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
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Data
2010
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Resumo
This 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.
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Successive projections algorithm, Multivariate calibration, Sequential regressions, Computational efficiency, Near-infrared spectrometry, Wheat
Citação
SOARES, 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.