Comparisons of different regressions tools in measurement of antioxidant activity in green tea using near infrared spectroscopy
文献类型： 外文期刊
作者： Chen, Quansheng 1 ; Guo, Zhiming 1 ; Zhao, Jiewen 1 ; Ouyang, Qin 1 ;
作者机构： 1.Jiangsu Univ, Sch Food & Biol Engn, Zhenjiang 212013, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： Near infrared (NIR) spectroscopy; Regression tool; Measurement; Tea; Antioxidant activity
期刊名称：JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS （ 影响因子：3.935； 五年影响因子：3.563 ）
ISSN： 0731-7085
年卷期： 2012 年 60 卷
页码：
收录情况： SCI
摘要： To rapidly and efficiently measure antioxidant activity (AA) in green tea, near infrared (NIR) spectroscopy was employed with the help of a regression tool in this work. Three different linear and nonlinear regressions tools (i.e. partial least squares (PLS), back propagation artificial neural network (BP-ANN), and support vector machine regression (SVMR)). were systemically studied and compared in developing the model. The model was optimized by a leave-one-out cross-validation, and its performance was tested according to root mean square error of prediction (RMSEP) and correlation coefficient (R(p)) in the prediction set. Experimental results showed that the performance of SVMR model was superior to the others, and the optimum results of the SVMR model were achieved as follow: RMSEP = 0.02161 and R(p) = 0.9691 in the prediction set. The overall results sufficiently demonstrate that the spectroscopy coupled with the SVMR regression tool has the potential to measure AA in green tea. (C) 2011 Elsevier B.V. All rights reserved.
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