最新刊期
卷 39 ， 期 10 ， 2020
- 摘要：Near infrared spectroscopy(NIRS) has distinguished itself as one of the most rapidly advancing analytical techniques over the last few decades.The fundamentals of vibrational spectroscopy,instrumentation and chemometrics are the three main sustaining pillars of modern near infrared spectroscopy analytical technology.In recent years,near infrared spectroscopy technology has made remarkable progresses in these three aspects.In this paper,the latest development of NIR analytical technology and its various applications in these three aspects are summarized,and the future perspectives are anticipated.关键词：near infrared spectroscopy;chemometrics;on line analysis;spectroscopic imaging;miniaturized instrument
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发布时间：2023-04-18 - 摘要：Near infrared spectroscopy(NIRS) is a rapid and nondestructive detection technique,which has the advantages of simple operation,low cost,no chemical reagent consumption and simultaneous detection of various quality parameters.In this review,research progress of near infrared spectroscopy(NIRS) technique in rapid determination on quality of oilseed and edible vegetable oils in China was reviewed,including oil content,crude protein content,fatty acid content and other quality parameters of oilseeds,as well as physicochemical properties,fatty acids and authenticity identification of edible oils.Moreover,the development prospects of NIRS technique in rapid determination of oilseed products quality were also anticipated.关键词：near infrared spectroscopy(NIRS);quality detection;oilseeds;edible oil;authentication
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发布时间：2023-04-18 - 摘要：Near-infrared(NIR) spectroscopy is a green,fast analytical technology,and thus has been widely used in scientific research,industrial production and routine detection.The application of chemometric algorithms plays an important role in the development of NIR spectroscopy.Chemometrics focuses on exploring the relation between the measured variables,modeling the differences among samples in a qualitative or quantitative way,finding out the underlying trend of intrinsic sample changes,and predicting unknown samples reasonably and accurately.This is also the thumb of the “big data” strategy.This review discusses the issues commonly encountered in NIR spectroscopy,concerning the weakness of spectral signals,the serious overlapping of NIR bands,the interference from background,noise,non informative variables or environmental factors,etc.,which could either mislead to an incorrect qualitative or quantitative analysis model relating the NIR spectroscopic measurements to target compositions of samples or worsen the model in terms of prediction capacity and accuracy.Furthermore,it also describes new chemometric methods with respect to spectral preprocessing,variable selection,multivariate calibration and calibration transfer.These methods have been proposed or developed in recent years to improve the reliability,accuracy and applicability of the chemometric NIR spectral models.关键词：near-infrared spectroscopy;chemometrics;spectral preprocessing;variable selection;multivariate calibration;calibration transfer
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发布时间：2023-04-18 - 摘要：Water plays an important role in chemical and biological processes,attracting wide interest in the structure of water.Based on the effect of temperature on structure of water,a temperature dependent near infrared spectroscopy was proposed and applied to the quantitative and structural analysis of aqueous solutions.Meanwhile,chemometrics was adopted to extract the temperature induced spectral variation of the water.In this article,recent works on the structural analysis of small molecules and the structural transformation of proteins and thermo responsive polymers were summarized.The structural changes and the interactions of them in aqueous solutions,as well as the role of water in the chemical processes were studied using the spectral variation of water with temperature.关键词：water spectroscopic probe;temperature dependent near infrared spectroscopy;chemometrics;structural analysis
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发布时间：2023-04-18 - 摘要：Crystallization which plays an important role in the separation and purification of high purity compounds,is a traditional separation and purification technology widely used in chemical,pharmaceutical and other fields.The spectral process analysis technology based on the concept of quality by design provides an efficient and intelligent support for the monitoring,feedback and control of crystallization process.In this paper,applications of several process analysis technologies,such as attenuated total reflection Fourier transform infrared spectroscopy,Raman spectroscopy,near infrared spectroscopy and focused beam reflectance measurement are reviewed.The status,advantages and disadvantages of the above technologies are systematically summarized,and their future development trends are prospected,in order to provide an effective reference for the on line monitoring of crystallization process.关键词：crystallization;quality by design;process analytical technology;spectroscopy;on line monitoring
