最新刊期
卷 39 ， 期 11 ， 2020
- 摘要：A near infrared spectroscopy(NIRS) was applied to the rapid and quantitative analysis of active components in the extraction process of Bupleuri Radix.Total of 126 extraction samples were selected,of which the contents of total flavonoids and polysaccharide were determined by ultraviolet-visible spectrophotometry,while the saikosaponin A and saikosaponin D were determined by high performance liquid chromatography(HPLC),and their spectra were collected in transmission mode.The quantitative calibration models for above four compounds were established by partial least squares(PLS),which were optimized by using different pretreatment methods,spectral bands and the number of principle components.Results indicated that there existed good fitting of the four quantitative calibration models between predicted values and the reference values,which exhibited high prediction accuracy with correlation coefficients in prediction sets(RP) greater than 0.9.The root mean square errors of prediction(RMSEP) were 3.46 μg/mL,0.743 mg/mL,1.53 μg/mL,0.406 μg/mL for total flavonoids,polysaccharide,saikosaponin A and saikosaponin D,respectively,and the relative standard errors of prediction(RSEP) were 1.65%,8.28%,5.74% and 7.52%,respectively.All the results confirmed that the combination of NIRS and PLS could be successfully applied to the monitoring of active components in the extraction of Bupleuri Radix with the advantages of rapidness,accuracy,non-destruction and environmental friendly.关键词：near infrared spectroscopy(NIRS);Bupleuri Radix;extraction process;active components;rapid analysis
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发布时间：2023-04-18 - 摘要：By screening the quality markers(Q-markers) of NF-κB inhibitory active ingredients,a method of near infrared spectroscopy(NIRS) was established for the rapid evaluation of anti inflammatory effect of Angelica sinensis(AS).The NF-κB inhibitory active ingredients in AS were screened by UPLC/Q-TOF combined with NF-κB double luciferase reporter gene assay system.To establish the Q-markers,the inhibitory effects were further evaluated using active ingredients,AS extract and equivalence verification.The Q-markers in multi batches of AS were quantitatively analyzed by UPLC and NIRS,and then the NIRS fitting algorithm was established.Meanwhile,the relationship between NF-κB inhibitory holistic activity of AS extract and the content of Q-markers was investigated.Finally,the prediction model for anti-inflammatory efficacy of AS was constructed based on Q-markers check analysis via NIRS technique.The screening results showed that chlorogenic acid(X1),senkyunolide I(X3) and Z-ligustilide(X4) in AS had significant NF-κB inhibitory effects,and the change of content was consistent with the capacity of NF-κB inhibitory action of AS extracts,which satisfied the following functions:Y=16.13-14.84X1+7.981X3+0.112 6X4.In addition,the measured values from NIRS simulation method for the Q-markers detection showed a good correlation with the predicted values.The developed method realized the rapid evaluation on the anti-inflammatory effects of AS based on Q-markers,and provided a new research paradigm and solution for the rapid quality evaluation of traditional Chinese medicine.关键词：Angelica sinensis(Oliv.) Diels;quality markers;NF-κB;near infrared spectroscopy(NIRS);quality evaluation
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发布时间：2023-04-18 - 摘要：According to the characteristics of X-ray fluorescence spectrum and visible near infrared spectrum for soil in Nanji area of Poyang Lake,three quantitative analysis models for data fusion,including equal right fusion,co-addition fusion and outer product fusion based on least squares vector machine(LS-SVM) were established.Results showed that the models for equal right fusion and outer product fusion have better accuracy and stability than the single spectral quantitative analysis model has,in which the model for outer product fusion exhibits the best performance with a determination coefficient(R2) of 0.85,a root mean squared error(RMSEC) of 0.09,a root mean square error of prediction(RMSEP) of 0.06 and a relative percent deviation(RPD) of 2.41,satisfying the detection requirements.With the advantages of accuracy and reliability,the developed method could provide a reference for the study of soil heavy metal classification and grading method in China.关键词：X-ray fluorescence spectroscopy;visible and near infrared spectra;least square support vector machine;cadmium content;outer product fusion;soil
