Method and system for detecting metabolic activity of yeast in beer fermentation process

Abstract

The invention discloses a method and a system for detecting yeast metabolic activity in a beer fermentation process, relates to the technical field of yeast metabolic activity detection, and aims to solve the problem of inaccurate acquisition of yeast metabolic activity change in the beer fermentation process. And the metabolite data is mapped into the classification rule, so that the accuracy and scientificity of classification are improved. The total relative abundance of each classification type and the relative abundance of different fermentation stages are calculated through statistical analysis, a reliable data basis is provided for dynamic change speculation, static metabolite data are concerned, relative abundance change data are obtained through dynamic change speculation, and the relative abundance of the fermentation stages is obtained. Therefore, metabolic activity changes of the yeast in the beer fermentation process can be reflected in real time, quantitative analysis and qualitative analysis are combined, obtained data are more comprehensive and accurate, and the metabolic activity of the yeast in the beer fermentation process can be known more deeply.

Description

Technical Field

[0001] The invention relates to the technical field of yeast metabolic activity detection, in particular to a method and a system for detecting yeast metabolic activity in a beer fermentation process. Background Art

[0002] The detection of brewer's yeast metabolic activity refers specifically to the evaluation of the physiological and biochemical functions of yeast cells during the fermentation process during beer production.

[0003] The Chinese patent with publication number CN102212603B discloses a method for detecting yeast metabolic activity during beer fermentation, which mainly involves starving the yeast collected from the fermentation broth, adjusting the initial pH of the bacterial suspension, adding glucose to induce yeast H+ outflow, calculating the maximum yeast H+ outflow rate, and using it to characterize the metabolic activity of the yeast. The method of the present invention can not only accurately reflect the difference in yeast strain activity, but also be used to evaluate the yeast metabolic activity during the fermentation process, and the results are accurate and reproducible. Although the above patent solves the problem of yeast metabolic activity detection, the following problems still exist in actual operation: 1. The fermentation data of beer samples was not monitored more accurately, which made it impossible to find out the key factors affecting fermentation efficiency and product quality.

[0004] 2. The monitoring data is not analyzed more effectively, resulting in the inability to accurately obtain metabolic activity.

[0005] 3. Failure to generate effective reports for the test data resulted in staff being unable to review the test data in a timely manner. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for detecting yeast metabolic activity during beer fermentation. By using chemical features and mass spectrometry features as mapping basis, metabolite data are mapped into classification rules, thereby improving the accuracy and scientificity of classification. The total relative abundance of each classification type and the relative abundance of different fermentation stages are calculated through statistical analysis, providing a reliable data basis for dynamic change speculation. Not only static metabolite data are focused on, but also relative abundance change data are obtained through dynamic change speculation, so that the changes in the metabolic activity of yeast during beer fermentation can be reflected in real time. The combination of quantitative analysis and qualitative analysis makes the obtained data more comprehensive and accurate, which helps to have a deeper understanding of the metabolic activity of yeast during beer fermentation, and can solve the problems in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for detecting yeast metabolic activity during beer fermentation, comprising the following steps: S1: Sample data collection: Use a sterile sampler to collect samples of beer at different fermentation stages, and obtain beer fermentation samples after sample collection; S2: Yeast activity monitoring: Use an optical microscope to monitor the yeast data of beer fermentation samples, and annotate the yeast monitoring data as activity monitoring data; S3: Temperature and pH monitoring: Use sensors to monitor the temperature data and pH value data in the yeast data monitoring process in real time, and synchronize the real-time monitoring data with the activity monitoring data to obtain comprehensive activity data after synchronization; S4: Metabolite analysis: Use detection instruments to conduct quantitative and qualitative analysis on the comprehensive activity data, integrate the analytical data of quantitative and qualitative analysis, and obtain metabolite data after component integration; S5: Yeast population analysis: The metabolite data are classified according to the classification rules, and the classified metabolite data are subjected to population analysis. After the population analysis, the structure and quantity changes of the yeast population are obtained, and the metabolic activity of the yeast under different conditions is obtained based on the structure and quantity changes of the yeast population.

