Investigating the variation of volatile compound composition in maotai-flavoured liquor during its multiple fermentation steps using statistical methods

Abstract

The use of multiple fermentations is one of the most specific characteristics of Maotai- -flavoured liquor production. In this research, the variation of volatile composition of Maotai-flavoured liquor during its multiple fermentations is investigated using statistical approaches. Cluster analysis shows that the obtained samples are grouped mainly according to the fermentation steps rather than the distillery they originate from, and the samples from the first two fermentation steps show the greatest difference, suggesting that multiple fermentation and distillation steps result in the end in similar volatile composition of the liquor. Back-propagation neural network (BNN) models were developed that satisfactorily predict the number of fermentation steps and the organoleptic evaluation scores of liquor samples from their volatile compositions. Mean impact value (MIV) analysis shows that ethyl lactate, furfural and some high-boiling-point acids play important roles, while pyrazine contributes much less to the improvement of the flavour and taste of Maotai-flavoured liquor during its production. This study contributes to further understanding of the mechanisms of Maotai-flavoured liquor production.