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successive projections algorithm combined with uninformative variable elimination for spectral variable selection

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2008年第2期91卷 194-199页

作者： Ye, Shengfeng Wang, Dong Min, Shungeng China Agr Univ Coll Sci Beijing 100094 Peoples R China

The UVE - SPA method, successive projections algorithm (SPA) combined with uninformative variable elimination (UVE) is proposed as a novel variable selection approach for multivariate calibration. UVE is used to select informative variables, and SPA is followed to select variables with minimum redundant information from the informative variables. The proposed method was applied to near-infrared (NIR) reflectance data for analysis of nicotine in tobacco lamina and NIR transmission data for active pharmaceutical ingredient (API) in single tablet. On the aspect of elimination of uninformative variables, the effect of UVE using first derivative spectra was better than that of using raw spectra. In terms of variable selection, fewer variables with better performance were selected by UVE - SPA method than by direct SPA method. For NIR spectral analysis of nicotine in tobacco lamina, the number of variables selected from 3001 spectral variables reduced from 48 by direct SPA to 35 by UVE - SPA, and the root mean squared error of prediction set (RMSEP) of the corresponding MLR models decreased from 0.174 (%, mg/mg) to 0.160. For NIR spectral analysis of API in each tablet, the number of variables selected from 650 spectral variables reduced from 46 by direct SPA to 17 by UVE - SPA, and RMSEP of the corresponding multiple linear regression (MLR) models decreased from 0.842 (%, mg/mg) to 0.473. MLR model using variables selected by UVE - SPA had better prediction performance than full-spectrum partial least-squares (PLS) model, and comparable to PLS model of UVE. (c) 2007 Elsevier B.V. All rights reserved.

关键词： successive projections algorithm uninformative variable elimination variable selection near-infrared spectroscopy multivariate calibration

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successive projections algorithm applied to spectral data for the simultaneous determination of flavour enhancers

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TALANTA 2010年第1期82卷 222-226页

作者： Acebal, Carolina C. Gruenhut, Marcos Lista, Adriana G. Fernandez Band, Beatriz S. Univ Nacl Sur FIA Lab Dept Chem INQUISURCONICET Buenos Aires DF Argentina

A novel variable selection strategy for multiple lineal regression (MLR), the successive projections algorithm (SPA), was applied to spectrophotometric data (190-320 nm) for the simultaneous determination of monosodium glutamate (MSG), guanosine-5'-monophosphate (GMP) and inosine-5'-monophosphate (IMP) in dehydrated broths samples. This selection method uses simple operations in a vector space to minimize variable collinearity and has become an interesting variable selection strategy for multivariate calibration. In this work, nine, six and four wavelengths for MSG, GMP and IMP, respectively, were selected to construct calibrations models in order to solve successfully the serious spectral overlapping in samples containing these analytes. The relative errors of prediction (REP) for the validation set were 2.3%, 0.9% and 1.8% for MSG, GMP and IMP, respectively. Commercial samples were analysed and a recovery study was carried out to verify the accuracy of the proposed method with satisfactory results. A continuous flow system was used to develop a simple, cheap and rapid method (sample throughput: 200 h(-1)), without any previous extraction step. Published by Elsevier B.V.

关键词： successive projections algorithm Flavour enhancers Continuous flow system Spectrophotometry

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successive projections algorithm-based three-band vegetation index for foliar phosphorus estimation

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ECOLOGICAL INDICATORS 2016年 67卷 12-20页

作者： Wang, Junjie Shi, Tiezhu Liu, Huizeng Wu, Guofeng Shenzhen Univ Natl Adm Surveying Mapping & Geoinformat Key Lab Geoenvironm Monitoring Coastal Zone Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Spatial Temporal Smart Sensing & Shenzhen 518060 Peoples R China Shenzhen Univ Coll Life & Marine Sci Shenzhen 518060 Peoples R China

