the high wavenumber region between 6500 and 9000 cm(-1) attributed to the first overtone of the hydroxyl stretching and second overtone of the C-H stretching mode; (b) the 5350-5900 cm(-1) region attributed to first overtone of the C-H stretching mode; and (c) the 4800-5300 cm(-1) region attributed to the combination O-H stretching and second overtone of the C=O stretching mode. For each of these regions, the lipids show distinctive spectra which allow their identification and characterization. NIR spectroscopy is a less used technique which does have great potential for the study of lipids, particularly to examine the behaviour of nanovesicles (liposomes) formed from lipids in aqueous suspensions. The study of such lipids is important since they are used as membrane models and prominent candidate for substance and drug delivery systems.

Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

PubMed Central

Acquah, Gifty E.; Via, Brian K.; Billor, Nedret; Fasina, Oladiran O.; Eckhardt, Lori G.

2016-01-01

As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel/chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIRS) together with linear discriminant analysis (LDA). Forest logging residue harvested from several Pinus taeda (loblolly pine) plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash (i.e., limbs and foliage). Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor/principal component (PC) was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR/FTIR could be employed as a tool to rapidly probe/monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality. PMID:27618901

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

NASA Astrophysics Data System (ADS)

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

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

Experimental Near Infrared Spectroscopy of Polycyclic Aromatic Hydrocarbons Between 0.7 to 2.5 microns

NASA Technical Reports Server (NTRS)

Mattioda, A. L.; Hudgins, D. M.; Allamandola, L. J.

2005-01-01

The near infrared (NIR) spectra and absolute band strengths of 27 polycyclic aromatic hydrocarbon (PAH) cations and anions ranging in size from C14H10 to C50H22, are reported. The spectra from 0.7 to 2.5 microns (14,000 to 4000/cm) are presented for the fifteen PAHs ranging in size from C40H18 to C50H22 whereas the spectra of the remaining twelve span the narrower range from 0.7 to 1.1 microns (14,000 to 9000/cm). The spectra of all the ionized PAHs we have studied to date have strong, broad absorption bands in the NIR arising from electronic transitions. This work shows that ionized PAHs have significant absorption bands at longer wavelengths than predicted by the current astronomical models which consider PAHs in their treatment of the radiation balance of the interstellar medium. Two implications are 1)-ionized interstellar PAHs should add weak, broad band structure to the NIR portion of the interstellar extinction curve and 2)- UV poor radiation fields can pump the PAH emission bands provided ionized PAHs are present.

Spectra-structure correlations in NIR region: Spectroscopic and anharmonic DFT study of n-hexanol, cyclohexanol and phenol.

PubMed

Beć, Krzysztof B; Grabska, Justyna; Czarnecki, Mirosław A

2018-05-15

We investigated near-infrared (7500-4000 cm -1 ) spectra of n-hexanol, cyclohexanol and phenol in CCl 4 (0.2 M) by using anharmonic quantum calculations. These molecules represent three major kinds of alcohols; linear and cyclic aliphatic, and aromatic ones. Vibrational second-order perturbation theory (VPT2) was employed to calculate the first overtones and binary combination modes and to reproduce the experimental NIR spectra. The level of conformational flexibility of these three alcohols varies from one stable conformer of phenol through four conformers of cyclohexanol to few hundreds conformers in the case of n-hexanol. To take into account the most relevant conformational population of n-hexanol, a systematic conformational search was performed. Accurate reproduction of the experimental NIR spectra was achieved and detailed spectra-structure correlations were obtained for these three alcohols. VPT2 approach provides less reliable description of highly anharmonic modes, i.e. OH stretching. In the present work this limitation was manifested in erroneous results yielded by VPT2 for 2νOH mode of cyclohexanol. To study the anharmonicity of this mode we solved the corresponding time-independent Schrödinger equation based on a dense-grid probing of the relevant vibrational potential. These results allowed for significant improvement of the agreement between the calculated and experimental 2νOH band of cyclohexanol. Various important biomolecules include similar structural units to the systems investigated here. A detailed knowledge on spectral properties of these three types of alcohols is therefore essential for advancing our understanding of NIR spectroscopy of biomolecules. Copyright © 2018 Elsevier B.V. All rights reserved.

Near-Infrared Spectroscopy and Imaging Studies of Fertilized Fish Eggs: In Vivo Monitoring of Egg Growth at the Molecular Level

NASA Astrophysics Data System (ADS)

Ishigaki, Mika; Kawasaki, Shoya; Ishikawa, Daitaro; Ozaki, Yukihiro

2016-01-01

In this work, the growth of fertilized Japanese medaka (Oryzias latipes) eggs was monitored in vivo at the molecular level using near-infrared (NIR) spectroscopy and NIR imaging. NIR spectra were recorded noninvasively for three major parts of a fertilized medaka egg, the embryonic body, the oil droplets, and the yolk, from the first day after fertilization to the day before hatching. Principal component analysis (PCA) revealed that water, protein, and lipid contents in the egg yolk and oil droplets changed significantly just before hatching. The ratio of the characteristic peaks due to proteins and lipids in the second derivative spectra suggested that the relative concentration of proteins to lipids was constant in the egg yolk, while it dramatically increased just before hatching in the oil droplets. Furthermore, linear discriminant analysis (LDA) predicted the hatching possibility on the next day with 100% and 99.3% accuracy for yolk and oil droplets data, respectively. Two types of NIR images were developed in situ using the band intensities of the lipids and proteins in the second derivative spectra. The egg’s protein and lipid content was successfully visualized noninvasively. This technique should enable noninvasive quality testing of fertilized eggs in the future.

Application of Near Infrared Reflectance Spectroscopy for Rapid and Non-Destructive Discrimination of Hulled Barley, Naked Barley, and Wheat Contaminated with Fusarium

PubMed Central

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Oh, Kyoungmin; Kim, Geonseob; Ham, Hyeonheui; Kim, Seongmin; Kim, Moon S.

2018-01-01

Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium. Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175–2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified. PMID:29301319

Functional assessment of the fundus autofluorescence pattern in Best vitelliform macular dystrophy.

PubMed

Parodi, Maurizio Battaglia; Iacono, Pierluigi; Del Turco, Claudia; Triolo, Giacinto; Bandello, Francesco

2016-07-01

To identify the fundus autofluorescence (FAF) patterns in Best vitelliform macular dystrophy (VMD). Patients affected by VMD in vitelliform, pseudohypopyon, and vitelliruptive stages underwent a complete ophthalmological examination, including best-corrected visual acuity (BCVA), short-wavelength FAF (SW-FAF), near-infrared FAF (NIR-FAF) and microperimetry. the identification of the correlation between SW-FAF and NIR-FAF patterns of the foveal region with BCVA, and central retinal sensitivity in eyes affected by VMD. The secondary outcomes included the definition of the frequency of foveal patterns on SW-FAF and NIR-FAF. Thirty-seven of 64 (58 %), 8 of 64 (12.5 %) and 19 of 64 (29.5 %) eyes showed vitelliform, pseudohypopyon, and vitelliruptive stages respectively. Three main FAF patterns were identified on both techniques: hyper-autofluorescent pattern, hypo-autofluorescent pattern, and patchy pattern. BCVA was significantly different in eyes with hypo-autofluorescent and patchy patterns with respect to eyes showing a hyper-autofluorescent pattern. Similar differences were registered in the FS according to SW-FAF classification. However, the FS differed in each subgroup in the NIR-FAF analysis. Subgroup analyses were performed on the patchy pattern, combining FAF and fundus abnormalities. Considering both FAF techniques, the BCVA differed between the vitelliform and pseudohypopyon stages, and between the vitelliform and vitelliruptive stages. In the NIR-FAF classification, there was a significant statistical difference in the FS between each subgroup; in the SW-FAF, there was a significant difference between the vitelliform and pseudohypopyon stages and the vitelliform and vitelliruptive stages. Three main FAF patterns can be identified in VMD. The patchy pattern is the most frequent, accounting for 70 % of eyes on SW-FAF and 80 % of eyes on NIR-FAF. A tighter correlation links the classification of NIR-FAF patterns and FS. Longitudinal investigations are warranted to evaluate the course of FAF patterns and their role in disease monitoring.

Simultaneous determination of the impurity and radial tensile strength of reduced glutathione tablets by a high selective NIR-PLS method.

PubMed

Li, Juan; Jiang, Yue; Fan, Qi; Chen, Yang; Wu, Ruanqi

2014-05-05

This paper establishes a high-throughput and high selective method to determine the impurity named oxidized glutathione (GSSG) and radial tensile strength (RTS) of reduced glutathione (GSH) tablets based on near infrared (NIR) spectroscopy and partial least squares (PLS). In order to build and evaluate the calibration models, the NIR diffuse reflectance spectra (DRS) and transmittance spectra (TS) for 330 GSH tablets were accurately measured by using the optimized parameter values. For analyzing GSSG or RTS of GSH tablets, the NIR-DRS or NIR-TS were selected, subdivided reasonably into calibration and prediction sets, and processed appropriately with chemometric techniques. After selecting spectral sub-ranges and neglecting spectrum outliers, the PLS calibration models were built and the factor numbers were optimized. Then, the PLS models were evaluated by the root mean square errors of calibration (RMSEC), cross-validation (RMSECV) and prediction (RMSEP), and by the correlation coefficients of calibration (R(c)) and prediction (R(p)). The results indicate that the proposed models have good performances. It is thus clear that the NIR-PLS can simultaneously, selectively, nondestructively and rapidly analyze the GSSG and RTS of GSH tablets, although the contents of GSSG impurity were quite low while those of GSH active pharmaceutical ingredient (API) quite high. This strategy can be an important complement to the common NIR methods used in the on-line analysis of API in pharmaceutical preparations. And this work expands the NIR applications in the high-throughput and extraordinarily selective analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

The Ultracool Typing Kit - An Open-Source, Qualitative Spectral Typing GUI for L Dwarfs

NASA Astrophysics Data System (ADS)

Schwab, Ellianna; Cruz, Kelle; Núñez, Alejandro; Burgasser, Adam J.; Rice, Emily; Reid, Neill; Faherty, Jacqueline K.; BDNYC

2018-01-01

The Ultracool Typing Kit (UTK) is an open-source graphical user interface for classifying the NIR spectral types of L dwarfs, including field and low-gravity dwarfs spanning L0-L9. The user is able to input an NIR spectrum and qualitatively compare the input spectrum to a full suite of spectral templates, including low-gravity beta and gamma templates. The user can choose to view the input spectrum as both a band-by-band comparison with the templates and a full bandwidth comparison with NIR spectral standards. Once an optimal qualitative comparison is selected, the user can save their spectral type selection both graphically and to a database. Using UTK to classify 78 previously typed L dwarfs, we show that a band-by-band classification method more accurately agrees with optical spectral typing systems than previous L dwarf NIR classification schemes. UTK is written in python, released on Zenodo with a BSD-3 clause license and publicly available on the BDNYC Github page.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

PubMed Central

Khan, M. Jawad; Hong, Melissa Jiyoun; Hong, Keum-Shik

2014-01-01

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology. PMID:24808844

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG.

PubMed

Fu, Yunfa; Xiong, Xin; Jiang, Changhao; Xu, Baolei; Li, Yongcheng; Li, Hongyi

2017-09-01

Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were placed midway between the NIRS probes. NIRS and EEG signals were acquired from six healthy subjects during six imagined hand clenching force and speed tasks involving the right hand. The results showed that NIRS combined with EEG is effective for simultaneously measuring brain activity of the sensorimotor area. The study also showed that in the duration of (0, 10) s for imagined force and speed of hand clenching, HbO first exhibited a negative variation trend, which was followed by a negative peak. After the negative peak, it exhibited a positive variation trend with a positive peak about 6-8 s after termination of imagined movement. During (-2, 1) s, the EEG may have indicated neural processing during the preparation, execution, and monitoring of a given imagined force and speed of hand clenching. The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted. The features of NIRS and EEG were combined to classify three levels of imagined force [at 20/50/80% MVGF (maximum voluntary grip force)] and speed (at 0.5/1/2 Hz) of hand clenching by SVM. The average classification accuracy of the NIRS-EEG fusion feature was 0.74 ± 0.02. These results may provide increased control commands of force and speed for a brain-controlled robot based on NIRS-EEG.

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

PubMed Central

Shin, Jaeyoung; Müller, Klaus-R; Hwang, Han-Jeong

2016-01-01

We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently. PMID:27824089

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic.

PubMed

Shin, Jaeyoung; Müller, Klaus-R; Hwang, Han-Jeong

2016-11-08

We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently.

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

NASA Technical Reports Server (NTRS)

Rampe, E. B.; Lanza, N. L.

2012-01-01

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Absolutely nondestructive discrimination of Huoshan Dendrobium nobile species with miniature near-infrared (NIR) spectrometer engine.

PubMed

Hu, Tian; Yang, Hai-Long; Tang, Qing; Zhang, Hui; Nie, Lei; Li, Lian; Wang, Jin-Feng; Liu, Dong-Ming; Jiang, Wei; Wang, Fei; Zang, Heng-Chang

2014-10-01

As one very precious traditional Chinese medicine (TCM), Huoshan Dendrobium has not only high price, but also significant pharmaceutical efficacy. However, different species of Huoshan Dendrobium exhibit considerable difference in pharmaceutical efficacy, so rapid and absolutely non-destructive discrimination of Huoshan Dendrobium nobile according to different species is crucial to quality control and pharmaceutical effect. In this study, as one type of miniature near-infrared (NIR) spectrometer, MicroNIR 1700 was used for absolutely nondestructive determination of NIR spectra of 90 batches of Dendrobium from five species of differ- ent commodity grades. The samples were intact and not smashed. Soft independent modeling of class analogy (SIMCA) pattern recognition based on principal component analysis (PCA) was used to classify and recognize different species of Dendrobium samples. The results indicated that the SIMCA qualitative models established with pretreatment method of standard normal variate transformation (SNV) in the spectra range selected by Qs method had 100% recognition rates and 100% rejection rates. This study demonstrated that a rapid and absolutely non-destructive analytical technique based on MicroNIR 1700 spectrometer was developed for successful discrimination of five different species of Huoshan Dendrobium with acceptable accuracy.

[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.

Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Power, Sarah D.; Falk, Tiago H.; Chau, Tom

2010-04-01

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% ± 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.

Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy

USDA-ARS?s Scientific Manuscript database

Adulteration of onion powder with cornstarch was identified by Fourier transform near-infrared (FT-NIR) and Fourier transform infrared (FT-IR) spectroscopy. The reflectance spectra of 180 pure and adulterated samples (1–35 wt% starch) were collected and preprocessed to generate calibration and predi...

Visible/near-infrared spectroscopy to predict water holding capacity in broiler breast meat

USDA-ARS?s Scientific Manuscript database

Visible/Near-infrared spectroscopy (Vis/NIRS) was examined as a tool for rapidly determining water holding capacity (WHC) in broiler breast meat. Both partial least squares (PLS) and principal component analysis (PCA) models were developed to relate Vis/NIRS spectra of 85 broiler breast meat sample...

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets.

PubMed

Naidu, Venkata Ramana; Deshpande, Rucha S; Syed, Moinuddin R; Deoghare, Piyush; Singh, Dharamvir; Wakte, Pravin S

2017-08-01

Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm -1 , 7515.24 to 7108.33 cm -1 , and 5257.00 to 5098.87 cm -1 were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm -1 was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process.

The Effect of Chain Length on Mid-Infrared and Near-Infrared Spectra of Aliphatic 1-Alcohols.

PubMed

Kwaśniewicz, Michał; Czarnecki, Mirosław A

2018-02-01

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH 3 , CH 2 , and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.

[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.

Design of an FT-NIR spectrometer for online quality analysis of traditional Chinese medicine manufacturing process

NASA Astrophysics Data System (ADS)

Zhu, Ren; Wu, Lan; Wang, Shiming; Ye, Linhua; Ding, Zhihua

2008-03-01

As a fast, non-destructive analysis method, Fourier transform (FT) near-infrared (NIR) spectroscopy is very suitable and effective for online quality analysis of traditional Chinese medicine (TCM) manufacturing process. In this thesis, the theoretics of FT-NIRS was analyzed and an FT-NIR spectrometer with 4 cm -1 resolution in the 12500-5000 cm -1 frequency range was designed. The spectrometer was based on a Michelson interferometer with Bromine tungsten lamp as the NIR light source and InGaAs detector to collect the interference signal. Each element was designed and chosen to provide maximum sensitivity in the NIR spectral region. A fiber-optic flow cell system was used to realize online analysis of traditional Chinese medicine. The performance of the spectrometer was evaluated and the feasibility of using FT-NIR spectrometer to get absorption spectra of traditional Chinese medicine was demonstrated.

Bacterial Phytochromes, Cyanobacteriochromes and Allophycocyanins as a Source of Near-Infrared Fluorescent Probes

PubMed Central

Oliinyk, Olena S.; Chernov, Konstantin G.

2017-01-01

Bacterial photoreceptors absorb light energy and transform it into intracellular signals that regulate metabolism. Bacterial phytochrome photoreceptors (BphPs), some cyanobacteriochromes (CBCRs) and allophycocyanins (APCs) possess the near-infrared (NIR) absorbance spectra that make them promising molecular templates to design NIR fluorescent proteins (FPs) and biosensors for studies in mammalian cells and whole animals. Here, we review structures, photochemical properties and molecular functions of several families of bacterial photoreceptors. We next analyze molecular evolution approaches to develop NIR FPs and biosensors. We then discuss phenotypes of current BphP-based NIR FPs and compare them with FPs derived from CBCRs and APCs. Lastly, we overview imaging applications of NIR FPs in live cells and in vivo. Our review provides guidelines for selection of existing NIR FPs, as well as engineering approaches to develop NIR FPs from the novel natural templates such as CBCRs. PMID:28771184

Exploring NIR technique in rapid prediction of cotton trash components

USDA-ARS?s Scientific Manuscript database

Near infrared (NIR) spectroscopy, a useful technique due to the speed, ease of use, and adaptability to on-line or off-line implementation, has been applied to perform the qualitative classification and quantitative prediction on a number of cotton quality indices, including cotton trash from HVI, S...

VizieR Online Data Catalog: NIR spectroscopy of Galactic WR stars. III (Kanarek+, 2015)

NASA Astrophysics Data System (ADS)

Kanarek, G.; Shara, M.; Faherty, J.; Zurek, D.; Moffat, A.

2016-02-01

This survey was previously described in Paper I (Shara et al., 2009AJ....138..402S). More than 88000 exposures were taken of the Galactic plane on the Cerro Tololo Inter-American Observatory (CTIO) 1.5-m telescope over approximately 200 nights during 2005-2006. IRTF: at the 3m NASA Infrared Telescope Facility (IRTF), we obtained NIR spectra of 150 candidate WR stars, selected using the criteria above, with the SpeX spectrograph. MDM 2011: during a run of excellent weather over the seven nights in 2011 June, we obtained 113 NIR spectra of candidate stars using TIFKAM in spectroscopic mode on the 2.4m Hiltner telescope at MDM Observatory. MDM 2012: during early 2012, the original survey data were reduced again, using different methods to produce better images. (10 data files).

Ar 3p photoelectron sideband spectra in two-color XUV + NIR laser fields

NASA Astrophysics Data System (ADS)

Minemoto, Shinichirou; Shimada, Hiroyuki; Komatsu, Kazma; Komatsubara, Wataru; Majima, Takuya; Mizuno, Tomoya; Owada, Shigeki; Sakai, Hirofumi; Togashi, Tadashi; Yoshida, Shintaro; Yabashi, Makina; Yagishita, Akira

2018-04-01

We performed photoelectron spectroscopy using femtosecond XUV pulses from a free-electron laser and femtosecond near-infrared pulses from a synchronized laser, and succeeded in measuring Ar 3p photoelectron sideband spectra due to the two-color above-threshold ionization. In our calculations of the first-order time-dependent perturbation theoretical model based on the strong field approximation, the photoelectron sideband spectra and their angular distributions are well reproduced by considering the timing jitter between the XUV and the NIR pulses, showing that the timing jitter in our experiments was distributed over the width of {1.0}+0.4-0.2 ps. The present approach can be used as a method to evaluate the timing jitter inevitable in FEL experiments.

Progress in the detection of neoplastic progress and cancer by Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bakker Schut, Tom C.; Stone, Nicholas; Kendall, Catherine A.; Barr, Hugh; Bruining, Hajo A.; Puppels, Gerwin J.

2000-05-01

Early detection of cancer is important because of the improved survival rates when the cancer is treated early. We study the application of NIR Raman spectroscopy for detection of dysplasia because this technique is sensitive to the small changes in molecular invasive in vivo detection using fiber-optic probes. The result of an in vitro study to detect neoplastic progress of esophageal Barrett's esophageal tissue will be presented. Using multivariate statistics, we developed three different linear discriminant analysis classification models to predict tissue type on the basis of the measured spectrum. Spectra of normal, metaplastic and dysplasia tissue could be discriminated with an accuracy of up to 88 percent. Therefore Raman spectroscopy seems to be a very suitable technique to detect dysplasia in Barrett's esophageal tissue.

Evaluation of portable near-infrared spectroscopy for organic milk authentication.

PubMed

Liu, Ningjing; Parra, Hector Aya; Pustjens, Annemieke; Hettinga, Kasper; Mongondry, Philippe; van Ruth, Saskia M

2018-07-01

Organic products are vulnerable to fraud due to their premium price. Analytical methodology helps to manage the risk of fraud and due to the miniaturization of equipment, tests may nowadays even be rapidly applied on-site. The current study aimed to evaluate portable near infrared spectroscopy (NIRS) in combination with chemometrics to distinguish organic milk from other types of milk, and compare its performance with benchtop NIRS and fatty acid profiling by gas chromatography. The sample set included 37 organic retail milks and 50 non-organic retail milks (of which 36 conventional and 14 green 'pasture' milks). Partial least squares discriminant analysis was performed to build classification models and kernel density estimation (KDE) functions were calculated to generate non-parametric distributions for samples' class probabilities. These distributions showed that portable NIRS was successful to distinguish organic milks from conventional milks, and so were benchtop NIRS and fatty acid profiling procedures. However, it was less successful when 'pasture' milks were considered too, since their patterns occasionally resembled those of the organic milk group. Fatty acid profiling was capable of distinguishing organic milks from both non-organic milks though, including the 'pasture' milks. This comparative study revealed that the classification performance of the portable NIRS for this application was similar to that of the benchtop NIRS. Copyright © 2018 Elsevier B.V. All rights reserved.

On the Origin of the Near-infrared Emission from the Neutron-star Low-mass X-Ray Binary GX 9+1

NASA Astrophysics Data System (ADS)

van den Berg, Maureen; Homan, Jeroen

2017-01-01

We have determined an improved position for the luminous persistent neutron-star low-mass X-ray binary and atoll source GX 9+1 from archival Chandra X-ray Observatory data. The new position significantly differs from a previously published Chandra position for this source. Based on the revised X-ray position we have identified a new near-infrared (NIR) counterpart to GX 9+1 in Ks-band images obtained with the PANIC and FourStar cameras on the Magellan Baade Telescope. NIR spectra of this {K}s=16.5+/- 0.1 mag star, taken with the FIRE spectrograph on the Baade Telescope, show a strong Br γ emission line, which is a clear signature that we discovered the true NIR counterpart to GX 9+1. The mass donor in GX 9+1 cannot be a late-type giant, as such a star would be brighter than the estimated absolute Ks magnitude of the NIR counterpart. The slope of the dereddened NIR spectrum is poorly constrained due to uncertainties in the column density NH and NIR extinction. Considering the source’s distance and X-ray luminosity, we argue that NH likely lies near the high end of the previously suggested range. If this is indeed the case, the NIR spectrum is consistent with thermal emission from a heated accretion disk, possibly with a contribution from the secondary. In this respect, GX 9+1 is similar to other bright atolls and the Z sources, whose NIR spectra do not show the slope that is expected for a dominant contribution from optically thin synchrotron emission from the inner regions of a jet. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Near infrared spectra are more sensitive to land use changes than physical, chemical and biological soil properties

NASA Astrophysics Data System (ADS)

Guerrero, C.; Zornoza, R.; Mataix-Solera, J.; Mataix-Beneyto, J.; Scow, K.

2009-04-01

We studied the sensibility of the near infrared spectra (NIR) of soils to the changes caused by land use, and we compared with the sensibility of different sets of physical, chemical and biological soil properties. For this purpose, we selected three land uses, constituted by forest, almond trees orchards, and orchards abandoned between 10 and 15 years previously to sampling. Sampling was carried out in four different locations from the province of Alicante (SE Spain). We used discriminant analysis (DA) using different sets of soil properties. The different sets tested in this study using DA were: (1) physical and chemical properties (organic carbon, total nitrogen, available phosphorus, pH, electrical conductivity, cation exchange capacity, aggregate stability, water holding capacity, and available Ca, Mg, K and Na), (2) biochemical properties (microbial biomass carbon, basal respiration and urease, phosphatase and β-glucosidase activities), (3) phospholipids fatty acids (PLFAs), (4) physical, chemical and biochemical properties (all properties of the previous sets), and (5) the NIR spectra of soils (scores of the principal components). In general, all sets of properties were sensible to land use. This was observed in the DAs by the separation (more or less clear) of samples in groups defined by land use (irrespective of site). The worst results were obtained using soil physical and chemical properties. The combination of physical, chemical and biological properties enhanced the separation of samples in groups, indicating higher sensibility. It is accepted than combination of properties of different nature is more effective to evaluate the soil quality. The microbial community structure (PLFAs) was highly sensible to the land use, grouping correctly the 100% of the samples according with the land use. The NIR spectra were also sensitive to land use. The scores of the first 5 components, which explained 99.97% of the variance, grouped correctly the 85% of the soil samples by land use, but were unable to group correctly the 100% of the samples. Surprisingly, when the scarce variance presents in components 5 to 40 was also used, the 100% of the samples were grouped by land use, as it was observed with PLFAs. But PLFAs analysis is expensive and time-consuming (some weeks). In contrast, only some minutes are needed for the obtainment of the NIR spectra. Additionally, no chemicals are need, decreasing the costs. The NIR spectrum of a soil contains relevant information about physical, chemical and biochemical properties. NIR spectrum could be considered as an integrated vision of soil quality, and as consequence offers an integrated vision of perturbations. Thus, NIR spectroscopy could be used as tool to monitoring soil quality in large areas. Acknowledgements: Authors acknowledge to "Bancaja-UMH" for the financial support of the project "NIRPRO"

Subcycle dynamics of high-order-harmonic generation of He atoms excited by attosecond pulses and driven by near-infrared laser fields: A self-interaction-free time-dependent density-functional-theory approach

NASA Astrophysics Data System (ADS)

Heslar, John; Telnov, Dmitry A.; Chu, Shih-I.

2014-05-01

In the framework of the self-interaction-free time-dependent density-functional theory, we have performed three-dimensional (3D) ab initio calculations of He atoms in near-infrared (NIR) laser fields subject to excitation by a single extreme ultraviolet (XUV) attosecond pulse (SAP). We have explored the dynamical behavior of the subcycle high harmonic generation (HHG) for transitions from the excited states to the ground state and found oscillation structures with respect to the time delay between the SAP and NIR fields. The oscillatory pattern in the photon emission spectra has a period of ˜1.3 fs which is half of the NIR laser optical cycle, similar to that recently measured in the experiments on transient absorption of He [M. Chini et al., Sci. Rep. 3, 1105 (2013), 10.1038/srep01105]. We present the photon emission spectra from 1s2p, 1s3p, 1s4p, 1s5p, and 1s6p excited states as functions of the time delay. We explore the subcycle Stark shift phenomenon in NIR fields and its influence on the photon emission process. Our analysis reveals several interesting features of the subcycle HHG dynamics and we identify the mechanisms responsible for the observed peak splitting in the photon emission spectra.

Monitoring of itaconic acid hydrogenation in a trickle bed reactor using fiber-optic coupled near-infrared spectroscopy.

PubMed

Wood, Joseph; Turner, Paul H

2003-03-01

Near-infrared (NIR) spectroscopy has been applied to determine the conversion of itaconic acid in the effluent stream of a trickle bed reactor. Hydrogenation of itaconic to methyl succinic acid was carried out, with the trickle bed operating in recycle mode. For the first time, NIR spectra of itaconic and methyl succinic acids in aqueous solution, and aqueous mixtures withdrawn from the reactor over a range of reaction times, have been recorded using a fiberoptic sampling probe. The infrared spectra displayed a clear isolated absorption band at a wavenumber of 6186 cm(-1) (wavelength 1.617 microm) resulting from the =C-H bonds of itaconic acid, which was found to decrease in intensity with increasing reaction time. The feature could be more clearly observed from plots of the first derivatives of the spectra. A partial least-squares (PLS) model was developed from the spectra of 13 reference samples and was used successfully to calculate the concentration of the two acids in the reactor effluent solution. Itaconic acid conversions of 23-29% were calculated after 360 min of reaction time. The potential of FT-NIR with fiber-optic sampling for remote monitoring of three-phase catalytic reactors and validation of catalytic reactor models is highlighted in the paper.

A Colloidal-Quantum-Dot-Based Self-Charging System via the Near-Infrared Band.

PubMed

Baek, Se-Woong; Cho, Jungmin; Kim, Joo-Seong; Kim, Changjo; Na, Kwangmin; Lee, Sang-Hoon; Jun, Sunhong; Song, Jung Hoon; Jeong, Sohee; Choi, Jang Wook; Lee, Jung-Yong

2018-05-11

A novel self-charging platform is proposed using colloidal-quantum-dot (CQD) photovoltaics (PVs) via the near-infrared (NIR) band for low-power electronics. Low-bandgap CQDs can convert invisible NIR light sources to electrical energy more efficiently than wider spectra because of reduced thermalization loss. This energy-conversion strategy via NIR photons ensures an enhanced photostability of the CQD devices. Furthermore, the NIR wireless charging system can be concealed using various colored and NIR-transparent fabric or films, providing aesthetic freedom. Finally, an NIR-driven wireless charging system is demonstrated for a wearable healthcare bracelet by integrating a CQD PVs receiver with a flexible lithium-ion battery and entirely embedding them into a flexible strap, enabling permanent self-charging without detachment. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Online Spectral Fit Tool for Analyzing Reflectance Spectra

NASA Astrophysics Data System (ADS)

Penttilä, A.; Kohout, T.

2015-11-01

The Online Spectral Fit Tool is developed for analyzing Vis-NIR spectral behavior of asteroids and meteorites. Implementation is done using JavaScript/HTML. Fitted spectra consist of spline continuum and gamma distributions for absorption bands.

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

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.

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.

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.

NIR Raman spectroscopy in medicine and biology: results and aspects

NASA Astrophysics Data System (ADS)

Schrader, B.; Dippel, B.; Erb, I.; Keller, S.; Löchte, T.; Schulz, H.; Tatsch, E.; Wessel, S.

1999-05-01

Analyses of biomaterial by 'classical' Raman spectroscopy with excitation in the visible range has not been possible since the fluorescence of many essential constituents of all animal and plant cells and tissues overlays the Raman spectra completely. Fluorescence, however, is virtually avoided, when Raman spectra are excited with the Nd : YAG laser line at 1064 nm. Within seven dissertations we explored different fields of potential applications to medical diagnostics. Identification and qualification of tissues and cells is possible. Tumors show small but significant differences to normal tissues; in order to develop a reliable tool for tumor diagnostics more research is necessary, especially a collection of reference spectra in a data bank is needed. Raman spectra of biomineralization structures in teeth and bones show pathological tissues as well as the development of new mineralized structures. NIR Raman spectra of flowers, leaves, and fruit show, without special preparation, their constituents: alkaloids, the essential oils, natural dyes, flavors, spices and drugs. They allow application to taxonomy, optimizing plant breeding and control of food.

Assessment of hyaline cartilage matrix composition using near infrared spectroscopy.

PubMed

Palukuru, Uday P; McGoverin, Cushla M; Pleshko, Nancy

2014-09-01

Changes in the composition of the extracellular matrix (ECM) are characteristic of injury or disease in cartilage tissue. Various imaging modalities and biochemical techniques have been used to assess the changes in cartilage tissue but lack adequate sensitivity, or in the case of biochemical techniques, result in destruction of the sample. Fourier transform near infrared (FT-NIR) spectroscopy has shown promise for the study of cartilage composition. In the current study NIR spectroscopy was used to identify the contributions of individual components of cartilage in the NIR spectra by assessment of the major cartilage components, collagen and chondroitin sulfate, in pure component mixtures. The NIR spectra were obtained using homogenous pellets made by dilution with potassium bromide. A partial least squares (PLS) model was calculated to predict composition in bovine cartilage samples. Characteristic absorbance peaks between 4000 and 5000 cm(-1) could be attributed to components of cartilage, i.e. collagen and chondroitin sulfate. Prediction of the amount of collagen and chondroitin sulfate in tissues was possible within 8% (w/dw) of values obtained by gold standard biochemical assessment. These results support the use of NIR spectroscopy for in vitro and in vivo applications to assess matrix composition of cartilage tissues, especially when tissue destruction should be avoided. Copyright © 2014. Published by Elsevier B.V.

Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Stolee, Jessica; Strong, Rick; Boulas, Pierre; Cho, Byoung-Kwan

2017-09-01

Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000-2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

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.

Applying support vector machine on hybrid fNIRS/EEG signal to classify driver's conditions (Conference Presentation)

NASA Astrophysics Data System (ADS)

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

2016-03-01

Driver's condition plays a critical role in driving safety. The fact that about 20 percent of automobile accidents occurred due to driver fatigue leads to a demand for developing a method to monitor driver's status. In this study, we acquired brain signals such as oxy- and deoxyhemoglobin and neuronal electrical activity by a hybrid fNIRS/EEG system. Experiments were conducted with 11 subjects under two conditions: Normal condition, when subjects had enough sleep, and sleep deprivation condition, when subject did not sleep previous night. During experiment, subject performed a driving task with a car simulation system for 30 minutes. After experiment, oxy-hemoglobin and deoxy-hemoglobin changes were derived from fNIRS data, while beta and alpha band relative power were calculated from EEG data. Decrement of oxy-hemoglobin, beta band power, and increment of alpha band power were found in sleep deprivation condition compare to normal condition. These features were then applied to classify two conditions by Fisher's linear discriminant analysis (FLDA). The ratio of alpha-beta relative power showed classification accuracy with a range between 62% and 99% depending on a subject. However, utilization of both EEG and fNIRS features increased accuracy in the range between 68% and 100%. The highest increase of accuracy is from 63% using EEG to 99% using both EEG and fNIRS features. In conclusion, the enhancement of classification accuracy is shown by adding a feature from fNIRS to the feature from EEG using FLDA which provides the need of developing a hybrid fNIRS/EEG system.

