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
1. [1]
2. [2]
3. [3]
  BIAN X H, LI S J, SHAO X G, LIU P. Chemom. Intell. Lab. Syst., 2016, 158(10):174-179.
4. [4]
  YUN Y H, LI H D, DENG B C, CAO D S. TrAC-Trends Anal. Chem., 2019, 113:102-115.
5. [5]
6. [6]
7. [7]
  LI H H, ZHU J J, JIAO T H, WANG B, WEI W Y, ALI S, OUYANG Q, ZUO M, CHEN Q S. Spectrochim. Acta, Part A, 2020(243):118765.
8. [8]
9. [9]
  ZHANG J, CUI X Y, CAI W S, SHAO X G. Sci. China:Chem., 2019, 62(2):271-279.
10. [10]
  AL-KAF H A G, ALDUAIS N A M, SAAD A H Y, CHIA K S, MOHSEN A M, ALHUSSIAN H, MAHDI A A M H, SALAM W S W. IEEE Access, 2020:3023681.
11. [11]
12. [12]
  YUN Y H, CAO D S, TAN M L, YAN J, REN D B, XU Q S, YU L, LIANG Y Z. Chemom. Intell. Lab. Syst., 2014, 130(2):76-83.
13. [13]
14. [14]
  YUN Y H, WANG W T, TAN M L, LIANG Y Z, LI H D, CAO D S, LU H M, XU Q S. Anal.Chim. Acta, 2014, 807:36-43.
15. [15]
  DENG B C, YUN Y H, CAO D S, YIN Y L, WANG W T, LU H M, LUO Q Y, LIANG Y Z. Anal. Chim. Acta, 2016, 908:63-74.
16. [16]
  YAN H, SONG X Z, TIAN K D, GAO J X, LI QQ, XIONG Y M, MIN S G. Spectrochim. Acta, Part A, 2019, 210:362-371.
17. [17]
  YUN Y H, WANG W T, DENG B C, LAI G B, LIU X B, REN D B, LIANG Y Z, FAN W, XU Q S. Anal. Chim. Acta, 2015, 862:14-23.
18. [18]
  YUN Y H, LI H D, WOOD L R E, FAN W, WANG JJ, CAO D S, XU Q S, LIANG Y Z. Spectrochim. Acta, Part A, 2013, 111:31-36.
19. [19]
  YAN H, ZHANG J X, GAO J X, HUANG Y M, XIONG Y M, MIN S G. Sci. Rep., 2018, 8:14729.
20. [20]
1. [1]
  Qi Wang , Yicong Gao , Feng Lu , Quli Fan . Preparation and Performance Characterization of the Second Near-Infrared Phototheranostic Probe: A New Design and Teaching Practice of Polymer Chemistry Comprehensive Experiment. University Chemistry, 2024, 39(11): 342-349. doi: 10.12461/PKU.DXHX202404141
2. [2]
  Jiahui CHEN , Tingting ZHENG , Xiuyun ZHANG , Wei LÜ . Research progress of near-infrared absorption inorganic nanomaterials in photothermal and photodynamic therapy of tumors. Chinese Journal of Inorganic Chemistry, 2024, 40(12): 2396-2414. doi: 10.11862/CJIC.20240106
3. [3]
  Jie ZHAO , Sen LIU , Qikang YIN , Xiaoqing LU , Zhaojie WANG . Theoretical calculation of selective adsorption and separation of CO₂ by alkali metal modified naphthalene/naphthalenediyne. Chinese Journal of Inorganic Chemistry, 2024, 40(3): 515-522. doi: 10.11862/CJIC.20230385
4. [4]
  Yinuo Wang , Siran Wang , Yilong Zhao , Dazhen Xu . Selective Synthesis of Diarylmethyl Anilines and Triarylmethanes via Multicomponent Reactions: Introduce a Comprehensive Experiment of Organic Chemistry. University Chemistry, 2024, 39(8): 324-330. doi: 10.3866/PKU.DXHX202401063
5. [5]
  Shihui Shi , Haoyu Li , Shaojie Han , Yifan Yao , Siqi Liu . Regioselectively Synthesis of Halogenated Arenes via Self-Assembly and Synergistic Catalysis Strategy. University Chemistry, 2024, 39(5): 336-344. doi: 10.3866/PKU.DXHX202312002
6. [6]
  Yunhao Zhang , Yinuo Wang , Siran Wang , Dazhen Xu . Progress in Selective Construction of Functional Aromatics from Nitrogenous Cycloalkanes. University Chemistry, 2024, 39(11): 136-145. doi: 10.3866/PKU.DXHX202401083
7. [7]
  Peiran ZHAO , Yuqian LIU , Cheng HE , Chunying DUAN . A functionalized Eu³⁺ metal-organic framework for selective fluorescent detection of pyrene. Chinese Journal of Inorganic Chemistry, 2024, 40(4): 713-724. doi: 10.11862/CJIC.20230355
8. [8]
  Jiakun BAI , Ting XU , Lu ZHANG , Jiang PENG , Yuqiang LI , Junhui JIA . A red-emitting fluorescent probe with a large Stokes shift for selective detection of hypochlorous acid. Chinese Journal of Inorganic Chemistry, 2024, 40(6): 1095-1104. doi: 10.11862/CJIC.20240002