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发布时间：2023-04-18 - 摘要：The quantitative analysis model for crude protein in corn straw was constructed in this paper,and the selection method for spectrum characteristic band was discussed and verified.Firstly,107 samples were preprocessed,then a DB2 wavelet transform method with default threshold 4-level decomposition was used to reconstruct the spectra after removing two abnormal samples.The determination coefficient of cross validation(R2CV) for crude protein model was increased from 0.788 9 to 0.920 8 after pretreatment.Interval partial least squares(IPLS) and its improved methods,i.e.backward interval partial least squares(BIPLS) and synergy interval partial least squares(SIPLS) were adopted to select the characteristic bands,that IPLS and it's improved approach BIPLS,SIPLS could locate the characteristic bands more effectively and accurately compared with principal component analysis(PCA),competitive adaptive reweighted sampling(CARS),correlation coefficient(CC),genetic algorithm(GA),moving windows partial least squares methods(MWPLS).When SIPLS was using 30 band interval,the optimal model validation results were obtained in the band ranges of 10 128-10 398 cm-1 and 11 196-11 462 cm-1,with the correlation coefficient of validation set is 0.978 4,the determination coefficient R-square of prediction set is 0.957 2 and the root mean square error of prediction set of 0.221 1.IPLS method exhibited a better real time accuracy,and it and has a certain practicability in data support for the determination of ammoniation and alkalization of corn straw.关键词：corn straw;crude protein;interval partial least squares(IPLS);near infrared spectroscopy(NIR);characteristic band
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发布时间：2023-04-18 - 摘要：In this paper,a nondestructive method was developed for the identification of different varieties of green tea by near infrared spectroscopy with chemometrics.The near infrared spectra of eight varieties of green tea samples were collected.Effects of single and optimized combination preprocessing methods on spectra were compared.The identification models were constructed by unsupervised principal component analysis(PCA) and supervised linear discriminant analysis(LDA).Results showed that the optimized combination preprocessing method achieved higher accuracy.The standard normal variable transformation preprocessing method was used to eliminate the spectral scattering effect caused by the uneven size of tea samples.Meanwhile,the first derivative preprocessing was used to eliminate the changing background,reduce the interference of baseline drift and underline the useful information from spectra.By combining the pretreatment methods with the principal component analysis,the tea samples could be identified more accurately,with an accuracy of 75.00%.In addition,a supervised linear discriminant analysis method was used to process the original spectral data,with an identification accuracy reaching up to 100%.However,a priori knowledge of categories was needed in the method.Therefore,the combination of near infrared spectroscopy and chemometrics could realize the rapid and non destructive identification of different green tea varieties.关键词：green tea;near infrared spectroscopy;spectral pretreatment;principal component analysis;linear discriminant analysis
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发布时间：2023-04-18 - 摘要：In order to alleviate the pressure of wood pulp supply in China and meet the actual demand of pulping with mixed pulpwood,a study was conducted on near infrared rapid analysis of mixed pulpwood.145 mixed samples of Eucalyptus urophylla× grandis-Acacia mangium were prepared,in which the content of Eucalyptus urophylla×grandis was manually controlled.The near infrared spectra of these samples were collected,and the contents of holocellulose,pentosan and Klason lignin were analyzed by traditional methods.After the original spectra were pretreated by first derivative and standard normal variate,the analysis models for contents of Eucalyptus urophylla×grandis,holocellulose,pentosan and Klason lignin were established by partial least squares method,support vector machine method,artificial neural network method and LASSO algorithm,respectively.Among them,models for contents of Eucalyptus urophylla×grandis and holocellulose established by LASSO algorithm were the best,with their root mean square error of prediction(RMSEP) values of 1.80% and 0.60%,and their absolute deviation(AD) ranges of -3.03%-3.17% and -1.03%-0.98%,respectively,which could be used for accurate and rapid analysis.Besides,the model for content of pentosan established by partial least squares was the best,with its RMSEP value of 0.75% and an absolute deviation range of -1.26%-1.33%,while the model for content of Klason lignin established by the support vector machine method was the best,with its RMSEP value of 0.48% and an absolute deviation range of -0.82%-0.86%.The performance of the two models was suitable for inaccurate analysis.This study provides the possibility for rapid analysis of mixed pulpwood,and also confirms the applicability of LASSO algorithm.关键词：near-infrared spectroscopy;LASSO algorithm;mixed pulpwood;pulping and papermaking;component content
