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Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression
Author(s) -
Luo RuiMin,
Tan ShiMiao,
Zhou YanPing,
Liu ShuJuan,
Xu Hui,
Song DanDan,
Cui YanFang,
Fu HaiYan,
Yang TianMing
Publication year - 2013
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2518
Subject(s) - partial least squares regression , boosting (machine learning) , support vector machine , calibration , multivariate statistics , principal component analysis , least squares support vector machine , regression , mathematics , regression analysis , linear regression , analytical chemistry (journal) , chemistry , spectroscopy , statistics , computer science , artificial intelligence , chromatography , physics , quantum mechanics
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 © 2013 John Wiley & Sons, Ltd.