The Experts below are selected from a list of 85953 Experts worldwide ranked by ideXlab platform
Quansheng Chen - One of the best experts on this subject based on the ideXlab platform.
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High-precision identification of the actual storage periods of edible oil by FT-NIR Spectroscopy combined with chemometric methods.
Analytical methods : advancing methods and applications, 2020Co-Authors: He Yingchao, Hui Jiang, Quansheng ChenAbstract:The actual storage period of edible oil is one of the important indicators of edible oil quality. A high-precision identification method based on the near-infrared (NIR) Spectroscopy technique for the actual storage period of edible oil is proposed in this study. Firstly, a Fourier transform NIR (FT-NIR) spectrometer was used to collect NIR spectra of edible oil samples in different storage periods, and the obtained spectra were pretreated by standard normal transformation (SNV). Then, the characteristics of the pretreated spectra were analyzed by principal component analysis (PCA), and the spatial distribution of edible oil samples in different storage periods was visually presented using a PCA score plot. Finally, three pattern recognition methods, which were K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were compared to establish a qualitative identification model of edible oil in different storage periods. The results showed that the recognition performance of the SVM model was significantly superior to that of the KNN and RF models, especially in terms of generalization performance, and the SVM model had a recognition rate of 100% when predicting independent samples in the prediction set. It is suggested that FT-NIR Spectroscopy combined with appropriate chemometric methods is feasible to realize fast and high-precision identification of actual storage periods of edible oil and provided an effective analysis tool for edible oil storage quality detection.
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Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm
Molecules (Basel Switzerland), 2019Co-Authors: Hui Jiang, Quansheng ChenAbstract:This work applied the FT-NIR Spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R2) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR Spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.
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prediction of amino acids caffeine theaflavins and water extract in black tea using ft nir Spectroscopy coupled chemometrics algorithms
Analytical Methods, 2018Co-Authors: Muhammad Zareef, Quansheng Chen, Qin Ouyang, Felix Y H Kutsanedzie, Md Mehedi Hassan, Annavaram Viswadevarayalu, Ancheng WangAbstract:Fourier transform near-infrared Spectroscopy (FT-NIRS), coupled with chemometrics techniques, was performed as a fast analysis technique to assess the quality of various components in black tea. Four PLS models, namely partial least square (PLS), synergy interval PLS (Si-PLS), genetic algorithm PLS (GA-PLS) and backward interval PLS (Bi-PLS), were established as calibration models for the quantitative prediction of amino acids, caffeine, theaflavins and water extract. The results are reported based on the lower root mean square error of cross prediction (RMSEP) and the root mean square error of cross-validation (RMSECV) as well as their correlation coefficient (R2) in the prediction set (RP) and the calibration set (RC). In addition, on the basis of fewer frequency variables, GA-PLS was found to be the best technique for the quantification of amino acids and water extract and Bi-PLS was found to be the best technique for the quantitative analysis of caffeine and theaflavins in this study. It was observed that NIR Spectroscopy can be successfully combined with various chemometric techniques for the rapid identification of the chemical composition of black tea. This study demonstrates that FT-NIR Spectroscopy, combined with chemometrics (GA-PLS and Bi-PLS), has the best stability and generalization performance for black tea analysis.
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Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR Spectroscopy and L1-PLS regression.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2018Co-Authors: Hui Jiang, Congli Mei, Yonghong Huang, Quansheng ChenAbstract:Abstract This study aimed to investigate the potential of FT-NIR Spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR Spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.
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Nondestructive Identification of Tea (Camellia sinensis L.) Varieties using FT-NIR Spectroscopy and Pattern Recognition
Czech Journal of Food Sciences, 2018Co-Authors: Quansheng Chen, Jiewen Zhao, Muhua Liu, Jianrong CaiAbstract:Chen Q., Zhao J., Liu M., Cai J . (2008): Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR Spectroscopy and pattern recognition. Czech J. Food Sci., 26: 360–367. Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea ( Camellia sinen sis L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) Spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR Spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.
