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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Abstract

This study established a Surface-enhanced Raman spectroscopy (SERS) strategy to predict green tea quality changes during storage. A PCA-SVM model, based on SERS data from green tea samples stored from 2015 to 2020, achieved 97.22% accuracy in predicting storage time. The Raman peak at 730 cm⁻¹ (attributed to myricetin) increased linearly with storage time and myricetin concentration, indicating myricetin's potential as an indicator for predicting green tea storage time.

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

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