Microorganism abundance prediction method and device for yeast fermentation and electronic equipment
By constructing a microbial abundance prediction model based on a time sequence neural network, using the environment, fermentation stage and metabolic compound data during the Daqu fermentation process, the problems of low efficiency and high cost of determining microbial abundance in the existing technology are solved, and an efficient and low-cost method for dynamic monitoring of microbial populations during the Daqu fermentation cycle is realized.
Patent Information
- Application Number
- CN202510056134.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-14
- Publication Date
- 2025-04-25
AI Technical Summary
The determination of microbial abundance during the existing Dako fermentation process relies on manual sampling and detection, which is time-consuming and labor-intensive, making it difficult to achieve dynamic monitoring of microbial populations during the fermentation cycle, with low detection efficiency and high cost.
By obtaining the fermentation data of each time point during the Daqu fermentation process, a Daqu fermentation data set was established, and the microbial abundance prediction model was trained based on the time sequence neural network model, and the microbial abundance was predicted using environmental data, fermentation stage data and metabolic compound data.
It improves the efficiency of determining microbial abundance, reduces costs, and realizes dynamic monitoring of microbial populations during the fermentation cycle, providing a scientific basis for the optimization of the Daqu fermentation process.
Smart Images
Figure CN119889428A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of winemaking, and in particular to a method and device for predicting the abundance of microorganisms in Daqu fermentation, and electronic equipment. Background Art
[0002] Daqu is a type of koji that has been fermented for a long time. It is usually made from wheat, yellow rice, peas and other raw materials through a series of fermentation, koji culture and cooking processes. Daqu contains a variety of microorganisms, including molds, yeasts and bacteria. Daqu is one of the main fermentation agents used in the production of traditional Chinese liquor, and is widely used in the brewing process of Luzhou-flavor and Maotai-flavor liquors. Daqu fermentation refers to the process of brewing liquor using Daqu as a saccharification fermentation agent.
[0003] As an important matrix of the traditional fermentation industry, the fermentation process of Daqu has a decisive influence on the flavor and quality of liquor, especially the abundance of microorganisms in the fermentation process has an important impact on the final liquor quality and fermentation effect. At present, the determination of the abundance of microorganisms in the Daqu fermentation process relies on manual sampling and detection, such as the use of genome sequencing, PLFA (phospholipid fatty acid analysis), high-throughput sequencing and other methods. These detection methods are time-consuming and labor-intensive, and it is difficult to achieve dynamic monitoring of microbial populations during the fermentation cycle. The detection efficiency is low and the cost is high. Summary of the invention
[0004] The present invention aims to solve the problems of low efficiency and high cost in the existing method for determining the abundance of microorganisms in Daqu fermentation, and proposes a method and device for predicting the abundance of microorganisms in Daqu fermentation, and an electronic device.
[0005] The technical solution adopted by the present invention to solve the above technical problems is:
[0006] In a first aspect, the present invention provides a method for predicting the abundance of microorganisms in Daqu fermentation, the method comprising:
[0007] Acquire fermentation data at each time point during the fermentation process of Daqu, and establish a Daqu fermentation data set according to the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data, and microbial abundance data;
[0008] Training a time series neural network model based on the Daqu fermentation data set to obtain a microbial abundance prediction model;
[0009] The environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted are obtained, and the microbial abundance of the Daqu to be predicted at the corresponding time point is obtained according to the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and based on the microbial abundance prediction model.
[0010] Furthermore, the environmental data includes temperature data and humidity data; the fermentation stage data is the time node in the Daqu fermentation cycle, and the unit of the time node is day or hour.
[0011] Furthermore, the metabolic compound data includes one or more compound content data of esters, alcohols, aldehydes, acids, ketones, pyrazines, furans and aromatics, the esters include at least ethyl hexadecanoate and ethyl hexanoate, the alcohols include at least phenylethanol and 2,3-butanediol, the acids include at least acetic acid and hexanoic acid, and the pyrazines include at least 2,5-dimethylpyrazine, trimethylpyrazine and 2,6-dimethylpyrazine.
[0012] Furthermore, the microbial abundance data includes the types and quantities of microbial populations during the Daqu fermentation process, and the microbial populations include at least Bacillus, Aspergillus, Saccharomyces cerevisiae, Pediococcus acidilactici and Staphylococcus.
[0013] Furthermore, the method also includes: standardizing the environmental data, fermentation stage data and metabolic compound data in the Daqu fermentation data set, and using the same processing method to standardize the environmental data, fermentation stage data and metabolic compound data of the predicted Daqu.
