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Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 304卷 123398页

作者： Phanomsophon, Thitima Jaisue, Natthapon Worphet, Akarawhat Tawinteung, Nukoon Khurnpoon, Lampan Lapcharoensuk, Ravipat Krusong, Warawut Pornchaloempong, Pimpen Sirisomboon, Panmanas Inagaki, Tetsuya Ma, Te Tsuchikawa, Satoru King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Agr Engn Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Agr Technol Dept Plant Prod Technol Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Food Ind Div Fermentat Technol Bangkok Thailand King Mongkuts Inst Technol Ladkrabang Sch Engn Dept Food Engn Bangkok Thailand Nagoya Univ Grad Sch Bioagr Sci Nagoya Japan

Farmers would be able to regulate fertilization and produce quality durian if they knew the nutrient concentration in durian leaves. A long period of time for traditional nutritional content determination is needed. Therefore, near-infrared spectroscopy is a good method for nondestructive and quick nutrient content evaluation. The leaf sample matrices (fresh leaves, dried ground leaves, and dried ground leaf pellets) were scanned by Fourier transform near-infrared (FT-NIR) with a wavelength of 12,500-3,600 cm- 1. Regression models were developed using partial least squares (PLS) with full wavelength, short wavelength, and selected wavelength by successive projections algorithm (SPA). In this study, the model for N and K concentration was acceptable and the prediction was considered good but for P content not had succeeded. As a result, the PLS-SPA model using fresh leaf samples for evaluating N content in durian leaves exhibited performance of r2 = 0.852, SEP = 0.14%, RPD = 2.63 and bias = -0.020%. The PLS-SPA model using dried ground leaf samples for evaluating K content in durian leaves exhibited performance of r2 = 0.820, SEP = 0.13%, RPD = 2.36 and bias = 0.006%. This research found that it is possible to apply NIR waves to predict N and K concentrations in durian leaves. It is not necessary to predict directly from the wavelengths associated with -N or -K bonds. Instead, NIR can measure them indirectly from the bonding of proteins, which are products formed by N and K. In addition, selecting the wavelength that is related to the value to be measured can produce results that are not significantly different from using full or short wavelengths. These models can assist farmers in rapidly predicting N and K content in durian leaves for immediate fertilizer adjustment.

关键词： Near infrared spectroscopy Primary macronutrients Durian leaf successive projections algorithm

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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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Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019年 219卷 367-374页

作者： Hu, Feng Zhou, Mengran Yan, Pengcheng Li, Datong Lai, Wenhao Zhu, Song Wang, Yu Anhui Univ Sci & Technol Sch Elect & Informat Engn 168 Taifeng Rd Huainan 232001 Anhui Peoples R China

In the process of prevention and control of water inrush disaster, it is of great significance to identify the type of water inrush source for coal mine safety production accurately and quickly. The application of laser induced fluorescence (LIF) technology to identify the water inrush in coal mine broke the shortage of the traditional hydrochemical method, which could realize the accurate and rapid identification of water inrush types. Firstly, in order to avoid the influence of random variations of spectral data, four kinds of common pretreatment methods were analyzed and compared, and the moving average smoothing method was chosen to preprocess the original fluorescence spectral data. Then, for the purpose of selecting the appropriate sample division method to improve the predictive performance of the model, four common sample division methods were compared, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the samples into training set and test set. Further, the 10 characteristic wavelengths were selected by successive projections algorithm (SPA) to reduce the amount of data. Finally, the selected data was taken as input, the sigmoid function was selected as the activation function of extreme learning machine (ELM), and the number of hidden layer neurons was set to 34, which realized the construction of water source identification model. The prediction accuracy of ELM model for the training set and test set were 99.0% and 94.0%, respectively. In addition, the water samples collected at different time were mixed in the same way to form the independent verification set, and the prediction accuracy of the ELM water source identification model for independent verification set was 91.5%. The results shown that it was feasible to select the characteristic wavelengths of fluorescence spectra by using the SPA. The data of 10 characteristic wavelengths could fully represent the effective information of whole band spectrum. And i

关键词： Laser induced fluorescence Characteristic wavelengths successive projections algorithm Mine water inrush

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Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

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SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY 2018年 143卷 12-17页

作者： Duan, Fajie Fu, Xiao Jiang, Jiajia Huang, Tingting Ma, Ling Zhang, Cong Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Tianjin 300072 Peoples R China Tsinghua Univ Elect Engn Dept Beijing 100084 Peoples R China

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA (C) 2018 Elsevier B.V. All rights reserved.