Hui Jiang - One of the best experts on this subject based on the ideXlab platform.
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High-precision identification of the actual storage periods of edible oil by FT-NIR Spectroscopy combined with chemometric methods.
Analytical methods : advancing methods and applications, 2020Co-Authors: He Yingchao, Hui Jiang, Quansheng ChenAbstract:The actual storage period of edible oil is one of the important indicators of edible oil quality. A high-precision identification method based on the near-infrared (NIR) Spectroscopy technique for the actual storage period of edible oil is proposed in this study. Firstly, a Fourier transform NIR (FT-NIR) spectrometer was used to collect NIR spectra of edible oil samples in different storage periods, and the obtained spectra were pretreated by standard normal transformation (SNV). Then, the characteristics of the pretreated spectra were analyzed by principal component analysis (PCA), and the spatial distribution of edible oil samples in different storage periods was visually presented using a PCA score plot. Finally, three pattern recognition methods, which were K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were compared to establish a qualitative identification model of edible oil in different storage periods. The results showed that the recognition performance of the SVM model was significantly superior to that of the KNN and RF models, especially in terms of generalization performance, and the SVM model had a recognition rate of 100% when predicting independent samples in the prediction set. It is suggested that FT-NIR Spectroscopy combined with appropriate chemometric methods is feasible to realize fast and high-precision identification of actual storage periods of edible oil and provided an effective analysis tool for edible oil storage quality detection.
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Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSS–PLS Algorithm
Molecules (Basel Switzerland), 2019Co-Authors: Hui Jiang, Quansheng ChenAbstract:This work applied the FT-NIR Spectroscopy technique with the aid of chemometrics algorithms to determine the adulteration content of extra virgin olive oil (EVOO). Informative spectral wavenumbers were obtained by the use of a novel variable selection algorithm of bootstrapping soft shrinkage (BOSS) during partial least-squares (PLS) modeling. Then, a PLS model was finally constructed using the best variable subset obtained by the BOSS algorithm to quantitative determine doping concentrations in EVOO. The results showed that the optimal variable subset including 15 wavenumbers was selected by the BOSS algorithm in the full-spectrum region according to the first local lowest value of the root-mean-square error of cross validation (RMSECV), which was 1.4487 % v/v. Compared with the optimal models of full-spectrum PLS, competitive adaptive reweighted sampling PLS (CARS–PLS), Monte Carlo uninformative variable elimination PLS (MCUVE–PLS), and iteratively retaining informative variables PLS (IRIV–PLS), the BOSS–PLS model achieved better results, with the coefficient of determination (R2) of prediction being 0.9922, and the root-mean-square error of prediction (RMSEP) being 1.4889 % v/v in the prediction process. The results obtained indicated that the FT-NIR Spectroscopy technique has the potential to perform a rapid quantitative analysis of the adulteration content of EVOO, and the BOSS algorithm showed its superiority in informative wavenumbers selection.
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Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR Spectroscopy and L1-PLS regression.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2018Co-Authors: Hui Jiang, Congli Mei, Yonghong Huang, Quansheng ChenAbstract:Abstract This study aimed to investigate the potential of FT-NIR Spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR Spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.
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Identification of Radix Puerariae starch from different geographical origins by FT-NIR Spectroscopy
International Journal of Food Properties, 2017Co-Authors: Hang Zhang, Hui Jiang, Guohai Liu, Congli Mei, Yonghong HuangAbstract:ABSTRACTFourier transform near-infrared (FT-NIR) Spectroscopy technique combined with multivariate calibration approach was employed to identify geographical origins of Radix Puerariae starch. First, the efficient spectral subintervals were selected by a synergy interval partial least squares (siPLS) method. Secondly, an iteratively retains informative variables (IRIV) algorithm was applied to select the characteristic wavelengths from the efficient spectral subintervals obtained by siPLS. Experimental results showed that the number of wavelength variables obtained by IRIV was 10. Meantime, only the first two PCs of principal component analysis (PCA) based on the selected 10 variables could explain 99.9769% of the total variance and the identification rate of validation set is 100% based on extreme learning machine (ELM) in this study. This work indicates that FT-NIR Spectroscopy analysis technique coupled with multivariate calibration is an excellent choice for discrimination of geographical origins of R...
