Journal Highlight: Quantitative analysis of tea using ytterbium-based internal standard near-infrared spectroscopy coupled with boosting least-squares support vector regression

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  • Published: Sep 16, 2013
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: Quantitative analysis of tea using ytterbium-based internal standard near-infrared spectroscopy coupled with boosting least-squares support vector regression
Ytterbium-based internal standard NIR spectroscopy coupled with multivariate calibration was used for the for quantitative analysis of tea, including total free amino acids and total polyphenols.

Quantitative analysis of tea using ytterbium-based internal standard near-infrared spectroscopy coupled with boosting least-squares support vector regression

Journal of Chemometrics, 2013, 27, 198-206
Rui-Min Luo, Shi-Miao Tan, Yan-Ping Zhou, Shu-Juan Liu, Hui Xu, Dan-Dan Song, Yan-Fang Cui, Hai-Yan Fu, Tian-Ming Yang

Abstract: 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.

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