A method for detecting water content during storage of Daqu

Abstract

The present application discloses a method for detecting the moisture content of Daqu during storage, comprising the following steps: obtaining a Daqu sample to be tested; measuring the temperature and humidity in the storage bin where the Daqu sample to be tested is located; calculating the moisture content of the Daqu sample to be tested according to the temperature and humidity in the storage bin where the Daqu sample to be tested is located and a pre-constructed standard curve; wherein the standard curve is a curve obtained according to the temperature and humidity in the storage bin where the Daqu sample is located during storage and the actual moisture content of the Daqu sample. By adopting this method, the moisture content of Daqu can be detected quickly and non-destructively during storage, so that the moisture change of Daqu can be monitored online and in real time during storage, the fermentation quality of Daqu can be monitored, and the efficiency and stability of production can be improved, which is of great significance to the automation and intelligence of Daqu production.

Description

Technical Field

[0001] The present application relates to the technical field of winemaking, and more specifically to a method for detecting the water content during the storage of Daqu. Background Art

[0002] Daqu is a saccharifying agent, fermentation agent and flavoring agent used in brewing Daqu wine. It contains a variety of microorganisms and enzymes, and its quality has an important impact on the quality of the base wine. Daqu is made of wheat as the main raw material, and is made through crushing, mixing, stepping, molding, fermentation and storage. During the fermentation process, the growth and decline of microorganisms in various parts of the qu block are inconsistent. Therefore, the Daqu after unpacking needs to be stored for a period of time before it can be used. Daqu achieves the effects of "removing impurities", "purifying" and "enhancing fragrance" through storage, so that the physical, chemical and biochemical properties of the qu block are in the best state. In the storage process of Daqu, moisture is a crucial indicator and plays a key role in the cultivation and storage of Daqu. On the one hand, water is an indispensable substance for microbial metabolism, a good solvent, and a necessary medium for maintaining the living environment of microorganisms and the exchange of substances between microorganisms. Excessive moisture will accelerate the metabolism of microorganisms, causing changes in the diversity of the microbial community in the qu block and the large-scale breeding of qu insects, affecting the quality of Daqu; on the other hand, too low moisture is not conducive to the aroma of the qu block and the purification of beneficial microorganisms. Therefore, monitoring the moisture content of Daqu is an important means to inspect and control the quality of Daqu.

[0003] Currently, the traditional methods for determining the moisture content of Daqu during storage are mainly the drying loss method and the near-infrared method. These methods require the Daqu samples to be taken out of the storage bin, which not only wastes manpower and material resources, is time-consuming and energy-intensive, but may also damage the Daqu samples to a certain extent. Summary of the invention

[0004] The present application provides a method for detecting the moisture content in Daqu during the storage process, the purpose of which is to be able to quickly and non-destructively detect the moisture content in Daqu during the storage process.

[0005] In order to achieve the above objectives, the technical solutions adopted in this application are as follows:

[0006] In some embodiments, the method of the present application includes the following steps: obtaining a Daqu sample to be tested; measuring the warehouse temperature and the warehouse humidity in the storage warehouse where the Daqu sample to be tested is located; calculating the moisture content of the Daqu sample to be tested based on the warehouse temperature and the warehouse humidity in the storage warehouse where the Daqu sample to be tested is located and a pre-constructed standard curve; wherein the standard curve is a curve obtained based on the warehouse temperature and the warehouse humidity in the storage warehouse where the Daqu sample is located during the storage process and the actual moisture content of the Daqu sample.

[0007] In some embodiments, the method for establishing a standard curve includes: establishing different standard curves for Daqu samples stored in different seasons according to the seasons in which the Daqu samples are stored.

[0008] In some embodiments, the method for calculating the actual moisture content includes: crushing and drying the Daqu sample, measuring the pre-drying mass of the Daqu sample before drying and the post-drying mass of the Daqu sample after drying, and calculating the actual moisture content of the Daqu sample based on the pre-drying mass and the post-drying mass.

[0009] In some embodiments, the actual moisture content includes an actual moisture percentage.

