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.

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.

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.

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.

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.

Rapid monitoring the water extraction process of Radix Paeoniae Alba using near infrared spectroscopy

Near infrared(NIR)spectroscopy has been developed into one of the most important process analytical techniques(PAT)in a wide field of applications.The feasibility of NIR spectroscopy with partial least square regression(PLSR)to monitor the concentration of paeoniflorin,albi-florin,gallic acid,and benzoyl paeoniforin during the water extraction process of Radix Paeoniae Alba was demonstrated and verified in this work.NIR spectra were collected in transmission mode and pretreated with smoothing and/or derivative,and then quantitative models were built up using PLSR.Interval partial least squares(iPLS)method was used for the selection of spectral variables.Determination coeficients(R2aI and R2red),root mean squares error of prediction(RMSEP),root mean squares error of calibration(RMSEC),and residual predictive deviation(RPD)were applied to verify the performance of the models,and the corresponding values were 0.9873 and 0.9855,0.0487 mg/mL,0.0545mg/mL and 8.4 for paeoniforin;0.9879,0.9888,0.0303 mg/mL,0.0321 mg/mL and 9.1 for albiflorin;0.9696,0.9644,0.0140 mg/mL,0.0145 mg/mL and 5.1 for gallic acid;0.9794,0.9781,0.00169 mg/mL,0.00171 mg/mL and 6.9 for benzoyl paeoniflorin,respectively.The results turned out that this approach was very efficient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba.

Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometrics methods

Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.

Rapid identification and preliminary evaluation of quality characters of oilseed sunflower by near infrared spectroscopy

To clarify the quality characters,understand the genetic diversity and screen elite lines among different oilseed sunflowers,the contents of crude fat,oleic acid,linoleic acid,palmitic acid and stearic acid of 525 oil sunflowers(including 375 germplasm accessions and 150 inbred lines)were detected by near-infrared spectroscopy(NIRS);the genetic variation and correlation analysis of quality traits were also performed.The results showed that oleic acid and linoleic acid had rich diversities with large variation ranges for each material type.Similar to the relation between crude fat content and palmitic acid content,significantly negative relation with high estimated value existed between oleic acid and linoleic acid content,while stearic acid content positively associated with oleic acid and palmitic acid content.Principal component analysis indicated that 5 quality traits were integrated into 2principal component factors(linoleic acid negative factor and palmitic acid factor)with the contribution rate of 88.191%,which could be used for evaluating sunflower quality.525 oilseed sunflowers were clustered into 3groups with obvious differences of quality characters,materials in Group I had high contents of oleic acid and low crude fat,but the opposite was found in GroupⅢ.59 superior quality accessions were obtained using large-scale and rapid near-infrared spectroscopy,and these excellent materials were verified by the traditional national chemical standard method.This research provided materials and significant reference for sunflower genetic research and quality breeding.

Nondestructive determination of paper properties of Populus × euramericana with near infrared spectroscopy

Five Populus x euramericana wood samples representing three different sites were selected and nearinfrared （NIR） spectra were obtained. For these sections, basis weight, brightness and three mechanical properties （tensile index, tearing index and bursting index） were determined by standard analytical methods. Calibrations were developed for each paper property using the NIR spectra, data on paper properties, using partial least squares （PLS） regression. The results show that the coefficients of correlation of calibration and validation for basis weight were 0.8824 and 0.8299, respectively; the standard error of calibration （SEC） and prediction （SEP） were 1.150 and 1.170, respectively. In testing for brightness, the correlation coefficient of calibration was 0.9621 and for validation 0.9612, while the SEC and SEP were 0.997 and 1.300, respectively; paper brightness and NIR spectroscopy were highly correlated. NIR spectroscopy can be used to predict tensile, tearing and bursting indices of paper samples rapidly. We found that the paper properties fitted by NIR and GB methods were highly correlated. The coefficients of correlation of calibration and validation for basis weight exceeded 0.8000, while the SEC and SEP were very small. These results reveal that the five paper properties of Populus x euramericana and those predicted by the NIR model were highly correlated. We conclude that the NIR models can be used for the prediction of paper properties.

Rapid Detection of Fat Content in Chenopodium quinoa Willd by Near Infrared Spectroscopy

This study was conducted to find a method for rapid determination of fat content in complete quinoa （ Chenopodium quinoa Willd） seeds. The near infrared spectra of 100 quinoa samples were collected, and a mathematic model was built using the near infrared spectra, so as to perform prediction. The results showed that within the wavelength range of 1 0 000-4 000 cm ^-1 , the quantification model of fat content built by first derivative ＋vector normalization spectral pre-processing had better calibration and prediction effects, and showed a determination coefficient of cross validation （ r cv^ 2 ） of 0.939 3 and a determination coefficient of validation （ rval^2 ） of 0.923 5. The near infrared spectral model of fat could be used for rapid detection of fat contents in quinoa.