Hungaria Asteroid Region Telescopic Spectral Survey (HARTSS) II: Spectral Homogeneity Among Hungaria Family Asteroids

NASA Astrophysics Data System (ADS)

Lucas, Michael P.; Emery, Joshua; Pinilla-Alonso, Noemi; Lindsay, Sean S.; MacLennan, Eric M.; Cartwright, Richard; Reddy, Vishnu; Sanchez, Juan A.; Thomas, Cristina A.; Lorenzi, Vania

2017-10-01

Spectral observations of asteroid family members provide valuable information regarding parent body interiors, the source regions of near-Earth asteroids, and the link between meteorites and their parent bodies. Hungaria family asteroids constitute the closest samples to the Earth from a collisional family (~1.94 AU), permitting observations of smaller fragments than accessible for Main Belt families. We have carried out a ground-based observational campaign - Hungaria Asteroid Region Telescopic Spectral Survey (HARTSS) - to record reflectance spectra of these preserved samples from the inner-most primordial asteroid belt. During HARTSS phase one (Lucas et al. [2017]. Icarus 291, 268-287) we found that ~80% of the background population is comprised of stony S-complex asteroids that exhibit considerable spectral and mineralogical diversity. In HARTSS phase two, we turn our attention to family members and hypothesize that the Hungaria collisional family is homogeneous. We test this hypothesis through taxonomic classification, albedo estimates, and spectral properties.During phase two of HARTSS we acquired near-infrared (NIR) spectra of 50 new Hungarias (19 family; 31 background) with SpeX/IRTF and NICS/TNG. We analyzed X-type family spectra for NIR color indices (0.85-J J-K), and a subtle ~0.9 µm absorption feature that may be attributed to Fe-poor orthopyroxene. Surviving fragments of an asteroid collisional family typically exhibit similar taxonomies, albedos, and spectral properties. Spectral analysis of X-type Hungaria family members and independently calculated WISE albedo determinations for 428 Hungaria asteroids is consistent with this scenario. Furthermore, ~1/4 of the background population exhibit similar spectral properties and albedos to family X-types.Spectral observations of 92 Hungaria region asteroids acquired during both phases of HARTSS uncover a compositionally heterogeneous background and spectral homogeneity down to ~2 km for collisional family members. Taxonomy, albedos, and spectral properties reveal that the Hungaria family progenitor was an igneous body that formed under reduced conditions, and was compositionally consistent with the enstatite achondrite (i.e., aubrite) meteorite group.

[Tobacco quality analysis of industrial classification of different producing area using near-infrared (NIR) spectrum].

PubMed

Wang, Yi; Ma, Xiang; Wen, Ya-Dong; Yu, Chun-Xia; Wang, Luo-Ping; Zhao, Long-Lian; Li, Jun-Hui

2012-10-01

In this study, tobacco quality analysis of industrial classification of different producing area was carried out applying spectrum projection and correlation methods. The group of industrial classification data was near-infrared (NIR) spectrum in 2010 year from different tobacco plant parts and colors of Hongta Tobacco (Group) Co., Ltd. 6 064 tobacco leaf samples of 17 classes from Yuxi, Chuxiong and Zhaotong, in Yunnan province and 6 industrial classifications were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of K326. The conclusion showed that, the probability of the grading belonging by the first dimension was 84%, the probability of the producing area belonging by the second dimension was 71%. The study can explain the difference of tobacco quality of industrial classification and producing area by a projection method to get the quantitative similarity values. The quantitative similarity values were instructive in combination of tobacco leaf blending.

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.

[Near infrared spectroscopy based process trajectory technology and its application in monitoring and controlling of traditional Chinese medicine manufacturing process].

PubMed

Li, Wen-Long; Qu, Hai-Bin

2016-10-01

In this paper, the principle of NIRS (near infrared spectroscopy)-based process trajectory technology was introduced.The main steps of the technique include:① in-line collection of the processes spectra of different technics; ② unfolding of the 3-D process spectra;③ determination of the process trajectories and their normal limits;④ monitoring of the new batches with the established MSPC (multivariate statistical process control) models.Applications of the technology in the chemical and biological medicines were reviewed briefly. By a comprehensive introduction of our feasibility research on the monitoring of traditional Chinese medicine technical process using NIRS-based multivariate process trajectories, several important problems of the practical applications which need urgent solutions are proposed, and also the application prospect of the NIRS-based process trajectory technology is fully discussed and put forward in the end. Copyright© by the Chinese Pharmaceutical Association.

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.

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.

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.

Near-Infrared Spectroscopy Assay of Key Quality-Indicative Ingredients of Tongkang Tablets.

PubMed

Pan, Wenjie; Ma, Jinfang; Xiao, Xue; Huang, Zhengwei; Zhou, Huanbin; Ge, Fahuan; Pan, Xin

2017-04-01

The objective of this paper is to develop an easy and fast near-infrared spectroscopy (NIRS) assay for the four key quality-indicative active ingredients of Tongkang tablets by comparing the true content of the active ingredients measured by high performance liquid chromatography (HPLC) and the NIRS data. The HPLC values for the active ingredients content of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin in Tongkang tablets were set as reference values. The NIRS raw spectra of Tongkang tablets were processed using first-order convolution method. The iterative optimization method was chosen to optimize the band for Cimicifuga glycoside and 5-O-methylvisamminol, and correlation coefficient method was used to determine the optimal band of calycosin glucoside and hesperidin. A near-infrared quantitative calibration model was established for each quality-indicative ingredient by partial least-squares method on the basis of the contents detected by HPLC and the obtained NIRS spectra. The correlation coefficient R 2 values of the four models of Cimicifuga glycoside, calycosin glucoside, 5-O-methylvisamminol and hesperidin were 0.9025, 0.8582, 0.9250, and 0.9325, respectively. It was demonstrated that the accuracy of the validation values was approximately 90% by comparison of the predicted results from NIRS models and the HPLC true values, which suggested that NIRS assay was successfully established and validated. It was expected that the quantitative analysis models of the four indicative ingredients could be used to rapidly perform quality control in industrial production of Tongkang tablets.

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

PubMed

Shin, Jaeyoung; Kim, Do-Won; Müller, Klaus-Robert; Hwang, Han-Jeong

2018-06-05

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Pinus taeda L. wood property calibrations based on variable numbers of near infrared spectra per core and cores per plantation

Treesearch

Laurence R. Schimleck; Justin A. Tyson; David Jones; Gary F. Peter; Richard F. Daniels; Alexander III Clark

2007-01-01

Near infrared (NIR) spectroscopy provides a rapid, non-destructive method for the estimation of several wood properties of increment cores. MR spectra are collected from adjacent sections of the same core; however, not all spectra are required for calibration purposes as spectra from the same core are autocorrelated. Previously, we showed that wood property...

A Survey of Near-infrared Diffuse Interstellar Bands

NASA Astrophysics Data System (ADS)

Hamano, Satochi; Kobayashi, Naoto; Kawakita, Hideyo; Ikeda, Yuji; Kondo, Sohei; Sameshima, Hiroaki; Arai, Akira; Matsunaga, Noriyuki; Yasui, Chikako; Mizumoto, Misaki; Fukue, Kei; Izumi, Natsuko; Otsubo, Shogo; Takenada, Keiichi

2018-04-01

We propose a study of interstellar molecules with near-infrared (NIR) high-resolution spectroscopy as a science case for the 3.6-m Devasthal Optical Telescope (DOT). In particular, we present the results obtained on-going survey of diffuse interstellar bands (DIBs) in NIR with the newly developed NIR high-resolution spectrograph WINERED, which offers a high sensitivity in the wavelength range of 0.91-1.36 µm. Using the WINERED spectrograph attached to the 1.3-m Araki telescope in Japan, we obtained high-quality spectra of a number of early-type stars in various environments, such as diffuse interstellar clouds, dark clouds and star-forming regions, to investigate the properties of NIR DIBs and constrain their carriers. As a result, we successfully identified about 50 new NIR DIBs, where only five fairly strong DIBs had been identified previously. Also, some properties of DIBs in the NIR are discussed to constrain the carriers of DIBs.

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.

Time- and Space-Resolved Spectroscopic Investigation on Pi-Conjugated Nanostructures - 2

DTIC Science & Technology

2016-01-12

15. SUBJECT TERMS Materials Characterization, Materials Chemistry, Nonlinear Optical Materials, Spectroscopy 16. SECURITY CLASSIFICATION...nanostructures will translate into new ground-breaking developments that not only allow the structure-property relationships to be probed in greater detail... spectroscopy . I. Experimental method 1. Steady-state Spectroscopy - UV-Vis-NIR Absorption & Emission Steady-state Spectroscopy - NIR

Application of near infrared spectroscopy to the analysis and fast quality assessment of traditional Chinese medicinal products

PubMed Central

Zhang, Chao; Su, Jinghua

2014-01-01

Near infrared spectroscopy (NIRS) has been widely applied in both qualitative and quantitative analysis. There is growing interest in its application to traditional Chinese medicine (TCM) and a review of recent developments in the field is timely. To present an overview of recent applications of NIRS to the identification, classification and analysis of TCM products, studies describing the application of NIRS to TCM products are classified into those involving qualitative and quantitative analysis. In addition, the application of NIRS to the detection of illegal additives and the rapid assessment of quality of TCMs by fast inspection are also described. This review covers over 100 studies emphasizing the application of NIRS in different fields. Furthermore, basic analytical principles and specific examples are used to illustrate the feasibility and effectiveness of NIRS in pattern identification. NIRS provides an effective and powerful tool for the qualitative and quantitative analysis of TCM products. PMID:26579382

VizieR Online Data Catalog: X-ray AGNs with Subaru/FMOS NIR observations (Suh+, 2015)

NASA Astrophysics Data System (ADS)

Suh, H.; Hasinger, G.; Steinhardt, C.; Silverman, J. D.; Schramm, M.

2016-03-01

We performed NIR spectroscopic observations for the AGN sources with the FMOS high-resolution spectrographs on the Subaru telescope; in J-short (0.92-1.12um), J-long (1.11-1.35um), H-short (1.40-1.60um), and H-long (1.60-1.80um) coverage with a spectral resolution of R~2200. The data span the 2012 Mar 25-2013 Oct 24 period. In addition to NIR spectra, we use existing optical spectroscopy (see section 3.2). (2 data files).

2D Vis/NIR correlation spectroscopy of cooked chicken meats

NASA Astrophysics Data System (ADS)

Liu, Yongliang; Chen, Yud-Ren; Ozaki, Yukihiro

2000-03-01

Cooking of chicken meats was investigated by the generalized two-dimensional visible/near-infrared (2D Vis/NIR) correlation spectroscopy. Synchronous and asynchronous spectra in the 400-700 nm visible region suggested that the 445 and 560 nm bands be ascribed to deoxymyoglobin and oxymyoglobin, and at least one of the 475, 520, and 585 nm bands is assignable to the denatured species (metmyoglobin). The asynchronous 2D NIR correlation spectrum showed that CH bands change their spectral intensities before the OH/NH groups during the cooking process, indicating that CH fractions are easily oxidized and degraded. In addition, strong correlation peaks were observed correlating the bands in the visible and NIR spectral regions.

Tracers of Star Formation in the Near Infrared

NASA Astrophysics Data System (ADS)

Martins, L.; Ardila, A.; Gruenwald, R.; de Souza, R.

2010-04-01

Starburst features in the optical are nowadays well known, but the use of this knowledge is not always possible (e.g. objects heavily obscured). In this case the near-IR is of unprecedented value. Recent models show that TP-AGB stars should dominate the NIR spectra of populations 0.3 to 2 Gyr old. While the optical spectra is insensitive to the presence of these stars, the near-IR changes dramatically. Not only does the absolute flux in the near-IR is affected, but also peculiar absorption features appear. These features can be used as indicators of 1 Gyr stellar population. In this work we used the IRTF Spex to create the first empirical database of NIR spectra of carefully selected starbursts, to test for the first time and in a consistent way the new stellar population models that account for the TP-AGB. The methodology used is to do stellar population synthesis in the optical and in the NIR, and compare the predictions of both spectral regions. We also compare the strength of important features of the TP-AGB stars, like the CN (1.1 microns) and CO (2.3 microns) bands with optical diagnostics.

Discrimination Between Maturity and Composition from Integrated Clementine UltraViolet-Visible and Near-Infrared Data

NASA Astrophysics Data System (ADS)

Le Mouelic, S.; Langevin, Y.; Erard, S.; Pinet, P.; Daydou, Y.; Chevrel, S.

1999-01-01

The Clementine UV-VIS dataset has greatly improved our understanding of the Moon. The UV-VIS camera was limited to five spectral channels from 415 to 1000 nm. The Clementine near-infrared (NIR) camera was designed to complement this spectral coverage. The NIR filter at 2000 run allows the discrimination between olivine and pyroxene within identified mare basalts. In addition, we will show that the integration of Clementine UV-VIS and NIR datasets allows a better evaluation of the ferrous 1-micron absorption band depth and gives access to the slope of the continuum. The discrimination between maturity and FeO composition can be achieved by a principal component analysis performed on spectral parameters. We selected 952 Clementine UV-VIS and NIR images to compute a multispectral cube covering the Aristarchus Plateau. Aristarchus Plateau is one of the most heterogeneous areas on the Moon. Highland-type materials, mare basalts, and dark mantle deposits have previously been mentioned. The mosaic represents a set of about 500 x 600 nine-channel spectra. UV-VIS filters at 415, 750, 900, 950, and 1000 run were calibrated using the ISIS software. We applied the reduction method described elsewhere to reduce the NIR filters at 1100, 1250, 1500 and 2000 nm. Absolute gain and offset values were refined for the NIR images by using eight telescopic spectra acquired as references. With this calibration test, we were able to reproduce the eight telescopic spectra with a maximum error of 1.8%. The integration of UV-VIS and NIR spectral channels allows the visualization of complete low-resolution spectra. In order to investigate the spectral effects of the space-weathering processes, we focused our analysis on a small mare crater and its immediate surroundings. According to the small size of the crater (about 2-km) and its location on an homogeneous mare area, we can reasonably assume that the content in FeO is homogeneous. The impact event has induced a variation of the maturity of the soil by excavating fresh material. Graphs displays five absolute reflectance spectra extracted from this area. One graph displays the same spectra divided by a continuum, which is considered to be a right line fitting the spectra at 0.75 and 1.5 micron. Spectrum 1 is extracted from the brightest part of the crater interior, and spectrum 5 is extracted from the surrounding mare material. Spectra 2, 3, and 4 are extracted from intermediate distances between the two areas. The 1-and-2 micron absorption band depths and the overall reflectance increase from spectrum 5 (corresponding to a mature area) to spectrum 1 (the most immature area). Conversely, the continuum slope decreases from spectrum 5 to spectrum 1. These three spectral effects of maturity have also been identified on laboratory spectra of lunar samples. Most of the lunar soils exhibit a signature near 1 micron. This absorption band is due to the presence of Fe2+ in mafic minerals such as orthopyroxene, clinopyroxene, and olivine. In the case of Clementine UV-VIS data alone, the depth of the 1-micron feature is evaluated by the 950/750-nm reflectance ratio. This ratio combined to the reflectance at 750nm has been used to evaluate the global content in FeO of the lunar surface. Near-infrared data makes a more precise evaluation of the 1 micron band depth possible by providing the right side of the band. The continuum in the vicinity of the band can be evaluated by an arithmetic mean or a geometric interpolation of both sides of the band, which are taken at 750 and 1500nm. The geometric interpolation is less sensitive to residual calibration uncertainties. With this method, the 1-micron absorption band depth for the Aristarchus; Plateau can be refined by as much as 10%. The difference is maximum on Fe-poor, highland-type materials. Similarly, the NIR data provide the possibility to investigate the continuum slope of the spectra. The continuum slope is a key parameter in any spectral analysis. The continuum slope variations seem to be mainly dominated by maturity effects, as suggested by the high correlation with the independent evaluation of maturity (OMAT parameter). We have also found a good correlation between the continuum slope and the OMAT parameter on laboratory spectra of lunar samples of the J. B. Adams collection. The discrimination between maturity effects and composition effects can be achieved by using a principal component analysis (PCA) on three spectral parameters, which are the reflectance at 0.75 micron the depth of the 1-micron feature, and the continuum slope. These parameters are mostly affected by maturity and FeO content. The effects of various glass content are assimilated to maturity. The aim of the PCA is to decorrelate the FeO content and maturity effects in the three input parameters. The integration of UV-VIS and NIR datasets allows for a better understanding of the spectral properties of the lunar surface by giving access to key parameters such as the 1 and 2-micron band depths and the continuum slope. The continuum slope can be combined with the depth of the mafic 1-micron absorption feature and the reflectance at 750 nm to discriminate between maturity and composition. NIR images of the sample return stations will be very interesting to refine absolute FeO content and maturity evaluations. Additional information is available in original.

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

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

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2014-02-01

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

MIR and NIR group spectra of n-alkanes and 1-chloroalkanes.

PubMed

Kwaśniewicz, Michał; Czarnecki, Mirosław A

2015-05-15

Numerous attempts were undertaken to resolve the absorption originating from different parts of alkanes. The separation of the contributions from the terminal and midchain methylene units was observed only in the spectra of solid alkanes at low temperatures. On the other hand, for liquid alkanes this effect was not reported as yet. In this study, ATR-IR, Raman and NIR spectra of eight n-alkanes and seven 1-chloroalkanes in the liquid phase were measured from 1000 to 12,000cm(-1). The spectra were analyzed by using two-dimensional (2D) correlation approach and chemometrics methods. It was shown that in 2D asynchronous contour plots, constructed from the spectra of n-alkanes and 1-chloroalkanes, the methylene band was resolved into two components. These two components were assigned to the terminal and midchain methylene groups. For the first time, the contributions from these two molecular fragments were resolved in the spectra of liquid n-alkanes and 1-chloroalkanes. MCR-ALS resolved these spectra into two components that were assigned to the ethyl and midchain methylene groups. These components represent the group spectra that can be used for assignment, spectral analysis and prediction of unknown spectra. The spectral prediction based on the group spectra provides very good results for n-alkanes, especially in the first and second overtone regions. Copyright © 2015 Elsevier B.V. All rights reserved.

Methodology for cork plank characterization (Quercus suber L.) by near-infrared spectroscopy and image analysis

NASA Astrophysics Data System (ADS)

Prades, Cristina; García-Olmo, Juan; Romero-Prieto, Tomás; García de Ceca, José L.; López-Luque, Rafael

2010-06-01

The procedures used today to characterize cork plank for the manufacture of cork bottle stoppers continue to be based on a traditional, manual method that is highly subjective. Furthermore, there is no specific legislation regarding cork classification. The objective of this viability study is to assess the potential of near-infrared spectroscopy (NIRS) technology for characterizing cork plank according to the following variables: aspect or visual quality, porosity, moisture and geographical origin. In order to calculate the porosity coefficient, an image analysis program was specifically developed in Visual Basic language for a desktop scanner. A set comprising 170 samples from two geographical areas of Andalusia (Spain) was classified into eight quality classes by visual inspection. Spectra were obtained in the transverse and tangential sections of the cork planks using an NIRSystems 6500 SY II reflectance spectrophotometer. The quantitative calibrations showed cross-validation coefficients of determination of 0.47 for visual quality, 0.69 for porosity and 0.66 for moisture. The results obtained using NIRS technology are promising considering the heterogeneity and variability of a natural product such as cork in spite of the fact that the standard error of cross validation (SECV) in the quantitative analysis is greater than the standard error of laboratory (SEL) for the three variables. The qualitative analysis regarding geographical origin achieved very satisfactory results. Applying these methods in industry will permit quality control procedures to be automated, as well as establishing correlations between the different classification systems currently used in the sector. These methods can be implemented in the cork chain of custody certification and will also provide a certainly more objective tool for assessing the economic value of the product.

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.

Regional calibration models for predicting loblolly pine tracheid properties using near-infrared spectroscopy

Treesearch

Mohamad Nabavi; Joseph Dahlen; Laurence Schimleck; Thomas L. Eberhardt; Cristian Montes

2018-01-01

This study developed regional calibration models for the prediction of loblolly pine (Pinus taeda) tracheid properties using near-infrared (NIR) spectroscopy. A total of 1842 pith-to-bark radial strips, aged 19–31 years, were acquired from 268 trees from 109 stands across the southeastern USA. Diffuse reflectance NIR spectra were collected at 10-mm...

[Research on NIR equivalent spectral measurement].

PubMed

Wang, Zhi-Hong; Liu, Jie; Sun, Yu-Yang; Teng, Fei; Lin, Jun

2013-04-01

When the spectra of the diffuse reflectance of low reflectivity samples or the transmittance of low transmisivity samples are measured by a portable near infrared (NIR) spectrometer, because there is the noise of the spectrometer, the smaller the reflectance or transmittance of the sample, the lower its SNR. Even if treated by denoise methods, the spectra can not meet the requirement of NIR analysis. So the equivalent spectrum measure method was researched. Based on the intensity of the reflected or transmitted signal by the sample under the traditional measure conditions, the light current of the spectrometer was enlarged, and then the signal of the measured sample increased; the reflected or transmitted light of the measure reference was reduced to avoid the signal of the measure reference over range. Moreover the equivalent spectrum of the sample was calculated in order to make it identical with the spectrum measured by traditional method. Thus the NIR spectral SNR was improved. The results of theory analysis and experiments show that if the light signal of the spectrometer was properly increased according to the reflected or transmitted signal of the low reflectivity or transmisivity sample, the equivalent spectrum was the same as the spectrum measured by traditional method and its SNR was improved.

Determination of chemical properties in 'calçot' (Allium cepa L.) by near infrared spectroscopy and multivariate calibration.

PubMed

Sans, Silvia; Ferré, Joan; Boqué, Ricard; Sabaté, José; Casals, Joan; Simó, Joan

2018-10-01

'Calçots', the immature floral stems of second-year onion resprouts, are an economically important traditional crop in Catalonia (Spain). Classical approaches to evaluating the chemical properties of 'calçots' are time consuming and expensive; near-infrared spectroscopy (NIRS) may be faster and cheaper. We used NIRS to develop partial least square (PLS) models to predict dry matter, soluble solid content, titratable acidity, and ash content in cooked 'calçots'. To guarantee the robustness of the models, calibration samples were grown and analyzed in a first season (2014-15) and validation samples in a second season (2015-16). NIRS on puree spectra estimated dry matter and soluble solid content with excellent accuracy (R 2 pred  = 0.953, 0.985 and RPD = 4.571, 8.068, respectively). However, good estimation of titratable acidity and ash content required using ground dried puree spectra (R 2 pred  = 0.852, 0.820 and RPD = 2.590, 1.987, respectively). NIRS can be a helpful tool for 'calçots' breeding and quality control. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantitative real-time monitoring of dryer effluent using fiber optic near-infrared spectroscopy.

PubMed

Harris, S C; Walker, D S

2000-09-01

This paper describes a method for real-time quantitation of the solvents evaporating from a dryer. The vapor stream in the vacuum line of a dryer was monitored in real time using a fiber optic-coupled acousto-optic tunable filter near-infrared (AOTF-NIR) spectrometer. A balance was placed in the dryer, and mass readings were recorded for every scan of the AOTF-NIR. A partial least-squares (PLS) calibration was subsequently built based on change in mass over change in time for solvents typically used in a chemical manufacturing plant. Controlling software for the AOTF-NIR was developed. The software collects spectra, builds the PLS calibration model, and continuously fits subsequently collected spectra to the calibration, allowing the operator to follow the mass loss of solvent from the dryer. The results indicate that solvent loss can be monitored and quantitated in real time using NIR for the optimization of drying times. These time-based mass loss values have also been used to calculate "dynamic" vapor density values for the solvents. The values calculated are in agreement with values determined from the ideal gas law and could prove valuable as tools to measure temperature or pressure indirectly.

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.

Application of near infrared spectroscopy (NIRS) to non-destructive internal quality inspection of tomatoes

NASA Astrophysics Data System (ADS)

Tao, Xuemei; He, Yong

2006-09-01

The internal quality of tomato such as acidity and sugar content is important to its taste thus influences the market. The objective of this paper was to demonstrate the feasibility of using a near-infrared spectroscopy (NIRS) to investigate the relationship between sugar content and acidity of tomato and absorption spectra. The N1RS reflectance of nondestructive tomatoes was measured with a Visible/NJR spectrophotometer in 325-1075 nm range. The sugar content and acidity of tomato were obtained with a handhold sugar content meter and a PH meter. The reflectance data set was recorded and analyzed with some mathematic methods. The PLS (Partial least squares) calibration method was developed for converting the NIRS reflectance of tomato into the data which determined the acidity value. BP (Back propagation) neural network was used to set up the relationship between the NIRS reflectance of tomato and sugar content. The acidity values were detected with an accuracy of 9O% and the sugar contents determined by the BP network were also very close to the measurements (coefficient of correlation r2=0.8764). NW spectra analysis would be very useful in the nondestructive internal quality inspecting of tomato.

Cloudless Atmospheres for Young Low-Gravity Substellar Objects

NASA Technical Reports Server (NTRS)

Tremblin, P.; Chabrier, G.; Baraffe, I.; Liu, Michael C.; Magnier, E. A.; Lagage, P.-O.; De Oliveira, C. Alves; Burgasser, A. J.; Amundsen, D. S.; Drummond, B.

2017-01-01

Atmospheric modeling of low-gravity (VL-G) young brown dwarfs remains challenging. The presence of very thick clouds is a possible source of this challenge, because of their extremely red near-infrared (NIR) spectra, but no cloud models provide a good fit to the data with a radius compatible with the evolutionary models for these objects. We show that cloudless atmospheres assuming a temperature gradient reduction caused by fingering convection provide a very good model to match the observed VL-G NIR spectra. The sequence of extremely red colors in the NIR for atmospheres with effective temperatures from approx. 2000 K down to approx. 1200 K is very well reproduced with predicted radii typical of young low-gravity objects. Future observations with NIRSPEC and MIRI on the James Webb Space Telescope (JWST) will provide more constraints in the mid-infrared, helping to confirm or refute whether or not the NIR reddening is caused by fingering convection. We suggest that the presence or absence of clouds will be directly determined by the silicate absorption features that can be observed with MIRI. JWST will therefore be able to better characterize the atmosphere of these hot young brown dwarfs and their low-gravity exoplanet analogs.

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.

In-vitro micro-Raman study of tissue samples for detecting cervical and ovarian cancer with 785-nm laser excitation

NASA Astrophysics Data System (ADS)

Sharma, S. K.; Kamemoto, L. E.; Misra, A. K.; Goodman, M. T.; Luk, H. W.; Killeen, J. L.

2010-04-01

We present results of in vitro micro-Raman spectroscopy of normal and cancerous cervical and ovarian tissues excited with 785 nm near-infrared (NIR) laser. Micro- Raman spectra of squamous cervical cells of both cervix and ovarian tissues show significant differences in the spectra of normal and cancerous cells. In particular, several well-defined Raman peaks in the 775-975 cm-1 region are observed in the spectra of normal cervix squamous cells but are completely missing in the spectra of invasive cervical cancer cells. In the high-frequency 2800-3100 cm-1 region it is shown that the peak area under CH stretching band is much lower than the corresponding area in the spectra of normal cells. In the case of ovarian tissues, the micro-Raman spectra show noticeable spectral differences between normal cells and ovarian serous cancer cells. In particular, we observed the accumulation of β-carotene in ovarian serous cancer cells compared to normal ovarian cells from women with no ovarian cancer. The NIR micro-Raman spectroscopy offers a potential molecular technique for detecting cervical and ovarian cancer from the respective tissues.

Best Practices for the Application of Functional Near Infrared Spectroscopy to Operator State Sensing

NASA Technical Reports Server (NTRS)

Harrivel, Angela R.; Hylton, Alan G.; Hearn, Tristan A.

2012-01-01

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuronal measurement technique with many advantages for application in operational and training contexts. Instrumentation and protocol improvements, however, are required to obtain useful signals and produce expeditiously self-applicable, comfortable and unobtrusive headgear. Approaches for improving the validity and reliability of fNIRS data for the purpose of sensing the mental state of commercial aircraft operators are identified, and an exemplary system design for attentional state monitoring is outlined. Intelligent flight decks of the future can be responsive to state changes to optimally support human performance. Thus, the identification of cognitive performance decrement, such as lapses in operator attention, may be used to predict and avoid error-prone states. We propose that attentional performance may be monitored with fNIRS through the quantification of hemodynamic activations in cortical regions which are part of functionally-connected attention and resting state networks. Activations in these regions have been shown to correlate with behavioral performance and task engagement. These regions lie beneath superficial tissue in head regions beyond the forehead. Headgear development is key to reliably and robustly accessing locations beyond the hair line to measure functionally-connected networks across the whole head. Human subject trials using both fNIRS and functional Magnetic Resonance Imaging (fMRI) will be used to test this system. Data processing employs Support Vector Machines for state classification based on the fNIRS signals. If accurate state classification is achieved based on sensed activation patterns, fNIRS will be shown to be useful for monitoring attentional performance.

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.

Reflectance calibration and shadow effect of VNIS spectra acquired by the Yutu rover

NASA Astrophysics Data System (ADS)

Hu, Sen; Lin, Yang-Ting; Liu, Bin; Yang, Wei; He, Zhi-Ping; Xing, Wei-Fan

2015-09-01

Yutu is the first lunar rover after the Apollo program and Luna missions. One of the payloads on the Yutu rover, the Visible and Near-infrared Imaging Spectrometer (VNIS), has acquired four VIS/NIR images and SWIR spectra near its landing site in Mare Imbrium. The radiance images were reduced through repairing bad lines and bad points, and applying flat field correction, and then were converted into reflectance values based on the solar irradiance and angles of incidence. A significant shadow effect was observed in the VIS/NIR image. The shadowed regions show lower reflectance with a darkening trend compared with illuminated regions. The reflectance increased by up to 24% for entire images and 17% for the VIS/NIR-SWIR overlapping regions after shadow correction. The correction for the shadow effect will remarkably decrease the estimate of FeO content, by up to 4.9 wt.% in this study. The derived FeO contents of CD-005∼008 after shadow correction are around 18.0 wt.%.

Vacancy-Rich Monolayer BiO 2-x as a Highly Efficient UV, Visible, and Near-Infrared Responsive Photocatalyst

DOE PAGES

Li, Jun; Wu, Xiaoyong; Pan, Wenfeng; ...

2017-09-08

Here in this paper, a full-spectrum responsive vacancy-rich monolayer BiO 2-x has been synthesized. The increased density of states at the conduction band (CB) minimum in the monolayer BiO 2-x is responsible for the enhanced photon response and photo-absorption, which were confirmed by UV/Vis-NIR diffuse reflectance spectra (DRS) and photocurrent measurements. Compared to bulk BiO 2-x, monolayer BiO 2-x has exhibited enhanced photocatalytic performance for rhodamine B and phenol removal under UV, visible, and near-infrared light (NIR) irradiation, which can be attributed to the vacancy VBi-O"' as confirmed by the positron annihilation spectra. The presence of V Bi-O"' defects inmore » monolayer BiO 2-x promoted the separation of electrons and holes. This finding provides an atomic level understanding for developing highly efficient UV, visible, and NIR light responsive photocatalysts.« less

An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize.

PubMed

Lee, Kyung-Min; Davis, Jessica; Herrman, Timothy J; Murray, Seth C; Deng, Youjun

2015-04-15

Three commercially available vibrational spectroscopic techniques, including Raman, Fourier transform near infrared reflectance (FT-NIR), and Fourier transform infrared (FTIR) were evaluated to help users determine the spectroscopic method best suitable for aflatoxin analysis in maize (Zea mays L.) grain based on their relative efficiency and predictive ability. Spectral differences of Raman and FTIR spectra were more marked and pronounced among aflatoxin contamination groups than those of FT-NIR spectra. From the observations and findings in our current and previous studies, Raman and FTIR spectroscopic methods are superior to FT-NIR method in terms of predictive power and model performance for aflatoxin analysis and they are equally effective and accurate in predicting aflatoxin concentration in maize. The present study is considered as the first attempt to assess how spectroscopic techniques with different physical processes can influence and improve accuracy and reliability for rapid screening of aflatoxin contaminated maize samples. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Vacancy-Rich Monolayer BiO 2-x as a Highly Efficient UV, Visible, and Near-Infrared Responsive Photocatalyst

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

Li, Jun; Wu, Xiaoyong; Pan, Wenfeng

Here in this paper, a full-spectrum responsive vacancy-rich monolayer BiO 2-x has been synthesized. The increased density of states at the conduction band (CB) minimum in the monolayer BiO 2-x is responsible for the enhanced photon response and photo-absorption, which were confirmed by UV/Vis-NIR diffuse reflectance spectra (DRS) and photocurrent measurements. Compared to bulk BiO 2-x, monolayer BiO 2-x has exhibited enhanced photocatalytic performance for rhodamine B and phenol removal under UV, visible, and near-infrared light (NIR) irradiation, which can be attributed to the vacancy VBi-O"' as confirmed by the positron annihilation spectra. The presence of V Bi-O"' defects inmore » monolayer BiO 2-x promoted the separation of electrons and holes. This finding provides an atomic level understanding for developing highly efficient UV, visible, and NIR light responsive photocatalysts.« less

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.

Nondestructive estimation of tracheid length from sections of radial wood strips by near infrared spectroscopy

Treesearch

Laurence R. Schimleck; P. David Jones; Gary F. Peter; F. Daniels; Alexander Clarklll

2004-01-01

The use of calibrated near infrared (NIR) spectroscopy for predicting tracheid length of Pinus taeda L. (loblolly pine) wood samples is described. Ten-mm sections of 14 P. taeda radial strips were selected and NIR spectra obtained from the radial longitudinal face of each section. The fibers in these sections were characterized in terms of arithmetic and length-...

Near infrared spectroscopy for the nondestructive estimation of clear wood properties of Pinus taeda L. from the southern United States

Treesearch

Laurence R. Schimleck; P. David Jones; Alexander Clark; Richard F. Daniels; Gary F. Peter

2005-01-01

The estimation of specific gravity (SG), modulus of elasticity (MOE), and modulus of rupture (MOR) of loblolly pine (Pinus taeda L.) clear wood samples from a diverse range of sites across the southern United States was investigated using near infrared (NIR) spectroscopy. NIR spectra were obtained from the radial and cross sectional (original, rough...

