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Combination of heuristic optimal partner bands for variable selection in near‐infrared spectral analysis
Author(s) -
Zhang Jin,
Cui Xiaoyu,
Cai Wensheng,
Shao Xueguang
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2971
Subject(s) - variable elimination , feature selection , monte carlo method , variable (mathematics) , mathematics , heuristic , near infrared spectroscopy , algorithm , selection (genetic algorithm) , projection (relational algebra) , partial least squares regression , independence (probability theory) , sampling (signal processing) , statistics , computer science , pattern recognition (psychology) , mathematical optimization , artificial intelligence , physics , inference , optics , mathematical analysis , filter (signal processing) , computer vision
Variable selection plays a critical role in the analysis of near‐infrared (NIR) spectra. A method for variable selection based on the principle of the successive projection algorithm (SPA) and optimal partner wavelength combination (OPWC) was proposed for NIR spectral analysis. The method determines a number of knot variables with sufficient independence by SPA, and candidate variable bands with a definite width are defined. The cooperative effect of the bands is then evaluated with the partial least squares regression model by using the method of OPWC. The performance of the proposed method was compared with those of SPA, OPWC, randomization test, competitive adaptive reweighted sampling, and Monte Carlo uninformative variable elimination by using NIR datasets for pharmaceutical tablets, corn, and soil. The results show that the proposed method can select informative variable bands with a cooperative effect and improves the model for quantitative analysis.