A Variable Selection Method of Near Infrared Spectroscopy Based on Automatic Weighting Variable Combination Population Analysis

作者：Zhao Huan; Huan Ke Wei; Shi Xiao Guang; Zheng Feng; Liu Li Ying; Liu Wei; Zhao Chun Ying

来源：Chinese Journal of Analytical Chemistry, 2018, 46(1): 136-142.

DOI：10.11895/j.issn.0253.3820.171158

• Near infrared spectroscopy
• Chemometrics
• Variable selection
• Automatic weighting variable combination population analysis
• Information vector

a Near. infrared spectroscopy (NIR) is widely used in the area of food quantitative and qualitative analysis. Variable selection technique is a critical step of the spectrum modeling with the development of chemometrics. In this study, a novel variable selection strategy, automatic weighting variable combination population analysis (AWVCPA), was proposed. Firstly, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, was used to produce a population of subsets to construct a population of sub. models. Then, the variable frequency (Fre) and partial least squares regression (Reg), which were two kinds of information vector (IVs) were weighted to obtain the value of the contribution of each spectral variables, the influence of two IVs of Rre and Reg was considered to each spectral variable. Finally, it used the exponentially decreasing function (EDF) to remove the low contribution wavelengths so as to select the characteristic variable. In the case of near infrared spectrum of beer and corn, the prediction model based on partial least squares (PLS) was established. Compared with other variable selection methods, the research showed that AWVCPA was the best variable selection strategy in the same situation. It had 72. 7% improvement compared AWVCPA. PLS with PLS and the predicted root mean square error (RMSEP) decreased from 0. 5348 to 0. 1457 on beer dataset. It had 64. 7% improvement compared AWVCPA. PLS with PLS and the RMSEP decreased from 0. 0702 to 0. 0248 on corn dataset.

• 出版日期2018-1
• 单位长春理工大学