Abstract
The selection abilities of the two well‐known techniques of variable selection, synergy interval‐partial least‐squares (SiPLS) and genetic algorithm‐partial least‐squares (GA‐PLS), have been examined and compared. By using different simulated and real (corn and metabolite) datasets, keeping in view the spectral overlapping of the components, the influence of the selection of either intervals of variables or individual variables on the prediction performances was examined. In the simulated datasets, with decrease in the overlapping of the spectra of components and cases with components of narrow bands, GA‐PLS results were better. In contrast, the performance of SiPLS was higher for data of intermediate overlapping. For mixtures of high overlapping analytes, GA‐PLS showed slightly better performance. However, significant differences between the results of the two selection methods were not observed in most of the cases. Although SiPLS resulted in slightly better performance of prediction in the case of corn dataset except for the prediction of the moisture content, the improvement obtained by SiPLS compared with that by GA‐PLS was not significant. For real data of less overlapped components (metabolite dataset), GA‐PLS that tends to select far fewer variables did not give significantly better root mean square error of cross‐validation (RMSECV), cross‐validated R^2^ (Q^2^), and root mean square error of prediction (RMSEP) compared with SiPLS. Irrespective of the type of dataset, GA‐PLS resulted in models with fewer latent variables (LVs). When comparing the computational time of the methods, GA‐PLS is considered superior to SiPLS. Copyright © 2010 John Wiley & Sons, Ltd.