Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

文献类型： 外文期刊

第一作者： Shan, Jiajia

作者： Shan, Jiajia;Wang, Xue;Zhou, Hao;Han, Shuqing;Al Riza, Dimas Firmanda;Kondo, Naoshi;Al Riza, Dimas Firmanda

作者机构：

关键词： flavonoids; synchronous fluorescence spectra; variable selection; clustering analysis; k-means clustering; kohonen self-organization map

期刊名称：METHODS AND APPLICATIONS IN FLUORESCENCE （ 影响因子：3.009； 五年影响因子：3.111 ）

ISSN： 2050-6120

年卷期： 2018 年 6 卷 2 期

页码：

收录情况： SCI

摘要： Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

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