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Quantitative analysis of yeast growth process based on FT-NIR Spectroscopy integrated with Gaussian mixture regression
RSC Advances, 2017Co-Authors: Wei Wang, Quansheng Chen, Hui Jiang, Guohai Liu, Congli Mei, Yonghong HuangAbstract:To improve the yield of industrial fermentation, herein, we report a method based on Fourier-transform near-infrared Spectroscopy (FT-NIR) to predict the growth of yeast. First, the spectra were obtained using an FT-NIR spectrometer during the process of yeast cultivation. Each spectrum was acquired over the range from 10 000 to 4000 cm−1, which resulted in spectra with 1557 variables. Moreover, the optical density (OD) value of each fermentation sample was determined via photoelectric turbidity method. Then, using a method based on competitive adaptive reweighted sampling (CARS), characteristic wavelength variables were selected from the preprocessed spectral data. Gaussian mixture regression (GMR) algorithm was employed to develop the prediction model for the determination of OD. The results of the model based on GMR were achieved as follows: only 13 characteristic wavelength variables were selected by CRAS, the coefficient of determination Rp2 was 0.98842, and the root mean square error of prediction (RMSEP) was 0.07262 in the validation set. Finally, compared to kernel partial least squares regression (KPLS), support vector machine (SVM), and extreme learning machine (ELM) models, GMR model showed excellent performance for prediction and generalization. This study demonstrated that FT-NIR Spectroscopy analysis technology integrated with appropriate chemometric approaches could be utilized to monitor the growth process of yeast, and GMR revealed its superiority in model calibration.
Jiewen Zhao - One of the best experts on this subject based on the ideXlab platform.
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Nondestructive Identification of Tea (Camellia sinensis L.) Varieties using FT-NIR Spectroscopy and Pattern Recognition
Czech Journal of Food Sciences, 2018Co-Authors: Quansheng Chen, Jiewen Zhao, Muhua Liu, Jianrong CaiAbstract:Chen Q., Zhao J., Liu M., Cai J . (2008): Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR Spectroscopy and pattern recognition. Czech J. Food Sci., 26: 360–367. Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea ( Camellia sinen sis L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) Spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR Spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.
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Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) Spectroscopy
LWT - Food Science and Technology, 2011Co-Authors: Quansheng Chen, Jianrong Cai, Xinmin Wan, Jiewen ZhaoAbstract:Abstract To address the rapid and nondestructive determination of pork storage time associated with its freshness, Fourier transform near infrared (FT-NIR) Spectroscopy technique, with the help of classification algorithm, was attempted in this work. To investigate the effects of different linear and non-linear classification algorithms on the discrimination results, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and back propagation artificial neural network (BP-ANN) were used to develop the discrimination models, respectively. The number of principal components (PCs) and other parameters were optimized by cross-validation in developing discrimination models. Experimental results showed that the performance of BP-ANN model was superior to others, and the optimal BP-ANN model was achieved when 5 PCs were included. The discrimination rates of the BP-ANN model were 99.26% and 96.21% in the training and prediction sets, respectively. The overall results sufficiently demonstrate that the FT-NIR Spectroscopy technique combined with BP-ANN classification algorithm has the potential to determine pork storage time associated with its freshness.
