Volatile Profiling of Tongcheng Xiaohua Tea from Different Geographical Origins: A Multimethod Investigation Using Sensory Analysis, E-Nose, HS-SPME-GC-MS, and Chemometrics

被引：0

作者：

Jin, Ge ^{[1
]}

Bi, Chenyue ^{[1
]}

Ji, Anqi ^{[1
]}

Hu, Jieyi ^{[1
]}

Zhang, Yuanrong ^{[1
]}

Yang, Lumin ^{[2
]}

Wu, Sunhao ^{[3
]}

Shen, Zhaoyang ^{[1
]}

Zhou, Zhou ^{[1
]}

Li, Xiao ^{[1
]}

Qin, Huaguang ^{[1
]}

Mu, Dan ^{[1
]}

Hou, Ruyan ^{[2
]}

Wu, Yan ^{[1
]}

机构：

[1] Anqing Normal Univ, Anqing Forestry Technol Innovat Res Inst, Sch Life Sci, Key Lab Biodivers Conservat & Characterist Resourc, North Jixian Rd 1318, Anqing 246133, Peoples R China

[2] Anhui Agr Univ, Natl Key Lab Tea Plant Germplasm Innovat & Resourc, Hefei 230036, Peoples R China

[3] Bengbu Univ, Sch Food & Biol Engn, Caoshan Rd 1866, Bengbu 233030, Peoples R China

来源：

FOODS | 2025年 / 14卷 / 11期

关键词：

green tea; volatile components; E-nose; HS-SPME-GC-MS; odor activity value;

D O I：

10.3390/foods14111996

中图分类号：

TS2 [食品工业];

学科分类号：

0832 ;

摘要：

The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and chemometrics. The E-nose results demonstrated that the volatile characteristics of Tongcheng Xiaohua tea exhibit distinct geographical signatures, confirming the regional specificity of its aroma. HS-SPME-GC-MS identified 66 volatile metabolites across samples, with 18 key odorants (OAV > 1) including linalool, geraniol, (Z)-jasmone, and beta-ionone driving aroma profiles. The partial least squares-discriminant analysis (PLS-DA) model, combined with variable importance in projection (VIP) scores and OAV, identified seven compounds that effectively differentiate the origins, among which alpha-pinene and beta-cyclocitral emerged as novel markers imparting unique regional characteristics. Further comparative analysis between standard and premium grades revealed 2-methyl butanal, 3-methyl butanal, and dimethyl sulfide as main differential metabolites. Notably, the influence of geographical origin on metabolite profiles was found to be more significant than batch effects. These findings establish a robust analytical framework for origin traceability, quality standardization, and flavor optimization in tea production, providing valuable insights for the tea industry.

引用

页数：17

共 38 条

[1]

[Anonymous], 2018, State Administration for Market Regulation

[2] Characterization of the key differential aroma compounds in five dark teas from different geographical regions integrating GC-MS, ROAV and chemometrics approaches [J].

Chen, Guohe ;

Zhu, Guangmei ;

Xie, He ;

Zhang, Jing ;

Huang, Jianan ;

Liu, Zhonghua ;

Wang, Chao .

FOOD RESEARCH INTERNATIONAL, 2024, 194

[3] Characterization of the Key Odorants in a High-Grade Chinese Green Tea Beverage (Camellia sinensis; Jingshan cha) by Means of the Sensomics Approach and Elucidation of Odorant Changes in Tea Leaves Caused by the Tea Manufacturing Process [J].

Flaig, Mario ;

Qi, Sally ;

Wei, Guodong ;

Yang, Xiaogen ;

Schieberle, Peter .

JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 2020, 68 (18) :5168-5179

[4] Characteristic flavor compounds in Guizhou green tea and the environmental factors influencing their formation: Investigation using stable isotopes, electronic nose, and headspace-gas chromatography ion migration spectrometry [J].

Ge, Yonghui ;

Wang, Ling ;

Huang, Yongji ;

Jia, Luyu ;

Wang, Jinhua .

LWT-FOOD SCIENCE AND TECHNOLOGY, 2024, 196

[5] Comparative analysis of volatile profiles in four pine-mushrooms using HS-SPME/GC-MS and E-nose [J].

Guo, Qiyong ;

Adelina, Nadya Mara ;

Hu, Jintao ;

Zhang, Ligang ;

Zhao, Yuhong .

FOOD CONTROL, 2022, 134

[6] Research progress of wine aroma components: A critical review [J].

He, Yang ;

Wang, Xinyuan ;

Li, Penghui ;

Lv, Yingchi ;

Nan, Hailong ;

Wen, Liankui ;

Wang, Zhitong .

FOOD CHEMISTRY, 2023, 402

[7] Digital Evaluation of Aroma Intensity and Odor Characteristics of Tea with Different Types-Based on OAV-Splitting Method [J].

Hu, Wenwen ;

Wang, Gege ;

Lin, Shunxian ;

Liu, Zhijun ;

Wang, Peng ;

Li, Jiayu ;

Zhang, Qi ;

He, Haibin .

FOODS, 2022, 11 (15)

[8] Machine Learning Algorithms Applied to Semi-Quantitative Data of the Volatilome of Citrus and Other Nectar Honeys with the Use of HS-SPME/GC-MS Analysis, Lead to a New Index of Geographical Origin Authentication [J].

Karabagias, Ioannis Konstantinos ;

Nayik, Gulzar Ahmad .

FOODS, 2023, 12 (03)

[9] High-sensitivity hyperspectral coupled self-assembled nanoporphyrin sensor for monitoring black tea fermentation [J].

Li, Luqing ;

Li, Menghui ;

Liu, Ying ;

Cui, Qingqing ;

Bi, Keyi ;

Jin, Shanshan ;

Wang, Yujie ;

Ning, Jingming ;

Zhang, Zhengzhu .

SENSORS AND ACTUATORS B-CHEMICAL, 2021, 346 (346)

[10] Evaluation of Chongqing Tuo Tea at Different Grades: An Integrated Approach by Artificial and Intelligent Sensory, Non-Volatile, and Volatile Compounds Analysis [J].

Miao, Yiwen ;

Wang, Lilei ;

Bai, Fei ;

Zheng, Shuting ;

Yan, Jingna ;

Wei, Hao ;

Meng, Qing ;

Tong, Huarong .

FOODS, 2024, 13 (06)

← 1 2 3 4 →