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Detection of fat content in peanut kernels based on chemometrics and hyperspectral imaging technology

INFRARED PHYSICS & TECHNOLOGY 2020年 105卷 103226-000页

作者： Sun, Jianfei Wang, Guangxian Zhang, Hui Xia, Lianming Zhao, Wenping Guo, Yemin Sun, Xia Shandong Univ Technol Sch Agr Engn & Food Sci 12 Zhangshou Rd Zibo 255049 Peoples R China Shandong Prov Engn Res Ctr Vegetable Safety & Qua 12 Zhangshou Rd Zibo 255049 Peoples R China Zibo City Key Lab Agr Prod Safety Traceabil Zibo Peoples R China

Hyperspectral imaging technology combined with chemometrics were applied to detect fat content in peanut kernels. Four varieties of peanuts were scanned to acquire hyperspectral images by using a "push-broom" system. Then, the spectral data was extracted from hyperspectral images. Principal component analysis (PCA) was used to detect outliers. After outliers removed, five different pre-processing methods were used to preprocess spectral data. successive projections algorithm (SPA) and regression coefficient (RC) were adopted to select effective wavelengths. Finally, based on the full wavelengths and the effective wavelengths, the models of partial least squares regression (PLSR), support vector machine regression (SVR) and multiple linear regression (MLR) were established respectively. Comparing these models, Baseline-SPA-MLR was the most excellent with determination coefficient (R-p(2)) of 0.9736, root mean square errors (RMSEp) of 0.4635% and residual prediction deviation (RPD) of 6.1273 in the prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology provided an efficient and non-destructive method for detecting the fat content in peanut kernels.

关键词： Hyperspectral imaging Fat content Peanut kernel successive projections algorithm Multiple linear regression

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Varietal classification and antioxidant activity prediction of Osmanthus fragrans Lour. flowers using UPLC-PDA/QTOF-MS and multivariable analysis

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FOOD CHEMISTRY 2017年第0期217卷 490-497页

作者： Zhou, Fei Peng, Jiyu Zhao, Yajing Huang, Weisu Jiang, Yirong Li, Maiquan Wu, Xiaodan Lu, Baiyi Zhejiang Univ Coll Biosyst Engn & Food Sci Fuli Inst Food SciZhejiang Key Lab Agrofood Proc Minist AgrKey Lab Agrofood Risk AssessmentZheji Hangzhou 310058 Zhejiang Peoples R China Zhejiang Econ & Trade Polytech Dept Appl Technol Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Anal Ctr Agrobiol & Environm Sci Hangzhou 310058 Zhejiang Peoples R China

This study was aimed to classify the varieties and predict the antioxidant activity of Osmanthus fragrans flowers by UPLC-PDA/QTOF-MS and multivariable analysis. The PLS-DA model successfully classified the four varieties based on both the 21 identified compounds and the effective compounds. For the antioxidant activity prediction, PLS performed well to predict the antioxidant activity of O. fragrans flowers. Furthermore, acteoside, suspensaside A, ligustroside, forsythoside A, phillygenin and caffeic acid were selected as effective compounds by UVE-SPA for prediction. On the basis of effective compounds, PLS, MLR and PCR were applied to establish the calibration models. The UVE-SPA MLR model was the optimal method to predict the antioxidant activity values with R-p of 0.9200, 0.9010 and 0.8905 for DPPH, ABTS and FRAP assays, respectively. The results revealed that the UPLC-PDA/QTOF-MS combined with chemometrics could be a new method to classify the varieties and predict the antioxidant activity of O. fragrans flowers. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Osmanthus fragrans Lour. flower Varietal classification Antioxidant activity Partial least squares Multiple linear regression successive projections algorithm Uninformative variable elimination Effective compound

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Detection of Aspartic Acid in Fermented Cordyceps Powder Using Near Infrared Spectroscopy Based on Variable Selection algorithms and Multivariate Calibration Methods

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FOOD AND BIOPROCESS TECHNOLOGY 2014年第2期7卷 598-604页

作者： Zhang, Chu Xu, Ning Luo, Liubin Liu, Fei Kong, Wenwen Feng, Lei He, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Technol Coll Pharmaceut Sci Hangzhou 310014 Zhejiang Peoples R China

