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A new framework for interval wavelength selection based on wavelength importance clustering

文献类型：期刊论文

作者Huang, Qing2,3; Zhu, Mingdong1,2; Xu, Zhenyu3; Kan, Ruifeng3
刊名ANALYTICA CHIMICA ACTA
出版日期2024-10-16
卷号1326
关键词Wavelength selection Wavelength importance Clustering Near infrared spectroscopy
ISSN号0003-2670
DOI10.1016/j.aca.2024.343153
通讯作者Huang, Qing(qhuang@aiofm.ac.cn) ; Zhu, Mingdong(uhz_uhz@hotmail.com) ; Kan, Ruifeng(rfkan@aiofm.ac.cn)
英文摘要Background: Wavelength selection is one of the key steps in spectral analysis and plays an irreplaceable role in improving model prediction accuracy and computational efficiency. High-dimensional spectral datasets contain substantial irrelevant information and redundant variables. Whereas, at current stage, such problem can be solved by existing abundant wavelength selection methods. However, it is difficult to achieve the balance between strong wavelength interpretability and prediction accuracy by those methods. As a result, there is an urgent need for a new method that can reach the point of balance. Results: we propose a new framework for wavelength selection based on wavelength importance clustering (WIC) which attempts to establish a hierarchical relationship between wavelength points and attributions of response through a clustering algorithm, consequently, performing combinatorial and filtering to obtain the optimal wavelength combinations. In this paper, a new wavelength selection method (WIC-WRCKF) is constructed based on WIC, and four commonly used wavelength selection methods are selected to be compared with WIC-WRCKF. A large number of experiments are carried out on three publicly available datasets as well, namely, wheat, corn, and tablets. Compared with other methods, WIC-WRCKF has the highest prediction accuracy with high stability on the three datasets, and the number of wavelengths selected is small and highly interpretative, indicating that WIC-WRCKF has a better predictive ability. Significance: The wavelength selection method can significantly improve the model prediction accuracy, and the WIC architecture can effectively exploit the essence of the spectral data, which has great potential in the application of wavelength selection.
WOS关键词PARTIAL LEAST-SQUARES ; FT-NIR SPECTROSCOPY ; VARIABLE SELECTION ; PLS-REGRESSION ; ALGORITHM ; TRANSMITTANCE ; OPTIMIZATION ; MODELS
资助项目National Key Research and Development Program of China[2020YFA0405703] ; Youth Innovation Promotion Association[20222451]
WOS研究方向Chemistry
语种英语
WOS记录号WOS:001303341600001
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; Youth Innovation Promotion Association
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/134999]  
专题中国科学院合肥物质科学研究院
通讯作者Huang, Qing; Zhu, Mingdong; Kan, Ruifeng
作者单位1.Hunan Acad Agr Sci, Hunan Rice Res Inst, State Key Lab Hybrid Rice, Changsha 410125, Peoples R China
2.Univ Sci & Technol China, Sch Environm Sci & Optoelect Technol, Hefei 230026, Anhui, Peoples R China
3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Qing,Zhu, Mingdong,Xu, Zhenyu,et al. A new framework for interval wavelength selection based on wavelength importance clustering[J]. ANALYTICA CHIMICA ACTA,2024,1326.
APA Huang, Qing,Zhu, Mingdong,Xu, Zhenyu,&Kan, Ruifeng.(2024).A new framework for interval wavelength selection based on wavelength importance clustering.ANALYTICA CHIMICA ACTA,1326.
MLA Huang, Qing,et al."A new framework for interval wavelength selection based on wavelength importance clustering".ANALYTICA CHIMICA ACTA 1326(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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