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 (root-mean-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.
successive projections algorithm; multivariate calibration; sequential regressions; computational efficiency; near-infrared spectrometry; wheat
