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Modelling and predicting population of core fungi through processing parameters in spontaneous starter (Daqu) fermentation.
Ban, Shibo; Chen, Lingna; Fu, Shuangxue; Wu, Qun; Xu, Yan.
Afiliación
  • Ban S; 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.
  • Chen L; 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.
  • Fu S; 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.
  • Wu Q; 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. Electronic address: wuq@jiangnan.edu.cn.
  • Xu Y; 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. Electronic address: yxu@jiangnan.edu.cn.
Int J Food Microbiol ; 363: 109493, 2022 Feb 16.
Article en En | MEDLINE | ID: mdl-34953345
ABSTRACT
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Bacterias / Microbiota Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Int J Food Microbiol Asunto de la revista: CIENCIAS DA NUTRICAO / MICROBIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Bacterias / Microbiota Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Int J Food Microbiol Asunto de la revista: CIENCIAS DA NUTRICAO / MICROBIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: China