[0014] Further, training a time series neural network model based on the Daqu fermentation data set includes:
[0015] The environmental data, fermentation stage data, and metabolic compound data at each time point are used as input features, and the corresponding microbial abundance data are used as output labels of the time series neural network model to construct training sets and validation sets;
[0016] Based on the training set and using a supervised learning method to train a time series neural network model, compare the output features of the time series neural network model with the corresponding output labels, calculate the corresponding loss function, and optimize the model through back propagation;
[0017] The time series neural network model is verified based on the verification set. When the loss function of the time series neural network model is minimized and the prediction error is less than the error threshold, the corresponding time series neural network model is used as a microbial abundance prediction model.
[0018] Furthermore, the time point of the output label is the same as the time point of the corresponding input feature;
[0019] Obtain the microbial abundance of the Daqu to be predicted at the corresponding time point, including:
[0020] After inputting the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted at a certain time point into the microbial abundance prediction model, the microbial abundance of the Daqu to be predicted at that time point is obtained.
[0021] Furthermore, the output label is the microbial abundance data corresponding to the input feature at a future time point;
[0022] Obtain the microbial abundance of the Daqu to be predicted at the corresponding time point, including:
[0023] After inputting the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted at a certain time point or multiple time points into the microbial abundance prediction model, the microbial abundance of the Daqu to be predicted at the future time point is obtained.
[0024] In a second aspect, the present invention provides a device for predicting the abundance of microorganisms in Daqu fermentation, the device comprising:
[0025] an acquisition unit, used to acquire fermentation data at each time point during the fermentation process of Daqu, and establish a Daqu fermentation data set according to the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data and microbial abundance data;
[0026] A training unit, used for training a time series neural network model based on the Daqu fermentation data set to obtain a microbial abundance prediction model;
[0027] The prediction unit is used to obtain the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted, and obtain the microbial abundance of the Daqu to be predicted at the corresponding time point based on the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and based on the microbial abundance prediction model.
[0028] In a third aspect, the present invention provides an electronic device, the electronic device comprising a processor, a memory and a communication bus;
[0029] The communication bus is used to realize the connection and communication between the processor and the memory;
[0030] The processor is used to execute one or more programs in the memory to implement the steps of the method for predicting microbial abundance in Daqu fermentation as described in the first aspect.
[0031] The beneficial effects of the present invention are as follows: the method and device for predicting the microbial abundance of Daqu fermentation provided by the present invention, and the electronic device, by constructing a microbial abundance prediction model, can predict the microbial abundance at the corresponding time point of Daqu fermentation based on environmental data, fermentation stage data, and metabolic compound data, thereby improving the efficiency of determining the microbial abundance and reducing costs. By determining the microbial abundance of Daqu fermentation, dynamic monitoring of the microbial population during the Daqu fermentation cycle can be achieved, providing a scientific basis for optimizing the Daqu fermentation process. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Figure 1 A schematic diagram of a process for predicting the abundance of microorganisms in Daqu fermentation provided in an embodiment;
[0033] Figure 2 A schematic diagram comparing the output features and corresponding output labels of a microbial abundance prediction model provided in an embodiment;
[0034] Figure 3 A schematic diagram of the structure of a device for predicting the abundance of microorganisms in Daqu fermentation provided in an embodiment;
[0035] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment. DETAILED DESCRIPTION
[0036] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.
[0037] In some of the processes described in the specification of the present invention and the above-mentioned figures, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The sequence numbers of the operations are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.
[0038] At present, the scheme for determining the abundance of microorganisms in the Daqu fermentation process is usually to detect after sampling, such as using genome sequencing, PLFA (phospholipid fatty acid analysis), high-throughput sequencing and other methods to detect microbial abundance. The inventors have found through research that these methods are time-consuming and labor-intensive, and it is difficult to achieve dynamic monitoring of microbial populations during the fermentation cycle. There are problems of low detection efficiency and high cost.
[0039] Based on this, the technical solution of the present invention is proposed. In the present invention, fermentation data at each time point in the Daqu fermentation process are obtained, and a Daqu fermentation data set is established according to the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data and microbial abundance data; a time series neural network model is trained based on the Daqu fermentation data set to obtain a microbial abundance prediction model; the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted are obtained, and the microbial abundance of the Daqu to be predicted at the corresponding time point is obtained according to the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and based on the microbial abundance prediction model.