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Determination of taste quality of green tea using FT-NIR Spectroscopy and variable selection methods
Guang pu xue yu guang pu fen xi = Guang pu, 2011Co-Authors: Jiewen Zhao, Quansheng Chen, Xingyi HuangAbstract:The present paper was attempted to study the feasibility to determine the taste quality of green tea using FT-NIR Spectroscopy combined with variable selection methods. Chemistry evaluation, as the reference measurement, was used to measure the total taste scores of green tea infusion. First, synergy interval PLS (siPLS) was implemented to select efficient spectral regions from SNV preprocessed spectra; then, optimal variables were selected using genetic algorithm (GA) from these selected spectral regions by siPLS, and the optimal model was achieved with Rp = 0.8908, RMSEP = 4.66 in the prediction set when 38 variables and 6 PLS factors were included. Experimental results showed that the performance of siPLS-GA model was superior to those of others. This study demonstrated that NIR spectra could be used successfully to measure taste quality of green tea and siPLS-GA algorithm has superiority to other algorithm in developing NIR spectral regression model.
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Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) Spectroscopy
Food Chemistry, 2011Co-Authors: Jianrong Cai, Quansheng Chen, Xinmin Wan, Jiewen ZhaoAbstract:Abstract Total volatile basic nitrogen (TVB-N) content is one of important index of pork’s freshness, and Warner–Bratzler shear force (WBSF) is seen as the important index of pork’s tenderness. This paper attempted the feasibility to determine TVB-N content and WBSF in pork by Fourier transform near infrared (FT-NIR) Spectroscopy. Synergy interval partial least square (SI-PLS) algorithm was performed to calibrate regression model. The number of PLS factors and the number of intervals were optimised simultaneously by cross-validation. The performance of the model was evaluated according to two correlation coefficients (R) in calibration and prediction sets. Experimental results showed that the correlations coefficients in the calibration set (Rc) and prediction set (Rp) were achieved as follows: Rc = 0.8398 and Rp = 0.8084 for TVB-N content model; Rc = 0.7533 and Rp = 0.7041 for WBSF model. The overall results demonstrated that NIR Spectroscopy combined with SI-PLS could be utilised to determinate TVB-N content and WBSF in pork.
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Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) Spectroscopy
Food Chemistry, 2009Co-Authors: Quansheng Chen, Jiewen Zhao, Sumpun Chaitep, Zhiming GuoAbstract:Abstract This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) Spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient ( R ). The correlations coefficients ( R ) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR Spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.
Congli Mei - One of the best experts on this subject based on the ideXlab platform.
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Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR Spectroscopy and L1-PLS regression.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2018Co-Authors: Hui Jiang, Congli Mei, Yonghong Huang, Quansheng ChenAbstract:Abstract This study aimed to investigate the potential of FT-NIR Spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR Spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.
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Identification of Radix Puerariae starch from different geographical origins by FT-NIR Spectroscopy
International Journal of Food Properties, 2017Co-Authors: Hang Zhang, Hui Jiang, Guohai Liu, Congli Mei, Yonghong HuangAbstract:ABSTRACTFourier transform near-infrared (FT-NIR) Spectroscopy technique combined with multivariate calibration approach was employed to identify geographical origins of Radix Puerariae starch. First, the efficient spectral subintervals were selected by a synergy interval partial least squares (siPLS) method. Secondly, an iteratively retains informative variables (IRIV) algorithm was applied to select the characteristic wavelengths from the efficient spectral subintervals obtained by siPLS. Experimental results showed that the number of wavelength variables obtained by IRIV was 10. Meantime, only the first two PCs of principal component analysis (PCA) based on the selected 10 variables could explain 99.9769% of the total variance and the identification rate of validation set is 100% based on extreme learning machine (ELM) in this study. This work indicates that FT-NIR Spectroscopy analysis technique coupled with multivariate calibration is an excellent choice for discrimination of geographical origins of R...
