Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Mathematic Models for Analysis of Quality Components in Sugarcane Juice with Fourier Transform Near Infrared Spectroscopy

With the technique of Fourier transform near infrared (FT-NIR) spectroscopy, the calibration models for quantitative analysis of sucrose and polarization in sugarcane juice were developed by using transmission mode and calibrating with partial least square (PLS) algorithm. The determination coefficients (R2) of the predicted models for sucrose and polarization in juice were 0. 9980 and 0. 9979 respectively; the root mean square errors of cross validation (RMSECV) were 0. 143 and 0. 155% for sucrose and polarization in juice respectively. The predictive errors measured by FT-NIR were close to those by routine laboratory methods. The results demonstrated that the FT-NIR methods had high accuracy and they were able to replace the routine laboratory analysis. It was also demonstrated that as a rapid and accurate measurement, the FT-NIR technique had potential applications in quality control of mill sugarcane, establishment of payment system based on sugarcane quality, and selection of clones in sugarcane breeding.

Application of Wavelet Transform in the Prediction of Navel Orange Vitamin C Content by Near-Infrared Spectroscopy

This study was to search for an approach for rapid measurement of orange vitamin C （Vc） content. By using different decomposing levels of Daubechies 3 wavelet transform, the near-infrared spectra signals obtained from intact fruits of 100 navel orange samples were denoised, and the results of the predicted Vc contents for the corresponding samples determined by the reconstructed spectra after denoising were validated by means of PLS-CV （partial least squared-cross validation）. It was shown that the prediction effects verified by PLS-CV analysis varied when different wavelet transform decomposing levels were employed. At the wavelet decomposing level 4, the best prediction effect was obtained, with the correlation coefficient R between the prediction and true values being 0.9574 and the expected variance RMSECV being as low as 3.9 mg 100 g^-1. Furthermore, the 11 different approaches for the pretreatment of the near-infrared spectrum were compared. It was found that the calibration model established by PLS using spectra pretreated by wavelet transform denoising provided the best prediction for Vc content, exhibiting the highest correlation between the prediction and true values by cross validation. In conclusion, the near infrared spectral model denoised by means of wavelet transform can be used for accurate, rapid, and nondestructive quantitative analysis on navel orange Vc content.

Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique

Background Fiber maturity is a key cotton quality property,and its variability in a sample impacts fiber processing and dyeing performance.Currently,the maturity is determined by using established protocols in laboratories under a controlled environment.There is an increasing need to measure fiber maturity using low-cost(in general less than $20000)and small portable systems.In this study,a laboratory feasibility was performed to assess the ability of the shortwave infrared hyperspectral imaging(SWIR HSI)technique for determining the conditioned fiber maturity,and as a comparison,a bench-top commercial and expensive(in general greater than $60000)near infrared(NIR)instrument was used.Results Although SWIR HSI and NIR represent different measurement technologies,consistent spectral characteristics were observed between the two instruments when they were used to measure the maturity of the locule fiber samples in seed cotton and of the well-defined fiber samples,respectively.Partial least squares(PLS)models were established using different spectral preprocessing parameters to predict fiber maturity.The high prediction precision was observed by a lower root mean square error of prediction(RMSEP)(<0.046),higher R_(p)^(2)(>0.518),and greater percentage(97.0%)of samples within the 95% agreement range in the entire NIR region(1000-2500 nm)without the moisture band at 1940 nm.Conclusion SWIR HSI has a good potential for assessing cotton fiber maturity in a laboratory environment.

Deep belief network-based drug identification using near infrared spectroscopy

Near infrared spectroscopy(NIRS)analysis technology,combined with chemometrics,can be effectively used in quick and nondestructive analysis of quality and category.In this paper,an effective drug identification method by using deep belief network(DBN)with dropout mecha-nism(dropout-DBN)to model NIRS is introduced,in which dropout is employed to overcome the overfitting problem coming from the small sample.This paper tests proposed method under datasets of different sizes with the example of near infrared diffuse refectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs,aluminum and nonaluminum packaged.Meanwhile,it gives experiments to compare the proposed method's performance with back propagation(BP)neural network,support vector machines(SVMs)and sparse denoising auto-encoder(SDAE).The results show that for both binary classification and multi-classification,dropout mechanism can improve the classification accuracy,and dropout-DBN can achieve best classification accuracy in almost all cases.SDAE is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability,which are higher than that of BP neural network and SVM methods.In terms of training time,dropout-DBN model is superior to SDAE model,but inferior to BP neural network and SVM methods.Therefore,dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size.

