Method for analyzing influence mechanism of physicochemical indexes on brewing function microorganisms in liquor brewing process based on model and application

Abstract

The invention belongs to the technical field of wine brewing, and relates to a method for analyzing an influence mechanism of physicochemical indexes on brewing functional microorganisms in a white spirit brewing process based on a model, which comprises the following steps: collecting a white spirit brewing sample; selecting physicochemical indexes and functional microbiological indexes in the white spirit brewing process as modeling indexes; determining the physicochemical indexes and the functional microbiological indexes to obtain index determination data; preprocessing the obtained index measurement data by using a min-max standardization method and outputting a new variable; establishing an analytical model according to the output new variables to obtain a structural equation; and explaining the influence mechanism of the physicochemical indexes in the liquor brewing process on the brewing function microorganisms according to an operation result of the structural equation model. The method comprises the following steps: selecting physicochemical indexes and functional microbiological indexes representing the white spirit brewing process as modeling indexes, measuring the modeling indexes, preprocessing by using a min-max standardization method, then establishing an analytical model, and explaining a mutual influence mechanism of the physicochemical indexes and functional brewing microorganisms in the white spirit brewing process through an operation result of the analytical model.

Description

Technical Field

[0001] The invention belongs to the technical field of wine brewing, and relates to a method and application of a model-based analysis of the mechanism of influence of physical and chemical indicators of a liquor brewing process on brewing functional microorganisms. Background Art

[0002] As the actual producer of liquor, the brewing microorganisms of liquor play a vital role in liquor production. In particular, the relationship between brewing microorganisms and the physical and chemical indicators of the brewing process has always been a hot topic and difficulty in liquor brewing research. At present, the relationship between brewing microorganisms and the physical and chemical indicators of the brewing process is mostly conducted through correlation analysis and regression analysis, such as the literature "Unraveling Core Functional Microbiota in Traditional Solid-State Fermentation by High-Throughput Amplicons and Metatranscriptomics Sequencing" analyzes the correlation network between physical and chemical indicators and brewing microorganisms, and the literature "Metatranscriptomics Unravel Composition, Drivers, and Functions of the Active Microorganisms in Light-Flavor Liquor Fermentation" analyzes the RDA correlation between physical and chemical indicators and brewing microorganisms.

[0003] However, these methods can only calculate the correlation between two variables. The size of the correlation coefficient cannot represent the importance weight of the indicator, and cannot show the indirect effects that may exist in the brewing process. When the relationship between the variables in the brewing process is not direct enough, unexplained impact results may occur. Summary of the invention

[0004] The present invention aims to provide a method and application for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on model. By collecting different liquor brewing samples, physical and chemical indicators and functional microbial indicators characterizing the liquor brewing process are selected as modeling indicators. After the modeling indicators are measured, they are preprocessed using the min-max standardization method, and then an analytical model is established. The coupling relationship between the physical and chemical indicators of the liquor brewing process and the functional brewing microbial elements is explained through the calculation results of the analytical model. The connection between brewing system elements such as microbial information and physical and chemical factors can be deeply characterized, and the mutual influence mechanism between the physical and chemical and functional microbial elements of the liquor brewing process can be carefully explored.

[0005] In a first aspect, the present invention provides a method for analyzing the mechanism of influence of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0006] Step 1: Collect samples: collect liquor brewing samples of different rounds, different fermentation times, and different fermentation points;

[0007] Step 2, index screening: select the physical and chemical indexes and functional microbial indexes that characterize the liquor brewing process as modeling indicators;

[0008] Step 3, index determination: the physical and chemical indexes and functional microbial indexes after the index screening in step 2 are determined to obtain index determination data;

[0009] Step 4, data preprocessing: Use the min-max standardization method to preprocess the obtained indicator measurement data and output new variables;

[0010] Step 5: Establishment of analytical model: Establish analytical model based on output new variables to obtain structural equation;

[0011] Step 6. Explain the influencing mechanism: Based on the calculation results of the structural equation model, explain the influence mechanism of the physical and chemical indicators of the liquor brewing process on the brewing functional microorganisms.

[0012] In some embodiments, the liquor brewing sample includes at least one of fermented grains, lees, and Daqu.

[0013] In some embodiments, in step 1, the method of collecting samples is specifically to take samples from the liquor brewing sample, collect 0.5-1.5 parts by weight of samples from the center of the inner layer of the fermentation pile > 0.4m deep or the center of the fermentation tank > 1m deep, and mix them.

[0014] In some embodiments, the physical and chemical indicators include acidity, sugar content, moisture content, starch content, lactic acid content, and acetic acid content.

[0015] In some embodiments, the functional microbial indicators include acidogenic microorganisms and alcohologenic microorganisms;

[0016] Preferably, the acid-producing microorganism is Lactobacillus;

[0017] Preferably, the alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, Schizosaccharomyces pombe, and Zygosaccharomyces cerevisiae.

