Combination of heuristic optimal partner bands for variable selection in near‐infrared spectral analysis

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EarlyView Article

  • Published: Nov 29, 2017
  • Author: Jin Zhang, Xiaoyu Cui, Wensheng Cai, Xueguang Shao


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.

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