A method for evaluating temperature distribution of Daqu in Daqu fermentation warehouse

Abstract

The present application belongs to the technical field of brewing, and particularly relates to a method for evaluating temperature distribution of Daqu in a Daqu fermentation bin, which comprises the following steps: S1, obtaining the average temperature of the whole bin of the Daqu fermentation bin; S2, calculating the values of the related parameters a and b in the evaluation model from the average temperature of the whole bin obtained in step S1 through a correlation, wherein the correlation is a linear correlation between the average temperature of the whole bin and the related parameters a and b in the evaluation model; and S3, substituting the values of the related parameters a and b obtained in step S2 into the evaluation model to obtain the evaluation result of the temperature distribution of the Daqu in the Daqu fermentation bin. The present application finds that the measured conditions of the temperature distribution of the Daqu in the Daqu fermentation bin can be highly fitted by using the probability density function and the cumulative distribution function through fitting and evaluation of the temperature distribution of the Daqu in the Daqu fermentation bin by different analysis models, and thus an evaluation method for the temperature distribution of the Daqu in the Daqu fermentation bin is established.

Description

Technical Field

[0001] This invention belongs to the field of brewing technology and relates to a method for evaluating the temperature distribution of Daqu (a type of starter culture) in a Daqu fermentation chamber. Background Technology

[0002] High-temperature Daqu (a type of starter culture) production is a crucial component of the Maotai-flavor Baijiu (Chinese liquor) brewing process. During fermentation in the fermentation chamber, the Daqu undergoes high-temperature screening and enrichment of a large number of thermophilic microorganisms, forming a unique microbial community system containing bacteria, molds, and yeasts. These microorganisms and the various enzymes they produce are the source of important microorganisms and enzyme systems in the later stages of Baijiu brewing. Maintaining high temperature is a key process in the high-temperature Daqu fermentation process. Sustained high temperatures allow thermophilic bacteria and other aroma-producing microorganisms to grow and proliferate into dominant species. Simultaneously, high temperatures promote the activity and function of various thermophilic enzymes, thereby promoting the formation of various flavor compounds and their precursors. The temperature during the microbial cultivation process in the fermentation chamber directly determines the quality of the finished Daqu. If the temperature is too low, the finished Daqu will lack Maotai aroma and have a high saccharification power. In actual production, the highest temperature for Maotai-flavor Daqu can reach over 60℃.

[0003] In the production of Daqu (a type of starter culture), the commonly used method for assessing Daqu temperature is to evaluate the temperature distribution in the fermentation chamber by detecting the temperature of multiple Daqu samples at random locations during the turning process. Due to the inherent randomness of this method, the sample representativeness is insufficient, making it difficult to represent the entire fermentation state of the chamber using temperature values ​​from a few sites. Furthermore, operational limitations such as the short turning time and confined space within the fermentation chamber make it difficult to detect temperatures at more sites. Therefore, the current Daqu temperature assessment method urgently needs further improvement to enable the evaluation of the entire Daqu temperature from a few sites. A new assessment method can address the lack of representativeness in the current method, allowing staff to gain a more comprehensive understanding of the production status. Simultaneously, it can lay the foundation for information-based improvements in Daqu fermentation technology within the context of a closer integration between traditional fermentation techniques and information technology. Summary of the Invention

[0004] In order to scientifically and accurately assess the temperature distribution of Daqu (a type of starter culture) in the entire fermentation chamber, this invention provides a method for assessing the temperature distribution of Daqu in the fermentation chamber.

[0005] On the one hand, the present invention provides a method for evaluating the temperature distribution of Daqu (a type of starter culture) in a Daqu fermentation chamber, the method comprising the following steps:

[0006] S1 obtains the average temperature of the entire fermentation chamber of Daqu;

[0007] S2 calculates the values ​​of relevant parameters a and b in the evaluation model using the average temperature of the entire warehouse obtained in step S1 through correlation calculation. The correlation is a linear correlation between the average temperature of the entire warehouse and the relevant parameters a and b in the evaluation model.

[0008] S3 substitutes the values ​​of the relevant parameters a and b obtained in step S2 into the evaluation model to obtain the evaluation results on the temperature distribution of Daqu in the fermentation chamber.

[0009] In some implementations, obtaining the average temperature of the entire warehouse includes the following steps:

[0010] S1.1 Measure the temperature of the sampled Daqu at the sampling point corresponding to the average temperature of the whole warehouse, and calculate the average value of the measured Daqu temperatures at the sampling point to obtain the average temperature of the whole warehouse.

[0011] In some implementation schemes, the sampling point is located at the location of the 39th-46th Daqu pieces at the bottom layer of the 5th stem in the Daqu fermentation chamber and / or the location of the 44th-50th Daqu pieces at the 3rd layer of the 2nd stem.

[0012] In some implementation schemes, determining the location of the sampling point includes the following steps:

[0013] 1) Obtain the temperature of each piece of Daqu in the fermentation chamber and calculate the average temperature of the entire fermentation chamber;

[0014] 2) Randomly group the Daqu (fermented starter culture) in each row and column of the fermentation chamber described in step 1);

[0015] 3) Detect the temperature of each group of Daqu obtained by random grouping in step 2). Compare the detected temperature data of each group of Daqu with the average temperature data of the whole warehouse calculated in step 1) at a significance level. If the comparison result between the temperature data of Daqu in the group and the average temperature data of the whole warehouse is not significant, then the location of Daqu in the group is determined as the sampling point location.

[0016] In some implementation schemes, obtaining the temperature of each piece of Daqu (a type of starter culture) within the Daqu fermentation chamber includes the following steps:

[0017] The calibrated thermometers were used to measure the temperature of each piece of koji in the fermentation chamber at the same time. The data read after the thermometer reading stabilized was the temperature of the corresponding koji.

[0018] In some implementation schemes, the same time period refers to the temperature of each piece of Daqu (a type of starter culture) being detected during the same turning process within the fermentation chamber.

[0019] In some implementations, the methods for comparing significance levels include: T-test, F-test, chi-square test, degrees of freedom test, and KS test; more preferably, the method for comparing significance levels is T-test.