关键词： LIBS Quantitative analysis Variable selection Genetic algorithm successive projections algorithm

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Mapping the Pungency of Green Pepper Using Hyperspectral Imaging

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FOOD ANALYTICAL METHODS 2018年第11期11卷 3042-3052页

作者： Rahman, Anisur Lee, Hoonsoo Kim, Moon S. Cho, Byoung-Kwan Chungnam Natl Univ Dept Biosyst Machinery Engn Coll Agr & Life Sci Daejeon 34134 South Korea Bangladesh Agr Univ Dept Farm Power & Machinery Mymensingh 2202 Bangladesh ARS Environm Microbial & Food Safety Lab USDA BARC East Powder Mill Rd Bldg 303 Beltsville MD 20705 USA

The pungency level of green peppers is dependent on the amounts of capsaicin and dihydrocapsaicin they contain. This study was conducted to develop a non-destructive method for the prediction and mapping of the capsaicin and dihydrocapsaicin contents in green pepper. Hyperspectral images of 200 total green peppers of three varieties were acquired in the wavelength range of 1000-1600nm, from which the mean spectra of each pepper variety were extracted. The reference capsaicin and dihydrocapsaicin contents of the samples were measured by high-performance liquid chromatography. Quantitative calibration models were built using partial least squares (PLS) regression with different spectral preprocessing techniques;the best performance was found by normalizing the preprocessed spectra with correlation coefficients (r(pred)) of 0.86 and 0.59, which showed the standard errors of prediction (SEPs) of 0.09 and 0.03mg/g for capsaicin and dihydrocapsaicin, respectively. Seventeen and 16 optimal wavebands were selected using the successive projections algorithm;r(pred) of 0.88 and 0.68 and SEPs of 0.084 and 0.027mg/g were obtained for capsaicin and dihydrocapsaicin, respectively, from the newly developed PLS calibration models using these optimal wavebands. The successive projections algorithm (SPA)-PLS model was used to map the capsaicin and dihydrocapsaicin contents of the green peppers. These maps provided detailed information on the pungency levels of the tested green peppers. The results of this study indicated that hyperspectral imaging is useful for the rapid and non-destructive evaluation of the pungency of green peppers.

关键词： Green pepper Pungency level Capsaicin Dihydrocapsaicin Hyperspectral imaging successive projections algorithm

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Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat

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LWT-FOOD SCIENCE AND TECHNOLOGY 2016年 72卷 322-329页

作者： Cheng, Weiwei Sun, Da-Wen Pu, Hongbin Liu, Yuwei S China Univ Technol Sch Food Sci & Engn Guangzhou 510641 Guangdong Peoples R China S China Univ Technol Acad Contemporary Food Engn Guangzhou Higher Educ Mega Ctr Guangzhou 510006 Guangdong Peoples R China Univ Coll Dublin Agr & Food Sci Ctr FRCFT Dublin 4 Ireland

K value is an important freshness indicator of meat. This study investigated the integration of spectral and textural data for enhancing the hyperspectral prediction ability of K value in pork meat. In this study, six feature wavebands (407, 481, 555, 578, 633, and 973 nm) were identified by successive projections algorithm (SPA). Meanwhile, the texture data of the grayscale images at the feature wavebands were extracted by gray level co-occurrence matrix (GLCM). The spectral and textural data were integrated by feature level fusion and the partial least square regression (PLSR) model built based on data fusion yielded excellent results, an improvement of at least 17.5% was obtained in model performance compared to those when either spectral data or textural data were used alone, indicating that data fusion is an effective way to enhance hyperspectral imaging ability for the determination of K values for freshness evaluation in pork meat. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Pork K value successive projections algorithm Gray level co-occurrence matrix Data fusion Partial least squares regression

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Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification

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TALANTA 2011年第4期85卷 2159-2165页

作者： Coelho Pontes, Marcio Jose Pereira, Claudete Fernandes Pimentel, Maria Fernanda Cruz Vasconcelos, Fernanda Vera Brito Silva, Alinne Girlaine Univ Fed Paraiba Dept Quim BR-58059900 Joao Pessoa PB Brazil Univ Fed Pernambuco Dept Quim Recife PE Brazil Univ Fed Pernambuco Dept Engn Quim Recife PE Brazil

This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0 mm (8814-3799 cm(-1)), 10 mm (11,329-5944 cm(-1) and 5531-4490 cm(-1)) and 20 mm (11,688-5952 cm(-1) and 5381-4679 cm(-1)). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20 mm. Otherwise, PLS-DA shows the best results for classification of 10 mm cell data, which achieved a correct prediction rate of 100% in the test set. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Diesel/biodiesel blends Near infrared spectrometry Screening analysis Partial least squares-discriminant analysis Linear discriminant analysis successive projections algorithm

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Simultaneous identification of the wood types in aged cachacas and their adulterations with wood extracts using digital images and SPA-LDA

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FOOD CHEMISTRY 2019年 273卷 77-84页

作者： de Sousa Fernandes, David Douglas de Almeida, Valber Elias Fontes, Marcelo Marques Ugulino de Araujo, Mario Cesar Veras, Germano Goncalves Dias Diniz, Paulo Henrique Univ Fed Paraiba Dept Quim POB 5093 BR-58051970 Joao Pessoa PB Brazil Inst SENAI Tecnol Dist Ind BR-58082004 Joao Pessoa PB Brazil Univ Estadual Paraiba Ctr Ciencia & Tecnol Dept Quim BR-58429500 Campina Grande PB Brazil Univ Fed Oeste Bahia Nucleo Quim Campus Reitor Edgard SantosRua Bertioga 892 BR-47810059 Barreiras BA Brazil

