Enhanced understanding of dark tea quality through integrated GC-IMS and E-Nose analysis

Abstract

Gas Chromatography Ion Mobility Spectrometry (GC-IMS) and electronic nose (E-Nose) were utilized to investigate the aroma components of Liupao tea and other dark teas, employing Principal component analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) for the statistical analysis of the detection data. A total of 58 aroma component forms were isolated using GC-IMS, with 39 of these components being clearly identified. Notably, Liupao tea exhibits distinct volatile characteristics, as evidenced by significantly higher concentrations of ethyl butanoate, pentyl butanoate, and methional compared to other dark teas (p < 0.01). The GC-IMS analyzes the foundational framework of nine common markers in dark tea, including acetone, 2-propanol, and acetic acid, specifically in Liupao tea, while the E-Nose captures the spatial distribution nuances of Liupao tea's overall aroma. This bimodal analytical approach effectively integrates the material basis with sensory representation, presenting a novel methodological paradigm for evaluating dark tea quality. The research findings not only establish a volatile characteristic map for Liupao tea but also utilize machine learning algorithms to identify key discriminant indicators. This provides a scientific basis for dark tea category identification, process optimization, and the protection of geographical indications.