关键词: 多模型融合, 波长选择, 偏最小二乘, 自适应

Abstract: Wavelength selection of spectral analysis for substances content in the liquid is studied. A multi model fusion algorithm is proposed to improve the prediction accuracy based on reducing the number of wavelengths. An adaptive weighted wavelength selection algorithm（iCARS） is proposed in this paper. Multi model fusion with iPLS and CARS combined with variable selection and vertical and adaptive optimization sample selection in the wavelength selection. And through wavelength band selection and wavelength selection by step the algorithm achieve wavelength optimization selection. Through the MATLAB simulation by using the PLS model for forecasting models in the original beer wort concentration prediction and experimental, iCARS algorithm selects 19 variables. By using these variables PLS prediction model get RMSEP 0.139. This algorithm makes the number of the variables significantly reduced, and the prediction ability is improved obviously. It can implement the optimal selection of variables.

Key words: multi-model?fusion, wavelength selection, partial least squares, adaptive