[0008] Preferably, in step S1, using a sterile sampler to collect samples of beer at different fermentation stages includes: Prepare and inspect the sterile sampler, which includes a sterile syringe and a sterile sampling bottle; Sterile syringes and sterile sampling bottles are sterilized at high temperature before sample collection; After sterilizing the sterile syringe and the sterile sampling bottle at high temperature, the beer sample collection time is confirmed; The sampling time of beer is the sampling time points of the initial fermentation period, the middle fermentation period and the post-fermentation period of beer fermentation; According to the beer sample collection time, a preset volume of sample is extracted using a sterile sampler after high temperature sterilization; The samples collected at different sample collection time points were labeled, including sampling time, fermentation stage, and sampling volume; After the sample labeling is completed, the beer fermentation sample is obtained.

[0009] Preferably, the yeast data monitoring of the beer fermentation sample using an optical microscope in step S2 includes: Before monitoring yeast data of beer fermentation samples, shake the beer fermentation samples first, then extract the beer fermentation samples with a sterile syringe under sterile conditions and drop them on a glass slide; The beer fermentation samples on the slides were examined for morphology and quantity of yeast cells using an optical microscope. The number of yeast cells was distinguished between live cells and dead cells using methylene blue staining technology, and the live cells were counted. The morphology and number of yeast cells were observed by optical microscopy, and the biomass was determined by turbidimetry; The beer fermentation samples after biomass determination were subjected to fermentation capacity test; The fermentation capacity test process is as follows: according to the result of biomass determination, the number of yeast cells in the beer fermentation sample is converted. The conversion method is to prepare a yeast suspension that meets the preset standard volume according to the number of yeast cells, and use turbidimetry to determine the absorbance value of each standard volume of yeast suspension. The standard curve is drawn with the absorbance value as the horizontal axis and the number of yeast cells as the vertical axis. According to the absorbance value of each standard volume of yeast suspension, the corresponding number of yeast cells is found on the standard curve; Inoculate the prepared yeast suspension that meets the preset standard volume into the yeast culture medium to conduct a fermentation experiment; Monitor fermentation parameters during the fermentation process, including carbon dioxide release, sugar concentration and alcohol concentration in the fermentation liquid; At the same time, the fermentation parameter change trends at different time points during the fermentation process are recorded, and the fermentation parameter change trends include the fermentation rate and the final fermentation product yield; Data correlation between biomass and fermentation parameters of beer fermentation samples; After data association, the fermentation capacity data of the beer fermentation samples were obtained.

[0010] Preferably, the method of monitoring yeast data of the beer fermentation sample using an optical microscope in step S2 further includes: Metabolic activity testing based on fermentation capacity data of beer fermentation samples; Metabolic activity tests include tests for sugar metabolism, alcohol production, and flavor compound production; Sugar metabolism test is to inoculate the beer fermentation sample that has completed the fermentation capacity test into the culture medium with preset sugar concentration, and monitor the sugar consumption rate in real time; The alcohol production test is to ferment the sample in the sugar metabolism test with yeast and take samples of the alcohol concentration at regular intervals; The flavor compound generation test is to detect the types and concentrations of flavor compounds in the alcohol generation test process using liquid chromatography; The metabolic activity test results were correlated with the fermentation capacity test results, and the relationship between yeast metabolic activity and fermentation performance was obtained after correlation analysis; The relationship between yeast metabolic activity and fermentation performance includes the relationship between yeast quantity and fermentation rate, the relationship between yeast activity and fermentation efficiency, the relationship between yeast morphology and fermentation stability, the relationship between biomass and alcohol yield, the relationship between sugar metabolism ability and fermentation progress, the relationship between alcohol tolerance and alcohol concentration at the end of fermentation, the relationship between flavor compound production and beer flavor, and the relationship between the trend of fermentation parameter changes and yeast metabolic activity; The relationship between yeast metabolic activity and fermentation performance is uniformly labeled as activity monitoring data.