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 ...

Metrology of the Solar Spectral Irradiance at the Top Of Atmosphere in the Near Infrared using Ground Based Instruments. Final results of the PYR-ILIOS campaign (Mauna Loa Observatory, June-July 2016).

NASA Astrophysics Data System (ADS)

Cessateur, G.; Bolsée, D.; Pereira, N.; Sperfeld, P.; Pape, S.

2017-12-01

The availability of reference spectra for the Solar Spectral Irradiance (SSI) is important for the solar physics, the studies of planetary atmospheres and climatology. The near infrared (NIR) part of these spectra is of great interest for its main role for example, in the Earth's radiative budget. Until recently, some large and unsolved discrepancies (up to 10 %) were observed in the 1.6 μm region between space instruments, models and ground-based measurements. We designed a ground-based instrumentation for SSI measurements at the Top Of Atmosphere (TOA) through atmospheric NIR windows using the Bouguer-Langley technique. The main instrument is a double NIR spectroradiometer designed by Bentham (UK), radiometrically characterized at the Royal Belgian Institute for Space Aeronomy. It was absolute calibrated against a high-temperature blackbody as primary standard for spectral irradiance at the Physikalisch-Technische Bundesanstalt (Germany). The PYR-ILIOS campaign was carried out in June to July 2016 at the Mauna Loa Observatory (Hawaii, USA, 3396 m a.s.l.) follows the four-month IRESPERAD campaign which was carried out in the summer 2011 at the Izaña Atmospheric Observatory (Canary Islands, 2367 m a.s.l.). We present here the results of the 3'week PYR-ILIOS campaign and compare them with the ATLAS 3 spectrum as well as from recently reprocessed NIR solar spectra obtained with SOLAR/SOLSPEC on ISS and SCIAMACHY on ENVISAT. The uncertainty budget of the PYR-ILIOS results will be discussed.

[Application of near infrared spectroscopy technology (NIRS) in forage field].

PubMed

Yan, Xu; Bai, Shi-Qie; Yan, Jia-Jun; Gan, You-Min; Dao, Zhi-Xue

2012-07-01

The majority of nutrients in ruminants and other herbivores come from forages. Forage quality not only affects the growth and production efficiency of livestock, but also determines the final output and quality of livestock products. Forage quality mainly depends on nutrient concentrations and their digestibility, palatability and the level of presence of antiquality factors and mycotoxins in forage. Near infrared reflectance spectroscopy (NIRS) has been widely used in many research areas because it is a inexpensive, rapid, simple and nondestructive technique offering the potential for qualitative and quantitative analysis. The present paper briefly introduces the principle and characteristics of NIRS, detailedly expounds the application of NIRS in forage quality. In addition, other applications of near infrared spectroscopy technique in forage are also discussed, including forage breeding, identification of variety and classification by kind. This paper comprehensively reviews the status quo of application of NIRS in forage filed, in order to contribute to promoting development of NIRS in this field in China.

Analysis of protein structures and interactions in complex food by near-infrared spectroscopy. 2. Hydrated gluten.

PubMed

Bruun, Susanne Wrang; Søndergaard, Ib; Jacobsen, Susanne

2007-09-05

Hydrated gluten, treated with various salts, was analyzed by near-infrared (NIR) spectroscopy to assess the ability of this method to reveal protein structure and interaction changes in perturbed food systems. The spectra were pretreated with second-derivative transformation and extended multiplicative signal correction for improving the band resolution and removing physical and quantitative spectral variations. Principal component analysis of the preprocessed spectra showed spectral effects that depended on salt type and concentration. Although both gluten texture and the NIR spectra were little influenced by treatment with salt solutions of low concentrations (0.1-0.2 M), they were significantly and diversely affected by treatment with 1.0 M salt solutions. Compared to hydration in water, hydration in 1.0 M sulfate salts caused spectral effects similar to a drying-out effect, which could be explained by salting-out.

Application of the near-infrared spectroscopy in the pharmaceutical technology.

PubMed

Jamrógiewicz, Marzena

2012-07-01

Near-infrared (NIR) spectroscopy is currently the fastest-growing and the most versatile analytical method not only in the pharmaceutical sciences but also in the industry. This review focuses on recent NIR applications in the pharmaceutical technology. This article covers monitoring, by NIR, of many manufacturing processes, such as granulation, mixing or drying, in order to determine the end-point of these processes. In this paper, apart from basic theoretical information concerning the NIR spectra, there are included determinations of the quality and quantity of pharmaceutical compounds. Some examples of measurements and control of physicochemical parameters of the final medicinal products, such as hardness, porosity, thickness size, compression strength, disintegration time and potential counterfeit are included. Biotechnology and plant drug analysis using NIR is also described. Moreover, some disadvantages of this method are stressed and future perspectives are anticipated. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

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.

At-line monitoring of key parameters of nisin fermentation by near infrared spectroscopy, chemometric modeling and model improvement.

PubMed

Guo, Wei-Liang; Du, Yi-Ping; Zhou, Yong-Can; Yang, Shuang; Lu, Jia-Hui; Zhao, Hong-Yu; Wang, Yao; Teng, Li-Rong

2012-03-01

An analytical procedure has been developed for at-line (fast off-line) monitoring of 4 key parameters including nisin titer (NT), the concentration of reducing sugars, cell concentration and pH during a nisin fermentation process. This procedure is based on near infrared (NIR) spectroscopy and Partial Least Squares (PLS). Samples without any preprocessing were collected at intervals of 1 h during fifteen batch of fermentations. These fermentation processes were implemented in 3 different 5 l fermentors at various conditions. NIR spectra of the samples were collected in 10 min. And then, PLS was used for modeling the relationship between NIR spectra and the key parameters which were determined by reference methods. Monte Carlo Partial Least Squares (MCPLS) was applied to identify the outliers and select the most efficacious methods for preprocessing spectra, wavelengths and the suitable number of latent variables (n (LV)). Then, the optimum models for determining NT, concentration of reducing sugars, cell concentration and pH were established. The correlation coefficients of calibration set (R (c)) were 0.8255, 0.9000, 0.9883 and 0.9581, respectively. These results demonstrated that this method can be successfully applied to at-line monitor of NT, concentration of reducing sugars, cell concentration and pH during nisin fermentation processes.

A simple method to fabricate an NIR detector by PbTe nanowires in a large scale

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

Baghchesara, Mohammad Amin; Yousefi, Ramin, E-mail: Yousefi.ramin@gmail.com; Cheraghizade, Mohsen

2016-05-15

Highlights: • PbTe nanowires were grown by tellurization of the Pb sheets for the first time. • It was observed a band gap value for the PbTe nanostructures in the NIR region. • NIR detector was fabricated in a large scale using a simple method. • Effect of Te concentration on morphology of PbTe nanostructures was investigated. - Abstract: A simple method was used to fabricate a near-infrared (NIR) detector using PbTe nanostructures. Samples were synthesized by tellurization of lead sheets in a tube furnace. PbTe nanostructures with wires and flakes shapes were grown on the lead sheets that weremore » placed at 300 and 330 °C, respectively, while, PbTe nanoporous were grown at 360 and 390 °C. X-ray diffraction patterns and X-ray photoelectron spectra results indicated that, the PbTe phase was formed in all samples. UV–vis diffuse reflectance spectra measurements showed a band gap for the PbTe nanostructures in the near-infrared region of the electromagnetic spectrum. Actually, the results indicated that, the band gap values of the PbTe nanowires and nanoporous were 1.54 eV and 1.61 eV, respectively. Finally, the PbTe nanostructures were used as a simple photoresponse device under a red light source. The photoresponse results revealed, PbTe nanowires are promising for photoelectrical applications in the NIR region.« less

NIR analysis of cellulose and lactose--application to ecstasy tablet analysis.

PubMed

Baer, Ines; Gurny, Robert; Margot, Pierre

2007-04-11

Cellulose and lactose are the most frequently used excipients in illicit ecstasy production. The aim of this project was to use near infrared reflectance spectroscopy (NIRS) for the determination of the different chemical forms of these two substances, as well as for the differentiation of their origin (producer). It was possible to distinguish between the different chemical forms of both compounds, as well as between their origins (producers), although within limits. Furthermore, the possibilities to apply NIR for the analysis of substances such as found in illicit tablets were studied. First, a few cellulose and lactose samples were chosen to make mixtures with amphetamine at three degrees of purity (5, 10 and 15%), in order to study the resulting changes in the spectra as well as to simultaneously quantify amphetamine and identify the excipient. A PLS2 model could be build to predict concentrations and excipient. Secondarily, the technique was to be applied to real ecstasy tablets. About 40 ecstasy seizures were analysed with the aim to determine the excipient and to check them against each other. Identification of the excipients was not always obvious, especially when more than one excipient were present. However, a comparison between tablets appeared to give groups of similar samples. NIR analysis results in spectra representing the tablet blend as a whole taking into account all absorbing compounds. Although NIRS seems to be an appropriate method for ecstasy profiling, little is known about intra- and intervariability of compression batches.

Phase-dependent Photometric and Spectroscopic Characterization of the MASTER-Net Optical Transient J212444.87+321738.3: An Oxygen-rich Mira

NASA Astrophysics Data System (ADS)

Ghosh, Supriyo; Mondal, Soumen; Das, Ramkrishna; Banerjee, D. P. K.; Ashok, N. M.; Hambsch, Franz-Josef; Dutta, Somnath

2018-05-01

We describe the time-dependent properties of a new spectroscopically confirmed Mira variable, which was discovered in 2013 as MASTER-Net Optical Transient J212444.87+321738.3 toward the Cygnus constellation. We have performed long-term optical/near-infrared (NIR) photometric and spectroscopic observations to characterize the object. From the optical/NIR light curves, we estimate a variability period of 465 ± 30 days. The wavelength-dependent amplitudes of the observed light curves range from ΔI ∼ 4 mag to ΔK ∼ 1.5 mag. The (J ‑ K) color index varies from 1.78 to 2.62 mag over phases. Interestingly, a phase lag of ∼60 days between optical and NIR light curves is also seen, as in other Miras. Our optical/NIR spectra show molecular features of TiO, VO, CO, and strong water bands that are a typical signature of oxygen-rich Mira. We rule out S- or C-type as ZrO bands at 1.03 and 1.06 μm and C2 band at 1.77 μm are absent. We estimate the effective temperature of the object from the Spectral Energy Distribution, and distance and luminosity from standard Period–Luminosity relations. The optical/NIR spectra display time-dependent atomic and molecular features (e.g., TiO, Na I, Ca I, H2O, CO), as commonly observed in Miras. Such spectroscopic observations are useful for studying pulsation variability in Miras.

Near-infrared microscopic methods for the detection and quantification of processed by-products of animal origin

NASA Astrophysics Data System (ADS)

Abbas, O.; Fernández Pierna, J. A.; Dardenne, P.; Baeten, V.

2010-04-01

Since the BSE crisis, researches concern mainly the detection, identification, and quantification of meat and bone meal with an important focus on the development of new analytical methods. Microscopic based spectroscopy methods (NIR microscopy - NIRM or/and NIR hyperspectral imaging) have been proposed as complementary methods to the official method; the optical microscopy. NIR spectroscopy offers the advantage of being rapid, accurate and independent of human analyst skills. The combination of an NIR detector and a microscope or a camera allows the collection of high quality spectra for small feed particles having a size larger than 50 μm. Several studies undertaken have demonstrated the clear potential of NIR microscopic methods for the detection of animal particles in both raw and sediment fractions. Samples are sieved and only the gross fraction (superior than 250 μm) is investigated. Proposed methodologies have been developed to assure, with an acceptable level of confidence (95%), the detection of at least one animal particle when a feed sample is adulterated at a level of 0.1%. NIRM and NIR hyperspectral imaging are running under accreditation ISO 17025 since 2005 at CRA-W. A quantitative NIRM approach has been developed in order to fulfill the new requirements of the European commission policies. The capacities of NIRM method have been improved; only the raw fraction is analyzed, both the gross and the fine fractions of the samples are considered, and the acquisition parameters are optimized (the aperture, the gap, and the composition of the animal feed). A mapping method for a faster collection of spectra is also developed. The aim of this work is to show the new advances in the analytical methods developed in the frame of the feed ban applied in Europe.

Noninvasive NIR measurement of tissue pH to assess hemorrhagic shock in swine

NASA Astrophysics Data System (ADS)

Soller, Babs R.; Zhang, Songbiao; Micheels, Ronald H.; Puyana, Juan C.

1999-07-01

Body-worn noninvasive physilogical sensors are needed to continuously monitor soldiers for hemorrhage and to provide real-time information for minimally skilled medics to treat the injured. In the hospital intramucosal pHi of the gut is used to monitor shock and its treatment. We hypothesize that abdominal wall muscle (AWM) pH can be measured noninvasively using near infrared (NIR) spectroscopy and partial least squares analysis (PLS) and will correlate with pHi. METHODS: AWM pH was measured with microelectrodes and gastric pHi was measured with a tonometric catheter simultaneously while NIR spectra were collected using prototype LED spectrometers placed on the pig's flanks. Animals were subject to hemorrhagic shock at 45 mm Hg for 45 minutes, then resuscitated with blood and lactated ringers. Relationships between electrode pH, pHi and NIR spectra were developed using PLS with cross validation. RESULTS: NIR spectral changes noninvasively acquired through the skin were shown to be from the muscle, not from changes in skin blood flow. Trending ability (R2) model accuracy (RMSD), and relative error were calculated for individual pigs. Using electrode pH as the reference, average R2 was 0.88 with a predicted accuracy of 0.17 pH units, a 9.3% relative error. Slightly degraded results were observed when pHi was used as a reference. CONCLUSIONS: NIR measurement of tissue pH can be used to noninvasively monitor for shock and guide its treatment in a swine model. These measurements correlate with gastric pHi, a clinically accepted measure of shock, providing an approach to develop similar methodology for humans.

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.

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.

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

PubMed

Chen, Ru-huang; Jin, Gang

2015-08-01

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

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

PubMed

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

2007-04-01

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

On the Chemical Abundances of Miras in Clusters: V1 in the Metal-rich Globular NGC 5927

NASA Astrophysics Data System (ADS)

D’Orazi, V.; Magurno, D.; Bono, G.; Matsunaga, N.; Braga, V. F.; Elgueta, S. S.; Fukue, K.; Hamano, S.; Inno, L.; Kobayashi, N.; Kondo, S.; Monelli, M.; Nonino, M.; Przybilla, N.; Sameshima, H.; Saviane, I.; Taniguchi, D.; Thevenin, F.; Urbaneja-Perez, M.; Watase, A.; Arai, A.; Bergemann, M.; Buonanno, R.; Dall’Ora, M.; Da Silva, R.; Fabrizio, M.; Ferraro, I.; Fiorentino, G.; Francois, P.; Gilmozzi, R.; Iannicola, G.; Ikeda, Y.; Jian, M.; Kawakita, H.; Kudritzki, R. P.; Lemasle, B.; Marengo, M.; Marinoni, S.; Martínez-Vázquez, C. E.; Minniti, D.; Neeley, J.; Otsubo, S.; Prieto, J. L.; Proxauf, B.; Romaniello, M.; Sanna, N.; Sneden, C.; Takenaka, K.; Tsujimoto, T.; Valenti, E.; Yasui, C.; Yoshikawa, T.; Zoccali, M.

2018-03-01

We present the first spectroscopic abundance determination of iron, α-elements (Si, Ca, and Ti), and sodium for the Mira variable V1 in the metal-rich globular cluster NGC 5927. We use high-resolution (R ∼ 28,000), high signal-to-noise ratio (∼200) spectra collected with WINERED, a near-infrared (NIR) spectrograph covering simultaneously the wavelength range 0.91–1.35 μm. The effective temperature and the surface gravity at the pulsation phase of the spectroscopic observation were estimated using both optical (V) and NIR time-series photometric data. We found that the Mira is metal-rich ([Fe/H] = ‑0.55 ± 0.15) and moderately α-enhanced ([α/Fe] = 0.15 ± 0.01, σ = 0.2). These values agree quite well with the mean cluster abundances based on high-resolution optical spectra of several cluster red giants available in the literature ([Fe/H] = ‑ 0.47 ± 0.06, [α/Fe] = + 0.24 ± 0.05). We also found a Na abundance of +0.35 ± 0.20 that is higher than the mean cluster abundance based on optical spectra (+0.18 ± 0.13). However, the lack of similar spectra for cluster red giants and that of corrections for departures from local thermodynamical equilibrium prevents us from establishing whether the difference is intrinsic or connected with multiple populations. These findings indicate a strong similarity between optical and NIR metallicity scales in spite of the difference in the experimental equipment, data analysis, and in the adopted spectroscopic diagnostics. Based on spectra collected with the WINERED spectrograph available as a visitor instrument at the ESO New Technology Telescope (NTT), La Silla, Chile (ESO Proposal: 098.D-0878(A), PI: G. Bono).

UV-Vis-NIR luminescence properties and energy transfer mechanism of LiSrPO4:Eu2+, Pr3+ suitable for solar spectral convertor.

PubMed

Chen, Yan; Wang, Jing; Liu, Chunmeng; Tang, Jinke; Kuang, Xiaojun; Wu, Mingmei; Su, Qiang

2013-02-11

An efficient near-infrared (NIR) phosphor LiSrPO(4):Eu(2+), Pr(3+) is synthesized by solid-state reaction and systematically investigated using x-ray diffraction, diffuse reflection spectrum, photoluminescence spectra at room temperature and 3 K, and the decay curves. The UV-Vis-NIR energy transfer mechanism is proposed based on these results. The results demonstrate Eu(2+) can be an efficient sensitizer for harvesting UV photon and greatly enhancing the NIR emission of Pr(3+) between 960 and 1060 nm through efficient energy feeding by allowed 4f-5d absorption of Eu(2+) with high oscillator strength. Eu(2+)/Pr(3+) may be an efficient donor-acceptor pair as solar spectral converter for Si solar cells.

Novel near-infrared sampling apparatus for single kernel analysis of oil content in maize.

PubMed

Janni, James; Weinstock, B André; Hagen, Lisa; Wright, Steve

2008-04-01

A method of rapid, nondestructive chemical and physical analysis of individual maize (Zea mays L.) kernels is needed for the development of high value food, feed, and fuel traits. Near-infrared (NIR) spectroscopy offers a robust nondestructive method of trait determination. However, traditional NIR bulk sampling techniques cannot be applied successfully to individual kernels. Obtaining optimized single kernel NIR spectra for applied chemometric predictive analysis requires a novel sampling technique that can account for the heterogeneous forms, morphologies, and opacities exhibited in individual maize kernels. In this study such a novel technique is described and compared to less effective means of single kernel NIR analysis. Results of the application of a partial least squares (PLS) derived model for predictive determination of percent oil content per individual kernel are shown.

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.

Impact of concentration of ellagitannins in oak wood on their levels and organoleptic influence in red wine.

PubMed

Michel, Julien; Jourdes, Michael; Silva, Maria A; Giordanengo, Thomas; Mourey, Nicolas; Teissedre, Pierre-Louis

2011-05-25

Some wood substances such as ellagitannins can be extracted during wine aging in oak barrels. The level of these hydrolyzable tannins in wine depends of some parameters of oak wood. Their impact on the organoleptic perception of red wine is poorly known. In our research, oak staves were classified in three different groups according to their level of ellagitannins estimated by NIRS (near infrared spectroscopy) online procedure (Oakscan). First, the ellagitannin level and composition were determine for each classified stave and an excellent correlation between the NIRS classification (low, medium and high potential level of ellagitannin) and the ellagitannin content estimated by HPLC-UV was found. Each different group of NIRS classified staves was then added to red wine during its aging in a stainless tank, and the extraction and evolution of the ellagitannins were monitored. A good correlation between the NIRS classification and the concentration of ellagitannins in red wine aging in contact with the classified staves was observed. The influence of levels of ellagitannins on the resulting wine perception was estimated by a trained judge's panel, and it reveals that the level of ellagitannins in wine has an impact on the roundness and amplitude of the red wine.

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.

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.

VizieR Online Data Catalog: M17 massive pms stars X-shooter spectra (Ramirez-Tannus+, 2017)

NASA Astrophysics Data System (ADS)

Ramirez-Tannus, M. C.; Kaper, L.; de Koter, A.; Tramper, F.; Bik, A.; Ellerbroek, L. E.; Ochsendorf, B. B.; Ramirez-Agudelo, O. H.; Sana, H.

2017-05-01

Normalized X-shooter spectra of the PMS and OB stars in M17 studied. The X-shooter spectra were obtained under good weather conditions with seeing ranging from 0.5" and 1" and clear sky. With the exception of the 2012 B289 spectrum and the 2009 B275 science verification spectrum, the spectrograph slit widths used were 1" (UVB, 300-590nm), 0.9" (VIS, 550-1020nm), and 0.4" (NIR, 1000-2480nm), resulting in a spectral resolving power of 5100, 8800, and 11300, respectively. The slit widths for the 2010 B275 observations were 1.6", 0.9", and 0.9" resulting in a resolving power of 3300, 8800, and 5600, respectively. For the 2012 B289 observations we used the 0.8", 0.7", and 0.4" slits corresponding to a resolving power of 6200, 11000, and 11300 for the UVB, VIS, and NIR arms, respectively. The spectra were taken in nodding mode and reduced using the X-shooter pipeline version 2.7.1 running under the ESO Reflex environment version 2.8.4. (2 data files).

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.

Interpreting the near infrared region of explosives.

PubMed

Zapata, Félix; Ferreiro-González, Marta; García-Ruiz, Carmen

2018-06-07

The NIR spectra from 1000 to 2500 nm of 18 different explosives, propellant powders and energetic salts were collected and interpreted. NIR spectroscopy is known to provide information about the combination bands and overtones of highly anharmonic vibrations as those occurring in XH bonds (CH, NH and OH). Particularly intense and complex were the bands corresponding to the first combination region (2500-1900 nm) and first overtone stretching mode (2ν) of CH and NH bonds (1750-1450 nm). Inorganic oxidizing salts including sodium/potassium nitrate, sodium/potassium chlorate, and sodium/potassium perchlorate displayed low intense or no NIR bands. Copyright © 2018 Elsevier B.V. All rights reserved.

Spectral analysis of lunar analogue samples

NASA Astrophysics Data System (ADS)

Offringa, Marloes; Foing, Bernard

2016-04-01

Analyses of samples derived from terrestrial analogue sites are used to study lunar processes in their geological context (Foing, Stoker, Ehrenfreund, 2011). For this study samples from the volcanic region of the Eifel, Germany collected during field campaigns (Foing et al., 2010), are analyzed with a variety of spectrometers. The aim is to obtain a database of analyzed samples that could be used as a reference for future in situ measurements. Equipment used in the laboratory consists of a Fourier Transform Infrared (FTIR) spectrometer, an X-Ray Fluorescence (XRF) spectrometer, a Raman laser spectrometer, as well as UV-VIS and NIR reflectance spectrometers. The Raman, UV-VIS and NIR are also used in combination with the EXoGeoLab mock-up lander during field campaigns (Foing, Stoker, Ehrenfreund, 2011). Calibration of the UV-VIS and NIR reflectance spectrometers is the main focus of this research in order to obtain the clearest spectra. The calibration of the UV-VIS and NIR reflectance spectrometers requires the use of a good light source as well as suitable optical fibers to create a signal that covers the widest range in wavelengths available. To eliminate noise towards the edges of this range, multiple measurements are averaged and data is processed by dividing the signal by reference spectra. Calibration of the devices by creating a new dark and reference spectra has to take place after every sample measurement. In this way we take into account changes that occur in the signal due to the eating of the devices during the measurements. Moreover, the integration time is adjusted to obtain a clear signal without leading to oversaturation in the reflectance spectrum. The typical integration times for the UV-VIS reflectance spectrometer vary between 1 - 18 s, depending on the amount of daylight during experiments. For the NIR reflectance spectrometer the integration time resulting in the best signals is approximately 150 ms in combination with a broad spectrum light source. Together with taking an average over ±600 measurements per sample this leads to the best spectral signals that can be acquired with this set-up. Obtained spectra can be tested for accuracy by comparing them with stationary laboratory spectrometers such as the FTIR spectrometer. Future campaigns involving the employment of the spectrometers on the ExoGeoLab lander would prove the applicability of the equipment in the field.

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®.

Signal processing for a single detector MOEMS based NIR micro spectrometer

NASA Astrophysics Data System (ADS)

Heberer, Andreas; Grüger, Heinrich; Zimmer, Fabian; Schenk, Harald; Kenda, Andreas; Frank, Albert; Scherf, Werner

2005-10-01

The examination of spectra in the NIR range is necessary for applications like process control, element analysis or medical systems. Typically integrated NIR spectrometers are based on optical setups with diffraction grating and detector arrays. The main disadvantage is price and availability of NIR array InGaAs-based detectors. The implementation of a scanning grating chip realized in a MOEMS technology which integrates the diffractive element makes it possible to detect spectra with single detectors time resolved. Either simple InGaAs photodiodes or cooled detectors may be used. The set up is a shrinked Czerny-Turner spectrometer. The light is coupled in by an optical fibre. After focussing the light passes the scanning grating moving at 150-500 Hz in a sinusoidal way. There it is split off in the different wavelength, the monochrome intensity is caught by a second mirror and led to the detector. The detector signal is amplified by a transimpedance stage and converted to digital with 12 bit resolution. The main part of the signal processing is done by a digital signal processor, which is used to unfold the sinusoidal position and calculate the final spectra. The data rate can be up to 3 MHz, then a spectrum is acquired every 2ms by using a 500Hz Mirror. Using the DSP, the spectrometer can operate autarkic without any PC. Then the spectrum is display on a 160 x 80 pixel graphic LCD. A keypad is used to control the functions. For communication a USB port is included, additional interfaces can be realized by a 16-pin expansion port, which is freely programmable, by the system firmware.

Extension of fourier transform vibrational circular dichroism into the near-infrared region: continuous spectral coverage from 800 to 10 000 cm(-1).

PubMed

Cao, Xiaolin; Shah, Rekha D; Dukor, Rina K; Guo, Changning; Freedman, Teresa B; Nafie, Laurence A

2004-09-01

We report the first vibrational circular dichroism (VCD) spectra with continuous coverage from 800 cm(-1) in the mid-infrared (MIR) region to 10 000 cm(-1) in the near-infrared (NIR) region. This coverage is illustrated with MIR and NIR absorbance and VCD spectra of 2,2-dimethyl-dioxolane-4-methanol (DDM), alpha-pinene, and camphor that serve as calibration samples over this entire region. Commercially available, dual-source Fourier transform (FT) MIR and NIR VCD spectrometers were equipped with appropriate light sources, optics, and detectors, and were modified for dual-polarization-modulation (DPM) operation. The combination of liquid-nitrogen- and thermoelectric-cooled HgCdTe (MCT) detectors, as well as InGaAs and Germanium (Ge) detectors operating at room temperature, permitted collection of the desired absorbance and VCD spectra across the range of vibrational fundamental, combination band, and overtone frequencies. The spectra of DDM and alpha-pinene were measured as neat liquids and recorded for both enantiomers in the various spectral regions. Spectra for camphor were all measured in CCl(4) solution at a concentration of 0.6 M, except for the carbonyl-stretching region, where a more dilute concentration was used. The typical anisotropy ratios (g) of the three molecules were estimated with respect to their strongest VCD bands in each spectral region. It was found that for all three molecules in the spectral regions above 2000 cm(-1), anisotropy ratios are approximately the same order (10(-5)) of magnitude. However, in the MIR region, the typical anisotropy ratios are significantly different for the three molecules. This study demonstrates that with modern FT-VCD spectrometers modified for DPM operation, VCD spectra can be measured continuously across a wide spectral range from the MIR to nearly the visible region with an unsurpassed combination of signal-to-noise ratio and spectral resolution.

Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study

PubMed Central

2013-01-01

Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state. PMID:23336819

Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario

PubMed Central

Verdière, Kevin J.; Roy, Raphaëlle N.; Dehais, Frédéric

2018-01-01

Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs. automated). The collected data were used to benchmark the performance of classical oxygenation features (i.e., Average, Peak, Variance, Skewness, Kurtosis, Area Under the Curve, and Slope) and connectivity features (i.e., Covariance, Pearson's, and Spearman's Correlation, Spectral Coherence, and Wavelet Coherence) to discriminate these two landing conditions. Classification performance was obtained by using a shrinkage Linear Discriminant Analysis (sLDA) and a stratified cross validation using each feature alone or by combining them. Our findings disclosed that the connectivity features performed significantly better than the classical concentration metrics with a higher accuracy for the wavelet coherence (average: 65.3/59.9 %, min: 45.3/45.0, max: 80.5/74.7 computed for HbO/HbR signals respectively). A maximum classification performance was obtained by combining the area under the curve with the wavelet coherence (average: 66.9/61.6 %, min: 57.3/44.8, max: 80.0/81.3 computed for HbO/HbR signals respectively). In a general manner all connectivity measures allowed an efficient classification when computed over HbO signals. Those promising results provide methodological cues for further implementation of fNIRS-based passive BCIs. PMID:29422841

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application

NASA Astrophysics Data System (ADS)

Kumar, Raj; Sharma, Vishal

2017-03-01

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%).

Constraining Aerosol Properties with the Spectrally-Resolved Phase Function of Pluto's Hazes

NASA Astrophysics Data System (ADS)

Parker, A. H.; Howett, C.; Olkin, C.; Protopapa, S.; Grundy, W. M.; Gladstone, R.; Young, L. A.; Horst, S. M.; Weaver, H. A., Jr.; Moore, J. M.; Ennico Smith, K.; Stern, A.

2017-12-01

The Multi-spectral Visible Imaging Camera (MVIC) and Lisa Hardaway Infrared Mapping Spectrometer (LEISA) aboard New Horizons imaged Pluto at high phase throughout departure from the system in July of 2015. The repeated MVIC color scans captured the phase behavior of Pluto's atmospheric hazes through phase angles of 165.0 to 169.5 degrees in four bandpasses in the visible and NIR. A spatially-resolved departure LEISA scan delivered moderate SNR NIR spectra of the hazes over wavelengths from 1.25 - 2.5 microns. Here we present our analysis of the departure MVIC and LEISA data, extracting high precision color phase curves of the hazes using the most up-to-date radiometric calibration and NIR gain drift corrections. We interpret these phase curves and spectra using Mie theory to constrain the size and composition of haze particles, with results indicating broad similarity to Titan aerosol analogues ("tholins"). Finally, we will explore the implications of the nature of these haze particles for the evolution of Pluto's surface as they settle out onto it over time.

Vacancy-Rich Monolayer BiO2-x as a Highly Efficient UV, Visible, and Near-Infrared Responsive Photocatalyst.

PubMed

Li, Jun; Wu, Xiaoyong; Pan, Wenfeng; Zhang, Gaoke; Chen, Hong

2018-01-08

Vacancy-rich layered materials with good electron-transfer property are of great interest. Herein, a full-spectrum responsive vacancy-rich monolayer BiO 2-x has been synthesized. The increased density of states at the conduction band (CB) minimum in the monolayer BiO 2-x is responsible for the enhanced photon response and photo-absorption, which were confirmed by UV/Vis-NIR diffuse reflectance spectra (DRS) and photocurrent measurements. Compared to bulk BiO 2-x , monolayer BiO 2-x has exhibited enhanced photocatalytic performance for rhodamine B and phenol removal under UV, visible, and near-infrared light (NIR) irradiation, which can be attributed to the vacancy V Bi-O ''' as confirmed by the positron annihilation spectra. The presence of V Bi-O ''' defects in monolayer BiO 2-x promoted the separation of electrons and holes. This finding provides an atomic level understanding for developing highly efficient UV, visible, and NIR light responsive photocatalysts. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An unbalanced spectra classification method based on entropy

NASA Astrophysics Data System (ADS)

Liu, Zhong-bao; Zhao, Wen-juan

2017-05-01

How to solve the problem of distinguishing the minority spectra from the majority of the spectra is quite important in astronomy. In view of this, an unbalanced spectra classification method based on entropy (USCM) is proposed in this paper to deal with the unbalanced spectra classification problem. USCM greatly improves the performances of the traditional classifiers on distinguishing the minority spectra as it takes the data distribution into consideration in the process of classification. However, its time complexity is exponential with the training size, and therefore, it can only deal with the problem of small- and medium-scale classification. How to solve the large-scale classification problem is quite important to USCM. It can be easily obtained by mathematical computation that the dual form of USCM is equivalent to the minimum enclosing ball (MEB), and core vector machine (CVM) is introduced, USCM based on CVM is proposed to deal with the large-scale classification problem. Several comparative experiments on the 4 subclasses of K-type spectra, 3 subclasses of F-type spectra and 3 subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS) verify USCM and USCM based on CVM perform better than kNN (k nearest neighbor) and SVM (support vector machine) in dealing with the problem of rare spectra mining respectively on the small- and medium-scale datasets and the large-scale datasets.

Detection and identification of bacteria in a juice matrix with Fourier transform-near infrared spectroscopy and multivariiate analysis.

PubMed

Rodriguez-Saona, L E; Khambaty, F M; Fry, F S; Dubois, J; Calvey, E M

2004-11-01

The use of Fourier transform-near infrared (FT-NIR) spectroscopy combined with multivariate pattern recognition techniques was evaluated to address the need for a fast and senisitive method for the detection of bacterial contamination in liquids. The complex cellular composition of bacteria produces FT-NIR vibrational transitions (overtone and combination bands), forming the basis for identification and subtyping. A database including strains of Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, Bacillus cereus, and Bacillus thuringiensis was built, with special care taken to optimize sample preparation. The bacterial cells were treated with 70% (vol/vol) ethanolto enhance safe handling of pathogenic strains and then concentrated on an aluminum oxide membrane to obtain a thin bacterial film. This simple membrane filtration procedure generated reproducible FT-NIR spectra that allowed for the rapid discrimination among closely related strains. Principal component analysis and soft independent modeling of class analogy of transformed spectra in the region 5,100 to 4,400 cm(-1) were able to discriminate between bacterial species. Spectroscopic analysis of apple juices inoculated with different strains of E. coli at approximately 10(5) CFU/ml showed that FT-NIR spectralfeatures are consistent with bacterial contamination and soft independent modeling of class analogy correctly predicted the identity of the contaminant as strains of E. coli. FT-NIR in conjunction with multivariate techniques can be used for the rapid and accurate evaluation of potential bacterial contamination in liquids with minimal sample manipulation, and hence limited exposure of the laboratory worker to the agents.