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发布时间：2023-04-18 - 摘要：Applications of near infrared spectroscopy(NIRS) and the applied research and practice of “NIRS+Internet” mode in the field of tobacco are reviewed in this paper.In the network environment,influences of the NIRS equipment differences and the shortcomings of common chemometrics method(algorithms) in modeling and data processing on the deep applications of NIRS are briefly discussed,and the possible solutions for cloud computing of NIRS are proposed.Finally,the application prospect for NIRS networking in big data period is also outlooked.关键词：near infrared spectroscopy(NIRS);internet;cloud computing;tobacco;review
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发布时间：2023-04-18 - 摘要：At present,as a new rapid detection method,near infrared spectroscopy rapid detection has been widely used in the fields of agriculture,food,beverage,oil,material,pharmacy,medicine,animal and plant quarantine and its industrial process.Based on the introduction of the principle and application of near infrared rapid detection,an idea is put forward in this paper to introduce the near infrared spectroscopy detection into the technical field of port safety,aiming at the application requirements such as intelligent classification of commodities,rapid identification of material composition,rapid quarantine identification of animal and plant products,as well as online monitoring of the loading and unloading process of commodities in port safety supervision.Meanwhile,a scientific and technological foundation is layed for the realization of the dual goals of port trade security and trade facilitation,and the research and application prospects of near infrared spectroscopy detection in port security supervision are presented.关键词：port safety supervision;near infrared spectroscopy;rapid detection;rapid quarantine;review
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发布时间：2023-04-18 - 摘要：Using the calibration model transfer of PLS-NIRs models for predicting contents of moisture,protein,fat and starch in corn,as well as total alkaloids in tobacco leaves as an example,effect of number of latent variables(nLVs) on the transfer errors of the models were investigated in this paper.It was found that the nLVs in PLS-NIRs models for corn and tobacco leaves selected by cumulative contribution rate greater than 99.9% were 1 and 13,respectively.The prediction reproducibilities for the four ingredients in corn between master and slave samples predicted by the PLS-NIRs models with one latent variable all satisfied the requirements of national standards.When the PLS-NIRs model predicting total alkaloids content built on the master with 13 latent variables was transferred to four slaves,mean of relative prediction errors(MRE) of tobacco leaves tested on the four slaves were all lower than 6% after piecewise direct standardization(PDS) correction.While the nLVs in PLS-NIRs models for corn and tobacco leaves determined by leaving one sample in turn as cross validation set or fourth fold cross validation method were 5-10,16 and 19,respectively.The prediction errors for the slave corn samples derived from the models with nLVs greater than 5 were significantly increased and exceeded the allowable error level.Even after being corrected by PDS method,most indices of prediction reproducibility for the four ingredients in corn between master and slave samples given by these models could not satisfy the requirements of national standards.The transfer errors of PLS-NIRs models for total alkaloids in tobacco leaves by selecting nLVs greater than 13 increased with the increase of nLVs,while PDS correction cannot guarantee the MRE for all slave instruments given by these models lower than 6%.Results indicated that selecting nLVs for PLS-NIRs models based on the principle of accumulative contribution rate greater than 99.9% or near to 99.9% could effectively avoid over fitting and improve the transfer performance of the models.关键词：near infrared spectroscopy model transfer;partial least square;number of latent variables;corn;tobacco
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发布时间：2023-04-18 - 摘要：The implementation of process analytical technology(PAT) in a commercial active pharmaceutical ingredient(API) recrystallization production process was introduced in this paper,in which the near infrared(NIR) spectroscopy technology was used to monitor the concentration adjustment process under the requirements of good manufacturing practice(GMP).The hardware and software of the monitoring system were qualified according to the requirements of a complex computerized system.Two prediction models for API concentration and moisture were developed by partial least square(PLS) algorithm,which performance was evaluated by root mean square error of calibration(RMSEC),root mean square error of cross validation(RMSECV),root mean square error of prediction(RMSEP) and determination coefficient(R2),respectively.In order to ensure the performance of the models,the linearity,range,accuracy,precision(repeatability),specificity and robustness were verified again according to the analytical method verification requirements.Finally,the system performance was tested to confirm that the PAT system meets the requirements for commercial operation.PAT control of the concentration adjustment process could greatly shorten the concentration adjustment time,save the steam energy consumption and testing costs,reduce the deviation of production,increase the consistency of different batches,and improve the product quality.关键词：good manufacturing practice(GMP);process analytical technology(PAT);near infrared(NIR) spectroscopy;on line monitoring;partial least square(PLS)
