MCCV stacked regression for model combination and fast spectral interval selection in multivariate calibration

The present paper deals with variable selection in multivariate calibration of spectral data. A machine learning method, stacked regression is improved and then used to linearly combine different regression models built on sequential spectral intervals. While automatically extracting the spectral intervals carrying useful information for quantitative analysis, the proposed method can achieve a combined regression model with minimum RMSEMCCV (root mean squared error of Monte Carlo cross validation) among all possible linear combinations of the interval models under certain reasonable constraints. As expected, this method demonstrates considerable immunity against overfitting yet holds good prediction property. Due to some inherent characteristics of stacked regression, the method is economical to compute and the computation time is acceptable for large data sets. Two real spectral data sets are investigated by this method and the results are compared with those obtained by simple interval PLS.