[0020] In some implementations, the T-test includes the following steps: running the t.test() function in R language on the temperature data of each group of Daqu randomly assigned in step 2) and the average temperature data of the whole warehouse calculated in step 1) to obtain the significance level results between the temperature data of each group of Daqu randomly assigned in step 2) and the average temperature data of the whole warehouse calculated in step 1).

[0021] In some implementation schemes, determining the location of the sampling point further includes the following steps:

[0022] 4) Feasibility verification of the sampling point location: The temperature of Daqu at the sampling point location in the other Daqu fermentation chambers of the same type is measured respectively; the measured temperature of Daqu at the sampling point location is compared with the average temperature of the whole chamber in the corresponding Daqu fermentation chamber at a significance level; it is determined whether there is a significant difference. If there is no significant difference in the significance comparison result, the sampling point location is feasible.

[0023] In some implementation schemes, the random grouping in step 2) includes the following steps: randomly grouping the Daqu (fermented starter culture) in each row and column of the fermentation chamber according to the number of 6-9 Daqu in each group.

[0024] In some implementations, the evaluation model is a probability function relationship, which includes: a probability density function and / or a cumulative distribution function, wherein the probability density function is: The cumulative distribution function is: Where a and b are the relevant parameters in the evaluation model.

[0025] In some implementation schemes, the linear correlation between the relevant parameters a and b in the evaluation model and the average temperature of the entire warehouse is as follows:

[0026] b = 0.8966 × ave + 7.9807;

[0027] Where ave is the average temperature of the entire warehouse.

[0028] In some implementations, step S3 includes: substituting the obtained values ​​of relevant parameters a and b into the probability density function and / or the cumulative distribution function to obtain an evaluation model of the temperature distribution of Daqu in the fermentation chamber, and obtaining an evaluation result of the temperature distribution of Daqu in the fermentation chamber based on the temperature distribution evaluation model; wherein, x in the temperature distribution evaluation model is the temperature of each piece of Daqu in the fermentation chamber.

[0029] In some embodiments, the temperature range of x is 40°C to 65°C; preferably, the temperature range of x is 41°C to 65°C; preferably, the temperature range of x is 44°C to 61°C; preferably, the temperature range of x is 50°C to 61°C.

[0030] In some implementations, the temperature range of the ave is 40°C to 68.1°C.

[0031] In some implementations, when the ave is 58.5°C, the relevant parameter a = 20.7074; the relevant parameter b = 60.4318.

[0032] In some implementations, the construction of the correlation includes the following steps:

[0033] Step 1: Obtain the temperature of each piece of koji in multiple koji fermentation chambers;

[0034] Step 2: Calculate the average temperature of each piece of Daqu in each of the Daqu fermentation chambers obtained in Step 1;

[0035] Step 3: Import the temperature data of each piece of Daqu in each fermentation chamber obtained in Step 1 into R language, and use the fitdistr() function in the MASS package to obtain the values ​​of relevant parameters a and b regarding the temperature distribution of Daqu in each fermentation chamber.

[0036] Step 4: Construct a linear regression equation using the linear regression method to establish the correlation between the average temperature obtained in Step 2 and the values ​​of relevant parameters a and b obtained in Step 3.

[0037] In some implementation schemes, in step one, the number of Daqu fermentation chambers is 3-5; preferably, the number of Daqu fermentation chambers is 4.

[0038] In some implementation schemes, step one, obtaining the temperature of each piece of koji in the plurality of koji fermentation chambers, includes the following steps:

[0039] The calibrated thermometers were used to measure the temperature of each piece of koji in the fermentation chamber at the same time. The data read after the thermometer reading stabilized was the temperature of the corresponding koji.

[0040] In some implementation schemes, the same time period refers to the temperature of each piece of Daqu (a type of starter culture) being detected during the same turning process within the fermentation chamber.

[0041] On the other hand, the present invention also provides an evaluation system for the temperature distribution of Daqu (a type of starter culture) in a Daqu fermentation chamber, the evaluation system comprising:

[0042] The data acquisition module is used to obtain the average temperature of the entire fermentation chamber of Daqu (a type of starter culture).

[0043] The data processing module is used to obtain the values ​​of relevant parameters a and b in the evaluation model from the average temperature of the entire warehouse obtained by the data acquisition module through a correlation relationship, wherein the correlation relationship is a linear correlation between the average temperature of the entire warehouse and the relevant parameters a and b in the evaluation model; and

[0044] The evaluation module is used to input the values ​​of relevant parameters a and b obtained by the data processing module into the evaluation model to obtain the evaluation results of the temperature distribution of Daqu in the fermentation chamber.

[0045] In some implementations, the evaluation model is a probability function relationship, which includes: a probability density function and / or a cumulative distribution function, wherein the probability density function is: The cumulative distribution function is: Where a and b are the relevant parameters in the evaluation model.

[0046] In some implementation schemes, the correlation between the relevant parameters a and b and the average temperature of the entire warehouse is as follows:

[0047] b = 0.8966 × ave + 7.9807; where ave is the average temperature of the entire warehouse.

[0048] In some implementation schemes, the processing procedure of the evaluation module includes: substituting the values ​​of relevant parameters a and b obtained by the data processing module into the probability density function and the cumulative distribution function to obtain a temperature distribution evaluation model for the Daqu fermentation chamber, and obtaining an evaluation result of the Daqu temperature distribution in the Daqu fermentation chamber based on the temperature distribution evaluation model; wherein, x in the temperature distribution evaluation model is the temperature of each piece of Daqu in the Daqu fermentation chamber.

[0049] In some embodiments, the temperature range of x is 40°C to 65°C; preferably, the temperature range of x is 41°C to 65°C; preferably, the temperature range of x is 44°C to 61°C; preferably, the temperature range of x is 50°C to 61°C.

[0050] In some implementations, the temperature range of the ave is 40℃ to 68.1℃; preferably, when the ave is 58.5℃, the relevant parameter a = 20.7074; the relevant parameter b = 60.4318.