[0010] In some embodiments, the actual moisture content is calculated as follows: (mass before drying - mass after drying) / mass before drying.

[0011] In some embodiments, drying is performed in a constant temperature drying oven.

[0012] In some embodiments, the drying temperature is a constant temperature of 125°C.

[0013] In some embodiments, the drying time is 1 hour.

[0014] In some embodiments, the method for measuring the temperature and humidity in the storage bin where the Daqu sample is located includes: measuring at least once a day for at least three consecutive weeks.

[0015] In some embodiments, the temperature inside the storage bin where the Daqu sample is located is the average value of the air temperature above the storage bin measured within three weeks.

[0016] In some embodiments, the humidity in the storage bin where the Daqu sample is located is the average humidity of the air above the storage bin measured within three weeks.

[0017] In some embodiments, the Daqu samples are samples that have been stored in a warehouse for 1 month to 6 months.

[0018] In some embodiments, the method for establishing a standard curve includes: linearly fitting the actual water content of the Daqu sample during storage with the temperature and humidity in the storage bin where the Daqu sample is located to obtain a standard curve.

[0019] In some embodiments, the fitting accuracy of the standard curve is expressed as R 2 express.

[0020] In some embodiments, the fitting model of the standard curve is: y=a+bx1+cx2, wherein x1 is the temperature inside the storage bin, x2 is the humidity inside the storage bin, and a and b are constants.

[0021] The method for detecting the moisture content of Daqu during the storage process of some embodiments of the present application can establish a standard curve in advance based on the correlation between the actual moisture content of the Daqu sample during the storage process and the temperature and humidity in the storage bin. When the moisture content of the Daqu sample to be tested is detected, the moisture content of the Daqu sample to be tested during the storage process can be predicted quickly and non-destructively based on the standard curve and the temperature and humidity in the storage bin where the Daqu sample to be tested is located. By adopting this scheme, the moisture content of Daqu can be detected quickly and non-destructively during the storage process, so that the moisture changes of Daqu can be monitored online in real time during the storage process, the fermentation quality of Daqu can be monitored, and the efficiency and stability of production can be improved, which is of great significance to the automation and intelligence of Daqu production. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The partial residual diagram of the actual moisture content of the Daqu sample stored in the spring and the temperature and humidity in the storage warehouse obtained by fitting the linear regression algorithm in Example 1;

[0023] Figure 2 The partial residual diagram of the actual moisture content of the Daqu samples stored in the warehouse in summer and the temperature and humidity in the storage warehouse obtained by the linear regression algorithm in Example 2;

[0024] Figure 3 This is the partial residual diagram of the actual moisture content of the Daqu samples stored in the warehouse in autumn and the temperature and humidity in the storage warehouse obtained by the linear regression algorithm in Example 3. DETAILED DESCRIPTION

[0025] The present invention is further described below by way of examples, which are not intended to further limit the content of the present invention. It should be understood by those skilled in the art that equivalent replacements or corresponding improvements made to the content of the present invention still fall within the scope of protection of the present invention.

[0026] Example 1

[0027] 1. Purpose of the experiment

[0028] (1) Establish a functional equation between the actual moisture content of Daqu samples stored in the spring and the temperature and humidity in the storage warehouse, so as to construct a moisture content detection model suitable for Daqu stored in the spring;

[0029] (2) Detect the moisture content of the Daqu samples stored in the warehouse in spring during storage.

[0030] 2. Experimental process

[0031] (1) Randomly select 15 Daqu samples from storage bins that were stored in spring and have different storage times (storage time is 1 to 6 months), and a total of 135 Daqu samples are tested for moisture content. The average value of the actual moisture content of the Daqu samples in the same storage bin is calculated, and the average temperature and humidity values ​​in the corresponding storage bins are monitored over a certain period of time. Preferably, the detection frequency of temperature and humidity is: once a day, for three consecutive weeks. Preferably, the average value of the air temperature above the storage bin measured within three weeks is calculated as the above-mentioned temperature average value, and / or the average value of the air humidity above the storage bin measured within three weeks is calculated as the above-mentioned humidity average value.