Near infrared (NIR) spectroscopy combined with chemometrics was employed to detect the aspartic acid content in fermented Cordyceps powder. One hundred sixty-nine samples were applied for calibration (n = 113) and prediction (n = 56). Six different pretreatment methods were compared to determine the optimal pretreatment method for analysis. The wavelength variables selected by regression coefficient analysis, successive projections algorithm, and genetic algorithm-partial least squares analysis (GAPLS) were used as the inputs of partial least-squares analysis, multiple linear regression (MLR), and least-squares support vector machine. The performances of these calibration methods were also compared to determine the best model. The results indicated that GAPLS-MLR obtained the highest precision with a correlation coefficient of prediction r (pre) = 0.9223, root mean square of prediction RMSEP = 0.0751 g/100 g, and coefficient of variation CV = 5.15 %. The overall results showed that NIR was feasible for the determination of aspartic acid in fermented Cordyceps powder and GAPLS could perform well with less input dimension and computation complexity in the aspartic acid estimation.

关键词： Near Infrared (NIR) Spectroscopy Aspartic Acid Variable Selection Regression Coefficient Analysis successive projections algorithm Genetic algorithm-Partial Least Squares Analysis

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Potential of visible/near-infrared hyperspectral imaging for rapid detection of freshness in unfrozen and frozen prawns

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JOURNAL OF FOOD ENGINEERING 2015年 149卷 97-104页

作者： Dai, Qiong Cheng, Jun-Hu Sun, Da-Wen Pu, Hongbin Zeng, Xin-An Xiong, Zhenjie South Chinci Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Food Refrigerat & Computerized Food Technol Agr & Dublin 4 Ireland

The potential of visible and near infrared (400-1000 nm) hyperspectral imaging as a rapid and non-invasive method was investigated to differentiate freshness of prawns. In both unfrozen and frozen groups (a total of 280 prawns), two different freshness levels were used for classification, respectively. Mean spectral data from the full surface of prawns were extracted automatically as the hyperspectral cubes. Both the first and second derivative spectra were performed for waveform analysis. successive projections algorithm (SPA) was conducted to select the individual feature wavelengths for classification. Least squares-support vector machine (LS-SVM), adaptive boosting (AdaBoost) algorithm and back-propagation neutral networks (BP-NN) were carried out for classification using the derivative spectrums based on both full wavelengths and selected feature wavelengths. The results demonstrated that SPA-LS-SVM achieved satisfactory average correct classification rate of 98.33% and 95% for prediction samples in unfrozen and frozen groups, respectively. Visualization map of classification of eight prawns (two groups) was also presented. The overall results revealed that hyperspectral imaging technique is promising for freshness classification of prawns rapidly and non-invasively. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Prawn Freshness Hyperspectral imaging successive projections algorithm Least-squares support vector machines Adaptive boosting Artificial neutral network

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DETERMINATION OF PIGMENTS CONCENTRATION OF OILSEED RAPE (BRASSICA NAPUS L.) LEAVES USING HYPERSPECTRAL IMAGING

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APPLIED ENGINEERING IN AGRICULTURE 2015年第1期31卷 23-30页

作者： He, Y. Zhang, C. Liu, F. Kong, W. W. Cui, P. Zhou, W. J. Huang, L. X. Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Coll Agr & Biotechnol Hangzhou 310003 Zhejiang Peoples R China Zhejiang Univ Coll Anim Sci Hangzhou 310003 Zhejiang Peoples R China

A hyperspectral imaging system covering the spectral range 380-1030 nm was used to estimate leaf pigment concentration. Spectral information of rape leaves were extracted from the hyperspectral images. Partial least squares (PLS), least squares-support vector machine (LS-SVM), and extreme learning machine (ELM) were applied to build calibration models using spectra of 500-900 nm to determine the concentrations of chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (tChl), and carotenoids (Car). For full spectra, ELM models performed best with correlation coefficient of prediction (r(p)) of 0.929, root mean square error of prediction (RMSEP) of 0.096 mg g(-1) and residual prediction deviation (RPD) of 2.37 for Chl a;r(p) of 0.883, RMSEP of 0.046 mg g(-1), and RPD of 2.01 for Chl b;r(p) of 0.912, RMSEP of 0.142 mg g(-1), and RPD of 2.29 for tChl;and r(p) of 0.914, RMSEP of 0.030 mg g(-1) and RPD of 2.29 for Car. The results showed that PLS, LS-SVM, and ELM models for chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids obtained strong performances. Seventeen, 15, 19, and 8 sensitive wavelengths were selected for chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids estimation by successive projections algorithm (SPA), respectively. For sensitive wavelengths, ELM models performed best with r(p) of 0.906, RMSEP of 0.108 mg g(-1) and RPD of 2.27 for Chl a;r(p) of 0.897, RMSEP of 0.043 mg g(-1), and RPD of 2.18 for Chl b;r(p) of 0.901, RMSEP of 0.145 mg g(-1), and RPD of 2.25 for tChl: and r(p) of 0.893, RMSEP of 0.034 mg g(-1) and RPD of 2.14 for Car. The results showed that PLS, LS-SVM, and ELM models based on the sensitive wavelengths also obtained good performance closed to the calibration models using full spectra. Compared with all developed models, the ELM models using full spectra and sensitive wavelengths obtained the best performances with highest re and lowest RMSEP, and highest RPD values for Chl a, Chl b, tChl, and Car conce

