Baking Degree Discrimination and Quality Prediction of Tieguanyin Oolong Tea Based on Near-infrared Spectroscopy

Authors

Lizhu HUANG, School of Intelligent Manufacturing , Jiangnan University , Wuxi 214122 , China
Yun BIAN, School of Intelligent Manufacturing , Jiangnan University , Wuxi 214122 , China
Zhenfeng LI, School of Intelligent Manufacturing , Jiangnan University , Wuxi 214122 , China
Xianfeng LI, Shandong Bihai Machinery Technology Co ., Ltd., Linyi 276600 , China
Chunfang SONG, School of Intelligent Manufacturing , Jiangnan University , Wuxi 214122 , ChinaFollow

Corresponding Author(s)

宋春芳（1974—），女，博士，教授，博士研究生导师，主要从事农产品无损检测与控制研究。E-mail：songcf@jiangnan.edu.cn

Abstract

[Objective ] This study aims to assess the quality of Tieguanyin oolong tea during the baking process.[Method ] Near-infrared spectroscopy was used for non-destructive detection of Tieguanyin oolong tea during the baking process and chemometrics was employed to analyze the changes in the intrinsic quality of the Tieguanyin oolong tea.Spectral preprocessing and dimensionality reduction were conducted for the near-infrared spectral data.Support vector machine (SVM ) and back propagation neural network (BPNN ) were adopted to construct discrimination models for the baking degree of Tieguanyin oolong tea.Combining the near-infrared spectral data with intrinsic quality data,partial least squares regression (PLSR ) was employed to build quality prediction models.[Result ] According to the changes in intrinsic quality and sensory evaluation results,the baking degree of Tieguanyin oolong tea can be graded into under baking,moderate baking,and over baking.Under the full-spectrum spectral data model,with preprocessing as multiple scatter correction (MSC ) and the discriminant model as BPNN,the discriminant effect was the best,and the accuracy rate of the test set was 100.00%.When predicting the four intrinsic quality properties,the prediction model combining the successive projections algorithm (SPA ) with PLSR had the best accuracy.The determination coefficients of prediction (R2P) of the best prediction model for free amino acids,tea polyphenols,catechins,and caffeine were 0.949 6,0.944 3,0.950 8,and 0.740 0,respectively.[Conclusion ] This study realized the accurate discrimination of the baking degree and the rapid prediction of quality of Tieguanyin oolong tea,providing a theoretical foundation for the accurate discrimination and control of oolong tea baking.

Publication Date

9-15-2025

First Page

153

Last Page

162

DOI

10.12441/spyswjs.20240919002

Recommended Citation

HUANG, Lizhu; BIAN, Yun; LI, Zhenfeng; LI, Xianfeng; and SONG, Chunfang (2025) "Baking Degree Discrimination and Quality Prediction of Tieguanyin Oolong Tea Based on Near-infrared Spectroscopy," Journal of Food Science and Biotechnology: Vol. 44: Iss. 9, Article 15.
DOI: 10.12441/spyswjs.20240919002
Available at: https://spsw.spyswjs.cnjournals.com/journal/vol44/iss9/15

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