Near Infrared Spectroscopy (NIRS) Model-Based Prediction for Protein Content in Cowpea

Cowpea (Vigna unguiculata L. Walp) is a multi-purpose legume with high quality protein for human consumption and livestock. The objective of this work was to develop near-infrared spectroscopy (NIRS) prediction models to estimate protein content in cowpea. A total of 116 cowpea breeding lines with a wide range of protein contents (19.28 % to 32.04%) were selected to build the model using whole seed and ground seed samples. Partial least-squares discriminant analysis (PLS-DA) regression technique with different pre-treatments (derivatives, standard normal variate, and multiplicative scatter correction) were carried out to develop the protein prediction model. Results showed: 1) spectral plots of both the whole seed and ground seed showed higher spectral scatter at higher wavelengths (>1450 nm), 2) data pre-processing affects prediction accuracy for bot whole seed and ground seed samples, 3) prediction using ground seed samples (0.64 R2 0.85) is better than the whole seed (0.33 R2 0.78), and 4) the data pre-processing second derivative with standard normal variate has the best prediction (R2_whole seed = 0.78, R2_ground seed = 0.85). The results will be of interest in cowpea breeding programs aimed at improving total seed protein content.

Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer

Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn stalks,and it is very important to determine its content in corn stalks.In this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied.In order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of NIRS.Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.

Investigation of Prefrontal Cortex Activity in University Students with Presenteeism: A Near-Infrared Spectroscopy (NIRS) Study

Presenteeism refers to impaired performance attributed to attending work with health problems. There has been no study examining the state of presenteeism with objective measures. We compared cerebral hemodynamic changes, measured by near-infrared spectroscopy (NIRS), during neuropsychological tests conducted by university students with presenteeism and healthy controls. Twenty-two university students participated in the study;11 of them with impaired performance caused by mental health problem were allocated to the presenteeism group and 11 without health problems to the control group. Presenteeism was assessed by the Presenteeism Scale for Students. To evoke hemodynamics changes, the participants completed a Word Fluency Test (WFT) and a Trail Making Test (TMT). The NIRS probes were located over the bilateral prefrontal area. Students with presenteeism had significantly higher incidences of depression than controls. However, there was no significant difference in behavioral performance examinations between the two groups. With regard to hemodynamics changes, the repeated measures analysis of covariance of the NIRS signals revealed significant interactions between group and task activation. Although we observed a significant increase in oxygenated hemoglobin concentration during the WFT among controls (simple main effect;left channel, F(1, 19) = 27.34, P F(1, 19) = 22.05, P < 0.001), no changes were found in students with presenteeism during either the WFT (simple main effect;left channel, F(1, 19) = 0.12, P F(1, 19) = 0.08, P t = ﹣0.94, P with Bonferroni correction = 0.745;right channel, t = ﹣2.19, P with Bonferroni correction < 0.113). This is the first study to reveal differences in activity in the cerebral cortex associated with presenteeism. The fact that students with presenteeism have prefrontal dysfunction might reinforce the concept of presenteeism.

Correlation of prefrontal activity measured by near-infrared spectroscopy (NIRS) with mood, BDNF genotype and serum BDNF level in healthy individuals

Depression has been known to reduce the prefrontal activity associated with the execution of certain cognitive tasks, although whether a temporarily depressed or anxious mood in healthy individuals affects the prefrontal blood oxygen level during cognitive tasks is unknown. Combining the measurement of prefrontal activity with near-infrared spectroscopy (NIRS) and the two cognitive tasks, namely the letter version of the verbal fluency test (VFT-l) and the Stroop test, we measured the effect of a depressed or anxious mood and gender on the changes in the prefrontal oxygenated hemoglobin (Oxy-Hb) levels during those cognitive tests in healthy individuals. Depressed mood or anxious mood was assessed by the Hospital Anxiety and Depression Scale (HADS). Thereby we aimed to explore the possibility of NIRS measurement for detecting the early subclinical manifestation of major depression. Moreover, we examined the possible relationships between prefrontal activation and the functional Val66Met polymorphisms of the brain derived neurotropic factor (BDNF) gene and serum BDNF level. As a result, the increased prefrontal Oxy-Hb levels during cognitive tasks were significantly correlated with the severity of depressed mood in males. The course of the prefrontal Oxy-Hb increase was different depending on the cognitive tasks, i.e., the VFT-l or the Stroop test, in both genders. Correlations of BDNF genotype and serum BDNF level with the prefrontal Oxy-Hb levels during those cognitive tasks were negative. Our results suggest that the early subclinical manifestation of depressed mood in males might be detected by the NIRS measurement, which is not correlated with the individual properties of BDNF.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

Evaluation of Expression Recognition Function in Autism Spectrum Disorder Using Near-Infrared Spectroscopy

