Food Science and Technology

Predicting the storage time of green tea by myricetin based on surface-enhanced Raman spectroscopy

M. Xiao, Y. Chen, et al.

This innovative study presents a Surface-enhanced Raman spectroscopy (SERS) strategy for predicting green tea quality changes during storage. The remarkable PCA-SVM model achieved 97.22% accuracy in predicting storage time, while highlighting myricetin as a key indicator. This effective method opens new avenues for monitoring tea quality over time, conducted by Mengxuan Xiao, Yingqi Chen, Fangling Zheng, Qi An, Mingji Xiao, Huiqiang Wang, Luqing Li, and Qianying Dai.

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Index

Abstract

The quality of green tea changes rapidly due to the oxidation and degradation of polyphenols during storage. Herein, a simple and fast Surface-enhanced Raman spectroscopy (SERS) strategy was established to predict changes in green tea during storage. Raman spectra of green tea with different storage times (2020–2015) were acquired by SERS with silver nanoparticles. The PCA-SVM model was established based on SERS to quickly predict the storage time of green tea, and the accuracy of the prediction set was 97.22%. The Raman peak at 730 cm⁻¹ caused by myricetin was identified as a characteristic peak, which increased with prolonged storage time and exhibited a linear positive correlation with myricetin concentration. Therefore, SERS provides a convenient method for identifying the concentration of myricetin in green tea, and myricetin can function as an indicator to predict the storage time of green tea.

Publisher

npj Science of Food

Published On

Jun 09, 2023

Authors

Mengxuan Xiao, Yingqi Chen, Fangling Zheng, Qi An, Mingji Xiao, Huiqiang Wang, Luqing Li, Qianying Dai

DOI

https://doi.org/https://doi.org/10.1038/s41538-023-00206-1

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