[0011] Preferably, adjusting the timing duration of alcohol concentration sampling also includes: Extract the alcohol concentration after each alcohol concentration sampling; comparing the alcohol concentration with a preset alcohol concentration threshold; When the alcohol concentration exceeds the preset alcohol concentration threshold, the alcohol concentration parameter, yeast concentration parameter, sugar concentration parameter, fermentation temperature and fermentation pH value corresponding to each alcohol concentration test of the sample in the sugar metabolism test after fermentation are retrieved; Obtaining a first duration adjustment coefficient using an alcohol concentration parameter, a yeast concentration parameter, and a sugar concentration parameter; The first duration adjustment coefficient is obtained by the following formula: ; Among them, S 01 represents the first time adjustment coefficient; n represents the number of alcohol concentration tests of the sample after fermentation in the sugar metabolism test; C i represents the alcohol concentration parameter corresponding to the i-th alcohol concentration test; C i+1 represents the alcohol concentration parameter corresponding to the alcohol concentration test for the i+1th time; C ni+1 represents the yeast concentration parameter corresponding to the alcohol concentration test for the i+1th time; C mi+1 represents the sugar concentration parameter corresponding to the alcohol concentration test for the i+1th time; C bi+1 Indicates the standard deviation of the alcohol concentration parameter corresponding to the i+1th alcohol concentration test; Comparing the first duration adjustment coefficient with a preset adjustment coefficient threshold; When the first duration adjustment coefficient is lower than the preset adjustment coefficient threshold, there is no need to adjust the timing duration; When the first duration adjustment coefficient exceeds a preset adjustment coefficient threshold, the timing duration is adjusted.

[0012] Preferably, when the first duration adjustment coefficient exceeds a preset adjustment coefficient threshold, adjusting the timing duration includes: When the first time adjustment coefficient exceeds a preset adjustment coefficient threshold, the fermentation temperature and the fermentation pH value are adjusted; The fermentation temperature and the fermentation pH value are combined with the first time adjustment coefficient to obtain a second time adjustment coefficient; The second duration adjustment coefficient is obtained by the following formula: ; Among them, S 02 Represents the second duration adjustment coefficient; S 01 represents the first time adjustment coefficient; n represents the number of alcohol concentration tests of the sample after fermentation in the sugar metabolism test; T i represents the fermentation temperature corresponding to the i-th alcohol concentration test; T i+1 represents the fermentation temperature corresponding to the i+1th alcohol concentration test; P i represents the fermentation pH value corresponding to the alcohol concentration test for the i-th time; P i+1 Indicates the fermentation pH value corresponding to the i+1th alcohol concentration test; The timing duration is adjusted using the second duration adjustment coefficient to obtain an adjusted timing duration, wherein the adjusted timing duration is obtained by the following formula: ; Among them, t x represents the timing duration after adjustment; t represents the timing duration before adjustment; S 02 Represents the second duration adjustment coefficient; S 01 Indicates the first duration adjustment coefficient.

[0013] Preferably, in step S3, the temperature data and pH value data in the yeast data monitoring process are monitored in real time by using sensors, and the real-time monitoring data is synchronized with the activity monitoring data, including: The sensor is installed before yeast data monitoring of beer fermentation samples; The sensor includes a temperature sensor and a pH sensor. The temperature sensor is installed in the middle of the beer fermentation sample, and the pH sensor is immersed in the beer fermentation sample. Develop a monitoring plan, which includes the frequency and time of data collection; According to the monitoring plan, the yeast data monitoring process of the beer fermentation sample is monitored in real time using sensors; When the real-time monitoring data exceeds the preset monitoring range, an alarm is automatically triggered to remind the staff to adjust the parameters in time to avoid fermentation failure; Integrate the real-time monitoring data with the activity monitoring data to obtain synchronized data, and annotate the synchronized data as comprehensive activity data; Among them, data integration is used to analyze the metabolic activity of yeast and provide a basis for fermentation technology.

