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A novel variable selection algorithm based on neural network for near-infrared spectral modeling

文献类型：期刊论文

作者Zhang, Pengfei4; Xu, Zhuopin4; Ma, Huimin1; Zheng, Lei3; Li, Xiaohong2,4; Zhang, Zhiyi2,4; Wu, Yuejin4; Wang, Qi4
刊名ANALYTICA CHIMICA ACTA
出版日期2024-11-22
卷号1330
ISSN号0003-2670
DOI10.1016/j.aca.2024.343291
通讯作者Wang, Qi(wangqi@ipp.ac.cn)
英文摘要Background: Partial least squares (PLS) is a widely used technique for modeling spectral data. Researchers have developed numerous PLS-based variable selection algorithms to enhance model predictive ability and interpretability. In recent years, as neural network technology has advanced, these algorithms have been increasingly applied to spectral data modeling. However, current research on neural network modeling tends to prioritize network structure over variable selection. Results: Our study introduces a neural network-based variable selection algorithm called VSNN (Variable Selection based on Neural Network). By iteratively eliminating unimportant variables using an exponentially decreasing function (EDF), the algorithm achieves the selection of variables in spectral data. VSNN can easily integrate different types of neural networks. In this study, we analyzed the impact of neural network types, activation functions, and variable importance vectors on algorithm performance. We tested the algorithm on four datasets: corn moisture, corn oil, tablets, and meat. The results indicate that VSNN significantly enhances the predictive ability of the model compared to partial least squares (PLS), neural networks (NN), and Joint Mutual Information Maximisation (JMIM). Specifically, non-linear activation functions markedly improve performance on non-linear meat datasets. Compared to PLS, the Root Mean Square Error of Prediction (RMSEP) values for the four datasets-corn moisture, corn oil, tablets, and meat-decreased from 0.0409, 0.0728, 3.97, and 3.2 to 0.002, 0.0236, 3.12, and 0.36, respectively, after applying the VSNN variable selection algorithm. Significance: VSNN can serve as a versatile framework to enhance variable selection, modeling, and predictive performance by adapting neural network types and variable importance evaluation indicators. As machine learning technology advances, the strength of VSNN is poised to increase. This study highlights the potential of VSNN as an effective algorithmic framework for variable selection in spectroscopy applications.
WOS关键词PARTIAL LEAST-SQUARES ; GENETIC ALGORITHMS ; MUTUAL INFORMATION ; NIR SPECTROSCOPY ; PART 1 ; REGRESSION
资助项目National Natural Science Foundation of China[32070399] ; Provincial Key Research and Development Project[2023n06020016] ; Provincial Key Research and Development Project[2023n06020028] ; Anhui Science and Technology Major Project[202103a06020014] ; HFIPS Directors Fund[YZJJKX202201]
WOS研究方向Chemistry
语种英语
WOS记录号WOS:001332633400001
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Provincial Key Research and Development Project ; Anhui Science and Technology Major Project ; HFIPS Directors Fund
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/134660]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Qi
作者单位1.Anhui Agr Univ, Hefei 230036, Peoples R China
2.Univ Sci & Technol China, Hefei 230026, Peoples R China
3.Hefei Univ Technol, Hefei 230036, Peoples R China
4.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Pengfei,Xu, Zhuopin,Ma, Huimin,et al. A novel variable selection algorithm based on neural network for near-infrared spectral modeling[J]. ANALYTICA CHIMICA ACTA,2024,1330.
APA Zhang, Pengfei.,Xu, Zhuopin.,Ma, Huimin.,Zheng, Lei.,Li, Xiaohong.,...&Wang, Qi.(2024).A novel variable selection algorithm based on neural network for near-infrared spectral modeling.ANALYTICA CHIMICA ACTA,1330.
MLA Zhang, Pengfei,et al."A novel variable selection algorithm based on neural network for near-infrared spectral modeling".ANALYTICA CHIMICA ACTA 1330(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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