Quantitative analysis of tea using ytterbium-based internal standard near-infrared spectroscopy coupled with boosting least-squares support vector regression
文献类型： 外文期刊
作者： Luo, Rui-Min 1 ; Tan, Shi-Miao 1 ; Zhou, Yan-Ping 1 ; Liu, Shu-Juan 2 ; Xu, Hui 1 ; Song, Dan-Dan 1 ; Cui, Yan-Fang 1 ; F 1 ;
作者机构： 1.Cent China Normal Univ, Coll Chem, Minist Educ, Key Lab Pesticide & Chem Biol, Wuhan 430079, Peoples R China
2.Hunan Acad Agr Sci, Tea Res Inst, Changsha 410125, Hunan, Peoples R China
3.South Cent Univ Nationalities, Coll Pharm, Wuhan 430074, Peoples R China
关键词： ytterbium;near-infrared spectroscopy;boosting least-squares support vector regression;total polyphenols;total free amino acids
期刊名称：JOURNAL OF CHEMOMETRICS （ 影响因子：2.467； 五年影响因子：2.513 ）
ISSN： 0886-9383
年卷期： 2013 年 27 卷 7-8 期
页码：
收录情况： SCI
摘要： The present study demonstrated the possibility of utilizing the ytterbium (Yb)-based internal standard near-infrared (NIR) spectroscopic measurement technique coupled with multivariate calibration for quantitative analysis of tea, including total free amino acids and total polyphenols in tea. Yb is a rare earth element aimed to compensate for the spectral variation induced by the alteration of sample quantity during the spectral measurement of the powdered samples. Boosting was invoked to be combined with least-squares support vector regression (LS-SVR), forming boosting least-squares support vector regression (BLS-SVR) for the multivariate calibration task. The results showed that the tea quality could be accurately and rapidly determined via the Yb-based internal standard NIR spectroscopy combined with BLS-SVR method. Moreover, the introduction of boosting drastically enhanced the performance of individual LS-SVR, and BLS-SVR compared favorably with partial least-squares regression. Copyright (c) 2013 John Wiley & Sons, Ltd.
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