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Hyperspectral imaging detection of decayed honey peaches based on their chlorophyll content

FOOD CHEMISTRY 2017年 235卷 194-202页

作者： Sun, Ye Wang, Yihang Xiao, Hui Gu, Xinzhe Pan, Leiqing Tu, Kang Nanjing Agr Univ Coll Food Sci & Technol 1 Weigang Rd Nanjing 210095 Jiangsu Peoples R China

Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617 nm, 675 nm, and 818 nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hyperspectral imaging Chlorophyll content successive projections algorithm Band ratio Peaches Diseases

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SPA Combined with Swarm Intelligence Optimization algorithms for Wavelength Variable Selection to Rapidly Discriminate the Adulteration of Apple Juice

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FOOD ANALYTICAL METHODS 2017年第6期10卷 1965-1971页

作者： Li, Ying Guo, Yajing Liu, Chang Wang, Wu Rao, Pingfan Fu, Caili Wang, Shaoyun Fuzhou Univ Coll Biol Sci & Engn Fuzhou 350108 Fujian Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350116 Fujian Peoples R China Zhejiang Gongshang Univ Sch Food Sci & Biotechnol Hangzhou 310018 Zhejiang Peoples R China

The application of wavelength variable selection before partial least squares (PLS) regression to rapidly discriminate the adulteration of apple juice by Fourier transform near-infrared (FT-NIR) was investigated in this study. successive projections algorithm (SPA) combined with four swarm intelligence optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), group search optimizer (GSO), and firefly algorithm (FA), was applied to extract effective wavelength variables. The results demonstrated that the variable number of SPA-PSO-PLS models was validly improved with a wavelength variable of four. The accuracy of model was satisfactory with the coefficients of determination of prediction (R-p(2) = 0.9986) and good root mean square errors of prediction (RMSEP = 0.0628). The results suggested that SPA combined with swarm intelligence optimization algorithms for wavelength variable selection could rapidly and efficiently discriminate the adulteration of apple juice.

关键词： FT-NIR spectroscopy successive projections algorithm Swarm intelligence optimization algorithms Wavelength selection

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Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy

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SENSORS 2018年第6期18卷 1963-1963页

作者： Liang, Hao Zhang, Meng Gao, Chao Zhao, Yandong Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Lab Urban & Rural Ecol Environm Beijing 100083 Peoples R China Key Lab State Forestry Adm Forestry Equipment & A Beijing 10083 Peoples R China

This article presents a non-destructive methodology to determine the modulus of elasticity (MOE) in static bending of wood through the use of near-infrared (NIR) spectroscopy. Wood specimens were obtained from Quercus mongolica growing in Northeast of China. The NIR spectra of specimens were acquired by using a one-chip NIR fiber optic spectrometer whose spectral range was 900 similar to 1900 nm. The raw spectra of specimens were pretreated by multiplication scatter correlation and Savitzky-Golay smoothing and differentiation filter. To reduce the dimensions of data and complexity of modeling, the synergy interval partial least squares and successive projections algorithm were applied to extract the characteristic wavelengths, which had closing relevance with the MOE of wood, and five characteristic wavelengths were selected from full 117 variables of a spectrum. Taking the characteristic wavelengths as input values, partial least square regression (PLSR) and the propagation neural network (BPNN) were implemented to establish calibration models. The predictive ability of the models was estimated by the coefficient of determination (r(p)) and the root mean square error of prediction (RMSEP) and in the prediction set. In comparison with the predicted results of the models, BPNN performed better results with the higher r(p) of 0.91 and lower RMSEP of 0.76. The results indicate that it is feasible to accurately determine the MOE of wood by using the NIR spectroscopy technique.

