A novel algorithm for spectral interval combination optimization

In this study, a new wavelength interval selection algorithm named as interval combination optimization (ICO) was proposed under the framework of model population analysis (MPA). In this method, the full spectra are divided into a fixed number of equal-width intervals firstly. Then the optimal interval combination is searched iteratively under the guide of MPA in a soft shrinkage manner, among which weighted bootstrap sampling (WBS) is employed as random sampling method. Finally, local search is conducted to optimize the widths of selected intervals. Three NIR datasets were used to validate the performance of ICO algorithm. Results show that ICO can select fewer wavelengths with better prediction performance when compared with other four wavelength selection methods, including VISSA, VISSA-iPLS, iVISSA and GA-iPLS. In addition, the computational intensity of ICO is also economical, benefit from fewer tune parameters and faster convergence speed.