[0018] In some embodiments, the acidity, sugar, moisture and starch are measured by near infrared spectroscopy;

[0019] Preferably, the method for determining the concentrations of lactic acid and acetic acid is chromatography.

[0020] In some embodiments, the method for determining the functional microbial index is a high-throughput microbial sequencing method.

[0021] In some embodiments, in step 4, the min-max normalization method evenly distributes the uneven modeling indicators between [0, 1];

[0022] Preferably, the min-max normalization method is specifically performed as follows:

[0023] S1. Find the minimum and maximum values ​​of each modeling indicator;

[0024] S2. For each data point in the data set, standardization is performed using the following formula: X'=X-min / max-min, where X' is the standardized data and X is the original data.

[0025] In some embodiments, establishing the analytical model in step 5 further comprises constructing a set of equations, wherein the set of equations is as follows:

[0026] A n =c 4n-3 C1+c 4n-2 C2+c 4n-1 C3+c 4n C4 (where n = 1, 2, 3, 4, 5)

[0027] B m =a m A1 (where m = 1, 2)

[0028] C1=b1·B1+b2·B2

[0029] Among them, the A n Indicates the abundance of different functional microorganisms (where n=1, 2, 3, 4, 5): A1 indicates the abundance of functional microorganisms (acid production), that is, the abundance of Lactobacillus. A2-A5 indicate the abundance of functional microorganisms (alcohol production), specifically, A2 indicates the abundance of Saccharomyces cerevisiae, A3 indicates the abundance of Pichia pastoris, A4 indicates the abundance of Schizosaccharomyces pombe, and A5 indicates the abundance of Zygosaccharomyces. m Indicates the main organic acid content (where m=1,2): B1 indicates lactic acid content; B2 indicates acetic acid content. C1-C4 indicate the main physical and chemical indicators of the brewing process: C1 indicates acidity; C2 indicates sugar content; C3 indicates water content; C4 indicates starch content.

[0030] The a mThe path coefficients (the magnitude of the influence between variables, where m=1,2) that represent the influence of the abundance of functional microorganisms (acid production) on the content of major organic acids are represented: Specifically, a1 represents the path coefficient of the influence of Lactobacillus on the lactic acid content; a2 represents the path coefficient of the influence of Lactobacillus on the acetic acid content. The b1-b2 represent the path coefficients of the influence of the content of major organic acids on acidity: Specifically, b1 represents the path coefficient of the influence of lactic acid content on acidity, and b2 represents the path coefficient of the influence of acetic acid content on acidity. The c 4n-3 、c 4n-2 、c 4n-1 and c 4n Respectively represent the path coefficients of the effects of acidity, sugar, water content, and starch content on the abundance of different functional microorganisms (where n = 1, 2, 3, 4, and 5): Specifically, when n = 1, the functional microorganism is Lactobacillus; when n = 2, the functional microorganism is Saccharomyces cerevisiae; when n = 3, the functional microorganism is Pichia pastoris; when n = 4, the functional microorganism is Schizosaccharomyces pombe; when n = 5, the functional microorganism is Zygosaccharomyces. The model analysis mainly focuses on the path coefficients.

[0031] In a second aspect, the present invention provides an application of any of the methods described above in the mechanism of the influence of physical and chemical indicators on brewing functional microorganisms in the liquor brewing process.

[0032] In summary, the present application includes at least one of the following beneficial technical effects:

[0033] By collecting different liquor brewing samples, the physicochemical indicators and functional microbial indicators that characterize the liquor brewing process are selected as modeling indicators. After measuring the modeling indicators, the min-max standardization method is used for preprocessing, and then an analytical model is established. The coupling relationship between the physicochemical indicators and functional brewing microbial elements of the liquor brewing process is explained through the calculation results of the analytical model. The connection between brewing system elements such as microbial information and physicochemical factors can be deeply characterized, and the mutual influence mechanism between the physicochemical and functional microbial elements of the liquor brewing process can be carefully explored. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a framework diagram of the effects of physical and chemical factors on brewing functional microorganisms during liquor brewing;

[0035] Figure 2 This is the structural equation model diagram of the physical and chemical factors of the liquor brewing process and the brewing functional microorganisms in Example 1, wherein *** means: P≤0.001, extremely significant; ** means: P≤0.01, relatively significant; * means: P≤0.05, significant. DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further described below by specific embodiments, which do not limit the protection scope of the present invention. Some non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the protection scope of the present invention.

[0037] At present, the relationship between brewing microorganisms and physical and chemical indicators of the brewing process is mostly carried out through correlation analysis and regression analysis, which can only calculate the correlation between two variables. The size of the correlation coefficient cannot represent the importance weight of the indicator and cannot show the indirect effects that may exist in the brewing process. When the relationship between the variables in the brewing process is not direct enough, the impact cannot be explained.

