Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)

doi:10.1155/2020/3590301

	Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)
	Jiang, Weiwei 4; Lu, Changhua 3,4; Zhang, Yujun 3; Ju, Wei 2; Wang, Jizhou 1; Hong, Feng 4; Wang, Tao 4; Ou, Chunsheng 4
刊名	JOURNAL OF SPECTROSCOPY
	2020-08-03
卷号	2020
ISSN号	2314-4920
DOI	10.1155/2020/3590301
通讯作者	Ou, Chunsheng(ouchunsheng@hfut.edu.cn)
英文摘要	The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.
资助项目	Major National Science and Technology Special Project of China[JZ2015KJZZ0254] ; Key Projects of Natural Science Research in Universities in Anhui, China[KJ2018A0544]
WOS关键词	LEAST-SQUARES REGRESSION ; SELECTION METHODS
WOS研究方向	Biochemistry & Molecular Biology ; Spectroscopy
语种	英语
出版者	HINDAWI LTD
WOS记录号	WOS:000561628800001
资助机构	Major National Science and Technology Special Project of China ; Key Projects of Natural Science Research in Universities in Anhui, China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/70644]
专题	中国科学院合肥物质科学研究院
通讯作者	Ou, Chunsheng
作者单位	1.Hefei Univ, Dept Elect, Hefei 230061, Peoples R China 2.Anhui Univ, Sch Internet, Hefei 230039, Peoples R China 3.Chinese Acad Sci, Anhui Inst Opt Fine Mech, Hefei 230031, Peoples R China 4.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Weiwei,Lu, Changhua,Zhang, Yujun,et al. Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)[J]. JOURNAL OF SPECTROSCOPY,2020,2020.
APA	Jiang, Weiwei.,Lu, Changhua.,Zhang, Yujun.,Ju, Wei.,Wang, Jizhou.,...&Ou, Chunsheng.(2020).Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS).JOURNAL OF SPECTROSCOPY,2020.
MLA	Jiang, Weiwei,et al."Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)".JOURNAL OF SPECTROSCOPY 2020(2020).