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发布时间：2023-04-18 - 摘要：A new classification method based on chemical image was established using ‘dynamic’ near infrared(NIR) spectroscopy with a deep learning based image recognition model GoogLeNet and transfer learning,with cashmere,cashmere/wool blends textiles,cotton and silk cotton textiles as the targets.Moisture perturbation was proposed to apply in this paper,collecting ‘dynamic’ spectra,expanding the spectral differences between samples of different types,and thus fusing the synchronous and asynchronous two dimensional map of dynamic spectra into a ‘chemical image’ which reflects the detailed differences between samples.A total of 234 textile samples were collected,and the samples with water contents of 0,5.4%,11.2% and 16.3% were prepared.Several preprocessing methods were employed before modeling.A total of 16 classification models were established,in which the best SIMCA and SVM models for cashmere vs cashmere/wool blends have the accuracies of 63.33% and 70.09%,while those of cotton and silk cotton textiles are 71.02% and 72.51%,respectively.Results demonstrated that the developed method is effective,the overall prediction correct rates of models are 92.59% for cashmere and blended and 94.74% for cotton and silk cotton.This contribution provides a successful demonstration for advanced identification techniques in the field of deep learning for solving chemical problems.关键词：near infrared spectroscopy;pattern identification;deep learning;transfer learning
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发布时间：2023-04-18 - 摘要：A new model calculation method,local weighted partial least squares(LWPLS),was proposed to solve the problem of nonlinear phenomena in near infrared spectroscopy.Taking Antai pill as the research object,the quantitative model for Antai pill was established by LWPLS algorithm,and the accuracies of quantitative models established by PLS and LWPLS were compared.Results indicated that,the correlation coefficient(R2) of PLS and LWPLS algorithms for ferulic acid were 0.785 5 and 0.971 9,the root mean square errors of prediction(RMSEP) were 0.126 6 and 0.043 8,and the relative prediction errors(RE) were 12.66% and 9.18%,respectively,while the R2 values of PLS and LWPLS algorithms for senkyunolide A were 0.886 4 and 0.964 9,the RMSEP values were 0.114 8 and 0.077 1,and the RE values were 14.01% and 7.81%,respectively,which showed that LWPLS algorithm was more accurate.Therefore,the LWPLS algorithm could improve the accuracy of quantitative model for Antai pill,exhibiting a wide generalization and application potential.关键词：local weighted partial least squares(LWPLS);near infrared spectroscopy;partial least squares(PLS);ferulic acid;senkyunolide A
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发布时间：2023-04-18 - 摘要：In this study,one dimensional scale invariant feature transform(SIFT) algorithm was used to find stable characteristic wavelengths of the near infrared spectra(NIRS) of tobacco leaves.The reproducibility rate and reproducibility were calculated according to the wavelengths selected by SIFT based on the precision tests of spectra.The orthogonal table of L9(33) was adopted to optimize the parameters in the SIFT algorithm to make the reproducibility rate and reproducibility as high as possible.Based on the optimized parameters and the spectra of 10 representative samples tested on the master instrument,10 stable characteristic wavelength sets were selected,and the spectral responses of the union of these wavelength sets were used as the independent variables to build a NIRS model for predicting the total alkaloids in tobacco leaves by partial least squares(PLS) method,which was shortened as SIFT-PLS model.After the model was directly transferred to three slave instruments,the average relative prediction errors(MREs) of the total alkaloids in tobacco leave samples tested on the three slaves were all smaller than 6%,meeting the internal control requirements of tobacco enterprises.Nevertheless,when the whole wavelength model(WW-PLS) was directly transferred to the three slaves,MRE of only one slave instrument satisfied the requirements.Even the spectra of the samples tested on the three salves were corrected by piecewise direct standardization(PDS) method,MRE of samples tested on only one slave was lower than 6%.The robust SIFT-PLS near infrared spectroscopy model established by selecting stable characteristic wavelengths with SIFT method could be directly shared by the three slave instruments,without needing transfer sets,and correcting spectra of slave instruments or spectral calibration models.The method proposed in this paper could be used to realize the NIRS calibration model transfer without standards in the true sense.关键词：scale invariant features transform;wavelength screening;near infrared spectroscopy calibration model transfer;total alkaloids in tobacco leaves
