Method for screening characteristic flavor compounds associated with fermented grains and base liquor based on core microorganisms

Abstract

The invention belongs to the technical field of biology, and relates to a method for screening characteristic flavor compounds associated with fermented grains and base liquor based on core microorganisms. Based on high-throughput sequencing and multi-omics analysis, a relation is established between core microorganisms and flavor substances in fermented grains and base liquor in a high-temperature accumulation process, and the method for constructing the flavor substances in the fermented grains and the base liquor based on core microorganism composition is formed. And a theoretical basis and a tool are provided for directional regulation and control, process optimization and quality improvement of white spirit flavor based on microbiome.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology and relates to a method for screening characteristic flavor compounds of fermented mash and base liquor based on core microorganisms. Background Technology

[0002] The sauce-flavored baijiu adopts the "12987" process, which means that one year is a cycle, and it involves two feedings, nine steamings, eight fermentations, and seven distillations. Among them, the stacking fermentation process is the key operation that determines the flavor of the liquor.

[0003] Stack fermentation employs an open, solid-state fermentation process, allowing the mash to fully contact with environmental microorganisms. These microorganisms grow and metabolize rapidly under high temperatures, producing various enzymes and metabolites. These substances are further transformed into flavor compounds in the mash through complex biochemical reactions, ultimately influencing the flavor characteristics of the base liquor. However, existing technologies primarily focus on the correlation between microbial communities and flavor compounds in base liquor during fermentation in cellars. There are few reports on the correlation between the microbial community of the mash during stack fermentation and flavor compounds in the base liquor; most studies concentrate on the correlation between microorganisms in the mash and flavor compounds.

[0004] Furthermore, in actual production, the fermentation process in the pits is closed, limiting the control of the process. High-temperature stacking, with its open operation, is known as "secondary koji making" and has become a key entry point for process optimization. Summary of the Invention

[0005] To provide theoretical data for a deeper understanding of the early causes of flavor differences in base liquor, this invention establishes a screening technique for core microorganisms during the high-temperature stacking process and establishes a correlation between these microorganisms and volatile flavor compounds in the mash and base liquor. This forms a method for constructing flavor compounds in mash and base liquor based on the composition of core microorganisms. This invention focuses on the screening method for core microorganisms in the stacked fermentation mash of Maotai-flavor liquor and the establishment of their correlation with the formation of key flavor compounds in the mash and final base liquor, aiming to provide an analytical method for elucidating the early causes of flavor differences in base liquor.

[0006] On one hand, the present invention provides a method for screening core microorganisms in fermented mash, comprising:

[0007] S1. Collect information on the relative abundance of microorganisms in the fermented mash;

[0008] S2. Based on the relative abundance information collected in step S1, screen for common microorganisms, absolutely dominant microorganisms, and symbiotic microorganisms;

[0009] S3. Use linear discriminant analysis to screen for differentially expressed microorganisms;

[0010] S4. Combine the ubiquitous microorganisms, absolute dominant microorganisms, and symbiotic microorganisms obtained from step S2 using a Venn diagram to obtain the marker microorganisms;

[0011] S5. Combine the differentially expressed microorganisms obtained in step S3 with the marker microorganisms obtained in step S4 to obtain the core microorganisms.

[0012] In some embodiments, in step S2, the ubiquitous microorganisms are microorganisms present in each accumulation stage; in some embodiments, the absolutely dominant microorganisms are microorganisms with a relative abundance greater than 10% in different stages; in some embodiments, the symbiotic microorganisms are microorganisms with high connectivity obtained based on co-occurrence network graph analysis.

[0013] In some implementations, in step S3, the linear discriminant analysis is performed by: using the Lefse linear discriminant analysis to analyze the OTU values ​​of all microorganisms, annotating them to the genus level, and screening for microorganisms with LDA values ​​greater than 4.0.

