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-electronic-nose (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 (R2Y = 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.

Key words: data fusion technology; black tea; aroma; fast gas chromatography-electronic-nose; gas chromatography-mass spectrometry