Phosphorus (P) is essential for plant growth and development. Very few studies have reported the use of hyperspectral three-band vegetation indices (TBVIs) in foliar P estimation. Further, the optimal TBVI is generally chosen from millions of all possible band combinations. This study aimed to investigate resampling and two wavelength selection methods (genetic algorithm (GA) and successive projections algorithm (SPA)) in deriving TBVIs for foliar P estimation and further to compare the performances of the newly developed TBVIs and published VIs. A total of 137 field-based canopy hyperspectral reflectance (350-2500 nm) of Carex (C. cinerascens) were obtained and reduced to 1603 wavelengths due to spectral noises. Considering both the original and first derivative reflectance spectra, their resampled wavelengths and selected wavelengths by GA and SPA were employed to derive TBVIs. A total of 24 selected TBVI models were calibrated for foliar P estimation with the training dataset, and they were independently validated with the test dataset. The root mean square error of validation (RMSEvai), determination coefficient of validation (R-val(2)) and residual prediction deviation (RPD) values were calculated to evaluate the performance of each model. The results demonstrated that 5474, 1972 and 1.2 s in average was taken in calculating all possible TBVIs using resampling, GA and SPA, respectively. Two SPA-based TBVIs, i.e. (rho 760-rho 2387)/(rho 723-rho 2387) (rho lambda, original reflectance) and (rho'(728)-rho'(319) + 2 rho'(714))/(rho'(729)+ rho'(1319)- 2 rho(714)) (gx, first derivative reflectance), had the best model performances (na, = 0.680, RMSEvai = 0.040%, RPD = 1.75;R-val(2) = 0.692, RMSEval = 0.039%, RPD =1.80) in foliar P estimation among the 24 TBVIs. Compared with 15 published VIs (R-val(2)< 0.64, RPD < 1.64), the two SPA-based TBVIs exhibited better validation performances. We concluded that SPA has the great potential for TBVI derivation due to the reduc

关键词： Genetic algorithm Hyperspectral remote sensing Foliar phosphorus successive projections algorithm Three-band vegetation index

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Selection of robust variables for transfer of classification models employing the successive projections algorithm

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ANALYTICA CHIMICA ACTA 2017年 984卷 76-85页

作者： Tavares Melo Milanez, Karla Danielle Araujo Nobrega, Thiago Cesar Nascimento, Danielle Silva Harrop Galvao, Roberto Kawakami Coelho Pontes, Marcio Jose Univ Fed Paraiba Dept Quim Joao Pessoa Paraiba Brazil Univ Nacl Sur Dept Quim INQUISUR CONICET Lab FIA Av Alem 1253B8000CPB Bahia Blanca Buenos Aires Argentina Inst Tecnol Aeronaut Div Engn Eletron Sao Jose Dos Campos SP Brazil

Multivariate models have been widely used in analytical problems involving quantitative and qualitative analyzes. However, there are cases in which a model is not applicable to spectra of samples obtained under new experimental conditions or in an instrument not involved in the modeling step. A solution to this problem is the transfer of multivariate models, usually performed using standardization of the spectral responses or enhancement of the robustness of the model. This present paper proposes two new criteria for selection of robust variables for classification transfer employing the successive projections algorithm (SPA). These variables are then used to build models based on linear discriminant analysis (LDA) with low sensitivity with respect to the differences between the responses of the instruments involved. For this purpose, transfer samples are included in the calculation of the cost for each subset of variables under consideration. The proposed methods are evaluated for two case studies involving identification of adulteration of extra virgin olive oil (EVOO) and hydrated ethyl alcohol fuel (HEAF) using UV-Vis and NIR spectroscopy, respectively. In both cases, similar or better classification transfer results (obtained for a test set measured on the secondary instrument) employing the two criteria were obtained in comparison with direct standardization (DS) and piecewise direct standardization (PDS). For the UV-Vis data, both proposed criteria achieved the correct classification rate (CCR) of 85%, while the best CCR obtained for the standardization methods was 81% for DS. For the NIR data, 92.5% of CCR was obtained by both criteria as well as DS. The results demonstrated the possibility of using either of the criteria proposed for building robust models as an alternative to the standardization of spectral responses for transfer of classification. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multivariate classification transfer Robust modeling successive projections algorithm Standardization methods UV-Vis spectroscopy NIR spectroscopy

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Pruning Extreme Learning Machines Using the successive projections algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2015年第12期13卷 3974-3979页

作者： Mesquita, D. P. P. Gomes, J. P. P. Rodrigues, L. R. Galvao, R. K. H. Univ Fed Ceara UFC Fortaleza Ceara Brazil Univ Fed Ceara UFC Engn Eletr Fortaleza Ceara Brazil Inst Aeronaut & Espaco IAE Sao Jose Dos Campos SP Brazil Inst Tecnol Aeronaut ITA Engn Eletron Sao Jose Dos Campos SP Brazil

Extreme Learning Machine (ELM) is a recently proposed machine learning method with successful applications in many domains. The key strengths of ELM are its simple formulation and the reduced number of hyper-parameters. Among these hyper-parameters, the number of hidden nodes has significant impact on ELM performance since too few/many hidden nodes may lead to underfitting/overfitting. In this work, we propose a pruning strategy for ELM using the successive projections algorithm (SPA) as an approach to automatically find the number of hidden nodes. The SPA was originally proposed for variable selection. In this work, it is adapted in order to prune ELMs. The proposed method is compared to the Optimally Pruned Extreme Learning Machine algorithm (OP-ELM), which is considered as a state of the art method. Real world datasets were used to assess the performance of the proposed method for regression and classification problems. The application of the proposed model resulted in much simpler models with similar performance compared to the OP-ELM. For some classification instances, the performance of the proposed method outperformed the OP-ELM method.

