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.

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.

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.

Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs

Background： The ability of near-infrared reflectance spectroscopy（NIRS） to determine the digestible energy（DE）and metabolizable energy（ME） content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set（n = 88） and a validation set（n = 29）. The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez（J Anim Sci. 71：3389–98,1993）. The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square（MPLS） regression method based on77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.Results： The NIRS equations obtained from actually determined DE, the coefficient of determination for calibration（RSQcal）, cross-validation（R^2CV）, and validation（RSQv） were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R^2CV, and RSQvvalues were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME（RPD_v= 2.38, 〈 2.50）, the other three equations built from actually determined energy and calculated DE produced good prediction performance（RPD_vranging from 2.53 to 2.69, 〉 2.50） when applied to validation samples.Conclusion： These results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.

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.

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.

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.

Application of NIR Reflectance Spectroscopy on Rapid Determination of Moisture Content of Wood Pellets

NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to various moisture levels between 0.63% and 14.16% (wet basis) and the moisture concentration was verified using a standard oven method. Samples from various moisture levels were separated into two groups, as calibration and validation sets. NIR absorption spectral data from 400 nm to 2500 nm with 0.5 nm intervals were collected using pellets within the calibration and validation sample sets. Spectral wavelength ranges were taken as independent variables and the MC of the pellets as the dependent variable for the analysis. Measurements were obtained on 30 replicates within each moisture level. Partial Least Square (PLS) analysis was performed on both raw and preprocessed spectral data of calibration set to determine the best calibration model based on Standard Error of Calibration (SEC) and coefficient of multiple determinations (R2). The PLS model that yielded the best fit was used to predict the moisture concentration of validation group pellets. Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were calculated to validate goodness of fit of the prediction model. Baseline and Multiple Scatter Corrected (MSC) reflectance spectra with 1st derivative model gave the highest RPD value of 4.46 and R2 of 0.95. Also it’s SEP (0.670) and RMSEP (0.782) were less than the other models those had RPD value more than 3.0 with less number of factors. Therefore, this model was selected as the best model for moisture content prediction of wood pellets.

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.

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

Determination of Protein and Starch Content in Whole Maize Kernel by Near Infrared Reflectance Spectroscopy

Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.

Visual Perception of Incomplete Human-Like Body Postures: A Near-Infrared Spectroscopy Study

Previous studies have reported that the mirror neuron system plays a crucial role in social cognition. We examined whether the higher-order cognitive functions are involved in the activations in the mirror neuron area when we perceive simplified pseudo-postures. We measured 14 participants’ brain activation during the posture-recognition task using near-infrared spectroscopy. The participants’ task was to observe five sequentially presented target pseudo-postures and judge whether a test pseudo-posture was identical to one of the preceding five target pseudo-postures. The results in the majority of participants (n = 10/14) revealed that the activity in the inferior frontal mirror neuron area is modulated by perception of human-likeness, but not in the remaining four participants (n = 4/14). These results suggest that the degree of the activation of higher-order cognitive functions, which may be engaged in the inhibitory and/or facilitative processing of human body or bodily movement, leads to the distinctive activities in the inferior frontal mirror neuron area.

FUNDAMENTAL RESEARCH ON RADIOFREQUENCY ABLATION OF RAT BRAIN TISSUE BASED ON NEAR-INFRARED SPECTROSCOPY AND MIE THEORY

Near-infrared spectroscopy(NIRS)technology and Mie theory are utilized for fundamental research on radiofrequency ablation of biological tissue.Firstly,NIRS is utilized to monitor rats undergoing radiofrequency ablation surgery in real time so as to explore the relationship between reduced scattering coefficient(μ_(s)')and the degree of thermally induced tissue coagulation.Then,Mie theory is utilized to analyze the morphological structure change of biological tissue so as to explore the basic mechanism of the change of optical parameters caused by thermally induced tissue coagulation.Results show that there is a close relationship between μ_(s)' and the degree of thermally induced tissue coagulation;the degree of thermal coagulation can be obtained by the value of μ_(s)';when biological tissue thermally coagulates,the average equivalent scattering particle decreases,the particle density increases,and the anisotropy factor decreases.

High-entropy rare earth stannate ceramics:Acid corrosion resistant radiative cooling materials with high atmospheric transparency window emissivity and high near-infrared solar reflectivity

In response to the development of the concepts of“carbon neutrality”and“carbon peak”,it is critical to developing materials with high near-infrared(NIR)solar reflectivity and high emissivity in the atmospheric transparency window(ATW;8–13μm)to advance zero energy consumption radiative cooling technology.To regulate emission and reflection properties,a series of high-entropy rare earth stannate ceramics(HE-RE_(2)Sn_(2)O_(7):(Y_(0.2)La_(0.2)Nd_(0.2)Eu_(0.2)Gd_(0.2))_(2)Sn_(2)O_(7),(Y_(0.2)La_(0.2)Sm_(0.2)Eu_(0.2)Lu_(0.2))_(2)Sn_(2)O_(7),and(Y_(0.2)La_(0.2)Gd_(0.2)Yb_(0.2)Lu_(0.2))_(2)Sn_(2)O_(7))with severe lattice distortion were prepared using a solid phase reaction followed by a pressureless sintering method for the first time.Lattice distortion is accomplished by introducing rare earth elements with different cation radii and mass.The as-synthesized HE-RE_(2)Sn_(2)O_(7)ceramics possess high ATW emissivity(91.38%–95.41%),high NIR solar reflectivity(92.74%–97.62%),low thermal conductivity(1.080–1.619 W·m^(−1)·K^(−1)),and excellent chemical stability.On the one hand,the lattice distortion intensifies the asymmetry of the structural unit to cause a notable alteration in the electric dipole moment,ultimately enlarging the ATW emissivity.On the other hand,by selecting difficult excitation elements,HE-RE_(2)Sn_(2)O_(7),which has a wide band gap(Eg),exhibits high NIR solar reflectivity.Hence,the multi-component design can effectively enhance radiative cooling ability of HE-RE_(2)Sn_(2)O_(7)and provide a novel strategy for developing radiative cooling materials.