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发布时间：2023-04-18 - 摘要：A method was proposed for the identification of citrus huanglongbing by near infrared(NIR) spectroscopy based on least angle regression combined with kernel extreme learning machine(LAR-KELM(RBF)) as the traditional detection method for the disease has some defects such as low accuracy and poor stability.Firstly,the acquired spectral data were preprocessed by wavelet transform,then the least angle regression(LAR) algorithm was used to select the spectral wavelength,and finally,with the help of KELM(RBF),the filtered spectral data were managed to classify.The NIR spectral data of orange leaves were taken to verify the performance of LAR-KELM(RBF) algorithm in the experiment.The classification accuracy of the algorithm could reach up to 99.91%,and standard deviation(STD) was 0.11.The experimental results of different training sets showed that LAR-KELM(RBF) model was more accurate and stable than extreme learning machine(ELM),summation wavelet extreme learning machine(SWELM),back propagation(BP(two layers)),KELM(RBF) and support vector machine(SVM) model,which could be widely used in the detection and differentiation of citrus huanglongbing.关键词：near infrared spectroscopy;huanglongbing of citrus;variable screening;kernel extreme learning machine;least angle regression
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发布时间：2023-04-18 - 摘要：Visible and near infrared reflectance spectroscopy is an effective method for the quantitative estimation of soil available phosphorus content.However,when the spectral data collected from the soil of one region are used for those of other regions,problems such as low prediction accuracy and model failure may occur.In order to solve these problems,a prediction model through transfer learning method was established in this paper,with soil samples from southern Anhui province as the source domain and soil samples from northern Anhui province as the target domain to improve the accuracy of soil available phosphorus model prediction.By comparing the prediction accuracy of the model before and after the transfer,it was found that the model for southern Anhui region could not be directly used for Northern Anhui region,in that case,model failure may occur.The coefficient of determination(R2) and the ratio of prediction to deviation(RPD) of the model were-0.19 and 0.92,respectively,and the root mean square error of prediction(RMSEP) of the model was 1.04.The predictive accuracy of modeling for the North Anhui region with a small sample size was low,with R2 and RPD of 0.61 and 1.60,respectively,and RMSEP of 0.60.Based on the transfer component analysis(TCA) and adding some samples from northern Anhui to build the model,the prediction accuracy of the model for available phosphorus in samples from northern Anhui was significantly improved,with the R2 and RPD improved to 0.79 and 2.18,respectively,and the RMSEP reduced to 0.44.Results showed that the prediction model based on TCA method for soil available phosphorus in southern Anhui region could be applied to that in northern Anhui region,improving the prediction accuracy and reducing the modeling cost,and it provides a new idea for the wide application of prediction model for soil available phosphorus.关键词：transfer learning;transfer component analysis;soil available phosphorus;visible and near infrared reflectance spectroscopy
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发布时间：2023-04-18 -
Propagation Effect of Instrument Noise in Partial Least Squares Models of Near infrared Spectroscopy
摘要：In the practical application of multivariate calibration,instrument noise is one of the key factors influencing the prediction ability of partial least squares(PLS) modeling.Plenty of research works have been reported to mitigate the negative influences of instrument noise by various de noised methods.However,effects of instrument noise in PLS modeling have not received adequate attention.The main point of this work is to illustrate how instrument noise could be involved in PLS model and demonstrate its propagation effect on calculations of weight vectors and loading vectors through theoretical analysis.Moreover,the prediction error of PLS model was divided into two parts,ie.the prediction error of noise free model and the error caused by noise propagation and accumulation.Results showed that instrument noise could not only decrease the prediction ability of PLS models,but also influence the model selection.关键词：near-infrared spectroscopy;partial least squares;multivariate calibration;instrument noise- 260
