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Variables selection for quantitative determination of cotton content in textile blends by near infrared spectroscopy

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INFRARED PHYSICS & TECHNOLOGY 2016年第0期77卷 65-72页

作者： Sun, Xu-dong Zhou, Ming-xing Sun, Yi-ze Donghua Univ Coll Mech Engn 2999 Renminbei Rd Shanghai 201620 Peoples R China East China Jiaotong Univ Coll Mech & Elect Engn Nanchang 330013 Peoples R China

Investigations were initiated to develop near infrared (NIR) techniques coupled with variables selection method to rapidly measure cotton content in blend fabrics of cotton and polyester. Multiplicative scatter correction (MSC), smooth, first derivative (1Der), second derivative (2Der) and their combination were employed to preprocess the spectra. Monte Carlo uninformative variables elimination (MCUVE), successive projections algorithm (SPA), and genetic algorithm (GA) were performed comparatively to choose characteristic variables associated with cotton content distributions. One hundred and thirty-five and fifty-nine samples were used to calibrate models and assess the performance of the models, respectively. Through comparing the performance of partial least squares (PLS) regression models with new samples, the optimal model of cotton content was obtained with spectral pretreatment method of 2 Der-Smooth MSC and variables selection method of MCUVE-SPA-PLS. The correlation coefficient of prediction (r(p)) and root mean square errors of prediction (RMSEP) were 0.988% and 2.100%, respectively. The results suggest that NIR technique combining with variables selection method of MCUVE-SPA has significant potential to quantitatively analyze cotton content in blend fabrics of cotton and polyester;moreover, it could indicate the related spectral contributions. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Near infrared spectroscopy Monte Carlo uninformative variables elimination successive projections algorithm Cotton Blend fabrics

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

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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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ESTIMATION OF ACETOLACTATE SYNTHASE ACTIVITY IN BRASSICA NAPUS UNDER HERBICIDE STRESS USING NEAR-INFRARED SPECTROSCOPY

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TRANSACTIONS OF THE ASABE 2012年第4期55卷 1631-1638页

作者： Liu, F. Kong, W. W. Tian, T. Fang, H. He, Y. Zhou, W. J. Zhejiang Univ Cyrus Tang Ctr Sensor Mat & Applicat Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Coll Agr & Biotechnol Hangzhou 310058 Zhejiang Peoples R China

The appropriateness of near-infrared spectroscopy was investigated for the fast and noninvasive estimation of acetolactate synthase (ALS) activity in oilseed rape (Brassica napus L.) leaves, which is important for studying the effects of the new herbicide propyl 4-(2-(4,6-dimethoxy-2-pyrimidinyloxy)benzylamino)benzoate (ZJ0273) on plant metabolism and physiological regulation. A total of 248 leaf samples were collected at different leaf positions and ZJ0273 concentrations. A complete comparison was performed on the preprocessing methods, which included smoothing, multiplicative scatter correction, standard normal variate, derivatives, detrending, and direct orthogonal signal correction (DOSC), as well as linear calibrations, i.e., multiple linear regression (MLR) and partial least squares (PLS), and nonlinear calibration (least squares-support vector machine, LS-SVM. In all linear (MLR and PLS) models, the optimal models were the successive projections algorithm (SPA)-MLR (DOSC) and SPA-PLS (DOSC) models, with the same prediction performance (r(p) = 0.938 and RMSEP = 6.587). The best prediction model was the nonlinear SPA-LS-SVM (MSC) model, with r(p) = 0.988 and RMSEP = 2.844. A direct linear function using raw and DOSC preprocessed spectra at 2094 nm was developed and obtained a good prediction performance. These results will be helpful for the design of ALS detection sensors and in-field monitoring systems for the growth status of oilseed rape. The results are also useful for study of the inhibitory effects, metabolic pathways, and environmental residues of the new herbicide ZJ0273 during the growth of oilseed rape.

