Near infrared spectral variable optimization by final complexity adapted models combined with uninformative variables elimination-a validation study

A combination method for spectral variable selection was proposed in this study. In this method, predictive property ranked variable reduction with final complexity adapted models (FCAM) was used for further variable refinement following method of uninformative variables elimination (UVE). In practice, two different near infrared spectral (NIRS) datasets were investigated to evaluate the quantitative performance of proposed method. Results showed that UVE-FCAM selected much fewer variables with better prediction than single UVE on both spectral datasets. Moreover, by contrast, both prediction and modeling stability of UVE-FCAM were proved to be better than a widely-used combination method as UVE-SPA. Overall results demonstrated UVE-FCAM could be a promising alternative method for optimizing variables, and FCAM had the potential to be an effective variable refinement following other variable selection methods.