[0040] Specifically, the present invention first constructs a Daqu fermentation data set including environmental data, fermentation stage data, metabolic compound data and microbial abundance data at each time point during the Daqu fermentation process, and then obtains a microbial abundance prediction model based on the Daqu fermentation data set training, and finally inputs the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted into the microbial abundance prediction model, and the microbial abundance of the Daqu to be predicted at the corresponding time point can be obtained. The present invention does not require a complex detection process, and compared with the prior art, it improves the efficiency of determining microbial abundance and reduces costs, and constructs a microbial abundance prediction model based on a time series neural network model, which can predict the microbial abundance at different time points, thereby realizing dynamic monitoring of the microbial population during the fermentation cycle.
[0041] The technical solution in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments.
[0042] Figure 1 A schematic diagram showing a method for predicting the abundance of microorganisms in Daqu fermentation is shown in FIG. Figure 1 , the method comprises the following steps:
[0043] Step 1: Obtain fermentation data at each time point during the Daqu fermentation process, and establish a Daqu fermentation data set based on the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data, and microbial abundance data.
[0044] In this embodiment, the environmental data includes temperature and humidity data. After obtaining the environmental data, the minimum and maximum normalization method can be used for standardization, and the maximum and minimum values are obtained based on all the data in the data set. For example, the temperature and humidity data at a certain time point are [35, 32]. Assuming that the maximum value of the temperature data is 60°C and the minimum value is 30°C, the maximum value of the humidity is 36 and the minimum value is 9, the temperature and humidity data [35, 32] are normalized to [0.14, 0.85].
[0045] In this embodiment, the fermentation stage data is a time node in the Daqu fermentation cycle, and the unit of the time node is day or hour. That is, the fermentation stage data is used to identify a specific moment in the current Daqu fermentation cycle, and can be expressed in days or hours. For example, if the fermentation time is the 1st day, it is expressed as 1, and if the fermentation time is the 16th day, it is expressed as 16. After obtaining the fermentation stage data, the minimum and maximum value normalization method can be used for standardization, for example, the 30th day is normalized to 1, and the 15th day is normalized to 0.5.
[0046] In the present embodiment, the metabolite compound data represents compound data related to Daqu fermentation, and the compound data may be compound content data. After obtaining the metabolite compound data, the metabolite compound data may be standardized using a minimum-maximum normalization method. For example, the metabolite compound data include one or more compound content data in esters, alcohols, aldehydes, acids, ketones, pyrazines, furans, and aromatics, wherein the esters include at least ethyl hexadecanoate and ethyl hexanoate, the alcohols include at least phenylethyl alcohol and 2,3-butanediol, the acids include at least acetic acid and hexanoic acid, and the pyrazines include at least 2,5-dimethylpyrazine, trimethylpyrazine, and 2,6-dimethylpyrazine. The unit of compound content data is ppm, and for example, the metabolite compound data at a certain moment is shown as: [1.017500007, 0.360192202, 0.28742875, 0.282392042, 0, 0.071789635, ...]. If the maximum value of the metabolite compound data is 4ppm and the minimum value is 0ppm, the metabolite compound data after normalization processing is shown as: [0.254375002, 0.090048051, 0.071857188, 0.070598011, 0, 0.017947409, …].
[0047] In this embodiment, the microbial abundance data includes the types and quantities of microbial populations during the fermentation of Daqu, and the microbial populations include at least Bacillus, Aspergillus, Saccharomyces cerevisiae, Pediococcus acidilactici and Staphylococcus. For example, the microbial abundance data at a certain moment is shown as: [0.01207594, 0.017445474, 0.022036947, ...]; the microbial abundance data itself is normalized, so normalization is not required, and in each set of microbial abundance data, the sum of the abundances of various microorganisms is 1.
[0048] In this embodiment, the fermentation data at each time point during the Daqu fermentation process may be daily fermentation data, and may be sampled and acquired at a sampling frequency of one day.
[0049] Step 2: training a time series neural network model based on the Daqu fermentation data set to obtain a microbial abundance prediction model.
[0050] In this embodiment, training a temporal neural network model based on the Daqu fermentation data set includes:
[0051] Step 21: Use the environmental data, fermentation stage data, and metabolic compound data at each time point as input features, and use the corresponding microbial abundance data as output labels of the time series neural network model to construct a training set and a validation set.
[0052] In this embodiment, the temporal neural network model may be, but is not limited to, a long short-term memory network (LSTM), a gated recurrent unit (GRU), or a temporal neural network model based on an attention mechanism.
