Optimization of Informative Spectral Variables for the Quantification of EGCG in Green Tea Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Multivariate Calibration

文献类型： 外文期刊

第一作者： Guo, Zhiming

作者： Guo, Zhiming;Huang, Wenqian;Zhao, Chunjiang;Chen, Quansheng;Chen, Liping;Zhang, Chi

作者机构：

关键词： Near-infrared spectroscopy; NIR spectroscopy; Multivariate calibration; Variable selection; Synergy interval partial least squares; PLS; Genetic algorithm

期刊名称：APPLIED SPECTROSCOPY （ 影响因子：2.388； 五年影响因子：2.296 ）

ISSN： 0003-7028

年卷期： 2011 年 65 卷 9 期

页码：

收录情况： SCI

摘要： Epigallocatechin-3-gallate (EGCG) is credited with the majority of the health benefits associated with green tea consumption. It has a high economic and medicinal value. The feasibility of using different variable selection approaches in Fourier transform near-infrared (FT-NIR) spectroscopy for a rapid and conclusive quantitative determination of EGCG in green tea was investigated. Graphically oriented multivariate calibration modeling procedures such as interval partial least squares (iPLS), synergy interval partial least squares (siPLS), and genetic algorithm optimization combined with siPLS (siPLS-GA) were applied to select the most efficient spectral variables that provided the lowest prediction error. The performance of the final model was evaluated according to the root mean square error of prediction (RMSEP) and coefficient of determination (R-2) for the prediction set. Experimental results showed that the siPLS-GA model obtained the best results in comparison to other models. The optimal models were achieved with R-p(2) = 0.97 and RMSEP = 032. The model can be obtained with only 36 variables retained and it provides a robust model with good estimation accuracy. This demonstrates the potential of NIR spectroscopy with multivariate calibration methods to quickly detect the bioactive component in green tea. Index Headings: Near-infrared spectroscopy; NIR spectroscopy; Multivariate calibration; Variable selection; Synergy interval partial least squares; PIS; Genetic algorithm.

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