[0014] In some implementations, in step S5, combining the differentially differentiated microorganisms obtained in step S3 and the marker microorganisms obtained in step S4 specifically means: combining the differentially differentiated microorganisms obtained in step S3 and the marker microorganisms obtained in step S4 to obtain the union set.

[0015] On the one hand, the present invention provides an application of the core microorganism screening method in the fermented mash, wherein the microorganism is any one or more of the following:

[0016] A1: Monitoring the growth and metabolism of microorganisms in the fermented mash;

[0017] A2: Related to flavor compounds in fermented mash and base spirit.

[0018] On the one hand, the present invention provides a method for associating characteristic flavor substances in mash and base liquor, which uses core microorganisms obtained by screening the core microorganisms in mash to associate characteristic flavor substances in mash and base liquor.

[0019] On one hand, the present invention provides a method for correlating characteristic flavor substances in mash and base liquor, comprising:

[0020] 1) Collect the content of volatile flavor compounds in the mash and base liquor;

[0021] 2) Based on PLS-DA, VIP and / or volcano maps, the contents collected in step 1) are screened to obtain characteristic flavor substances;

[0022] 3) The core microorganisms obtained by the core microorganism screening method in the described fermentation mash, and

[0023] The characteristic flavor compounds obtained in step 2) were subjected to Spearman correlation analysis to establish a correlation network.

[0024] In some embodiments, the method for collecting volatile flavor compounds in the mash is GC-FID; in some embodiments, the method for collecting volatile flavor compounds in the base liquor is LLME-GC-MS; in some embodiments, the screening conditions in step 2) are VIP>1.0 and FC threshold>1.4.

[0025] In some implementations, step 3) specifically involves: establishing a correlation coefficient matrix based on Spearman correlation analysis, drawing a correlation network diagram, and using the screening criteria of |R|>0.40 and p<0.05.

[0026] On the one hand, the present invention provides an application of the method for linking characteristic flavor substances in mash and base liquor. Based on the established linking network, the process can be adjusted in real time to achieve staged controllable fermentation, or based on the established linking network, the influence nodes of the core microorganisms on key flavor substances can be accurately located. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of temperature changes during the accumulation of fermented mash in Example 1.

[0028] Figure 2 This is a schematic diagram of the sampling method in Example 1.

[0029] Figure 3 This is a flowchart of the association method of the present invention.

[0030] Figure 4 This is a Venn diagram of common microorganisms in Example 2.

[0031] Figure 5 This is a schematic diagram of the analysis of the dominant microorganism in Example 2.

[0032] Figure 6 This is a schematic diagram of the symbiotic microorganism analysis in Example 2.

[0033] Figure 7 This is a schematic diagram of the differential microbial analysis in Example 2.

[0034] Figure 8 This is a Venn diagram of the marker microorganisms in Example 2.

[0035] Figure 9 This is an analysis diagram of key flavor compounds in the mash and base liquor in Example 3.

[0036] Figure 10This is a network diagram showing the role of core microorganisms in the fermentation of mash to the formation of base liquor flavor, established in Example 4. Detailed Implementation

[0037] The following specific embodiments further illustrate the technical solution of the present invention. These specific embodiments do not represent a limitation on the scope of protection of the present invention. Non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the scope of protection of the present invention.

[0038] Example 1

[0039] S1 Fermentation Mash Sample Collection: All fermentation mash samples were taken from the 2025 production year's sand-making and one round of high-temperature stacking processes at a sauce-aroma baijiu distillery in Zunyi City. This invention is based on the temperature changes during the fermentation mash stacking process (…). Figure 1 Sampling was conducted, with samples taken at 1d, 2d, 3d, and 4d for sand production. Samples were also taken at 1d, 3d, 5d, and 7d for each round, representing the four stages of pile completion (WD), early to mid-stage pile formation (ES), late to mid-stage pile formation (LS), and final pile formation (XJ), respectively. To ensure the uniformity of the sampling, the sampling method was as follows: Figure 2 As shown, 48 samples from 6 locations at each stage were collected and immediately placed into sterile bags and stored in a -80°C freezer.

