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International Journal of Analytical Chemistry
Volume 2019, Article ID 7314916, 12 pages
Research Article

A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data

1School of Science, Kunming University of Science and Technology, Kunming 650500, China
2Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

Correspondence should be addressed to Lun-Zhao Yi; nc.ude.tsumk@oahznuliy

Received 29 April 2019; Revised 23 June 2019; Accepted 2 July 2019; Published 1 August 2019

Academic Editor: Jiu-Ju Feng

Copyright © 2019 Guang-Hui Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.