Near-infrared imaging spectroscopy for counterfeit drug detection

NASA Astrophysics Data System (ADS)

Arnold, Thomas; De Biasio, Martin; Leitner, Raimund

2011-06-01

Pharmaceutical counterfeiting is a significant issue in the healthcare community as well as for the pharmaceutical industry worldwide. The use of counterfeit medicines can result in treatment failure or even death. A rapid screening technique such as near infrared (NIR) spectroscopy could aid in the search for and identification of counterfeit drugs. This work presents a comparison of two laboratory NIR imaging systems and the chemometric analysis of the acquired spectroscopic image data. The first imaging system utilizes a NIR liquid crystal tuneable filter and is designed for the investigation of stationary objects. The second imaging system utilizes a NIR imaging spectrograph and is designed for the fast analysis of moving objects on a conveyor belt. Several drugs in form of tablets and capsules were analyzed. Spectral unmixing techniques were applied to the mixed reflectance spectra to identify constituent parts of the investigated drugs. The results show that NIR spectroscopic imaging can be used for contact-less detection and identification of a variety of counterfeit drugs.

Infrared and NIR Raman spectroscopy in medical microbiology

NASA Astrophysics Data System (ADS)

Naumann, Dieter

1998-04-01

FTIR and FT-NIR Raman spectra of intact microbial cells are highly specific, fingerprint-like signatures which can be used to (i) discriminate between diverse microbial species and strains, (ii) detect in situ intracellular components or structures such as inclusion bodies, storage materials or endospores, (iii) detect and quantify metabolically released CO2 in response to various different substrate, and (iv) characterize growth-dependent phenomena and cell-drug interactions. The characteristic information is extracted from the spectral contours by applying resolution enhancement techniques, difference spectroscopy, and pattern recognition methods such as factor-, cluster-, linear discriminant analysis, and artificial neural networks. Particularly interesting applications arise by means of a light microscope coupled to the spectrometer. FTIR spectra of micro-colonies containing less than 103 cells can be obtained from colony replica by a stamping technique that transfers micro-colonies growing on culture plates to a special IR-sample holder. Using a computer controlled x, y- stage together with mapping and video techniques, the fundamental tasks of microbiological analysis, namely detection, enumeration, and differentiation of micro- organisms can be integrated in one single apparatus. FTIR and NIR-FT-Raman spectroscopy can also be used in tandem to characterize medically important microorganisms. Currently novel methodologies are tested to take advantage of the complementary information of IR and Raman spectra. Representative examples on medically important microorganisms will be given that highlight the new possibilities of vibrational spectroscopies.

On-line prediction of the glucose concentration of CHO cell cultivations by NIR and Raman spectroscopy: Comparative scalability test with a shake flask model system.

PubMed

Kozma, Bence; Hirsch, Edit; Gergely, Szilveszter; Párta, László; Pataki, Hajnalka; Salgó, András

2017-10-25

In this study, near-infrared (NIR) and Raman spectroscopy were compared in parallel to predict the glucose concentration of Chinese hamster ovary cell cultivations. A shake flask model system was used to quickly generate spectra similar to bioreactor cultivations therefore accelerating the development of a working model prior to actual cultivations. Automated variable selection and several pre-processing methods were tested iteratively during model development using spectra from six shake flask cultivations. The target was to achieve the lowest error of prediction for the glucose concentration in two independent shake flasks. The best model was then used to test the scalability of the two techniques by predicting spectra of a 10l and a 100l scale bioreactor cultivation. The NIR spectroscopy based model could follow the trend of the glucose concentration but it was not sufficiently accurate for bioreactor monitoring. On the other hand, the Raman spectroscopy based model predicted the concentration of glucose in both cultivation scales sufficiently accurately with an error around 4mM (0.72g/l), that is satisfactory for the on-line bioreactor monitoring purposes of the biopharma industry. Therefore, the shake flask model system was proven to be suitable for scalable spectroscopic model development. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.

PubMed

Shin, Jaeyoung; von Lühmann, Alexander; Kim, Do-Won; Mehnert, Jan; Hwang, Han-Jeong; Müller, Klaus-Robert

2018-02-13

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for 'target' versus 'non-target' (dataset A) and symbol 'O' versus symbol 'X' (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques.

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

PubMed Central

Shin, Jaeyoung; von Lühmann, Alexander; Kim, Do-Won; Mehnert, Jan; Hwang, Han-Jeong; Müller, Klaus-Robert

2018-01-01

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for ‘target’ versus ‘non-target’ (dataset A) and symbol ‘O’ versus symbol ‘X’ (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques. PMID:29437166

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.

Synthesis, structure, and luminescence property of a series of Ag–Ln coordination polymers with the N-heterocyclic carboxylato ligand

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

Jin, Jing, E-mail: jinjing_crystal@126.com; Chen, Chong; Gao, Yan

Six Ln–Ag coordination polymers {[LnAg_2(IN)_4(H_2O)_5]·NO_3·2H_2O}{sub n} (Ln=Ho (1) and Tb (2), HIN=isonicotinic acid), {[PrAg_2(IN)_4(H_2O)_2]·NO_3·H_2O}{sub n} (3), [LnAg(pdc){sub 2}]{sub n} (Ln=Eu(4) and Pr (5), H{sub 2}pdc=3,4-pyridine-dicarboxylic acid) and [NdAg(bidc){sub 2}(H{sub 2}O){sub 4}]{sub n} (6) (H{sub 2}bidc=benzimidazole-5,6-dicarboxylic acid) have been hydrothermally synthesized and characterized by single crystal X-ray diffraction, elemental analysis, IR, UV–vis-NIR absorption spectra, fluorescence spectra and thermogravimetric analysis. Structural analyses reveal that the six polymers exhibit 0D (polymer (1)), 1D (polymer (2)), 2D (polymers (3) and (5)) and 3D (polymers (4) and (6)) infinite structures, respectively. Polymers (1)–(6) exhibit the Ln(III) characteristic emission in the near-infrared (NIR) region or inmore » the visible region. Especially, the NIR emission bands of polymers 1, 5 and 6 evidently present shift or splitting due to formation of the Ln–Ag coordination polymers. This can be attributed to the tune of inner levels in Ln–Ag system caused by the interact and influence between the 4d orbital of the Ag(I) ion and the 4f orbital of the Ln(III) ion, which can be confirmed by the UV–vis-NIR absorption spectra of the polymers. In addition, the distortion of coordination geometry as well as difference of the coordination number around the Ag(I) ion affect the structure framework. - Graphical abstract: Six Ag–Ln coordination polymers have been hydrothermally synthesized and characterized. The photoluminescence properties were studied. The distortion of coordination geometry of Ag(I) ion affect structure framework. Introduction of Ag(I) cause wonderful changes to the NIR emission of Ln(III) ions. - Highlights: • Six Ln–Ag polymers have been synthesized and characterized. • The distortion of coordination geometry of Ag(I) ion affect structure framework. • Introduction of Ag(I) cause wonderful changes to the NIR emission of Ln(III) ions.« less

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.

Quantitative interpretations of Visible-NIR reflectance spectra of blood.

PubMed

Serebrennikova, Yulia M; Smith, Jennifer M; Huffman, Debra E; Leparc, German F; García-Rubio, Luis H

2008-10-27

This paper illustrates the implementation of a new theoretical model for rapid quantitative analysis of the Vis-NIR diffuse reflectance spectra of blood cultures. This new model is based on the photon diffusion theory and Mie scattering theory that have been formulated to account for multiple scattering populations and absorptive components. This study stresses the significance of the thorough solution of the scattering and absorption problem in order to accurately resolve for optically relevant parameters of blood culture components. With advantages of being calibration-free and computationally fast, the new model has two basic requirements. First, wavelength-dependent refractive indices of the basic chemical constituents of blood culture components are needed. Second, multi-wavelength measurements or at least the measurements of characteristic wavelengths equal to the degrees of freedom, i.e. number of optically relevant parameters, of blood culture system are required. The blood culture analysis model was tested with a large number of diffuse reflectance spectra of blood culture samples characterized by an extensive range of the relevant parameters.

Multi-spectrometer calibration transfer based on independent component analysis.

PubMed

Liu, Yan; Xu, Hao; Xia, Zhenzhen; Gong, Zhiyong

2018-02-26

Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.

Spectral classification with the International Ultraviolet Explorer: An atlas of B-type spectra

NASA Technical Reports Server (NTRS)

Rountree, Janet; Sonneborn, George

1993-01-01

New criteria for the spectral classification of B stars in the ultraviolet show that photospheric absorption lines in the 1200-1900A wavelength region can be used to classify the spectra of B-type dwarfs, subgiants, and giants on a 2-D system consistent with the optical MK system. This atlas illustrates a large number of such spectra at the scale used for classification. These spectra provide a dense matrix of standard stars, and also show the effects of rapid stellar rotation and stellar winds on the spectra and their classification. The observational material consists of high-dispersion spectra from the International Ultraviolet Explorer archives, resampled to a resolution of 0.25 A, uniformly normalized, and plotted at 10 A/cm. The atlas should be useful for the classification of other IUE high-dispersion spectra, especially for stars that have not been observed in the optical.

Techniques for Producing Coastal Land Water Masks from Landsat and Other Multispectral Satellite Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hall, Callie

2005-01-01

Coastal erosion and land loss continue to threaten many areas in the United States. Landsat data has been used to monitor regional coastal change since the 1970s. Many techniques can be used to produce coastal land water masks, including image classification and density slicing of individual bands or of band ratios. Band ratios used in land water detection include several variations of the Normalized Difference Water Index (NDWI). This poster discusses a study that compares land water masks computed from unsupervised Landsat image classification with masks from density-sliced band ratios and from the Landsat TM band 5. The greater New Orleans area is employed in this study, due to its abundance of coastal habitats and its vulnerability to coastal land loss. Image classification produced the best results based on visual comparison to higher resolution satellite and aerial image displays. However, density sliced NDWI imagery from either near infrared (NIR) and blue bands or from NIR and green bands also produced more effective land water masks than imagery from the density-sliced Landsat TM band 5. NDWI based on NIR and green bands is noteworthy because it allows land water masks to be generated from multispectral satellite sensors without a blue band (e.g., ASTER and Landsat MSS). NDWI techniques also have potential for producing land water masks from coarser scaled satellite data, such as MODIS.

Techniques for Producing Coastal Land Water Masks from Landsat and Other Multispectral Satellite Data

NASA Technical Reports Server (NTRS)

Spruce, Joe; Hall, Callie

2005-01-01

Coastal erosion and land loss continue to threaten many areas in the United States. Landsat data has been used to monitor regional coastal change since the 1970's. Many techniques can be used to produce coastal land water masks, including image classification and density slicing of individual bands or of band ratios. Band ratios used in land water detection include several variations of the Normalized Difference Water Index (NDWI). This poster discusses a study that compares land water masks computed from unsupervised Landsat image classification with masks from density-sliced band ratios and from the Landsat TM band 5. The greater New Orleans area is imployed in this study, due to its abundance of coastal habitats and ist vulnerability to coastal land loss. Image classification produced the best results based on visual comparison to higher resolution satellite and aerial image displays. However, density-sliced NDWI imagery from either near infrared (NIR) and blue bands or from NIR and green bands also produced more effective land water masks than imagery from the density-sliced Landsat TM band 5. NDWI based on NIR and green bands is noteworthy because it allows land water masks to be generated form multispectral satellite sensors without a blue band (e.g., ASTER and Landsat MSS). NDWI techniques also have potential for producing land water masks from coarser scaled satellite data, such as MODIS.

Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface.

PubMed

Naseer, Noman; Hong, Keum-Shik

2013-10-11

This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BCI). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10s task period. Moreover, when the analysis time was confined to the 2-7s span within the overall 10s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value. Copyright © 2013 Elsevier Ireland Ltd. 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.

Spectroscopic properties for identifying sapphire samples from Ban Bo Kaew, Phrae Province, Thailand

NASA Astrophysics Data System (ADS)

Mogmued, J.; Monarumit, N.; Won-in, K.; Satitkune, S.

2017-09-01

Gemstone commercial is a high revenue for Thailand especially ruby and sapphire. Moreover, Phrae is a potential gem field located in the northern part of Thailand. The studies of spectroscopic properties are mainly to identify gemstone using advanced techniques (e.g. UV-Vis-NIR spectrophotometry, FTIR spectrometry and Raman spectroscopy). Typically, UV-Vis-NIR spectrophotometry is a technique to study the cause of color in gemstones. FTIR spectrometry is a technique to study the functional groups in gem-materials. Raman pattern can be applied to identify the mineral inclusions in gemstones. In this study, the natural sapphires from Ban Bo Kaew were divided into two groups based on colors including blue and green. The samples were analyzed by UV-Vis-NIR spectrophotometer, FTIR spectrometer and Raman spectroscope for studying spectroscopic properties. According to UV-Vis-NIR spectra, the blue sapphires show higher Fe3+/Ti4+ and Fe2+/Fe3+ absorption peaks than those of green sapphires. Otherwise, green sapphires display higher Fe3+/Fe3+ absorption peaks than blue sapphires. The FTIR spectra of both blue and green sapphire samples show the absorption peaks of -OH,-CH and CO2. The mineral inclusions such as ferrocolumbite and rutile in sapphires from this area were observed by Raman spectroscope. The spectroscopic properties of sapphire samples from Ban Bo Kaew, Phrae Province, Thailand are applied to be the specific evidence for gemstone identification.

UV / Visible / Near-Infrared Reflectance Models for the Rapid and Non-Destructive Prediction and Classification of Cotton Color and Physical Indices

USDA-ARS?s Scientific Manuscript database

High volume instrumentation (HVI), utilized in the cotton industry to determine the qualities and classifications of cotton fibers, is time consuming, and prone to day-to-day and location-to-location variations. UV / visible / NIR spectroscopy, a rapid and easy sampling technique, was investigated a...

Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples.

PubMed

Liu, Yan; Cai, Wensheng; Shao, Xueguang

2016-12-05

Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses. Copyright © 2016 Elsevier B.V. All rights reserved.

Explaining Space-Weathering Effects on UV-Vis-NIR Spectra with Light-Scattering Methods

NASA Astrophysics Data System (ADS)

Penttilä, Antti; Väisänen, Timo; Martikainen, Julia; Kohout, Tomas; Muinonen, Karri

2015-11-01

Space-weathering (SW) introduces changes to the asteroid reflectance spectra. In silicate minerals, SW is known to darken the spectra and reduce the silicate absorption band depths. In olivine, the neutral slope in Vis and NIR wavelengths is becoming positive [1]. In pyroxene, the positive slope over the 1 µm absorption band is decreasing, and the negative slope over the 2 µm band is increasing towards positive values with increasing SW [2].The SW process generates small nanophase iron (npFe0) inclusions in the surface layers of mineral grains. The inclusions are some tens of nm in size. This mechanism has been linked to the Moon and to a certain extent also to the silicate-rich S-complex asteroids.We offer two simple explanations from light-scattering theory to explain the SW effects on the spectral slope. First, the npFe0 will introduce a posititive general slope (reddening) to the spectra. The npFe0 inclusions (~10 nm) are in the Rayleigh domain with the wavelength λ in the UV-Vis-NIR range. Their absorption cross-section follows approximately the 1/λ-relation from the Rayleigh theory. Absorption is more efficient in the UV than in the NIR wavelengths, therefore the spectra are reddening.Second, the effect of npFe0 absorption is more efficient for originally brighter reflectance values. Explanation combines the effective medium theory and the exponential attenuation in the medium. When adding a small amount of highly absorbing npFe0, the effective absorption coefficient k will increase approximately the same Δk for the typical values of silicates. This change will increase more effectively the exponential attenuation if the original k was very small, and thus the reflectance high. Therefore, both positive and negative spectral slopes will approach zero with SW.We conclude that the SW will introduce a general reddening, and neutralize local slopes. This is verified using the SIRIS code [3], which combines geometric optics with small internal diffuse scatterers in the radiative transfer domain.[1] Kohout T. et al. (2014), Icarus 237(15), 75-83.[2] Kohout T. et al. (2015), Workshop on Space Weathering of Airless Bodies, Abstract.[3] Muinonen K. et al. (2009), JQSRT 110, 1628-1639.

Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

PubMed Central

2011-01-01

Background For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. Methods 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). Results The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Conclusions Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation. PMID:21682906

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.

[Achievement of the noninvasive measurement for human blood glucose with NIR diffusion reflectance spectrum method].

PubMed

Zhang, Hong-yan; Ding, Dong; Song, Li-qiang; Gu, Lin-na; Yang, Peng; Tang, Yu-guo

2005-06-01

The noninvasive measurement of human blood glucose was achieved with NIR diffusion reflectance spectrum method. The thumb fingertip NIR diffusion reflectance spectra of six different age healthy volunteers were collected using Nexus-870 and its NIR fiber port smart accessory. The test was implemented with changing the blood glucose concentration for the limosis and satiation of every volunteer. The calibration model was set up using PLS method with the smoothing, baseline correction and first derivatives pretreatment spectrum in the 7500-8500 cm(-1) region for single volunteer, the same age combination and that of different age. When the spectrum was obtained, the actual blood glucose value of every spectrun sample was demarcated using ultraviolet spectrophotometer. The correlation between the calibration value and true value for single volunteer is better than that for the combination of volunteers, the correlative coefficients are all over 0.90471, RMSECs are all less than 0.171.

Nondestructive estimation of wood chemical composition of sections of radial wood strips by diffuse reflectance near infrared spectroscopy

Treesearch

P. David Jones; Laurence R. Schimleck; Gary F. Peter; Richard F. Daniels; Alexander Clark

2006-01-01

The use of calibrated near infrared (NIR) spectroscopy for predicting the chemical composition of Pirus taeda L. (loblolly pine) wood samples is investigated. Seventeen P. taeda radial strips, representing seven different sites were selected and NlR spectra were obtained from the radial longitudinal face of each strip. The spectra...

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.

[Temperature compensation for portable Vis/NIR spectrometer measurement of apple fruit soluble solids contents].

PubMed

Wang, Jia-hua; Qi, Shu-ye; Tang, Zhi-hui; Jia, Shou-xing; Li, Yong-yu

2012-05-01

Visible (Vis)/near infrared (NIR) spectroscopy has been used successfully to measure soluble solids content (SSC) in fruit. However, for practical implementation, the NIR technique needs to be able to compensate for fruit temperature fluctuations, as it was observed that the sample temperature affects the NIR spectrum. A portable Vis/NIR spectrometer was used to collect diffused transmittance spectra of apples at different temperatures (0-30 degrees C). The spectral data of apple at 20 degrees C was used to develop a norm partial least squares (PLS) model. Slope/bias technique was found to well suits to control the accuracy of the calibration model for SSC concerning temperature fluctuations. The correctional PLS models were used to predict the SSC of apple at 0, 10 and 30 degrees C, respectively. The correctional method was found to perform well with Q values of 0.810, 0.822 and 0.802, respectively. When no precautions are taken, the Q value on the SSC may be as small as 0.525-0.680. The results obtained highlight the potential of portable Vis/NIR instruments for assessing internal quality of fruits on site under varying weather conditions.

Estimation of Sensory Analysis Cupping Test Arabica Coffee Using NIR Spectroscopy

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Comparative Variable Temperature Studies of Polyamide II with a Benchtop Fourier Transform and a Miniature Handheld Near-Infrared Spectrometer Using 2D-COS and PCMW-2D Analysis.

PubMed

Unger, Miriam; Pfeifer, Frank; Siesler, Heinz W

2016-07-01

The main objective of this communication is to compare the performance of a miniaturized handheld near-infrared (NIR) spectrometer with a benchtop Fourier transform near-infrared (FT-NIR) spectrometer. Generally, NIR spectroscopy is an extremely powerful analytical tool to study hydrogen-bonding changes of amide functionalities in solid and liquid materials and therefore variable temperature NIR measurements of polyamide II (PAII) have been selected as a case study. The information content of the measurement data has been further enhanced by exploiting the potential of two-dimensional correlation spectroscopy (2D-COS) and the perturbation correlation moving window two-dimensional (PCMW2D) evaluation technique. The data provide valuable insights not only into the changes of the hydrogen-bonding structure and the recrystallization of the hydrocarbon segments of the investigated PAII but also in their sequential order. Furthermore, it has been demonstrated that the 2D-COS and PCMW2D results derived from the spectra measured with the miniaturized NIR instrument are equivalent to the information extracted from the data obtained with the high-performance FT-NIR instrument. © The Author(s) 2016.

Near Infrared Spectroscopy Facilitates Rapid Identification of Both Young and Mature Amazonian Tree Species.

PubMed

Lang, Carla; Costa, Flávia Regina Capellotto; Camargo, José Luís Campana; Durgante, Flávia Machado; Vicentini, Alberto

2015-01-01

Precise identification of plant species requires a high level of knowledge by taxonomists and presence of reproductive material. This represents a major limitation for those working with seedlings and juveniles, which differ morphologically from adults and do not bear reproductive structures. Near-infrared spectroscopy (FT-NIR) has previously been shown to be effective in species discrimination of adult plants, so if young and adults have a similar spectral signature, discriminant functions based on FT-NIR spectra of adults can be used to identify leaves from young plants. We tested this with a sample of 419 plants in 13 Amazonian species from the genera Protium and Crepidospermum (Burseraceae). We obtained 12 spectral readings per plant, from adaxial and abaxial surfaces of dried leaves, and compared the rate of correct predictions of species with discriminant functions for different combinations of readings. We showed that the best models for predicting species in early developmental stages are those containing spectral data from both young and adult plants (98% correct predictions of external samples), but even using only adult spectra it is still possible to attain good levels of identification of young. We obtained an average of 75% correct identifications of young plants by discriminant equations based only on adults, when the most informative wavelengths were selected. Most species were accurately predicted (75-100% correct identifications), and only three had poor predictions (27-60%). These results were obtained despite the fact that spectra of young individuals were distinct from those of adults when species were analyzed individually. We concluded that FT-NIR has a high potential in the identification of species even at different ontogenetic stages, and that young plants can be identified based on spectra of adults with reasonable confidence.

Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging.

PubMed

Manley, Marena; du Toit, Gerida; Geladi, Paul

2011-02-07

The combination of near infrared (NIR) hyperspectral imaging and chemometrics was used to follow the diffusion of conditioning water over time in wheat kernels of different hardnesses. Conditioning was attempted with deionised water (dH(2)O) and deuterium oxide (D(2)O). The images were recorded at different conditioning times (0-36 h) from 1000 to 2498 nm with a line scan imaging system. After multivariate cleaning and spectral pre-processing (either multiplicative scatter correction or standard normal variate and Savitzky-Golay smoothing) six principal components (PCs) were calculated. These were studied visually interactively as score images and score plots. As no clear clusters were present in the score plots, changes in the score plots were investigated by means of classification gradients made within the respective PCs. Classes were selected in the direction of a PC (from positive to negative or negative to positive score values) in almost equal segments. Subsequently loading line plots were used to provide a spectroscopic explanation of the classification gradients. It was shown that the first PC explained kernel curvature. PC3 was shown to be related to a moisture-starch contrast and could explain the progress of water uptake. The positive influence of protein was also observed. The behaviour of soft, hard and very hard kernels was different in this respect, with the uptake of water observed much earlier in the soft kernels than in the harder ones. The harder kernels also showed a stronger influence of protein in the loading line plots. Difference spectra showed interpretable changes over time for water but not for D(2)O which had a too low signal in the wavelength range used. NIR hyperspectral imaging together with exploratory chemometrics, as detailed in this paper, may have wider applications than merely conditioning studies. Copyright © 2010 Elsevier B.V. All rights reserved.

Mid-Infrared (MIR) and Near-Infrared (NIR) Detection of Rhizoctonia solani AG 2-2 IIIB on Barley-Based Artificial Inoculum.

PubMed

Webb, Kimberly M; Calderón, Francisco J

2015-10-01

The amount of Rhizoctonia solani in the soil and how much must be present to cause disease in sugar beet (Beta vulgaris L.) is relatively unknown. This is mostly because of the usually low inoculum densities found naturally in soil and the low sensitivity of traditional serial dilution assays. We investigated the usefulness of Fourier transform mid-infrared (MIR) and near-infrared (NIR) spectroscopic properties in identifying the artificial colonization of barley grains with R. solani AG 2-2 IIIB and in detecting R. solani populations in plant tissues and inoculants. The objectives of this study were to compare the ability of traditional plating assays to NIR and MIR spectroscopies to identify R. solani in different-size fractions of colonized ground barley (used as an artificial inoculum) and to differentiate colonized from non-inoculated barley. We found that NIR and MIR spectroscopies were sensitive in resolving different barley particle sizes, with particles that were <0.25 and 0.25-0.5 mm having different spectral properties than coarser particles. Moreover, we found that barley colonized with R. solani had different MIR spectral properties than the non-inoculated samples for the larger fractions (0.5-1.0, 1.0-2.0, and >2.0 mm) of the ground barley. This colonization was confirmed using traditional plating assays. Comparisons with the spectra from pure fungal cultures and non-inoculated barley suggest that the MIR spectrum of colonized barley is different because of the consumption of C substrates by the fungus rather than because of the presence of fungal bands in the spectra of the colonized samples. We found that MIR was better than NIR spectroscopy in differentiating the colonized from the control samples.

Remote sensing and the optical properties of the narrow cylindrical leaves of Juncus roemerianus

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, A.

2004-01-01

To develop a more complete foundation for remote sensing of the marsh grass Juncus roemerianus, we measured the optical properties of its cylindrical leaves at sites of different canopy height, biomass composition and amount, and connectivity to ocean flushing. To measure the leaf optical properties, we adapted a technique used for conifer needles. After establishing the reliability and limits of the adapted technique to the wider J.roemerianus leaves, mean transmittance and reflectance spectra were compared to associated leaf diameters from two dates in 1999 and 2002 and at each site. Transmittance was inversely related to leaf diameter. Mean transmittance and reflectance generated from reoccupation of many field sites in 2002 indicated little or no difference in transmittance between years, a slight reflectance difference in the visible (<2%) and a slightly higher reflectance difference in the near infrared (NIR) (<4%). Site comparison indicated limited ability to separate leaf transmittance but not reflectance by marsh type (e.g., low, medium, high) or biomass. Excluding one outlier, we found leaf transmittances could be adequately represented as 1% ?? 0.2% in the visible and 9% ?? 1% in the NIR and leaf reflectances represented from 14% to 16% in the visible and 71% to 75% in the NIR (the reflectance ranges represent 1999 and 2002 means). Reflectance and transmittance spectra associated with the dead J. roemerianus leaves displayed a spectrally flat increase from the visible to the NIR wavelengths. In total, we documented the atypical optical properties of the cylindrical J. roemerianus leaves and showed that to a first approximation, single means could represent leaf transmittance and visible leaf reflectance across all marsh zones and, after accounting for sample standardization, possibly the NIR reflectance as well.

Spectroscopic photoacoustics for assessing ischemic kidney damage

NASA Astrophysics Data System (ADS)

Berndl, Elizabeth S. L.; He, Xiaolin; Yuen, Darren A.; Kolios, Michael C.

2018-02-01

Ischemic reperfusion injuries (IRIs) are caused by return of blood to a tissue or organ after a period without oxygen or nutrients. Damage in the microvasculature causes an inflammatory response and heterogeneous scarring, which is associated with an increase in collagen in the extracellular matrix. Although most often associated with heart attacks and strokes, IRI also occurs when blood reperfuses a transplanted organ. Currently, monitoring for IRI is limited to biopsies, which are invasive and sample a limited area. In this work, we explored photoacoustic (PA) biomarkers of scarring. IRI events were induced in mice (n=2) by clamping the left renal artery, then re-establishing flow. At 53 days post-surgery, kidneys were saline perfused and cut in half laterally. One half was immediately imaged with a VevoX system (Fujifilm-VisualSonics, Toronto) in two near infrared ranges - 680 to 970 nm (NIR), and 1200 to 1350 nm (NIR II). The other half was decellularized and then imaged at NIR and NIR II. Regions of interest were manually identified and analyzed for each kidney. For both cellularized and decellularized samples, the PA signal ratio based on irradiation wavelengths of 715:930 nm was higher in damaged kidneys than for undamaged kidneys (p < 0.0001 for both). Damaged kidneys had ROIs with spectra indicating the presence of collagen in the NIR II range, while healthy kidneys did not. Collagen rich spectra were more apparent in decellularized kidneys, suggesting that in the cellularized samples, other components may be contributing to the signal. PA imaging using spectral ratios associated with collagen signatures may provide a non-invasive tool to determine areas of tissue damage due to IRIs.

Mental stress assessment using simultaneous measurement of EEG and fNIRS

PubMed Central

Al-Shargie, Fares; Kiguchi, Masashi; Badruddin, Nasreen; Dass, Sarat C.; Hani, Ahmad Fadzil Mohammad; Tang, Tong Boon

2016-01-01

Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively. PMID:27867700

[Tobacco quality analysis of industrial classification of different years using near-infrared (NIR) spectrum].

PubMed

Wang, Yi; Xiang, Ma; Wen, Ya-Dong; Yu, Chun-Xia; Wang, Luo-Ping; Zhao, Long-Lian; Li, Jun-Hui

2012-11-01

In this study, tobacco quality analysis of main Industrial classification of different years was carried out applying spectrum projection and correlation methods. The group of data was near-infrared (NIR) spectrum from Hongta Tobacco (Group) Co., Ltd. 5730 tobacco leaf Industrial classification samples from Yuxi in Yunnan province from 2007 to 2010 year were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of HONGDA. The conclusion showed that, when the samples were divided to two part by the ratio of 2:1 randomly as analysis and verification sets in the same year, the verification set corresponded with the analysis set applying spectrum projection because their correlation coefficients were above 0.98. The correlation coefficients between two different years applying spectrum projection were above 0.97. The highest correlation coefficient was the one between 2008 and 2009 year and the lowest correlation coefficient was the one between 2007 and 2010 year. At the same time, The study discussed a method to get the quantitative similarity values of different industrial classification samples. The similarity and consistency values were instructive in combination and replacement of tobacco leaf blending.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging

NASA Astrophysics Data System (ADS)

Jiang, Yu; Li, Changying; Takeda, Fumiomi

2016-10-01

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 - 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging.

PubMed

Jiang, Yu; Li, Changying; Takeda, Fumiomi

2016-10-21

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 - 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising.

Tissue classification and diagnostics using a fiber probe for combined Raman and fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Cicchi, Riccardo; Anand, Suresh; Rossari, Susanna; Sturiale, Alessandro; Giordano, Flavio; De Giorgi, Vincenzo; Maio, Vincenza; Massi, Daniela; Nesi, Gabriella; Buccoliero, Anna Maria; Tonelli, Francesco; Guerrini, Renzo; Pimpinelli, Nicola; Pavone, Francesco S.

2015-03-01

Two different optical fiber probes for combined Raman and fluorescence spectroscopic measurements were designed, developed and used for tissue diagnostics. Two visible laser diodes were used for fluorescence spectroscopy, whereas a laser diode emitting in the NIR was used for Raman spectroscopy. The two probes were based on fiber bundles with a central multimode optical fiber, used for delivering light to the tissue, and 24 surrounding optical fibers for signal collection. Both fluorescence and Raman spectra were acquired using the same detection unit, based on a cooled CCD camera, connected to a spectrograph. The two probes were successfully employed for diagnostic purposes on various tissues in a good agreement with common routine histology. This study included skin, brain and bladder tissues and in particular the classification of: malignant melanoma against melanocytic lesions and healthy skin; urothelial carcinoma against healthy bladder mucosa; brain tumor against dysplastic brain tissue. The diagnostic capabilities were determined using a cross-validation method with a leave-one-out approach, finding very high sensitivity and specificity for all the examined tissues. The obtained results demonstrated that the multimodal approach is crucial for improving diagnostic capabilities. The system presented here can improve diagnostic capabilities on a broad range of tissues and has the potential of being used for endoscopic inspections in the near future.

Tissue classification and diagnostics using a fiber probe for combined Raman and fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Cicchi, Riccardo; Anand, Suresh; Crisci, Alfonso; Giordano, Flavio; Rossari, Susanna; De Giorgi, Vincenzo; Maio, Vincenza; Massi, Daniela; Nesi, Gabriella; Buccoliero, Anna Maria; Guerrini, Renzo; Pimpinelli, Nicola; Pavone, Francesco S.

2015-07-01

Two different optical fiber probes for combined Raman and fluorescence spectroscopic measurements were designed, developed and used for tissue diagnostics. Two visible laser diodes were used for fluorescence spectroscopy, whereas a laser diode emitting in the NIR was used for Raman spectroscopy. The two probes were based on fiber bundles with a central multimode optical fiber, used for delivering light to the tissue, and 24 surrounding optical fibers for signal collection. Both fluorescence and Raman spectra were acquired using the same detection unit, based on a cooled CCD camera, connected to a spectrograph. The two probes were successfully employed for diagnostic purposes on various tissues in a good agreement with common routine histology. This study included skin, brain and bladder tissues and in particular the classification of: malignant melanoma against melanocytic lesions and healthy skin; urothelial carcinoma against healthy bladder mucosa; brain tumor against dysplastic brain tissue. The diagnostic capabilities were determined using a cross-validation method with a leave-one-out approach, finding very high sensitivity and specificity for all the examined tissues. The obtained results demonstrated that the multimodal approach is crucial for improving diagnostic capabilities. The system presented here can improve diagnostic capabilities on a broad range of tissues and has the potential of being used for endoscopic inspections in the near future.

Optical and Near-infrared Study of Nova V2676 Oph 2012

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

Raj, A.; Das, R. K.; Walter, F. M., E-mail: ashish.raj@iiap.res.in

2017-02-01

We present optical spectrophotometric and near-infrared (NIR) photometric observations of the nova V2676 Oph covering the period from 2012 March 29 through 2015 May 8. The optical spectra and photometry of the nova have been taken from SMARTS and Asiago; the NIR photometry was obtained from SMARTS and Mt. Abu. The spectra were dominated by strong H i lines from the Balmer series, Fe ii, N i, and [O i] lines in the initial days, typical of an Fe ii type nova. The measured FWHM for the H β and H α lines was 800–1200 km s{sup −1}. There wasmore » pronounced dust formation starting 90 days after the outburst. The J − K color was the largest among recent dust-forming novae.« less

Optical properties of trinuclear metal chalcogenolate complexes - room temperature NIR fluorescence in [Cu2Ti(SPh)6(PPh3)2].