[0038] In order to better explain the mechanism of the influence of physical and chemical indicators of the liquor brewing process on the functional microorganisms of brewing, the present invention attempts to use the powerful computing power of computers to more deeply fit and analyze how the functional microorganisms of the liquor brewing process are affected by important environmental variables of the brewing process. For example, the structural equation model (SEM) is a method for establishing, estimating and testing causal relationship models. The model contains both observable explicit variables and potential variables that cannot be directly observed. SEM can analyze the relationship between single indicators. By comparing the path coefficients of different directions of the same path, the difference in the degree of mutual influence between the two variables can be analyzed, so as to derive the party with greater influence, thereby improving the influence mechanism.

[0039] In the process of exploring the establishment of the analytical model, the present invention finds that the model parameters constructed by the indicators of different physicochemical index information and functional microorganism information are different. First, acidity, sugar, moisture, starch content, lactic acid content, acetic acid content, dextrin and saccharification power are screened as physicochemical indicators, acid-producing microorganisms and alcohol-producing microorganisms are used as functional microorganisms, and the acid-producing microorganisms are Lactobacillus; alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, fission yeast, and zygosaccharomyces. As a result, the analytical model established failed to be established and could not be used to explain the impact mechanism. Therefore, the present invention further screens the physicochemical indicators and functional microorganisms, and finally finds that acidity, sugar, moisture, starch content, lactic acid content, and acetic acid content are screened as physicochemical indicators, acid-producing microorganisms and alcohol-producing microorganisms are used as functional microorganisms, and the acid-producing microorganisms are Lactobacillus; alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, fission yeast, and zygosaccharomyces, and the analytical model is successfully established. In addition, the determination method of physical and chemical indicators and the data preprocessing method also play a key role in the successful establishment of the analytical model. The present invention explored and found that by determining the acidity, sugar, moisture and starch indicators by near-infrared spectroscopy, determining the concentrations of lactic acid and acetic acid by chromatography, and obtaining the abundance of Lactobacillus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and fission yeast by high-throughput microbial sequencing, and using the min-max normalization method for data preprocessing, the analytical model can be successfully established, and the influence mechanism of the physical and chemical indicators of the liquor brewing process on the brewing functional microorganisms can be successfully analyzed.

[0040] The structural equation model established by the present invention is not a pairwise correlation, but an overall model based on the selected data, and can reflect the cause-effect relationship between each physicochemical index and the key brewing microorganisms, and the intensity of the pairwise interaction under the overall background. The present invention takes into account the unidirectional influence of the fermentation physicochemical indexes on the brewing microorganisms, and further considers the reverse influence of the brewing functional microorganisms on the physicochemical indexes, enriching the interaction network between the physicochemical indexes and the brewing functional microorganisms in the liquor brewing process. Therefore, based on the interaction between the fermentation physicochemical indexes and the brewing microorganisms, the analytical method involved in the present invention is very important for establishing a method for accurately predicting and regulating microorganisms, which helps to analyze the physicochemical index environment for better growth and metabolism of brewing functional microorganisms, and helps to improve the quality of liquor.

[0041] The present invention integrates a variety of physical and chemical indicators and functional microbial data of liquor brewing process, and develops a new method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms, which can provide important scientific basis and practical value for further in-depth research on the coupling relationship between physical and chemical indicators of liquor brewing process and brewing functional microorganisms. The structural equation model analysis method can well explain the coupling relationship between physical and chemical indicators of liquor brewing process and functional brewing microbial elements, and can deeply characterize the connection between brewing system elements such as microbial information and physical and chemical factors, and carefully explore the mutual influence mechanism between physical and chemical and functional microbial elements of liquor brewing process. There is a coupling relationship between physical and chemical indicators and microorganisms in liquor brewing process. Physical and chemical indicators can affect brewing functional microorganisms, and organic matter produced by brewing functional microorganism metabolism can affect physical and chemical indicators of brewing process. At present, the analysis of the interaction between the two is not clear. In the model of the present invention, by comparing the path coefficients of different directions of the same path, the difference in the degree of mutual influence between the two variables can be analyzed, so as to obtain the party with greater influence, thereby improving the influence mechanism.

[0042] Example 1

[0043] A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0044] Step 1. Collect samples: Collect liquor brewing samples from different rounds, different fermentation times, and different fermentation points.

[0045] Step 2: Indicator screening: Select physical and chemical indicators and functional microbial indicators that characterize the liquor brewing process as modeling indicators.

[0046] Step 3, indicator determination: the physical and chemical indicators and functional microbial indicators after the indicator screening in step 2 are measured to obtain indicator measurement data.

[0047] Step 4: Data preprocessing: Use the min-max standardization method to preprocess the obtained indicator measurement data and output new variables.

[0048] Step 5: Establish an analytical model: Establish an analytical model based on the output new variables to obtain the structural equation.

[0049] Step 6. Explain the influencing mechanism: Based on the calculation results of the structural equation model, explain the influence mechanism of the physical and chemical indicators of the liquor brewing process on the brewing functional microorganisms.