[0040] S2 Base Liquor Sample Collection: A fermentation pit is divided into upper, middle, and lower layers for distillation. Based on the actual fermentation method, and considering the sand-making and first-round stacking process, the mash at points A and B belongs to the lower layer of the fermentation pit, the mash at points C and E belongs to the middle layer, and the mash at points D and F belongs to the upper layer. Figure 2 Take two 50mL bottles of base liquor from each layer and label them as XJ (lower cellar) 1, XJ2, ZJ (middle cellar) 1, ZJ2, SJ (upper cellar) 1, and SJ2, and store them in a sealed container at room temperature in the laboratory.

[0041] S3 Core Microbial Screening Method: This invention utilizes Spearman correlation analysis and Gephi (0.10.1) to construct a microbial co-occurrence network diagram, with 62 nodes for each fermentation round and 24 nodes for the first round. Relative abundance analysis of microbial genera is also performed. Combined with Venn diagram screening of common microorganisms (present in every stage), marker microorganisms in the fermented mash are obtained. Lefse linear discriminant analysis is then used to identify differentially expressed microorganisms between the two rounds, with a significance level filtering threshold of 0.05 and an LDAscore filtering threshold of 4.0. By combining marker microorganisms and differentially expressed microorganisms, the core microorganisms in the fermented mash from the two rounds of fermentation are obtained.

[0042] Total DNA was extracted from the fermented mash samples using the EZNATMMag-Bind Soil DNA Kit. For detailed operating procedures, please refer to the kit instructions.

[0043] Microbial data were sequenced using the Illumina MiSeq platform. Bacterial 16S rRNA gene V3-V4 regions were amplified and sequenced using universal primers 341F (5'-CCTACGGGNGGCWGCAG-3') and 805R (5'-GACTACHVGGGTATCTAATCC-3'). Fungal ITS3-ITS4 regions were amplified and sequenced using universal primers ITS3 (5'-GCATCGATGAAGAACGCAGC-3') and ITS4 (5'-TCCTCCGCTTATTGATATGC3'). PCR amplification conditions were: 95℃ pre-denaturation for 3 min; 94℃ denaturation for 20 s, 55℃ annealing for 20 s, 72℃ extension for 30 s, 25 cycles, with a final extension at 72℃ for 5 min, followed by cooling to 4℃. The PCR reaction system was as follows: Add 15 μL of Robust PCR Master Mix, 1 μL each of forward and reverse primers, and 20–30 ng of DNA template. Use ddH2O to bring the total volume to 30 μL.

[0044] The measured abundance of microbial genera is shown in Tables 1 and 2.

[0045] Table 1. Relative abundance of bacterial genera in sand-making cycles.

[0046] 1d 2d 3d 4d Acetobacter 21.80292 0.196886 2.585936 3.911514 Bacillus 4.458266 13.62043 3.715245 12.4901 Gluconobacter 27.95334 0.674876 1.147426 0.105155 Kroppenstedtia 21.69179 38.11968 10.22659 22.60837 Lactiplantibacillus 0.94507 1.057268 45.49243 25.32682 Oceanobacillus 1.525 2.935084 1.248883 2.296538 Pediococcus 0.716274 1.151728 7.611663 1.526698 Scopulibacillus 0.308175 1.664997 0.339397 0.630931 Staphylococcus 0.525765 1.383894 0.330942 0.852926 Thermoactinomyces 1.403597 2.839487 1.136556 2.840489 Virgibacillus 7.651146 22.04898 5.440008 12.11102 Weissella 1.798622 3.240087 6.383313 2.444534 Others 9.220037 11.0666 14.34162 12.8549

[0047] Table 2. Relative abundance of fungal genera in sand-making cycles.

[0048]

[0049]

[0050] Table 3. Relative abundance of bacterial genera in the first round.

