Key wavelength selection using CARS method in near infrared spectra

被引：0

作者：

Gong, Huili ^{[1
]}

Xu, Xiaowei ^{[1
,3
]}

Su, Jiandong ^{[2
]}

Yang, Jutian ^{[2
]}

机构：

[1] College of Information Science and Engineering, Ocean University of China, Qingdao

[2] Shandong Linyi Tobacco Co. Ltd, Linyi

[3] Qingdao Haier Intelligent Home Appliance Technology Co., Ltd, Qingdao

来源：

Journal of Information and Computational Science | 2014年 / 11卷 / 17期

关键词：

CARS; Modeling; Near Infrared Spectra; Noise; Wavelength Selection;

D O I：

10.12733/jics20105029

中图分类号：

学科分类号：

摘要：

In near infrared spectroscopy, there exist a lot of wavelength points, and bands are difficult to be attributed. In addition, there exit large measurement error because of serious spectral overlap. So, it is difficult to build multivariate calibration model based on all the spectra. Moreover, the overlap of the strong absorption background wave section of water and the other components in the near infrared spectra will reduce the model robustness and metastatic. Therefore, the CARS method was adopted for the wavelength variable selection in this paper. The selected key points are used to build model for total sugar, total nitrogen and total nicotine index. Compared with full spectrum, the performance of quantitative model has been greatly improved. ©, 2014, Binary Information Press.

引用

页码：6427 / 6435

页数：8

共 50 条

[21] Interval interaction moving window partial least squares for wavelength interval selection in near infrared spectroscopy [J].

Yang, Wuye ;

Xiong, Yinran ;

Wang, Honghong ;

Wu, Ting ;

Du, Yiping .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2023, 241

[22] Application of Intelligent Optimization Algorithms to Wavelength Selection of Near-Infrared Spectroscopy [J].

Bin Jun ;

Fan Wei ;

Zhou Ji-heng ;

Li Xin ;

Liang Yi-zeng .

SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37 (01) :95-102

[23] Near Infrared Spectral Wavelength Selection Based on Improved Team Progress Algorithm [J].

Gao Mei-feng ;

Tao Huan-ming .

SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (10) :3032-3038

[24] Wavelength selection to increase robustness of the multivariate calibration model in near infrared spectroscopy [J].

Li Qingbo ;

Li Xiang ;

Zhang Guangjun ;

Xu Kexin ;

Wang Yan .

SIGNAL ANALYSIS, MEASUREMENT THEORY, PHOTO-ELECTRONIC TECHNOLOGY, AND ARTIFICIAL INTELLIGENCE, PTS 1 AND 2, 2006, 6357

[25] Rough set based wavelength selection in near-infrared spectral analysis [J].

Dong, Ying ;

Xiang, Bingren ;

Geng, Ying ;

Yuan, Wenbo .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2013, 126 :21-29

[26] Variable selection by double competitive adaptive reweighted sampling for calibration transfer of near infrared spectra [J].

Zheng, Kaiyi ;

Feng, Tao ;

Zhang, Wen ;

Huang, Xiaowei ;

Li, Zhihua ;

Zhang, Di ;

Yao, Yuan ;

Zou, Xiaobo .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2019, 191 :109-117

[27] Discriminating varieties of waxberry using near infrared spectra [J].

He, Y ;

Li, XL .

JOURNAL OF INFRARED AND MILLIMETER WAVES, 2006, 25 (03) :192-+

[28] Wavelength Selection Method of Near-Infrared Spectrum Based on Random Forest Feature Importance and Interval Partial Least Square Method [J].

Chen, Rui ;

Wang, Xue ;

Wang Zi-wen ;

Qu, Hao ;

Ma Tie-min ;

Chen Zheng-guang ;

Gao, Rui .

SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43 (04) :1043-1050

[29] Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple [J].

Li, Lisha ;

Jang, Xiaogang ;

Li, Bin ;

Liu, Yande .

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 190 (190)

[30] Using consensus interval partial least square in near infrared spectra analysis [J].

Ji, Guoli ;

Huang, Guangzao ;

Yang, Zijiang ;

Wu, Xiaohui ;

Chen, Xiaojing ;

Yuan, Mingshun .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2015, 144 :56-62

← 1 2 3 4 5 →