[Successive projections algorithm and its application to selecting the wheat near-infrared spectral variables]

Successive projections algorithm combined with partial least squares regression, termed as SPA-PLS approach, was implemented as a novel variable selection approach to multivariate calibration. The proposed approach was applied to near-infrared reflectance data for analyzing moisture in wheat. The number of variables selected from 701 spectral variables was reduced to 16 by SPA, and the root mean squared error of prediction set (RMSEP) of the corresponding partial least squares regression models was decreased to 0.205 5% as well. The result indicates that the SPA-PLS approach by performing SPA prior to calibration not only can improve the model accuracy, but also decreases the number of spectral variables, so its resulting model becomes more concise. Moreover, as compared with genetic algorithm for wavelength selection, SPA is a deterministic search technique whose results are reproducible and it is more robust with respect to the choice of the validation set.