[0051] 1d 3d 5d 7d Acetobacter 0.037346 0.003503 0.015387 0 Bacillus 17.34592 13.45846 16.5487 10.90174 Gluconobacter 0 0 0 0 Kroppenstedtia 17.83621 26.7943 22.45538 20.50413 Lactiplantibacillus 0.162792 0.267996 1.125173 0.56382 Oceanobacillus 17.25591 12.39085 12.35094 22.65197 Pediococcus 0.163749 0.107724 0.369288 0.413223 Scopulibacillus 4.839698 2.760529 4.557432 2.144169 Staphylococcus 3.244341 1.039578 1.059778 1.242424 Thermoactinomyces 0.341862 0.228584 0.365441 0.584022 Virgibacillus 30.59141 35.35527 30.03731 30.9348 Weissella 0.166622 0.172533 0.512579 0.229568 Others 8.014134 7.420674 10.60259 9.830119

[0052] Table 4. Relative abundance of fungal genera in the first round.

[0053] 1d 3d 5d 7d Agaricus 0 0 6.17849 0 Lichtheimia 78.20513 26.19048 39.81693 54.30622 Monascus 0 0 0 0 Paecilomyces 0 0 0 0 Pichia 0 0 0 0 Rhizopus 2.564103 1.587302 0.457666 0.956938 Saccharomyces 1.282051 2.380952 1.601831 0.717703 Thermoascus 0 0 0 0 Thermomyces 0 0 0 0 Torulaspora 15.38462 69.04762 51.02975 44.01914 Wickerhamomyces 0 0 0 0 Zygosaccharomyces 2.564103 0.396825 0.915332 0 unclassified_Fungi 0 0 0 0 Others 0 0.396825 0 0

[0054] The results of this method indicate that the sand-making cycle contains 10 microorganisms, including Bacillus, and the first cycle contains 7 microorganisms, including Lichtehimia.

[0055] Screening method for key flavor substances in S4 mash and base liquor: This invention detected the volatile flavor substances in mash and base liquor. Based on PLS-DA, VIP and volcano diagram analysis, 14 and 20 key differential flavor substances, such as 3-methylpyrazine, were screened in the sand making and first round, respectively.

[0056] Volatile flavor compounds were detected using gas chromatography-flame ionization detector (GC-FID) and liquid-liquid microextraction-gas chromatography-mass spectrometry (LLME-GC-MS).

[0057] GC-FID Detection of Flavor Skeleton Components in Distilled Fermentation Mash

[0058] Weigh 10g of fermented mash into a 50mL centrifuge tube, add 20mL of 53% ethanol solution and sonicate for 30min. After centrifugation, filter through a 0.22µm organic filter membrane. Accurately pipette 1mL of the solution and add 10μL of internal standard (297.46mg / L tert-amyl alcohol, 297.59mg / L n-amyl acetate and 302.37mg / L 2-ethylbutyric acid), mix well and set aside. The gas chromatography conditions were as follows: CP97723 CP-Wax57 CB capillary column (50m×0.25mm, 0.25μm), injection port temperature 230℃, injection volume 10μL, split ratio 50:1, and temperature program as follows: 40℃ held for 3 min, increased to 70℃ at 1.5℃ / min, without holding, increased to 80℃ at 2℃ / min, without holding, and finally increased to 215℃ at 7.5℃ / min and held for 20 min; the carrier gas flow rate was 0.3mL / min.