9. [9]
  Xilin Zhao , Xingyu Tu , Zongxuan Li , Rui Dong , Bo Jiang , Zhiwei Miao . Research Progress in Enantioselective Synthesis of Axial Chiral Compounds. University Chemistry, 2024, 39(11): 158-173. doi: 10.12461/PKU.DXHX202403106
10. [10]
  Tianlong Zhang , Rongling Zhang , Hongsheng Tang , Yan Li , Hua Li . Online Monitoring and Mechanistic Analysis of 3,5-diamino-1,2,4-triazole (DAT) Synthesis via Raman Spectroscopy: A Recommendation for a Comprehensive Instrumental Analysis Experiment. University Chemistry, 2024, 39(6): 303-311. doi: 10.3866/PKU.DXHX202312006
11. [11]
  Junjie Zhang , Yue Wang , Qiuhan Wu , Ruquan Shen , Han Liu , Xinhua Duan . Preparation and Selective Separation of Lightweight Magnetic Molecularly Imprinted Polymers for Trace Tetracycline Detection in Milk. University Chemistry, 2024, 39(5): 251-257. doi: 10.3866/PKU.DXHX202311084
12. [12]
  Jun LUO , Baoshu LIU , Yunchang ZHANG , Bingkai WANG , Beibei GUO , Lan SHE , Tianheng CHEN . Europium(Ⅲ) metal-organic framework as a fluorescent probe for selectively and sensitively sensing Pb²⁺ in aqueous solution. Chinese Journal of Inorganic Chemistry, 2024, 40(12): 2438-2444. doi: 10.11862/CJIC.20240240
13. [13]
  Chongjing Liu , Yujian Xia , Pengjun Zhang , Shiqiang Wei , Dengfeng Cao , Beibei Sheng , Yongheng Chu , Shuangming Chen , Li Song , Xiaosong Liu . Understanding Solid-Gas and Solid-Liquid Interfaces through Near Ambient Pressure X-Ray Photoelectron Spectroscopy. Acta Physico-Chimica Sinica, 2025, 41(2): 100013-. doi: 10.3866/PKU.WHXB202309036
14. [14]
  Zhuomin Zhang , Hanbing Huang , Liangqiu Lin , Jingsong Liu , Gongke Li . Course Construction of Instrumental Analysis Experiment: Surface-Enhanced Raman Spectroscopy for Rapid Detection of Edible Pigments. University Chemistry, 2024, 39(2): 133-139. doi: 10.3866/PKU.DXHX202308034
15. [15]
  Jingyi Chen , Fu Liu , Tiejun Zhu , Kui Cheng . Practice of Integrating Ideological and Political Education into Raman Spectroscopy Analysis Experiment Course. University Chemistry, 2024, 39(2): 140-146. doi: 10.3866/PKU.DXHX202310111
16. [16]
  Chun-Lin Sun , Yaole Jiang , Yu Chen , Rongjing Guo , Yongwen Shen , Xinping Hui , Baoxin Zhang , Xiaobo Pan . Construction, Performance Testing, and Practical Applications of a Home-Made Open Fluorescence Spectrometer. University Chemistry, 2024, 39(5): 287-295. doi: 10.3866/PKU.DXHX202311096
17. [17]
  Tianlong Zhang , Jiajun Zhou , Hongsheng Tang , Xiaohui Ning , Yan Li , Hua Li . Virtual Simulation Experiment for Laser-Induced Breakdown Spectroscopy (LIBS) Analysis. University Chemistry, 2024, 39(6): 295-302. doi: 10.3866/PKU.DXHX202312049
18. [18]
  Wei Peng , Baoying Wen , Huamin Li , Yiru Wang , Jianfeng Li . Exploration and Practice on Raman Scattering Spectroscopy Experimental Teaching. University Chemistry, 2024, 39(8): 230-240. doi: 10.3866/PKU.DXHX202312062
19. [19]
  Zhaoyue Lü , Zhehao Chen , Yi Ni , Duanbin Luo , Xianfeng Hong . Multi-Level Teaching Design and Practice Exploration of Raman Spectroscopy Experiment. University Chemistry, 2024, 39(11): 304-312. doi: 10.12461/PKU.DXHX202402047
20. [20]
  Mengyao Shi , Kangle Su , Qingming Lu , Bin Zhang , Xiaowen Xu . Determination of Potassium Content in Tobacco Stem Ash by Flame Atomic Absorption Spectroscopy. University Chemistry, 2024, 39(10): 255-260. doi: 10.12461/PKU.DXHX202404105

Get Citation

PDF

XML

Metrics

PDF Downloads(7)
Abstract views(546)
HTML views(71)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142