Application of genetic algorithm-support vector regression (GA-SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used...

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Vydáno v:Journal of chemometrics Ročník 26; číslo 7; s. 353 - 360
Hlavní autoři: Xin, Ni, Gu, Xiaofeng, Wu, Hao, Hu, Yuzhu, Yang, Zhonglin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Chichester, UK John Wiley & Sons, Ltd 01.07.2012
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Témata:
Algorithms
Analytical chemistry
Correlation coefficient
Fingerprinting
fingerprints
genetic algorithm (GA)
Genetic algorithms
Herbal medicine
herbal medicines (HM)
Qualitative research
Quality control
Random Forests (RF)
Regression analysis
support vector regression (SVR)
ISSN:0886-9383, 1099-128X
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Shrnutí:In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd. Genetic algorithm‐support vector regression (GA‐SVR) coupled approach is proposed for quantitative analysis of herbal medicines. GA‐Random Forests and GA‐partial least squares regression were also employed and compared with the GA‐SVR method. The performance has been tested on a simulated system, high performance liquid chromatography data set, and near‐infrared data set, and the results show that GA‐SVR model is helpful for the quantitative analysis of herbal medicines.
Bibliografie:ark:/67375/WNG-6WRXGQ6V-5
istex:7AA230BDD453F9F52B3DF6FFF6004456352FBB33
ArticleID:CEM2435
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0886-9383
1099-128X
DOI:10.1002/cem.2435