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Broad bean paste-meju was fermented by a mixture of broad bean koji and saline; koji fermentation is an essential process for the production of broad bean paste-meju. Aspergillus oryzae was the most widely used in sauce fermentation. The purpose of this study was to research the factory adaptability of the highly efficient A. oryzae PNM003 and further evaluate the effect of fermentation conditions and fermentation strains on koji. A. oryzae PNM003 was compared with the widely used strain HN 3.042 not only in the laboratory but also in factory conditions (large scale). Results showed that the koji made with the same starter in the factory had a greater amount of fungi than that in the laboratory. Bacteria and yeast levels in HN_L koji were higher than in PN_L koji. As for fungi constitution, almost only Aspergillus survived in the end through the microorganism self-purification process during koji fermentation. As for the bacterial constitution, koji was grouped by fermentation conditions instead of fermentation starter. PN koji had higher protease activity and a higher content of total acids, amino acid nitrogen, amino acids, and organic acids in the laboratory conditions. Nevertheless, in factory conditions, PN koji and HN koji had similar indexes. As for volatile flavor compounds, koji made with the two starters in the same condition was grouped together. As for the same starter, there were more flavor compounds metabolized in the factory condition than in the laboratory condition, especially esters and alcohols. The results showed PN was a highly efficient strain to ferment koji, but the advantages were expressed more remarkably in laboratory conditions. In brief, the fermented condition had a greater influence than the fermentation starter for broad bean koji.

IntroductionThis study compared differences in physicochemical characteristics of the vinegar made by a mixed culture (MC) of Saccharomyces cerevisiae and Lactiplantibacillus plantarum and a pure culture (PC) of Saccharomyces cerevisiae.MethodsThe fermentation process was monitored, and metabolomics analysis by Liquid Chromagraphy-Mass Spectrometry (LC-MS) was applied to the compositional differences between PC and MC vinegars, combined with quantification of organic acids, amino acids and B vitamins.ResultsA total of 71 differential metabolites including amino acids, organic acids and carbohydrates, and six possible key metabolic pathways were identified. MC enhanced the malic acid utilization and pyruvate acid metabolism during fermentation, increasing substrate-level phosphorylation, and supplying more energy for cellular metabolism. Higher acidity at the beginning of acetic acid fermentation, resulting from lactic acid production by Lactiplantibacillus plantarum in MC, suppressed the cellular metabolism and growth of Acetobacter pasteurianus, but enhanced its alcohol metabolism and acetic acid production in MC. MC vinegar contained more vitamin B, total flavonoids, total organic acids, amino acids and had a higher antioxidant capacity. MC enhanced the volatile substances, particularly ethyl lactate, ethyl caprate and ethyl caproate, which contributed to a stronger fruity aroma.DiscussionThese results indicated the mixed culture in alcoholic fermentation can effectively enhance the flavor and quality of apple cider vinegar.

Additional file 1: Table S1. List of primers involved in this work. Table S2. List of primers involved in RT-PCR. Fig. S1. SDS-PAGE analysis of 2# recombinant strain. Lanes 1-3 were the blank control (GS115/pPIC9K), lanes 4-6 were the 2# recombinant strain (GS115/pPIC9K-col), and lane M was standard molecular weights. Fig. S2. Target gene copy number of double-driving recombinant strain. Fig. S3. N-terminal sequencing results of the target protein. Fig. S4. C-terminal sequencing results of the target protein. Fig. S5. Western blotting analysis of the native hlCOLIII (after Native-PAGE). Lane 1 was the hlCOLIII, and lane M was standard molecular weights.