Wavelet-based classification and influence matrix analysis method for the fast discrimination of Chinese herbal medicines according to the geographical origins with near infrared spectroscopy

A discriminant analysis technique using wavelet transformation(WT)and influence matrixanalysis(CAIMAN)method is proposed for the near infrared(NIR)spectroscopy classifi-cation.In the proposed methodology,NIR spectra are decomposed by WT for data com-pression and a forward feature selection is further employed to extract the relevant informationfrom the wavelet coefficients,reducing both classification errors and model complexity.Adiscriminant-CAIMAN(D-CAIMAN)method is utilized to build the classification model inwavelet domain on the basis of reduced wavelet coefficients of spectral variables.NIR spectradata set of 265 salviae miltiorrhizae radia samples from 9 different geographical origins is usedas an example to test the classification performance of the algorithm.For a comparison,k-nearest neighbor(KNN),linear discriminant analysis(LDA)and quadratic discriminant analysis(QDA)methods are also employed.D-CAIMAN with wavelet-based feature selection(WD-CAIMAN)method shows the best performance,achieving the total classification rate of ioo%in both cross-validation set and prediction set.It is worth noting that the WD-CAIMANclassifier also shows improved sensitivity,selectivity and model interpretability in thecla.ssifications.

DETECTING BILATERAL FUNCTIONAL CONNECTIVITY IN THE PREFRONTAL CORTEX DURING A STROOP TASK BY NEAR-INFRARED SPECTROSCOPY

Near infrared spectroscopy(NIRS)is generally accepted as a functional brain imaging technology for brain activation study.With multichannel highly sensitive NIRS instruments,it has become possible to assess functional connectivity of different brain negions by NIRS.However,the feasibility needs to be validated in complex cognitive activities.In this study,we recorded the hemodynamic activity of the bilateral prefrontal cortex(PFC)during a color-word matching Stroop task.Wavelet transform coberence(WTC)analysis was applied to ascss the functional conectivity of all homologous channel pairs within the left/right PFC.Both the behavioral and brain activ ation results showed signifcant Stroop effects.The results of WTC analysis revealed that,bilateral functional connectivity was significantly stronger during both the incongruent stimuli and neutral stimuli compared to that of the rest period.It also showed significant Stroop effect.Our findings demonstrate that,NIRS bcomes a valuable tool to elucidate the functional conectivity of brain cortex in complex cognitive activities.

Study on Detection of Pesticide Residues on Winter Jujube Surface by Near-infrared Spectroscopy Combined with PLS and SPA

With fresh winter jujube from the southern region of Xinjiang as the object of study, the method for detecting pesticide residues on winter jujube surface by near-infrared spectroscopy （NIR） combined with successive projections algorithm （SPA） and partial least squares （PLS） was investigated. The absorbance information of winter jujube sample surface was obtained through NIR technology, for the building of a full-wave band PLS model of fresh winter jujube sample sprayed with different concentrations of pesticide （taking chlorpyrifos as an example） as well as an SPA-PLS model which was built with the characteristic wavelengths extracted with SPA as the input variables for PLS, and the prediction precision of the two kinds of models was compared. The model built with the five characteristic waveslengths extected by SPA method only used the variables 0.32% of all the variables in the full wave band, but its accuracy and precision were better than the model built with the full wave band. It is feasible to build a model for different concentrations of chlorpyrifos on fresh winter jujube surfarce by NIR technology combined with SPA and PLS, and SPA method could simplify the complexity of the model and improve the precision and stablity of the model.

