Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving NIR spectrometric snalysis of wheat samples

2018-05-102018-05-102010SOARES, Anderson S.; GALVÃO FILHO, Arlindo R.; GALVÃO, Roberto K. H.; ARAÚJO, Mário César U. Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving NIR spectrometric snalysis of wheat samples. Journal of the Brazilian Chemical Society, Campinas, v. 21, n. 4, p. 760-763, 2010.e- 1678-4790http://repositorio.bc.ufg.br/handle/ri/14856This short report proposes a sequential regression implementation for the successive projections algorithm (SPA), which is a variable selection technique for multiple linear regression. An example involving the near-infrared determination of protein in wheat is presented for illustration. The resulting model predictions exhibited a correlation coefficient of 0.989 and an RMSEP (rootmean- square error of prediction) value of 0.2% m/m in the range 10.2-16.2% m/m. The proposed implementation provided computational gains of up to five-fold.engAcesso AbertoSuccessive projections algorithmMultivariate calibrationSequential regressionsComputational efficiencyNear-infrared spectrometryWheatImproving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving NIR spectrometric snalysis of wheat samplesArtigo10.1590/S0103-50532010000400024