Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry

Abstract 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.

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