@article{WANG2024, 
author = {Lilei WANG and Yanqin YANG and Jialing XIE and Yiwen MIAO and Qiwei WANG and Yongwen JIANG and Yuliang DENG and Huarong TONG and Haibo YUAN},
title = {Aroma Quality Evaluation of High-Quality and Quality-Deficient Black Tea by Electronic Nose Coupled with Gas Chromatography-Mass Spectrometry},
year = {2024},
journal = {Food Science},
volume = {45},
number = {2},
pages = {274-282},
keywords = {data fusion technology, black tea, aroma, fast gas chromatography-electronic-nose, gas chromatography-mass spectrometry},
url = {https://www.sciopen.com/article/10.7506/spkx1002-6630-20230417-163},
doi = {10.7506/spkx1002-6630-20230417-163},
abstract = {According to the results of sensory evaluation performed by experts, 14 black tea samples were divided into two groups based on their aroma quality: high-quality and quality-deficient black tea. Using fast gas chromatography-electronicnose (GC-E-Nose) and gas chromatography-mass spectrometry (GC-MS) combined with multivariate statistical analysis, discriminant analysis of the two groups were carried out, and the key differential components between these groups were selected. The results showed that 117-dimensional dataset was obtained by the fusion of the GC-E-Nose (44-dimensional) and GC-MS (73-dimensional) data and used to establish a model for accurate classification of the two types of black tea employing orthogonal partial least squares-discriminant analysis (OPLS-DA). The model’s explanatory and predictive capacity (RY2 =0.976, Q2=0.959) were better than those of the model established based on the GC-E-Nose or GC-MS data. Based on variable important in projection (VIP) scores > 1.6 and P < 0.05, eight key aroma components including dimethyl sulfide (B3 and B25), β-ionone (A59), (3E)-4,8-dimethylnon-1,3,7-triene (A20), dihydroactinidiolide (A64), linalool (A17), phenylethyl alcohol (A19), δ-octyl lactone (A41) and γ-nonalatone (A45) were selected, which played an important role in the classification. These results showed that GC-E-Nose combined with GC-MS allows rapid and accurate discrimination between quality-deficient and high-quality black tea, which can be used as a supplement to traditional sensory evaluation, providing technical support for quality control and improvement of black tea.}
}