Research Of Variable Selection Method On Near-infrared Spectrum Modeling

There are many applications of Near-Infrared Spectroscopy in various fields for its high efficiency, rapid detection, low cost and so unique advantages. However, the Wavelength points in some certain wavelengths not only have any contribution to the analysis and modeling and even affect the quality of the model. It will lead to more complex models and the decline of predictive ability. Therefore choose to represent an important variable sample information has been the important content in modeling and analysis of Near Infrared Spectroscopy.In this paper, in the complex four methods were used:moving window partial least square, uninformative variables elimination, genetic algorithms, successive projections algorithm, extracting useful information and with support vector machine modeling. Comparing with four kinds of variable selection methods, the method of continuous projection algorithm variable selection could enhance quantitative spectral calibration model prediction accuracy and modeling efficiency and the NIR calibration models with good predictive ability and stability could also be acquired.