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A consensus successive projections algorithm - multiple linear regression method for analyzing near infrared spectra

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ANALYTICA CHIMICA ACTA 2015年第1期858卷 16-23页

作者： Liu, Ke Chen, Xiaojing Li, Limin Chen, Huiling Ruan, Xiukai Liu, Wenbin Wenzhou Univ Coll Phys & Elect Engn Informat Wenzhou Zhejiang Peoples R China

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named "consensus SPA-MLR" (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Consensus model Variable selection successive projections algorithm Near infrared spectra Multiple linear regression

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Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm

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TALANTA 2018年 181卷 38-43页

作者： de Almeida, Valber Elias Gomes, Adriano de Araujo de Sousa Fernandes, David Douglas Casimiro Goicoechea, Hector Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar Univ Fed Paraiba CCEN Lab Automacao & Instrumentacao Quim Analit & Quim Dept Quim Caixa Postal 5093 BR-58051970 Joao Pessoa PB Brazil Univ Fed Sul & Sudoeste Para Inst Ciencias Exatas Fac Quim Folha 17Quadra 04 BR-68505080 Maraba Para Brazil Univ Natl Litoral CONICET Lab Desarrollo Analit Quimiometr LADAQ Fac Bioquim & Ciencias BiolCatedra Quim Analit 1 Ciudad Univ RA-3000 Santa Fe Argentina Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the successive projections algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.

关键词： Nonlinear multivariate calibration Kernel partial least squares successive projections algorithm Variable selection Near infrared spectrometry Sugar

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Correlation weighted successive projections algorithm as a novel method for variable selection in QSAR studies: investigation of anti-HIV activity of HEPT derivatives

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JOURNAL OF CHEMOMETRICS 2007年第5-6期21卷 239-250页

作者： Kompany-Zareh, Mohsen Akhlaghi, Yousef Inst Adv Studies Basic Sci Dept Chem Zanjan 45195 Iran Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran

Correlation weighted successive projections algorithm (CWSPA), as a modified version of successive projections algorithm (SPA), is proposed for selection of descriptors in the non-linear quantitative structure-activity relationship QSAR) study of a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine] (HEPT) derivatives, as non-nucloside reverse transcriptase inhibitors (NNRTIs). In the proposed procedure the correlation coefficient of each descriptor with the activities (r(g)) was an additional criterion for selection of descriptors. The extent of contribution of r in the selection of variables, m, was also optimized and r(g)(4)-CWSPA was the selected condition (m = 4). Three layer radial basis function networks (RBFNs) and molecular descriptors derived solely from molecular structure were used to construct the non-linear QSAR models. Utilizing r(g)(4)-CWSPA a limited number of uncorrelated and informative descriptors were selected. The relative standard error percent in anti-HIV activity predictions for the training set by the application of cross-validation (RSECV%) was 9.77%, and for prediction set (RSEP%) was 8.61% when the selected number of descriptors were 20. The obtained model outperforms those given in the literature in both the fitting and predicting stages. RBFN analysis yielded predicted activities in an acceptable agreement with the experimentally obtained values (cross-validation r = 0.924, prediction r = 0.939). Compared to SPA, r(g)(m)-CWSPA resulted in a lower RSECV% and RSEP% values using lower number of selected variables. The results show that considering the correlation of variables to the independent variables increase the performance of selection, as a result, the quality of the set of selected variables. Finally, a simple procedure for selection of variables using r(g)(A)-CWSPA was proposed in which there was no need to test all possible initial descriptors. The results from the simple procedure were comparable to the procedure in whi

关键词： QSAR radial basis function networks successive projections algorithm anti-HIV HEPT derivatives

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Determination of fat content in chicken hamburgers using NIR spectroscopy and the successive projections algorithm for interval selection in PLS regression (iSPA-PLS)

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018年 189卷 300-306页

作者： Krepper, Gabriela Romeo, Florencia de Sousa Fernandes, David Douglas Goncalves Dias Diniz, Paulo Henrique Ugulino de Araujo, Mario Cesar Susana Di Nezio, Maria Fabian Pistonesi, Marcelo Eugenia Centurion, Maria UNS CONICET INQUISUR Dept Quim Av Alem 1253B8000CPB Bahia Blanca Buenos Aires Argentina Univ Fed Paraiba Dept Quim Lab Automacao & Instrumentacao Quim Analt Quimiome Caixa Postal 5093 BR-58051970 Joao Pessoa Paraiba Brazil Univ Fed Oeste Bahia Programa Posgrad Quim Pura & Aplicada Campus Reitor Edgard SantosRua Bertioga 892 BR-47810059 Barreiras BA Brazil

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an altemative analytical method based on Near Infrared Spectroscopy (NIR) and successive projections algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12 mg kg(-1) were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (w w(-1)). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PIS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59 mg kg(-1), REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Fat Hamburgers NIR spectroscopy Interval selection successive projections algorithm Partial Least Squares

