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

Highlights: [•] 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. [ABSTRACT FROM AUTHOR]