关键词： Extreme learning machine Hyperspectral imaging Leaf pigments Oilseed rape successive projections algorithm

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Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA

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FOOD ANALYTICAL METHODS 2014年第8期7卷 1712-1718页

作者： Goncalves Dias Diniz, Paulo Henrique Gomes, Adriano Araujo Fabian Pistonesi, Marcelo Fernandez Band, Beatriz Susana Ugulino de Araujo, Mario Cesar Univ Fed Paraiba Dept Quim Ctr Ciencias Exatas & Nat BR-58051970 Joao Pessoa Paraiba Brazil Univ Nacl Sur Dept Quim INQUISUR RA-8000 Bahia Blanca Buenos Aires Argentina

Due to the human health benefits already scientifically proven, tea (Camellia sinensis) has been widely studied in the literature. Several studies report the classification of the variety or geographical origin of teas, separately. Thus, this paper has proposed a methodology for simultaneous classification of tea samples according to their varieties (green or black) and geographical origins (Brazil, Argentina, or Sri Lanka). For this purpose, near-infrared (NIR) spectroscopy and three differing supervised pattern recognition techniques, namely SIMCA (soft independent modeling of class analogy), PLS-DA (partial least squares-discriminant analysis), and SPA-LDA (successive projections algorithm associated with linear discriminant analysis) have been used. Despite having good results, both full-spectrum PLS-DA and SIMCA were not able to achieve 100 % classification accuracy, regardless of the significance level for the F test in the case of the SIMCA model. On the other hand, the resulting SPA-LDA model successfully classified all studied samples into five differing tea classes (Argentinean green tea;Brazilian green tea;Argentinean black tea;Brazilian black tea;and Sri Lankan black tea) using 12 wave numbers alone.

关键词： Camellia sinensis Classification Geographical origin NIR spectroscopy successive projections algorithm Linear discriminant analysis

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Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves

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FOOD AND BIOPROCESS TECHNOLOGY 2011年第7期4卷 1314-1321页

作者： Liu, Fei Jin, Zonglai L. Naeem, Muhammad Shahbaz Tian, Tian Zhang, Fan He, Yong Fang, Hui Ye, Qingfu F. Zhou, Weijun J. Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Zhejiang Peoples R China Zhejiang Univ Coll Agr & Biotechnol Hangzhou 310029 Zhejiang Peoples R China

Near-infrared (NIR) spectroscopy was investigated to determine the total amino acids (TAA) in oilseed rape (Brassica napus L.) leaves under a new herbicide-propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273)-stress. In full-spectrum partial least squares (PLS) models, direct orthogonal signal correction (DOSC) was the best preprocessing method. successive projections algorithm (SPA) was used to select the relevant variables. Multiple linear regression (MLR), PLS, and least squares-support vector machine (LS-SVM) were used for calibration. The DOSC-SPA-LS-SVM model achieved the best prediction performance with correlation coefficients r = 0.9968 and root mean squares error of prediction (RMSEP) = 0.2950 comparing all SPA-MLR, SPA-PLS, and SPA-LS-SVM models. Some parsimonious direct functions were also developed based on the DOSC-SPA wavelength (1,340 nm) such as linear, index, logarithmic, binominal, and exponential functions. The best performance was achieved by direct exponential function with r = 0.9968 and RMSEP = 0.2943. The overall results indicated that NIR was able to determine the TAA in herbicide-stressed oilseed rape leaves, and the DOSC-SPA was quite helpful for the development of detection sensors and the monitoring of the growing status and herbicide effect on field crop oilseed rape.