关键词： near-infrared spectroscopy the modulus of elasticity in static bending synergy interval partial least squares successive projections algorithm characteristic wavelengths

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successive projections algorithm applied to spectral data for the simultaneous determination of flavour enhancers

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TALANTA 2010年第1期82卷 222-226页

作者： Acebal, Carolina C. Gruenhut, Marcos Lista, Adriana G. Fernandez Band, Beatriz S. Univ Nacl Sur FIA Lab Dept Chem INQUISURCONICET Buenos Aires DF Argentina

A novel variable selection strategy for multiple lineal regression (MLR), the successive projections algorithm (SPA), was applied to spectrophotometric data (190-320 nm) for the simultaneous determination of monosodium glutamate (MSG), guanosine-5'-monophosphate (GMP) and inosine-5'-monophosphate (IMP) in dehydrated broths samples. This selection method uses simple operations in a vector space to minimize variable collinearity and has become an interesting variable selection strategy for multivariate calibration. In this work, nine, six and four wavelengths for MSG, GMP and IMP, respectively, were selected to construct calibrations models in order to solve successfully the serious spectral overlapping in samples containing these analytes. The relative errors of prediction (REP) for the validation set were 2.3%, 0.9% and 1.8% for MSG, GMP and IMP, respectively. Commercial samples were analysed and a recovery study was carried out to verify the accuracy of the proposed method with satisfactory results. A continuous flow system was used to develop a simple, cheap and rapid method (sample throughput: 200 h(-1)), without any previous extraction step. Published by Elsevier B.V.

关键词： successive projections algorithm Flavour enhancers Continuous flow system Spectrophotometry

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Application of NIR transmission spectroscopy with effective wavelength selection in non-destructive determination of essential amino acid content of foxtail millet

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QUALITY ASSURANCE AND SAFETY OF CROPS & FOODS 2017年第2期9卷 237-248页

作者： Chen, J. Ren, X. Shen, Q. China Agr Univ Coll Food Sci & Nutr Engn 17 Tsinghua East Rd Beijing 100083 Peoples R China Natl Engn Res Ctr Fruits & Vegetables Proc 17 Tsinghua East Rd Beijing 100083 Peoples R China

The near infrared (NIR) spectroscopy models developed for rapid and accurate determination of nine essential amino acids and protein in foxtail millet were investigated. The effects of the status of the samples ( intact or ground), amino acid/protein correlation and variable selection methods on the predictive ability of the models were analysed. According to results, although the average spectral patterns of the intact and ground samples were similar, the absolute values of peaks and valleys for the intact samples were slightly higher. The modelling results of intact samples were similar to those obtained with ground samples. In the results of the partial least squares models with full spectra, 81-94% of the amino acid variance could be explained, except for lysine, tryptophan and methionine with the coefficients of determination of cross-validation less than 0.70. The correlation between various amino acids and protein might affect the predictive ability of NIR spectroscopy for essential amino acids. After variable selection, the models for isoleucine, leucine, phenylalanine, threonine, tryptophan and valine were remarkably improved by the successive projections algorithm method. The results indicated that short-wave NIR spectroscopy coupled with variable selection is a promising approach for determination of essential amino acids in foxtail millet.

关键词： near infrared transmission spectroscopy successive projections algorithm interval partial least squares jack-knife algorithm

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Vis/NIR Chemical Imaging Technique for Predicting Sodium Humate Contents in Aquaculture Environment

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WATER AIR AND SOIL POLLUTION 2017年第5期228卷 177-177页

作者： Qu, Jia-Huan Sun, Da-Wen Pu, Hongbin South China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Guangzhou Higher Educ Mega Ctr Acad Contemporary Food Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Higher Educ Mega Ctr Engn & Technol Res Ctr Guangdong Prov Intelligent Guangzhou 510006 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr FRCFT Dublin 4 Ireland

