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

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...

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Bibliographic Details
Published in:Food analytical methods Vol. 10; no. 7; pp. 2281 - 2292
Main Authors: Hu, Leqian, Yin, Chunling
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2017
Springer Nature B.V
Subjects:
Acetone
Amino acids
Analytical Chemistry
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Classification
Clustering
data collection
Decomposition
Discriminant analysis
Emission analysis
Fluorescence
fluorescence emission spectroscopy
Food Science
formaldehyde
Green tea
Methods
Microbiology
Pattern recognition
principal component analysis
Principal components analysis
Quality assessment
Spectrometry
Spectroscopy
Tea
Multidimensional pattern recognition methods
Amino acid
Green tea
Three-dimensional fluorescence spectrometry
Fluorescence derivative
ISSN:1936-9751, 1936-976X
Online Access:Get full text
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Summary: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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ISSN:1936-9751
1936-976X
DOI:10.1007/s12161-017-0798-1