|Dual stacked partial least squares for analysis of near-infrared spectra|
|Bi, Yiming; Xie, Qiong; Peng, Silong; et al.; Qiong Xie
|Source Publication||ANALYTICA CHIMICA ACTA
|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.|
|Keyword||Partial Least Squares
selective Weighting Rule
|WOS Headings||Science & Technology
; Physical Sciences
|WOS Keyword||MULTIVARIATE CALIBRATION
; ENSEMBLE METHODS
; NIR SPECTRA
|WOS Research Area||Chemistry
|WOS Subject||Chemistry, Analytical
|Corresponding Author||Qiong Xie|
Bi, Yiming,Xie, Qiong,Peng, Silong,et al. Dual stacked partial least squares for analysis of near-infrared spectra[J]. ANALYTICA CHIMICA ACTA,2013,792(792):19-27.
Bi, Yiming,Xie, Qiong,Peng, Silong,et al.,&Qiong Xie.(2013).Dual stacked partial least squares for analysis of near-infrared spectra.ANALYTICA CHIMICA ACTA,792(792),19-27.
Bi, Yiming,et al."Dual stacked partial least squares for analysis of near-infrared spectra".ANALYTICA CHIMICA ACTA 792.792(2013):19-27.
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