[0014] Preferably, in step S4, the quantitative analysis and qualitative analysis of the comprehensive activity data are performed using a detection instrument, and the analytical data of the quantitative analysis and the qualitative analysis are integrated, including: Before conducting quantitative and qualitative analysis, the fermentation samples corresponding to the comprehensive activity data were first processed, including dilution, filtration and centrifugation; The fermented sample after sample treatment is put into atomic absorption spectrometry for quantitative analysis; After quantitative analysis, metabolite concentration data, relative content data, time variation curve data and statistical data of metabolites in the fermentation sample are obtained, wherein the statistical data include standard deviation data and confidence interval data; The fermented sample after sample treatment is put into infrared spectrum for qualitative analysis; After qualitative analysis, metabolite type data, chemical structure data, characteristic peaks and biosynthetic pathway data of metabolites in fermentation samples were obtained; Merge the analytical data obtained from quantitative analysis and qualitative analysis; The integrated components are uniquely coded and labeled, and the unique code includes the chemical formula, retention time and mass spectrum characteristics of the beer fermentation sample; The data after the unique coding label is completed are marked as metabolite data.

[0015] Preferably, in step S5, the metabolite data is classified according to the classification rules, and the classified metabolite data is subjected to population analysis, and the structure and quantity changes of the yeast population are obtained after the population analysis, including: Retrieving classification rules from a database, the classification rules including chemical properties, molecular structures, biosynthetic pathways and functional attributes of metabolites; Metabolite data were mapped to classification rules using chemical features and mass spectrometry features as mapping basis; After the mapping is completed, the classification data of the metabolite data are obtained; The classification data of metabolite data were statistically analyzed, and the total relative abundance of each classification species was calculated after statistical analysis; Metabolite analysis was performed on beer fermentation samples at different fermentation stages, and the relative abundance of each stage was calculated; The total relative abundance and the relative abundance of each stage are dynamically estimated, and the relative abundance change data are obtained after the dynamic change estimation; The classification data of metabolite data were used to confirm the changes in quantity and distribution under different fermentation conditions, and the yeast population structure was obtained based on the changes in quantity and distribution; The metabolic activity of yeast was inferred based on the relative abundance change data and the yeast population structure; Metabolic activity inference involves retrieving a yeast metabolic network model from a database, mapping the relative abundance change data and yeast population structure into the yeast metabolic network model, and identifying active metabolic pathways and nodes after the mapping is completed; The mapped yeast metabolic network model was used to simulate metabolic flows under different conditions; After the metabolic flow simulation is completed, the metabolic activity data of yeast under different conditions are obtained. The different conditions are various factors that affect the metabolic activity of yeast during beer fermentation, including fermentation stage, temperature, pH value, sugar concentration and yeast population.

[0016] A system for detecting yeast metabolic activity during beer fermentation, comprising: Metabolic activity test report generation and display unit, used for: Generate a test report based on the metabolic activity data of yeast under different conditions; The test report is generated by summarizing the metabolic activity data of yeast under different conditions. The summarized data include the classification of metabolite data, relative abundance change data, yeast population structure and metabolic flow simulation results; Convert each summary data into graphic data visualization; After visualization conversion, the metabolic activity data of yeast under different conditions were obtained; The visualization data is transmitted to the display terminal for display, and the staff views the visualization data on the display terminal.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides a method and system for detecting yeast metabolic activity during beer fermentation. The method and system utilize a microscope and methylene blue staining technology to accurately distinguish and count living cells and dead cells, thereby improving the accuracy of biomass determination. By real-time monitoring of carbon dioxide release, sugar concentration, and alcohol concentration during fermentation, the progress and state of fermentation can be understood in a timely manner, facilitating process adjustment. By in-depth research on the relationship between metabolic activity and fermentation performance, the key factors affecting fermentation efficiency and product quality can be identified, thereby optimizing yeast use and fermentation conditions.