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发布时间：2023-04-18 - 摘要：In order to realize the intelligent grain preparation of fermented grains in the process of liquor production,an online near infrared(NIR) spectroscopy system was developed in this paper for the real time detection of the water,starch and acid contents of fermented grains.The online signal was converted as 4-20 mA current for the intelligent allocation of grains in the process of Chinese liquor brewing.This real time detection system was carried out at liuchixiang Chinese liquor brewing plant in Yibin,Sichuan province.The average errors for Lab NIR analysis of water,starch,acid in fermented grains were -0.25%,0.38% and 0.29 mmol/10 g,while the standard errors of prediction(SEP) of water,starch and acid were 0.60%,0.75% and 0.18 mmol/10 g,respectively.The average errors for online NIR quantitative analysis of water,starch,acid in fermented grains were -0.75%,0.48% and 0.29 mmol/10 g,while the SEP of water,starch and acid were 0.66%,0.97%and 0.22 mmol/10 g,respectively.Compared with the results of Lab NIR analysis,the average errors and standard deviations of this online analysis were amplified,but the precision of online NIR analysis could be qualified for grains addition in the automation of the whole production line.关键词：near infrared spectroscopy;online monitoring;Chinese liquor;fermented grains
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发布时间：2023-04-18 - 摘要：Under the optimal test conditions explored in the early stage,the online original near infrared spectrum for the polyester/cotton blend fabrics samples was collected using a self developed “high efficiency identification and sorting device for the main components of fiber products”.Based on the original online spectrogram,an optimal spectral pretreatment method was proposed as S-G smoothing+MMN+S-G derivative.Meanwhile,an online near infrared quantitative analysis model for polyester/cotton blend fabrics was established by partial least square method.The root mean standard error of cross validation(RMSECV) was 1.47,the correlation coefficient of calibration(RC) and correlation coefficient of validation(RV) were not less than 0.99,the relative predictive deviation of calibration(RPDC) was 18.17,the relative predictive deviation of validation(RPDV) was 13.13,and the relative predictive deviation of cross validation(RPDCV) was 1176.To verify the reliability of the model,30 external samples were selected for online verification.The linear equation for the verified results was y=(1.00±0.01)x-(0.88±0.56),with an accurate rate of 93.3%.After importing the model into the “textile online master control program” of the sorting device,the sorting category of the equipment was set for different polyester content fabrics,then the content prediction for the polyester/cotton fabrics samples were performed,and the samples were automatically purged into the corresponding collection frame through the device's blowing and sorting system.The time for each sample to be predicted and sorted was less than 2 s,and the automatic mechanical sorting result was correct.Therefore,the established model and sorting device could be used for efficient online measurement and automatic sorting of waste polyester/cotton blended textiles.关键词：waste textiles;online near infrared;quantitative analysis model;efficient identification;automatic sorting
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发布时间：2023-04-18 - 摘要：A rapid and non-destructive identification method for shiitake geographical origins was developed based on near infrared(NIR) spectroscopy combined with chemometrics.The NIR spectra of dried shiitake samples from different regions were collected in the diffuse reflectance mode.The origin discrimination models for shiitake cultivated in Jilin,Hubei and Fujian provinces were respectively established by partial least squares discriminant analysis(PLSDA),then optimized by spectral preprocessing and wavelength screening technique,and finally verified with samples.Results showed that the model established with the original spectrum could initially achieve the identification of the origin.The spectral preprocessing was used to subtract the background information in the spectrum,and the wavelength selection technique was used to select a specific wavelength to optimize the model.The prediction results could be further improved based on spectral preprocessing and wavelength selection.This method provides a new approach for tracing the origin of shiitake production,which has an important practical significance in the development of shiitake industry.关键词：shiitake;geographical origins;near infrared spectroscopy;chemometrics
