Characterization of the aroma-active compounds in Xiaokeng green tea by three pretreatment methods combined with gas chromatography-olfactometry (GC-O).

Chinese Xiaokeng green tea (XKGT) possesses elegant and fascinating aroma characteristics, but its key odorants are still unknown. In this study, 124 volatile compounds in the XKGT infusion were identified by headspace-solid phase microextraction (HS-SPME), stir bar sorptive extraction (SBSE), and solvent extraction-solid phase extraction (SE-SPE) combined with gas chromatography-mass spectrometry (GC-MS). Comparing these three pretreatments, we found HS-SPME was more efficient for headspace compounds while SE-SPE was more efficient for volatiles with higher boiling points. Furthermore, SBSE showed more sensitive to capture ketones then was effective to the application of pretreatment of aroma analysis in green tea. The aroma intensities (AIs) were further identified by gas chromatography-olfactometry (GC-O). According to the AI and relative odor activity value (rOAV), 27 compounds were identified as aroma-active compounds. Quantitative descriptive analysis (QDA) showed that the characteristic aroma attributes of XKGT were chestnut-like, corn-like, fresh, and so on. The results of network analysis showed that (E, Z)-2,6-nonadienal, nonanal, octanal and nerolidol were responsible for the fresh aroma. Similarly, dimethyl sulfide, (E, E)-2,4-heptadienal, (E)-2-octenal and ß-cyclocitral contributed to the corn-like aroma. Furthermore, indole was responsible for the chestnut-like and soybean-like aroma. This study contributes to a better understanding of the molecular mechanism of the aroma characteristics of XKGT.

E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea.

Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality.