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Development of a New Three-Dimensional Fluorescence Spectroscopy Method Coupling with Multilinear Pattern Recognition to Discriminate the Variety and Grade of Green Tea

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Abstract

According to the different types and contents of amino acids in green tea, a new method was proposed for green tea classification and quality evaluation based on excitation-emission matrix (EEM) fluorescence spectroscopy coupled with multilinear pattern recognition in this work. Amino acids in green tea samples were first derived with formaldehyde and acetyl acetone solution. Derivatives of green teas were then scanned with a three-dimensional fluorescence spectrometry. Multilinear pattern recognition methods, including multilinear principal component analysis (M-PCA), self-weight alternative trilinear decomposition (SWATLD), and multilinear partial least squares discriminant analysis (N-PLS-DA) methods, were used to decompose the EEM data sets. All of these multilinear pattern recognition methods showed the clustering tendency for five different kinds of green tea. Compared with the other two methods, N-PLS-DA got more accurate and reliable classification result because it made full use of all the fluorescence information of the derivative green tea samples. At the same time, this method also revealed the possibility of evaluating the grade of green tea.

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Acknowledgements

The work was financially supported by Foundation of Henan University of Technology (grant no. 2014JCYJ08).

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  1. School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou, 450001, China

    Leqian Hu & Chunling Yin

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  1. Leqian Hu
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  2. Chunling Yin
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Correspondence to Leqian Hu.

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Leqian Hu declares that he has no conflict of interest. Chunling Yin declares that she has no conflict of interest.

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Hu, L., Yin, C. Development of a New Three-Dimensional Fluorescence Spectroscopy Method Coupling with Multilinear Pattern Recognition to Discriminate the Variety and Grade of Green Tea. Food Anal. Methods 10, 2281–2292 (2017). https://doi.org/10.1007/s12161-017-0798-1

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