Development of analytical “aroma wheels” for Oolong tea infusions (Shuixian and Rougui) and prediction of dynamic aroma release and colour changes during “Chinese tea ceremony” with machine learning

The flavour of tea as a worldwide popular beverage has been studied extensively. This study aimed to apply established flavour analysis techniques (GC-MS, GC-O-MS and APCI-MS/MS) in innovative ways to characterise the flavour profile of oolong tea infusions for two types of oolong tea (type A- Shuixian, type B- Rougui). GC-MS identified 48 aroma compounds, with type B having a higher abundance of most compounds. GC-O-MS analysis determined the noticeable aroma difference based on 20 key aroma compounds, facilitating the creation of an analytical "Aroma Wheel" with 8 key odour descriptors. APCI-MS/MS assessed real-time aroma release during successive brews linked with the "Chinese tea ceremony" (Gongfu Cha). Multivariate Polynomial Regression (MPR) and Long Short-Term Memory (LSTM) network approaches were applied to aroma and colour data from seven successive brews. The results revealed a progressive decline in both colour and aroma with seven repeated brews, particularly notable after the fourth brew.