[0059] LLME-GC-MS Detection of Trace and Pyrazine Components in Base Wine

[0060] Pipette 2 mL of wine sample into a 25 mL stoppered test tube, and dilute the base wine to 10% alcohol by adding saturated sodium chloride solution. Then add 10 μL of 2-octanol (final concentration: 19.82 mg / L) and 2-methoxy-3-methylpyrazine (final concentration: 20.23 mg / L) internal standard solution, respectively. Finally, add 2 mL of ether:n-hexane (1:1) extractant for extraction, vortex for 1 min, and after the solution separates into layers, take 1 μL of the upper organic phase for upper-level analysis. GC-MS conditions 1: Splitless injection mode with a flow rate of 1 mL / min was used. The temperature program was set as follows: initial temperature 40℃, hold for 1 min, increase to 80℃ at 8℃ / min, then increase to 115℃ at 2.5℃ / min, then increase to 155℃ at 8℃ / min, then increase to 220℃ at 5℃ / min, and finally increase to 230℃ at 5℃ / min. Data was acquired using SIM mode with a solvent delay of 7 min. GC-MS Condition 2: Splitless injection was used at a flow rate of 1 mL / min. The temperature program was set as follows: initial temperature 40℃, hold for 1 min, increase to 100℃ at 2.5℃ / min, hold for 1 min, then increase to 160℃ at 3℃ / min, and finally increase to 230℃ at 5℃ / min, hold for 10 min. Data was acquired using SIM mode with a solvent delay of 8 min.

[0061] The content of flavor substances in the base liquor produced by each distillation round is shown in Table 5.

[0062] Table 5

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] The content of flavor compounds in the base liquor produced in the first round is shown in Table 6.

[0069] Table 6

[0070]

[0071]

[0072]

[0073]

[0074] S5 Core Microorganism-Fermentation Mash-Base Liquor Correlation Analysis Method: Based on Spearman correlation analysis of core microorganisms and flavor compounds in fermentation mash and base liquor, this invention shows that 1,2-propanediol in fermentation mash and base liquor from each fermentation round exhibits the same correlation with thermophilic fungi Thermomyces and Pichia; in one round, ethyl phenylacetate in fermentation mash and base liquor shows a negative correlation with Torulaspora.

[0075] Example 2: Screening Method for Core Microorganisms

[0076] 1.1 Common Microorganisms

[0077] Universal microorganisms are those present in every stage of sedimentation and possess stability. Based on high-throughput sequencing results, Venn diagrams were used to screen microorganisms present in different stages of both sedimentation rounds. A total of 96 species were found in the sedimentation rounds, namely A2, Acetobacter, Acidipropionibacterium, Acidovorax, Acinetobacter, Acutalibacter, Aerococcus, Agrobacterium, Alicyclobacillus, Amylolactobacillus, Anaerosalibacter, Aneurinibacillus, and A... pibacter, Azospirillum, Bacillus, Bavariicoccus, Bifidobacterium, Blastococcus, Blastomonas, Bosea, Brachybacterium, Bradyrhizobium, Brevibacterium, Brevundimonas, Brucella, CAG-352, Chryseobacterium, Clostridium, Comamonas, Companilactobacillus, Corynebacterium, Cupri avidus, Curtobacterium, Dankookia, Desertimonas, Devosia, Dubosiella, Duffyella, DVSBS1, Elstera, Empedobacter, Enteractinococcus, Enter obacter, Enterococcus, Erythrobacter, Escherichia-Shigella, Faecalimonas, Flavobacterium, Fructilactobacillus, Furfurilactobacillus, Gluconobacter, Glutamicibacter, Gordonia, Hymenobacter, Intestinimonas, Isosphaera, Ketogulonicigenium, Klebsiella, Kocuria, Komagataeibacter, Kroppenstedtia, Lacticaseibacillus, Lactiplantibacillus, Lactobacillus, Lactococcus, Lapidilactobacillus, Lichenibacterium,Massilia, Nocardiopsis, norank_Peptococcaceae, Priestia, SN8, unclassified_Isosphaeraceae, Zygosaccharomyces, Wickerhamomyces, Wickerhamiella, unclassified_Fungi, Torulaspora, Thermomyces, Thermoascus, Starmerella, Saccharomycopsis, Saccharomyces, Rhizopus, Rhizomucor, Pichia, Penicillium, Paecilomyces, Monascus, Microascus, Kodamaea, Kazachstania, Hyphopichia, Geotrichum, Cyberlindnera, and Aspergillus.