Development and implementation of incident response near-infrared models for analyzing contaminated medicines containing diethylene glycol solvent

Samples of preparations contaminated by diethylene glycol(DEG),diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra.Then the iden-tification models were developed using the collected spectra and the spectra of distilled water,propylene glyool and the preparations without diethylene glyool.Besides,the quantification model was also established for determining the concentration of diethylene glyool in the pre-parations.V alidation results show the identification and quantification models have ideal pre-diction performance.The emergency NIR models are rapid,easy to use and accurate,and can be implemented for identifying diethylene glycol raw material,screening the preparations contam-inated by diethylene glycol in the markets and analyzing the concentrations of DEG.

Titanium dioxide as an adsorbent to enhance the detection ability of near-infrared diffuse reflectance spectroscopy

A method for quantitative determination of fish sperm deoxyribonucleic acid(fsDNA)was developed by using titanium dioxide(TiO2)as an adsorbent and near-infrared diffuse reflectance spectroscopy(NIRDRS).The selective enrichment of fsDNA was proved by comparing the adsorption efficiency of bovine serum albumin,tyrosine and tryptophan,and the low adsorption background of TiO2 was illustrated by comparing the spectra of four commonly-used inorganic adsorbents(alkaline aluminium oxide,neutral aluminium oxide,nano-hydroxyapatite and silica).The spectral feature of fsDNA can be clearly observed in the spectrum of the sample.Partial least squares(PLS)model was built for quantitative determination of fsDNA using 28 solutions,and 13 solutions with interferences were used for validation of the model.The results showed that the correlation coefficient(R)between the predicted and the reference concentration is 0.9727 and the recoveries of the validation samples are in the range of 98.2%-100.7%.

Determination of Cysteine Using Near-infrared Diffuse Reflectance Spectroscopy with Enrichment via Thiol-maleimide Click Reaction

Enrichment technique has been proved to be an efficient way to make the near-infrared diffuse reflectancespectroscopy（NIRDRS） suitable for micro analysis. However, low selectivity presented by conventional enrichmentmethods makes the quantitative analysis easy to be affected by the coexisting components. In this study, a specificenrichment method with chemical bonding via thiol-maleimide click reaction was used to achieve the reduction of theinterferences. Taking cysteine as the analyzing target, maleimide-functionalized SiO2 nanoparticles were prepared forthe enrichment of cysteine. Then determination of cysteine in aqueous solution and human serum was studied usingthe partial least squares model built from the NIRDRS spectra of the adsorbate. The results show that the concentra-tion that can be quantitatively detected is as low as 2.0 μg/mL, and the correlation coefficient（R） between thereference and predicted concentration is 0.9871 for the validation samples. The recoveries are in the range of89.5%-113.8% for human serum samples in the concentration range of 0--16.2 μg/mL.

Determination of triglycerides in human serum by near-infrared diffuse reflectance spectroscopy using silver mirror as a substrate

Near infrared diffuse reflectance spectroscopy(NIRDRS) has gained wide attention due to its convenience for rapid quantitative analysis of complex samples. A method for rapid analysis of triglycerides in human serum using NIRDRS with silver mirror as the substrate is developed. Due to the even and high reflectance of the silver mirror, the spectral response is enhanced and the background interference is reduced.Furthermore, both linear and nonlinear modeling strategies were investigated adopting the partial least squares(PLS) and least squares support vector regression(LS-SVR), continuous wavelet transform(CWT)was used for spectral preprocessing, and variable selection was tried using Monte Carlo uninformative variable elimination(MC-UVE), randomization test(RT) and competitive adaptive reweighted sampling(CARS) for optimization the models. The results show that the determination coefficient(R) between the predicted and reference concentration is 0.9624 and the root mean squared error of prediction(RMSEP) is 0.21. The maximum deviation of the prediction results is as low as 0.473 mmol/L. The proposed method may provide an alternative method for routine analysis of serum triglycerides in clinical applications.

NIRS定量分析油菜种子含油量、蛋白质含量数学模型的创建

用近红外光谱 (NIRS)分析油菜品质。采用残余法测试了近 2 116份甘蓝型油菜品种资源种子的含油量 ,用近红外仪采集数据 ,选择 12 88份代表性样品 ,建立了数学模型。用该模型测试 96份待测样品 ,其NIRS的测试值与残余法测试的油菜种子含油量实测值相关系数为 0 .950 3 ,相对误差小于 3 .5% ,用凯氏定氮法测试了 63 7份油菜籽饼粕的蛋白质含量 ,选择 168份代表性样品 ,建立数学模型。 3 0份样品检测模型 ,NIRS测试值与凯氏定氮法测试的油菜籽饼粕蛋白质含量的实测值相关系数为 0 .9515,相对误差小于 6%。结果表明 ,这 2个数学模型已经可用来准确、快速、无污染。