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Quantitative analysis of yeast growth process based on FT-NIR Spectroscopy integrated with Gaussian mixture regression
RSC Advances, 2017Co-Authors: Wei Wang, Quansheng Chen, Hui Jiang, Guohai Liu, Congli Mei, Yonghong HuangAbstract:To improve the yield of industrial fermentation, herein, we report a method based on Fourier-transform near-infrared Spectroscopy (FT-NIR) to predict the growth of yeast. First, the spectra were obtained using an FT-NIR spectrometer during the process of yeast cultivation. Each spectrum was acquired over the range from 10 000 to 4000 cm−1, which resulted in spectra with 1557 variables. Moreover, the optical density (OD) value of each fermentation sample was determined via photoelectric turbidity method. Then, using a method based on competitive adaptive reweighted sampling (CARS), characteristic wavelength variables were selected from the preprocessed spectral data. Gaussian mixture regression (GMR) algorithm was employed to develop the prediction model for the determination of OD. The results of the model based on GMR were achieved as follows: only 13 characteristic wavelength variables were selected by CRAS, the coefficient of determination Rp2 was 0.98842, and the root mean square error of prediction (RMSEP) was 0.07262 in the validation set. Finally, compared to kernel partial least squares regression (KPLS), support vector machine (SVM), and extreme learning machine (ELM) models, GMR model showed excellent performance for prediction and generalization. This study demonstrated that FT-NIR Spectroscopy analysis technology integrated with appropriate chemometric approaches could be utilized to monitor the growth process of yeast, and GMR revealed its superiority in model calibration.
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Rapid identification of fermentation stages of bioethanol solid-state fermentation (SSF) using FT-NIR Spectroscopy: comparisons of linear and non-linear algorithms for multiple classification issues
Anal. Methods, 2017Co-Authors: Hui Jiang, Congli Mei, Quansheng ChenAbstract:Solid-state fermentation (SSF) is a critical step in bioethanol production, and a means for the effective monitoring of the SSF process is urgently needed due to the rapid changes in the SSF industry, which demands fast tools that could provide real time information to ensure the quality of the final product. The aim of the present study was to investigate the FT-NIR Spectroscopy technique associated with supervised pattern recognition methods in order to develop a means to monitor the time-related molecular changes that occur during the SSF of bioethanol. Principal component analysis as an exploratory tool was employed to uncover details on the molecular modifications of the spectral data during the SSF process. Furthermore, identification models were constructed using partial least squares discriminant analysis (PLS-DA), back propagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM) algorithms. The parameters of the four algorithms were optimized by leave-one-out cross-validation (LOOCV) for the calibration of the identification models. The experimental results showed that the nonlinear identification models achieved strong classification performance to identify the fermentation stages in the SSF of bioethanol. Moreover, compared with the BPNN and SVM models, the ELM model achieved a slightly better generalization performance with an identification rate of 92.60% in the validation process. The overall results show that the ELM-FT-NIR methodology was efficient in accurately identifying the fermentation stages during the SSF of bioethanol, thus demonstrating its potential for application in the in situ monitoring and control of large-scale industrial processes.
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Identification of solid state fermentation degree with FT-NIR Spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2015Co-Authors: Hui Jiang, Quansheng Chen, Hang Zhang, Congli Mei, Guohai LiuAbstract:The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR Spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.
Guohai Liu - One of the best experts on this subject based on the ideXlab platform.
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Identification of Radix Puerariae starch from different geographical origins by FT-NIR Spectroscopy
International Journal of Food Properties, 2017Co-Authors: Hang Zhang, Hui Jiang, Guohai Liu, Congli Mei, Yonghong HuangAbstract:ABSTRACTFourier transform near-infrared (FT-NIR) Spectroscopy technique combined with multivariate calibration approach was employed to identify geographical origins of Radix Puerariae starch. First, the efficient spectral subintervals were selected by a synergy interval partial least squares (siPLS) method. Secondly, an iteratively retains informative variables (IRIV) algorithm was applied to select the characteristic wavelengths from the efficient spectral subintervals obtained by siPLS. Experimental results showed that the number of wavelength variables obtained by IRIV was 10. Meantime, only the first two PCs of principal component analysis (PCA) based on the selected 10 variables could explain 99.9769% of the total variance and the identification rate of validation set is 100% based on extreme learning machine (ELM) in this study. This work indicates that FT-NIR Spectroscopy analysis technique coupled with multivariate calibration is an excellent choice for discrimination of geographical origins of R...