关键词： Feedfoward Networks Extreme Learning Machine successive projections algorithm

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Unfolded partial least squares/residual bilinearization combined with the successive projections algorithm for interval selection: enhanced excitation-emission fluorescence data modeling in the presence of the inner filter effect

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2015年第19期407卷 5649-5659页

作者： Gomes, Adriano de Araujo Schenone, Agustina V. Goicoechea, Hector C. de Araujo, Mario Cesar U. Univ Fed Paraiba CCEN Dept Quim Lab Automacao & Instrumentacao Quim Analit & Qui BR-58051970 Joao Pessoa Paraiba Brazil Univ Nacl Litoral Lab Desarrollo Analit & Quimiometria LADAQ Fac Bioquim & Ciencias Biol Catedra Quim Analit 1 Santa Fe Argentina

The use of the successive projections algorithm (SPA) for elimination of uninformative variables in interval selection, and unfold partial least squares regression (U-PLS) modeling of excitation-emission matrices (EEM), when under the inner filter effect (IFE) is reported for first time. Post-calibration residual bilinearization (RBL) was employed against events of unknown components in the test samples. The inner filter effect can originate changes in both the shape and intensity of analyte spectra, leading to trilinearity losses in both modes, and thus invalidating most multiway calibration methods. The algorithm presented in this paper was named iSPA-U-PLS/RBL. Both simulated and experimental data sets were used to compare the prediction capability during: (1) simulated EEM;and (2) quantitation of phenylephrine (PHE) in the presence of paracetamol (PAR) (or acetaminophen) in water samples. Test sets containing unexpected components were built in both systems [a single interference was taken into account in the simulated data set, while water samples were added with varying amounts of ibuprofen (IBU), and acetyl salicylic acid (ASA)]. The prediction results and figures of merit obtained with the new algorithm were compared with those obtained with U-PLS/RBL (without intervals selection), and with the well-known parallel factors analysis (PARAFAC). In all cases, U-PLS/RBL displayed better EEM handling capability in the presence of the inner filter effect compared with PARAFAC. In addition, iSPA-U-PLS/RBL improved the results obtained with the full U-PLS/RBL model, in this case demonstrating the potential of variable selection.

关键词： Interval selection successive projections algorithm Unfolded-partial least squares Second order calibration Inner filter effect

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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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Screening analysis of beer ageing using near infrared spectroscopy and the successive projections algorithm for variable selection

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TALANTA 2012年 89卷 286-291页

作者： Ghasemi-Varnamkhasti, Mandi Mohtasebi, Seyed Saied Luz Rodriguez-Mendez, Maria Gomes, Adriano A. Ugulino Araujo, Mario Cesar Galvao, Roberto K. H. Shahrekord Univ Dept Agr Machinery Engn Fac Agr Shahrekord Iran Univ Valladolid ETS Ingenieros Ind Dept Inorgan Chem Valladolid Spain Univ Tehran Fac Agr Engn & Technol Agr Machinery Engn Dept Karaj Iran 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

This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the successive projections algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40 degrees C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500-5405 cm(-1) was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the nonalcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550 cm(-1) for non-alcoholic samples and 7228 cm(-1) for alcoholic samples. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Beer Ageing Near infrared spectroscopy Wavelength selection Classification successive projections algorithm Linear Discriminant Analysis

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A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2023年第1期31卷 24-32页

作者： Lakshmanan, Madan Kumar Boelt, Birte Gislum, Rene CSIR Cent Elect Engn Res Inst CSIR Madras Complex Chennai India Aarhus Univ Dept Agroecol Crop Hlth Slagelse Denmark CSIR Cent Elect Engn Res Inst CSIR Madras Complex Chennai 600113 India

This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitszky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds' viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.

关键词： Seed quality assessment near infrared spectroscopy chemometrics variable selection for data classification successive projections algorithm

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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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