[0051] In some implementation schemes, the data acquisition module obtains the average temperature of the entire warehouse by including the following steps:

[0052] The average temperature of the entire warehouse is obtained by taking the temperature of the sampling point at the Daqu location, and calculating the average value of the Daqu temperature at the sampling point location.

[0053] In some implementations, the sampling point locations include: the location of the 39th-46th Daqu pieces at the bottom layer of the 5th stem in the Daqu fermentation chamber and / or the location of the 44th-50th Daqu pieces at the 3rd layer of the 2nd stem.

[0054] On the other hand, the present invention also provides an application of the method or system described herein in evaluating the temperature distribution of Daqu (a type of starter culture) in a Daqu fermentation chamber.

[0055] In summary, this application includes at least one of the following beneficial technical effects:

[0056] (1) To scientifically and accurately analyze and evaluate the temperature distribution of Daqu (a type of starter culture) within a fermentation chamber, this invention provides a method for evaluating the temperature distribution of Daqu within a fermentation chamber. This method, for the first time, statistically analyzes the temperature distribution of Daqu throughout the fermentation process and derives a corresponding evaluation model based on a probability function. The model is determined by sampling points based on the average temperature of the entire fermentation chamber, and the correlation between relevant parameters a and b and the average temperature of the entire chamber is constructed. This successfully yields a solution, ultimately achieving a scientific and accurate evaluation of the temperature distribution of Daqu within the fermentation chamber. The evaluation model provided by this invention can highly fit the actual temperature distribution of each piece of Daqu within the fermentation chamber, overcoming the problem of insufficient representativeness in current evaluation methods. It also allows staff to gain a more comprehensive understanding of the fermentation state of Daqu and lays the foundation for information-based improvements in the development of Daqu fermentation technology in the context of closer integration between traditional fermentation industry and information technology.

[0057] (2) The evaluation method provided by this invention further explores for the first time the selection of a few sites that can reflect the average temperature of the whole fermentation chamber in Daqu, and proposes a few detection sites that can reflect the average temperature of the whole fermentation chamber. By detecting the temperature of a few Daqu sites at the sampling points of the determined average temperature of the whole fermentation chamber, the average temperature of the whole fermentation chamber in Daqu can be determined. Then, the Daqu temperature distribution in Daqu fermentation chamber can be evaluated through the evaluation model. This overcomes the problem that the existing method of evaluating the fermentation temperature of Daqu by random sites has problems such as insufficient representativeness, which makes it difficult to evaluate the temperature of the whole fermentation chamber in Daqu by the selected sites. Attached Figure Description

[0058] Figure 1 This is a plan view of the measured temperature distribution of Daqu fermentation in fermentation chamber No. 1 in this embodiment of the invention.

[0059] Figure 2 The graph shows the fitting results of the probability density function (frequency distribution) and cumulative distribution function (probability distribution) of the measured temperature distribution of Daqu in the fermentation chamber in Example 1 of this invention.

[0060] Figure 3 This is a QQ plot test used in Embodiment 1 of the present invention to evaluate the fitting effect of Model 5 on the actual temperature distribution of Daqu in the fermentation chamber.

[0061] Figure 4 This is a PP plot test used in Embodiment 1 of the present invention to evaluate the fitting effect of Model 5 on the actual temperature distribution of Daqu in the fermentation chamber.

[0062] Figure 5 5- (1) is a graph showing the fitting effect of the probability density function obtained by fitting the measured temperature distribution of Daqu in the fermentation chamber under test using the evaluation model provided by the present invention in Example 4 of the present invention; Figure 5 (2) is a cumulative distribution function fitting effect diagram obtained by fitting the measured temperature distribution of Daqu in the fermentation chamber under test using the evaluation model provided by the present invention in Example 4 of the present invention; Figure 5 (3) is the QQ plot verification result of fitting the measured temperature distribution of Daqu in the fermentation chamber to be tested using the evaluation model provided by the present invention in Example 4 of the present invention; Figure 5 (4) is the PP plot verification result of fitting the measured temperature distribution of Daqu in the fermentation chamber under test using the evaluation model provided by the present invention in Example 4 of the present invention;

[0063] Explanation of the labels in the attached images: 1-First joke; 2-Second joke; 3-Third joke; 4-Fourth joke; 5-Fifth joke; 6-Sixth joke. Detailed Implementation

[0064] 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.

[0065] It should be noted that, in the embodiments of the present invention, the distribution of Daqu (a type of starter culture) in each Daqu fermentation chamber is consistent with... Figure 1 The distribution of Daqu (a type of starter culture) in fermentation chamber No. 1 shown in the figure is consistent, and in the actual production process, the specifications of the Daqu fermentation chambers used in the preparation and fermentation of high-temperature Daqu are also the same.

[0066] The average temperature of the entire fermentation chamber refers to the average temperature of all the Daqu (a type of starter culture) within the fermentation chamber during the preparation and fermentation process of high-temperature Daqu.

[0067] Example 1: A method for constructing an evaluation model for the temperature distribution of Daqu (a type of starter culture) throughout a fermentation chamber.

[0068] S1 acquires fermentation temperature data of each piece of Daqu (a type of starter culture) in the fermentation chamber under natural fermentation conditions.

[0069] In this embodiment, the sample fermentation chamber is fermentation chamber No. 1. The location distribution of each piece of koji in fermentation chamber No. 1 is as follows. Figure 1 As shown, there are 6 stems in total, each stem has 4 layers, and each layer contains multiple pieces of Daqu (fermented koji). In this embodiment, the temperature of each piece of Daqu in fermentation chamber No. 1 was measured, and fermentation temperature data of a total of 1200 pieces of Daqu located at different positions in fermentation chamber No. 1 were obtained. The obtained fermentation temperature distribution information of each piece of Daqu in fermentation chamber No. 1 is shown in the attached figure. Figure 1 As shown in the attached document Figure 1 As shown, the distribution of Daqu (a type of starter culture) in the fermentation chamber is as follows: from near to far, they are the first stem, the second stem, the third stem, the fourth stem, the fifth stem, and the sixth stem; from the bottom to the top of each stem, they are the first layer, the second layer, the third layer, and the fourth layer; from left to right of each layer, they are the first piece, the second piece, the third piece, the fourth piece, and the fifth piece...