This study aimed to develop a visible and near-infrared (Vis/NIR) chemical imaging (400-1000 nm) technique to provide rapid prediction of the contents of sodium humate dissolved in aquaculture environment. Gray reference image with 5% reflectance value was first used to correct the obtained raw images in order to promote the reflectance values as compared to that with 99% reflectance for further spectral analysis. successive projection algorithm (SPA) was introduced to extract four optimal wavelengths, which were then used for the establishment of back-propagation artificial neural network (BP-ANN) models. The results revealed that the BP-ANN model based on the selected four optimal wavelengths better performed (R-C(2) = 0.986, R-CV(2) = 0.985, R-P(2) = 0. 9 9 3, RMSEC = 0. 329 m g /L, RMSECV = 0.433, RMSEP = 0.734 mg/L) than that based on the whole 381 wavelengths (R-C(2) = 0.978, R-CV(2) = 0.996, R-P(2) = 0.977, RMSEC = 0.388 mg/L, RMSECV = 0.625, RMSEP = 0.734 mg/L). Finally, a series of chemical images were developed to clearly display the concentration distribution of the sodium humate dissolved in water, demonstrating that Vis/NIR chemical imaging technique was feasible to quantify the contents of sodium humate in the aquatic environment and could be further used for real-time monitoring the quality of aquaculture water.

关键词： Imaging spectroscopy Vis/NIR Humic Gray reference image successive projections algorithm Back-propagation artificial neural network

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Genetic algorithm Interval Partial Least Squares Regression Combined successive projections algorithm for Variable Selection in Near-Infrared Quantitative Analysis of Pigment in Cucumber Leaves

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APPLIED SPECTROSCOPY 2010年第7期64卷 786-794页

作者： Zou Xiaobo Zhao Jiewen Mao Hanpin Shi Jiyong Yin Xiaopin Li Yanxiao Jiangsu Univ Sch Food & Biol Engn Agr Prod Nondestruct Detect Lab Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Agr Engn Key Lab China Zhenjiang 212013 Jiangsu Peoples R China

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A method based on a genetic algorithm interval partial least squares regression (GAiPLS) combined successive projections algorithm (SPA) was proposed for variable selection in NIR spectroscopy. GAiPLS was used to select informative interval regions among the spectrum, and then SPA was employed to select the most informative variables and to minimize collinearity between those variables in the model. The performance of the proposed method was compared with the full-spectrum model, conventional interval partial least squares regression (iPLS), and backward interval partial least squares regression (BiPLS) for modeling the NIR data sets of pigments in cucumber leaf samples. The multiple linear regression (MLR) model was obtained with eight variables for chlorophylls and five variables for carotenoids selected by SPA. When the SPA model was applied to the prediction of the validation set, the correlation coefficients of the predicted value by MLR and the measured value for the validation data set (re) of chlorophylls and carotenoids were 0.917 and 0.932, respectively. Results show that the proposed method was able to select important wavelengths from the NIR spectra and makes the prediction more robust and accurate in quantitative analysis.

关键词： Near-infrared spectroscopy NIR spectroscopy Multivariate calibration Genetic algorithm Interval partial least squares PLS successive projections algorithm

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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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Online monitoring of red meat color using hyperspectral imaging

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MEAT SCIENCE 2016年 116卷 110-117页

作者： Kamruzzaman, Mohammed Makino, Yoshio Oshita, Seiichi Univ Tokyo Tokyo 1138654 Japan Bangladesh Agr Univ Fac Agr Engn & Technol Dept Food Technol & Rural Ind Mymensingh 2202 Bangladesh

A hyperspectral imaging system in the spectral range of 400-1000 nm was tested to develop an online monitoring system for red meat (beef, lamb, and pork) color in the meat industry. Instead of selecting different sets of important wavelengths for beef, lamb, and pork, a set of feature wavelengths were selected using the successive projection algorithm for red meat colors (L*, a*, b) for convenient industrial application. Only six wavelengths (450, 460, 600, 620, 820, and 980 nm) were further chosen as predictive feature wavelengths for predicting L*, a*, and b* in red meat. Multiple linear regression models were then developed and predicted L*, a*, and b* with coefficients of determination (R-p(2)) of 0.97, 0.84, and 0.82, and root mean square error of prediction of 1.72, 1.73, and 1.35, respectively. Finally, distribution maps of meat surface color were generated. The results indicated that hyperspectral imaging has the potential to be used for rapid assessment of meat color. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Hyperspectral imaging Beef Lamb Pork Multivariate analysis Image processing successive projections algorithm

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