[0078] There are 60 species in one round, namely Zygosaccharomyces, Weizmannia, Weissella, Virgibacillus, unclassified_Pseudonocardiaceae, unclassified_Orbaceae, unclassified_Neisseriaceae, unclassified_Mycobacteriales, unclassified_Micrococcales, unclassified_Lactobacillales, unclassified_Flavobacteriaceae, unclassified_Cyanobacteriales, unclassified_Bacteria, unclassified_Bacilli, unclassified_Bacillaceae, unclassified_Alphaproteobacteria, Torulaspora, Thermoactinomyces, Streptomyces, Staphylococcus, Sphingomonas, Solitalea, Scopulibacillus, Saccharopolyspora, Saccharomyces, Ruania, Ralstonia, Pseudomonas, Pseudogracilibacillus, Proteus, Pediococcus, Pantoea, Oceanobacillus, norank_Rhizobiaceae, norank_Caulobacteraceae, norank_Bacillaceae, Methylobacterium, Mammaliicoccus, Loigolactobacillus, Limosilactobacillus, Ligilactobacillus, Lichtheimia, Levilactobacillus, Lentilactobacillus, Lederbergia, Latilactobacillus, Lactococcus, Lactobacillus, Lactiplantibacillus, Kroppenstedtia, Kocuria, Fructilactobacillus, Enterococcus, Corynebacterium, Companilactobacillus, BrevibacteriumBrachybacterium, Bacillus, Aeromicrobium and Acinetobacter(, Figure 4 ).

[0079] 1.2 Dominant Microorganisms

[0080] Bacterial genera with a relative abundance greater than 10% at different stages are defined as absolutely dominant microorganisms. The sand-forming cycle includes 8 species such as Kroppenstedtia and Pichia, while the first cycle mainly consists of 7 species such as Virgibacillus and Torulaspora. Figure 5 ).

[0081] 1.3 Symbiotic Microorganisms

[0082] Spearman correlation analysis among microorganisms revealed 210 strong correlations among 62 genera of microorganisms in the first sand-forming cycle, and 32 strong correlations among 24 genera of microorganisms in the second sand-forming cycle. Figure 6 In each sand-building cycle, 20 nodes had more than 9 connecting edges, and in one cycle, 6 nodes had more than 3 connecting edges (Table 7). This indicates that these microorganisms play an important role in community succession. Therefore, these 26 microorganisms are considered symbiotic microorganisms. The sand-building cycles are, in order: Lichtheimia, Thermoactinomyces, Thermomyces, Thermoascus, Brevibacterium, Bacillus, Aspergillus, Monascus, unclassified_Mycobacteriales, Rhizomucor, Rhizopus, Pa ecilomyces, unclassified_Fungi, Virgibacillus, Kroppenstedtia, Corynebacterium, Oceanobacillus, Saccharomycopsis, Mammaliicoccus and Hyphopichia, and in turn Lichtheimia, Thermoactinomyces, Fructilactobacillus, Oceanobacillus, Companilactobacillus and Scopulibacillus.

[0083] Table 7. Number of microbial connection nodes in the collinear network diagram of the two rounds.

[0084]

[0085]

[0086] 1.4 Differential Microorganisms

[0087] Leffe linear discriminant analysis was used to determine the OTU values ​​of all microorganisms, annotated to the genus level, and microorganisms with LDA values ​​greater than 4.0 were screened. A total of 17 microorganisms were screened at the genus level. The differentially expressed microorganisms from each sand-forming cycle were *Acetobacter*, *Sphingomonas*, *Pichia*, *Lactiplantibacillus*, *Kroppenstedtia*, *Paecilomyces*, *Thermomyces*, *Wickerhamomyces*, *Monascus*, and *Thermoascus*. *Bacillus*, *Oceanobacillus*, *Virgibacillus*, *Lactobacillus*, *Zygosaccharomyces*, *Lichtheimia*, and *Torulaspora* were differentially expressed microorganisms from the first cycle. Figure 7 ).