关键词： Acetolactate synthase activity Herbicide ZJ0273 Least squares-support vector machine Near-infrared spectroscopy successive projections algorithm

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Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of 'Ya' Pear

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FOOD ANALYTICAL METHODS 2014年第9期7卷 1891-1902页

作者： Li, Jiangbo Huang, Wenqian Chen, Liping Fan, Shuxiang Zhang, Baohua Guo, Zhiming Zhao, Chunjiang Beijing Acad Agr & Forestry Sci Beijing Res Ctr Intelligent Equipment Agr Beijing 100097 Peoples R China China Agr Univ Coll Engn Beijing 100083 Peoples R China

Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA;MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in 'Ya' pear. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of 'Ya' pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in 'Ya' pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r (2)), root mean square error of prediction, and resi

关键词： Near infrared spectroscopy Monte Carlo-uninformative variable elimination successive projections algorithm Variable selection Soluble solids content 'Ya' pear

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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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A global model for the determination of prohibited addition in pesticide formulations by near infrared spectroscopy

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INFRARED PHYSICS & TECHNOLOGY 2020年 105卷 103191-000页

作者： Li, Qianqian Xie, Jianchun Zhang, Jixiong Yan, Hong Xiong, Yanmei Liu, Wei Min, Shungeng Beijing Univ Chinese Med Sch Chinese Mat Med Beijing 100029 Peoples R China Beijing Technol & Business Univ Beijing Lab Food Qual & Safety Beijing 100048 Peoples R China China Agr Univ Coll Sci Beijing 100193 Peoples R China Chongqing Grain & Oil Qual Supervis & Inspect Stn Chongqing 400026 Peoples R China

Generally, a pesticide was determined by a specific model, so different models were established for different pesticides as the backgrounds between pesticides formulations were with relatively huge discrepancy. It is expected to develop a more adaptive model which could not only improve the efficiency for prohibited addition detection but also afford an extensive application of the target pesticide. Moreover, less time would be spent on maintaining the model and thus the model supervision ability would be improve. In this paper, a global model for the determination of fipronil in a mixed matrix of imidacloprid and cypermethrin formulations was developed. The global model was established by means of two chemometrics methods, namely: full spectrum partial least squares (PLS) and competitive adaptive reweighted sampling-successive projections algorithm (CARS-SPA). Furthermore, the global model was investigated by the following two aspects: algorithm strategies of PLS and CARS-SPA, and spectroscopy interpretation. Results revealed that CARS-SPA combined with NIR could be efficiently used for rapid identification of prohibited addition in imidacloprid and cypermethrin formulations. Simultaneously, this study provided a theoretical support in the aspect of developing a global model for quantitative analysis.

关键词： Global model Competitive adaptive reweighted sampling successive projections algorithm Partial least squares Quantitative analysis

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Predicting intramuscular fat content variations in boiled pork muscles by hyperspectral imaging using a novel spectral pre-processing technique

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LWT-FOOD SCIENCE AND TECHNOLOGY 2018年 94卷 119-128页

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

For better predicting intramuscular fat contents in pork muscles using hyperspectral imaging, a novel correlation optimised warping (COW) technique was employed with the first derivative on the full spectra and the feature wavelengths selected by successive projections algorithm. Images of 104 pork longissimus dorsi samples cooked in boiling water for eight different periods were taken using a Vis-NIR (400-1000 nm) imaging system. Duplex method was used to divide the images into training and predicting sets. Reference measured intramuscular fat contents of each sample were correlated with the spectra extracted from ROI within the corresponding samples. Support vector regression models were developed and results proved positive effects by COW combined with first derivative transforms as spectral pre-processing techniques. The simplified model developed based on eight important wavelengths (403, 435, 438, 556, 586, 596, 739 and 951 nm) predicted accurately the intramuscular fat contents with R-P(2) of 0.9635 and RMSEP of 0.885 g/kg. Some other algorithms were used and listed as control algorithms to enhance data analysis, including Savitzky Golay (SG)-smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC) and partial least squares regression (PLSR).

关键词： Intramuscular fat content Correlation optimised warping First derivative successive projections algorithm Support vector regression

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A variable differential consensus method for improving the quantitative near-infrared spectroscopic analysis

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Science China Chemistry 2012年第9期55卷 1946-1952页

作者： DU GuoRong CAI WenSheng SHAO XueGuang Research Center for Analytical Sciences College of ChemistryNankai UniversityTianjin 300071China

Consensus methods have presented promising tools for improving the reliability of quantitative models in near-infrared(NIR) spectroscopic analysis.A strategy for improving the performance of consensus methods in multivariate calibration of NIR spectra is *** the approach,a subset of non-collinear variables is generated using successive projections algorithm(SPA) for each variable in the reduced spectra by uninformative variables elimination(UVE).Then sub-models are built using the variable subsets and the calibration subsets determined by Monte Carlo(MC) re-sampling,and the sub-model that produces minimal error in cross validation is selected as a member *** repetition of the MC re-sampling,a series of member models are built and a consensus model is achieved by averaging all the member *** member models are built with the best variable subset and the randomly selected calibration subset,both the quality and the diversity of the member models are insured for the consensus *** NIR spectral datasets of tobacco lamina are used to investigate the proposed *** superiority of the method in both accuracy and reliability is demonstrated.