[0070] The specific method for detecting the temperature of each piece of Daqu (a type of starter culture) in fermentation chamber No. 1 is as follows: When detecting the temperature of each piece of Daqu in fermentation chamber, a calibrated thermometer needs to be used to detect the temperature of each piece of Daqu in the fermentation chamber at the same time period. The data read after the thermometer reading stabilizes is the temperature of the corresponding Daqu. Generally, Daqu needs to undergo two turnings during fermentation in the fermentation chamber. During the turning process, the placement of Daqu needs to be adjusted to facilitate the full fermentation of each piece of Daqu. Therefore, the time period for detecting the temperature of each piece of Daqu in fermentation chamber No. 1 is the same time period during the turning process. The same time period for detecting the temperature of Daqu refers to the same turning process during which the temperature of Daqu is detected.

[0071] Construction of an evaluation model for the temperature distribution throughout the S2 Daqu fermentation chamber

[0072] S2.1 Evaluation Model Screening

[0073] To construct a suitable functional relationship for evaluating the overall temperature distribution within the fermentation chamber of Daqu (a type of starter culture), the inventors explored various evaluation models during the research process, as follows:

[0074] Evaluation Model 1: This model is an exponential distribution model, and the relationship is as follows:

[0075]

[0076] Where λ represents the relevant parameters in evaluation model one;

[0077] Evaluation Model 2: This model uses a gamma distribution, and the relationship is as follows:

[0078]

[0079] Where α and β are the relevant parameters in evaluation model two;

[0080] Evaluation Model 3: This model uses a normal distribution, and the relationship is as follows:

[0081]

[0082] Where μ and σ are the relevant parameters in evaluation model three;

[0083] Evaluation Model 4: This model is based on a skewed distribution and the density function of the normal distribution mentioned above. The input data is processed accordingly, such as taking the inverse, taking the logarithm, or standardization.

[0084] Evaluation Model 5: This model is a probability distribution function, and the probability distribution function relationship is as follows:

[0085]

[0086]

[0087] Where a and b are the relevant parameters in evaluation model five; Formula 1 is the probability density function; and Formula 2 is the cumulative distribution function.

[0088] S2.2 Determine the relevant parameters in the evaluation model

[0089] Using the R language, the fitdistr() function from the MASS package is used to calculate the relevant parameters in the evaluation model in step S2.1, as follows:

[0090] The fermentation temperature data of each piece of Daqu in fermentation chamber 1 obtained in step S1 is imported into R language, and the relevant parameter values ​​in the above evaluation model function relationship are determined by using the fitdistr() function in the MASS package.

[0091] The input value of the fitdistr() function is a dataset consisting of the temperatures of the entire batch of Daqu (a type of starter culture) in fermentation chamber 1. The output value of the fitdistr() function is the specific value of the relevant parameter in the probability function relationship of the different models mentioned above.

[0092] Taking evaluation model five as an example, the fermentation temperature data of each piece of koji in fermentation chamber 1 obtained in step S1 is imported into R language. The fitdistr() function in the MASS package is used to determine the specific values ​​of the relevant parameters a and b in the above evaluation model function relationship. The input value of the fitdistr() function is the dataset consisting of the temperature of the entire koji in fermentation chamber 1. The output value of the fitdistr() function is the value of the relevant parameters a and b in the probability function relationship in evaluation model five.

[0093] S2.3 Determine an evaluation model that can be used to assess the temperature distribution of Daqu (a type of starter culture) within the fermentation chamber.

[0094] Substitute the relevant parameter values ​​obtained from the output of the fitdistr() function into the functional relationships of the above evaluation models one to five to calculate the distribution of Daqu fermentation temperature in the Daqu fermentation chamber.

[0095] Based on the above distribution calculation results, the remaining evaluation models selected, such as the functional relationships of evaluation models one to four, cannot truly reflect the distribution of fermentation temperature of each piece of Daqu in the fermentation chamber. The functional relationships of evaluation models one to four have poor fit with the measured fermentation temperature distribution of each piece of Daqu in the fermentation chamber, and therefore cannot be used to evaluate the fermentation temperature distribution of each piece of Daqu in the fermentation chamber.

[0096] Furthermore, the calculation results based on the above distribution further demonstrate that the probability function relationships (Formula 1 and Formula 2) of the above evaluation model five can provide a good fit to the measured temperature distribution of Daqu within the fermentation chamber. In other words, the probability function relationships (Formula 1 and Formula 2) of the above evaluation model five can provide a good assessment of the temperature distribution of Daqu within the fermentation chamber. The specific fitting effect of the probability function relationships of evaluation model five on the measured fermentation temperature distribution of Daqu within the fermentation chamber is as follows: Figure 2 As shown.

[0097] Figure 2 The left side represents the calculation result of Formula 1 above, that is, the proportion of a certain temperature (x) in the fermentation chamber of Daqu. Figure 2 The right side of the figure represents the calculation result of Formula 2, which is the proportion of Daqu (a type of starter culture) in the fermentation chamber between 35℃ (the lowest temperature) and a certain temperature (x). Figure 2 The results show that the results obtained by fitting the total Daqu temperature in the fermentation chamber using Formula 1 and Formula 2 are consistent with the actual Daqu temperature distribution within the fermentation chamber, and the evaluation results also conform to the actual situation of the fermentation chamber. The probability function relationship of the above-mentioned evaluation model five can fit the measured Daqu fermentation temperature distribution in the fermentation chamber very well. That is, the probability function relationship of evaluation model five provided by the present invention can achieve an accurate evaluation of the Daqu fermentation temperature distribution in the fermentation chamber.

[0098] S3 verifies the fit of the evaluation model determined in step S2.

