Dual stacked partial least squares for analysis of near-infrared spectra

• Dual stacking steps are used for multivariate calibration of near-infrared spectra.
• A selective weighting strategy is introduced that only a subset of all available sub-models is used for model fusion.
• Using two public near-infrared datasets, the proposed method achieved competitive results.
• The method can be widely applied in many fields, such as Mid-infrared spectra data and Raman spectra data.

A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications.

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