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International Conference on Components, Packaging and Manufacturing Technology

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

ISBN: (纸本)9780878492138

successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.

关键词： Visible/near infrared spectroscopy successive projections algorithm least square-support vector machine lemon vinegar nondestructive testing

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An improved successive projections algorithm version to variable selection in multiple linear regression

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ANALYTICA CHIMICA ACTA 2023年第1期1274卷 341560页

作者： Canova, Luciana dos Santos Vallese, Federico Danilo Pistonesi, Marcelo Fabian Gomes, Adriano de Araujo Univ Fed Rio Grande do Sul Inst Quim Av Bento Goncalves9500 Agron BR-91501970 Porto Alegre RS Brazil Univ Nacl Sur Dept Quim INQUISUR Av Alem 1253 RA-8000 Bahia Blanca Buenos Aires Argentina

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including firstderivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

关键词： Variable selection successive projections algorithm Multilinear regression Partial least squares NIR spectrometry

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Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the successive projections algorithm feature-selection technique

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EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES 2014年第1期51卷 189-195页

作者： Goodarzi, Mohammad Saeys, Wouter Ugulino de Araujo, Mario Cesar Harrop Galvao, Roberto Kawakami Heyden, Yvan Vander Katholieke Univ Leuven KULeuven Fac Biosci Engn Dept Biosyst B-3001 Heverlee Belgium Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa PB Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil Vrije Univ Brussel Ctr Pharmaceut Res CePhaR B-1090 Brussels Belgium

Chalcones are naturally occurring aromatic ketones, which consist of an alpha-, beta-unsaturated carbonyl system joining two aryl rings. These compounds are reported to exhibit several pharmacological activities, including antiparasitic, antibacterial, antifungal, anticancer, immunomodulatory, nitric oxide inhibition and anti-inflammatory effects. In the present work, a Quantitative Structure-Activity Relationship (QSAR) study is carried out to classify chalcone derivatives with respect to their antileishmanial activity (active/inactive) on the basis of molecular descriptors. For this purpose, two techniques to select descriptors are employed, the successive projections algorithm (SPA) and the Genetic algorithm (GA). The selected descriptors are initially employed to build Linear Discriminant Analysis (LDA) models. An additional investigation is then carried out to determine whether the results can be improved by using a non-parametric classification technique (One Nearest Neighbour, INN). In a case study involving 100 chalcone derivatives, the INN models were found to provide better rates of correct classification than LDA, both in the training and test sets. The best result was achieved by a SPA-INN model with six molecular descriptors, which provided correct classification rates of 97% and 84% for the training and test sets, respectively. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Antileishmanial activity successive projections algorithm Linear Discriminant Analysis Genetic algorithm One Nearest Neighbour

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Rapid non-destructive identification of blueberry origin based on near infrared spectroscopy combined with wavelength selection

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INFRARED PHYSICS & TECHNOLOGY 2025年 145卷

作者： Wang, Guannan Wang, Na Dong, Ying Liu, Jinming Gao, Peng Hou, Rui Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Heilongjiang Peoples R China Guangdong Prov Key Lab Intelligent Port Secur Insp Guangzhou 510700 Peoples R China Huangpu Customs Technol Ctr Sanyuan Rd 66 Dongguan 523000 Peoples R China

To realize the nondestructive identification of blueberry origin, near-infrared spectroscopy was used to obtain the original spectral data of blueberry. Given the problems of spectral bandwidth, severe overlap, and complicated information analysis in the collection of near-infrared spectral data, we integrated successive projection algorithm (SPA) and sparrow search algorithm (SSA) with partial least squares regression (PLS) and support vector machine (SVM), respectively, resulting in the construction of two wavelength selection (WS) models: SPAPLS and SSA-SVM for WS from blueberry spectral data, 30 and 148 wavelength variables were selected respectively. To further enhance the accuracy of blueberry origin identification, we incorporated SSA into both Optimal Latin hypercube idea and Osprey algorithm, creating a multi-strategy hybrid sparrow search algorithm (ZOSSA). This approach reduced the number of selected wavelengths from 148 to 36. Using wavelengths selected from three different techniques as input subsets, a blueberry origin recognition model is constructed by placing them separately into a support vector machine. The experimental results prove that the performance of the wavelength-optimized model is higher than that of the full spectra performance, and the wavelength variables screened by ZOSSA have the best effect. The wavelength variables identified by ZOSSA exhibit superior performance with an accuracy rate of 96.21%, precision rate of 95.12 %, recall rate of 94.78 %, and F1 score of 94.94 % on the test set;surpassing those obtained using SPA (89.39 %, 87.43 %, 88.72 %, and 88.08 %) as well as SSA (90.15 %, 87.90 %, 88.16 %, and 88.02 %). The method strikes a balance between selecting an appropriate number of wavelengths while maintaining high model performance levels;thus meeting requirements for fast, accurate, nondestructive origin identification not only for blueberries but also providing novel insights for identifying origins in other agricultural

关键词： Near-infrared spectroscopy Blueberry Sparrow search algorithm Wavelength selection Origin identification successive projections algorithm

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Spatial analysis of hyperspectral images for detecting adulteration levels in bon-sorkh (Allium jesdianum L.) seeds: Application of voting classifiers

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SMART AGRICULTURAL TECHNOLOGY 2025年 10卷