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发布时间：2023-04-18 - 摘要：Gray wolf optimizer(GWO) algorithm, which is based on swarm intelligence, is easy to implement due to its few parameters and simple structure. However, to our knowledge, few studies used GWO for the spectral analysis. In this study, the GWO was introduced into the variable selection of NIR spectra.Taking corn dataset as an example,the performance,numbers of iterationsnumbers of wolves and efficiency of GWO algorithm were investigated.Based on this,a partial least squares(PLS) model was established to determine the protein,fat,moisture and starch contents in corn samples.Results showed that GWO algorithm was very efficient.With optimized parameters,the retention variable numbers of GWO algorithm for protein,fat,moisture and starch were 19,19,14 and 34,respectively.Compared with root mean square error of prediction(RMSEP) values of the full wavelength PLS model for the four components,those of the GWO-PLS model decreased from 0.245 8,0.122 4,0.339 8 and 1.105 8 to 0.147 7,0.080 1,0.176 2 and 0.739 8,with their decreasing percentages of 40%,35%,48% and 33%,respectively.Meanwhile,the correlation coefficients were increased accordingly.Therefore,GWO algorithm could improve the prediction accuracy of the PLS model apparently with high efficiency and fewer selected variables.It is a promising method for variable selection of NIR spectroscopy.关键词：near infrared spectra;variable selection;gray wolf optimizer(GWO);partial least squares(PLS)
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发布时间：2023-04-18 - 摘要：Hyperspectral remote sensing，which plays an important role in the field of earth observation and remote sensing,could be used to obtain more accurate and rich remote sensing information,thus covering various levels and full links of the various countries′ aerial,spaceflight and small range of ground observation.However,hyperspectral data sets are often very large and contain redundant information,which brings inconvenience to subsequent processing.In this study,Laplacian Eigen mapping was used to reduce the dimension and fulfil the feature extraction of hyperspectral data.Then a weighted naive Bayes classification algorithm was proposed,while the classic naive Bayes classifier was improved by the method of rewarding weight.The algorithm was verified by the open source data.Results indicated that the accuracy for the proposed method in identification of the object information reached to 92.7%,which was greatly improved compared with that for the traditional method.关键词：hyperspectral;feature extraction;target recognition;naive Bayes classification algorithm;Laplacian Eigen mapping
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发布时间：2023-04-18 - 摘要：A high temperature portable gas analyzer based on Fourier transform infrared(FTIR) spectroscopy was developed to meet the needs of supervision for on site environment and verification for continuous emission monitoring system of stationary sources.The gas analyzer was used for the detection of SO2 and NO under the condition of simulation exhaust gas in lab,with the absolute errors less than 1.5 μmol/mol and 1.0 μmol/mol for SO2 and NO,respectively.Meanwhile,the equipment was used to monitor the flue gas of a waste incinerator before denitrification.The monitoring results showed a good correspondence with the continuous emission monitoring system(CEMS) results of boiler,and the absolute error for the average value of the monitoring results was within the standard allowable error range,indicating that the analyzer could meet the requirements for on site detection of flue gas with good accuracy and on site application capability.关键词：gas analyzer;Fourier transform infrared spectrometer;flue gas detection
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发布时间：2023-04-18 - 摘要：In order to quickly and accurately grasp the hydrolytic nitrogen content of soil in the whole Kunming area,963 soil samples of different types were collected.Partial least squares(PLS) combined with competitive adaptive reweighted sampling(CARS) method used to screen the spectral wavelength variables was adopted to establish an analysis model for hydrolytic nitrogen.Results showed that the parameters for the model were improved after the optimization of wavelength variables by CARS,and the root mean square error of cross validation(RMSECV) was reduced from 31.63 to 25.55,the correlation coefficient of cross validation(Rcv) was increased from 0.78 to 0.84,and the external verification results of the model were basically consistent with the internal cross verification results.Meanwhile,near infrared spectroscopy combined with CARS method could be used to effectively establish a near infrared(NIR) mathematical model for hydrolytic nitrogen content in different soil types in Kunming area under the modeling of a large number of representative samples,which is suitable for the NIR detection of other components in soil and has an important guiding significance.关键词：near-infrared spectroscopy;CARS variable selection;soil;hydrolytic nitrogen
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发布时间：2023-04-18
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