[0050] In step 1 of this embodiment, the liquor brewing sample includes at least one of fermented grains, distiller's grains, and Daqu, such as fermented grains or Daqu. Specifically, the liquor brewing sample of this embodiment is fermented grains.

[0051] Furthermore, the specific method of collecting samples in step 1 of this embodiment is: sampling in the liquor brewing sample, collecting 0.5-1.5 parts by weight of samples from the center of the inner layer of the fermentation pile > 0.4m deep or the center of the fermentation tank > 1m deep, and mixing.

[0052] Further, the indicator selection method in step 2 is: select indicators around the functional microorganisms in the brewing sample and the physical and chemical indicators of the microenvironment in which they are located. Considering that the main products of the brewing process are ethanol and organic acids, the functional microorganism data mainly include microorganisms that can metabolize the main products, that is, alcohol-producing functional microorganisms and acid-producing functional microorganisms. Among them, the acid-producing microorganisms are mainly lactobacillus, Weissella, Pediococcus, Leuconostoc and Lactococcus, and the alcohol-producing microorganisms are mainly saccharomyces cerevisiae, Pichia pastoris, fission yeast and zygosaccharomyces. The physical and chemical indicators of the microenvironment in which the functional microorganisms are located are composed of acidity, sugar, moisture, starch content, lactic acid content, acetic acid content, dextrin and saccharification power, and strive to fully and comprehensively cover the physical and chemical indicators of the microenvironment. However, as in Comparative Example 1, when the above 8 physical and chemical indicators are used to construct the SEM equation, the evaluation indicators of the structural equation model diagram of the physical and chemical indicators of the liquor brewing process obtained on the mechanism of the influence of brewing functional microorganisms do not meet the parameter requirements. Similarly, as in comparative example 3, when the above five acid-producing microorganisms were used to construct the SEM equation, the evaluation index of the structural equation model diagram of the influence mechanism of the physical and chemical indicators of the liquor brewing process on the brewing functional microorganisms did not meet the parameter requirements. Considering that the information required by the model may need to be further summarized, the above eight physical and chemical indicators and five acid-producing microorganisms were removed one by one to simplify the model, and finally six physical and chemical indicators of acidity, sugar, water, starch content, lactic acid content and acetic acid content, as well as Lactobacillus were selected as the physical and chemical indicators of the model.

[0053] Furthermore, the indicator selection results in step 2 are as follows:

[0054] Physical and chemical index information includes: acidity, sugar, moisture, starch content, lactic acid content, acetic acid content;

[0055] Functional microbial indicator information includes: acid-producing microorganisms and alcohol-producing microorganisms. The acid-producing microorganisms are Lactobacillus; the alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, fission yeast and Zygosaccharomyces.

[0056] Further, the specific determination method in step 3 is as follows:

[0057] The acidity, sugar, moisture and starch indexes were measured by near infrared spectroscopy; the specific measurement method of near infrared spectroscopy is: stir the brewing sample to be tested evenly, remove obvious impurities, load it into the sample dish of the near infrared spectrometer in a non-agglomerated state, and ensure that the irradiated surface is flat and seamless, and the sample does not precipitate liquid. The near infrared spectrum of the same sample is collected three times, and the average near infrared spectrum of the sample is synthesized; then the acidity, sugar, moisture and starch content indexes of the fermented mash to be tested are obtained using the calibration model of the acidity, sugar, moisture and starch content of the fermented mash.

[0058] The concentration of lactic acid and acetic acid measured by chromatography; The specific determination method of chromatography is: dilute the brewing sample to be tested with a certain amount of ultrapure water, and extract the lactic acid and ethanol therein by vortex oscillation, and centrifuge at a speed of 10000rpm, take the supernatant and add a certain amount of ultrapure water to dilute it, and pass the diluted extract through a 0.22um membrane and detect it on a liquid chromatograph. The chromatographic column uses a 4.6×50mm, 1.8um Agilent C18 chromatographic column, 0.12% phosphoric acid solution as mobile phase A, and 100% methanol as mobile phase B for gradient elution; the column temperature of the chromatographic column is 30°C, the detection wavelength is 214nm, the flow rate is 0.2mL / min, and the injection volume is 10uL. Based on the corresponding peak area of ​​the gradient lactic acid and acetic acid standard curves, the lactic acid and acetic acid content indicators of the tested mash are converted.

[0059] The abundance of Lactobacillus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and Schizosaccharomyces obtained by high-throughput microbial sequencing; the specific determination method of high-throughput microbial sequencing is: extract and purify the total microbial DNA of the brewing sample, and perform PCR amplification and product electrophoresis detection as appropriate. The qualified DNA samples are added with fragmentation buffer as appropriate, randomly sheared by ultrasonic crusher, and the short fragment DNA after shearing is used to construct the gene library. The qualified samples are sequenced by PE150 or PE250 on the Illumina NovaSeq 6000 high-throughput sequencing platform to obtain the original image data file, which is converted into original sequencing data by base recognition analysis. After quality control by Trimmomactic software, the cleanreads are assembled and predicted, and the Unigenes sequence of the gene set is compared with the NCBI-NR database for species annotation to obtain the composition and abundance information of each species.