Application of near infrared spectroscopy in monitoring the moisture content in freeze-drying process of human coagulation factor VIII

As an important process analysis tool,near infrared spectroscopy(NIRS)has been widely used in process monitoring.In the present work,the feasibility of NIRS for monitoring the moisture content of human coagulation factor VIII(FVIII)in freeze-drying process was investigated.A partial least squares regression(PLS-R)model for moisture content determination was built with 88 samples.Different pre-processing methods were explored,and the best method found was standard normal variate(SNV)transformation combined with 1st derivation with Savitzky–Golay(SG)15 point smoothing.Then,four different variable selection methods,including uninformative variable elimination(UVE),interval partial least squares regression(iPLS),competitive adaptive reweighted sampling(CARS)and manual method,were compared for eliminating irrelevant variables,and iPLS was chosen as the best variable selection method.The correlation coe±cient(R),correlation coe±cient of calibration set(Rcal),correlation coefficient of validation set(Rval),root mean square errors of cross-validation(RMSECV)and root mean square errors of prediction(RMSEP)of PLS model were 0.9284,0.9463,0.8890,0.4986% and 0.4514%,respectively.The results showed that the model for moisture content determination has a wide range,good linearity,accuracy and precision.The developed approach was demonstrated to be a potential for monitoring the moisture content of FVIII in freeze-drying process.

Study on evaluation of gamma oryzanol of germinated brown rice by near infrared spectroscopy

Germinated brown rice(GBR)is rich in gamma oryzanol which increase its consumption popularity,particularly in the health food market.The objective of this research was to apply the near infraredspectroscopy(NIRS)for evaluation of gamma oryzanol of the germinated brown rice.The germinated brown rice samples were prepared from germinated rough rice(soaked for 24 and 48 h,incubated for 0,6,12,18,24,30 and 36 h)and purchased from local supermar kets.The germinated brown rice sampleswere subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extractionmet hodology.The prediction model was established by partial least square regression(PLSR)andvalidated by full cross validation method.The NIRS model established from various varieties of germinated brown rice bought from diferent markets by first derivatives+vector normalizationpretreated spectra showed the optimal prediction with the correlation of determination(R?),root mean squared error of cross validation(RMSECV),and bias of 0.934,8.84×10^(-5) mg/100 g drymatter and 1.06×10^(-5) mg/100 g dry matter,respectively.This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice.This information is veryuseful to the germinated brown rice production factory and consumers.

Rapid Non-destructive Detection for Molds Colony of Paddy Rice Based on Near Infrared Spectroscopy

Near infrared spectrometer technology under a wavelength range of 918-1045 nm was used to rapidly detect paddy rice that was stored at 5℃, 15℃ and 25℃. A total of 121 paddy rice samples were collected from artificial infection with moulds to build the calibration models to calculate the total number colony of moulds based on the principal component regression method and multiple linear regression method. The results of statistical analysis indicated that multiple linear regression method was applicable to the detection of the total number colony of moulds. The correlation of calibration data set was 0.943. The correlation of prediction data set was 0.897. Therefore, the result showed that near infrared spectroscopy could be a useful instrumental method for determining the total number colony of moulds in paddy rice. The near infrared spectroscopy methodology could be applied for monitoring mould contamination in postharvest paddy rice during storage and might become a powerful tool for monitoring the safety of the grain.

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.

On-Line Measurement of the Chemical Oxygen Demand in Wastewater in a Pulp and Paper Mill Using Near Infrared Spectroscopy

Although near infrared (NIR) spectroscopy has been evaluated for numerous applications, the number of actual on-line or even on-site industrial applications seems to be very limited. In the present paper, the attempts to produce online predictions of the chemical oxygen demand (COD) in wastewater from a pulp and paper mill using NIR spectroscopy are described. The task was perceived as very challenging, but with a root mean square error of prediction of 149 mg/l, roughly corresponding to 1/10 of the studied concentration interval, this attempt was deemed as successful. This result was obtained by using partial least squares model regression, interpolated reference values for calibration purposes, and by evenly distributing the calibration data in the concentration space. This work may also represent the first industrial application of online COD measurements in wastewater using NIR spectroscopy.

