Citation: CHEN Xiao, HUAN Ke-Wei, ZHAO Huan, FAN Heng-Ye, HAN Xue-Yan. Variable Selection of Near Infrared Spectroscopy Based on Variable Frequency Weighted Bootstrap Sampling[J]. Chinese Journal of Analytical Chemistry, ;2021, 49(10): 1743-1749. doi: 10.19756/j.issn.0253-3820.210456 shu

Variable Selection of Near Infrared Spectroscopy Based on Variable Frequency Weighted Bootstrap Sampling

School of Science, Changchun University of Science and Technology, Changchun 130022, China

Corresponding author: HUAN Ke-Wei, huankewei@126.com
Received Date: 22 April 2021
Revised Date: 28 July 2021

Fund Project: Supported by the Jilin Province Science and Technology Development Plan Project (No.20190701024GH).

Near-infrared spectroscopy (NIRS) has been widely used in various fields such as food detection and quantitative analysis. As a key step in NIRS modeling and analysis, variable selection plays an important role in improving the stability and predictive performance of model. A new variable selection method for NIRS, variable frequency weighted bootstrap sampling (FWBS), was proposed in this work. Different variable subsets were generated by random combination of binary matrix sampling (BMS), and sub-models of different variable subsets were established by partial least squares (PLS). The frequency of each variable in the sub-model was counted and normalized to get the initial weight of the variable. Finally, the best characteristic variables were selected by weighted bootstrap sampling (WBS). Taking the public data base of NIRS spectroscopy of corn and milk as examples, the prediction models of starch content in corn and protein content in milk were established. The results showed that, comparing with uninformative variable elimination by PLS (UVE-PLS), competitive adapative reweighted sampling by PLS (CARS-PLS) and bootstrapping soft shrinkage by PLS (BOSS-PLS), the root mean square error of prediction (RMSEP) of the FWBS-PLS on the corn dataset decreased from 0.2523, 0.1162 and 0.0831 to 0.0740, respectively. The prediction accuracy increased by 70.7%, 36.3% and 11.0%, respectively. The relative percent deviation (RPD) of FWBS-PLS was 11.1328. The RMSEP on the milk dataset decreased from 0.1743, 0.1437 and 0.1432 to 0.0887, respectively. The prediction accuracy increased by 49.1%, 38.3% and 38.1%, respectively. The RPD of FWBS-PLS was 12.2701. The variable selection of NIRS based on FWBS could simplify model and improve the prediction accuracy of model greatly.
- Near infrared spectroscopy,
- Variable selection,
- Weight,
- Binary matrix sampling,
- Weighted bootstrap sampling
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