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Investigating the Variation of Volatile Compound Composition in Maotai-Flavoured Liquor During Its Multiple Fermentation Steps Using Statistical Methods

机译:用统计方法研究茅台风味酒在多次发酵过程中挥发性成分的变化

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摘要

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.
机译:多次发酵的使用是茅台风味酒生产的最具体特征之一。在这项研究中,使用统计方法研究了茅台风味酒在多次发酵过程中挥发性成分的变化。聚类分析表明,获得的样品主要按照发酵步骤进行分组,而不是根据其来源的酿酒厂进行分类,前两个发酵步骤的样品差异最大,这表明多个发酵和蒸馏步骤最终导致结果相似液体的挥发性成分。建立了反向传播神经网络(BNN)模型,该模型可从其挥发性成分中令人满意地预测发酵步骤的数量和酒样品的感官评估得分。平均冲击值(MIV)分析表明,乳酸乙酯,糠醛和一些高沸点酸起重要作用,而吡嗪在茅台风味酒生产过程中对改善风味和风味的贡献要小得多。这项研究有助于进一步了解茅台风味白酒的生产机理。

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