Cortical activation pattern during shoulder simple versus vibration exercises： a functional near infrared spectroscopy study

To date, the cortical effect of exercise has not been fully elucidated. Using the functional near infrared spectroscopy, we attempted to compare the cortical effect between shoulder vibration exercise and shoulder simple exercise. Eight healthy subjects were recruited for this study. Two different exercise tasks（shoulder vibration exercise using the flexible pole and shoulder simple exercise） were performed using a block paradigm. We measured the values of oxygenated hemoglobin in the four regions of interest： the primary sensory-motor cortex（SM1 total, arm somatotopy, and leg and trunk somatotopy）, the premotor cortex, the supplementary motor area, and the prefrontal cortex. During shoulder vibration exercise and shoulder simple exercise, cortical activation was observed in SM1（total, arm somatotopy, and leg and trunk somatotopy）, premotor cortex, supplementary motor area, and prefrontal cortex. Higher oxygenated hemoglobin values were also observed in the areas of arm somatotopy of SM1 compared with those of other regions of interest. However, no significant difference in the arm somatotopy of SM1 was observed between the two exercises. By contrast, in the leg and trunk somatotopy of SM1, shoulder vibration exercise led to a significantly higher oxy-hemoglobin value than shoulder simple exercise. These two exercises may result in cortical activation effects for the motor areas relevant to the shoulder exercise, especially in the arm somatotopy of SM1. However, shoulder vibration exercise has an additional cortical activation effect for the leg and trunk somatotopy of SM1.

pH value monitoring during human albumin purification with near infrared spectroscopy and chemometrics

Human albumin(HA)is a very important blood product which requires strict quality controlstrategy.Acid precipitation is a key step which has a great effect on the quality of final product.Therefore,a new method based on quality by design(QbD)was proposed to investigate thefeasibility of realizing online quality control with the help of near infrared spectroscopy(NIRS)and chemometrics.The pH value is the critical process parameter(CPP)in acid precipitationprocess,which is used as the end-point indicator.Six batches,a total of 74 samples of acidprecipitation process,were simulated in our lab.Four batches were selected randomly as cali-bration set and remaining two batches as validation set.Then,the analysis based on materialinformation and three dfferent variable selection methods,including interval partial least squaresregression(iPLS),competitive adaptive reweighted sampling(CARS)and correlation coeficient(CC)were compared for eliminating irrelevant variables,Fimally,iPLS was used for variablesselection.The quantitative model was built up by partial least squares regression(PLSR).Thevalues of determination coeficients(R^(2)_(C) and R^(2)_(P)),root mean squares error of prediction(RMSEP),root mean squares error of calibration(RMSEC)and root mean squared error of crossvalidation(RMSECV)were 0.969,0.953,0.0496,0.0695 and 0.0826,respectively.The paired t test and repeatability test showed that the model had good prediction ability and stability.The results indicated that PLSR model could give accurate measurement of the pH value.

Rapid Detection of Cement Raw Meal Composition Based on Near Infrared Spectroscopy

The composition of cement raw materials was detected by near-infrared spectroscopy.It was found that the BiPLS-SiPLS method selected the NIR spectral band of cement raw materials,and the partial least squares regression algorithm was adopted to establish a quantitative correction model of cement raw materials with good prediction effect.The root-mean-square errors of SiO_(2),Al_(2)O_(3),Fe_(2)O_(3) and CaO calibration were 0.142,0.072,0.034 and 0.188 correspondingly.The results show that the NIR spectroscopy method can detect the composition of cement raw meal rapidly and accurately,which provides a new perspective for the composition detection of cement raw meal.