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发布时间：2023-04-18 - 摘要：In order to investigate the feasibility of spectral transfer between near infrared spectrometric(NIRS) models of grating type and Fourier transform near infrared analyzer,the spectral transfer of original spectrum was performed by piecewise direct standardization(PDS) method,with domestic fish meal as the NIRS sample,DS2500F as the source instrument and MPA as the target one.The NIRS quantitative analysis prediction models for moisture,crude protein,crude fat,methionine and lysine were established,whose spectrum difference,applicability and accuracy were verified by correlation coefficient of cross validation(R2cv),root mean square error of cross validation(RMSECV),Mahalanobis distance(MD),bias,root mean square error of prediction(RMSEP) and relative prediction deviation(RPD),respectively.According to the verification and evaluation results,the various parameters of the prediction models established for moisture,crude protein,crude fat,methionine and lysine were not significantly different from the NIR spectrum of DS2500F instrument transferring to the target spectrum of MPA instrument.It showed that the NIR spectrum of domestic fish meal on the DS2500F instrument could be used to replace the original MPA spectrum by transmission,and model transfer was thus realized indirectly,which improved the application efficiency of NIR prediction model with good shareability and applicability.关键词：near infrared spectroscopy;fish meal;predictive model;spectral transfer;model transfer
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发布时间：2023-04-18 - 摘要：A novel near infrared spectroscopy(NIRS) combined with supervised pattern recognition was proposed for the rapid classification and discrimination of types of cigarettes.Standard normal variables(SNV),multiplicative signal correction(MSC),first derivative(FD),second derivative(SD) and Savitzky-Golay filt(SG) and their combined spectral pre processing methods were used for the spectral data preprocessing of finished cut tobacco.A discriminant model was established by NIRS combined with three pattern recognition methods include principal component analysis(PCA),partial least squares discriminant analysis(PLS-DA) and orthogonal partial least squares discriminant analysis(OPLS-DA),and the prediction accuracy of classification identification was used as an evaluation index.The experimental results showed that:(1) the principal component distribution maps were intertwined,and PCA could not identify five types of finished cut tobacco.(2) The PLS-DA model for finished cut tobacco spectrum after MSC+FD pretreatment could achieve better classification and recognition results,and the prediction accuracies for the calibration set and test set were 100% and 98.3%,respectively.(3) The identification of the OPLS-DA model for the finished cut tobacco spectrum after MSC+SD pretreatment was the best,and the parameters of the model,including the fraction of the variation of X explained by the model(R2X),the fraction of the variation of Y explained by the model(R2Y),and the fraction of the variation of Y that can be predicted by the model according to the cross validation(Q2) were 0.485,0.907 and 0.748,respectively.The prediction accuracies for the calibration set and test set both reached to 100%.Results showed that the classification model based on NIRS combined with OPLS-DA was efficient,quick,accurate and non destructive,and provided a new and rapid identification approach for finished cut tobacco classification.关键词：near infrared spectroscopy;finished cut tobacco;classify discrimination;principal component analysis(PCA);partial least squares discriminant analysis(PLS-DA);orthogonal partial least squares discriminant analysis(OPLS-DA)
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发布时间：2023-04-18 - 摘要：Protein content is an important indicator for the evaluation of the quality of fishmeal.In this paper,a near infrared(NIR) spectral analysis technique combined with a feature selection method was adopted to establish a rapid quantitative analytical model detecting the protein content of fishmeal samples.Combining the interval partial least squares(iPLS) with the differential evolution(DE) algorithms of binary mutation strategy,a novel optimization mode,ie.interval partial least squares differential evolution(iPLS-DE) was established for the wavelength selection of the NIR spectral data for fishmeal samples.9 optimal feature wavebands were first selected by iPLS-DE through adjusting the number of equally divided intervals in iPLS,and then the discrete characteristic wavelength combinations in the optimal wavebands were further to screened out by the DE algorithm of binary mutation strategy.According to the evaluation indexes for the model,the optimal model of iPLS-DE was determined,and compared with the optimal model of iPLS.Results showed that,when the full spectrum was equally divided into 5 intervals,50 discrete characteristic wavelengths were screened out by iPLS-DE to establish an optimal model.The prediction root mean square error and relative prediction derivation of the iPLS-DE optimization model for the test set samples were 1.033% and 4.058,while the prediction root mean square error and relative prediction derivation of the iPLS optimization model for the test set samples were 1.131% and 3.855,respectively.In comparison with the common iPLS models,the iPLS-DE model is more feasible to improve the predictive ability of NIR analytical model applied to the quantitative detection of fishmeal protein.关键词：near infrared spectroscopy;fishmeal protein;feature extraction;interval partial least squares;differential evolution
