Short communication, Note
https://doi.org/10.17113/ftb.54.02.16.4582
Investigating the Variation of Volatile Compound Composition in Maotai-Flavoured Liquor During Its Multiple Fermentation Steps Using Statistical Methods
Zheng-Yun Wu
; Department of Food Engineering, College of Light Industry, Textile and Food Engineering, Sichuan University, Chengdu, PR China
Xue-Jun Lei
; Department of Food Engineering, College of Light Industry, Textile and Food Engineering, Sichuan University, Chengdu, PR China
De-Wen Zhu
; Department of Food Engineering, College of Light Industry, Textile and Food Engineering, Sichuan University, Chengdu, PR China
Ai-Min Luo
; Department of Food Engineering, College of Light Industry, Textile and Food Engineering, Sichuan University, Chengdu, PR China
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.
Keywords
Maotai-flavoured liquor; multiple fermentations; volatile compounds; statistical analysis; back-propagation neural network
Hrčak ID:
160380
URI
Publication date:
24.6.2016.
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