Variable selection in near infrared spectroscopy for quantitative models of homologous analogs of cephalosporins

Two universal spectral ranges(4550-4100 cm^(-1) and 6190-5510 cm^(-1))for construction of quantitative models of homologous analogs of cephalosporins were proposed by evaluating theperformance of five spectral ranges and their combinations,using three data sets of cephalos-porins for injection,ie.,cefuroxime sodium,cetriaxone sodium and cefoperazone sodium.Subsequently,the proposed ranges were validated by using eight calibration sets of otherhomologous analogs of cephalosporins for injection,namely cefmenoxime hydrochloride,ceftezole sodium,cefmetazole,cefoxitin sodium,cefotaxime sodium,cefradine,cephazolin sodium and ceftizoxime sodium.All the constructed quantitative models for the eight kinds of cephalosporinsusing these universal ranges could fulill the requirements for quick quantification.After that,competitive adaptive reweighted sampling(CARS)algorithm and infrared(IR)-near infrared(NIR)two-dimensional(2D)correlation spectral analysis were used to determine the scientific basis of these two spectral ranges as the universal regions for the construction of quantitativemodels of cephalosporins.The CAR.S algorithm demonstrated that the ranges of 4550-4100 cm^(-1) and 6190-5510 cm^(-1) included some key wavenumbers which could be attributed to content changes of cephalosporins.The IR-NIR 2D spectral analysis showed that certain wavenumbersin these two regions have strong correlations to the structures of those cephalosporins that wereeasy to degrade.

The relevance study of effective information between near infrared spectroscopy and chondroitin sulfate in ethanol precipitation process

Near infrared spectroscopy(NIRS)is based on molecular overtone and combination vibrations.It is difficult to assign specific features under complicated system.So it is necessary to find the relevance between NIRS and target compound.For this purpose,the chondroitin sulfate(CS)ethanol precipitation process was selected as the research model,and 90 samples of 5 different batches were collected and the content of CS was determined by modifed carbazole method.The relevance between NIRS and CS was studied throughout optical pathlength,pretreat ment methods and variables selection methods.In conclusion,the first derivative with Savitzky--Golay(SG)smoothing was selected as the best pretreatment,and the best spectral region was selected using interval partial least squares(iPLS)method under 1 mm optical cell.A multivariate cali-bration model was established using PLS algorithm for determining the content of CS,and the root mean square error of prediction(RMSEP)is 3.934gL-1.This method will have great potential in process analytical technology in the future.

Drug discrimination of Near Infrared spectroscopy based on the scaled convex hull classifier

Near Infrared spectroscopy(NIRS)has been widely used in the discrimination(classification)of pharmaceutical drugs.In real applications,however,the class imbalance of the drug samples,i.e.,the number of one drug sample may be much larger than the number of the other drugs,deceasesdrastically the discrimination performance of the classification models.To address this classimbalance problem,a new computational method--the scaled convex hull(SCH)-basedmaximum margin classifier is proposed in this paper.By a suitable selection of the reductionfactor of the SCHs generated by the two classes of drug samples,respectively,the maximalmargin classifier bet ween SCHs can be constructed which can obtain good classification per-formance.With an optimization of the parameters involved in the modeling by Cuckoo Search,a satisfied model is achieved for the classification of the drug.The experiments on spectra samplesproduced by a pharmaceutical company show that the proposed method is more effective androbust than the existing ones.

Investigation.of human visual cortex responses to flickering light using functional near infrared spectroscopy and constrained ICA

The human visual sensitivity to the flickering light has been under investigation for decades.The finding of research in this area can contribute to the understanding of human visual system mechanism and visual disorders,and establishing diagnosis and treatment of diseases.The aim of this study is to investigate the ffects of the flickering light to the visual cortex by monitoring the hemodynamic responses of the brain with the functional near infrared spectrosoopy(ENIRS)method.Since the acquired fNIRS signals are afected by physiological factors and measurement artifacts,constrained independent component analysis(eICA)was applied to extract the actual fNIRS responses from the obtained data.The experimental results revealed significant changes(p<0.0001)of the hemodynamic responses of the visual cortex.from the baseline when the flickering stimulation was activated.With the uses of cICA,the contrast to noise ratio(CNR),reflecting the contrast of hemodynamic concentration between rest and task,became larger.This indicated the improvement of the NIRS signals when the noise was eliminated.In subsequent studies,statistical analysis was used to infer the correlation between the NIRS signals and the visual stimulus.We found that there was a slight decrease of the oxygenated hemoglobin con-centration(about 5.69%)over four frequencies when the modulation increased.However,the variations of oxy and deoxy-hemoglobin were not statistically significant.