Sensory characterization of Pu-erh tea and sensory wheel

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Authors: Zhi‐guo Shan, Zilong Wang, Jiali Zhang, Bai-qing Zeng, Junqing Yang, Xue-xing Yan, Ru-qiu Pu, Chun-hua Zhang, Yuan-yan Zhao
Cited by: 0
References: 2

Abstract

Abstract: In this study, to develop a comprehensive flavor wheel for Pu-erh tea and analyze its sensory characteristics, 52 varieties of Pu-erh tea, differing in production dates, regions, and manufacturers, were collected as the research materials. A sensory panel comprising seven assessors conducted an analysis of sensory descriptors, resulting in the identification of 104 descriptors, including 40 related to aroma, 23 to infusion, and 41 to flavor, which facilitated the mapping of the Pu-erh tea flavor wheel. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were employed to refine the selection of sensory descriptors. Ultimately, 44 descriptors were identified as representative of Pu-erh tea's sensory characteristics, encompassing 20 aroma descriptors, 12 infusion descriptors, and 12 flavor descriptors. Through correlation analysis, four tea samples exhibiting distinct styles were selected. Flavor radar diagrams were subsequently constructed to visualize and compare these differences, effectively and quantitatively illustrating the sensory commonalities and flavor distinctions among the samples. The descriptors identified in this study effectively characterize the sensory profile of Pu-erh tea, as well as the variations in its sensory composition. This serves as a valuable framework for flavor research, quality assessment, and quality control of Pu-erh tea. Moreover, these flavor profile descriptors provide a clearer and more intuitive representation of the flavor characteristics of ripe Pu-erh tea compared to traditional descriptors.

Fields of Study & Subjects

Fermentation and Sensory Analysis Tea Polyphenols and Effects Traditional Chinese Medicine Analysis

Publication details

Journal / source
Measurement Food
Publisher
Elsevier BV
Volume
22
Pages
100297–100297
Publication date
25 May 2026
Language
EN