Figure S1: Pathway functional enrichment of Coenzyme Q0 (7µM) vs control group. Bubble diagram showed KEGG pathway enrichment analysis. X-axis is enrichment ratio [Term Candidate Gene Number/Term Gene Number ], Y-axis is KEGG Pathway, the size of bubbles represents the number of genes annotated to KEGG Pathway, the color represents enrichment Q value, and the darker the color represents Q value, the smaller the value. Figure S2: Enrichment of DEGs in GO compared with the 7µM coenzyme Q0 treatment group. Gene ontology is divided into three major functional categories: molecular function (molecular function), cellular component (cellular component) and biological process (biological process). Perform functional classification based on the results of differential genetic testing. Under each major category, there are sub-categories of respective levels. Plot the GO annotation classification results of differential genes. The X axis represents the number of genes annotated into the GO cylinder, and the Y axis represents GO functional classification. Table S1: The expression levels of differentially expressed genes identified in three comparison groups ( CTL, 7μM CoQ0 treatment, 14μM CoQ0 treatment). Figure S1: Pathway functional enrichment of Coenzyme Q0 (7µM) vs control group. Bubble diagram showed KEGG pathway enrichment analysis. X-axis is enrichment ratio [Term Candidate Gene Number/Term Gene Number ], Y-axis is KEGG Pathway, the size of bubbles represents the number of genes annotated to KEGG Pathway, the color represents enrichment Q value, and the darker the color represents Q value, the smaller the value. Figure S2: Enrichment of DEGs in GO compared with the 7µM coenzyme Q0 treatment group. Gene ontology is divided into three major functional categories: molecular function (molecular function), cellular component (cellular component) and biological process (biological process). Perform functional classification based on the results of differential genetic testing. Under each major category, there are sub-categories of respective levels. Plot the GO annotation classification results of differential genes. The X axis represents the number of genes annotated into the GO cylinder, and the Y axis represents GO functional classification. Table S1: The expression levels of differentially expressed genes identified in three comparison groups ( CTL, 7μM CoQ0 treatment, 14μM CoQ0 treatment).

Butyrate, one of the key aroma compounds in Luzhou-flavor baijiu, is synthesized through two alternative pathways: butyrate kinase (buk) and butyryl-CoA: acetate CoA-transferase (but). A lack of knowledge of butyrate-producing microorganisms hinders our ability to understand the flavor formation mechanism of baijiu. Here, temporal dynamics of microbial metabolic profiling in fermented grains (FG) was explored via PICRUSt based on 16S rRNA gene sequences. We found Bacilli and Bacteroidia were the major potential butyrate producers in buk pathway at the beginning of fermentation, while later Clostridia dominated the two pathways. Clone library analysis also revealed that Clostridia (~73% OTUs) was predominant in buk pathway throughout fermentation, followed by Bacilli and Bacteroidia, and but pathway was merely possessed by Clostridia. Afterward, Clostridia-specific 16S rRNA gene sequencing demonstrated Clostridium might be the major butyrate-producing genus in two pathways, which was subsequently evaluated using culture approach. Seventeen Clostridium species were isolated from FG based on 16S rRNA gene sequence-guided medium prediction method. Profiles of short-chain fatty acids and but and buk genes in these species demonstrated phylogenetic and functional diversities of butyrate-producing Clostridium in FG. These findings add to illustrate the diversity of potential butyrate producers during brewing and provide a workflow for targeting functional microbes in complex microbial community.

Image's noise, edge and contrast are the important factors that influence image's quality. If a method of image enhancement is solely applied to the degraded medical digital image, image's quality is improved in part, if a combined method of image enhancement is applied to the degraded medical digital image, it can increase the image's quality greatly.

High-resolution optical mapping with voltage-sensitive dyes has become a powerful tool to depict complex propagation patterns of cardiac transmembrane potentials. Many studies have proved that this optical signal obtained from tissue surface is the response of the transmembrane potential averaged upon depth rather than only surface. In order to investigate the differential between the two transmembrane potentials, in this paper, we simulated cardiac propagation using Luo-Rudy (L-R) model and calculated the transmembrane potentials average over depth by an optical decay constant from 0.0 mm to 3.0mm. Our results suggest that the transmembrane potentials weighted average over depth is different from that on the tissue surface, the discrepancy between them depends on the depth of the fluorescence emission of the tissue. If only top layer of tissue (< 1.0mm) contributes the fluorescence, optical mapping is an almost accurate representation of surface activation dynamics