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发布时间：2023-04-18 - 摘要：Four kinds of edible oils,ie.sunflower oil,soybean oil,corn oil and peanut oil from different botanical origins were characterized by gas chromatography(GC) and near infrared spectroscopy(NIR),and the discriminant analysis models were established based on the characterization data.Meanwhile,the feasibility of data level data fusion was explored.A partial least squares discriminant analysis(PLS-DA) model was constructed based on chromatographic and spectral data fusion to classify the edible oils of botanical origins.Principal component analysis(PCA) results showed that the discriminant analysis by GC was mainly based on fatty acid composition,while that by NIR was mainly based on the characterization of hydrogen contained chemical bonds in samples.The sensitivity and specificity of the data fusion model were both 1.000,and the classification error were 0.000.Thus,low level data fusion reduced average cross validation(CV) classification error,and the model exhibited a good robustness.Compared with the results of the model based on single data of gas chromatography or near infrared spectroscopy,the data fusion strategy improved the classification performance of the model.关键词：edible oil;botanical origins;discriminant analysis;gas chromatography(GC);near infrared spectroscopy(NIR);data fusion
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发布时间：2023-04-18 - 摘要：A near-infrared spectroscopic(NIRS) method using competitive adaptive reweighted sampling(CARS) for variable screening was established for the rapid determination of luteolin,with imidazole ionic liquid as adsorbent enriching luteolin in dilute solution.Effects of amount of adsorbent,pH value and oscillation time on the adsorption efficiency were investigated,and the adsorption capacity of the adsorbent was explored.The adsorbent enriching luteolin was detected by near infrared diffuse reflectance spectroscopy,and a quantitative correction model for luteolin was constructed by CARS method for variable screening combined with partial least squares regression(PLS).Results showed that,under the optimal conditions of 0.15 g of adsorbent,pH 7,and 20 min of oscillation time,the adsorption rate reached to 90.9%,and the adsorption conformed to the Langmuir isotherm adsorption model,with a maximum adsorption capacity of 7.1 mg/g.In the NIRS modeling,the developed model was compared with that of the non CARS variable screening process,and the comparison showed that the results of the CARS variable screening method were better,which were also confirmed by the continuous wavelet transform(CWT) spectral preprocessing.The results exhibited that,the RPD value increased after CWT treatment,which further explain the reliability of the model.This method could be used for effective enrichment of luteolin in dilute solution,and the NIRS method combined with CWT spectrum pretreatment using CARS variable screening could realize the sensitive and rapid detection of luteolin in dilute solution.关键词：imidazole ionic liquid adsorbent;luteolin;near infrared spectroscopy;enrichment;CARS variable screening;partial least squares
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发布时间：2023-04-18 - 摘要：Nicotine is the most important component in E cigarette liquid,whose content determines the flavor and the safety of the product.In order to improve the detecting efficiency of the nicotine content,a novel near infrared spectroscopy(NIR) combined with extreme learning machine regression(ELMR) algorithm was adopted to establish an NIR-ELMR prediction model for nicotine content in E cigarette liquid.The experimental results showed that,compared with traditional partial least squares regression(PLSR) model and principal component regression(PCR) model,the NIR-ELMR model was much better with a determination coefficient(R2) of 0.926 2,which was higher than 0.859 0 for PCR prediction model and 0.860 4 for PLSR prediction model.Besides,the root mean square error of prediction(RMSEP) for NIR-ELMR model was 0.026 8,which was smaller than 0.043 1 for PCR model and 0.040 9 for PLSR model.The above results indicated the established model could be applied to the rapid and accurate determination of the nicotine content of E cigarette liquid,which lay a foundation for the online analysis of nicotine content and the rapid determination of other quality parameters.关键词：E-cigarette liquid;nicotine content;near infrared spectroscopy (NIR);extreme learning machine (ELM);rapid determination