关键词： near infrared spectroscopy multivariate calibration consensus model variable selection uninformative variable elim-ination successive projections algorithm

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Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain

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ANALYTICA CHIMICA ACTA 2009年第1-2期642卷 12-18页

作者： Coelho Pontes, Marcio Jose Cortez, Juliana Harrop Galvao, Roberto Kawakami Pasquini, Celio Ugulino Araujo, Mario Cesar Coelho, Ricardo Marques Chiba, Marcio Koiti de Abreu, Monica Ferreira Madari, Beata Emoeke Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analit Quimio BR-58051970 Joao Pessoa Paraiba Brazil Univ Estadual Campinas Inst Quim Campinas SP Brazil Inst Tecnol Aeronaut Div Engn Elect Sao Jose Dos Campos SP Brazil Ctr Pesquisa & Desenvolvimento Solos & Recursos A Inst Agron Campinas SP Brazil Embrapa Solos Rio De Janeiro Brazil

This paper proposes a novel analytical methodology for soil classification based on the use of laser-induced breakdown spectroscopy (LIBS) and chemometric techniques. In the proposed methodology, linear discriminant analysis (LDA) is employed to build a classification model on the basis of a reduced subset of spectral variables. For the purpose of variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA), and a stepwise formulation (SW). The use of a data compression procedure in the wavelet domain is also proposed to reduce the computational workload involved in the variable selection process. The methodology is validated in a case study involving the classification of 149 Brazilian soil samples into three different orders (Argissolo, Latossolo and Nitossolo). For means of comparison, soft independent modelling of class analogy (SIMCA) models are also employed. The best discrimination of soil types was attained by SPA-LDA, which achieved an average classification rate of 90% in the validation set and 72% in cross-validation. Moreover, the proposed wavelet compression procedure was found to be of value by providing a 100-fold reduction in computational workload without significantly compromising the classification accuracy of the resulting models. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Brazilian soils Laser-induced breakdown spectroscopy Classification Wavelet compression successive projections algorithm Linear discriminant analysis

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Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging

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INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES 2014年 21卷 179-187页

作者： Cheng, Jun-Hu Sun, Da-Wen Zeng, Xin-An Pu, Hong-Bin S China 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

Hyperspectral imaging technique in the spectral wavelength range of 400-1000 nm was implemented in this study to determine the total volatile basic nitrogen (TVB-N) contents of grass carp fillets during the frozen storage. The quantitative calibration models were built between the spectral data extracted from the hyperspectral images and the reference measured TVB-N values by using partial least squares regression (PLSR) and least squares support vector machines (LS-SVM). The LS-SVM model using full spectral range had a better performance than the PLSR model for prediction of TVB-N value with the corresponding coefficients of determination (R(2)p) of 0.916 and 0.905, and root-mean-square errors of prediction (RMSEP) of 2346% and 2.749%, respectively. Nine optimal wavelengths (420, 466, 523, 552, 595, 615, 717,850 and 955 nm) were selected using successive projections algorithm (SPA), and R2p values of 0.902 and 0.891 with the corresponding RMSEP of 2.782% and 2.807% were obtained from the new optimized models established based on the selected valuable wavelengths. The best SPA-LS-SVM model was used to achieve the visualization map of TVB-N content distribution of the tested fish fillet samples. The results of this study indicated that hyperspectral imaging technique as an objective and promising tool is capable of determining TVB-N values for evaluation of fish freshness quality in a rapid and non-destructive way. Industrial relevance: The study showed that VIS-NIR hyperspectral imaging technique was an effective and powerful tool for rapid and non-destructive determination and assessment of fish fillet freshness for the fish industry. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Hyperspectral imaging Non-destructive TVB-N value successive projections algorithm Wavelength selection Grass carp

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