[0032] (2) Fit the average values ​​of temperature, humidity, and water content to establish a standard curve, such as Figure 1 As shown, Figure 1 This is the partial residual diagram of the actual moisture content of the Daqu sample stored in the spring and the temperature and humidity in the storage warehouse obtained by linear regression algorithm fitting in Example 1. Among them, the equation of the standard curve obtained by linear regression algorithm is: y=5.32412-0.15402x1+0.14171x2(R 2 ≥0.96627).

[0033] (3) In each of the two Daqu storage warehouses that were put into storage in spring, three Daqu samples were selected as the Daqu samples to be tested, and they were numbered 1-1, 1-2, 1-3, 2-1, 2-2, and 2-3, respectively. The average temperature and average humidity in each warehouse were monitored for the first three weeks. Then, the average temperature and average humidity were substituted into the equation y=5.32412-0.15402x1+0.14171x2 obtained in step (2), and the predicted moisture content of the Daqu samples to be tested was calculated, as shown in Table 1.

[0034] (4) After all parts of the six Daqu samples to be tested in the two storage bins in step (3) are crushed, their actual moisture contents are detected, and the average values ​​of the actual moisture contents of the Daqu samples to be tested in each storage bin are calculated, as shown in Table 1. Table 1 shows the comparison results of the actual moisture content and the predicted moisture content of the Daqu samples stored in the spring during storage.

[0035] Table 1

[0036]

[0037] It can be seen from the comparison results in Table 1 that the relative error between the actual moisture content of the Daqu sample to be tested and the calculated predicted moisture content is within 5%, indicating that this method is feasible.

[0038] Example 2

[0039] 1. Purpose of the experiment

[0040] (1) Establish a functional equation between the actual moisture content of Daqu samples stored in the summer during storage and the temperature and humidity in the storage warehouse, so as to construct a moisture content detection model suitable for Daqu stored in the summer;

[0041] (2) Detect the moisture content of the Daqu samples stored in the warehouse in summer during the storage process.

[0042] 2. Experimental process

[0043] (1) Randomly select 15 Daqu samples from storage bins that were stored in the summer and have different storage times (storage time is 1 to 6 months), and a total of 135 Daqu samples are tested for moisture content. The average value of the actual moisture content of the Daqu samples in the same storage bin is calculated, and the average temperature and humidity values ​​in the corresponding storage bins are monitored over a certain period of time. Preferably, the detection frequency of temperature and humidity is: once a day, for three consecutive weeks. Preferably, the average value of the upper air temperature in the storage bin measured within three weeks is calculated as the above-mentioned temperature average value, and / or the average value of the upper air humidity in the storage bin measured within three weeks is calculated as the above-mentioned humidity average value.

[0044] (2) Fit the average values ​​of temperature, humidity, and water content to establish a standard curve, such as Figure 2 As shown, Figure 2 This is the partial residual diagram of the actual moisture content of the Daqu sample stored in the warehouse in summer and the temperature and humidity in the storage warehouse obtained by the linear regression algorithm in Example 2. Among them, the equation of the standard curve for calculating the moisture content of the Daqu sample to be tested obtained by the linear regression algorithm is: y = -7.41979-0.10043x1+0.27021x2(R 2 ≥0.92444).

[0045] (3) In each of the two summer storage warehouses, three Daqu samples were selected as the Daqu samples to be tested, numbered 1-1, 1-2, 1-3, 2-1, 2-2, and 2-3, respectively. The average temperature and humidity values ​​in each warehouse were monitored for the first three weeks. The average temperature and humidity values ​​were then substituted into the regression equation y=-7.41979-0.10043x1+0.27021x2 obtained in step (2) to calculate the predicted moisture content of the Daqu samples to be tested, as shown in Table 2.