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Quantitative analysis of yeast growth process based on FT-NIR Spectroscopy integrated with Gaussian mixture regression
RSC Advances, 2017Co-Authors: Wei Wang, Quansheng Chen, Hui Jiang, Guohai Liu, Congli Mei, Yonghong HuangAbstract:To improve the yield of industrial fermentation, herein, we report a method based on Fourier-transform near-infrared Spectroscopy (FT-NIR) to predict the growth of yeast. First, the spectra were obtained using an FT-NIR spectrometer during the process of yeast cultivation. Each spectrum was acquired over the range from 10 000 to 4000 cm−1, which resulted in spectra with 1557 variables. Moreover, the optical density (OD) value of each fermentation sample was determined via photoelectric turbidity method. Then, using a method based on competitive adaptive reweighted sampling (CARS), characteristic wavelength variables were selected from the preprocessed spectral data. Gaussian mixture regression (GMR) algorithm was employed to develop the prediction model for the determination of OD. The results of the model based on GMR were achieved as follows: only 13 characteristic wavelength variables were selected by CRAS, the coefficient of determination Rp2 was 0.98842, and the root mean square error of prediction (RMSEP) was 0.07262 in the validation set. Finally, compared to kernel partial least squares regression (KPLS), support vector machine (SVM), and extreme learning machine (ELM) models, GMR model showed excellent performance for prediction and generalization. This study demonstrated that FT-NIR Spectroscopy analysis technology integrated with appropriate chemometric approaches could be utilized to monitor the growth process of yeast, and GMR revealed its superiority in model calibration.
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Identification of solid state fermentation degree with FT-NIR Spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2015Co-Authors: Hui Jiang, Quansheng Chen, Hang Zhang, Congli Mei, Guohai LiuAbstract:The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR Spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.
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qualitative and quantitative analysis in solid state fermentation of protein feed by ft nir Spectroscopy integrated with multivariate data analysis
Analytical Methods, 2013Co-Authors: Hui Jiang, Guohai Liu, Congli Mei, Quansheng CheAbstract:The potential of Fourier-transform near-infrared (FT-NIR) Spectroscopy for qualitative and quantitative analysis in solid-state fermentation (SSF) of protein feed was verified based on FT-NIR Spectroscopy combined with multivariate data analysis. The raw spectra were processed and analyzed by multivariate analyses, which integrated the approaches of discrete wavelet transform (DWT), principal component analysis and extreme learning machine (ELM) modeling. The noise of raw spectra was filtered and latent information was extracted by DWT, and then the characteristic information obtained by DWT was visualized in principal component space, in which the structures with the time course of the SSF were explored. Thereafter, some parameters of the calibration models were optimized by cross-validation. The results of the final models were achieved as follows: root mean square error of prediction (RMSEP) = 0.0987/Rp2 = 0.9322 for pH model, RMSEP = 0.0092 w/w/Rp2 = 0.8991 for moisture content model, and an identification rate of 91.43% for the discrimination model of the fermentation phase in the validation set. Finally, compared with partial least squares (PLS)/PLS-discriminant analysis and back propagation artificial neural network models, the ELM model showed excellent performance for prediction and generalization. This study demonstrates that FT-NIR Spectroscopy coupled with appropriate chemometrics approaches could be utilized to monitor the SSF, and ELM reveals its superiority in model calibration.
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Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR Spectroscopy and synergy interval PLS algorithm.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2012Co-Authors: Hui Jiang, Guohai Liu, Congli Mei, Xiahong Xiao, Yuhan DingAbstract:The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) Spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR Spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR Spectroscopy technique has a potential to be utilized in SSF industry.