One of the characteristics of Autism Spectrum Disorder (ASD) is social disorder. The specificity of facial and expression recognition for people with ASD is gathering attention as a factor of this social disorder. The study examined the hemodynamic activities in the prefrontal cortex using near-infrared spectroscopy (NIRS) when a person with ASD performed an expression recognition task. The subjects were twenty males (18 - 22 years old) with ASD and without intellectual disabilities. Forty-five healthy males matched for age and sex were included as a control group. In both groups, the degree of autistic tendencies was evaluated using the Autism-Spectrum Quotient (AQ). Using eight standard emotional expressions of Japanese people, two expression recognition tasks were set. An NIRS was used to measure the prefrontal cortex blood mobilization during the expression-processing process. The AQ was significantly higher in the ASD group, while the rate of overall correct expression response was significantly lower (p &rho;= &minus;0.40 p < 0.001). In the automatic expression-processing task, no activation in the prefrontal cortex was found in either the ASD or the control group. In the conscious expression-processing task, the activation of the left and right lateral prefrontal cortex was weaker in the ASD group compared to the control group. Unlike in the control group, a mild activation of posterior prefrontal cortex was found in the ASD group. The expression-processing process of the ASD group was found to be different from that of the control group. NIRS was effective in detecting a brain function disorder in people with ASD during an expression-processing process.

RAPID DETERMINATION OF PROTEIN IN MILLET BY FOURIER TRANSFORM NEAR-INFRARED(FTNIR)DIFFUSE REFLECTANCE SPECTROSCOPY

In this paper,the Fourier transform near-infrared(FTNIR)diffuse reflectance spectroscopy is applied for the rapid determination of protein in millet.The partial least-squares(PLS)regression is successfully used as an effective multivariate calibration technique.The calibration set is composed of 20 standard millet samples that the protein contents were determined by the traditional Kjeldahl method.The optimal model dimension is found to be 5 by cross-validation.22 millet samples were determined by the proposed FTNIR-PLS method.The correlation coefficient between the concentration values obtained by the FTNIR-PLS method and the traditional Kjeldahl method is 0.9805.The standard error of prediction(SEP)is 0.28% and the mean recovery is 100.2%.The proposed method has been successfully applied for the routine analysis of protein in about 10,000 grain samples.

玉米秸秆组分近红外漫反射光谱(NIRS)测定方法的建立

玉米秸秆是我国产量最大的秸秆生物质资源，但目前还没有快速高效的组分分析方法，本研究利用傅里叶变换近红外漫反射光谱（NIRS）技术，采用偏最小二乘法（PLS），在国内首次建立了NIRS测定玉米秸秆中灰分、半纤维素、纤维素、Klason木质素、酸不溶灰分和水分含量的校正模型，该模型稳定，适合不同地区、不同品种的玉米秸秆及其不同部位。实验结果表明，采用一阶导数＋KarlNorris滤波预处理，谱区在4100-7500cm^-1，能得到理想的预测模型。该模型对玉米秸秆各组分的交叉验证均方差（RMSECV）范围为0．0903～1．45，预测误差（RMSEP）范围为0．2569％～2．5819％，预测相关系数≥0．8711。该研究对加速我国秸秆生物质的工业转化具有重要意义。

Application of near-infrared spectroscopy to predict sweetpotato starch thermal properties and noodle quality

Sweetpotato starch thermal properties and its noodle quality were analyzed using a rapid predictive method based on near-infrared spectroscopy （NIRS）. This method was established based on a total of 93 sweetpotato genotypes with diverse genetic background. Starch samples were scanned by NIRS and analyzed for quality properties by reference methods. Results of statistical modelling indicated that NIRS was reasonably accurate in predicting gelatinization onset temperature （To） （standard error of prediction SEP=2.014 ℃, coefficient of determination RSQ=0.85）, gelatinization peak temperature （Tp） （SEP=-1.371 ℃, RSQ=0.89）, gelatinization temperature range （Tr） （SEP=2.234 ℃, RSQ=0.86）, and cooling resistance （CR） （SEP=0.528, RSQ=0.89）. Gelatinization completion temperature （To）, enthalpy of gelatinization （△H）, cooling loss （CL） and swelling degree （SWD）, were modelled less well with RSQ between 0.63 and 0.84. The present results suggested that the NIRS based method was sufficiently accurate and practical for routine analysis of sweetpotato starch and its noodle quality.

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

An enrichment device of silica-based monolithic material and its application to determine micro-carbaryl by NIRS

Silica-based monolithic column material was synthesized and an enrichment device was fabricated with the material by assembling the material inside a glass column. The enrichment device was applied for the determination of micro-carbaryl with near-infrared spectroscopy （NIRS）. The aqueous solutions of carbaryl passed through the device and the carbaryl was enriched on the surface of the material where diffuse reflection NIR spectra were measured. These procedures of enrichment and measurement ensured to concentrate analytes for the measurement, so that the sensitivity of determination of MRS could be improved. NIR spectra of carbaryl solutions （0.01-1.00μg mL^-1）, measured after the application of the enrichment device, were pretreated with multiplicative scatter correction （MSC） and regressed against the concentrations of the carbaryl solutions with partial least squares （PLS） method. The results showed that the minimum value of root-mean-square error of prediction （RMSEP） was 0.1771 μg mL^-1 when the number of latent variables was 3 of PLS regression. Therefore, the number of latent variables 3 was selected as the optimum value. RMSEP was not very low but acceptable considering that NIRS is commonly used in macro amount analysis and it is quite difficult for NIRS to determine micro amount analytes, especially, less than 1 μg mL^-1.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.