PubMed

Kühn, Michael; Lebedkin, Sergei; Weigend, Florian; Eichhöfer, Andreas

2017-01-31

The optical properties of four isostructural trinuclear chalcogenolato bridged metal complexes [Cu 2 Sn(SPh) 6 (PPh 3 ) 2 ], [Cu 2 Sn(SePh) 6 (PPh 3 ) 2 ], [Ag 2 Sn(SPh) 6 (PPh 3 ) 2 ] and [Cu 2 Ti(SPh) 6 (PPh 3 ) 2 ] have been investigated by absorption and photoluminescence spectroscopy and time-dependent density functional theory (TDDFT) calculations. All copper-tin compounds demonstrate near-infrared (NIR) phosphorescence at ∼900-1100 nm in the solid state at low temperature, which is nearly absent at ambient temperature. Stokes shifts of these emissions are found to be unusually large with values of about 1.5 eV. The copper-titanium complex [Cu 2 Ti(SPh) 6 (PPh 3 ) 2 ] also shows luminescence in the NIR at 1090 nm but with a much faster decay (τ ∼ 10 ns at 150 K) and a much smaller Stokes shift (ca. 0.3 eV). Even at 295 K this fluorescence is found to comprise a quantum yield as high as 9.5%. The experimental electronic absorption spectra well correspond to the spectra simulated from the calculated singlet transitions. In line with the large Stokes shifts of the emission spectra the calculations reveal for the copper-tin complexes strong structural relaxation of the excited triplet states whereas those effects are found to be much smaller in the case of the copper-titanium complex.

On-line analysis of algae in water by discrete three-dimensional fluorescence spectroscopy.

PubMed

Zhao, Nanjing; Zhang, Xiaoling; Yin, Gaofang; Yang, Ruifang; Hu, Li; Chen, Shuang; Liu, Jianguo; Liu, Wenqing

2018-03-19

In view of the problem of the on-line measurement of algae classification, a method of algae classification and concentration determination based on the discrete three-dimensional fluorescence spectra was studied in this work. The discrete three-dimensional fluorescence spectra of twelve common species of algae belonging to five categories were analyzed, the discrete three-dimensional standard spectra of five categories were built, and the recognition, classification and concentration prediction of algae categories were realized by the discrete three-dimensional fluorescence spectra coupled with non-negative weighted least squares linear regression analysis. The results show that similarities between discrete three-dimensional standard spectra of different categories were reduced and the accuracies of recognition, classification and concentration prediction of the algae categories were significantly improved. By comparing with that of the chlorophyll a fluorescence excitation spectra method, the recognition accuracy rate in pure samples by discrete three-dimensional fluorescence spectra is improved 1.38%, and the recovery rate and classification accuracy in pure diatom samples 34.1% and 46.8%, respectively; the recognition accuracy rate of mixed samples by discrete-three dimensional fluorescence spectra is enhanced by 26.1%, the recovery rate of mixed samples with Chlorophyta 37.8%, and the classification accuracy of mixed samples with diatoms 54.6%.

VizieR Online Data Catalog: Multiwavelenght photometry of Sh 2-138 YSOs (Baug+, 2015)

NASA Astrophysics Data System (ADS)

Baug, T.; Ojha, D. K.; Dewangan, L. K.; Ninan, J. P.; Bhatt, B. C.; Ghosh, S. K.; Mallick, K. K.

2016-07-01

Optical BVRI imaging observations of the Sh2-138 region were carried out on 2005 September 8 using the Himalaya Faint Object Spectrograph and Camera (HFOSC) mounted on the 2 m Himalayan Chandra Telescope (HCT). In order to identify strong Hα emission sources in the Sh2-138 region, slitless Hα spectra were obtained using the HFOSC on 2007 November 16. Optical spectroscopic observations of the central brightest source were performed using the HFOSC on 2014 November 18. The newly installed TIFR Near Infrared Spectrometer and Imager Camera (TIRSPEC) on the HCT was used for NIR observations on 2014 November 18 under photometric conditions with an average seeing of 1.4 arcsec. We obtained NIR spectra of the central brightest source on 2014 May 29, using the TIRSPEC, in NIR Y (1.02-1.20um), J (1.21-1.48um), H (1.49-1.78um), and K (2.04-2.35um) bands. We conducted optical narrow-band imaging observations of the region in Hα filter (λ~6563Å, Δλ~100Å) with exposure times of 600s, 250s, and 50s on 2005 September 8 using the HFOSC. (1 data file).

NIR DLP hyperspectral imaging system for medical applications

NASA Astrophysics Data System (ADS)

Wehner, Eleanor; Thapa, Abhas; Livingston, Edward; Zuzak, Karel

2011-03-01

DLP® hyperspectral reflectance imaging in the visible range has been previously shown to quantify hemoglobin oxygenation in subsurface tissues, 1 mm to 2 mm deep. Extending the spectral range into the near infrared reflects biochemical information from deeper subsurface tissues. Unlike any other illumination method, the digital micro-mirror device, DMD, chip is programmable, allowing the user to actively illuminate with precisely predetermined spectra of illumination with a minimum bandpass of approximately 10 nm. It is possible to construct active spectral-based illumination that includes but is not limited to containing sharp cutoffs to act as filters or forming complex spectra, varying the intensity of light at discrete wavelengths. We have characterized and tested a pure NIR, 760 nm to 1600 nm, DLP hyperspectral reflectance imaging system. In its simplest application, the NIR system can be used to quantify the percentage of water in a subject, enabling edema visualization. It can also be used to map vein structure in a patient in real time. During gall bladder surgery, this system could be invaluable in imaging bile through fatty tissue, aiding surgeons in locating the common bile duct in real time without injecting any contrast agents.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application.

PubMed

Kumar, Raj; Sharma, Vishal

2017-03-15

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%). Copyright © 2016 Elsevier B.V. All rights reserved.

[Gaussian process regression and its application in near-infrared spectroscopy analysis].

PubMed

Feng, Ai-Ming; Fang, Li-Min; Lin, Min

2011-06-01

Gaussian process (GP) is applied in the present paper as a chemometric method to explore the complicated relationship between the near infrared (NIR) spectra and ingredients. After the outliers were detected by Monte Carlo cross validation (MCCV) method and removed from dataset, different preprocessing methods, such as multiplicative scatter correction (MSC), smoothing and derivate, were tried for the best performance of the models. Furthermore, uninformative variable elimination (UVE) was introduced as a variable selection technique and the characteristic wavelengths obtained were further employed as input for modeling. A public dataset with 80 NIR spectra of corn was introduced as an example for evaluating the new algorithm. The optimal models for oil, starch and protein were obtained by the GP regression method. The performance of the final models were evaluated according to the root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (r). The models give good calibration ability with r values above 0.99 and the prediction ability is also satisfactory with r values higher than 0.96. The overall results demonstrate that GP algorithm is an effective chemometric method and is promising for the NIR analysis.

Mid-infrared and near-infrared spectroscopic study of selected magnesium carbonate minerals containing ferric iron-Implications for the geosequestration of greenhouse gases.

PubMed

Frost, Ray L; Reddy, B Jagannadha; Bahfenne, Silmarilly; Graham, Jessica

2009-04-01

The proposal to remove greenhouse gases by pumping liquefied CO(2) several kilometres below the ground implies that many carbonate containing minerals will be formed. Among these minerals brugnatellite and coalingite are probable. Two ferric ion bearing minerals brugnatellite and coalingite with a hydrotalcite-like structure have been characterised by a combination of infrared and near-infrared (NIR) spectroscopy. The infrared spectra of the OH stretching region are characterised by OH and water stretching vibrations. Both the first and second fundamental overtones of these bands are observed in the NIR spectra in the 7030-7235 cm(-1) and 10,490-10,570 cm(-1) regions. Intense (CO(3))(2-) symmetric and antisymmetric stretching vibrations support the concept that the carbonate ion is distorted. The position of the water bending vibration indicates the water is strongly hydrogen bonded in the mineral structure. Split NIR bands at around 8675 and 11,100 cm(-1) indicate that some replacement of magnesium ions by ferrous ions in the mineral structure has occurred. Near-infrared spectroscopy is ideal for the assessment of the formation of carbonate minerals.

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.

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.

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.

Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

PubMed

Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M

2016-10-01

A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Testing forward model against OCO-2 and TANSO-FTS/GOSAT observed spectra in near infrared range

NASA Astrophysics Data System (ADS)

Zadvornykh, Ilya V.; Gribanov, Konstantin G.

2015-11-01

An existing software package FIRE-ARMS (Fine InfraRed Explorer for Atmospheric Remote MeasurementS) was modified by embedding vector radiative transfer model VLIDORT. Thus the program tool includes both thermal (TIR) and near infrared (NIR) regions. We performed forward simulation of near infrared spectra on the top of the atmosphere for outgoing radiation accounting multiple scattering in cloudless atmosphere. Simulated spectra are compared with spectra measured by TANSO-FTS/GOSAT and OCO-2 in the condition of cloudless atmosphere over Western Siberia. NCEP/NCAR reanalysis data were used to complete model atmosphere.

Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations.

PubMed

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M

2013-08-08

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

Potential of On-Line Visible and Near Infrared Spectroscopy for Measurement of pH for Deriving Variable Rate Lime Recommendations

PubMed Central

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M.

2013-01-01

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate. PMID:23966186

Vesta and the HED Meteorites: Comparison of Spectral Properties

NASA Technical Reports Server (NTRS)

Ammannito, E.; De Sanctis, M. C.; Fonte, S.; Magni, G.; Capaccioni, F.; Tosi, F.; Capria, M. T.; Blewett, D.; Combe, J. P.; Farina, M.;

Industrial defect discrimination applying infrared imaging spectroscopy and artificial neural networks

NASA Astrophysics Data System (ADS)

Garcia-Allende, Pilar Beatriz; Conde, Olga M.; Madruga, Francisco J.; Cubillas, Ana M.; Lopez-Higuera, Jose M.

2008-03-01

A non-intrusive infrared sensor for the detection of spurious elements in an industrial raw material chain has been developed. The system is an extension to the whole near infrared range of the spectrum of a previously designed system based on the Vis-NIR range (400 - 1000 nm). It incorporates a hyperspectral imaging spectrograph able to register simultaneously the NIR reflected spectrum of the material under study along all the points of an image line. The working material has been different tobacco leaf blends mixed with typical spurious elements of this field such as plastics, cardboards, etc. Spurious elements are discriminated automatically by an artificial neural network able to perform the classification with a high degree of accuracy. Due to the high amount of information involved in the process, Principal Component Analysis is first applied to perform data redundancy removal. By means of the extension to the whole NIR range of the spectrum, from 1000 to 2400 nm, the characterization of the material under test is highly improved. The developed technique could be applied to the classification and discrimination of other materials, and, as a consequence of its non-contact operation it is particularly suitable for food quality control.

In vivo real time non invasive monitoring of brain penetration of chemicals with near-infrared spectroscopy: Concomitant PK/PD analysis.

PubMed

Crespi, Francesco; Cattini, Stefano; Donini, Maurizio; Bandera, Andrea; Rovati, Luigi

2016-01-30

Near-infrared spectroscopy (NIRS) is a non-invasive technique that monitors changes in oxygenation of haemoglobin. The absorption spectra of near-infrared light differ for the oxygenation-deoxygenation states of haemoglobin (oxygenate (HbO2) and deoxygenate (Hb), respectively) so that these two states can be directly monitored. Different methodologies report different basal values of HbO2 and Hb absolute concentrations in brain. Here, we attempt to calculate basal HbO2 levels in rat CNS via evaluation of the influence of exogenous oxygen or exogenous carbon dioxide on the NIRS parameters measured in vivo. Furthermore the possibility that changes of haemoglobin oxygenation in rat brain as measured by NIRS might be a useful index of brain penetration of chemical entities has been investigated. Different compounds from different chemical classes were selected on the basis of parallel ex vivo and in vivo pharmacokinetic (PK/PD) studies of brain penetration and overall pharmacokinetic profile. It appeared that NIRS might contribute to assess brain penetration of chemical entities, i.e. significant changes in NIRS signals could be related to brain exposure, conversely the lack of significant changes in relevant NIRS parameters could be indicative of low brain exposure. This work is proposing a further innovation on NIRS preclinical applications i.e. a "chemical" NIRS [chNIRS] approach for determining penetration of drugs in animal brain. Therefore, chNIRS could became a non invasive methodology for studies on neurobiological processes and psychiatric diseases in preclinical but also a translational strategy from preclinical to clinical investigations. Copyright © 2015 Elsevier B.V. All rights reserved.

Laboratory and Field Spectroscopy of Moon analogue material

NASA Astrophysics Data System (ADS)

Offringa, Marloes; Foing, Bernard H.

2016-07-01

Samples derived from terrestrial analogue sites are studied to gain insight into lunar processes in their geological context (Foing, Stoker, Ehrenfreund, 2011). For this study samples from the volcanic region of the Eifel, Germany collected during our latest field campaigns in November 2015 and February 2016 (Foing et al., 2010), are analyzed with a variety of spectrometers. The aim is to obtain a database of analyzed samples that could be used as a reference for future in situ measurements. We also use a documented set of Moon-Mars relevant minerals curated at VU Amsterdam. We are using systematically for all samples UV-VIS and NIR reflectance spectrometers, and sporadically a Fourier Transform Infrared (FTIR) spectrometer, an X-Ray Fluorescence (XRF) spectrometer and a Raman laser spectrometer on control samples. Calibration of the UV-VIS and NIR reflectance spectrometers is the main focus of this research in order to obtain the clearest spectra. The calibration of the UV-VIS and NIR reflectance spectrometers requires the use of a good light source as well as suitable optical fibers to create a signal that covers the widest range in wavelengths available. To eliminate noise towards the edges of this range, multiple measurements are averaged and data is processed by dividing the signal by reference spectra. Obtained spectra can be tested for accuracy by comparing them with stationary laboratory spectrometers such as the FTIR spectrometer. The Raman, UV-VIS and NIR are also used in combination with the ExoGeoLab mock-up lander during field campaigns (Foing, Stoker, Ehrenfreund, 2011) also brought again to Eifel in February 2016, to prove the applicability of the equipment in the field. Acknowledgements: we thank Dominic Doyle for ESTEC optical lab support, Euan Monaghan (Leiden U) for FTIR measurement support, Wim van Westrenen for access to VU samples, Oscar Kamps (Utrecht U./ESTEC), Aidan Cowley (EAC) and Matthias Sperl (DLR) for support discussions

The Zugspitze radiative closure experiment for quantifying water vapor absorption over the terrestrial and solar infrared - Part 2: Accurate calibration of high spectral-resolution infrared measurements of surface solar radiation

NASA Astrophysics Data System (ADS)

Reichert, Andreas; Rettinger, Markus; Sussmann, Ralf

2016-09-01

Quantitative knowledge of water vapor absorption is crucial for accurate climate simulations. An open science question in this context concerns the strength of the water vapor continuum in the near infrared (NIR) at atmospheric temperatures, which is still to be quantified by measurements. This issue can be addressed with radiative closure experiments using solar absorption spectra. However, the spectra used for water vapor continuum quantification have to be radiometrically calibrated. We present for the first time a method that yields sufficient calibration accuracy for NIR water vapor continuum quantification in an atmospheric closure experiment. Our method combines the Langley method with spectral radiance measurements of a high-temperature blackbody calibration source (< 2000 K). The calibration scheme is demonstrated in the spectral range 2500 to 7800 cm-1, but minor modifications to the method enable calibration also throughout the remainder of the NIR spectral range. The resulting uncertainty (2σ) excluding the contribution due to inaccuracies in the extra-atmospheric solar spectrum (ESS) is below 1 % in window regions and up to 1.7 % within absorption bands. The overall radiometric accuracy of the calibration depends on the ESS uncertainty, on which at present no firm consensus has been reached in the NIR. However, as is shown in the companion publication Reichert and Sussmann (2016), ESS uncertainty is only of minor importance for the specific aim of this study, i.e., the quantification of the water vapor continuum in a closure experiment. The calibration uncertainty estimate is substantiated by the investigation of calibration self-consistency, which yields compatible results within the estimated errors for 91.1 % of the 2500 to 7800 cm-1 range. Additionally, a comparison of a set of calibrated spectra to radiative transfer model calculations yields consistent results within the estimated errors for 97.7 % of the spectral range.

Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy.

PubMed

Mamani-Linares, L W; Gallo, C; Alomar, D

2012-02-01

Visible and near infrared reflectance spectroscopy (VIS-NIRS) was used to discriminate meat and meat juices from three livestock species. In a first trial, samples of Longissimus lumborum muscle, corresponding to beef (31) llamas (21) and horses (27), were homogenised and their spectra collected in reflectance (NIRSystems 6500 scanning monochromator, in the range of 400-2500 nm). In the second trial, samples of meat juice (same muscle) from the same species (20 beef, 19 llama and 19 horse) were scanned in folded transmission (transflectance). Discriminating models (PLS regression) were developed against "dummy" variables, testing different mathematical treatments of the spectra. Best models indentified the species of almost all samples by their meat (reflectance) or meat juice (transflectance) spectra. A few (three of beef and one of llama, for meat samples; one of beef and one of horse, for juice samples) were classified as uncertain. It is concluded that NIRS is an effective tool to recognise meat and meat juice from beef, llama and horses. Copyright © 2011 Elsevier Ltd. All rights reserved.

Wavelength-dependent penetration depth of near infrared radiation into cartilage.

PubMed

Padalkar, M V; Pleshko, N

2015-04-07

Articular cartilage is a hyaline cartilage that lines the subchondral bone in the diarthrodial joints. Near infrared (NIR) spectroscopy is emerging as a nondestructive modality for the evaluation of cartilage pathology; however, studies regarding the depth of penetration of NIR radiation into cartilage are lacking. The average thickness of human cartilage is about 1-3 mm, and it becomes even thinner as OA progresses. To ensure that spectral data collected is restricted to the tissue of interest, i.e. cartilage in this case, and not from the underlying subchondral bone, it is necessary to determine the depth of penetration of NIR radiation in different wavelength (frequency) regions. In the current study, we establish how the depth of penetration varies throughout the NIR frequency range (4000-10 000 cm(-1)). NIR spectra were collected from cartilage samples of different thicknesses (0.5 mm to 5 mm) with and without polystyrene placed underneath. A separate NIR spectrum of polystyrene was collected as a reference. It was found that the depth of penetration varied from ∼1 mm to 2 mm in the 4000-5100 cm(-1) range, ∼3 mm in the 5100-7000 cm(-1) range, and ∼5 mm in the 7000-9000 cm(-1) frequency range. These findings suggest that the best NIR region to evaluate cartilage with no subchondral bone contribution is in the range of 4000-7000 cm(-1).

500 days of SN 2013dy: spectra and photometry from the ultraviolet to the infrared

NASA Astrophysics Data System (ADS)

Pan, Y.-C.; Foley, R. J.; Kromer, M.; Fox, O. D.; Zheng, W.; Challis, P.; Clubb, K. I.; Filippenko, A. V.; Folatelli, G.; Graham, M. L.; Hillebrandt, W.; Kirshner, R. P.; Lee, W. H.; Pakmor, R.; Patat, F.; Phillips, M. M.; Pignata, G.; Röpke, F.; Seitenzahl, I.; Silverman, J. M.; Simon, J. D.; Sternberg, A.; Stritzinger, M. D.; Taubenberger, S.; Vinko, J.; Wheeler, J. C.

2015-10-01

SN 2013dy is a Type Ia supernova (SN Ia) for which we have compiled an extraordinary data set spanning from 0.1 to ˜ 500 d after explosion. We present 10 epochs of ultraviolet (UV) through near-infrared (NIR) spectra with Hubble Space Telescope/Space Telescope Imaging Spectrograph, 47 epochs of optical spectra (15 of them having high resolution), and more than 500 photometric observations in the BVrRiIZYJH bands. SN 2013dy has a broad and slowly declining light curve (Δm15(B) = 0.92 mag), shallow Si II λ 6355 absorption, and a low velocity gradient. We detect strong C II in our earliest spectra, probing unburned progenitor material in the outermost layers of the SN ejecta, but this feature fades within a few days. The UV continuum of SN 2013dy, which is strongly affected by the metal abundance of the progenitor star, suggests that SN 2013dy had a relatively high-metallicity progenitor. Examining one of the largest single set of high-resolution spectra for an SN Ia, we find no evidence of variable absorption from circumstellar material. Combining our UV spectra, NIR photometry, and high-cadence optical photometry, we construct a bolometric light curve, showing that SN 2013dy had a maximum luminosity of 10.0^{+4.8}_{-3.8} × 10^{42} erg s-1. We compare the synthetic light curves and spectra of several models to SN 2013dy, finding that SN 2013dy is in good agreement with a solar-metallicity W7 model.

Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state.

PubMed

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

2011-12-01

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. For the most part, previous research has investigated the development of NIRS-BCIs operating under synchronous control paradigms, which require the user to exert conscious control over their mental activity whenever the system is vigilant. Though functional, this is mentally demanding and an unnatural way to communicate. An attractive alternative to the synchronous control paradigm is system-paced control, in which users are required to consciously modify their brain activity only when they wish to affect the BCI output, and can remain in a more natural, 'no-control' state at all other times. In this study, we investigated the feasibility of a system-paced NIRS-BCI with one intentional control (IC) state corresponding to the performance of either mental arithmetic or mental singing. In particular, this involved determining if these tasks could be distinguished, individually, from the unconstrained 'no-control' state. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while eight able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a six-dimensional feature set, an overall classification accuracy of 71.2% across participants was achieved for the mental arithmetic versus no-control classification problem. While the mental singing versus no-control classification was less successful across participants (62.7% on average), four participants did attain accuracies well in excess of chance, three of which were above 70%. Analyses were performed offline. Collectively, these results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one IC state corresponding to either mental arithmetic or mental singing.

Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state

NASA Astrophysics Data System (ADS)

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

2011-10-01

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. For the most part, previous research has investigated the development of NIRS-BCIs operating under synchronous control paradigms, which require the user to exert conscious control over their mental activity whenever the system is vigilant. Though functional, this is mentally demanding and an unnatural way to communicate. An attractive alternative to the synchronous control paradigm is system-paced control, in which users are required to consciously modify their brain activity only when they wish to affect the BCI output, and can remain in a more natural, 'no-control' state at all other times. In this study, we investigated the feasibility of a system-paced NIRS-BCI with one intentional control (IC) state corresponding to the performance of either mental arithmetic or mental singing. In particular, this involved determining if these tasks could be distinguished, individually, from the unconstrained 'no-control' state. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while eight able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a six-dimensional feature set, an overall classification accuracy of 71.2% across participants was achieved for the mental arithmetic versus no-control classification problem. While the mental singing versus no-control classification was less successful across participants (62.7% on average), four participants did attain accuracies well in excess of chance, three of which were above 70%. Analyses were performed offline. Collectively, these results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one IC state corresponding to either mental arithmetic or mental singing.

Multivariate classification of the infrared spectra of cell and tissue samples

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

Haaland, D.M.; Jones, H.D.; Thomas, E.V.

1997-03-01

Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

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

Library of UV-Vis-NIR reflectance spectra of modern organic dyes from historic pattern-card coloured papers.

PubMed

Montagner, Cristina; Bacci, Mauro; Bracci, Susanna; Freeman, Rachel; Picollo, Marcello

2011-09-01

An accurate characterisation of the organic dyes used in artworks, especially those made of paper, is an important factor in designing safe conservation treatments. In the case of synthetic organic dyes used in modern works of art, for example, one frequently encountered difficulty is that some of these dyes are not still commercially available. Recognizing this problem, the authors of this paper present the results of an analysis of UV-Vis-NIR fibre optic reflectance spectra of 82 samples of dyed paper prepared with 41 dyes. The samples come from a historic book, The Dyeing of Paper in the Pulp, which was published by Interessen-Gemeinschaft (I.G.) Farbenindustrie in 1925. The dyes used in the paper pulp belong to the azo compounds, acridine, anthraquinone, azine, diphenylmethane, indigoid, methine, nitro, quinoline, thiazine, triphenylmethane, sulphur and xanthene classes. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate curve resolution using a combination of mid-infrared and near-infrared spectra for the analysis of isothermal epoxy curing reaction

NASA Astrophysics Data System (ADS)

Yamasaki, Hideki; Morita, Shigeaki

2018-05-01

Multivariate curve resolution (MCR) was applied to a hetero-spectrally combined dataset consisting of mid-infrared (MIR) and near-infrared (NIR) spectra collected during the isothermal curing reaction of an epoxy resin. An epoxy monomer, bisphenol A diglycidyl ether (BADGE), and a hardening agent, 4,4‧-diaminodiphenyl methane (DDM), were used for the reaction. The fundamental modes of the Nsbnd H and Osbnd H stretches were highly overlapped in the MIR region, while their first overtones could be independently identified in the NIR region. The concentration profiles obtained by MCR using the hetero-spectral combination showed good agreement with the results of calculations based on the Beer-Lambert law and the mass balance. The band assignments and absorption sites estimated by the analysis also showed good agreement with the results using two-dimensional (2D) hetero-correlation spectroscopy.

Multivariate curve resolution using a combination of mid-infrared and near-infrared spectra for the analysis of isothermal epoxy curing reaction.

PubMed

Yamasaki, Hideki; Morita, Shigeaki

2018-05-15

Multivariate curve resolution (MCR) was applied to a hetero-spectrally combined dataset consisting of mid-infrared (MIR) and near-infrared (NIR) spectra collected during the isothermal curing reaction of an epoxy resin. An epoxy monomer, bisphenol A diglycidyl ether (BADGE), and a hardening agent, 4,4'-diaminodiphenyl methane (DDM), were used for the reaction. The fundamental modes of the NH and OH stretches were highly overlapped in the MIR region, while their first overtones could be independently identified in the NIR region. The concentration profiles obtained by MCR using the hetero-spectral combination showed good agreement with the results of calculations based on the Beer-Lambert law and the mass balance. The band assignments and absorption sites estimated by the analysis also showed good agreement with the results using two-dimensional (2D) hetero-correlation spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

Gastric cancer differentiation using Fourier transform near-infrared spectroscopy with unsupervised pattern recognition

NASA Astrophysics Data System (ADS)

Yi, Wei-song; Cui, Dian-sheng; Li, Zhi; Wu, Lan-lan; Shen, Ai-guo; Hu, Ji-ming

2013-01-01

The manuscript has investigated the application of near-infrared (NIR) spectroscopy for differentiation gastric cancer. The 90 spectra from cancerous and normal tissues were collected from a total of 30 surgical specimens using Fourier transform near-infrared spectroscopy (FT-NIR) equipped with a fiber-optic probe. Major spectral differences were observed in the CH-stretching second overtone (9000-7000 cm-1), CH-stretching first overtone (6000-5200 cm-1), and CH-stretching combination (4500-4000 cm-1) regions. By use of unsupervised pattern recognition, such as principal component analysis (PCA) and cluster analysis (CA), all spectra were classified into cancerous and normal tissue groups with accuracy up to 81.1%. The sensitivity and specificity was 100% and 68.2%, respectively. These present results indicate that CH-stretching first, combination band and second overtone regions can serve as diagnostic markers for gastric cancer.

Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.

PubMed

Khan, Muhammad Jawad; Hong, Keum-Shik

2017-01-01

In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.

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.

Detection of fatty product falsifications using a portable near infrared spectrometer

NASA Astrophysics Data System (ADS)

Kalinin, A. V.; Krasheninnikov, V. N.

2017-01-01

Spreading sales of counterfeited fatty-oil foods leads to a development of portable and operational analyzer of typical fatty acids (FA) which may be a near infrared (NIR) spectrometer. In this work the calibration models for prediction of named FA were built with the spectra of FT-NIR spectrometer for different absorption bands of the FA. The best parameters were obtained for the wavelength sub-band 1.0-1.8 μ, which includes the 2nd and 3rd overtones of C-H stretching vibrations (near 1.7 and 1.2 μ) and the combination band (1.42 μ). Applicability of the portable spectrometer based on linear NIR array photosensor for the quality analysis of spread, butter and fish oil by the typical FA has been tested.

[Analysis of different forms Linderae Radix based on HPLC and NIRS fingerprints].

PubMed

Du, Wei-Feng; Yue, Xian-Ke; Wu, Yao; Ge, Wei-Hong; Lu, Tu-Lin; Wang, Zhi-Min

2016-10-01

Three different forms of Linderae Radix were evaluated by HPLC combined with NIRS fingerprint. The Linderae Radix was divided into three forms, including spindle root, straight root and old root. The HPLC fingerprints were developed, and then cluster analysis was performed using the SPSS software. The near-infrared spectra of Linderae Radix was collected, and then established the discriminant analysis model. The similarity values of the spindle root and straight root all were above 0.990, while the similarity value of the old root was less than 0.850. Two forms of Linderae Radix were obviously divided into three parts by the NIRS model and Cluster analysis. The results of HPLC and FT-NIR analysis showed the quality of Linderae Radix old root was different from the spindle root and straight root. The combined use of the two methods could identify different forms of Linderae Radix quickly and accurately. Copyright© by the Chinese Pharmaceutical Association.

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.

New low-resolution spectrometer spectra for IRAS sources

NASA Astrophysics Data System (ADS)

Volk, Kevin; Kwok, Sun; Stencel, R. E.; Brugel, E.

1991-12-01

Low-resolution spectra of 486 IRAS point sources with Fnu(12 microns) in the range 20-40 Jy are presented. This is part of an effort to extract and classify spectra that were not included in the Atlas of Low-Resolution Spectra and represents an extension of the earlier work by Volk and Cohen which covers sources with Fnu(12 microns) greater than 40 Jy. The spectra have been examined by eye and classified into nine groups based on the spectral morphology. This new classification scheme is compared with the mechanical classification of the Atlas, and the differences are noted. Oxygen-rich stars of the asymptotic giant branch make up 33 percent of the sample. Solid state features dominate the spectra of most sources. It is found that the nature of the sources as implied by the present spectral classification is consistent with the classifications based on broad-band colors of the sources.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

PubMed

Oyama, Katsunori; Sakatani, Kaoru

2016-01-01

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

Evaluation of a Compact Hybrid Brain-Computer Interface System

PubMed Central

Müller, Klaus-Robert; Schmitz, Christoph H.

2017-01-01

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation. PMID:28373984

Evaluation of a Compact Hybrid Brain-Computer Interface System.

PubMed

Shin, Jaeyoung; Müller, Klaus-Robert; Schmitz, Christoph H; Kim, Do-Won; Hwang, Han-Jeong

2017-01-01

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

Normal Raman and surface enhanced Raman spectroscopic experiments with thin layer chromatography spots of essential amino acids using different laser excitation sources.

PubMed

István, Krisztina; Keresztury, Gábor; Szép, Andrea

2003-06-01

A comparative study of the feasibility and efficiency of Raman spectroscopic detection of thin layer chromatography (TLC) spots of some weak Raman scatterers (essential amino acids, namely, glycine and L-forms of alanine, serine, valine, proline, hydroxyproline, and phenylalanine) was carried out using four different visible and near-infrared (NIR) laser radiations with wavelengths of 532, 633, 785, and 1064 nm. Three types of commercial TLC plates were tested and the possibility of inducing surface enhanced Raman scattering (SERS) by means of Ag-sol was also investigated. The spectra obtained from spotted analytes adsorbed on TLC plates were of very different quality strongly depending on the excitation wavelength, the wetness of the samples, and the compounds examined. The best results were obtained with the simple silica TLC plate, and it has been established that the longest wavelength (lowest energy) NIR excitation of a Nd:YAG laser is definitely more suitable for generating normal Raman scattering of analyte spots than any of the visible radiations. Concerning SERS with application of Ag-sol to the TLC spots, 1-3 orders of magnitude enhancement was observed with wet samples, the greatest with the 532 nm radiation and gradually smaller with the longer wavelength excitations. It is shown, however, that due to severe adsorption-induced spectral distortions and increased sensitivity to microscopic inhomogeneity of the sample, none of the SERS spectra obtained with the dispersive Raman microscope operating in the visible region were superior to the best NIR normal FT-Raman spectra, as far as sample identification is concerned.

Normal Raman and surface enhanced Raman spectroscopic experiments with thin layer chromatography spots of essential amino acids using different laser excitation sources

NASA Astrophysics Data System (ADS)

István, Krisztina; Keresztury, Gábor; Szép, Andrea

2003-06-01

A comparative study of the feasibility and efficiency of Raman spectroscopic detection of thin layer chromatography (TLC) spots of some weak Raman scatterers (essential amino acids, namely, glycine and L-forms of alanine, serine, valine, proline, hydroxyproline, and phenylalanine) was carried out using four different visible and near-infrared (NIR) laser radiations with wavelengths of 532, 633, 785, and 1064 nm. Three types of commercial TLC plates were tested and the possibility of inducing surface enhanced Raman scattering (SERS) by means of Ag-sol was also investigated. The spectra obtained from spotted analytes adsorbed on TLC plates were of very different quality strongly depending on the excitation wavelength, the wetness of the samples, and the compounds examined. The best results were obtained with the simple silica TLC plate, and it has been established that the longest wavelength (lowest energy) NIR excitation of a Nd:YAG laser is definitely more suitable for generating normal Raman scattering of analyte spots than any of the visible radiations. Concerning SERS with application of Ag-sol to the TLC spots, 1-3 orders of magnitude enhancement was observed with wet samples, the greatest with the 532 nm radiation and gradually smaller with the longer wavelength excitations. It is shown, however, that due to severe adsorption-induced spectral distortions and increased sensitivity to microscopic inhomogeneity of the sample, none of the SERS spectra obtained with the dispersive Raman microscope operating in the visible region were superior to the best NIR normal FT-Raman spectra, as far as sample identification is concerned.

Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms.

PubMed

Malegori, Cristina; Nascimento Marques, Emanuel José; de Freitas, Sergio Tonetto; Pimentel, Maria Fernanda; Pasquini, Celio; Casiraghi, Ernestina

2017-04-01

The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits. Copyright © 2016 Elsevier B.V. All rights reserved.

Evidence of ventricular contamination of the optical signal in preterm neonates with post hemorrhagic ventricle dilation

NASA Astrophysics Data System (ADS)

Kishimoto, J.; Diop, M.; McLachlan, P.; de Ribaupierre, S.; Lee, D. S. C.; St. Lawrence, K.