[0060] Furthermore, the specific method of using the min-max normalization method for data preprocessing in step 4 is as follows:

[0061] S1. Find the minimum (min) and maximum (max) values ​​of each observed variable;

[0062] S2. For each data point in the data set, standardization is performed using the following formula: X'=(X-min) / (max-min), where X' is the standardized data and X is the original data.

[0063] The standardized data X' will fall between [0,1]. The purpose of preprocessing is to evenly distribute the uneven variables between [0,1].

[0064] Furthermore, the specific model building method in step 5 is: based on SPSS Pro software, input data and the equation group, calculate and output chi-square freedom ratio, GFI, RMR, CFI, NFI evaluation indicators, and judge the effectiveness of the model according to the chi-square freedom ratio, GFI, RMR, CFI, NFI evaluation indicators. The details are as follows:

[0065] Based on SPSSpro software, a structural equation model (SEM) was constructed. Paths were established based on the output new variables, and data were imported, path construction was performed, and model testing was performed. The selected modeling indicators were used as features to construct the model. According to the actual situation, the abundance of functional microorganisms (acid production) would affect the lactic acid and acetic acid content of brewing samples, and the lactic acid and acetic acid content would affect the acidity of brewing samples. The acidity, sugar, water content, and starch content of brewing samples would jointly affect the abundance of functional microorganisms (acid production) and functional microorganisms (alcohol production). Therefore, a path pointing to a specific variable was established ( Figure 1 ), construct the system of equations:

[0066] A n =c 4n-3 C1+c 4n-2 C2+c 4n-1 C3+c 4n C4 (where n = 1, 2, 3, 4, 5)

[0067] B m =a m A1 (where m = 1, 2)

[0068] C1=b1·B1+b2·B2

[0069] Among them, the A nrepresents the abundance of different functional microorganisms (where n=1, 2, 3, 4, 5), A1 represents the abundance of functional microorganisms (acid production), that is, the abundance of Lactobacillus. A2-A5 represent the abundance of functional microorganisms (alcohol production), specifically, A2 represents the abundance of Saccharomyces cerevisiae, A3 represents the abundance of Pichia pastoris, A4 represents the abundance of fission yeast, and A5 represents the abundance of Zygosaccharomyces. m Indicates the main organic acid content (where m=1,2), B1 indicates the lactic acid content; B2 indicates the acetic acid content. C1-C4 indicate the main physical and chemical indicators of the brewing process, C1 indicates acidity; C2 indicates sugar; C3 indicates water; and C4 indicates starch content.

[0070] The a m The path coefficients (the magnitude of the influence between variables, where m=1,2) that represent the influence of the abundance of functional microorganisms (acid production) on the content of major organic acids are represented: Specifically, a1 represents the path coefficient of the influence of Lactobacillus on the lactic acid content; a2 represents the path coefficient of the influence of Lactobacillus on the acetic acid content. The b1-b2 represent the path coefficients of the influence of the content of major organic acids on acidity: Specifically, b1 represents the path coefficient of the influence of lactic acid content on acidity, and b2 represents the path coefficient of the influence of acetic acid content on acidity. The c 4n-3 、c 4n-2 、c 4n-1 and c 4n They respectively represent the path coefficients of the effects of acidity, sugar, water content and starch content on the abundance of different functional microorganisms (where n=1, 2, 3, 4, 5): specifically, when n=1, the functional microorganism is Lactobacillus; when n=2, the functional microorganism is Saccharomyces cerevisiae; when n=3, the functional microorganism is Pichia pastoris; when n=4, the functional microorganism is fission yeast; when n=5, the functional microorganism is Zygosaccharomyces cerevisiae.

[0071] The obtained model chi-square freedom ratio was 3.967, GFI was 0.900, RMR was 0.002, CFI was 0.922, and NFI was 0.900 (satisfying chi-square freedom ratio ≤ 5, GFI ≥ 0.90, RMR ≤ 0.10, CFI ≥ 0.90, and NFI ≥ 0.90), and the model was successfully established.

