Research on a Quantitative Regression Model of the Infrared Spectrum Based on the Integrated Learning Algorithm

doi:10.3964/j.issn.1000-0593(2021)04-1119-06

	Research on a Quantitative Regression Model of the Infrared Spectrum Based on the Integrated Learning Algorithm
	Jiang Wei-wei 1; Lu Chang-hua 1,2; Zhang Yu-jun 2; Ju Wei 3; Wang Ji-zhou 4; Ou Chun-sheng 1; Xiao Ming-xia 1
刊名	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2021-04-01
卷号	41
关键词	Integrated learning Support vector machine GBDT Quantitative regression
ISSN号	1000-0593
DOI	10.3964/j.issn.1000-0593(2021)04-1119-06
通讯作者	Ou Chun-sheng(ouchunsheng@mail.hfut.edu.cn)
英文摘要	In recent years, deep learning has been studied more and more in the field of data mining, and the integrated learning algorithm in deep learning has been applied to classification and quantitative regression more and more, but the application of integrated learning in the field of infrared spectrum analysis is little. In this paper, an integrated learning quantitative regression algorithm based on Blending model is proposed. GBDT algorithm, linear kernel support vector machine (LinearSVM) and radial kernel support vector machine (RBF SVM) are used as the basic learners, and the prediction results of the basic learners are fused by LinearSVM. The first derivative preprocessing was carried out for the spectral data. The prediction results of the model were analyzed and compared by using the GBDT, LinearSVM, RBF SVM and the Blending integrated learning model respectively. RBF SVM model is the best model for predicting the content of active substance and hardness, R-2 is the highest, the RMSEP is the smallest, and the RPD is the largest, and the GBDT model is the worst. The R2 of tablet quality predicted by Blending model is the highest, reaching 0.8374, while the RMSEP of RBF SVM is the lowest, 2.1406, and the RPD of RBF SVM, 7.4878, is the largest. For the boiling point, flash point and total aromatics of diesel oil, Blending model is the best one, which is better than the single model. For the cetane number, GBDT model and RBF SVM model are better than Blending model. For the density property, the single model and the integrated model have better prediction results, except that the R-2 of LinearSVM model is 0.9445, R2 of other models are all higher than 0.99. For the prediction of freezing point properties, RBF SVM and LinearSVM are both better than Blending model. For the prediction of viscosity, only RBF SVM is better than Blending model. It can be seen from the results that the Blending model integrates the characteristics of GBDT, LinearSVM and RBF SVM model, compared with the single model, the prediction of Blending is better or optimal. It is proved that Blending integrated learning model has strong applicability for infrared quantitative regression, and has a high prediction accuracy and generalization ability. It is of great significance for further research on the application of integrated learning algorithm in infrared quantitative regression.
WOS研究方向	Spectroscopy
语种	英语
出版者	OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
WOS记录号	WOS:000646723300021
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/122611]
专题	中国科学院合肥物质科学研究院
通讯作者	Ou Chun-sheng
作者单位	1.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Anhui Inst Opt Fine Mech, Hefei 230031, Peoples R China 3.Anhui Univ, Sch Internet, Hefei 230039, Peoples R China 4.Hefei Univ, Dept Elect, Hefei 230061, Peoples R China
推荐引用方式 GB/T 7714	Jiang Wei-wei,Lu Chang-hua,Zhang Yu-jun,et al. Research on a Quantitative Regression Model of the Infrared Spectrum Based on the Integrated Learning Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2021,41.
APA	Jiang Wei-wei.,Lu Chang-hua.,Zhang Yu-jun.,Ju Wei.,Wang Ji-zhou.,...&Xiao Ming-xia.(2021).Research on a Quantitative Regression Model of the Infrared Spectrum Based on the Integrated Learning Algorithm.SPECTROSCOPY AND SPECTRAL ANALYSIS,41.
MLA	Jiang Wei-wei,et al."Research on a Quantitative Regression Model of the Infrared Spectrum Based on the Integrated Learning Algorithm".SPECTROSCOPY AND SPECTRAL ANALYSIS 41(2021).