[0088] 1.5 Screening methods for core microorganisms

[0089] The microorganisms screened in sections 1.1–1.3 were merged using a Venn diagram, ultimately yielding four marker microorganisms: Bacillus and Kroppenstedtia for sand-forming rounds, and Lichtheimia and Oceanobacillus for first-round formation. Figure 8 The marker microorganisms, possessing stability, absolute dominance, and symbiotic relationships, play a crucial role in this round of accumulation.

[0090] The marker microorganisms were combined with the differentially expressed microorganisms screened in Section 1.4 (using the union) to obtain the core microorganisms. The first sand-forming cycle included 11 species of Bacillus and Kroppenstedtia, while the second cycle included 7 species of Lichtehimia and Oceanobacillus. The core microorganisms not only represent the core microbial community that plays an important role in this cycle, but also allow for reasonable differentiation of microbial differences between cycles.

[0091] Example 3: Screening Method for Key Flavor Components in Fermentation Mash and Base Wine

[0092] Key flavor compounds were screened using partial least squares discriminant analysis (PLS-DA) combined with the difference fold (FC value threshold). Based on VIP>1.0 and FC threshold>1.4, 14 key flavor compounds were identified in the fermentation mash and base liquor from the sand-making round, including 2-pentanone, 2-pentanol, propanol, propionaldehyde, methanol, ethyl butyrate, butyl hexanoate, propionic acid, 3-octanol, 1-octen-3-ol, 3-hydroxy-2-butanone, isoamyl acetate, ethyl oleate, and ethyl palmitate. One round includes 20 substances such as trimethylpyrazine, 2,3-dimethylpyrazine, isobutyraldehyde, isovaleraldehyde, 3-hydroxy-2-butanone, furfural, isobutyric acid, meso-2,3-butanediol, ethyl phenylacetate, β-phenylethanol, hexyl acetate, 2-acetylfuran, 5-methyl-2-furfural, 2-methylpyrazine, 2,5-dimethylpyrazine, 2-ethyl-6-methylpyrazine, 1,2-propanediol, butyric acid, valeric acid, and isovaleraldehyde. Figure 9 ).

[0093] Example 4: Correlation Analysis Method of Core Microorganisms-Fermentation Mash-Base Wine

[0094] By comprehensively analyzing the Spearman correlation coefficient matrix, a correlation network diagram was drawn using Cytoscape (3.8.0). The screening criteria were |R|>0.40 and p<0.05. A network diagram of the role of the core microbial community in the fermentation of mash to the formation of base liquor flavor was constructed to accurately locate the nodes that affect the production of key flavor substances.

[0095] In the fermentation round, 1,2-propanediol was observed to be significantly positively correlated with Thermomyces in both the mash and the base liquor (p<0.05), and significantly negatively correlated with Pichia (p<0.05), indicating that these two correlations were consistent in both the mash and the base liquor. However, the correlations between other substances and microorganisms showed significant differences between the mash and the base liquor: 3-hydroxy-2-butanone was negatively correlated with Monascus and Thermomyces in the mash (p<0.05), but turned to a significant positive correlation in the base liquor (p<0.05); propionaldehyde in the base liquor... In the mash, propionic acid showed a significant positive correlation with Pichia (p<0.05), but a negative correlation in the base liquor; propionic acid showed a positive correlation with Thermomyces and a negative correlation with Pichia in the mash, but a negative correlation with Thermomyces and a positive correlation with Pichia in the base liquor; acetic acid showed a positive correlation with Monascus, Paecilomyces, and Thermoascus in the mash, and a negative correlation with Lactiplantibacillus, but its correlation with these core microorganisms in the base liquor was in the opposite direction. In one round of fermentation, ethyl phenylacetate was found to be significantly negatively correlated with Torulaspora in both the mash and the base liquor (p<0.05), caprylic acid was significantly negatively correlated with Zygosaccharomyces (p<0.05), and acetic acid was also negatively correlated with Torulaspora (p<0.05). These correlations were consistent in both the stacking and cellaring stages (i.e., mash and base liquor). However, there were also cases where the correlations between mash and base liquor were opposite: 3-hydroxy-2-butanone was positively correlated with Bacillus in the mash (p<0.05), but negatively correlated in the base liquor. The correlation was positive (p<0.05); ethyl phenylacetate was negatively correlated with Zygosaccharomyces in the mash (p<0.05), but positively correlated with it in the base liquor (p<0.05); caprylic acid was significantly negatively correlated with Torulaspora in the mash (p<0.05), but the correlation was reversed in the base liquor; acetic acid was negatively correlated with Zygosaccharomyces in the mash (p<0.05), but positively correlated in the base liquor; in addition, Bacillus was positively correlated with acetal in the mash, but the correlation was reversed in the base liquor.