[0072] Furthermore, the specific impact mechanism in step 6 is as follows:

[0073] like Figure 1As shown in the figure, the acidity difference caused by Lactobacillus is the key reason for the heterogeneous succession of alcohol-producing microorganisms dominated by Pichia pastoris during liquor brewing. Among them, acidity is driven by Lactobacillus (P<0.01), while the core alcohol-producing microorganisms are driven by environmental parameters such as acidity (P<0.05). This shows that Lactobacillus can regulate the core alcohol-producing microorganisms by adjusting environmental parameters. According to the path coefficient and P value of the model, Lactobacillus regulates acidity by synthesizing lactic acid and acetic acid (P<0.01), while acidity significantly affects Pichia pastoris (P<0.01) and Saccharomyces cerevisiae (P<0.05). In addition, the difference in sugar content has a significant positive effect on the succession process of Lactobacillus (P<0.01). This may be the reason why reducing sugar has a negative effect on Pichia pastoris (P<0.01). Starch content and moisture parameters directly change Lactobacillus and Zygosaccharomyces (P<0.01). In summary, the model reveals the potential connection between environmental parameters and heterogeneous brewing microorganisms at spatial scales.

[0074] Comparative Example 1

[0075] A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0076] Step 1. Collect samples: Collect liquor brewing samples from different rounds, different fermentation times, and different fermentation points.

[0077] Step 2, index screening: select the physical and chemical index information and functional microbial information that characterize the liquor brewing process; specifically, the physical and chemical index information includes: acidity, sugar, water, starch content, lactic acid concentration, acetic acid concentration, dextrin and saccharifying power;

[0078] Functional microbial indicator information includes: acid-producing microorganisms and alcohol-producing microorganisms. Acid-producing microorganisms include Lactobacillus; alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, fission yeast, and Zygosaccharomyces.

[0079] Step 3, index determination: perform index determination on the physical and chemical index information and functional microbial information after the index screening in step 2 to obtain index determination data;

[0080] The acidity, sugar, moisture and starch indexes were measured by near-infrared spectroscopy; the concentrations of lactic acid and acetic acid were measured by chromatography; the abundance of Lactobacillus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and Schizosaccharomyces were obtained by high-throughput microbial sequencing; the above determination methods were the same as those in Example 1.

[0081] Step 4, data preprocessing: Based on the obtained index measurement data, the min-max standardization method of Example 1 is used to preprocess the index measurement data of the physical and chemical index information and the functional microbial information and output new variables.

[0082] Step 5, model building: Establish a path based on the screened indicators. By importing data, building the path, and testing the model, the chi-square freedom ratio of the model obtained was 13.295, GFI was 0.417, RMR was 0.476, CFI was 0.533, and NFI was 0.732 (it did not meet the chi-square freedom ratio ≤ 5, GFI ≥ 0.90, RMR ≤ 0.10, CFI ≥ 0.90, and NFI ≥ 0.90). The model building failed and the subsequent steps of explaining the impact mechanism could not be carried out.

[0083] The difference between the comparative example 1 and the embodiment 1 is that in step 2, the physical and chemical index information screened is different. It can be seen that the dextrin and saccharification power measured by near infrared are not suitable for constructing the analytical model of the present invention.

[0084] Comparative Example 2

[0085] A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0086] Step 1. Collect samples: Collect liquor brewing samples from different rounds, different fermentation times, and different fermentation points.

[0087] Step 2, index screening: select the physical and chemical index information and functional microbial information that characterize the liquor brewing process; specifically, the physical and chemical index information includes: acidity, sugar, water, starch content, lactic acid content, acetic acid content;

[0088] Functional microbial indicator information includes: total number of microorganisms, total number of wine-producing yeast colonies, total number of non-wine-producing yeast colonies and total number of lactic acid bacteria colonies.

[0089] Step 3, index determination: perform index determination on the physical and chemical index information and functional microbial information after index screening in step 2 to obtain index determination data.

[0090] Acidity, sugar, moisture and starch indicators were determined by near-infrared spectroscopy; lactic acid and acetic acid concentrations were measured by chromatography; high-throughput microbial sequencing obtained the total number of microbial examinations, the total number of wine-producing yeast colonies, the total number of non-wine-producing yeast colonies and the total number of lactic acid bacteria colonies.

[0091] Step 4, data preprocessing: Based on the obtained index measurement data, the index measurement data of the physical and chemical index information and the functional microbial information are preprocessed using the min-max standardization method and new variables are output;

[0092] Step 5, model establishment: Establish a path based on the screened indicators. By importing data, building the path, and testing the model, the chi-square freedom ratio of the model obtained is 25.963, GFI is 0.254, RMR is 0.867, CFI is 0.447, and NFI is 0.362 (it does not meet the chi-square freedom ratio ≤ 5, GFI ≥ 0.90, RMR ≤ 0.10, CFI ≥ 0.90, and NFI ≥ 0.90). The model establishment failed and the subsequent steps of explaining the impact mechanism could not be carried out.

[0093] The difference between the above-mentioned comparative example 2 and embodiment 1 is that in step 2, the functional microbial indicator information screened is different and the corresponding determination method is different. It can be seen that the total number of microscopic microorganisms, the total number of wine-producing yeast colonies, the total number of non-wine-producing yeast colonies and the total number of lactic acid bacteria colonies measured by traditional microbial detection methods are not suitable for constructing the analytical model of the present invention.

