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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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UV-Vis spectrometric classification of coffees by SPA-LDA

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FOOD CHEMISTRY 2010年第1期119卷 368-371页

作者： Carvalho Polari Souto, Urijatan Teixeira Coelho Pontes, Marcio Jose Silva, Edvan Cirino Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar Castriani Sanches, Fatima Aparecida Silva Cunha, Francisco Antonio Ribeiro Oliveira, Maria Socorro Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analit Quimio BR-58051970 Joao Pessoa Paraiba Brazil Inst Tecnol Aeronaut Div Engn Eletron Sao Jose Dos Campos SP Brazil

UV-Vis spectrometry and chemometric techniques were used to classify aqueous extracts of Brazilian ground roast coffee with respect to type (caffeinated/decaffeinated) and conservation state (expired and non-expired shelf-life). Two classification methods were compared: soft independent modelling of class analogy (SIMCA) and linear discriminant analysis (LDA) with wavelength selection by the successive projections algorithm (SPA). The best results were obtained by SPA-LDA, which correctly classified all test samples. The classification accuracy of this model remained high (96%) even after the introduction of artificial spectral noise. These results suggest that UV-Vis spectrometry and SPA-LDA modelling provide a promising alternative for assessment of conservation state and decaffeination condition of coffee samples. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Coffee classification UV-Vis spectrometry Linear discriminant analysis successive projections algorithm Soft independent modelling of class analogy

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Growing Area Determination of Tremella Fuciformis Using Visible and Near-Infrared Spectroscopy

Growing Area Determination of Tremella Fuciformis Using Visi...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Xiaojing Chen Meng Xu Xiangou Zhu College of Physics and Electronic Information Engineering,Wenzhou University,Wenzhou 325027,China

Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella *** component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal ***-square support vector machine (LS-SVM) was used to establish the calibration *** projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS) which have 675 spectra *** eleven variables were *** variables based LS-SVM model obtain 100% determination correct *** was proved that SPA was an effective algorithm for spectra variable *** a conclusion,Vis-NIR spectroscopy could be used to determine the growing areas of Tremella fuciformis fast and accurately.

关键词： Visible and near infrared Principal component analysis Least-square support vector machine Tremella fuciformis successive projections algorithm

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Robust modeling for multivariate calibration transfer by the successive projections algorithm

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第1期76卷 65-72页

作者： Honorato, FA Galvao, RKH Pimentel, MF Neto, BD Araújo, MCU de Carvalho, FR Univ Fed Pernambuco Dept Engn Quim Cidade Univ BR-50740521 Recife PE Brazil Univ Fed Pernambuco Dept Quim Fundamental Recife PE Brazil Univ Fed Pernambuco Dept Quim Fundamental Recife PE Brazil

The use of the successive projections algorithm (VSPA) to select variables for building robust transferable Multiple Linear Regression (MLR) models is investigated. The robustness requirement is explicitly taken into account by introducing some spectra acquired by the slave instrument in the validation set that guides VSPA selection. The transfer samples are selected by the classic Kennard-Stone (KS) algorithm or a modified version of VSPA (SSPA) that operates across the rows of the instrumental response matrix, instead of the columns. The proposed approach was tested in two data sets, each set consisting of spectra obtained by infrared spectrometry in two different instruments. The first data set consists of gasoline spectra, which are employed to predict the distillation temperature at which 90% of the sample has evaporated (T 90%). The second data set consists of corn spectra, which are employed for moisture determination. The robust MLR models were compared to a PLS model employing Piecewise Direct Standardization (PDS) to correct the slave spectra. In both data sets, the mean prediction errors at the slave instrument for the robust VSPA-MLR and the PLS-PDS models were comparable, and slightly better for VSPA-MLR. The proposed approach appears to be a valid alternative to the commonly used PLS-PDS technique. (c) 2004 Elsevier B.V. All rights reserved.

关键词： multivariate calibration transfer robust modeling successive projections algorithm FT-IR spectrometry Piecewise Direct Standardization Kennard-Stone algorithm

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The successive projections algorithm for spectral variable selection in classification problems

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第1-2期78卷 11-18页

作者： Pontes, MJC Galvao, RKH Araújo, MCU Nogueira, P Moreira, T Neto, ODP José, GE Saldanha, TCB Univ Fed Paraiba Dept Quim CCEN BR-58051970 Joao Pessoa Paraiba Brazil

The successive projections algorithm (SPA) has been shown to be a useful tool for variable selection in the framework of multivariate calibration. In this paper, the collinearity minimization role of SPA is exploited in the context of classification methods for which collinearity is a known cause of generalization problems. For this purpose, a cost function associated to the average risk of misclassification by Linear Discriminant Analysis (LDA) is used to guide SPA selection. The proposed approach is illustrated in two classification problems. The first problem involves four types of vegetable oils (corn, soya, canola, sunflower). In this case, UV-VIS spectrometry is adopted to emphasize the ability of SPA-LDA to deal with low-resolution spectra with strong overlapping, which are associated to the wide absorption bands in this region. In the second problem, NIR spectrometry is employed to discriminate diesel samples with respect to the concentration level of sulphur. This application illustrates the use of SPA-LDA in a large-scale variable selection scenario. In these two examples, SPA-LDA is compared with the commonly used SIMCA classification method, as well as with a genetic algorithm (GA). The results show that SPA-LDA is superior to SIMCA and comparable to GA-LDA with respect to classification accuracy in an independent prediction set. Moreover, SPALDA is found to be less sensitive to instrumental noise than GA-LDA. (c) 2005 Elsevier B.V. All rights reserved.