[0099] The evaluation model five determined in step S2 was tested using QQ and PP graphs drawn according to conventional statistical methods. Specific results are as follows: Figure 3 and Figure 4 As shown, Figure 3 To evaluate the QQ graph test results of Model 5, Figure 4 To evaluate the PP plot test results of Model 5. From Figure 3The QQ graph test results show that when the fermentation temperature range of Daqu in the fermentation chamber is 44℃~61℃, the above evaluation model 5 can achieve a good fit to the actual distribution of Daqu fermentation temperature in the fermentation chamber. However, when the fermentation temperature range of Daqu in the fermentation chamber is in a lower temperature range, such as below 44℃, or a higher temperature range, such as above 61℃, dispersion occurs, resulting in a large deviation between the distribution calculation results of the above evaluation model 5 and the actual detection results. The reliability of using evaluation model 5 to calculate the Daqu fermentation temperature distribution in the fermentation chamber is low. Therefore, it is further determined that when the fermentation temperature range of Daqu in the fermentation chamber is 44℃~61℃, the evaluation model 5 constructed by the above method can have a good evaluation effect on the temperature distribution of the whole Daqu in the fermentation chamber.

[0100] A P-P plot is a graph drawn based on the relationship between the cumulative proportion of a variable and the cumulative proportion of a specified distribution. P-P plots can be used to test whether data conforms to a specified distribution. When data conforms to a specified distribution, the points on a P-P plot approximate a straight line. In a QQ plot, Q represents a quantile. A QQ plot is a probability graph that graphically compares two probability distributions by comparing their two quantiles. The purpose of QQ plots is exactly the same as that of P-P plots; only the testing methods differ. To use a QQ plot to determine whether sample data approximates a normal distribution, simply observe whether the points on the QQ plot are approximately aligned with a straight line.

[0101] Through the above process, the evaluation model for assessing the temperature distribution of the entire Daqu fermentation chamber has been constructed. The resulting evaluation model is model five, and its probability function relationship is as follows:

[0102]

[0103]

[0104] Where a and b are the relevant parameters in the evaluation model five; Formula 1 is the probability density function relationship in the evaluation model five; Formula 2 is the cumulative distribution function relationship in the evaluation model five.

[0105] Example 2: Constructing the correlation between relevant parameters a and b and the average temperature of the entire Daqu fermentation chamber in the evaluation model. S1: Detecting the fermentation temperature of each Daqu in multiple Daqu fermentation chambers.

[0106] The method for detecting the fermentation temperature of each piece of Daqu in multiple Daqu fermentation chambers is the same as in Example 1. In this example, data from four Daqu fermentation chambers were used for experimental calculation.

[0107] S2 uses conventional statistical methods to calculate the average temperature of the whole fermentation of Daqu in the four fermentation chambers obtained in step S1, and denoted as ave1, ave2, ave3, and ave4.

[0108] S3 imports the fermentation temperature data of each piece of Daqu in the four fermentation chambers obtained in step S1 into R language. The fitdistr() function in the MASS package is used to determine the specific values ​​of the relevant parameters a and b in the evaluation model. The input value of the fitdistr() function is the dataset consisting of the temperature of the whole Daqu in each fermentation chamber. The output value of the fitdistr() function is the specific value of the relevant parameters a and b for the corresponding Daqu fermentation chamber. The values ​​of the relevant parameter a in the four fermentation chambers are recorded as a1, a2, a3, a4; and the relevant parameter b is recorded as b1, b2, b3, b4.

[0109] S4 Calculation of the Correlation Between Average Temperature and Related Parameters

[0110] The average temperatures (ave1, ave2, ave3, ave4) and related parameters a (a1, a2, a3, a4) and b (b1, b2, b3, b4) obtained in steps S2 and S3 are used to construct regression equations using linear regression. This establishes the correlation between the average temperature ave and related parameters a and b. The specific correlations obtained using this method are as follows:

[0111]

[0112] b = 0.8966 × ave + 7.9807 (Formula 4)

[0113] In exploring which analytical method could truly reflect the correlation between relevant parameters a and b in each fermentation chamber and the average temperature of the entire chamber, the inventors unexpectedly discovered that when linear regression was used to construct the relationship between the two, the relevant parameters a and b had a good linear relationship with the average temperature ave, which could truly reflect the correlation between the relevant parameters a and b and the average temperature ave. Therefore, this invention adopts the above method to construct the correlation between the relevant parameters a and b and the average temperature ave.

[0114] As shown in Formula 3, since a > 0, the value of ave must be less than 68.1℃. The initial temperature of the Daqu (fermented koji) is the same. During fermentation in the Daqu fermentation chamber, the temperature of the Daqu will rise, undergoing varying degrees of temperature increase. During this temperature increase, the temperature of the Daqu is generally above 40℃. Therefore, when applying the above evaluation model to the temperature increase process of Daqu in the fermentation chamber, the value of ave should be greater than 40℃. In conclusion, when the average temperature detected in the Daqu fermentation chamber, ave, is between 40 and 68.1℃, the above evaluation model can effectively fit the temperature distribution of the Daqu within the fermentation chamber.

[0115] Example 3: Determination of sampling points for the average temperature of the entire fermentation chamber in Daqu (a type of starter culture)

[0116] The evaluation model constructed based on Examples 1 and 2 is used to evaluate the temperature distribution of the entire Daqu fermentation chamber. This requires obtaining the average temperature of the entire chamber to assess the temperature distribution using the aforementioned evaluation model. However, conventionally, obtaining the average temperature requires measuring the fermentation temperature of each Daqu piece and calculating the average. This example explores how to accurately assess the temperature distribution of the entire Daqu fermentation chamber by detecting the temperature of a few Daqu sites, based on the aforementioned evaluation model. This example investigates the sampling locations for the average temperature of the entire Daqu fermentation chamber. By detecting the temperature of a few Daqu sites at these sampling points, the average temperature of the entire Daqu fermentation chamber can be obtained. The following example provides an implementation method for determining the sampling locations for the average temperature of the entire Daqu fermentation chamber:

[0117] S1 uses conventional statistical methods to calculate the average temperature of the entire fermentation chamber of No. 1 based on the fermentation temperature data of each koji obtained in Example 1.

[0118] S2 initially determined the sampling point location for the average temperature of the entire fermentation chamber of Daqu.

[0119] S2.1 The Daqu (fermentation starter) in each row and column of the No. 1 fermentation chamber is randomly grouped into groups of 6-9 Daqu in each group. That is, the more than 1,200 Daqu pieces in the No. 1 fermentation chamber are randomly divided into multiple groups of 6-9 Daqu in each group.