[0094] Comparative Example 3

[0095] A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0096] Step 1. Collect samples: Collect liquor brewing samples from different rounds, different fermentation times, and different fermentation points.

[0097] Step 2, index screening: select the physical and chemical index information and functional microbial information that characterize the liquor brewing process; specifically, the physical and chemical index information includes: acidity, sugar, water, starch content, lactic acid content, acetic acid content;

[0098] Functional microbial indicator information includes: the abundance of Lactobacillus, Weissella, Pediococcus, Leuconostoc, Lactococcus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and Schizosaccharomyces pombe.

[0099] Step 3, index determination: perform index determination on the physical and chemical index information and functional microbial information after index screening in step 2 to obtain index determination data.

[0100] The acidity, sugar, moisture and starch indicators were measured by near-infrared spectroscopy technology; the lactic acid and acetic acid concentrations were measured by chromatography; the abundance of Lactobacillus, Weissella, Pediococcus, Leuconostoc, Lactococcus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and Schizosaccharomyces pombe was measured by high-throughput microbial sequencing to obtain the indicator measurement data.

[0101] Step 4, data preprocessing: Based on the obtained index measurement data, the index measurement data of the physical and chemical index information and the functional microbial information are preprocessed using the min-max standardization method and new variables are output;

[0102] Step 5, model establishment: Establish a path based on the screened indicators. By importing data, building the path, and testing the model, the chi-square freedom ratio of the model obtained is 18.395, GFI is 0.189, RMR is 0.683, CFI is 0.409, and NFI is 0.685 (it does not meet the chi-square freedom ratio ≤ 5, GFI ≥ 0.90, RMR ≤ 0.10, CFI ≥ 0.90, and NFI ≥ 0.90). The model establishment failed and the subsequent steps of explaining the impact mechanism could not be carried out.

[0103] The difference between the above-mentioned comparative example 3 and embodiment 1 is that in step 2, the functional microbial indicator information screened is different and the corresponding determination methods are different. It can be seen that Weissella, Pediococcus, Leuconostoc and Lactococcus measured by the microbial sequencing method are not suitable for constructing the analytical model of the present invention.

[0104] Comparative Example 4

[0105] A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0106] Step 1. Collect samples: Collect liquor brewing samples from different rounds, different fermentation times, and different fermentation points.

[0107] Step 2, index screening: select the physical and chemical index information and functional microbial information that characterize the liquor brewing process; specifically, the physical and chemical index information includes: acidity, sugar, moisture, starch content, lactic acid content, acetic acid content; the functional microbial index information includes: acid-producing microorganisms and alcohol-producing microorganisms, acid-producing microorganisms include Lactobacillus; alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, fission yeast, and Zygosaccharomyces.

[0108] Step 3, index determination: perform index determination on the physical and chemical index information and functional microbial information after index screening in step 2 to obtain index determination data.

[0109] The acidity, sugar, moisture, starch, lactic acid and acetic acid concentration indicators were measured by near-infrared spectroscopy technology; the abundance of Lactobacillus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and Schizosaccharomyces pombe was measured by high-throughput microbial sequencing to obtain indicator measurement data.

[0110] Step 4, data preprocessing: Based on the obtained index measurement data, the index measurement data of the physical and chemical index information and the functional microbial information are preprocessed using the min-max standardization method and new variables are output;

[0111] Step 5, model establishment: Establish a path based on the screened indicators. By importing data, building the path, and testing the model, the chi-square freedom ratio of the model obtained is 9.886, GFI is 0.808, RMR is 0.120, CFI is 0.832, and NFI is 0.867 (satisfying the chi-square freedom ratio ≤ 5, GFI ≥ 0.90, RMR ≤ 0.10, CFI ≥ 0.90, NFI ≥ 0.90). The model establishment failed, and the subsequent steps of explaining the impact mechanism could not be carried out.

[0112] The difference between the comparative example 4 and the example 1 is that in step 2, the determination method of the lactic acid and acetic acid contents is different. It can be seen that the lactic acid and acetic acid contents measured by near infrared are not suitable for constructing the analytical model of the present invention.

[0113] Comparative Example 5

[0114] A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, comprising the following steps:

[0115] Step 1. Collect samples: Collect liquor brewing samples from different rounds, different fermentation times, and different fermentation points.

[0116] Step 2, index screening: select the physical and chemical index information and functional microbial information that characterize the liquor brewing process; specifically, the physical and chemical index information includes: acidity, sugar, water, starch content, lactic acid content, acetic acid content;

[0117] Functional microbial indicator information includes: acid-producing microorganisms and alcohol-producing microorganisms. Acid-producing microorganisms include Lactobacillus; alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, fission yeast, and Zygosaccharomyces.

[0118] Step 3, index determination: perform index determination on the physical and chemical index information and functional microbial information after index screening in step 2 to obtain index determination data.