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The successive projections algorithm for variable selection in spectroscopic multicomponent analysis

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2001年第2期57卷 65-73页

作者： Araújo, MCU Saldanha, TCB Galvao, RKH Yoneyama, T Chame, HC Visani, V Univ Fed Paraiba Dept Quim BR-58051970 Joao Pessoa PB Brazil

The "successive projections algorithm", a forward selection method which uses simple operations in a vector space to minimize variable collinearity, is proposed as a novel variable selection strategy for multivariate calibration. The algorithm was applied to UV-VIS spectrophotometric data for simultaneous analysis of complexes of Co2+ ,Cu2+, Mn2+, Ni2+ e Zn2+ with 4-(2-piridilazo)resorcinol in samples containing the analytes in the 0.02-0.5 mg 1(-1) concentration range. A convenient spectral window was first chosen by a procedure also proposed here and applying successive projections algorithm to this range allowed an improvement of the predictive capabilities of Principal Component Regression, Partial Least Squares and Multiple Linear Regression models using only 20% of the number of wavelengths. successive projections algorithm selection resulted in a root mean square error of prediction at the test set of 0.02 mg l(-1), while the best and worst realizations of a genetic algorithm used for comparison yielded 0.01 and 0.03 mg l(-1). However, genetic algorithm took 200 times longer than successive projections algorithm, and this ratio tends to increase dramatically with the number of wavelengths employed. Finally, unlike genetic algorithm, successive projections algorithm is a deterministic search technique whose results are reproducible and it is more robust with respect to the choice of the validation set. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： successive projections algorithm variable selection multicomponent analysis UV-VIS spectrophotometry multivariate calibration

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A graphical user interface for variable selection employing the successive projections algorithm

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2012年 118卷 260-266页

作者： Paiva, Henrique Mohallem Carreiro Soares, Sofacles Figueredo Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa PB Brazil Mectron Engn Ind & Comercio SA BR-12227000 Sao Jose Dos Campos Brazil Inst Tecnol Aeronaut Div Engn Elet BR-12228900 Sao Jose Dos Campos Brazil

The present work describes a MATLAB graphical user interface for the successive projections algorithm (SPA), which is a variable selection technique aimed at reducing collinearity problems in Multiple Linear Regression (MLR) modelling. The interface also offers the possibility of pre-processing the data using Savitzky-Golay smoothing/differentiation and/or wavelet denoising. Sample selection routines for dividing the samples into calibration and validation sets are also implemented. At the end, prediction statistics (PRESS, RMSEP, SDV, WAS and correlation coefficient) are calculated to evaluate the performance of the resulting MLR model. All these operations can be carried out by the user without the need for MATLAB programming skills. For illustration, an example involving near-infrared spectrometric determination of moisture in corn samples is presented. The software is freely available at ***/similar to kawakami/spa. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Multivariate calibration Variable selection successive projections algorithm Graphical user interface MATLAB

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A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2008年第1期92卷 83-91页

作者： Galvao, Roberto Kawakami Harrop Ugulino Araujo, Mario Cesar Fragoso, Wallace Duarte Silva, Edvan Cirino Jose, Gledson Emidio Carreiro Soares, Sofacles Figueredo Paiva, Henrique Mohallem Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa Paraiba Brazil Inst Tecnol Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil EMBRAER BR-12227901 Sao Jose Dos Campos SP Brazil

The successive projections algorithm (SPA) is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR). This paper proposes a modification to the basic SPA formulation aimed at further improving the parsimony of the resulting MLR model. For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test. The utility of the proposed modification is illustrated in a simulation study, as well as in two application examples involving the analysis of diesel and com samples by near-infrared (NIR) spectroscopy. The results demonstrate that the number of variables selected by SPA can be reduced without significantly compromising prediction performance. In addition, SPA is favourably compared with classic Stepwise Regression and full-spectrum PLS. A graphical user interface for SPA is available at ***/similar to kawakami/spa/. (C) 2008 Elsevier B.V. All rights reserved.

关键词： multiple linear regression variable selection successive projections algorithm near-infrared spectrometry diesel analysis com analysis

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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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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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A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents

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ANALYTICA CHIMICA ACTA 2011年第1期689卷 22-28页

作者： Carreiro Soares, Sofacles Figueredo Harrop Galvao, Roberto Kawakami Ugulino Araujo, Mario Cesar da Silva, Edvan Cirino Pereira, Claudete Fernandes Evangelista de Andrade, Stefani Fury Leite, Flaviano Carvalho Univ Fed Paraiba CCEN Dept Quim BR-58051970 Joao Pessoa PB Brazil Inst Technol & Aeronaut Div Engn Eletron BR-12228900 Sao Jose Dos Campos SP Brazil

This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet-visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Variable selection successive projections algorithm Interferents Ultraviolet-visible and near-infrared spectrometry Colorants Gasoline

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