[0053] In this step, the time point of outputting the label can be the same as the time point of inputting the feature. At this time, the trained microbial abundance prediction model is used to predict the microbial abundance at the same time point, that is, the input of the microbial abundance prediction model is the environmental data, fermentation stage data and metabolic compound data at a certain time point, and the output is the microbial abundance at the same time point.
[0054] In this step, the time point of outputting the label can also be the future time point of the input feature. At this time, the microbial abundance prediction model is trained to predict the microbial abundance at future time points, that is, the input of the microbial abundance prediction model is the environmental data, fermentation stage data and metabolic compound data at a certain time point or multiple time points, and the output is the microbial abundance at future time points.
[0055] Step 22: Based on the training set, the time series neural network model is trained using a supervised learning method, the output features of the time series neural network model are compared with the corresponding output labels, the corresponding loss function is calculated, and the model is optimized through back propagation.
[0056] Step 23: verify the time series neural network model based on the verification set. When the loss function of the time series neural network model is minimized and the prediction error is less than the error threshold, the corresponding time series neural network model is used as the microbial abundance prediction model.
[0057] In practical applications, the training set and the validation set can be divided into a ratio of 4:1. The training set can be used to train the time series neural network model, and the validation set can be used to verify the prediction error of the time series neural network model. When the loss function of the validation set is the smallest and the prediction error is less than the error threshold, the microbial abundance prediction model can be obtained. Among them, the loss function can be an error metric function suitable for regression tasks, such as mean square error and mean absolute error, and the model optimization can use the Adam optimizer.
[0058] After training is completed, the output features of the time series neural network model and the corresponding output labels are compared. Figure 2 ,exist Figure 2 In the figure, the horizontal axis represents the output features (predicted values) and output labels (true values) of multiple sample data in the validation set, and the vertical axis represents the abundance data of multiple microorganisms (different colors represent different microorganisms).
[0059] Step 3: Obtain the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted, and obtain the microbial abundance of the Daqu to be predicted at the corresponding time point based on the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and the microbial abundance prediction model.
[0060] In practical applications, if it is necessary to determine the microbial abundance of the Daqu to be predicted, the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted are first obtained, and standardized using the same processing method, and then input into the microbial abundance prediction model to obtain the microbial abundance of the Daqu to be predicted at the corresponding time point.
[0061] In this embodiment, during the training process of the microbial abundance prediction model, when the time point of the output label is the same as the time point of the corresponding input feature, the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted at a certain time point are input into the microbial abundance prediction model to obtain the microbial abundance of the Daqu to be predicted at that time point.
[0062] In this embodiment, during the training process of the microbial abundance prediction model, when the output label is the microbial abundance data of the corresponding input feature at a future time point, the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted at a certain time point or multiple time points are input into the microbial abundance prediction model to obtain the microbial abundance of the Daqu to be predicted at the future time point.
[0063] In summary, the method for predicting the microbial abundance of Daqu fermentation provided in this embodiment can realize the prediction of the microbial abundance at the corresponding time point of Daqu fermentation by constructing a microbial abundance prediction model based on environmental data, fermentation stage data and metabolic compound data, thereby improving the efficiency of determining the microbial abundance and reducing costs. In addition, this embodiment can also realize the prediction of the microbial abundance at future time points, further improving the efficiency of determining the microbial abundance. By determining the microbial abundance of Daqu fermentation, dynamic monitoring of the microbial population in the Daqu fermentation cycle can be achieved, providing a scientific basis for the optimization of the Daqu fermentation process.
[0064] Figure 3 A schematic diagram of a device for predicting the abundance of microorganisms in Daqu fermentation is shown in FIG. Figure 3 , the device comprises:
[0065] an acquisition unit, used to acquire fermentation data at each time point during the fermentation process of Daqu, and establish a Daqu fermentation data set according to the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data and microbial abundance data;
[0066] A training unit, used for training a time series neural network model based on the Daqu fermentation data set to obtain a microbial abundance prediction model;
[0067] The prediction unit is used to obtain the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted, and obtain the microbial abundance of the Daqu to be predicted at the corresponding time point based on the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and based on the microbial abundance prediction model.
[0068] Figure 4 A schematic diagram of the structure of an electronic device is shown. Figure 4 , the electronic device includes a processor, a memory and a communication bus;
[0069] The communication bus is used to realize the connection and communication between the processor and the memory;
[0070] The processor is used to execute one or more programs in the memory to implement the steps of the method for predicting the abundance of microorganisms in Daqu fermentation as described in this embodiment.