2015-03-01

Dilation of the cerebral ventricles is a common condition in preterm neonates with intraventricular hemorrhage (IVH). This post hemorrhagic ventricle dilation (PHVD) can lead to lifelong neurological impairment through ischemic injury due to increased intracranial pressure (ICP). Interventions, such as ventricular tapping to remove cerebrospinal fluid (CSF), are used to prevent injury, but determining the optimal time for treatment is difficult as clinical signs of increased ICP lack sensitivity. There is a growing interest in using near-infrared spectroscopy (NIRS) because of its ability to monitor cerebral oxygen saturation (StO2) at the bedside. However, the accuracy of NIRS may be affected by signal contamination from enlarged ventricles, especially if there are blood breakdown products (bbp) in CSF following IVH. To investigate this, serial NIR spectra from the head and from CSF samples were acquired over a month from seven IVH patients undergoing treatment for PHVD. Over time, the visual appearance of the CSF samples progressed from dark brown ("tea color") to clear yellow, reflecting the reduction in bbp concentration as confirmed by the stronger absorption around 760 nm at the earlier time points. All CSF samples contained strong absorption at 960 nm due to water. More importantly the same trend in these absorption features was observed in the in vivo spectra, and Monte Carlo simulations confirmed the potential for signal contamination from enlarged ventricles. These findings highlight the challenges of accurately measuring StO2 in this patient population and the necessity of using a hyperspectral NIRS system to resolve the additional chromophores.

A case study of real-time monitoring of solid-state phase transformations in acoustically levitated particles using near infrared and Raman spectroscopy.

PubMed

Rehder, Sönke; Wu, Jian X; Laackmann, Julian; Moritz, Hans-Ulrich; Rantanen, Jukka; Rades, Thomas; Leopold, Claudia S

2013-01-23

The objective of this study was to monitor the amorphous-to-crystalline solid-state phase transformation kinetics of the model drug ibuprofen with spectroscopic methods during acoustic levitation. Chemical and physical information was obtained by real-time near infrared (NIRS) and Raman spectroscopy measurements. The recrystallisation kinetic parameters (overall recrystallisation rate constant β and the time needed to reach 50% of the equilibrated level t(50)), were determined using a multivariate curve resolution approach. The acoustic levitation device coupled with non-invasive spectroscopy enabled monitoring of the recrystallisation process of the difficult-to-handle (adhesive) amorphous sample. The application of multivariate curve resolution enabled isolation of the underlying pure spectra, which corresponded well with the reference spectra of amorphous and crystalline ibuprofen. The recrystallisation kinetic parameters were estimated from the recrystallisation profiles. While the empirical recrystallisation rate constant determined by NIR and Raman spectroscopy were comparable, the lag time for recrystallisation was significantly lower with Raman spectroscopy as compared to NIRS. This observation was explained by the high energy density of the Raman laser beam, which might have led to local heating effects of the sample and thus reduced the recrystallisation onset time. It was concluded that acoustic levitation with NIR and Raman spectroscopy combined with multivariate curve resolution allowed direct determination of the recrystallisation kinetics of amorphous drugs and thus is a promising technique for monitoring solid-state phase transformations of adhesive small-sized samples during the early phase of drug development. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Quantitative determination and classification of energy drinks using near-infrared spectroscopy.

PubMed

Rácz, Anita; Héberger, Károly; Fodor, Marietta

2016-09-01

Almost a hundred commercially available energy drink samples from Hungary, Slovakia, and Greece were collected for the quantitative determination of their caffeine and sugar content with FT-NIR spectroscopy and high-performance liquid chromatography (HPLC). Calibration models were built with partial least-squares regression (PLSR). An HPLC-UV method was used to measure the reference values for caffeine content, while sugar contents were measured with the Schoorl method. Both the nominal sugar content (as indicated on the cans) and the measured sugar concentration were used as references. Although the Schoorl method has larger error and bias, appropriate models could be developed using both references. The validation of the models was based on sevenfold cross-validation and external validation. FT-NIR analysis is a good candidate to replace the HPLC-UV method, because it is much cheaper than any chromatographic method, while it is also more time-efficient. The combination of FT-NIR with multidimensional chemometric techniques like PLSR can be a good option for the detection of low caffeine concentrations in energy drinks. Moreover, three types of energy drinks that contain (i) taurine, (ii) arginine, and (iii) none of these two components were classified correctly using principal component analysis and linear discriminant analysis. Such classifications are important for the detection of adulterated samples and for quality control, as well. In this case, more than a hundred samples were used for the evaluation. The classification was validated with cross-validation and several randomization tests (X-scrambling). Graphical Abstract The way of energy drinks from cans to appropriate chemometric models.

Multipoint NIR spectroscopy for gross composition analysis of powdered infant formula under various motion conditions.

PubMed

Cama-Moncunill, Raquel; Markiewicz-Keszycka, Maria; Dixit, Yash; Cama-Moncunill, Xavier; Casado-Gavalda, Maria P; Cullen, Patrick J; Sullivan, Carl

2016-07-01

Powdered infant formula (PIF) is a worldwide, industrially produced, human milk substitute. Manufacture of PIF faces strict quality controls in order to ensure that the product meets all compositional requirements. Near-infrared (NIR) spectroscopy is a rapid, non-destructive and well-qualified technique for food quality assessments. The use of fibre-optic NIR sensors allows measuring in-line and at real-time, and can record spectra from different stages of the process. The non-contact character of fibre-optic sensors can be enhanced by fitting collimators, which allow operation at various distances. The system, based on a Fabry-Perot interferometer, records four spectra concurrently, rather than consecutively as in the "quasi-simultaneous" multipoint NIR systems. In the present study, a novel multipoint NIR spectroscopy system equipped with four fibre-optic probes with collimators was assessed to determine carbohydrate and protein contents of PIF samples under static and motion conditions (0.02, 0.15 and 0.30m/s) to simulate possible industrial scenarios. Best results were obtained under static conditions providing a R(2) of calibration of 0.95 and RMSEP values of 1.89%. Yet, considerably low values of RMSEP, for instance 2.70% at 0.15m/s, were provided with the in-motion predictions, demonstrating the system's potential for in/on-line applications at various levels of speed. The current work also evaluated the viability of using general off-line calibrations developed under static conditions for on/in-line applications subject to motion. To this end, calibrations in both modes were developed and compared. Best results were obtained with specific calibrations; however, reasonably accurate models were obtained with the general calibration. Furthermore, this work illustrated independency of the collimator-probe setup by characterizing PIF samples simultaneously recorded according to their carbohydrate content, even when measured under different conditions. Therefore, the improved multipoint NIR approach constitutes a potential in/on-line tool for quality evaluation of PIF over the manufacturing process. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparison of time-gated surface-enhanced Raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples.

PubMed

Kögler, Martin; Paul, Andrea; Anane, Emmanuel; Birkholz, Mario; Bunker, Alex; Viitala, Tapani; Maiwald, Michael; Junne, Stefan; Neubauer, Peter

2018-06-08

The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here we compare time-gated Raman (TG-Raman)-, continuous wave NIR-process Raman (NIR-Raman) and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis. This article is protected by copyright. All rights reserved. © 2018 American Institute of Chemical Engineers.

Near-infrared Raman spectroscopy to detect anti-Toxoplasma gondii antibodies in blood sera of domestic cats

NASA Astrophysics Data System (ADS)

Duarte, Janaina; Pacheco, Marcos T. T.; Silveira, Landulfo, Jr.; Machado, Rosangela Z.; Martins, Rodrigo A. L.; Zangaro, Renato A.; Villaverde, Antonio G. J. B.

2001-05-01

Near-infrared (NIR) Raman spectroscopy has been studied for the last years for many biomedical applications. It is a powerful tool for biological materials analysis. Toxoplasmosis is an important zoonosis in public health, cats being the principal responsible for the transmission of the disease in Brazil. The objective of this work is to investigate a new method of diagnosis of this disease. NIR Raman spectroscopy was used to detect anti Toxoplasma gondii antibodies in blood sera from domestic cats, without sample preparation. In all, six blood serum samples were used for this study. A previous serological test was done by the Indirect Immunoenzymatic Assay (ELISA) to permit a comparative study between both techniques and it showed that three serum samples were positive and the other three were negative to toxoplasmosis. Raman spectra were taken for all the samples and analyzed by using the principal components analysis (PCA). A diagnosis parameter was defined from the analysis of the second and third principal components of the Raman spectra. It was found that this parameter can detect the infection level of the animal. The results have indicated that NIR Raman spectroscopy, associated to the PCA can be a promising technique for serological analysis, such as toxoplasmosis, allowing a fast and sensitive method of diagnosis.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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.

Eyes-closed hybrid brain-computer interface employing frontal brain activation.

PubMed

Shin, Jaeyoung; Müller, Klaus-Robert; Hwang, Han-Jeong

2018-01-01

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroencephalography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA- from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 ± 10.3%) was shown to be significantly higher than those of unimodal EEG-based (77.3 ± 15.9%) and NIRS-based BCI (75.9 ± 6.3%). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.

Seasonal trends in separability of leaf reflectance spectra for Ailanthus altissima and four other tree species

NASA Astrophysics Data System (ADS)

Burkholder, Aaron

This project investigated the spectral separability of the invasive species Ailanthus altissima, commonly called tree of heaven, and four other native species. Leaves were collected from Ailanthus and four native tree species from May 13 through August 24, 2008, and spectral reflectance factor measurements were gathered for each tree using an ASD (Boulder, Colorado) FieldSpec Pro full-range spectroradiometer. The original data covered the range from 350-2500 nm, with one reflectance measurement collected per one nm wavelength. To reduce dimensionality, the measurements were resampled to the actual resolution of the spectrometer's sensors, and regions of atmospheric absorption were removed. Continuum removal was performed on the reflectance data, resulting in a second dataset. For both the reflectance and continuum removed datasets, least angle regression (LARS) and random forest classification were used to identify a single set of optimal wavelengths across all sampled dates, a set of optimal wavelengths for each date, and the dates for which Ailanthus is most separable from other species. It was found that classification accuracy varies both with dates and bands used. Contrary to expectations that early spring would provide the best separability, the lowest classification error was observed on July 22 for the reflectance data, and on May 13, July 11 and August 1 for the continuum removed data. This suggests that July and August are also potentially good months for species differentiation. Applying continuum removal in many cases reduced classification error, although not consistently. Band selection seems to be more important for reflectance data in that it results in greater improvement in classification accuracy, and LARS appears to be an effective band selection tool. The optimal spectral bands were selected from across the spectrum, often with bands from the blue (401-431 nm), NIR (1115 nm) and SWIR (1985-1995 nm), suggesting that hyperspectral sensors with broad wavelength sensitivity are important for mapping and identification of Ailanthus.

On the construction of a new stellar classification template library for the LAMOST spectral analysis pipeline

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

Wei, Peng; Luo, Ali; Li, Yinbi

2014-05-01

The LAMOST spectral analysis pipeline, called the 1D pipeline, aims to classify and measure the spectra observed in the LAMOST survey. Through this pipeline, the observed stellar spectra are classified into different subclasses by matching with template spectra. Consequently, the performance of the stellar classification greatly depends on the quality of the template spectra. In this paper, we construct a new LAMOST stellar spectral classification template library, which is supposed to improve the precision and credibility of the present LAMOST stellar classification. About one million spectra are selected from LAMOST Data Release One to construct the new stellar templates, andmore » they are gathered in 233 groups by two criteria: (1) pseudo g – r colors obtained by convolving the LAMOST spectra with the Sloan Digital Sky Survey ugriz filter response curve, and (2) the stellar subclass given by the LAMOST pipeline. In each group, the template spectra are constructed using three steps. (1) Outliers are excluded using the Local Outlier Probabilities algorithm, and then the principal component analysis method is applied to the remaining spectra of each group. About 5% of the one million spectra are ruled out as outliers. (2) All remaining spectra are reconstructed using the first principal components of each group. (3) The weighted average spectrum is used as the template spectrum in each group. Using the previous 3 steps, we initially obtain 216 stellar template spectra. We visually inspect all template spectra, and 29 spectra are abandoned due to low spectral quality. Furthermore, the MK classification for the remaining 187 template spectra is manually determined by comparing with 3 template libraries. Meanwhile, 10 template spectra whose subclass is difficult to determine are abandoned. Finally, we obtain a new template library containing 183 LAMOST template spectra with 61 different MK classes by combining it with the current library.« less

Raman scattering or fluorescence emission? Raman spectroscopy study on lime-based building and conservation materials.

PubMed

Kaszowska, Zofia; Malek, Kamilla; Staniszewska-Slezak, Emilia; Niedzielska, Karina

2016-12-05

This work presents an in-depth study on Raman spectra excited with 1064 and 532nm lasers of lime binders employed in the past as building materials and revealed today as valuable conservation materials. We focus our interest on the bands of strong intensity, which are present in the spectra of all binders acquired with laser excitation at 1064nm, but absent in the corresponding spectra acquired with laser excitation at 532nm. We suggest, that the first group of spectra represents fluorescence phenomena of unknown origin and the second true Raman scattering. In our studies, we also include two other phases of lime cycle, i.e. calcium carbonate (a few samples of calcite of various origins) and calcium oxide (quicklime) to assess how structural and chemical transformations of lime phases affect the NIR-Raman spectral profile. Furthermore, we analyse a set of carbonated limewashes and lime binders derived from old plasters to give an insight into their spectral characteristics after excitation with the 1064nm laser line. NIR-Raman micro-mapping results are also presented to reveal the spatial distribution of building materials and fluorescent species in the cross-section of plaster samples taken from a 15th century chapel. Our study shows that the Raman analysis can help identify lime-based building and conservation materials, however, a caution is advised in the interpretation of the spectra acquired using 1064nm excitation. Copyright © 2016. Published by Elsevier B.V.

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.

Near-Infrared Spectroscopy Using a Supercontinuum Laser: Application to Long Wavelength Transmission Spectra of Barley Endosperm and Oil.

PubMed

Ringsted, Tine; Dupont, Sune; Ramsay, Jacob; Jespersen, Birthe Møller; Sørensen, Klavs Martin; Keiding, Søren Rud; Engelsen, Søren Balling

2016-07-01

The supercontinuum laser is a new type of light source, which combines the collimation and intensity of a laser with the broad spectral region of a lamp. Using such a source therefore makes it possible to focus the light onto small sample areas without losing intensity and thus facilitate either rapid or high-intensity measurements. Single seed transmission analysis in the long wavelength (LW) near-infrared (NIR) region is one area that might benefit from a brighter light source such as the supercontinuum laser. This study is aimed at building an experimental spectrometer consisting of a supercontinuum laser source and a dispersive monochromator in order to investigate its capability to measure the barley endosperm using transmission experiments in the LW NIR region. So far, barley and wheat seeds have only been studied using NIR transmission in the short wavelength region up to 1100 nm. However, the region in the range of 2260-2380 nm has previously shown to be particularly useful in differentiating barley phenotypes using NIR spectroscopy in reflectance mode. In the present study, 350 seeds (consisting of 70 seeds from each of five barley genotypes) in 1 mm slices were measured by NIR transmission in the range of 2235-2381 nm and oils from the same five barley genotypes were measured in a cuvette with a 1 mm path length in the range of 2003-2497 nm. The spectra of the barley seeds could be classified according to genotypes by principal component analysis; and spectral covariances with reference analysis of moisture, β-glucan, starch, protein and lipid were established. The spectral variations of the barley oils were compared to the fatty acid compositions as measured using gas chromotography-mass spectrometry (GC-MS). © The Author(s) 2016.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

PubMed

Peng, Yi; Xiong, Xiong; Adhikari, Kabindra; Knadel, Maria; Grunwald, Sabine; Greve, Mogens Humlekrog

2015-01-01

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l'Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the 'upland model' was able to more accurately predict SOC compared with the 'upland & wetland model'. However, the separately calibrated 'upland and wetland model' did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

PubMed Central

Peng, Yi; Xiong, Xiong; Adhikari, Kabindra; Knadel, Maria; Grunwald, Sabine; Greve, Mogens Humlekrog

2015-01-01

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM). PMID:26555071

EARLY OBSERVATIONS AND ANALYSIS OF THE TYPE Ia SN 2014J IN M82

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

Marion, G. H.; Vinkó, J.; Sand, D. J.

2015-01-01

We present optical and near infrared (NIR) observations of the nearby Type Ia SN 2014J. Seventeen optical and 23 NIR spectra were obtained from 10 days before (–10d) to 10 days after (+10d) the time of maximum B-band brightness. The relative strengths of absorption features and their patterns of development can be compared at one day intervals throughout most of this period. Carbon is not detected in the optical spectra, but we identify C I λ1.0693 in the NIR spectra. Mg II lines with high oscillator strengths have higher initial velocities than other Mg II lines. We show that the velocity differences canmore » be explained by differences in optical depths due to oscillator strengths. The spectra of SN 2014J show that it is a normal SN Ia, but many parameters are near the boundaries between normal and high-velocity subclasses. The velocities for O I, Mg II, Si II, S II, Ca II, and Fe II suggest that SN 2014J has a layered structure with little or no mixing. That result is consistent with the delayed detonation explosion models. We also report photometric observations, obtained from –10d to +29d, in the UBVRIJH and K{sub s} bands. The template fitting package SNooPy is used to interpret the light curves and to derive photometric parameters. Using R{sub V} = 1.46, which is consistent with previous studies, SNooPy finds that A{sub V} = 1.80 for E(B – V){sub host} = 1.23 ± 0.06 mag. The maximum B-band brightness of –19.19 ± 0.10 mag was reached on February 1.74 UT ± 0.13 days and the supernova has a decline parameter, Δm {sub 15}, of 1.12 ± 0.02 mag.« less

Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans.

PubMed

Tolessa, Kassaye; Rademaker, Michael; De Baets, Bernard; Boeckx, Pascal

2016-04-01

The growing global demand for specialty coffee increases the need for improved coffee quality assessment methods. Green bean coffee quality analysis is usually carried out by physical (e.g. black beans, immature beans) and cup quality (e.g. acidity, flavour) evaluation. However, these evaluation methods are subjective, costly, time consuming, require sample preparation and may end up in poor grading systems. This calls for the development of a rapid, low-cost, reliable and reproducible analytical method to evaluate coffee quality attributes and eventually chemical compounds of interest (e.g. chlorogenic acid) in coffee beans. The aim of this study was to develop a model able to predict coffee cup quality based on NIR spectra of green coffee beans. NIR spectra of 86 samples of green Arabica beans of varying quality were analysed. Partial least squares (PLS) regression method was used to develop a model correlating spectral data to cupping score data (cup quality). The selected PLS model had a good predictive power for total specialty cup quality and its individual quality attributes (overall cup preference, acidity, body and aftertaste) showing a high correlation coefficient with r-values of 90, 90,78, 72 and 72, respectively, between measured and predicted cupping scores for 20 out of 86 samples. The corresponding root mean square error of prediction (RMSEP) was 1.04, 0.22, 0.27, 0.24 and 0.27 for total specialty cup quality, overall cup preference, acidity, body and aftertaste, respectively. The results obtained suggest that NIR spectra of green coffee beans are a promising tool for fast and accurate prediction of coffee quality and for classifying green coffee beans into different specialty grades. However, the model should be further tested for coffee samples from different regions in Ethiopia and test if one generic or region-specific model should be developed. Copyright © 2015 Elsevier B.V. All rights reserved.

Melamine detection in infant formula powder using near- and mid-infrared spectroscopy.

PubMed

Mauer, Lisa J; Chernyshova, Alona A; Hiatt, Ashley; Deering, Amanda; Davis, Reeta

2009-05-27

Near- and mid-infrared spectroscopy methods (NIR, FTIR-ATR, FTIR-DRIFT) were evaluated for the detection and quantification of melamine in infant formula powder. Partial least-squares (PLS) models were established for correlating spectral data to melamine concentration: R(2) > 0.99, RMSECV ≤ 0.9, and RPD ≥ 12. Factorization analysis of spectra was able to differentiate unadulterated infant formula powder from samples containing 1 ppm melamine with no misclassifications, a confidence level of 99.99%, and selectivity > 2. These nondestructive methods require little or no sample preparation. The NIR method has an assay time of 1 min, and a 2 min total time to detection. The FTIR methods require up to 5 min for melamine detection. Therefore, NIR and FTIR methods enable rapid detection of 1 ppm melamine in infant formula powder.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging

PubMed Central

Jiang, Yu; Li, Changying; Takeda, Fumiomi

2016-01-01

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 − 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising. PMID:27767050

Is there Place for Perfectionism in the NIR Spectral Data Reduction?

NASA Astrophysics Data System (ADS)

Chilingarian, Igor

2017-09-01

"Despite the crucial importance of the near-infrared spectral domain for understanding the star formation and galaxy evolution, NIR observations and data reduction represent a significant challenge. The known complexity of NIR detectors is aggravated by the airglow emission in the upper atmosphere and the water absorption in the troposphere so that up until now, the astronomical community is divided on the issue whether ground based NIR spectroscopy has a future or should it move completely to space (JWST, Euclid, WFIRST). I will share my experience of pipeline development for low- and intermediate-resolution spectrographs operated at Magellan and MMT. The MMIRS data reduction pipeline became the first example of the sky subtraction quality approaching the limit set by the Poisson photon noise and demonstrated the feasibility of low-resolution (R=1200-3000) NIR spectroscopy from the ground even for very faint (J=24.5) continuum sources. On the other hand, the FIRE Bright Source Pipeline developed specifically for high signal-to-noise intermediate resolution stellar spectra proves that systematics in the flux calibration and telluric absorption correction can be pushed down to the (sub-)percent level. My conclusion is that even though substantial effort and time investment is needed to design and develop NIR spectroscopic pipelines for ground based instruments, it will pay off, if done properly, and open new windows of opportunity in the ELT era."

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy.

PubMed

Kusumaningrum, Dewi; Lee, Hoonsoo; Lohumi, Santosh; Mo, Changyeun; Kim, Moon S; Cho, Byoung-Kwan

2018-03-01

The viability of seeds is important for determining their quality. A high-quality seed is one that has a high capability of germination that is necessary to ensure high productivity. Hence, developing technology for the detection of seed viability is a high priority in agriculture. Fourier transform near-infrared (FT-NIR) spectroscopy is one of the most popular devices among other vibrational spectroscopies. This study aims to use FT-NIR spectroscopy to determine the viability of soybean seeds. Viable and artificial ageing seeds as non-viable soybeans were used in this research. The FT-NIR spectra of soybean seeds were collected and analysed using a partial least-squares discriminant analysis (PLS-DA) to classify viable and non-viable soybean seeds. Moreover, the variable importance in projection (VIP) method for variable selection combined with the PLS-DA was employed. The most effective wavelengths were selected by the VIP method, which selected 146 optimal variables from the full set of 1557 variables. The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

In-line NIR spectroscopy for the understanding of polymer-drug interaction during pharmaceutical hot-melt extrusion.

PubMed

Saerens, Lien; Dierickx, Lien; Quinten, Thomas; Adriaensens, Peter; Carleer, Robert; Vervaet, Chris; Remon, Jean Paul; De Beer, Thomas

2012-05-01

The aim was to evaluate near-infrared spectroscopy for the in-line determination of the drug concentration, the polymer-drug solid-state behaviour and molecular interactions during hot-melt extrusion. Kollidon® SR was extruded with varying metoprolol tartrate (MPT) concentrations (20%, 30% and 40%) and monitored using NIR spectroscopy. A PLS model allowed drug concentration determination. The correlation between predicted and real MPT concentrations was good (R(2)=0.97). The predictive performance of the model was evaluated by the root mean square error of prediction, which was 1.54%. Kollidon® SR with 40% MPT was extruded at 105°C and 135°C to evaluate NIR spectroscopy for in-line polymer-drug solid-state characterisation. NIR spectra indicated the presence of amorphous MPT and hydrogen bonds between drug and polymer in the extrudates. More amorphous MPT and interactions could be found in the extrudates produced at 135°C than at 105°C. Raman spectroscopy, DSC and ATR FT-IR were used to confirm the NIR observations. Due to the instability of the formulation, only in-line Raman spectroscopy was an adequate confirmation tool. NIR spectroscopy is a potential PAT-tool for the in-line determination of API concentration and for the polymer-drug solid-state behaviour monitoring during pharmaceutical hot-melt extrusion. Copyright © 2012 Elsevier B.V. All rights reserved.

An Application for the Quantitative Analysis of Pharmaceutical Tablets Using a Rapid Switching System Between a Near-Infrared Spectrometer and a Portable Near-Infrared Imaging System Equipped with Fiber Optics.

PubMed

Murayama, Kodai; Ishikawa, Daitaro; Genkawa, Takuma; Ozaki, Yukihiro

2018-04-01

We present a rapid switching system between a newly developed near-infrared (NIR) spectrometer and its imaging system to select the spot size of a diffuse reflectance (DR) probe. In a previous study, we developed a portable NIR imaging system, known as D-NIRs, which has significant advantages over other systems. Its high speed, high spectral resolution, and portability are particularly useful in the process of monitoring pharmaceutical tablets. However, the spectral accuracies relating to the changes in the formulation of the pharmaceutical tablets have not been fully discussed. Therefore, we improved the rapid optical switching system and present a new model of D-NIRs (ND-NIRs) here. This system can automatically switch the optical paths of the DR and NIR imaging probes, greatly contributing to the simultaneous measurement of both the imaging and spot. The NIR spectra of the model tablets, including 0-10% ascorbic acid, were measured and simultaneous NIR images of the tablets were obtained. The predicted results using spot sizes for the DR probe of 1 and 5 mm diameter, resulted in concentrations of R2 = 0.79 and 0.94, with root mean square errors (RMSE) of 1.78 and 0.89, respectively. For tablets with a high concentration of ascorbic acid, the NIR imaging results showed inhomogeneity in concentration. However, the predicted values for the low concentration samples appeared higher than the known concentration of the tablets, although the homogeneity of the concentration was confirmed. In addition, the optimal spot size using NIR imaging data was estimated to be 5-7 mm. The results obtained in this study show that the spot size of the fiber probe, attached to a spectrometer, is important in developing a highly reliable model to determine the component concentration of a tablet.

Advances in R&D in near-infrared spectroscopy in Japan

NASA Astrophysics Data System (ADS)

Kawano, Sumio; Iwamoto, Mutsuo

1991-02-01

More than 20 years ago when Mr. K. H. Norris firstly introduced the near infrared spectroscopy (NIRS) as a powerful technology in the field of composition analysis of cereals those who were interested in the area of classical spectroscopy would not like to recognize its potential. This tendency still remains at present however it leaves no room for doubt that from viewpoints of applied spectroscopy the NIRS has consolidated its position. From a viewpoint of NIRS application in the field of nondestructive or non invasive measuring techniques history of this technology is only the last decade in Japan. However since the technology was firstly introduced to composition analysis of agricultural commodities in the same manner as in other countries R and D have been growing more actively in diversified fields such as agriculture and industry as well as medical science. In addition the NIRS technology are becoming of general interest by combining other techniques to create various hyphenated instrumentations such as FTNIR MCFTNIR NIRCT and NIR-NMR. In this paper new trends of R D on NIR spectroscopy which are being conducted in Japan will be reviewed. 2. S1JMMARY OF PRESENT R D ON NIRS IN JAPAN NIRS applications reported in the last 3 years are summarized in Table 1. Table 1 Applications of NIRS in Japan Application for Agriculture Taste evaluation of rice and coffee Determination of chemical compositions rice for breeding Determination of chemical compositions in tea Determination of sugar contents in intact peaches Japanese pears Satsuma oranges and apples Determination of sugars and acids in intact tomatoes Determination of forage composition Application for Industry Analysis of state of water in foods Application of analyzing Maillard Reaction''s Process Pattern recognition of NIR spectra as related to process control of roasting coffee beans Quality control of tea processing Determination of moisture content of Surimi products 2 / SPIE Vol. 1379 Optics in Agriculture (1990)

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.

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

Spectral evidence for multi-pathway contribution to the upconversion pathway in NaYF4:Yb3+,Er3+ phosphors.

PubMed

Cho, Youngho; Song, Si Won; Lim, Soo Yeong; Kim, Jae Hun; Park, Chan Ryang; Kim, Hyung Min

2017-03-08

Although upconversion phosphors have been widely used in nanomedicine, laser engineering, bioimaging, and solar cell technology, the upconversion luminescence mechanism of the phosphors has been fiercely debated. A comprehensive understanding of upconversion photophysics has been significantly impeded because the number of photons incorporated in the process in different competitive pathways could not be resolved. Few convincing results to estimate the contribution of each of the two-, three-, and four-photon channels of near-infrared (NIR) energy have been reported in yielding upconverted visible luminescence. In this study, we present the energy upconversion process occurring in NaYF 4 :Yb 3+ ,Er 3+ phosphors as a function of excitation frequency and power density. We investigated the upconversion mechanism of lanthanide phosphors by comparing UV/VIS one-photon excitation spectra and NIR multi-photon spectra. A detailed analysis of minor transitions in one-photon spectra and luminescence decay enables us to assign electronic origins of individual bands in multi-photon upconversion luminescence and provides characteristic transitions representing the corresponding upconversion channel. Furthermore, we estimated the quantitative contribution of multiple channels with respect to irradiation power and excitation energy.

Combining near infrared spectra of feces and geostatistics to generate forage nutritional quality maps across landscapes

NASA Astrophysics Data System (ADS)

Jean, Pierre-Olivier; Bradley, Robert; Tremblay, Jean-Pierre

2015-04-01

An important asset for the management of wild ungulates is the ability to recognize the spatial distribution of forage quality across heterogeneous landscapes. To do so typically requires knowledge of which plant species are eaten, in what abundance they are eaten, and what their nutritional quality might be. Acquiring such data may be, however, difficult and time consuming. Here, we are proposing a rapid and cost-effective forage quality monitoring tool that combines near infrared (NIR) spectra of fecal samples and easily obtained data on plant community composition. Our approach rests on the premise that NIR spectra of fecal samples collected within low population density exclosures reflect the optimal forage quality of a given landscape. Forage quality can thus be based on the Mahalanobis distance of fecal spectral scans across the landscape relative to fecal spectral scans inside exclosures (referred to as DISTEX). The Gi* spatial autocorrelation statistic can then be applied among neighbouring DISTEX values to detect and map 'hot-spots' and 'cold-spots' of nutritional quality over the landscape. We tested our approach in a heterogeneous boreal landscape on Anticosti Island (Qu

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy.

PubMed

Liu, Yan-De; Ying, Yi-Bin; Fu, Xia-Ping

2005-03-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy*

PubMed Central

Liu, Yan-de; Ying, Yi-bin; Fu, Xia-ping

2005-01-01

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r 2) of 0.759, low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way. PMID:15682498

Additive assisted hydrothermal synthesis, characterization and optical properties of one dimensional DyPO4:Ce3+ nanostructures

NASA Astrophysics Data System (ADS)

Khajuria, H.; Kumar, M.; Singh, R.; Ladol, J.; Nawaz Sheikh, H.

2018-05-01

One dimensional nanostructures of cerium doped dysprosium phosphate (DyPO4:Ce3+) were synthesized via hydrothermal route in the presence of different surfactants [sodium dodecyl sulfate (SDS), dodecyl sulfosuccinate (DSS), polyvinyl pyrollidone (PVP)] and solvent [ethylene glycol and water]. The prepared nanostructures were characterized by Powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), Field emission scanning electron microscopy (FE-SEM), Transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), UV-VIS-NIR absorption spectrophotometer and photoluminescence (PL) studies. The PXRD and FTIR results indicate purity, good crystallinity and effective doping of Ce3+ in nanostructures. SEM and TEM micrographs display nanorods, nanowires and nanobundles like morphology of DyPO4:Ce3+. Energy-dispersive X-ray spectra (EDS) of DyPO4:Ce3+nanostructures confirm the presence of dopant. UV-VIS-NIR absorption spectra of prepared compounds are used to calculate band gap and explore their optical properties. Luminescent properties of DyPO4:Ce3+ was studied by using PL emission spectra. The effect of additives and solvents on the uniformity, morphology and optical properties of the nanostructures were studied in detail.

The investigation of brain-computer interface for motor imagery and execution using functional near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, Zhen; Jiao, Xuejun; Xu, Fengang; Jiang, Jin; Yang, Hanjun; Cao, Yong; Fu, Jiahao

2017-01-01

Functional near-infrared spectroscopy (fNIRS), which can measure cortex hemoglobin activity, has been widely adopted in brain-computer interface (BCI). To explore the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion. We measured changes of oxygenated hemoglobin (HBO) and deoxygenated hemoglobin (HBR) on PFC and Motor Cortex (MC) when 15 subjects performing hand extension and finger tapping tasks. The mean, slope, quadratic coefficient and approximate entropy features were extracted from HBO as the input of support vector machine (SVM). For the four-class fNIRS-BCI classifiers, we realized 87.65% and 87.58% classification accuracy corresponding to hand extension and finger tapping tasks. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®

Optical polarimetric and near-infrared photometric study of the RCW95 Galactic H II region

NASA Astrophysics Data System (ADS)

Vargas-González, J.; Roman-Lopes, A.; Santos, F. P.; Franco, G. A. P.; Santos, J. F. C.; Maia, F. F. S.; Sanmartim, D.

2018-02-01

We carried out an optical polarimetric study in the direction of the RCW 95 star-forming region in order to probe the sky-projected magnetic field structure by using the distribution of linear polarization segments which seem to be well aligned with the more extended cloud component. A mean polarization angle of θ = 49.8° ± 7.7°7 was derived. Through the spectral dependence analysis of polarization it was possible to obtain the total-to-selective extinction ratio (RV) by fitting the Serkowski function, resulting in a mean value of RV = 2.93 ± 0.47. The foreground polarization component was estimated and is in agreement with previous studies in this direction of the Galaxy. Further, near-infrared (NIR) images from Vista Variables in the Via Láctea (VVV) survey were collected to improve the study of the stellar population associated with the H II region. The Automated Stellar Cluster Analysis algorithm was employed to derive structural parameters for two clusters in the region, and a set of PAdova and TRieste Stellar Evolution Code (PARSEC) isochrones was superimposed on the decontaminated colour-magnitude diagrams to estimate an age of about 3 Myr for both clusters. Finally, from the NIR photometry study combined with spectra obtained with the Ohio State Infrared Imager and Spectrometer mounted at the Southern Astrophysics Research Telescope we derived the spectral classification of the main ionizing sources in the clusters associated with IRAS 15408-5356 and IRAS 15412-5359, both objects classified as O4V stars.

Classification of ion mobility spectra by functional groups using neural networks

NASA Technical Reports Server (NTRS)

Bell, S.; Nazarov, E.; Wang, Y. F.; Eiceman, G. A.