[0018] 2. The present invention provides a method and system for detecting the metabolic activity of yeast during beer fermentation. Quantitative analysis by atomic absorption spectroscopy can obtain detailed data such as the type of metabolites, chemical structure, characteristic peaks and biosynthetic pathways. Qualitative analysis by infrared spectroscopy provides information such as metabolite concentration, relative content, time change curve and statistical data. These data are helpful in evaluating the dynamic changes and overall distribution of metabolites. The combination of quantitative analysis and qualitative analysis makes the obtained data more comprehensive and accurate, which helps to have a deeper understanding of the metabolic activity of yeast in the beer fermentation process and provide strong support for optimizing the fermentation process and improving the quality of beer.

[0019] 3. The present invention provides a method and system for detecting yeast metabolic activity during beer fermentation, which uses chemical features and mass spectrometry features as mapping basis to map metabolite data to classification rules, thereby improving the accuracy and scientificity of classification. The total relative abundance of each classification type and the relative abundance of different fermentation stages are calculated through statistical analysis, providing a reliable data basis for dynamic change speculation. It not only focuses on static metabolite data, but also obtains relative abundance change data through dynamic change speculation, thereby being able to reflect the changes in yeast metabolic activity during beer fermentation in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the steps for detecting yeast metabolic activity during beer fermentation of the present invention; Figure 2 The present invention is a schematic diagram of the process of detecting yeast metabolic activity during beer fermentation. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In order to solve the problem that the fermentation data of beer samples is not monitored more accurately in the prior art, which leads to the inability to find out the key factors affecting the fermentation efficiency and product quality, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: A method for detecting yeast metabolic activity during beer fermentation, comprising the following steps: S1: Sample data collection: Use a sterile sampler to collect samples of beer at different fermentation stages, and obtain beer fermentation samples after sample collection; Among them, high temperature sterilization can ensure that the microorganisms inside and outside the sampler are effectively killed, avoiding the deviation of experimental results caused by sampler contamination; S2: Yeast activity monitoring: Use an optical microscope to monitor the yeast data of beer fermentation samples, and annotate the yeast monitoring data as activity monitoring data; Among them, through in-depth research on the relationship between metabolic activity and fermentation performance, the key factors affecting fermentation efficiency and product quality can be found, thereby optimizing the use of yeast and fermentation conditions; S3: Temperature and pH monitoring: Use sensors to monitor the temperature data and pH value data in the yeast data monitoring process in real time, and synchronize the real-time monitoring data with the activity monitoring data to obtain comprehensive activity data after synchronization; Among them, real-time monitoring reduces the errors caused by manual sampling and testing, and improves the reliability of data; S4: Metabolite analysis: Use detection instruments to conduct quantitative and qualitative analysis on the comprehensive activity data, integrate the analytical data of quantitative and qualitative analysis, and obtain metabolite data after component integration; The combination of quantitative and qualitative analysis makes the data obtained more comprehensive and accurate, which helps to gain a deeper understanding of the metabolic activity of yeast during beer fermentation. S5: Yeast population analysis: Classify the metabolite data according to the classification rules, and perform population analysis on the classified metabolite data. After the population analysis, the structure and quantity changes of the yeast population are obtained, and the metabolic activity of yeast under different conditions is obtained based on the structure and quantity changes of the yeast population; Among them, by mapping the relative abundance change data and yeast population structure into the yeast metabolic network model, active metabolic pathways and nodes can be identified, providing strong support for predicting the metabolic activity of yeast.

[0023] In step S1, a sterile sampler is used to collect samples of beer at different fermentation stages, including: Prepare and inspect the sterile sampler, which includes a sterile syringe and a sterile sampling bottle; Sterile syringes and sterile sampling bottles are sterilized at high temperature before sample collection; After sterilizing the sterile syringe and the sterile sampling bottle at high temperature, the beer sample collection time is confirmed; The sampling time of beer is the sampling time points of the initial fermentation period, the middle fermentation period and the post-fermentation period of beer fermentation; According to the beer sample collection time, a preset volume of sample is extracted using a sterile sampler after high temperature sterilization; The samples collected at different sample collection time points were labeled, including sampling time, fermentation stage, and sampling volume; After the sample labeling is completed, the beer fermentation sample is obtained.