[0120] S2.2 The temperature of each group of Daqu randomly assigned in step S2.1 is measured, and the significance level of the temperature of each Daqu piece in each group is compared with the average temperature of the entire fermentation chamber No. 1 calculated in step S1 using the T-test method in statistics. The T-test method is as follows: In R language, run the t.test() function on the temperature data of each group of Daqu randomly assigned in step S2.1 and the average temperature data of the entire fermentation chamber No. 1 calculated in step S1 to obtain the significance level result between the two groups of data. Alternatively, F-test, chi-square test, degrees of freedom test, and KS test can also be used. After comparison, if there is no significant difference between the temperature data of the Daqu in the corresponding group and the average temperature of the entire fermentation chamber (P>0.05), then the location of the Daqu in the corresponding group is preliminarily determined as the sampling point location for the average temperature of the entire fermentation chamber.

[0121] Based on the above calculations and T-test comparisons, it was found that the temperatures of the 39-46th pieces of Daqu at the bottom of the 5th stem and the 44-50th pieces of Daqu in the 3rd layer of the 2nd stem in fermentation chamber 1 were not significantly different from the average temperature of the entire chamber calculated in step S1. Therefore, it was preliminarily determined that the locations of the 39-46th pieces of Daqu at the bottom of the 5th stem and the 44-50th pieces of Daqu in the 3rd layer of the 2nd stem in fermentation chamber 1 of this embodiment were the sampling points for the average temperature of the entire Daqu fermentation chamber.

[0122] Feasibility verification of the location of the average temperature sampling point for the entire S3 warehouse

[0123] Based on the locations of the 39th-46th pieces of Daqu at the bottom of the 5th stem and the 44th-50th pieces of Daqu at the 3rd layer of the 2nd stem in fermentation chamber No. 1, which were initially determined in step S2, the sampling points for the average temperature of the entire fermentation chamber No. 1 were used to measure the temperature of Daqu at the above locations in fermentation chamber No. 2. The measured temperatures of the corresponding Daqu were then compared with the average temperature of the entire fermentation chamber No. 2 at a significance level. The results showed that there was no significant difference between the temperatures of the corresponding Daqu and the average temperature of the entire fermentation chamber No. 2. That is, the sampling point locations for the average temperature of the entire fermentation chamber No. 1, which were initially determined in step S2, are also applicable to the measurement of the average temperature of the entire fermentation chamber No. 2 of the same type.

[0124] Similarly, based on the locations of the 39th-46th pieces of Daqu at the bottom of the 5th stem and the 44th-50th pieces of Daqu at the 3rd layer of the 2nd stem in fermentation chamber No. 1, which were initially determined in step S2, the sampling points for the average temperature of the entire fermentation chamber No. 1 were used to measure the temperature of Daqu at the above locations in fermentation chamber No. 3. The measured temperatures of Daqu at the corresponding locations were compared with the average temperature of the entire fermentation chamber No. 3 at a significant level. The results showed that there was no significant difference between the temperatures of Daqu at the corresponding locations and the average temperature of the entire fermentation chamber No. 3. That is, the sampling point locations for the average temperature of the entire fermentation chamber No. 1, which were initially determined in step S2, are also applicable to the measurement of the average temperature of the entire fermentation chamber No. 3 of the same type.

[0125] Following the same verification method described above, the feasibility of sampling points for the average temperature of the entire fermentation chamber initially determined in step S2 was verified using multiple similar fermentation chambers. It was found that the temperatures located at the bottom layer of the 5th stem (blocks 39-46) and the 3rd layer of the 2nd stem (blocks 44-50) accurately reflect the average temperature of the entire fermentation chamber. In other words, the temperatures at these locations are effectively applicable to assessing the average temperature of the entire fermentation chamber of the same type.

[0126] The above feasibility verification shows that the sampling points for determining the average temperature of the entire fermentation chamber using the above method are feasible, and the temperature of the Daqu at the corresponding location in the fermentation chamber can accurately assess and reflect the average temperature of the entire fermentation chamber. In other words, the temperatures of the Daqu at the bottom layer of the 5th stem (pieces 39-46) and the Daqu at the 3rd layer of the 2nd stem (pieces 44-50) in the fermentation chamber determined by the above method can accurately reflect the average temperature of the entire fermentation chamber of the same type of Daqu.

[0127] The above method determined the sampling point location for the average temperature of the entire fermentation chamber, overcoming the problem that existing methods of evaluating the fermentation temperature of Daqu by random sites have insufficient representativeness, making it difficult to evaluate the temperature of the entire Daqu chamber by the selected sites.

[0128] Example 4: A method for evaluating the temperature distribution of Daqu fermentation in the whole fermentation chamber.

[0129] S1 obtains the average temperature of the entire fermentation chamber.

[0130] The temperatures of the bottom 39-46 pieces of Daqu (fermentation starter culture) in the 5th stem and the 44-50 pieces of Daqu in the 3rd layer of the 2nd stem were measured in the fermentation chamber. The average temperature of the measured Daqu was calculated, and the average temperature of the Daqu was the average temperature of the entire fermentation chamber. After testing and calculation, the average temperature of the bottom 39-46 pieces of Daqu in the 5th stem and the 44-50 pieces of Daqu in the 3rd layer of the 2nd stem was 58.5℃, that is, the average temperature of the entire fermentation chamber ave = 58.5℃.

[0131] The relevant parameters a and b in the S2 calculation and evaluation model

[0132] Substituting the average temperature of the entire fermentation chamber of the Daqu (a starter culture) measured in step S1 (ave = 58.5℃) into the following formula relating the average temperature of the entire chamber to the relevant parameters a and b in the evaluation model, the relevant parameters a and b in the evaluation model are further calculated as follows:

[0133]

[0134] b = 0.89G6 × ave + 7.9807 (Formula 4)

[0135] The calculated values ​​for the relevant parameter a are 20.7074 and the relevant parameter b is 60.4318.