1999-01-01

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis

NASA Astrophysics Data System (ADS)

Chauhan, H.; Krishna Mohan, B.

2014-11-01

The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise crop spectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to select precise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only on broad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. As unevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstanding the conventional method, the data precision has been performed effectively to serve the purpose of the present research work. The effectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discrimination values compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectra selected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectral similarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectra respectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for field spectra selection based on spectral similarity measure.

Prediction of Cortisol and Progesterone Concentrations in Cow Hair Using Near-Infrared Reflectance Spectroscopy (NIRS).

PubMed

Tallo-Parra, Oriol; Albanell, Elena; Carbajal, Annais; Monclús, Laura; Manteca, Xavier; Lopez-Bejar, Manel

2017-08-01

Concentrations of different steroid hormones have been used in cows as a measure of adrenal or gonadal activity and, thus, as indicators of stress or reproductive state. Detecting cortisol and progesterone in cow hair provides a long-term integrative value of retrospective adrenal or gonadal/placental activity, respectively. Current techniques for steroid detection require a hormone-extraction procedure that involves time, several types of equipment, management of reagents, and some assay procedures (which can also be time-consuming and can destroy the samples). In contrast, near-infrared reflectance spectroscopy (NIRS) is a multi-component predictor technique, characterized as rapid, nondestructive for the sample, and reagent-free. However, as a predictor technique, NIRS needs to be calibrated and validated for each matrix, hormone, and species. The main objective of this study was to evaluate the predictive value of the NIRS technique for hair cortisol and progesterone quantification in cows by using specific enzyme immunoassay as a reference method. Hair samples from 52 adult Friesian lactating cows from a commercial dairy farm were used. Reflectance spectra of hair samples were determined with a NIR reflectance spectrophotometer before and after trimming them. Although similar results were obtained, a slightly better relationship between the reference data and NIRS predicted values was found using trimmed samples. Near infrared reflectance spectroscopy demonstrated its ability to predict cortisol and progesterone concentrations with certain accuracy (R 2  = 0.90 for cortisol and R 2  = 0.87 for progesterone). Although NIRS is far from being a complete alternative to current methodologies, the proposed equations can offer screening capability. Considering the advantages of both fields, our results open the possibility for future work on the combination of hair steroid measurement and NIRS methodology.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

NASA Astrophysics Data System (ADS)

de Oliveira Silveira, Eduarda Martiniano; de Menezes, Michele Duarte; Acerbi Júnior, Fausto Weimar; Castro Nunes Santos Terra, Marcela; de Mello, José Márcio

2017-07-01

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares

NASA Astrophysics Data System (ADS)

Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

2018-01-01

Milk is among the most popular nutrient source worldwide, which is of great interest due to its beneficial medicinal properties. The feasibility of the classification of milk powder samples with respect to their brands and the determination of protein concentration is investigated by NIR spectroscopy along with chemometrics. Two datasets were prepared for experiment. One contains 179 samples of four brands for classification and the other contains 30 samples for quantitative analysis. Principal component analysis (PCA) was used for exploratory analysis. Based on an effective model-independent variable selection method, i.e., minimal-redundancy maximal-relevance (MRMR), only 18 variables were selected to construct a partial least-square discriminant analysis (PLS-DA) model. On the test set, the PLS-DA model based on the selected variable set was compared with the full-spectrum PLS-DA model, both of which achieved 100% accuracy. In quantitative analysis, the partial least-square regression (PLSR) model constructed by the selected subset of 260 variables outperforms significantly the full-spectrum model. It seems that the combination of NIR spectroscopy, MRMR and PLS-DA or PLSR is a powerful tool for classifying different brands of milk and determining the protein content.

Component Analysis and Identification of Black Tahitian Cultured Pearls From the Oyster Pinctada margaritifera Using Spectroscopic Techniques

NASA Astrophysics Data System (ADS)

Shi, L.; Wang, Y.; Liu, X.; Mao, J.

2018-03-01

Raman spectroscopy, ultraviolet, visible, and near infrared (UV-Vis-NIR) reflectance spectroscopy, and X-ray fluorescence (XRF) spectroscopy were used to characterize black Tahitian cultured pearls and imitations of these saltwater cultured pearls produced by γ-irradiation, and by coloring of cultured pearls with silver nitrate or organic dyes. Raman spectra indicated that aragonite was the major constituent of these four types of pearl. Using Raman spectroscopy at an excitation wavelength of 514 nm, black Tahitian cultured pearls exhibited characteristic 1100-1700 cm-1 bands. These bands were attributed to various organic components, including conchiolin and other black biological pigments. The peaks shown by saltwater cultured pearls colored with organic dyes varied with the type of dye used. Tahitian cultured and organic-dye-treated saltwater cultured pearls were easily identified by Raman spectroscopy. UV-Vis-NIR reflectance spectra showed bands at 408, 497, and 700 nm derived from porphyrin pigment and other black pigments. The spectra of dye-treated black saltwater pearls showed absorption peaks at 216, 261, 300, and 578 nm. The 261-nm absorption band disappeared from the spectra of γ-irradiated saltwater cultured pearls. This suggests the degradation of conchiolin in the γ-irradiated saltwater cultured pearls. XRF analysis revealed the presence of Ag on the surface of silver nitrate-dyed saltwater cultured pearls.

Detection of Rotational Spectral Variation on the M-type Asteroid (16) Psyche

NASA Astrophysics Data System (ADS)

Sanchez, Juan A.; Reddy, Vishnu; Shepard, Michael K.; Thomas, Cristina; Cloutis, Edward A.; Takir, Driss; Conrad, Albert; Kiddell, Cain; Applin, Daniel

2017-01-01

The asteroid (16) Psyche is of scientific interest because it contains ˜1% of the total mass of the asteroid belt and is thought to be the remnant metallic core of a protoplanet. Radar observations have indicated the significant presence of metal on the surface with a small percentage of silicates. Prior ground-based observations showed rotational variations in the near-infrared (NIR) spectra and radar albedo of this asteroid. However, no comprehensive study that combines multi-wavelength data has been conducted so far. Here we present rotationally resolved NIR spectra (0.7-2.5 μm) of (16) Psyche obtained with the NASA Infrared Telescope Facility. These data have been combined with shape models of the asteroid for each rotation phase. Spectral band parameters extracted from the NIR spectra show that the pyroxene band center varies from ˜0.92 to 0.94 μm. Band center values were used to calculate the pyroxene chemistry of the asteroid, whose average value was found to be Fs30En65Wo5. Variations in the band depth (BD) were also observed, with values ranging from 1.0% to 1.5%. Using a new laboratory spectral calibration method, we estimated an average orthopyroxene content of 6% ± 1%. The mass-deficit region of Psyche, which exhibits the highest radar albedo, also shows the highest value for the spectral slope and the minimum BD. The spectral characteristics of Psyche suggest that its parent body did not have the typical structure expected for a differentiated body or that the sequence of events that led to its current state was more complex than previously thought.

The He-rich stripped-envelope core-collapse supernova 2008ax

NASA Astrophysics Data System (ADS)

Taubenberger, S.; Navasardyan, H.; Maurer, J. I.; Zampieri, L.; Chugai, N. N.; Benetti, S.; Agnoletto, I.; Bufano, F.; Elias-Rosa, N.; Turatto, M.; Patat, F.; Cappellaro, E.; Mazzali, P. A.; Iijima, T.; Valenti, S.; Harutyunyan, A.; Claudi, R.; Dolci, M.

2011-05-01

Extensive optical and near-infrared (NIR) observations of the Type IIb supernova (SN IIb) 2008ax are presented, covering the first year after the explosion. The light curve is mostly similar in shape to that of the prototypical SN IIb 1993J, but shows a slightly faster decline rate at late phases and lacks the prominent narrow early-time peak of SN 1993J. From the bolometric light curve and ejecta expansion velocities, we estimate that about 0.07-0.15 M⊙ of 56Ni was produced during the explosion and that the total ejecta mass was between 2 and 5 M⊙, with a kinetic energy of at least 1051 erg. The spectral evolution of SN 2008ax is similar to that of SN Ib/IIb 2007Y, exhibiting high-velocity Ca II features at early phases and signs of ejecta-wind interaction from Hα observations at late times. NIR spectra show strong He I lines similar to SN Ib 1999ex and a large number of emission features at late times. Particularly interesting are the strong, double-peaked He I lines in late NIR spectra, which - together with the double-peaked [O I] emission in late optical spectra - provide clues for the asymmetry and large-scale Ni mixing in the ejecta. a Phase in days with respect to the explosion date (JD =245 4528.80 ± 0.15). B-band maximum light occurred on day 18.3. b Average seeing in arcsec over all filter bands. c CAFOS = Calar Alto 2.2m Telescope + CAFOS; DOLORES = 3.58m Telescopio Nazionale Galileo + DOLORES; AFOSC = Asiago 1.82m Copernico Telescope + AFOSC.

DETECTION OF ROTATIONAL SPECTRAL VARIATION ON THE M-TYPE ASTEROID (16) PSYCHE

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

Sanchez, Juan A.; Thomas, Cristina; Reddy, Vishnu

The asteroid (16) Psyche is of scientific interest because it contains ∼1% of the total mass of the asteroid belt and is thought to be the remnant metallic core of a protoplanet. Radar observations have indicated the significant presence of metal on the surface with a small percentage of silicates. Prior ground-based observations showed rotational variations in the near-infrared (NIR) spectra and radar albedo of this asteroid. However, no comprehensive study that combines multi-wavelength data has been conducted so far. Here we present rotationally resolved NIR spectra (0.7–2.5 μ m) of (16) Psyche obtained with the NASA Infrared Telescope Facility.more » These data have been combined with shape models of the asteroid for each rotation phase. Spectral band parameters extracted from the NIR spectra show that the pyroxene band center varies from ∼0.92 to 0.94 μ m. Band center values were used to calculate the pyroxene chemistry of the asteroid, whose average value was found to be Fs{sub 30}En{sub 65}Wo{sub 5}. Variations in the band depth (BD) were also observed, with values ranging from 1.0% to 1.5%. Using a new laboratory spectral calibration method, we estimated an average orthopyroxene content of 6% ± 1%. The mass-deficit region of Psyche, which exhibits the highest radar albedo, also shows the highest value for the spectral slope and the minimum BD. The spectral characteristics of Psyche suggest that its parent body did not have the typical structure expected for a differentiated body or that the sequence of events that led to its current state was more complex than previously thought.« less

AUTOMATED UNSUPERVISED CLASSIFICATION OF THE SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA USING k-MEANS CLUSTERING

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

Sanchez Almeida, J.; Allende Prieto, C., E-mail: jos@iac.es, E-mail: callende@iac.es

2013-01-20

Large spectroscopic surveys require automated methods of analysis. This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior physical framework. We work with a representative set of stellar spectra associated with the Sloan Digital Sky Survey (SDSS) SEGUE and SEGUE-2 programs, which consists of 173,390 spectra from 3800 to 9200 A sampled on 3849 wavelengths. We classify the original spectra as well as the spectra with the continuum removed. The second set only contains spectral lines,more » and it is less dependent on uncertainties of the flux calibration. The classification of the spectra with continuum renders 16 major classes. Roughly speaking, stars are split according to their colors, with enough finesse to distinguish dwarfs from giants of the same effective temperature, but with difficulties to separate stars with different metallicities. There are classes corresponding to particular MK types, intrinsically blue stars, dust-reddened, stellar systems, and also classes collecting faulty spectra. Overall, there is no one-to-one correspondence between the classes we derive and the MK types. The classification of spectra without continuum renders 13 classes, the color separation is not so sharp, but it distinguishes stars of the same effective temperature and different metallicities. Some classes thus obtained present a fairly small range of physical parameters (200 K in effective temperature, 0.25 dex in surface gravity, and 0.35 dex in metallicity), so that the classification can be used to estimate the main physical parameters of some stars at a minimum computational cost. We also analyze the outliers of the classification. Most of them turn out to be failures of the reduction pipeline, but there are also high redshift QSOs, multiple stellar systems, dust-reddened stars, galaxies, and, finally, odd spectra whose nature we have not deciphered. The template spectra representative of the classes are publicly available in the online journal.« less

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.

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.

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

Multivariate classification of infrared spectra of cell and tissue samples

DOEpatents

Haaland, David M.; Jones, Howland D. T.; Thomas, Edward V.

1997-01-01

Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.

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

PubMed

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

2018-04-24

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

A morpho-kinematic and spectroscopic study of the bipolar nebulae: M 2-9, Mz 3, and Hen 2-104

NASA Astrophysics Data System (ADS)

Clyne, N.; Akras, S.; Steffen, W.; Redman, M. P.; Gonçalves, D. R.; Harvey, E.

2015-10-01

Context. Complex bipolar shapes can be generated either as a planetary nebula or a symbiotic system. The origin of the material ionised by the white dwarf is very different in these two scenarios, and it complicates the understanding of the morphologies of planetary nebulae. Aims: The physical properties, structure, and dynamics of the bipolar nebulae, M 2-9, Mz 3, and Hen 2-104, are investigated in detail with the aim of understanding their nature, shaping mechanisms, and evolutionary history. Both a morpho-kinematic study and a spectroscopic analysis, can be used to more accurately determine the kinematics and nature of each nebula. Methods: Long-slit optical echelle spectra are used to investigate the morpho-kinematics of M 2-9, Mz 3, and Hen 2-104. The morpho-kinematic modelling software SHAPE is used to constrain both the morphology and kinematics of each nebula by means of detailed 3D models. Near-infrared (NIR) data, as well as optical, spectra are used to separate Galactic symbiotic-type nebulae from genuine planetary nebulae by means of a 2MASS J-H/H-Ks diagram and a λ4363/Hγ vs. λ5007/Hβ diagnostic diagram, respectively. Results: The best-fitted 3D models for M 2-9, Mz 3, and Hen 2-104 provide invaluable kinematical information on the expansion velocity of its nebular components by means of synthetic spectra. The observed spectra match up very well with the synthetic spectra for each model, thus showing that each model is tightly constrained both morphologically and kinematically. Kinematical ages of the different structures of M 2-9 and Mz 3 have also been determined. Both diagnostic diagrams show M 2-9 and Hen 2-104 to fall well within the category of having a symbiotic source, whereas Mz 3 borders the region of symbiotic and young planetary nebulae in the optical diagram but is located firmly in the symbiotic region of the NIR colour-colour diagram. The optical diagnostic diagram is shown to successfully separate the two types of nebulae, however, the NIR colour-colour diagram is not as accurate in separating these objects. Conclusions: The morphology, kinematics, and evolutionary history of M 2-9, Mz 3, and Hen 2-104 are better understood using the interactive 3D modelling tool shape. The expansion velocities of the components for each nebula are better constrained and fitted with a vector field to reveal their direction of motion. The optical and NIR diagnostic diagrams used are important techniques for separating Galactic symbiotic-type nebulae from genuine planetary nebulae.

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.

[Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

PubMed

Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

2013-12-01

The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

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.

Automated detection of radioisotopes from an aircraft platform by pattern recognition analysis of gamma-ray spectra.

PubMed

Dess, Brian W; Cardarelli, John; Thomas, Mark J; Stapleton, Jeff; Kroutil, Robert T; Miller, David; Curry, Timothy; Small, Gary W

2018-03-08

A generalized methodology was developed for automating the detection of radioisotopes from gamma-ray spectra collected from an aircraft platform using sodium-iodide detectors. Employing data provided by the U.S Environmental Protection Agency Airborne Spectral Photometric Environmental Collection Technology (ASPECT) program, multivariate classification models based on nonparametric linear discriminant analysis were developed for application to spectra that were preprocessed through a combination of altitude-based scaling and digital filtering. Training sets of spectra for use in building classification models were assembled from a combination of background spectra collected in the field and synthesized spectra obtained by superimposing laboratory-collected spectra of target radioisotopes onto field backgrounds. This approach eliminated the need for field experimentation with radioactive sources for use in building classification models. Through a bi-Gaussian modeling procedure, the discriminant scores that served as the outputs from the classification models were related to associated confidence levels. This provided an easily interpreted result regarding the presence or absence of the signature of a specific radioisotope in each collected spectrum. Through the use of this approach, classifiers were built for cesium-137 ( 137 Cs) and cobalt-60 ( 60 Co), two radioisotopes that are of interest in airborne radiological monitoring applications. The optimized classifiers were tested with field data collected from a set of six geographically diverse sites, three of which contained either 137 Cs, 60 Co, or both. When the optimized classification models were applied, the overall percentages of correct classifications for spectra collected at these sites were 99.9 and 97.9% for the 60 Co and 137 Cs classifiers, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.

Probing insect backscatter cross section and melanization using kHz optical remote detection system

NASA Astrophysics Data System (ADS)

Gebru, Alem; Brydegaard, Mikkel; Rohwer, Erich; Neethling, Pieter

2017-01-01

A kHz optical remote sensing system is implemented to determine insect melanization features. This is done by measuring the backscatter signal in the visible and near-infrared (VIS-NIR) and short-wave infrared (SWIR) in situ. It is shown that backscatter cross section in the SWIR is insensitive to melanization and absolute melanization can be derived from the ratio of backscatter cross section of different bands (SWIR/VIS-NIR). We have shown that reflectance from insect is stronger in the SWIR as compared to NIR and VIS. This reveals that melanization plays a big role to determine backscatter cross section. One can use this feature as a tool to improve insect species and age classification. To support the findings, we illustrated melanization feature using three different insects [dead, dried specimens of snow white moth (Spilosoma genus), fox moth (Macrothylacia), and leather beetle (Odontotaenius genus)]. It is shown that reflectance from the leather beetle in the VIS and NIR is more affected by melanization as compared with snow white moth.

Identifying ADHD children using hemodynamic responses during a working memory task measured by functional near-infrared spectroscopy.

PubMed

Gu, Yue; Miao, Shuo; Han, Junxia; Liang, Zhenhu; Ouyang, Gaoxiang; Yang, Jian; Li, Xiaoli

2018-06-01

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. The results showed that 86.0% ([Formula: see text]) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.

Examination of U valence states in the brannerite structure by near-infrared diffuse reflectance and X-ray photoelectron spectroscopies

NASA Astrophysics Data System (ADS)

Finnie, Kim S.; Zhang, Zhaoming; Vance, Eric R.; Carter, Melody L.

2003-04-01

The valence state of uranium doped into a f 0 thorium analog of brannerite (i.e., thorutite) has been examined using near-infrared (NIR) diffuse reflectance (DRS) and X-ray photoelectron (XPS) spectroscopies. NIR transitions of U 4+, which are not observed in spectra of brannerite, have been detected in the samples of U xTh 1- xTi 2O 6, and we propose that strong specular reflectance is responsible for the lack of U 4+ features in UTi 2O 6. Characteristic U 5+ bands have been identified in samples in which sufficient Ca 2+ has been added to nominally effect complete oxidation to U 5+. XPS results support the assignments of U 4+ and U 5+ by DRS. The presence of residual U 4+ bands in the spectra of the Ca-doped samples is consistent with segregation of Ca 2+ to the grain boundaries during high temperature sintering.

UV Raman detection of 2,4-DNT in contact with sand particles

NASA Astrophysics Data System (ADS)

Blanco, Alejandro; Pacheco-Londoño, Leonardo C.; Peña-Quevedo, Alvaro J.; Hernández-Rivera, Samuel P.

2006-05-01

Deep Ultra Violet Raman Spectroscopy (DUV-RS) is an emerging tool for vibrational spectroscopy analysis and can be used in Point Detection mode to detect explosive components of landmines and Improvised Explosive Devices (IED). Interactions of explosives with different substrates can be measured by using quantitative vibrational signal shift information of scattered Raman light associated with these interactions. In this research, grounds were laid for detection of explosives using UV-Raman Spectroscopy equipped with 244 nm laser excitation line from a 488 nm frequency doubled Coherent FreD laser. In other experiments, samples of 2,4-DNT were allowed to interact with Ottawa Sand and were studied using DUV-RS. Characteristic vibrational signals of energetic compounds were analyzed in the ranges: 400-1200 cm -1, 1200-1800 cm -1, and 2800-3500 cm -1. In addition these Raman spectra were compared with dispersive spectra that were acquired using Raman Microscopy equipped with 514.5 nm (VIS) 785 nm (NIR) and 1064 nm (NIR) excitation lasers.

A spectroscopic comparison of selected Chinese kaolinite, coal bearing kaolinite and halloysite--a mid-infrared and near-infrared study.

PubMed

Cheng, Hongfei; Yang, Jing; Liu, Qinfu; Zhang, Jinshan; Frost, Ray L

2010-11-01

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy have been compared and evaluated for differentiating kaolinite, coal bearing kaolinite and halloysite. Kaolinite, coal bearing kaolinite and halloysite are the three relative abundant minerals of the kaolin group, especially in China. In the MIR spectra, the differences are shown in the 3000-3600 cm⁻¹ between kaolinite and halloysite. It cannot obviously differentiate the kaolinite and halloysite, leaving alone kaolinite and coal bearing kaolinite. However, NIR, together with MIR, gives us the sufficient evidence to differentiate the kaolinite and halloysite, especially kaolinite and coal bearing kaolinite. There are obvious differences between kaolinite and halloysite in all range of their spectra, and they also show some difference between kaolinite and coal bearing kaolinite. Therefore, the reproducibility of measurement, signal to noise ratio and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for mineral analysis. Copyright © 2010 Elsevier B.V. All rights reserved.

Near infrared spectroscopy for body fat sensing in neonates: quantitative analysis by GAMOS simulations.

PubMed

Mustafa, Fatin Hamimi; Jones, Peter W; McEwan, Alistair L

2017-01-11

Under-nutrition in neonates is closely linked to low body fat percentage. Undernourished neonates are exposed to immediate mortality as well as unwanted health impacts in their later life including obesity and hypertension. One potential low cost approach for obtaining direct measurements of body fat is near-infrared (NIR) interactance. The aims of this study were to model the effect of varying volume fractions of melanin and water in skin over NIR spectra, and to define sensitivity of NIR reflection on changes of thickness of subcutaneous fat. GAMOS simulations were used to develop two single fat layer models and four complete skin models over a range of skin colour (only for four skin models) and hydration within a spectrum of 800-1100 nm. The thickness of the subcutaneous fat was set from 1 to 15 mm in 1 mm intervals in each model. Varying volume fractions of water in skin resulted minimal changes of NIR intensity at ranges of wavelengths from 890 to 940 nm and from 1010 to 1100 nm. Variation of the melanin volume in skin meanwhile was found to strongly influence the NIR intensity and sensitivity. The NIR sensitivities and NIR intensity over thickness of fat decreased from the Caucasian skin to African skin throughout the range of wavelengths. For the relationship between the NIR reflection and the thickness of subcutaneous fat, logarithmic relationship was obtained. The minimal changes of NIR intensity values at wavelengths within the ranges from 890 to 940 nm and from 1010 to 1100 nm to variation of volume fractions of water suggests that wavelengths within those two ranges are considered for use in measurement of body fat to solve the variation of hydration in neonates. The stronger influence of skin colour on NIR shows that the melanin effect needs to be corrected by an independent measurement or by a modeling approach. The logarithmic response obtained with higher sensitivity at the lower range of thickness of fat suggests that implementation of NIRS may be suited for detecting under-nutrition and monitoring nutritional interventions for malnutrition in neonates in resource-constrained communities.

Early pregnancy diagnosis in sheep using near-infrared spectroscopy on blood plasma.

PubMed

Andueza, Donato; Alabart, José L; Lahoz, Belén; Muñoz, Fernando; Folch, José

2014-02-01

The objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to discriminate between pregnant and nonpregnant ewes in early stages of pregnancy after artificial insemination (AI) from blood plasma. Samples were collected using jugular puncture at 18 and 25 days after AI from 188 Rasa Aragonesa and Ansotana ewes. Plasma samples were analyzed for pregnancy-associated glycoprotein (PAG) and progesterone (P4) using ELISA commercial kits. The spectra of plasma samples were recorded in the visible and near-infrared ranges. The performance of these tests were compared, using as criterion standard the pregnancy status determined using transabdominal ultrasonography at 45 days after AI. Pregnancy rate was 47.9% (90/188). At Day 18, sensitivity was similar in NIRS and P4 tests (98.9% vs. 100%; not significant) and greater than PAG (32.2%; both P < 0.001). Specificity was similar in NIRS and PAG tests (both 100%) and greater than that of P4 (84.7%; P < 0.001). At Day 25, sensitivity and specificity of NIRS and PAG were both 100%. It can be concluded that NIRS was an accurate method of diagnosis of pregnancy at Days 18 and 25 after AI in ewes. Copyright © 2014 Elsevier Inc. All rights reserved.

[Rapid assessment of critical quality attributes of Chinese materia medica (II): strategy of NIR assignment].

PubMed

Pei, Yan-Ling; Wu, Zhi-Sheng; Shi, Xin-Yuan; Zhou, Lu-Wei; Qiao, Yan-Jiang

2014-09-01

The present paper firstly reviewed the research progress and main methods of NIR spectral assignment coupled with our research results. Principal component analysis was focused on characteristic signal extraction to reflect spectral differences. Partial least squares method was concerned with variable selection to discover characteristic absorption band. Two-dimensional correlation spectroscopy was mainly adopted for spectral assignment. Autocorrelation peaks were obtained from spectral changes, which were disturbed by external factors, such as concentration, temperature and pressure. Density functional theory was used to calculate energy from substance structure to establish the relationship between molecular energy and spectra change. Based on the above reviewed method, taking a NIR spectral assignment of chlorogenic acid as example, a reliable spectral assignment for critical quality attributes of Chinese materia medica (CMM) was established using deuterium technology and spectral variable selection. The result demonstrated the assignment consistency according to spectral features of different concentrations of chlorogenic acid and variable selection region of online NIR model in extract process. Although spectral assignment was initial using an active pharmaceutical ingredient, it is meaningful to look forward to the futurity of the complex components in CMM. Therefore, it provided methodology for NIR spectral assignment of critical quality attributes in CMM.

Development of near-infrared ratiometric fluorescent probe based on cationic conjugated polymer and CdTe/CdS QDs for label-free determination of glucose in human body fluids.

PubMed

Yu, Mengze; Zhao, Kunli; Zhu, Xiaohua; Tang, Shiyun; Nie, Zhou; Huang, Yan; Zhao, Peng; Yao, Shouzhuo

2017-09-15

Quantum dots (QDs) have attracted extensive attention in biomedical applications， because of their broad excitation spectra, narrow and symmetric emission peaks etc. Furthermore, near-infrared (NIR) QDs have further advantages including low autofluorescence, good tissue penetration and low phototoxicity. In this work, the electrostatic interaction and fluorescence resonance energy transfer (FRET) between NIR CdTe/CdS QDs and cationic conjugated polymer (CCP) was studied for the first time. Based on the newly discovered phenomena and the result that hydrogen peroxide (H 2 O 2 ) can efficiently quench the fluorescence of NIR CdTe/CdS QDs, a novel NIR ratiometric fluorescent probe for determination of H 2 O 2 and glucose was developed. Under the optimized conditions, the detection limit of H 2 O 2 and glucose assay were 0.1mM and 0.05mM (S/N=3), with a linear range of 0.2-4mM and 0.1-5mM, respectively. Because of the NIR spectrum, this ratiometric probe can be also applied for the determination of glucose in whole blood samples directly, providing a valuable platform for glucose sensing in clinic diagnostic and drug screening. Copyright © 2017. Published by Elsevier B.V.

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.

A compact circumstellar shell as the source of high-velocity features in SN 2011fe

NASA Astrophysics Data System (ADS)

Mulligan, Brian W.; Wheeler, J. Craig

2018-05-01

High-velocity features (HVFs), especially of Ca II, are frequently seen in Type Ia supernova observed prior to B-band maximum (Bmax). These HVFs evolve in velocity from more than 25 000 km s-1, in the days after first light, to about 18 000 km s-1 near Bmax. To recreate the evolution of the Ca II near-infrared triplet (CaNIR) HVFs in SN 2011fe, we consider the interaction between a model Type Ia supernova and compact circumstellar shells with masses between 0.003 and 0.012 M⊙. We fit the observed CaNIR feature using synthetic spectra generated from the models using SYN++. The CaNIR feature is better explained by the supernova model interacting with a shell than the model without a shell, with a shell of mass 0.005 M⊙ tending to be better fitting than the other shells. The evolution of the optical depth of CaNIR suggests that the ionization state of calcium within the ejecta and shell is not constant. We discuss the method used to measure the observed velocity of CaNIR and other features and conclude that HVFs or other components can be falsely identified. We briefly discuss the possible origin of the shells and the implications for the progenitor system of the supernova.

A Spectroscopic and Mineralogical Study of Multiple Asteroid Systems

NASA Astrophysics Data System (ADS)

Lindsay, Sean S.; Emery, J. P.; Marchis, F.; Enriquez, J.; Assafin, M.

2013-10-01

There are currently ~200 identified multiple asteroid systems (MASs). These systems display a large diversity in heliocentric distance, size/mass ratio, system angular momentum, mutual orbital parameters, and taxonomic class. These characteristics are simplified under the nomenclature of Descamps and Marchis (2008), which divides MASs into four types: Type-1 - large asteroids with small satellites; Type-2 - similar size double asteroids; Type-3 - small asynchronous systems; and Type-4 - contact-binary asteroids. The large MAS diversity suggests multiple formation mechanisms are required to understand their origins. There are currently three broad formation scenarios: 1) ejecta from impacts; 2) catastrophic disruption followed by rotational fission; and 3) tidal disruption. The taxonomic class and mineralogy of the MASs coupled with the average density and system angular momentum provide a potential means to discriminate between proposed formation mechanisms. We present visible and near-infrared (NIR) spectra spanning 0.45 - 2.45 μm for 23 Main Belt MASs. The data were primarily obtained using the Southern Astrophysical Research Telescope (SOAR) Goodman High Throughput Spectrograph (August 2011 - July 2012) for the visible data and the InfraRed Telescope Facility (IRTF) SpeX Spectrograph (August 2008 - May 2013) for the IR data. Our data were supplemented using previously published data when necessary. The asteroids' Bus-DeMeo taxonomic classes are determined using the MIT SMASS online classification routines. Our sample includes 3 C-types, 1 X-type, 1 K-type, 1 L-type, 4 V-types, 10 S-types, 2 Sq- or Q-types, and 1 ambiguous classification. We calculate the 1- and 2-μm band centers, depths, and areas to determine the pyroxene mineralogy (molar Fs and Wo) of the surfaces using empirically derived equations. The NIR band analysis allows us to determine the S-type subclasses, S(I) - S(VII), which roughly tracks olivine-pyroxene chemistry. A comparison of the orbital parameters, physical parameters (size, density, and angular momentum), collisional family membership, and taxonomy is presented in an effort to find correlations, which may give insights to how these MASs formation mechanisms.

Spectra Transfer Between a Fourier Transform Near-Infrared Laboratory and a Miniaturized Handheld Near-Infrared Spectrometer.

PubMed

Hoffmann, Uwe; Pfeifer, Frank; Hsuing, Chang; Siesler, Heinz W

2016-05-01

The aim of this contribution is to demonstrate the transfer of spectra that have been measured on two different laboratory Fourier transform near-infrared (FT-NIR) spectrometers to the format of a handheld instrument by measuring only a few samples with both spectrometer types. Thus, despite the extreme differences in spectral range and resolution, spectral data sets that have been collected and quantitative as well as qualitative calibrations that have been developed thereof, respectively, over a long period on a laboratory instrument can be conveniently transferred to the handheld system. Thus, the necessity to prepare completely new calibration samples and the effort required to develop calibration models when changing hardware platforms is minimized. The enabling procedure is based on piecewise direct standardization (PDS) and will be described for the data sets of a quantitative and a qualitative application case study. For this purpose the spectra measured on the FT-NIR laboratory spectrometers were used as "master" data and transferred to the "target" format of the handheld instrument. The quantitative test study refers to transmission spectra of three-component liquid solvent mixtures whereas the qualitative application example encompasses diffuse reflection spectra of six different current polymers. To prove the performance of the transfer procedure for quantitative applications, partial least squares (PLS-1) calibrations were developed for the individual components of the solvent mixtures with spectra transferred from the master to the target instrument and the cross-validation parameters were compared with the corresponding parameters obtained for spectra measured on the master and target instruments, respectively. To test the retention of the discrimination ability of the transferred polymer spectra sets principal component analyses (PCAs) were applied exemplarily for three of the six investigated polymers and their identification was demonstrated by Mahalanobis distance plots for all polymers. © The Author(s) 2016.

Tensile properties of polyhydroxyalkanoate/polycaprolactone blends studied by rheo-optical near-infrared (NIR) spectroscopy

NASA Astrophysics Data System (ADS)

Nishida, Masahiro; Ogura, Takashi; Shinzawa, Hideyuki; Nishida, Masakazu; Kanematsu, Wataru

2016-11-01

In order to improve the mechanical properties of Polyhydroxyalkanoate (PHA), the polycaprolactone (PCL) pellet was blended with a PHA-based pellet. The effects of the mixing ratio on the tensile properties, Young's modulus, tensile strength and elongation at break, were examined using a universal testing machine. When the mixing ration of PCL increased to 50%, the elongation at break of the polymer blend increased and the gauge area of tensile test specimens whitened and became porous. In order to understand this behavior, a rheo-optical characterization technique based on near-infrared (NIR) spectroscopy was applied to the mechanical deformation of the polymer blends during static tensile tests. Two-dimensional (2D) correlation of NIR spectra was then examined. It was found from peaks of ethyl group or methyl group that PCL was preferentially deformed. The difference in the deformation behavior is thought to be the cause of the porous structure.

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

Batch Statistical Process Monitoring Approach to a Cocrystallization Process.

PubMed

Sarraguça, Mafalda C; Ribeiro, Paulo R S; Dos Santos, Adenilson O; Lopes, João A

2015-12-01

Cocrystals are defined as crystalline structures composed of two or more compounds that are solid at room temperature held together by noncovalent bonds. Their main advantages are the increase of solubility, bioavailability, permeability, stability, and at the same time retaining active pharmaceutical ingredient bioactivity. The cocrystallization between furosemide and nicotinamide by solvent evaporation was monitored on-line using near-infrared spectroscopy (NIRS) as a process analytical technology tool. The near-infrared spectra were analyzed using principal component analysis. Batch statistical process monitoring was used to create control charts to perceive the process trajectory and define control limits. Normal and non-normal operating condition batches were performed and monitored with NIRS. The use of NIRS associated with batch statistical process models allowed the detection of abnormal variations in critical process parameters, like the amount of solvent or amount of initial components present in the cocrystallization. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Assessment of Transition Element Speciation in Glasses Using a Portable Transmission Ultraviolet-Visible-Near-Infrared (UV-Vis-NIR) Spectrometer.