[0071] It can be understood that since the device and electronic device for predicting the microbial abundance of Daqu fermentation described in this embodiment are devices and electronic devices for implementing the method for predicting the microbial abundance of Daqu fermentation described in the embodiment, for the device and electronic device disclosed in the embodiment, since they correspond to the method disclosed in the embodiment, the description is relatively simple. For relevant matters, please refer to the partial description of the method, which will not be repeated here.
Claims
1. A method for predicting the abundance of microorganisms in Daqu fermentation, characterized in that: The method comprises: Acquire fermentation data at each time point during the fermentation process of Daqu, and establish a Daqu fermentation data set according to the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data, and microbial abundance data; Training a time series neural network model based on the Daqu fermentation data set to obtain a microbial abundance prediction model; The environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted are obtained, and the microbial abundance of the Daqu to be predicted at the corresponding time point is obtained according to the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and based on the microbial abundance prediction model.
2. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 1, characterized in that: The environmental data includes temperature data and humidity data; the fermentation stage data is the time node in the Daqu fermentation cycle, and the unit of the time node is day or hour.
3. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 1, characterized in that: The metabolic compound data include one or more compound content data of esters, alcohols, aldehydes, acids, ketones, pyrazines, furans and aromatics, the esters include at least ethyl hexadecanoate and ethyl hexanoate, the alcohols include at least phenylethanol and 2,3-butanediol, the acids include at least acetic acid and hexanoic acid, and the pyrazines include at least 2,5-dimethylpyrazine, trimethylpyrazine and 2,6-dimethylpyrazine.
4. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 1, characterized in that: The microbial abundance data include the types and quantities of microbial populations during the Daqu fermentation process, and the microbial populations include at least Bacillus, Aspergillus, Saccharomyces cerevisiae, Pediococcus acidilactici and Staphylococcus.
5. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 1, characterized in that: The method further includes: performing standardization processing on the environmental data, fermentation stage data and metabolic compound data in the Daqu fermentation data set, and performing standardization processing on the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted using the same processing method.
6. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 1, characterized in that: Training a time series neural network model based on the Daqu fermentation data set includes: The environmental data, fermentation stage data, and metabolic compound data at each time point are used as input features, and the corresponding microbial abundance data are used as output labels of the time series neural network model to construct training sets and validation sets; Based on the training set and using a supervised learning method to train a time series neural network model, compare the output features of the time series neural network model with the corresponding output labels, calculate the corresponding loss function, and optimize the model through back propagation; The time series neural network model is verified based on the verification set. When the loss function of the time series neural network model is minimized and the prediction error is less than the error threshold, the corresponding time series neural network model is used as a microbial abundance prediction model.
7. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 6, characterized in that: The time point of the output label is the same as the time point of the corresponding input feature; Obtain the microbial abundance of the Daqu to be predicted at the corresponding time point, including: After inputting the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted at a certain time point into the microbial abundance prediction model, the microbial abundance of the Daqu to be predicted at that time point is obtained.
8. The method for predicting the abundance of microorganisms in Daqu fermentation according to claim 6, characterized in that: The output label is the microbial abundance data corresponding to the input feature at a future time point; Obtain the microbial abundance of the Daqu to be predicted at the corresponding time point, including: After inputting the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted at a certain time point or multiple time points into the microbial abundance prediction model, the microbial abundance of the Daqu to be predicted at the future time point is obtained.
9. A device for predicting the abundance of microorganisms in Daqu fermentation, characterized in that: The device comprises: an acquisition unit, used to acquire fermentation data at each time point during the fermentation process of Daqu, and establish a Daqu fermentation data set according to the fermentation data, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data and microbial abundance data; A training unit, used for training a time series neural network model based on the Daqu fermentation data set to obtain a microbial abundance prediction model; The prediction unit is used to obtain the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted, and obtain the microbial abundance of the Daqu to be predicted at the corresponding time point based on the environmental data, fermentation stage data and metabolic compound data of the Daqu to be predicted and based on the microbial abundance prediction model.
10. An electronic device, characterized in that The electronic device includes a processor, a memory and a communication bus; The communication bus is used to realize the connection and communication between the processor and the memory; The processor is used to execute one or more programs in the memory to implement the steps of the method for predicting the microbial abundance of Daqu fermentation as described in any one of claims 1 to 8.