Work

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

Year: 2024

Type: article

Abstract: 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. Graphical abstract • Type B (Rougui) had mostly higher aroma intensity than type A (Shuixian). • Innovative use of GC-O-MS generated a specific analytical “Aroma Wheel”. • APCI-MS/MS showed dynamic changes in aroma release during multiple tea infusions. • A progressive loss of aroma and colour for seven brews were modelled by MPR and LSTM. • A minimal release of aroma and colour was found after the fourth brew.

Source: Food Chemistry

DOI: 10.1016/j.foodchem.2024.141537