Development of particle swarm optimization-support vector regression (PSO-SVR) coupled with microwave plasma torch-atomic emission spectrometry for quality control of ginsengs
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- 作者：Yangwei Ying ; Wei Jin ; Haixiang Yu ; Bingwen Yu ; Jin Shan ; Shaowu Lv ; Dan Zhu ; Qinhan Jin and Ying Mu
- 刊名：Journal of Chemometrics
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：31
- 期：1
- 全文大小：1005K
- ISSN：1099-128X
文摘
As people have become more focused on their own health, the role of ginseng for medical uses has begun to receive substantial interest. However, the quality control of ginseng remains in question because different species vary considerably in this respect. In this paper, particle swarm optimization–support vector regression combined with microwave plasma torch–atomic emission spectrometry (MPT-AES) was used, for the first time, for quality control of ginseng. To build calibration models, quantitative determination of target element concentrations in ginseng samples was conducted by MPT-AES because ginseng quality was closely related to the place of origin and can thus be judged by the elemental composition. Characteristic spectral lines were extracted via principal component analysis to reduce the computational effort and improve the representativeness of the input variables. Two heuristic algorithms, particle swarm optimization and a genetic algorithm, were selected to optimize the parameters (eg, c, g, and ε) that were extremely significant in the construction of the support vector regression (SVR) models. Another linear regression approach, partial least squares regression (PLSR), was also used and compared. The comparisons were based on evaluation indexes, namely, the root mean square error and the squared correlation coefficient (R2). A significant difference between SVR and PLSR showed that SVR outperformed PLSR in such a multivariate regression problem. The acquired results showed that particle swarm optimization was slightly better than a genetic algorithm. In conclusion, the proposed MPT-AES combined with particle swarm optimization–support vector regression is appropriate for quantitative elemental analysis and further application in the quality control of ginseng.