Gledson Emidio José

TL;DR: A new method to divide a pool of samples into calibration and validation subsets for multivariate modelling is proposed, and the results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.

TL;DR: The successive projections algorithm (SPA) as discussed by the authors is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR) models, and it has been shown that the number of variables selected by SPA can be reduced without significantly compromising prediction performance.

TL;DR: The collinearity minimization role of SPA is exploited in the context of classification methods for which coll inearity is a known cause of generalization problems, and it is shown that SPA-LDA is superior to SIMCA and comparable to GA-Lda with respect to classification accuracy in an independent prediction set.

TL;DR: It can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels and the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.

TL;DR: In this article, a sample selection strategy based on the Successive Projections Algorithm (SPA) was proposed for variable selection, which selects a subset of samples that are minimally redundant but still representative of the data set.