关键词： successive projections algorithm classification linear discriminant analysis genetic algorithm SIMCA UVVIS spectrometry NIR spectrometry vegetable oils diesel

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Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

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ANALYTICA CHIMICA ACTA 2009年第1期635卷 45-52页

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

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n=180), validation (n=90) and prediction (n=90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs. and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer. (c) 2009 Elsevier B.V. All rights reserved.

关键词： Visible/near infrared spectroscopy successive projections algorithm Independent component analysis Variable selection Least squares-support vector machine Beer

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Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis

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TALANTA 2009年第5期77卷 1660-1666页

作者： Gambarra-Neto, Francisco Fernandes Marino, Glimaldo Ugulino Araujo, Mario Cesar Harrop Galvao, Roberto Kawakami Coelho Pontes, Marcio Jose de Medeiros, Everaldo Paulo Lima, Renato Sousa Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analit Quimio BR-58051970 Joao Pessoa Paraiba Brazil Inst Tecnol Aeronaut Div Engn Eletron Sao Jose Dos Campos SP Brazil EMBRAPA Ctr Nacl Pesquisa Algodao Campina Grande PB Brazil

This paper proposes a simple and non-expensive electroanalytical methodology for classification of edible vegetable oils with respect to type (canola, sunflower, corn and soybean) and conservation state (expired and non-expired shelf life). The proposed methodology employs an alcoholic extraction procedure followed by square wave voltammetry (SWV). Two chemometric methods were compared for classification of the resulting voltammograms, namely Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) with variable selection by the successive projections algorithm (SPA). The results were evaluated in terms of errors in a set of samples not included in the modelling process. The best results were obtained with the SPA-LDA method, which correctly classified all samples in terms of type and conservation state. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Vegetable oil Square wave voltammetry Soft Independent Modelling of Class Analogy Linear Discriminant Analysis 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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Determination of Protein Content of Auricularia auricula Using Near Infrared Spectroscopy Combined with Linear and Nonlinear Calibrations

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JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2009年第11期57卷 4520-4527页

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

Near infrared (NIR) spectroscopy was investigated to determine the protein content of Auricularia auricula (commonly called black woody ear or tree ear) using partial least-squares (PLS), multiple linear regression (MLR), and least-squares-support vector machine (LS-SVM). The performances of different preprocessing were compared including Savitzky-Golay (SG) smoothing, standard normal variate, multiplicative scatter correction (MSC), first derivative, second derivative, and direct orthogonal signal correction. A successive projections algorithm (SPA) was applied for relevant effective wavelengths selection. The combinations of various pretreatment and calibration methods were compared based on the prediction performance. The optimal full-spectrum PLS model was achieved by raw spectra, whereas the optimal SPA-MLR, SPA-PLS, and SPA-LS-SVM models were achieved by MSC spectra. The best prediction performance was achieved by the SPA-LS-SVM model, with correlation coefficients (0 = 0.9839 and a root mean squares error of prediction (RMSEP) = 0.16. The results indicated that NIR spectroscopy combined with SPA-LS-SVM was the most successful to determine the protein content of A. auricula.

关键词： Near infrared spectroscopy direct orthogonal signal correction successive projections algorithm least-squares-support vector machine Auricularia auricula protein content

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A Weak Signal Extraction Method for Human Blood Glucose Noninvasive Measurement using Near Infrared Spectroscopy

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JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES 2009年第11期30卷 1191-1204页

作者： Li, Li-Na Li, Qing-Bo Zhang, Guang-Jun Beihang Univ Sch Instrumentat Sci & Optoelect Engn Key Lab Educ Minist Beijing 100191 Peoples R China

Background interference from optical absorption of matrix components, low spectral selectivity and low spectral sensitivity are the main interference factors for human blood glucose noninvasive measurement using near infrared (NIR) spectroscopy. In order to extract the weak glucose concentration information, a modified uninformative variable elimination (mUVE) method combined with successive projections algorithm (SPA) named as mUVE-SPA, is proposed. mUVE is used to eliminate matrix background and high-frequency noise by wavelet multi-resolution technology. SPA is followed to select variables with minimum colinearity by projection algorithm in a vector space. The proposed method was applied in two NIR spectra data sets (plasma samples experiment in vitro and human blood glucose noninvasive measurement experiment in vivo) respectively. The performance and adaptability of the proposed strategy were discussed. The results indicate that the proposed hybrid method can give an alternative path to extract weak glucose information and yield more parsimonious models with higher precision.

关键词： Uninformative variable elimination successive projections algorithm Blood glucose noninvasive measurement Near infrared spectroscopy

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