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发布时间：2023-04-18 - 摘要：Based on near infrared diffuse reflectance spectroscopy for the qualitative discrimination on food packaging materials polyethylene and polypropylene using different spectral pretreatment methods with different wavebands,principal component analysis(PCA) combined with three pattern recognition methods of SIMCA,Bayes discriminant and K-Nearest neighbor were adopted to establish three qualitative prediction models,and the prediction performances of the models were compared according to their correct recognition rates,in order to select the best model.Results showed that the qualitative correction models established by SIMCA,Bayes discriminant analysis and K-Nearest neighbor are better in the wavelength range of 1 050-1 550 nm.Six types of spectral preprocessing methods,i.e vector normalization,standard normal variable transformation,Centralization,moving average filtering,Savitzky-Golay filtering and first order differential combined with three pattern recognition methods of SIMCA,Bayes discrimination and K-Nearest neighbors were used to process the near infrared spectra of plastic samples.In the range of 1 050-1 550 nm,the principal component factor was 3.The qualitative correction model by K-Nearest neighbor using the original spectrum was the best,whose correct recognition rates for the sample's calibration set and prediction set were both 100%.It could provide a reference for the rapid identification of polyethylene and polypropylene as food packaging materials.关键词：near infrared spectroscopy;plastic;principal component analysis;correct recognition rate
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发布时间：2023-04-18 - 摘要：Using hyperspectral analysis technology combined with pattern recognition, the classification and detection methods of eight potato micro seed potatoes(Daxiyang,Holland-14,Holland fifteen 041,Holland fifteen Q8,Jizhangshu 12,Jizhangshu 8,Xingjia 2 and Y2) were established.276 seed tuber samples were collected.The original spectra of 860-1 700 nm were preprocessed by standardize,11 points Savitzky-Golay smoothing and 4 points differential first derivative.Principal component analysis showed that the cumulative contribution rate of the first three principal components was 95.12%,including most information of the original spectra,and could be used as classification variables.Then,linear discriminant analysis,BP neural network and support vector machine were used for classification modeling.Finally,the classification models of 8 potato micro seed potatos were established by stratification and step by step.Firstly,the linear discriminant analysis model was used to distinguish Daxiyang,Holland-14,Holland fifteen 041 and other varieties.The average correct recognition rate was 88.79%.Then BP neural network model was established to divide the samples of other varieties into two categories:Jizhangshu 8,Y2,and Holland fifteen Q8,Jizhangshu 12,Xingjia 2,with an average correct recognition rate of 93.24%.Finally,the BP neural network model was used to distinguish Jizhangshu 8 and Y2,with the average correct recognition rate of 77.78%;and the support vector machine classification model was used to distinguish Holland fifteen Q8, Jizhangshu 12 and Xingjia 2,with the average correcct recognition rate of 8723%.The method was applied to the classification detection of eight potato seed potatos with the average correct recognition rate of 89.75%,which indicated that the hyperspectral analysis technology could be used for the classification and detection of potato micro seed potatos.关键词：potato;micro seed potato;hyperspectral;classification detection
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发布时间：2023-04-18 - 摘要：A rapid detection model for 2,3-butanedione and 3-hydroxy-2-butanone in base liquor was established using near infrared spectroscopy(NIR).182 base liquor samples from Dukang winery in Luoyang were selected as materials.The chemical values of the two ketones were detected by gas chromatography(GC).Meanwhile,the spectral data in the range of 12 000-4 000 cm-1 were collected.The calibration models for 2,3-butanedione and 3-hydroxy-2-butanone were established by partial least square(PLS) combined with internal cross validation.By comparing effects of the PLS models under different spectral pretreatments for optimization,the optimal preprocessing methods for 2,3-butanedione and 3-hydroxy-2-butanone were determined as first derivative with multiple scattering correction and second derivative,respectively,while the optimal spectral ranges are 9 403.2-7 497.9 cm-1 and 9 403.2-7 497.9 cm-1 + 6 101.7-5 449.8 cm-1.The determination coefficients(R2) for the chemical values and the NIR predicted values of the 2,3-butanedione and 3-hydroxy-2-butanone calibration set samples after optimization were 0.960 2 and 0.963 2,while the corresponding root mean square errors of cross validation(RMSECV) were 0.39 mg/100 mL and 0.22 mg/100 mL,respectively.Through external validation,the R2 for the validation set samples were 0.957 6 and 0.957 8,while the predicted root mean square errors(RMSEP) were 0.40 mg/100 mL and 0.24 mg/100 mL,respectively.Results showed that the model established by near infrared spectroscopy combined with chemometrics could meet the requirements for rapid detection of ketones in liquor production with high accuracy.关键词：base liquor;near infrared spectroscopy;2,3-butanedione;3-hydroxy-2-butanone
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发布时间：2023-04-18
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