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      Dynamic changes in microbial communities and flavor during different fermentation stages of proso millet Baijiu, a new product from Shanxi light-flavored Baijiu

      1 , 2 , * ,
      Frontiers in Microbiology
      Frontiers Media S.A.
      proso millet, Baijiu, fermentation, microbial community, flavor

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          Proso millet, a high-quality fermentation material used for Chinese yellow wine production, can produce special flavored substances; however, its role in improving the flavor and altering microbial communities of light-flavored Baijiu during fermentation remain unknown. Thus, we aimed to investigate the effect of proso millet on improving the flavor of light-flavored Baijiu and altering microbial communities during different fermentation stages.


          The dynamic changes in the microbial communities and flavor of proso millet (50%) + sorghum (50%) mixed fermentation samples were analyzed through intermittent sampling on days 7, 14, 21, and 28 of the fermentation process. Microbial high-throughput sequencing and the analysis of flavor characteristics were conducted through 16S DNA/ ITS amplicon sequencing and gas chromatography (multi-capillary column)-ion mobility spectrometry, respectively.


          Proso millet significantly changed the core flavor compound composition of traditional light-flavored Baijiu from ethyl acetate, ethyl hexanoate, ethyl hexanoate dimer, ethyl butanoate, ethyl lactate, and butyl acetate to oct-2-ene, 2-butanol, propyl propanoate, 2-pentenal, and 4-methylpentanal. The amplicon sequencing analysis revealed that the alpha diversity parameters of bacterial and fungal communities, including the Chao1, Pielou_e, Shannon, and Simpson indices, for proso millet–sorghum mixed fermentation samples were significantly higher than those for sorghum fermentation samples ( p < 0.05). Of the 40 most significant microbial genera in two treatments, proso millet significantly increased the abundance of 12 bacterial and 18 fungal genera. Among the 40 most significant bacterial and fungal species, 23 bacterial species belonged to the Lactobacillus genus, whereas the 30 primary fungal species belonged to 28 different genera. The analysis of the relationship between microbial changes and the main flavor compounds of light-flavored Baijiu showed that bacteria from the Weissella, Acinetobacter, Bacteroides, Psychrobacter, Pseudarthrobacter, Lactococcus, Chloroplast, Saccharopolyspora, Psychrobacter, Saccharopolyspora, Pseudonocardiaceae, Bacteroides genera and fungi from the Thermoascus, Aspergillus, Pichia, Rhizomucor, Papiliotrema, Hyphopichia, and Mucor genera significantly inhibited the synthesis of ethyl hexanoate, ethyl butanoate, ethyl lactate ethyl lactate, and butyl acetate but increased the synthesis of ethyl acetate ( p < 0.05). Moreover, these microbes exhibited a significantly greater abundance in proso millet–sorghum mixed fermentation samples than in sorghum samples. The synthesis of special flavored compounds in proso millet Baijiu was significantly positively correlated with the presence of fungi from the Rhizopus, Papiliotrema, Wickerhamomyces, Aspergillus, and Thermoascus genera but negative correlated with the presence of bacteria from the Weissella, Acinetobacter, Psychrobacter, Pseudarthrobacter, Bacteroides, and Saccharopolyspora genera. Regarding ethanol content, the low alcohol content of Fenjiu may be due to the significantly high abundance of fungi from the Psathyrella genus and bacteria from the Staphylococcus, Kroppenstedtia, Brevibacterium, and Acetobacter genera during fermentation. In summary, proso millet significantly altered the flavor of light-flavored Baijiu by inducing the formation of a special microbial community; however, it did not increase alcohol concentration.


          This study lays the foundation for future research on Baijiu fermentation. Additionally, the study findings may help improve the production efficiency and elevate the quality and flavor of the final product.

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          Most cited references55

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          QIIME allows analysis of high-throughput community sequencing data.

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            PEAR: a fast and accurate Illumina Paired-End reAd mergeR

            Motivation: The Illumina paired-end sequencing technology can generate reads from both ends of target DNA fragments, which can subsequently be merged to increase the overall read length. There already exist tools for merging these paired-end reads when the target fragments are equally long. However, when fragment lengths vary and, in particular, when either the fragment size is shorter than a single-end read, or longer than twice the size of a single-end read, most state-of-the-art mergers fail to generate reliable results. Therefore, a robust tool is needed to merge paired-end reads that exhibit varying overlap lengths because of varying target fragment lengths. Results: We present the PEAR software for merging raw Illumina paired-end reads from target fragments of varying length. The program evaluates all possible paired-end read overlaps and does not require the target fragment size as input. It also implements a statistical test for minimizing false-positive results. Tests on simulated and empirical data show that PEAR consistently generates highly accurate merged paired-end reads. A highly optimized implementation allows for merging millions of paired-end reads within a few minutes on a standard desktop computer. On multi-core architectures, the parallel version of PEAR shows linear speedups compared with the sequential version of PEAR. Availability and implementation: PEAR is implemented in C and uses POSIX threads. It is freely available at http://www.exelixis-lab.org/web/software/pear. Contact: Tomas.Flouri@h-its.org
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              Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA.

              The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA.

                Author and article information

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                URI : https://loop.frontiersin.org/people/2142994/overviewRole: Role: Role: Role: Role: Role: Role:
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                22 January 2024
                : 15
                : 1333466
                [1] 1Department of Biological Science and Technology, Jinzhong University , Jinzhong, China
                [2] 2College of Food Science and Engineering, Shanxi Agriculture University , Jinzhong, China
                Author notes

                Edited by: Antonio Alfonzo, University of Palermo, Italy

                Reviewed by: Bin-Bin Hu, Yunnan Academy of Tobacco Agricultural Sciences, China

                Zhilei Fu, Hebei Normal University for Nationalities, China

                *Correspondence: Zhenfeng Gao, 862157168@ 123456163.com
                Copyright © 2024 Zhao and Gao.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 05 November 2023
                : 09 January 2024
                Page count
                Figures: 9, Tables: 2, Equations: 0, References: 56, Pages: 19, Words: 12224
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported financially by the Shanxi Provincial Higher Education Science and Technology Innovation Project (2022L498), the Shanxi Province Science Foundation for Youths (20210302124512), the Doctoral Foundation of Shanxi Academy of Agricultural Sciences (YBSJJ2001, YBSJJ2002, and YBSJJ2013), the Agricultural Science and Technology Innovation Research Project of Shanxi Academy of Agricultural Sciences (YCX2020BH3), and an earmarked fund for the China Agriculture Research System (CARS-06-14.5-A30).
                Original Research
                Custom metadata
                Food Microbiology

                Microbiology & Virology
                proso millet,baijiu,fermentation,microbial community,flavor
                Microbiology & Virology
                proso millet, baijiu, fermentation, microbial community, flavor


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