Determination of Rice Amylose Content by Near-infrared Spectroscopy

Amylose contents in 298 rice samples were determined by conventional method, and a near-infrared spectral model of rice amylose content was established by partial least squares. The calibration determination coefficient （RC） was 0.95; the standard error of calibration （SEC） was 1.58; and the determination coefficient of cross validation （RP） was 0.91, and the standard error of prediction SEP was 1.92. External validation was performed with 20 samples, the predicted values and the determined values were not significantly different, and the correlation coefficient between them was over 95%. The calibration model has good prediction perfor- mance, and could rapidly determine rice amylose content instead of chemical ana- lytical method.

Influence of Transparent Anode on Luminescent Performance of Near-infrared Organic Light-emitting Diodes

The optical transmission（200--2000 nm）, sheet resistance and work functions of indium-tin oxide（ITO）（100 Ω/）, ITO（12 Ω/）, zinc-oxide（ZnO）, aluminum-doped ZnO（AZO） and polyaniline（PANI） films were investigated. Near-infrared organic light-emitting diodes（NIR-OLEDs） emitting around 1.54 μm based on Er（DBM）3Phen with ITO（100 Ω/）, ITO（12 Ω/） and PANI as anodes, respectively, were fabricated. The device structure was anode/4＂-tris（N-3-methylphenyl-N-phenyl-amino）-triphenylamine（m-MTDATA）/ N,N＇-di-l-naphthyl- N,N＇-diphenylbenzidine（NPB）/Er（DBM）3Phen/tris-（8-hydroxyquinoline） aluminum（Alq3）/A1. The results suggest that the performance of NIR-OLEDs with ITO（100 Ω/）, which has a lower Sn content, as anodes appear to be better than that of NIR-OLEDs with ITO（12 Ω/） and PANI as anodes, respectively. The high N1R transmittance of ITO（100 Ω/） is a major reason for the relatively high NIR EL efficiency. The more balanced holes and electrons in the device based on ITO（100 Ω/） are another reasons.

Rapid determination of production date for green tea by near-infrared spectroscopy

Coupled with partial least squares(PLS),near infrared(NIR)spectroscopy was applied to develop a fast and nondestructive method to identify the production date of Rizhao green tea aiming at the deficiencies of the existing methods.In the modeling process,the raw spectra were first processed by five-point smoothing and first derivative.And then,moving window back propagation artificial neural network(MW-BP-ANN)was applied to select the characteristic spectral variables.After that,the calibration model was built by PLS,and the optimum model was achieved when 9 principal component scores(PCs)were included.The performances of the calibration models were evaluated according to root mean square error of predictionεRMSEP,correlation coefficient(C p)and residual prediction deviation(σRPD).The optimum results of the calibration model was achieved,andεRMSEP=19.965,C p=0.943 andσRPD=3.07.The overall results sufficiently demonstrate that NIR spectroscopy combined with PLS can be efficiently applied in the rapid identification of green tea production date.

Multi-watt near-infrared light therapy as a neuroregenerative treatment for traumatic brain injury

Infrared light represents a broad spectrum of light with wavelengths from 700 nm to 1 million nm（1,000 microns）.At its shortest wavelengths（referred to as near-infrared）,it merges with the red spectrum of visible light.At the longest end（referred to as far-infrared）,it blends into the range of microwaves.

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Fructus cnidii （Chinese name shechuangzi） is the fruit produced by Cnidium monnieri （L.） Cusson （Umbelliferae）. It is a perennial herb that is used to treat skin-related diseases and gynecopathyell. Recent pharmacological studies have revealed crude extracts or components isolated from fructus cnidii possess antiallergic, antipruritic, antidermatophytic, antibacterial, antifungal, and antiosteoporotic activities. Osthole and imperatorin are the major compounds present in shechuangzi. They are often used as standards for the evaluation of the quality of shechuangzi products.

Geographic Classification of Chinese Grape Wines by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.

Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

Capillary method for measuring near-infrared spectra of microlitre volume liquids

The present study theoretically explored the feasibility of the capillary method for measuring near-infrared (NIR) spectra of liquid or solution samples with microlitre volume, which was proposed in our previous studies. Lambert-Beer absorb- ance rule was applied to establish a model for the integral absorbance of capillary, which was then implemented in numerical analyses of the effects of capillary on various spectral features and dynamic range of absorption measurement. The theoretical speculations indicated that the capillary method might be used in NIR spectroscopy, which was further supported by the empirical data collected from our experiments by comparison between capillary NIR spectra of several organic solvents and cuvette cell NIR spectra.