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Improved partial least squares regression for rapid determination of reducing sugar of potato flours by near infrared spectroscopy and variable selection method

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2015年第1期9卷 95-103页

作者： Sun, Xudong Dong, Xiaoling East China Jiaotong Univ Sch Mechatron Engn Nanchang 330013 Peoples R China East China Jiaotong Univ Sch Foreign Langue Nanchang 330013 Peoples R China

The feasibility of near infrared (NIR) spectroscopy coupled with variable selection methods for rapid determination of reducing sugar content in potato flours was investigated. Monte Carlo uninformative variable elimination (MCUVE), successive projections algorithm (SPA) and genetic algorithm (GA) were performed comparatively to choose characteristic variables associated with reducing sugar distributions. Eighty three and twenty seven samples were used to calibrate models and assess the performance of the models, respectively. Through comparing the performance of partial least squares regression models with new samples, the optimal models of reducing sugar was obtained with 12 selected variables by combination of MCUVE and SPA method. The correlation coefficient of prediction (r(p)) and root mean square errors of prediction (RMSEP) were 0.981 and 0.243, respectively. The results suggest that NIR technique combining with MCUVE and SPA has significant potential to quantitatively analyze reducing sugar in potato flours;moreover, it could indicate the related spectral contributions.

关键词： Near infrared spectroscopy Monte Carlo uninformative variable elimination successive projections algorithm Reducing sugar Potato

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Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets

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FOOD CHEMISTRY 2015年 185卷 245-253页

作者： Cheng, Jun-Hu Sun, Da-Wen Pu, Hongbin Zhu, Zhiwei S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Food Refrigerat & Computerized Food Technol Dublin 4 Ireland

K value is an important freshness index widely used for indication of nucleotide degradation and assessment of chemical spoilage. The feasibility of hyperspectral imaging (400-1000 nm) for determination of K value in grass carp and silver carp fillets was investigated. Partial least square (PLS) regression and least square support vector machines (LS-SVM) models established using full wavelengths showed excellent performances and the PLS model was better with higher determination coefficients of prediction (R-P(2) = 0.936) and lower root mean square errors of prediction (RMSEP = 5.21%). The simplified PLS and LS-SVM models using the seven optimal wavelengths selected by successive projections algorithm (SPA) also presented good performances. The spatial distribution map of K value was generated by transferring the SPA-PLS model to each pixel of the images. The current study showed the suitability of using hyperspectral imaging to determine K value for evaluation of chemical spoilage and freshness of fish fillets. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Hyperspectral imaging K value successive projections algorithm Grass carp Silver carp

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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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Identification of adulteration in ground roasted coffees using UV-Vis spectroscopy and SPA-LDA

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LWT-FOOD SCIENCE AND TECHNOLOGY 2015年第2期63卷 1037-1041页

作者： de Carvalho Polari Souto, Urijatan Teixeira Barbosa, Mayara Ferreira Dantas, Hebertty Vieira de Pontes, Aline Santos Lyra, Wellington da Silva Goncalves Dias Diniz, Paulo Henrique Ugulino de Araujo, Mario Cesar da Silva, Edvan Cirino Univ Fed Paraiba Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Univ Estadual Paraiba Ctr Ciencia & Tecnol Dept Quim BR-58429500 Campina Grande PB Brazil

The pleasant taste and flavor of coffee are responsible for making it one of the most preferred and consumed beverages around the world. Industrial coffee plants usually roast both coffee and adulterants together, which affects directly the quality of the product, specially the sensorial characteristics of the drink. Therefore, this work aims the identification of adulterations in ground roasted coffees (due to the presence of husks and sticks) using ultraviolet visible spectroscopy and the successive projections algorithm for variable selection in association with Linear Discriminant Analysis (SPA-LDA). For this purpose, extracts of ground roasted coffees prepared in hot water alone (representing the final product as ingested by the consumers) were then analyzed. The proposed methodology obtained a 100% classification in both training and test sets, providing a simple and fast analysis of the aqueous extracts of ground roasted coffees. Moreover, it can also afford security to coffee consumers and regulatory agencies, preventing the fraudulent labeling. (C) 2015 Published by Elsevier Ltd.

关键词： Ground roasted coffee Adulteration UV-Vis spectroscopy successive projections algorithm Linear Discriminant Analysis

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Screening for Coffee Adulteration Using Digital Images and SPA-LDA

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FOOD ANALYTICAL METHODS 2015年第6期8卷 1515-1521页

作者： de Carvalho Polari Souto, Urijatan Teixeira Barbosa, Mayara Ferreira Dantas, Hebertty Vieira de Pontes, Aline Santos Lyra, Wellington da Silva Goncalves Dias Diniz, Paulo Henrique Ugulino de Araujo, Mario Cesar da Silva, Edvan Cirino Univ Fed Paraiba Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Univ Estadual Paraiba Dept Quim Ctr Ciencia & Tecnol Campina Grande PB Brazil