[0119] Acidity, sugar, moisture and starch indicators were determined by near-infrared spectroscopy; lactic acid and acetic acid concentrations were measured by chromatography; and the abundance of Lactobacillus, Saccharomyces cerevisiae, Zygosaccharomyces, Pichia pastoris and Schizosaccharomyces pombe was obtained by high-throughput microbial sequencing.

[0120] Step 4: Data preprocessing: Based on the obtained indicator measurement data, the indicator measurement data of physical and chemical indicator information and functional microbial information are directly used to establish the subsequent model.

[0121] Step 5, model establishment: Establish a path based on the screened indicators. By importing data, building the path, and testing the model, the chi-square freedom ratio of the model obtained is 10.628, GFI is 0.528, RMR is 0.387, CFI is 0.618, and NFI is 0.399 (satisfying the chi-square freedom ratio ≤ 5, GFI ≥ 0.90, RMR ≤ 0.10, CFI ≥ 0.90, and NFI ≥ 0.90). The model establishment failed, and the subsequent steps of explaining the impact mechanism could not be carried out.

[0122] The difference between the comparative example 5 and the embodiment 1 is that no preprocessing is performed in step 4 of the comparative example 5. It can be seen that the data without preprocessing is not suitable for constructing the analytical model of the present invention.

[0123] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for analyzing the influence mechanism of physical and chemical indicators of liquor brewing process on brewing functional microorganisms based on a model, characterized in that: The following steps are involved: Step 1: Collect samples: collect liquor brewing samples of different rounds, different fermentation times, and different fermentation points; Step 2, index screening: select the physical and chemical indexes and functional microbial indexes that characterize the liquor brewing process as modeling indicators; Step 3, index determination: the physical and chemical indicators and functional microbial indicators after the index screening in step 2 are measured to obtain index determination data; Step 4, data preprocessing: Use the min-max standardization method to preprocess the obtained indicator measurement data and output new variables; Step 5: Establishment of analytical model: Establish an analytical model based on the output new variables to obtain the structural equation; Step 6. Explain the influencing mechanism: Based on the calculation results of the structural equation model, explain the influence mechanism of the physical and chemical indicators of the liquor brewing process on the brewing functional microorganisms.

2. The method according to claim 1, characterized in that The liquor brewing sample includes at least one of fermented grains, lees and Daqu.

3. The method according to claim 1, characterized in that In step 1, the sample collection method is specifically as follows: sampling is performed in the liquor brewing sample, and 0.5-1.5 parts by weight of the sample is collected from the center of the inner layer of the fermentation pile > 0.4m deep or the center of the fermentation tank > 1m deep, and the samples are mixed.

4. The method according to claim 1, characterized in that The physical and chemical indicators include acidity, sugar content, moisture content, starch content, lactic acid content, and acetic acid content.

5. The method according to claim 1, characterized in that The functional microbial indicators include acid-producing microorganisms and alcohol-producing microorganisms; Preferably, the acid-producing microorganism is Lactobacillus; Preferably, the alcohol-producing microorganisms include Saccharomyces cerevisiae, Pichia pastoris, Schizosaccharomyces pombe, and Zygosaccharomyces cerevisiae.

6. The method according to claim 4, characterized in that The acidity, sugar, moisture and starch are determined by near infrared spectroscopy; Preferably, the method for determining the concentrations of lactic acid and acetic acid is chromatography.

7. The method according to claim 1, characterized in that The method for determining the functional microbial index is a high-throughput microbial sequencing method.

8. The method according to claim 1, characterized in that In step 4, the min-max normalization method evenly distributes the uneven modeling indicators between [0, 1]; Preferably, the min-max normalization method is specifically performed as follows: S1. Find the minimum and maximum values ​​of each modeling indicator; S2. For each data point in the data set, standardization is performed using the following formula: X'=X-min / max-min, where X' is the standardized data and X is the original data.

9. The method according to claim 1, characterized in that The step 5 of establishing the analytical model further includes constructing a set of equations, which are as follows: A n =c 4n-3 ·C1+c 4n-2 ·C2+c 4n-1 ·C3+c 4n ·C4 B m =a m ·A1 C1=b1·B1+b2·B2 Among them, the A n Represents the abundance of different functional microorganisms, where n = 1, 2, 3, 4, 5; m Represents the main organic acid content, where m=1,2; C1-C4 represent the main physical and chemical indicators of the brewing process; The a m represents the path coefficient of the influence of the abundance of acid-producing functional microorganisms on the content of major organic acids, wherein m=1,2; b1-b2 represents the path coefficient of the influence of the content of major organic acids on acidity; c 4n-3 、c 4n-2 、c 4n-1 and c 4n They represent the path coefficients of the effects of acidity, sugar, water, and starch content on the abundance of different functional microorganisms, respectively, where n=1, 2, 3, 4, and 5.

10. Application of the method according to any one of claims 1 to 9 in the influence mechanism of physical and chemical indicators on brewing functional microorganisms in the liquor brewing process.

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