关键词： Near-infrared spectroscopy Direct orthogonal signal correction successive projections algorithm Oilseed rape Amino acids Least squares-support vector machine

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Simultaneous spectrophotometric quantification of dinitrobenzene isomers in water samples using multivariate calibration methods

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 154卷 72-79页

作者： Lu, Tao Yuan, Yuan Jiao, Yan Wen, Zhining Wang, Lu Zhao, Yihuan Zhang, Yuxiang Li, Menglong Pu, Xuemei Xu, Tao Sichuan Univ Coll Chem Chengdu 610064 Peoples R China China Acad Engn Phys Inst Chem Mat Mianyang 621900 Peoples R China Southwest Univ Nationalities Coll Management Chengdu 610041 Peoples R China

A facile yet efficient strategy was proposed by means of a combination of ultraviolet (UV) spectrophotometry with multivariate calibration methods, through which 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene in water samples could be simultaneously determined without any pre-separation process. The competitive adaptive reweighted sampling combined with successive projections algorithm (CARS SPA) approach was used to diminish uninformative variables and select the important ones from spectral data measured. The multivariate calibration models were constructed by partial least squares (PLS-1) regression with high accuracy, in which the coefficients of determination of prediction (R-pred(2)) were 0.9935, 0.9969, and 0.9971 and the root mean square error of prediction (RMSEP) were 0.7850, 0.5411 and 0.5414 for 1,2-dinitrobenzene, 1,3-dinitrobenzene and 1,4-dinitrobenzene, respectively. The optimized model was successfully applied to simultaneously determine the content of the three studied analytes in several real water samples with good recovery close to 100%. Finally, the elliptical joint confidence region (EJCR) tests further confirm that the proposed method has no proportional and constant error in the predicted concentrations, providing a statistic support for the accuracy of the model. These results indicate that it is promise for UV spectroscopy coupled to the multivariate calibration technique to establish a simple, quick, accurate and reliable analysis method for simultaneous determination of some nitroaromatic compounds in real environments. Also, the strategy proposed by the work will advance the analytical methods used in the other complicated sample systems. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Ultraviolet spectrophotometry Partial least square regression successive projections algorithm Competitive adaptive reweighted sampling Dinitrobenzene isomers Quantitative analysis

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The determination of the fatty acid content of sea buckthorn seed oil using near infrared spectroscopy and variable selection methods for multivariate calibration

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VIBRATIONAL SPECTROSCOPY 2016年 84卷 24-29页

作者： Li, Zongpeng Wang, Jian Xiong, Yating Li, Ziwen Feng, Siwen China Natl Res Inst Food & Fermentat Ind 24 Jiuxianqiao Middle Rd Beijing 100015 Peoples R China

This study proposes an analytical method for the simultaneous near infrared (NIR) spectrometric determination of palmitic, oleic, linoleic and linolenic acids in sea buckthorn seed oil. For this purpose, four different combinations of multivariate calibration methods and variable selections were evaluated: partial least squares (PLS) with full spectrum;PLS with uninformative variables elimination (WE);PLS with competitive adaptive reweighted sampling (CARS);and multiple linear regression (MLR) with uninformative variable elimination combined with successive projections algorithm (UVE-SPA). An independent set of samples was employed to evaluate the performance of the resulting models. The UVE-SPA-MLR model developed with a few spectral variables provided the best results for each parameter. The values of relative errors of prediction (REP) from the UVE-SPA-MLR model for palmitic, oleic, linoleic and linolenic acids are 1.77%, 1.20%, 1.02% and 1.40%, respectively. These results indicate that this method is a feasible and fast method for the determination of the fatty acid content of sea buckthorn seed oil. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Sea buckthorn seed oil Near infrared spectrometry Uninformative variables elimination Competitive adaptive reweighted sampling successive projections algorithm

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Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

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ANALYTICA CHIMICA ACTA 2009年第1期635卷 45-52页

作者： Liu, Fei Jiang, Yihong He, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Zhejiang Peoples R China

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n=180), validation (n=90) and prediction (n=90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs. and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Visible/near infrared spectroscopy successive projections algorithm Independent component analysis Variable selection Least squares-support vector machine Beer

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