[0046] (4) After all parts of the six Daqu samples to be tested in the two storage bins in step (3) were crushed, their actual moisture contents were detected, and the average actual moisture contents of the Daqu samples to be tested in each storage bin were calculated, as shown in Table 2. Table 2 shows the comparison results of the actual moisture content and the predicted moisture content of the Daqu samples stored in the warehouse during the summer.

[0047] Table 2

[0048]

[0049] It can be seen from the comparison results in Table 1 that the relative error between the actual moisture content of the Daqu sample to be tested and the calculated predicted moisture content is within 5%, indicating that this method is feasible.

[0050] Example 3

[0051] 1. Purpose of the experiment

[0052] (1) Establish a standard curve function equation between the actual moisture content of Daqu samples stored in autumn during storage and the temperature and humidity in the storage warehouse, so as to construct a moisture content detection model suitable for Daqu stored in autumn;

[0053] (2) Detect the moisture content of the Daqu samples stored in the warehouse in autumn during storage.

[0054] 2. Experimental process

[0055] (1) Randomly select 15 Daqu samples from storage bins that were stored in autumn and have different storage times (storage time is 1 to 6 months), and a total of 135 Daqu samples are tested for moisture content. The average moisture content of the Daqu samples in the same storage bin is calculated, and the average temperature and humidity values ​​in the corresponding storage bins are monitored over a certain period of time. Preferably, the detection frequency of temperature and humidity is: once a day, for three consecutive weeks. Preferably, the average value of the upper air temperature in the storage bin measured within three weeks is calculated as the above-mentioned temperature average value, and / or the average value of the upper air humidity in the storage bin measured within three weeks is calculated as the above-mentioned humidity average value.

[0056] (2) Fit the average values ​​of temperature, humidity, and water content to establish a standard curve, such as Figure 3 As shown, Figure 3 This is the partial residual diagram of the actual moisture content of the Daqu sample stored in the warehouse in autumn and the temperature and humidity in the storage warehouse obtained by the linear regression algorithm in Example 3. Among them, the equation of the standard curve for calculating the moisture content of the Daqu sample to be tested obtained by the linear regression algorithm is: y = -8.87174-0.0272x1+0.25326x2(R 2 ≥0.96079).

[0057] (3) In each of the two Daqu storage warehouses that were put into storage in autumn, three Daqu samples were selected as the Daqu samples to be tested, and they were numbered 1-1, 1-2, 1-3, 2-1, 2-2, and 2-3, respectively. The average temperature and average humidity in each warehouse were monitored for the first three weeks. Then, the average temperature and average humidity were substituted into the regression equation y=-8.87174-0.0272x1+0.25326x2 obtained in step (2), and the predicted moisture content of the Daqu samples to be tested was calculated, as shown in Table 3.

[0058] (4) After all parts of the six Daqu samples to be tested in the two storage bins in step (3) were crushed, their actual moisture contents were detected, and the average actual moisture contents of the Daqu samples to be tested in each storage bin were calculated, as shown in Table 3. Table 3 shows the comparison results of the actual moisture content and the predicted moisture content of the Daqu samples stored in the autumn during storage.

[0059] Table 3

[0060]

[0061]

[0062] It can be seen from the comparison results in Table 3 that the relative error between the actual moisture content of the Daqu sample to be tested and the predicted moisture content obtained by calculation is within 5%, indicating that this method is feasible.

[0063] Example 4

[0064] 1. Purpose of the experiment

[0065] When verifying the construction of the moisture content model, the measurement period of the temperature and humidity in the storage bin is preferably three weeks.

[0066] 2. Experimental process

[0067] Randomly select 15 Daqu samples from storage bins that were stored in spring and stored for different periods of time (storage time ranges from 1 to 6 months), and a total of 135 Daqu samples for moisture content determination. Calculate the average moisture content of Daqu samples in the same storage bin, monitor the average temperature and humidity in the corresponding storage bins for two weeks, and fit the average temperature, humidity, and moisture content to establish a standard curve. The R value of the standard curve fitted by the average temperature and humidity in the storage bins monitored for two weeks and the actual moisture content of the Daqu samples is 0. 2 =0.94894, which is smaller than R in Example 1 2 (0.96627), wherein the average values ​​of temperature and humidity in Example 1 are the average values ​​of the air temperature above the storage bin measured within three weeks, and the average values ​​of the air humidity above the storage bin measured within three weeks as the above-mentioned average humidity values.