[0136] S3 Improved evaluation model's probability function relationship

[0137] Substitute the relevant parameters a = 20.7074 and b = 60.4318 obtained in step S2 into the probability function relationship of the following evaluation model to calculate the temperature distribution of the whole fermentation of Daqu in the fermentation chamber under test:

[0138]

[0139]

[0140] The following evaluation results were obtained regarding the temperature distribution of the entire Daqu fermentation chamber within the test chamber: a probability distribution function of the temperature distribution of the entire Daqu fermentation chamber. This enabled the evaluation of the temperature distribution of Daqu within the fermentation chamber, as detailed below:

[0141]

[0142]

[0143] The evaluation model provided by this invention is used to evaluate the temperature distribution of Daqu fermentation in the fermentation chamber, and the results are obtained by fitting the evaluation with the actual detection results of the temperature distribution of Daqu in the fermentation chamber to be tested. The results are shown in the appendix. Figure 5 And as shown in Table 1 below:

[0144] Table 1. Comparison between the evaluation model and actual test results

[0145] Temperature zones in the fermentation chamber of Daqu >60℃ 55℃-60℃ 50℃-55℃ <50℃ Evaluation model calculation results 42.23％ 44.51％ 11.30％ 1.96％ Actual test results 42.33％ 43.13％ 13.82％ 0.72％

[0146] Depend on Figure 5 As shown in the comparison results in Table 1 above, the evaluation results of the evaluation method provided by this invention on the temperature distribution of the whole Daqu fermentation chamber are basically consistent with the actual detection results. Furthermore, the P-value of this evaluation method is 1, which is greater than 0.05. This further demonstrates that the evaluation method provided by this invention has high reliability and can well fit the actual temperature distribution of the whole Daqu fermentation chamber. The P-value refers to the probability that, in a probability model, the statistical summary is the same as or even greater than the actual observed data. In other words, it is the probability that the null hypothesis is true or more severe. If the P-value is smaller than the selected significance level (0.05 or 0.01), the null hypothesis is rejected and unacceptable. If the P-value is greater than 0.05, it indicates that the hypothesis has high reliability.

[0147] When the temperature range of the fermentation chamber is >60℃, the evaluation model provided by this invention provides almost identical results to the actual detection of the Daqu temperature within the fermentation chamber. Furthermore, when the temperature ranges of the fermentation chamber are 55℃-60℃ and 50℃-55℃, the deviation between the evaluation model's results and the actual detection results is also minimal. This further demonstrates that the evaluation model provided by this invention can effectively fit the actual temperature distribution of the entire Daqu within the fermentation chamber, thereby efficiently and accurately assessing the temperature distribution of the entire Daqu within the fermentation chamber, and further efficiently evaluating the fermentation state of the Daqu.

[0148] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for evaluating the temperature distribution of Daqu (a type of starter culture) in a Daqu fermentation chamber, characterized in that, The method includes the following steps: S1 obtains the average temperature of the entire fermentation chamber of Daqu; S2 calculates the relevant parameters in the evaluation model using the correlation relationship based on the average temperature of the entire warehouse obtained in step S1. value, The value of , the correlation is the relationship between the average temperature of the entire warehouse and the relevant parameters in the evaluation model. , The linear correlation; S3 will use the relevant parameters obtained in step S2 value, Substituting the values ​​into the evaluation model, we obtain the evaluation results regarding the temperature distribution of Daqu (a type of starter culture) within the fermentation chamber. The acquisition of the average temperature of the entire warehouse includes the following steps: S1.1 Measure the temperature of the Daqu at the sampling point corresponding to the average temperature of the entire warehouse, calculate the average temperature of the Daqu at the sampling point, and obtain the average temperature of the entire warehouse; The sampling point is: the location of the 39th-46th Daqu pieces at the bottom of the 5th stem in the Daqu fermentation warehouse and / or the location of the 44th-50th Daqu pieces at the 3rd layer of the 2nd stem. The Daqu fermentation warehouse has a total of 6 stems, each stem has 4 layers, and each layer has multiple Daqu pieces. The relevant parameters in the evaluation model , The linear correlation between the temperature and the average temperature of the entire warehouse is as follows: ; ;in, This represents the average temperature of the entire warehouse. The evaluation model is a probability function relationship, which includes a probability density function and a cumulative distribution function. The probability density function is: The cumulative distribution function is: ;in, , These are the relevant parameters in the evaluation model.

2. The method as described in claim 1, characterized in that, Determining the location of the sampling point includes the following steps: 1) Obtain the temperature of each piece of Daqu in the fermentation chamber and calculate the average temperature of the entire fermentation chamber; 2) Randomly group the Daqu (fermented starter culture) in each row and column of the fermentation chamber described in step 1); 3) Detect the temperature of each group of Daqu obtained by random grouping in step 2). Compare the detected temperature data of each group of Daqu with the average temperature data of the whole warehouse calculated in step 1) at a significance level. If the comparison result between the temperature data of Daqu in the group and the average temperature data of the whole warehouse is not significant, then the location of Daqu in the group is determined as the sampling point location.

3. The method as described in claim 2, characterized in that, The temperature of each piece of Daqu (a type of starter culture) in the fermentation chamber is obtained through the following steps: The calibrated thermometers were used to measure the temperature of each piece of koji in the fermentation chamber at the same time. The data read after the thermometer reading stabilized was the temperature of the corresponding koji. The same time period refers to the temperature measurement of each piece of koji during the same turning process in the koji fermentation chamber.

4. The method as described in claim 2, characterized in that, The methods for comparing significance levels include: T-test, F-test, chi-square test, degrees of freedom test, and KS test.

5. The method as described in claim 2, characterized in that, The method for comparing the significance level is the T-test; the T-test includes the following steps: in R language, run the t.test() function on the temperature data of each group of Daqu randomly assigned in step 2) and the average temperature data of the whole warehouse calculated in step 1) to obtain the significance level results between the temperature data of each group of Daqu randomly assigned in step 2) and the average temperature data of the whole warehouse calculated in step 1).