Claims

1. A method for screening core microorganisms in fermented mash, characterized in that, include: S1. Collect information on the relative abundance of microorganisms in the fermented mash; S2. Based on the relative abundance information collected in step S1, screen for common microorganisms, absolutely dominant microorganisms, and symbiotic microorganisms; S3. Use linear discriminant analysis to screen for differentially expressed microorganisms; S4. Combine the ubiquitous microorganisms, absolute dominant microorganisms, and symbiotic microorganisms obtained from step S2 using a Venn diagram to obtain the marker microorganisms; S5. Combine the differentially expressed microorganisms obtained in step S3 with the marker microorganisms obtained in step S4 to obtain the core microorganisms.

2. The screening method as described in claim 1, characterized in that, In step S2, the common microorganisms are microorganisms present in each accumulation stage; Preferably, the absolutely dominant microorganism is a microorganism with a relative abundance of more than 10% at different stages; Preferably, the symbiotic microorganism is a highly connected microorganism obtained based on co-occurrence network graph analysis.

3. The screening method as described in claim 1, characterized in that, In step S3, the linear discriminant analysis is performed by using the Lefse linear discriminant analysis to analyze the OTU values ​​of all microorganisms, annotating them to the genus level, and screening for microorganisms with an LDA value greater than 4.

0.

4. The screening method as described in claim 1, characterized in that, In step S5, combining the differentially differentiated microorganisms obtained in step S3 and the marker microorganisms obtained in step S4 specifically means: combining the differentially differentiated microorganisms obtained in step S3 and the marker microorganisms obtained in step S4 to obtain the union set.

5. The application of the screening method as described in claim 1 is any one of the following: A1: Monitoring the growth and metabolism of microorganisms in the fermented mash; A2: Related to flavor compounds in fermented mash and base spirit.

6. A method for correlating characteristic flavor compounds in fermented mash and base liquor, characterized in that, Based on the screening method described in claim 1, the core microorganisms obtained are used to correlate the characteristic flavor substances in the mash and base liquor.

7. A method for correlating characteristic flavor compounds in fermented mash and base liquor, characterized in that, include: 1) Collect the content of volatile flavor compounds in the mash and base liquor; 2) Based on PLS-DA, VIP and / or volcano maps, the contents collected in step 1) are screened to obtain characteristic flavor substances; 3) The core microorganisms obtained by the screening method described in claim 1, and The characteristic flavor compounds obtained in step 2) were subjected to Spearman correlation analysis to establish a correlation network.

8. The method as described in claim 7, characterized in that, The method for collecting volatile flavor compounds in the fermented mash is GC-FID; Preferably, the method for collecting volatile flavor compounds in the base wine is LLME-GC-MS; Preferably, the filtering conditions in step 2) are VIP>1.0 and FC threshold>1.

4.

9. The method as described in claim 7, characterized in that, Step 3) specifically involves: establishing a correlation coefficient matrix based on Spearman correlation analysis, drawing a correlation network diagram, and selecting conditions that are |R|>0.40 and p<0.

05.

10. The application of the method as described in claim 7, characterized in that, Based on the established correlation network, process adjustments are made in real time to achieve phased controllable fermentation, or Based on the established network of connections, the nodes that influence the core microorganisms on key flavor compounds can be precisely located.

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