PubMed

Hunault, Myrtille; Lelong, Gérald; Gauthier, Michel; Gélébart, Frédéric; Ismael, Saindou; Galoisy, Laurence; Bauchau, Fanny; Loisel, Claudine; Calas, Georges

2016-05-01

A new low-cost experimental setup based on two compact dispersive optical spectrometers has been developed to measure optical absorption transmission spectra over the 350-2500 nm energy range. We demonstrate how near-infrared (NIR) data are essential to identify the coloring species in addition to ultraviolet visible data. After calibration with reference glasses, the use of an original sample stage that maintains the window panel in the vertical position enables the comparison of ancient and modern glasses embedded in a panel from the Sainte-Chapelle of Paris, without any sampling. The spectral resolution enables to observe fine resonances arising in the absorption bands of Cr(3+), and the complementary information obtained in the NIR enables to determine the contribution of Fe(2+), a key indicator of glassmaking conditions. © The Author(s) 2016.

Single-trial lie detection using a combined fNIRS-polygraph system

PubMed Central

Bhutta, M. Raheel; Hong, Melissa J.; Kim, Yun-Hee; Hong, Keum-Shik

2015-01-01

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph. PMID:26082733

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.

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.

Multimodal 2D Brain Computer Interface.

PubMed

Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

2015-08-01

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

Laboratory simulations of the Vis-NIR spectra of comet 67P using sub-μm sized cosmochemical analogues

NASA Astrophysics Data System (ADS)

Rousseau, B.; Érard, S.; Beck, P.; Quirico, É.; Schmitt, B.; Brissaud, O.; Montes-Hernandez, G.; Capaccioni, F.; Filacchione, G.; Bockelée-Morvan, D.; Leyrat, C.; Ciarniello, M.; Raponi, A.; Kappel, D.; Arnold, G.; Moroz, L. V.; Palomba, E.; Tosi, F.; Virtis Team

2018-05-01

Laboratory spectral measurements of relevant analogue materials were performed in the framework of the Rosetta mission in order to explain the surface spectral properties of comet 67P. Fine powders of coal, iron sulphides, silicates and their mixtures were prepared and their spectra measured in the Vis-IR range. These spectra are compared to a reference spectrum of 67P nucleus obtained with the VIRTIS/Rosetta instrument up to 2.7 μm, excluding the organics band centred at 3.2 μm. The species used are known to be chemical analogues for cometary materials which could be present at the surface of 67P. Grain sizes of the powders range from tens of nanometres to hundreds of micrometres. Some of the mixtures studied here actually reach the very low reflectance level observed by VIRTIS on 67P. The best match is provided by a mixture of sub-micron coal, pyrrhotite, and silicates. Grain sizes are in agreement with the sizes of the dust particles detected by the GIADA, MIDAS and COSIMA instruments on board Rosetta. The coal used in the experiment is responsible for the spectral slope in the visible and infrared ranges. Pyrrhotite, which is strongly absorbing, is responsible for the low albedo observed in the NIR. The darkest components dominate the spectra, especially within intimate mixtures. Depending on sample preparation, pyrrhotite can coat the coal and silicate aggregates. Such coating effects can affect the spectra as much as particle size. In contrast, silicates seem to play a minor role.

Effects of temperature on the near-infrared spectroscopic measurement of glucose

NASA Astrophysics Data System (ADS)

Jung, Byungjo; McShane, Michael J.; Rastegar, Sohi; Cote, Gerard L.

1998-05-01

The noninvasive monitoring of sugars, and in particular, glucose using near-IR (NIR) spectroscopy would be useful for a number of applications including regulating the nutrients in cell culture medium, monitoring on-line processes in the food industry, and in vivo monitoring for control of glucose in DIabetic patients. The focus of this research was the investigation of the temperature effects across a 10.6 to 40.4 degrees C range on Fourier filtered and unfiltered single-beam as well as absorbance glucose and water NIR spectra. It is known that the positions of water absorption bands centered at 1.923 and 2.623 micrometers depend heavily on temperature effects while the glucose bands are temperature insensitive across this range. The water absorption bands were shown to shift to lower wavelengths while the distance between these bands increased with increasing temperatures. Partial least squares (PLS) calibration models were constructed at five separate temperatures, 15.7, 20.5, 25.5, 35.6, and 40.4 degrees C. When absorbance spectra were used with reference scans taken at the same temperature and PLS models were used, no significant difference in the standard error of prediction (SEP) was noted with temperature. Using PLS calibration with single-beam spectra at one temperature showed large SEPs at the other temperatures. The use of Fourier filtered single-beam spectra reduced the SEP but still showed an increase as large temperature differences were produced and the filtered single beam approach did not reduce the SEP to the level achieved with the absorbance spectra.

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.

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

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis.

PubMed

Zhang, Chu; Liu, Fei; He, Yong

2018-02-01

Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.

[Proximate analysis of straw by near infrared spectroscopy (NIRS)].

PubMed

Huang, Cai-jin; Han, Lu-jia; Liu, Xian; Yang, Zeng-ling

2009-04-01

Proximate analysis is one of the routine analysis procedures in utilization of straw for biomass energy use. The present paper studied the applicability of rapid proximate analysis of straw by near infrared spectroscopy (NIRS) technology, in which the authors constructed the first NIRS models to predict volatile matter and fixed carbon contents of straw. NIRS models were developed using Foss 6500 spectrometer with spectra in the range of 1,108-2,492 nm to predict the contents of moisture, ash, volatile matter and fixed carbon in the directly cut straw samples; to predict ash, volatile matter and fixed carbon in the dried milled straw samples. For the models based on directly cut straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.92% and 0.76% for moisture, 0.94% and 0.84% for ash, 0.88% and 0.82% for volatile matter, and 0.75% and 0.65% for fixed carbon, respectively. For the models based on dried milled straw samples, the determination coefficient of independent validation (R2v) and standard error of prediction (SEP) were 0.98% and 0.54% for ash, 0.95% and 0.57% for volatile matter, and 0.78% and 0.61% for fixed carbon, respectively. It was concluded that NIRS models can predict accurately as an alternative analysis method, therefore rapid and simultaneous analysis of multicomponents can be achieved by NIRS technology, decreasing the cost of proximate analysis for straw.

Near infrared reflectance spectroscopy studies of Chinese giant salamanders in aquaculture production

USDA-ARS?s Scientific Manuscript database

NIR spectra were collected at three surface locations for Chinese giant salamanders to ascertain whether spectral signatures could be separated by anatomical, presumably physiologically-based, locations. The first location was the smooth area immediately above the cloaca on the animal’s abdomen, whi...

Simple route to (NH4)xWO3 nanorods for near infrared absorption

NASA Astrophysics Data System (ADS)

Guo, Chongshen; Yin, Shu; Dong, Qiang; Sato, Tsugio

2012-05-01

Described here is how to synthesize one-dimensional ammonium tungsten bronze ((NH4)xWO3) by a facile solvothermal approach in which ethylene glycol and acetic acid were employed as solvents and ammonium paratungstate was used as a starting material, as well as how to develop the near infrared absorption properties of (NH4)xWO3 nanorods for application as a solar light control filter. The as-obtained product was characterized by field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetry (TG), atomic force microscope (AFM) and UV-Vis-NIR spectra. The SEM and TEM images clearly revealed that the obtained sample possessed rod/fiber-like morphologies with diameters around 120 nm. As determined by UV-Vis-NIR optical measurement, the thin film consisted of (NH4)xWO3 nanoparticles, which can selectively transmit most visible lights, but strongly absorb the near-infrared (NIR) lights and ultraviolet rays. These interesting optical properties make the (NH4)xWO3 nanorods suitable for the solar control windows.Described here is how to synthesize one-dimensional ammonium tungsten bronze ((NH4)xWO3) by a facile solvothermal approach in which ethylene glycol and acetic acid were employed as solvents and ammonium paratungstate was used as a starting material, as well as how to develop the near infrared absorption properties of (NH4)xWO3 nanorods for application as a solar light control filter. The as-obtained product was characterized by field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetry (TG), atomic force microscope (AFM) and UV-Vis-NIR spectra. The SEM and TEM images clearly revealed that the obtained sample possessed rod/fiber-like morphologies with diameters around 120 nm. As determined by UV-Vis-NIR optical measurement, the thin film consisted of (NH4)xWO3 nanoparticles, which can selectively transmit most visible lights, but strongly absorb the near-infrared (NIR) lights and ultraviolet rays. These interesting optical properties make the (NH4)xWO3 nanorods suitable for the solar control windows. Electronic supplementary information (ESI) available. See DOI: 10.1039/c2nr30612c

Classification of Salmonella Enterica serotypes with selective bands using visible/NIR hyperspectral imaging

USDA-ARS?s Scientific Manuscript database

Optical detection of foodborne bacteria such as Salmonella classifies bacteria by analyzing spectral data, and has potential for rapid detection. In this experiment hyperspectral microscopy is explored as a means for classifying five Salmonella serotypes. Initially, the microscope collects 89 spect...

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

NASA Technical Reports Server (NTRS)

Hoffbeck, Joseph P.; Landgrebe, David A.

1994-01-01

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Potential prospects in archaeological research by using optical spectroscopy through a black glass ocular

NASA Astrophysics Data System (ADS)

Cosyns, P.; Meulebroeck, W.; Thienpont, H.; Nys, K.

The aim of this paper is to draw attention to the potential usefulness of optical spectroscopy within the archaeological discourse. We therefore use the standardized color coordinates and the transmittance spectra in the region between 350- 1650 nm of nine fragmented Roman black glass artifacts from archaeological contexts in Avenches (Switzerland) and an intact piece from Tongeren (Belgium). Firstly, we demonstrate how the use of UV-Vis-NIR spectroscopy can help the archaeologist in understanding the various excavated features containing glass artifacts. The analysis of the optical spectra of Roman black glass artifacts demonstrates in the first place that an object has a very homogenous composition. The clustering of the different fragments with characteristic spectra permits to connect the pieces from various areas of an excavation to one single object or to several objects from the same batch. These results provide the archaeologist the possibility to merge recognized layers or to connect different features in the surrounding area. Secondly, we demonstrate how the use of UV-Vis-NIR spectroscopy can help improve the analysis process. This inexpensive method can facilitate a more convenient and purposive sampling by means of a preliminary inquiry, selecting the most interesting pieces out of a large group of artifacts suitable for chemical analysis.

Multi-Spectroscopic Analysis of Seed Quality and 13C-Stable-Iotopologue Monitoring in Initial Growth Metabolism of Jatropha curcas L.

PubMed Central

Komatsu, Takanori; Ohishi, Risa; Shino, Amiu; Akashi, Kinya; Kikuchi, Jun

2014-01-01

In the present study, we applied nuclear magnetic resonance (NMR), as well as near-infrared (NIR) spectroscopy, to Jatropha curcas to fulfill two objectives: (1) to qualitatively examine the seeds stored at different conditions, and (2) to monitor the metabolism of J. curcas during its initial growth stage under stable-isotope-labeling condition (until 15 days after seeding). NIR spectra could non-invasively distinguish differences in storage conditions. NMR metabolic analysis of water-soluble metabolites identified sucrose and raffinose family oligosaccharides as positive markers and gluconic acid as a negative marker of seed germination. Isotopic labeling patteren of metabolites in germinated seedlings cultured in agar-plate containg 13C-glucose and 15N-nitrate was analyzed by zero-quantum-filtered-total correlation spectroscopy (ZQF-TOCSY) and 13C-detected 1H-13C heteronuclear correlation spectroscopy (HETCOR). 13C-detected HETOCR with 13C-optimized cryogenic probe provided high-resolution 13C-NMR spectra of each metabolite in molecular crowd. The 13C-13C/12C bondmer estimated from 1H-13C HETCOR spectra indicated that glutamine and arginine were the major organic compounds for nitrogen and carbon transfer from roots to leaves. PMID:25401292

Diffuse Reflectance Spectroscopy of Hidden Objects, Part I: Interpretation of the Reflection-Absorption-Scattering Fractions in Near-Infrared (NIR) Spectra of Polyethylene Films.

PubMed

Pomerantsev, Alexey L; Rodionova, Oxana Ye; Skvortsov, Alexej N

2017-08-01

Investigation of a sample covered by an interfering layer is required in many fields, e.g., for process control, biochemical analysis, and many other applications. This study is based on the analysis of spectra collected by near-infrared (NIR) diffuse reflectance spectroscopy. Each spectrum is a composition of a useful, target spectrum and a spectrum of an interfering layer. To recover the target spectrum, we suggest using a new phenomenological approach, which employs the multivariate curve resolution (MCR) method. In general terms, the problem is very complex. We start with a specific problem of analyzing a system, which consists of several layers of polyethylene (PE) film and underlayer samples with known spectral properties. To separate information originating from PE layers and the target, we modify the system versus both the number of the PE layers as well as the reflectance properties of the target sample. We consider that the interfering spectrum of the layer can be modeled using three components, which can be tentatively called transmission, absorption, and scattering contributions. The novelty of our approach is that we do not remove the reflectance and scattering effects from the spectra, but study them in detail aiming to use this information to recover the target spectrum.

[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.

Use of near infared spectroscopy to predict the mechanical properties of six softwoods

Treesearch

Stephen S. Jelley; Timothy G. Rials; Leslie H. Groom; Chi-Leung So

2004-01-01

The visible and near infrared (NIR)(500-2400 nm) spectra and mechanical properties of almost 1000 small clear-wood samples from six softwood species: Pinus taeda L. (loblolly pine), Pinus palustris, Mill. (longleaf pine), Pinus elliottii Engelm. (slash pine), Pinus echinata Mill. (shortleaf...

Optical spectroscopy for food and beverages control

NASA Astrophysics Data System (ADS)

Mignani, Anna Grazia; Ciaccheri, Leonardo; Mencaglia, Andrea Azelio

2011-08-01

A selection of spectroscopy-based, fiber optic and micro-optic devices is presented. They have been designed and tested for monitoring the quality and safety of typical foodstuffs. The VIS-NIR spectra, considered as product fingerprints, allowed to discriminating the geographic region of production and to detecting nutritional and nutraceutic indicators.

Analysis of near infrared spectra for age-grading of wild populations of Anopheles gambiae

USDA-ARS?s Scientific Manuscript database

A greater understanding of 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 affect this structure. The use of near-infrared spectroscopy (NIRS) for a...

Parametric Models of NIR Transmission and Reflectivity Spectra for Dyed Fabrics

DTIC Science & Technology

2015-07-29

forms of Eqs. (1a,b) and (3a,b) are based on trend features following those of the Beer - Lambert law . Their parameterizations, however, are...0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law

[A New Distance Metric between Different Stellar Spectra: the Residual Distribution Distance].

PubMed

Liu, Jie; Pan, Jing-chang; Luo, A-li; Wei, Peng; Liu, Meng

2015-12-01

Distance metric is an important issue for the spectroscopic survey data processing, which defines a calculation method of the distance between two different spectra. Based on this, the classification, clustering, parameter measurement and outlier data mining of spectral data can be carried out. Therefore, the distance measurement method has some effect on the performance of the classification, clustering, parameter measurement and outlier data mining. With the development of large-scale stellar spectral sky surveys, how to define more efficient distance metric on stellar spectra has become a very important issue in the spectral data processing. Based on this problem and fully considering of the characteristics and data features of the stellar spectra, a new distance measurement method of stellar spectra named Residual Distribution Distance is proposed. While using this method to measure the distance, the two spectra are firstly scaled and then the standard deviation of the residual is used the distance. Different from the traditional distance metric calculation methods of stellar spectra, when used to calculate the distance between stellar spectra, this method normalize the two spectra to the same scale, and then calculate the residual corresponding to the same wavelength, and the standard error of the residual spectrum is used as the distance measure. The distance measurement method can be used for stellar classification, clustering and stellar atmospheric physical parameters measurement and so on. This paper takes stellar subcategory classification as an example to test the distance measure method. The results show that the distance defined by the proposed method is more effective to describe the gap between different types of spectra in the classification than other methods, which can be well applied in other related applications. At the same time, this paper also studies the effect of the signal to noise ratio (SNR) on the performance of the proposed method. The result show that the distance is affected by the SNR. The smaller the signal-to-noise ratio is, the greater impact is on the distance; While SNR is larger than 10, the signal-to-noise ratio has little effect on the performance for the classification.

Spectral identification of abiotic O2 buildup from early runaways and rarefied atmospheres

NASA Astrophysics Data System (ADS)

Schwieterman, Edward; Meadows, Victoria; Domagal-Goldman, Shawn; Arney, Giada; Robinson, Tyler D.; Luger, Rodrigo; Barnes, Rory

2016-01-01

The spectral detection of oxygen (O2) in a planetary atmosphere has been considered a robust signature of life because O2 is highly reactive on planets with Earth-like redox buffers and because significant continuous abiotic sources were thought to be implausible. However, recent work has revealed the possibility that significant O2 may build-up in terrestrial atmospheres through (1) photochemical channels or (2) through the escape of hydrogen. We focus on the latter category here. Significant amounts of abiotic O2 could remain in the atmospheres of planets in the habitable zones of late type stars, where an early runaway greenhouse and massive hydrogen escape during the pre-main-sequence phase could have irreversibly oxidized the crust and mantle (Luger & Barnes 2015). Additionally, it has been hypothesized that O2 could accumulate in the atmospheres of planets with sufficiently low abundances of non-condensable gases such as N2 where water would not be cold trapped in the troposphere, leading to H-escape from UV photolysis in a wet stratosphere (Wordsworth & Pierrehumbert 2014). We self-consistently model the climate, photochemistry, and spectra of both rarefied and post-runaway, high-O2 atmospheres. Because an early runaway might not have lasted long enough for the entire water inventory to escape, we explore both completely desiccated scenarios and cases where a surface ocean remains. We find "habitable" surface conditions for a wide variety of oxygen abundances, atmospheric masses, and CO2 mixing ratios. If O2 builds up from massive or sustained H escape, the O2 abundance should be very high, and could be spectrally indicated by the presence of O2-O2 (O4) collisionally-induced absorption (CIA) features. We generate synthetic direct-imaging and transit transmission spectra of these atmospheres and calculate the strength of the UV/Visible and NIR O4 features. We find that while both the UV/Visible and NIR O4 features are strong in the radiance spectra of very high-O2 atmospheres, only the NIR O4 features are strong in transmission spectra. We also conclude that detection of N2-N2 CIA near 4.2 μm in transmission or direct-imaging spectra could rule out O2 origination from H-escape from thin atmospheres.

Classification of Korla fragrant pears using NIR hyperspectral imaging analysis

USDA-ARS?s Scientific Manuscript database

Korla fragrant pears are small oval pears characterized by light green skin, crisp texture, and a pleasant perfume for which they are named. Anatomically, the calyx of a fragrant pear may be either persistent or deciduous; the deciduous-calyx fruits are considered more desirable due to taste and tex...

[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.

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

Concordance of obesity classification between body mass index and percent body fat among school children in Saudi Arabia.

PubMed

Al-Mohaimeed, Abdulrahman; Ahmed, Saifuddin; Dandash, Khadiga; Ismail, Mohammed Saleh; Saquib, Nazmus

2015-03-05

In Saudi Arabia, where childhood obesity is a major public health issue, it is important to identify the best tool for obesity classification. Hence, we compared two field methods for their usefulness in epidemiological studies. The sample consisted of 874 primary school (grade I-IV) children, aged 6-10 years, and was obtained through a multi-stage random sampling procedure. Weight and height were measured, and BMI (kg/m(2)) was calculated. Percent body fat was determined with a Futrex analyzer that uses near infrared reactance (NIR) technology. Method specific cut-off values were used for obesity classification. Sensitivity, specificity, positive and negative predictive values were determined for BMI, and the agreement between BMI and percent body fat was calculated. Compared to boys, the mean BMI was higher in girls whereas the mean percent body fat was lower (p-values < 0.0001). According to BMI, the prevalence of overweight or obesity was significantly higher in girls (34.3% vs. 17.3%); as oppose to percent body fat, which was similar between the sexes (6.6% vs. 7.0%). The sensitivity of BMI to classify overweight or obesity was high (boys = 93%, girls = 100%); and its false-positive detection rate was also high (boys = 63%, girls = 81%). The agreement rate was low between these two methods (boys = 0.48, girls =0.24). There is poor agreement in obesity classification between BMI and percent body fat, using NIR method, among Saudi school children.

Detection of regulated herbs and plants in plant food supplements and traditional medicines using infrared spectroscopy.

PubMed

Deconinck, E; Djiogo, C A Sokeng; Bothy, J L; Courselle, P

2017-08-05

The identification of a specific toxic or regulated plant in herbal preparations or plant food supplements is a real challenge, since they are often powdered, mixed with other herbal or synthetic powders and compressed into tablets or capsules. The classical identification approaches based on micro- and macroscopy are therefore not possible anymore. In this paper infrared spectroscopy, combined with attenuated total reflectance was evaluated for the screening of plant based preparations for nine specific plants (five regulated and four common plants for herbal supplements). IR and NIR spectra were recorded for a series of self-made triturations of the targeted plants. After pretreatment of the spectral data chemometric classification techniques were applied to both data sets (IR and NIR) separately and the combination of both. The results show that the screening of herbal preparations or plant food supplements for specific plants, using infrared spectroscopy, is feasible. The best model was obtained with the Mid-IR data, using SIMCA as modelling technique. During validation of the model, using an external test set, 21 of 25 were correctly classified and six of the nine targeted plants showed no misclassifications for the selected test set. For the other three a success rate of 50% was obtained. Mid-IR combined with SIMCA can therefore be applied as a first step in the screening of unknown samples, before applying more sophisticated fingerprint approaches or identification tests described in several national and international pharmacopoeia. As a proof of concept five real suspicious samples were successfully screened for the targeted regulated plants. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

PubMed

Xu, Lu; Shi, Peng-Tao; Ye, Zi-Hong; Yan, Si-Min; Yu, Xiao-Ping

2013-12-01

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market. Copyright © 2013 Elsevier Ltd. All rights reserved.

Near-Infrared Spectra of Type Ia Supernovae

NASA Technical Reports Server (NTRS)

Marion, G. H.; Hoeflich, P.; Vacca, W. D.; Wheeler, J. C.

2003-01-01

We report near-infrared (NIR) spectroscopic observations of 12 'branch-normal' Type Ia supernovae (SNe Ia) that cover the wavelength region from 0.8 to 2.5 microns. Our sample more than doubles the number of SNe Ia with published NIR spectra within 3 weeks of maximum light. The epochs of observation range from 13 days before maximum light to 18 days after maximum light. A detailed model for a Type Ia supernovae is used to identify spectral features. The Doppler shifts of lines are measured to obtain the velocity and thus the radial distribution of elements. The NIR is an extremely useful tool to probe the chemical structure in the layers of SNe Ia ejecta. This wavelength region is optimal for examining certain products of the SNe Ia explosion that may be blended or obscured in other spectral regions. We identify spectral features from Mg II, Ca II, Si II, Fe II, Co II, Ni II, and possibly Mn II. We find no indications for hydrogen, helium, or carbon in the spectra. The spectral features reveal important clues about the physical characteristics of SNe Ia. We use the features to derive upper limits for the amount of unburned matter, to identify the transition regions from explosive carbon to oxygen burning and from partial to complete silicon burning, and to estimate the level of mixing during and after the explosion. Elements synthesized in the outer layers during the explosion appear to remain in distinct layers. That provides strong evidence for the presence of a detonation phase during the explosion as it occurs in delayed detonation or merger models. Mg II velocities are found to exceed 11,000 - 15,000 km/s, depending on the individual SNe Ia. That result suggests that burning during the explosion reaches the outermost layers of the progenitor and limits the amount of unburned material to less than 10% of the mass of the progenitor. Small residuals of unburned material are predicted by delayed detonation models but are inconsistent with pure deflagration or merger models. Differences in the spectra of the individual SNe Ia demonstrate the variety of these events.

Mapping iron oxides and the color of Australian soil using visible-near-infrared reflectance spectra

NASA Astrophysics Data System (ADS)

Viscarra Rossel, R. A.; Bui, E. N.; de Caritat, P.; McKenzie, N. J.

2010-12-01

Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moisture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil's Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. The aims of this paper are (1) to measure the abundance of hematite and goethite in Australian soils from their vis-NIR spectra, (2) to compare these results to measurements of soil color, and (3) to describe the spatial variability of hematite, goethite, and soil color and map their distribution across Australia. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The work presented here was validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils.

Laboratory Spectroscopy of Large Carbon Molecules and Ions in Support of Space Missions. A New Generation of Laboratory & Space Studies

NASA Technical Reports Server (NTRS)

Salama, Farid; Tan, Xiaofeng; Cami, Jan; Biennier, Ludovic; Remy, Jerome

2006-01-01

Polycyclic Aromatic Hydrocarbons (PAHs) are an important and ubiquitous component of carbon-bearing materials in space. A long-standing and major challenge for laboratory astrophysics has been to measure the spectra of large carbon molecules in laboratory environments that mimic (in a realistic way) the physical conditions that are associated with the interstellar emission and absorption regions [1]. This objective has been identified as one of the critical Laboratory Astrophysics objectives to optimize the data return from space missions [2]. An extensive laboratory program has been developed to assess the properties of PAHs in such environments and to describe how they influence the radiation and energy balance in space. We present and discuss the gas-phase electronic absorption spectra of neutral and ionized PAHs measured in the UV-Visible-NIR range in astrophysically relevant environments and discuss the implications for astrophysics [1]. The harsh physical conditions of the interstellar medium characterized by a low temperature, an absence of collisions and strong VUV radiation fields - have been simulated in the laboratory by associating a pulsed cavity ringdown spectrometer (CRDS) with a supersonic slit jet seeded with PAHs and an ionizing, penning-type, electronic discharge. We have measured for the {\\it first time} the spectra of a series of neutral [3,4] and ionized [5,6] interstellar PAHs analogs in the laboratory. An effort has also been attempted to quantify the mechanisms of ion and carbon nanoparticles production in the free jet expansion and to model our simulation of the diffuse interstellar medium in the laboratory [7]. These experiments provide {\\it unique} information on the spectra of free, large carbon-containing molecules and ions in the gas phase. We are now, for the first time, in the position to directly compare laboratory spectral data on free, cold, PAH ions and carbon nano-sized carbon particles with astronomical observations in the UV-NIR range (interstellar UV extinction, DIBs in the NUV-NIR range). This new phase offers tremendous opportunities for the data analysis of current and upcoming space missions geared toward the detection of large aromatic systems Le., the "new frontier space missions" (Spitzer, HST, COS, JWST, SOFIA,...).

Prediction of CP and starch concentrations in ruminal in situ studies and ruminal degradation of cereal grains using NIRS.

PubMed

Krieg, J; Koenzen, E; Seifried, N; Steingass, H; Schenkel, H; Rodehutscord, M

2018-03-01

Ruminal in situ incubations are widely used to assess the nutritional value of feedstuffs for ruminants. In in situ methods, feed samples are ruminally incubated in indigestible bags over a predefined timespan and the disappearance of nutrients from the bags is recorded. To describe the degradation of specific nutrients, information on the concentration of feed samples and undegraded feed after in situ incubation ('bag residues') is needed. For cereal and pea grains, CP and starch (ST) analyses are of interest. The numerous analyses of residues following ruminal incubation contribute greatly to the substantial investments in labour and money, and faster methods would be beneficial. Therefore, calibrations were developed to estimate CP and ST concentrations in grains and bag residues following in situ incubations by using their near-infrared spectra recorded from 680 to 2500 nm. The samples comprised rye, triticale, barley, wheat, and maize grains (20 genotypes each), and 15 durum wheat and 13 pea grains. In addition, residues after ruminal incubation were included (at least from four samples per species for various incubation times). To establish CP and ST calibrations, 620 and 610 samples (grains and bag residues after incubation, respectively) were chemically analysed for their CP and ST concentration. Calibrations using wavelengths from 1250 to 2450 nm and the first derivative of the spectra produced the best results (R 2 Validation=0.99 for CP and ST; standard error of prediction=0.47 and 2.10% DM for CP and ST, respectively). Hence, CP and ST concentration in cereal grains and peas and their bag residues could be predicted with high precision by NIRS for use in in situ studies. No differences were found between the effective ruminal degradation calculated from NIRS estimations and those calculated from chemical analyses (P>0.70). Calibrations were also calculated to predict ruminal degradation kinetics of cereal grains from the spectra of ground grains. Estimation of the effective ruminal degradation of CP and ST from the near-infrared spectra of cereal grains showed promising results (R 2>0.90), but the database needs to be extended to obtain more stable calibrations for routine use.

Particle analysis using laser ablation mass spectroscopy

DOEpatents

Parker, Eric P.; Rosenthal, Stephen E.; Trahan, Michael W.; Wagner, John S.

2003-09-09

The present invention provides a method of quickly identifying bioaerosols by class, even if the subject bioaerosol has not been previously encountered. The method begins by collecting laser ablation mass spectra from known particles. The spectra are correlated with the known particles, including the species of particle and the classification (e.g., bacteria). The spectra can then be used to train a neural network, for example using genetic algorithm-based training, to recognize each spectra and to recognize characteristics of the classifications. The spectra can also be used in a multivariate patch algorithm. Laser ablation mass specta from unknown particles can be presented as inputs to the trained neural net for identification as to classification. The description below first describes suitable intelligent algorithms and multivariate patch algorithms, then presents an example of the present invention including results.

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.

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.

Multiparametric Flow Cytometry Using Near-Infrared Fluorescent Proteins Engineered from Bacterial Phytochromes

PubMed Central

Telford, William G.; Shcherbakova, Daria M.; Buschke, David; Hawley, Teresa S.; Verkhusha, Vladislav V.

2015-01-01

Engineering of fluorescent proteins (FPs) has followed a trend of achieving longer fluorescence wavelengths, with the ultimate goal of producing proteins with both excitation and emission in the near-infrared (NIR) region of the spectrum. Flow cytometers are now almost universally equipped with red lasers, and can now be equipped with NIR lasers as well. Most red-shifted FPs of the GFP-like family are maximally excited by orange lasers (590 to 610 nm) not commonly found on cytometers. This has changed with the development of the iRFP series of NIR FPs from the protein family of bacterial phytochromes. The shortest wavelength variants of this series, iRFP670 and iRFP682 showed maximal excitation with visible red lasers. The longer wavelength variants iRFP702, iRFP713 and iRFP720 could be optimally excited by NIR lasers ranging from 685 to 730 nm. Pairs of iRFPs could be detected simultaneously by using red and NIR lasers. Moreover, a novel spectral cytometry technique, which relies on spectral deconvolution rather than optical filters, allowed spectra of all five iRFPs to be analyzed simultaneously with no spectral overlap. Together, the combination of iRFPs with the advanced flow cytometry will allow to first image tissues expressing iRFPs deep in live animals and then quantify individual cell intensities and sort out the distinct primary cell subpopulations ex vivo. PMID:25811854

Luminous Obscured AGN Unveiled in the Stripe 82 X-ray Survey

NASA Astrophysics Data System (ADS)

LaMassa, Stephanie; Glikman, Eilat; Brusa, Marcella; Rigby, Jane; Tasnim Ananna, Tonima; Stern, Daniel; Lira, Paulina; Urry, Meg; Salvato, Mara; Alexandroff, Rachael; Allevato, Viola; Cardamone, Carolin; Civano, Francesca Maria; Coppi, Paolo; Farrah, Duncan; Komossa, S.; Lanzuisi, Giorgio; Marchesi, Stefano; Richards, Gordon; Trakhtenbrot, Benny; Treister, Ezequiel

2018-01-01

Stripe 82X is a wide-area (30 deg2) X-ray survey overlapping the legacy Sloan Digital Sky Survey (SDSS) Stripe 82 field, designed to uncover rare, high luminosity active galactic nuclei (AGN). We report on the results of an on-going near-infrared (NIR) spectroscopic campaign to follow-up reddened AGN candidates with Palomar TripleSpec, Keck NIRSPEC, and Gemini GNIRS. We identified 8 AGN in our bright NIR sample (K < 16, Vega), selected to have red R-K colors (> 4, Vega); four of these sources had existing optical spectra in SDSS. We targeted four out of 34 obscured AGN candidates in our faint NIR sample (K > 17, Vega), all of which are undetected in the single-epoch SDSS imaging, making them the best candidates for the most obscured and/or the most distant reddend AGN in Stripe 82X. All twelve sources are Type 1 AGN, with the FWHM of at least one permitted emission line exceeding 1300 km/s. We find that our nearly complete bright NIR sample (12/13 obscured AGN candidates have spectroscopic redshifts) is more distant (z > 0.5) than a matched sample of blue Type 1 AGN from Stripe 82X; these AGN tend to be more luminous than their blue, unobscured counterparts. Results from our pilot program of faint NIR-selected obscured AGN candidates demonstrate that our selection recovers reddened quasars missed by SDSS.

Multiparametric flow cytometry using near-infrared fluorescent proteins engineered from bacterial phytochromes.

PubMed

Telford, William G; Shcherbakova, Daria M; Buschke, David; Hawley, Teresa S; Verkhusha, Vladislav V

2015-01-01

Engineering of fluorescent proteins (FPs) has followed a trend of achieving longer fluorescence wavelengths, with the ultimate goal of producing proteins with both excitation and emission in the near-infrared (NIR) region of the spectrum. Flow cytometers are now almost universally equipped with red lasers, and can now be equipped with NIR lasers as well. Most red-shifted FPs of the GFP-like family are maximally excited by orange lasers (590 to 610 nm) not commonly found on cytometers. This has changed with the development of the iRFP series of NIR FPs from the protein family of bacterial phytochromes. The shortest wavelength variants of this series, iRFP670 and iRFP682 showed maximal excitation with visible red lasers. The longer wavelength variants iRFP702, iRFP713 and iRFP720 could be optimally excited by NIR lasers ranging from 685 to 730 nm. Pairs of iRFPs could be detected simultaneously by using red and NIR lasers. Moreover, a novel spectral cytometry technique, which relies on spectral deconvolution rather than optical filters, allowed spectra of all five iRFPs to be analyzed simultaneously with no spectral overlap. Together, the combination of iRFPs with the advanced flow cytometry will allow to first image tissues expressing iRFPs deep in live animals and then quantify individual cell intensities and sort out the distinct primary cell subpopulations ex vivo.

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.

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.