6. The method as described in claim 2, characterized in that, The determination of the sampling point location also includes the following steps: 4) Feasibility verification of the sampling point location: The temperature of Daqu at the sampling point location in several other Daqu fermentation chambers of the same type is measured respectively; the measured temperature of Daqu at the sampling point location is compared with the average temperature of the whole chamber in the corresponding Daqu fermentation chamber at a significance level; it is determined whether there is a significant difference. If the significance comparison result is that there is no significant difference, then the sampling point location is feasible.

7. The method as described in claim 2, characterized in that, The random grouping in step 2) includes the following steps: randomly grouping the Daqu (fermented koji) in each row and column of the fermentation chamber according to the number of 6-9 Daqu in each group.

8. The method as described in claim 1, characterized in that, Step S3 includes: obtaining relevant parameters The value and Substituting the value into the probability density function and / or the cumulative distribution function, an evaluation model for the temperature distribution of Daqu within the Daqu fermentation chamber is obtained. Based on the temperature distribution evaluation model, an evaluation result for the temperature distribution of Daqu within the Daqu fermentation chamber is obtained; wherein, x in the temperature distribution evaluation model represents the temperature of each piece of Daqu within the Daqu fermentation chamber.

9. The method as described in claim 8, characterized in that, The temperature range of x is 40℃~65℃.

10. The method as described in claim 8, characterized in that, The temperature range of x is 41℃~65℃.

11. The method as described in claim 8, characterized in that, The temperature range of x is 44℃~61℃.

12. The method as described in claim 8, characterized in that, The temperature range of x is 50℃~61℃.

13. The method as described in claim 1, characterized in that, The The temperature range is 40℃~68.1℃.

14. The method as described in claim 1, characterized in that, The At 58.5℃, the relevant parameters =20.7074; the relevant parameters =60.4318.

15. The method as described in claim 1, characterized in that, The construction of the correlation includes the following steps: Step 1: Obtain the temperature of each piece of koji in multiple koji fermentation chambers; Step 2: Calculate the average temperature of each piece of Daqu in each of the Daqu fermentation chambers obtained in Step 1; Step 3: Import the temperature data of each piece of Daqu (fermentation starter) obtained in Step 1 into R language, and use the fitdistr() function in the MASS package to obtain relevant parameters about the temperature distribution of Daqu in each fermentation chamber. value, The value; Step 4: Construct a linear regression equation using the linear regression method to compare the average temperature obtained in Step 2 with the relevant parameters obtained in Step 3. value, The correlation between the values.

16. The method as described in claim 15, characterized in that, In step one, the number of fermentation chambers for Daqu (a type of starter culture) is 3-5.

17. The method as described in claim 15, characterized in that, The number of fermentation chambers for Daqu (a type of starter culture) is 4.

18. The method as described in claim 15, characterized in that, In step one, obtaining the temperature of each piece of koji in the multiple koji fermentation chambers includes the following steps: The calibrated thermometers were used to measure the temperature of each piece of Daqu (a type of starter culture) in the fermentation chamber at the same time. The data read after the thermometer reading stabilized was taken as the temperature of the corresponding Daqu. The same time period refers to the temperature measurement of each piece of Daqu during the same turning process in the fermentation chamber.

19. A system for evaluating the temperature distribution of Daqu (a type of starter culture) in a Daqu fermentation chamber, characterized in that, The evaluation system includes: The data acquisition module is used to acquire the average temperature of the entire fermentation chamber of Daqu. The acquisition of the average temperature of the entire chamber includes the following steps: acquiring the temperature of Daqu at the sampling point locations of the average temperature of the entire chamber, and calculating the average value of the Daqu temperature at the sampling point locations, that is, acquiring the average temperature of the entire chamber; wherein, the sampling point locations include: the location of Daqu pieces 39-46 at the bottom layer of the 5th stem in the fermentation chamber and / or the location of Daqu pieces 44-50 at the 3rd layer of the 2nd stem. The fermentation chamber of Daqu has a total of 6 stems, each stem has 4 layers, and each layer has multiple Daqu pieces. The data processing module is used to process the average temperature of the entire warehouse obtained by the data acquisition module to obtain relevant parameters in the evaluation model through correlation processing. value, The value of , the correlation is the relationship between the average temperature of the entire warehouse and the relevant parameters in the evaluation model. , The linear correlation; and, The evaluation module is used to process the relevant parameters obtained by the data processing module. value, The value is input into the evaluation model to obtain the evaluation result of the temperature distribution of Daqu in the fermentation chamber; The evaluation model is a probability function relationship, which includes: a probability density function and / or a cumulative distribution function. The probability density function is: The cumulative distribution function is: ;in, , To evaluate the relevant parameters in the model; Relevant parameters in the evaluation model , The correlation between the temperature and the average temperature of the entire warehouse is as follows: ; ;in, This represents the average temperature of the entire warehouse.

20. The evaluation system as described in claim 19, characterized in that, The evaluation module's processing procedure includes: processing the relevant parameters obtained by the data processing module. The value and Substituting the values ​​into the probability density function and / or the cumulative distribution function, an evaluation model for the temperature distribution of the Daqu (fermentation starter culture) in the Daqu fermentation chamber is obtained. Based on the temperature distribution evaluation model, an evaluation result for the temperature distribution of the Daqu in the Daqu fermentation chamber is obtained; wherein, in the temperature distribution evaluation model... This refers to the temperature of each piece of Daqu (a type of starter culture) within the Daqu fermentation chamber.

21. The evaluation system as described in claim 19, characterized in that, The The temperature range is 40℃~65℃.

22. The evaluation system as described in claim 19, characterized in that, The temperature of the Daqu The temperature range is 41℃~65℃.

23. The evaluation system as described in claim 19, characterized in that, The temperature of the Daqu The temperature range is 44℃~61℃.

24. The evaluation system as described in claim 19, characterized in that, The temperature of the Daqu The temperature range is 50℃~61℃.

25. The evaluation system as described in claim 19, characterized in that, The The temperature range is 40℃~68.1℃.

26. The evaluation system as described in claim 19, characterized in that, The At 58.5℃, the relevant parameters =20.7074; the relevant parameters =60.4318.

27. The application of the method as described in any one of claims 1-18 or the evaluation system as described in any one of claims 19-26 in evaluating the temperature distribution of Daqu in Daqu fermentation chambers.

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