[0068] Example 5

[0069] 1. Purpose of the experiment

[0070] It is verified that the model for linearly fitting the actual moisture content of the Daqu sample during storage with the temperature and humidity in the storage bin where the Daqu sample is located is preferably y=a+bx1+cx2.

[0071] 2. Experimental process

[0072] (1) Fifteen pieces of Daqu were randomly selected from storage bins that were stored in spring for different periods of time (storage time ranged from 1 to 6 months), and the moisture content of a total of 135 samples was measured. The average moisture content of the Daqu in the same storage bin was calculated, and the average temperature and humidity of the corresponding storage bins were monitored over three weeks.

[0073] (2) The temperature, humidity and water content average values ​​were fitted using a variety of linear fitting models, and the fitting results are shown in Table 4. Table 4 shows the comparison of fitting results based on different fitting models.

[0074] Table 4

[0075]

[0076] Note: y is the dependent variable moisture; x1 is the variable temperature in the warehouse; x2 is the variable humidity in the warehouse

[0077] It can be seen from Table 4 that the standard error obtained by fitting the fitting model y=a+bx1+cx2 is the smallest, R 2 The value is the largest, indicating that the fitting model y=a+bx1+cx2 has the best fitting effect and the highest accuracy. Therefore, this fitting model can be preferably used to fit the standard curve, thereby improving the accuracy of Daqu moisture detection.

[0078] The above-mentioned implementation modes are only preferred implementation modes of the present application and cannot be used to limit the scope of protection of the present application. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present application shall fall within the scope of protection required by the present application.

Claims

1. A method for detecting the water content during the storage of Daqu, characterized in that: The method comprises: Obtain a Daqu sample to be tested; Measuring the temperature and humidity in the storage bin where the Daqu sample to be tested is located; Calculate the water content of the Daqu sample to be tested according to the temperature and humidity in the storage bin where the Daqu sample to be tested is located and a pre-constructed standard curve; The method for establishing the standard curve includes: performing linear fitting on the actual water content of the Daqu sample during storage with the temperature and humidity in the storage bin where the Daqu sample is located to obtain the standard curve; and the fitting accuracy of the standard curve is expressed as R 2 It means that the fitting model of the standard curve is: y=a+bx1+cx2; Among them, x1 is the temperature inside the storage warehouse, x2 is the humidity inside the storage warehouse, and a and b are constants.

2. The method according to claim 1, characterized in that: The method for establishing the standard curve includes: according to the season when the Daqu samples are stored in the warehouse, establishing different standard curves for the Daqu samples stored in the warehouse in different seasons.

3. The method according to claim 1, characterized in that: The method for calculating the actual moisture content comprises: crushing and drying the Daqu sample, measuring the mass of the Daqu sample before drying and the mass of the Daqu sample after drying, and calculating the actual moisture content of the Daqu sample according to the mass before drying and the mass after drying; The actual water content is the actual water content; The calculation formula of the actual moisture content is: (the mass before drying-the mass after drying) / the mass before drying.

4. The method according to claim 3, characterized in that The drying is carried out in a constant temperature drying oven; The drying temperature is a constant temperature of 125°C; The drying time was 1 hour.

5. The method according to claim 1, characterized in that The method for measuring the temperature and humidity in the storage bin where the Daqu samples are located includes: measuring at least once a day for at least three consecutive weeks.

6. The method according to claim 5, characterized in that The temperature in the storage bin where the Daqu samples are stored is the average of the air temperatures above the storage bin measured within three weeks; and / or, The humidity in the storage bin where the Daqu samples are located is the average value of the air humidity above the storage bin measured within three weeks.

7. The method according to claim 1, characterized in that The Daqu samples are samples that have been stored in the warehouse for 1 month to 6 months.

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