Abstract: Abstract： The variable selection method can realize the dimensionality reduction of high-dimensional data， reduce the complexity of the calibration model as well as improve the predictive ability and interpretability of the model， which is important for establishing an efficient and reliable prediction model. In this paper， model population analysis （MPA） is used for variable selection in the modeling process of NIR spectral calibration. A subset index reuse kernel - partial least squares （SIRK-PLS） fusion modeling approach is proposed by combining the characteristics of MPA to repeatedly extract subsets in the same space. The method essentially avoids redundant calculations in the process of cross-validation of variable selection subsets and regression coefficient solving under the MPA framework by indexing the pre-calculated covariance matrix， and improves modeling efficiency. In addition， the SIRK-PLS modeling approach allows for automatic optimal switching of modeling algorithms based on the ratio of the number of samples to the number of variables. The algorithm performance is validated with a nominal near-infrared spectral corn data set. The results show that the SIRK-PLS modeling method proposed in this paper has fast convergence speed and high accuracy， and is suitable for automatic and fast dimensionality reduction modeling of mobile infrared spectroscopy devices， which has some application prospects.

Key words: Key words： partial least squares, model population analysis, infrared spectroscopy technique, variable selection, subspace modelling