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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Novel imaging system for positioning of the indocyanine green (ICG) target;visible projection of the near-infrared fluorescence image

Background: Even though NIR fluorescence imaging has many advantages in SLN mapping and cancer detection, NIR fluorescence imaging shows a serious drawback that NIR cannot be detected by the naked eye without any detectors. This limitation further disturbs accurate SLN detection and adequate tumor resection resulting in the presence of cancerous cells near the boundaries of surgically removed tissues. Materials and methods: To overcome the drawback of the conventional NIR imaging method, we suggest a novel NIR imaging system which can make the NIR fluorescence image visible to the naked eye as NIR fluorescence image detected by a video camera is processed by a computer and then projected back onto the NIR fluorescence excitation position with a projector using conspicuous color light. Image processing techniques were used for projection onto the exact position of the NIR fluorescence image. Also, we implemented a phantom experiment to evaluate the performance of the developed NIR fluorescence projection system by use of the ICG. Results: The developed NIR fluorescence projection system was applied in normal mouse model to confirm the usefulness of the system in the clinical field. A BALB/c nude mouse was prepared to be applied in normal mouse model and 0.25 mg/ml stock solution of the ICG was injected through a tail vein of the mouse. From the application in normal mouse model, we could confirm that the injected ICG stayed in the liver of the mouse and verify that the projection system projected the ICG fluorescence image at the exact location of the ICG by performing laparotomy of the mouse. Conclusions: From the application in normal mouse model, we could verify that the ICG fluorescence image was precisely projected back on the site where ICG fluorescence generated. It can be demonstrated that the NIR fluorescence projection system can make it possible to visualize the invisible NIR fluorescence image and to realize that SLN mapping and cancer detection in clinical surgery.

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.

近红外光谱技术(NIRS)在草地生态学研究中的应用

近红外光谱技术(NIRS)是一种快速、高效、无损的现代检测技术,已经在许多领域广泛应用。文章阐述了NIRS应用于草地生态学研究的意义,介绍了NIRS用于测定牧草营养成分、矿物质、土壤养分含量,分析了牧草混合物的组成、动物对所采食牧草的反应、牧草病虫害抗性等一些复杂特性,以及进行生化标记、同位素鉴别研究。综合这些研究可以看出,NIRS能够作为一种整体研究工具应用于草地生态学的许多研究领域中,可以检测各种常规化学成分、分析草地生态系统的各种动态指标和系统运行的多项整体特性。希望通过本文的总结分析,推动NIRS在中国草地生态学研究中的应用,加速该领域研究手段的现代化。