In this paper, we propose a new methodology to identify adulterations in ground roasted coffees (due to the presence of husks and sticks) using digital images and the successive projections algorithm for variable selection in association with linear discriminant analysis (SPA-LDA). A simple document scanner was used for capturing the images, and a Petri dish support with eight circular holes (one for each sample) to be scanned was employed. Color histograms in the hue-luminosity-saturation (HLS) channels extracted from the digital images were used as input data and statistically evaluated using supervised pattern recognition techniques. For comparison with SPA-LDA, soft independent modeling by class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) were also used. In general, SPA-LDA provided significantly better performance than the other classification models, reaching a mean accuracy of 92.5 % for both the training and test sets, while SIMCA and PLS-DA attained only 71.5 and 85.5 %, respectively. More specifically, all of the models presented high rates (above 90 %) for sensitivity and specificity (in the test set samples classification), except SIMCA, which presented a specificity rate of 76 %. Moreover, the SPA-LDA model generally showed the lowest classification error rates. As such, it is a more adequate chemometric tool for discriminating pure coffee samples and adulterated by husks and sticks. The proposed strategy avoided laborious sample preparation, and additional operational costs, assessing coffee adulteration by husks and sticks.

关键词： Coffee Adulteration Digital images Color histograms successive projections algorithm

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Digital image-based classification of biodiesel

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TALANTA 2015年 139卷 50-55页

作者： Costa, Gean Bezerra Sousa Fernandes, David Douglas Almeida, Valber Elias Policarpo Araujo, Thomas Souto Melo, Jessica Priscila Goncalves Dias Diniz, Paulo Henrique Veras, Germano Univ Estadual Paraiba Programa Posgrad Ciencias Agr BR-58429500 Campina Grande PB Brazil Univ Estadual Paraiba Dept Quim Ctr Ciencias & Tecnol BR-58429500 Campina Grande PB Brazil

This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cotton-seed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the successive projections algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected successive projections algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Biofuel Webcam Color histograms Pattern recognition successive projections algorithm

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Application of Vis-NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles

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INTERNATIONAL JOURNAL OF REFRIGERATION 2015年 50卷 10-18页

作者： Ma, Ji Pu, Hongbin Sun, Da-Wen Gao, Wenhong Qu, Jia-Huan Ma, Kai-Yue S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Univ Coll Dublin Agr & Food Sci Ctr Dublin 4 Ireland

Fresh and frozen-thawed (F-T) pork meats were classified by Vis-NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (RLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination. (C) 2014 Elsevier Ltd and IIR. All rights reserved.

关键词： Hyperspectral imaging Pork Fresh Frozen-thawed successive projections algorithm Gray-level-gradient co-occurrence matrix Partial least squares-discriminate analysis Correct classification rate

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Non-destructive prediction of thiobarbituric acid reactive substances (TSARS) value for freshness evaluation of chicken meat using hyperspectral imaging

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FOOD CHEMISTRY 2015年 179卷 175-181页

作者： Xiong, Zhenjie Sun, Da-Wen Pu, Hongbin Xie, Anguo Han, Zhong Luo, Man S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Food Refrigerat & Computerised Food Technol Dublin 4 Ireland

This study examined the potential of hyperspectral imaging (HSI) for rapid prediction of 2-thiobarbituric acid reactive substances (TBARS) content in chicken meat during refrigerated storage. Using the spectral data and the reference values of TBARS, a partial least square regression (PLSR) model was established and yielded acceptable results with regression coefficients in prediction (R-p) of 0.944 and root mean squared errors estimated by prediction (RMSEP) of 0.081. To simplify the calibration model, ten optimal wavelengths were selected by successive projections algorithm (SPA). Then, a new SPA-PLSR model based on the selected wavelengths was built and showed good results with R-p of 0.801 and RMSEP of 0.157. Finally, an image algorithm was developed to achieve image visualization of TBARS values in some representative samples. The encouraging results of this study demonstrated that HSI is suitable for determination of TBARS values for freshness evaluation in chicken meat. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Hyperspectral imaging Non-destructive analysis Chicken meat TEARS Freshness successive projections algorithm Partial least square regression

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Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine

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JOURNAL OF FORENSIC SCIENCES 2015年第5期60卷 1199-1205页

作者： da Silva, Neirivaldo Cavalcante Honorato, Ricardo Saldanha Pimentel, Maria Fernanda Garrigues, Salvador Luisa Cervera, Maria de la Guardia, Miguel Univ Fed Pernambuco Dept Quim Fundamental Recife PE Brazil Superintendencia Reg Pernambuco Dept Policia Fed BR-50030230 Recife PE Brazil Univ Fed Pernambuco Dept Engn Quim Recife PE Brazil Univ Valencia Dept Analyt Chem Valencia Spain

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2%w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.

关键词： forensic science herbal medicines sibutramine near infrared partial least squares discriminant analysis partial least squares regression multiple linear regression successive projections algorithm

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