Cachaca is a sugarcane-derived alcoholic spirit exclusively produced in Brazil. It can be aged in barrels made from different types of wood, similar to other distilled beverages. The choice of wood type promotes different effects on color, flavor, aroma and consequently the price of cachaca, favoring fraudulent activities. This paper proposes the simultaneous identification of different wood types in aged cachacas and their adulterations with wood extracts using a digital-image based methodology employing color histograms obtained from digital images associated with pattern recognition methods, without any sample preparation step. Linear Discriminant Analysis, coupled with successive projections algorithm for variable selection (SPA-LDA), obtained the best results, reaching accuracy, sensitivity, and specificity rates higher than 90.0% in the test set. This can be a rapid and reliable tool to prevent fraudulent labeling;ensuring that what is on the label reflects the quality of aged cachacas, affording security to consumers and regulatory agencies.

关键词： Cachaca Aging Wood types Wood extracts Adulteration successive projections algorithm

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Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice

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ANALYTICA CHIMICA ACTA 2010年第1-2期659卷 229-237页

作者： Wu, Di He, Yong Nie, Pengcheng Cao, Fang Bao, Yidan Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Zhejiang Peoples R China Nanchang Hangkong Univ Coll Elect & Informat Engn Nanchang 330069 Peoples R China

Several wavelength variable selection algorithms were compared to analyze visible and near-infrared (Vis-NIR) spectra for the non-invasive quantitative determination of soluble solids content (SSC) and pH in grape juice. In order to eliminate useless variables and improve the signal/noise ratio, the pretreated full spectra were firstly calculated by different informative variable selection methods. Uninformation variable elimination (UVE) did better than interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS). successive projections algorithm (SPA) was further operated to select variables. Finally, nine and eleven variables were obtained for respectively SSC and pH analyses. The better results of UVE-SPA-PLS models compared to those of SPA-PLS models in both SSC and pH analyses show that it is necessary to execute UVE before SPA, which can both reduce the calculation time and increase the model's performance. Furthermore, two common used calibration methods, PLS and multiple linear regression (MLR), were compared. UVE-SPA-MLR obtained better results than UVE-SPA-PLS in both SSC and pH analyses. The coefficients of determination for prediction set (r(p)(2)) and residual predictive deviation (RPD) obtained by UVE-SPA-MLR are 0.979 and 6.971 for SSC, and 0.951 and 5.432 for pH. The overall results demonstrate that it is feasible to non-invasively determine SSC and pH of grape juice using Vis-NIR spectroscopy, UVE-SPA is a powerful tool to select the efficient variables, and UVE-SPA-MLR is simple and excellent for the spectral calibration. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Visible and near-infrared spectroscopy Grape juice Uninformation variable elimination Interval partial least squares successive projections algorithm

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PREDICTING SOIL NITROGEN AND ORGANIC CARBON USING NEAR INFRARED SPECTROSCOPY COUPLED WITH VARIABLE SELECTION

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APPLIED ENGINEERING IN AGRICULTURE 2014年第4期30卷 641-647页

作者： Jia, S. Zhang, J. Li, G. Yang, X. Zhejiang Univ Sch Biosyst Engn & Food Sci Hangzhou 310003 Zhejiang Peoples R China Minist Agr Key Lab Equipment & Informatizat Environm Control Hangzhou Zhejiang Peoples R China Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310003 Zhejiang Peoples R China

The aim of this work was to select informative variables for modeling near-infrared spectra to soil nitrogen (N) and organic carbon (OC) and to provide interpretation for the selected variables. The dataset that consisted of 225 soil samples was randomly spilt into calibration set, validation set, and prediction set. Spectra in the calibration set were conducted for variable selection by the method of Monte Carlo uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA). Partial least squares regression (PLSR) and multiple linear regression (MLR) were used to construct calibration models for each property based on the selected variables. The proposed model MC-UVE-PLSR achieved the optimal performance for soil N and OC comparing with full spectra PLSR and SPA-MLR. The coefficients of determination (R-2), residual prediction deviation (RPD) were, respectively, 0.87, 0.88, and 2.8, 2.9 for N and OC. The results indicate that MC-UVE is an effective tool for spectral variable selection and is able to promote model prediction accuracy and efficiency. Analysis of the feature variables show that some of the selected variables of soil N and OC are directly related to their functional groups, while some influence the prediction results by measuring soil moisture content. It is also considered that soil N is better predicted by its own feature variables rather than calculation by the autocorrelation of soil OC.

关键词： Monte Carlo uninformative variable elimination Near-infrared spectroscopy Soil nitrogen Soil organic carbon successive projections algorithm

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