作者： Fathi, Golshid Mireei, Seyed Ahmad Jafari, Mehrnoosh Sadeghi, Morteza Karimmojeni, Hassan Nazeri, Majid Isfahan Univ Technol Coll Agr Dept Biosyst Engn Esfahan *** Iran Isfahan Univ Technol Coll Agr Dept Agron & Plant Breeding Esfahan *** Iran Univ Kashan Fac Phys Dept Laser & Photon Kashan *** Iran

Bon-sorkh (Allium jesdianum L.) is a flowering plant with various reported beneficial properties. The rising price of bon-sorkh seeds has led some opportunists to distribute counterfeit seeds. This study assessed the feasibility of hyperspectral imaging to detect different adulteration levels (10%, 20%, 30%, 40%, and 50%) in bon-sorkh seeds adulterated with shallot seeds, the most similar to bon-sorkh based on spectral features. Hyperspectral images of pure and adulterated seed samples were acquired in the spectral range of 400-1000 nm. After image preprocessing using median blur and bilateral filters, pixel-wise classification models were developed using artificial neural networks, random forest, and voting classifiers to detect pure bon-sorkh and shallot seeds. A hard voting strategy was employed in the voting classifier, combining gradient boosting and histogram gradient boosting models. Additionally, competitive adaptive reweighted sampling (CARS), the successive projections algorithm, and extreme gradient boosting were used for feature selection to identify efficient wavelengths. Despite the promising performance of artificial neural networks in distinguishing pure bon-sorkh and shallot seeds, the voting classifiers demonstrated better generalization in classifying pure test images and were therefore applied to detect adulteration levels and identify the spatial distribution of bon-sorkh and shallot seeds. The results indicated the superiority of the voting model based on the median blur filter, which distinguished various adulteration levels with a total root mean squares error (RMSE) of 3.94%. Among the different feature selection methods, CARS-selected features performed best, with the corresponding voting model satisfactorily detecting adulteration levels, resulting in a RMSE of 6.69%. This study highlights the potential of hyperspectral imaging combined with voting classifiers to detect varying levels of shallot adulteration in bon-sorkh seeds.

关键词： Shallot adulterant Random forest Histogram gradient boosting Competitive adaptive reweighted sampling successive projections algorithm

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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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An Efficient Procedure for Identifying the Prediction Model Between Residual Stress and Barkhausen Noise

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JOURNAL OF NONDESTRUCTIVE EVALUATION 2013年第4期32卷 341-349页

作者： Sorsa, Aki Leiviska, Kauko Santa-aho, Suvi Vippola, Minnamari Lepisto, Toivo Univ Oulu Control Engn Lab Oulu 90014 Finland Tampere Univ Technol Dept Mat Sci FIN-33101 Tampere Finland

Residual stress of case-hardened steel samples is predicted in this paper with the linear multivariable regression model. The development of the prediction model is based on the huge set of features calculated from the Barkhausen noise measurement signal among which the most suitable ones are chosen. The selection uses a genetic algorithm with leave-multiple-out cross-validation in the objective function. The original feature set contains collinear features that make the selection task even more complex. Thus a feature elimination procedure based on the successive projections algorithm is studied in this paper. Also the standard genetic algorithm is slightly modified to better serve the feature selection task. The obtained results are good showing that the proposed procedures suit well for residual stress predictions. Also the applied feature elimination procedure is applicable and can be safely used to reduce the dimensionality of the selection problem.

关键词： Quantitative modelling Variable selection Genetic algorithm successive projections algorithm

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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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Discrimination of edible olive oils by means of synchronous fluorescence spectroscopy with multivariate data analysis

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GRASAS Y ACEITES 2013年第4期64卷 425-431页

作者： Dankowska, A. Malecka, M. Kowalewski, W. Poznan Univ Econ Fac Commod Sci Poznan Poland Adam Mickiewicz Univ Fac Math & Comp Sci Poznan Poland

The potential of fluorescence spectroscopy for the classification of olive oils was investigated. Synchronous fluorescence spectra were collected in the region of 240-700 nm with the wavelength intervals of 10, 30, 60 and 80 nm. successive projection algorithm (SPA) was applied for the determination of representative wavelengths while the linear discriminant analysis (LDA) method was used to classify olive oils. The classification error of the method was low (0,9-6,4%) for measurements collected at all wavelength intervals. The best classification accuracy was obtained for synchronous fluorescence intensities acquired at 10 selected wavelengths with the wavelength interval equal to 10 nm.

关键词： Extra virgin olive oil Pomace olive oil Refined olive oil successive projections algorithm Synchronous fluorescence spectroscopy

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Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

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ANALYTICA CHIMICA ACTA 2012年 726卷 57-66页

作者： Wu, Di Shi, Hui Wang, Songjing He, Yong Bao, Yidan Liu, Kangsheng Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China Natl Univ Ireland Univ Coll Dublin FRCFT Grp Dublin 4 Ireland Zhejiang Univ Dept Math Hangzhou 310027 Zhejiang Peoples R China

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380-1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on 'Manual Prawn Mask' and 'Automatic Prawn Mask', respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428, 445, 544, 569, 629, 672, 697, 760, 827, 917, 958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Hyperspectral imaging Prawn successive projections algorithm Image processing Wavelength selection Imaging spectroscopy

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