This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of acidity, refractive index and viscosity in four types of edible vegetable oils (corn, soya, canola and sunfl...
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This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of acidity, refractive index and viscosity in four types of edible vegetable oils (corn, soya, canola and sunflower). For this purpose, a combination of spectral range selection by interval partial least squares (iPLS) and variable selection by the successive projections algorithm (SPA) is proposed to obtain simple multiple linear regression (MLR) models based on a small subset of wavenumbers. An independent set of samples was employed to evaluate the prediction ability of the resulting MLR-SPA models. As a result, correlation values of 0.94, 0.98, and 0.96 were obtained between model predictions and reference values for acidity, refractive index, and viscosity, respectively. The results show that a single calibration can be successfully performed for each parameter, without the need for developing a separate model for each vegetable oil type. (c) 2008 Elsevier Ltd. All rights reserved.
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 mul...
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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.
The successive projections algorithm (SPA) was recently proposed as a variable selection strategy to minimize collinearity problems in multivariate calibration. Although SPA has been successfully applied to UV-VIS spe...
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The successive projections algorithm (SPA) was recently proposed as a variable selection strategy to minimize collinearity problems in multivariate calibration. Although SPA has been successfully applied to UV-VIS spectrophotometric multicomponent analysis, no evidence of its ability to deal with variable sets with both high and low signal-to-noise ratios has been presented. This issue is addressed by the present work, which applies SPA to the simultaneous determination of Mn, Mo, Cr, Ni and Fe using a low-resolution plasma spectrometer/diode array detection system. This problem is of particular interest since strong interanalyte spectral interferences arise and regions with high and low signal intensity alternate in the spectra. Results show that multiple linear regression (MLR) on the wavelengths selected by SPA yields models with better prediction capabilities than principal component regression (PCR) and partial least squares (PLS) models. A standard genetic algorithm (GA) used for comparison yielded results similar to SPA for Mn, Cr and Fe, and better predictions for Mo and Ni. However, in all cases, the GA resulted in models less parsimonious than SPA. The average of the root mean square relative error of prediction (RMSREP) obtained for the five analytes was 1.4% for MLR-SPA, 1.0% for MLR-GA, 2.2% for PCR, and 2.1% for PLS. Since the computational time demanded by SPA grows with the square of the number of spectral variables, a pre-selection procedure based on the identification of emission peaks is proposed. This procedure decreased selection time by a factor of 20, without significantly degrading the results. (C) 2001 Elsevier Science B.V. All rights reserved.
A sample selection strategy based on the successive projections algorithm (SPA), which is a technique originally developed for variable selection, is proposed. The strategy selects a subset of samples that are minimal...
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A sample selection strategy based on the successive projections algorithm (SPA), which is a technique originally developed for variable selection, is proposed. The strategy selects a subset of samples that are minimally redundant but still representative of the data set. The selection takes into account both X and Y statistics, thus tailoring the choice of samples according to the spectral profiles of the chemical species involved in the analysis. Such procedure is of value to reduce the experimental and computational workload involved in the multivariate calibration, as well as in the transfer of calibration between different instruments. The strategy was applied to UV-VIS spectrometric simultaneous multicomponent analysis of complexes of Co2+, Cu2+, Mn2+, Ni2+ and Zn2+ with 4-(2-piridilazo)resorcinol and also to total sulphur determination in diesel by NIR spectrometry. The selection of samples was preceded by wavelength selection to avoid ill-conditioning problems in the multiple linear regression (MLR) modeling employed by SPA. In both applications, SPA reduced the number of variables and samples considerably, especially in the NIR data set, where it provided an impressive reduction in the number of wavelengths from 3071 to 10 and in the number of samples from 92 to 10. MLR models developed with the selected calibration samples displayed no significant loss of prediction ability when compared to MLR and PLS1 models built with the full set of calibration samples. This finding shows that the selected samples do convey the information needed for modeling. Moreover, in the NIR application, sample selection by SPA provided significantly better results than the classic Kennard-Stone (KS) algorithm. (C) 2004 Elsevier B.V. All rights reserved.
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