Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi 214122, China.
Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi 214122, China.
Int J Food Microbiol. 2022 Feb 16;363:109493. doi: 10.1016/j.ijfoodmicro.2021.109493. Epub 2021 Nov 26.
Traditional fermented foods are usually produced by spontaneous fermentation with multiple microorganisms. Environmental factors play important roles in microbial succession. However, it is still unclear how the processing parameters regulate the microbiota during fermentation. Here, we reveal the effects of processing parameters on the core microbiota in spontaneous fermentation of Chinese liquor starter. Rhizopus, Pichia, Wickerhamomyces, Saccharomycopsis, Aspergillus and Saccharomyces were identified as core microbiota using amplicon sequencing and metaproteomics analysis. Fermentation moisture gradually decreased from 34.8% to 14.2%, and fermentation temperature varied between 17.0 °C and 35.3 °C during the fermentation. Mantel test showed that fermentation moisture (P < 0.001) and fermentation temperature (P < 0.05) significantly affected the core microbiota. Moreover, structural equation modelling analysis indicated that fermentation moisture (P < 0.001) and fermentation temperature (P < 0.001) were respectively influenced by the processing parameters, room humidity and room temperature. The succession of Rhizopus, Pichia, Wickerhamomyces, Saccharomycopsis and Aspergillus were significantly affected by room humidity (P < 0.05), and the succession of Saccharomyces was significantly affected by room temperature (P < 0.001). Further, models were constructed to predict the population of core microbiota by room humidity and room temperature, using Gaussian process regression and linear regression (P < 0.05). This work would be beneficial for regulating microorganisms via controlling processing parameters in spontaneous food fermentations.
传统发酵食品通常是通过多种微生物的自然发酵生产的。环境因素在微生物演替中起着重要作用。然而，目前尚不清楚加工参数如何在发酵过程中调节微生物区系。在这里，我们揭示了加工参数对中国白酒曲自发发酵核心微生物群的影响。通过扩增子测序和宏蛋白质组学分析，鉴定出根霉属、毕赤酵母属、有孢汉逊酵母属、酒香酵母属、曲霉属和酿酒酵母属为核心微生物群。发酵水分从 34.8%逐渐降至 14.2%，发酵温度在 17.0°C 和 35.3°C 之间变化。Mantel 检验表明，发酵水分（P<0.001）和发酵温度（P<0.05）显著影响核心微生物群。此外，结构方程模型分析表明，发酵水分（P<0.001）和发酵温度（P<0.001）分别受加工参数、室温和湿度的影响。根霉属、毕赤酵母属、有孢汉逊酵母属、酒香酵母属和曲霉属的演替受室温（P<0.05）显著影响，而酿酒酵母的演替受室温（P<0.001）显著影响。进一步，通过高斯过程回归和线性回归（P<0.05），构建了由室温和湿度预测核心微生物群的模型。这项工作有助于通过控制自然发酵食品加工参数来调节微生物。
