title: Dual stacked partial least squares for analysis of near-infrared spectra.
abstract: 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.
authors.affiliation: Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China.
authors.forename: Yiming
authors.initials: Y
authors.lastname: Bi
authors.forename: Qiong
authors.initials: Q
authors.lastname: Xie
authors.forename: Silong
authors.initials: S
authors.lastname: Peng
authors.forename: Liang
authors.initials: L
authors.lastname: Tang
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
authors.forename: Jie
authors.initials: J
authors.lastname: Tan
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Zhao
authors.forename: Changwen
authors.initials: C
authors.lastname: Li
mesh_terms: D016018:Least-Squares Analysis; D015233:Models, Statistical; D019265:Spectroscopy, Near-Infrared
keywords: Ensemble learning; Multivariate calibration; Near-infrared spectra; Partial least squares; Selective weighting rule