[0024] Specifically, sterile samplers (including sterile syringes and sterile sampling bottles) are used for sample collection. These tools are sterilized at high temperature before sampling, which can effectively kill potential microorganisms, thereby avoiding the risk of sample contamination during the collection process. The selection of sample collection time points (primary fermentation period, middle fermentation period and post-fermentation period) covers the entire cycle of beer fermentation and can fully reflect the metabolic activity of yeast at different stages. By accurately controlling the sampling time and flow rate of the sterile syringe and adjusting the depth of the steel pipe, it can be ensured that the sampling head can obtain representative samples, thereby improving the accuracy and reliability of the test results. The sample collection process strictly follows the aseptic operation specifications, reducing tedious cleaning steps and verification processes, and improving the detection efficiency. The sample markings are clear and unambiguous, including information such as sampling time, fermentation stage and sampling volume, which is convenient for subsequent data analysis and result interpretation. High temperature sterilization can ensure that microorganisms inside and outside the sampler are effectively killed, avoiding experimental result deviations caused by sampler contamination.

[0025] In step S2, the yeast data of beer fermentation samples is monitored by optical microscopy, including: Before monitoring yeast data of beer fermentation samples, shake the beer fermentation samples first, then extract the beer fermentation samples with a sterile syringe under sterile conditions and drop them on a glass slide; The beer fermentation samples on the slides were examined for morphology and quantity of yeast cells using an optical microscope. The number of yeast cells was distinguished between live cells and dead cells using methylene blue staining technology, and the live cells were counted. The morphology and number of yeast cells were observed by optical microscopy, and the biomass was determined by turbidimetry; The beer fermentation samples after biomass determination were subjected to fermentation capacity test; The fermentation capacity test process is as follows: according to the result of biomass determination, the number of yeast cells in the beer fermentation sample is converted. The conversion method is to prepare a yeast suspension that meets the preset standard volume according to the number of yeast cells, and use turbidimetry to determine the absorbance value of each standard volume of yeast suspension. The absorbance value is used as the horizontal axis and the number of yeast cells is used as the vertical axis to draw a standard curve. According to the absorbance value of each standard volume of yeast suspension, the corresponding number of yeast cells is found on the standard curve; Inoculate the prepared yeast suspension that meets the preset standard volume into the yeast culture medium to conduct a fermentation experiment; Monitor fermentation parameters during the fermentation process, including carbon dioxide release, sugar concentration and alcohol concentration in the fermentation liquid; At the same time, the fermentation parameter change trends at different time points during the fermentation process are recorded, and the fermentation parameter change trends include the fermentation rate and the final fermentation product yield; Data correlation between biomass and fermentation parameters of beer fermentation samples; After data association, the fermentation capacity data of the beer fermentation samples were obtained.

[0026] Metabolic activity testing based on fermentation capacity data of beer fermentation samples; Metabolic activity tests include tests for sugar metabolism, alcohol production, and flavor compound production; Sugar metabolism test is to inoculate the beer fermentation sample that has completed the fermentation capacity test into the culture medium with preset sugar concentration, and monitor the sugar consumption rate in real time; The alcohol production test is to ferment the sample in the sugar metabolism test with yeast and take samples of the alcohol concentration at regular intervals; The flavor compound generation test is to detect the types and concentrations of flavor compounds in the alcohol generation test process using liquid chromatography; The metabolic activity test results were correlated with the fermentation capacity test results, and the relationship between yeast metabolic activity and fermentation performance was obtained after correlation analysis; The relationship between yeast metabolic activity and fermentation performance includes the relationship between yeast quantity and fermentation rate, the relationship between yeast activity and fermentation efficiency, the relationship between yeast morphology and fermentation stability, the relationship between biomass and alcohol yield, the relationship between sugar metabolism ability and fermentation progress, the relationship between alcohol tolerance and alcohol concentration at the end of fermentation, the relationship between flavor compound production and beer flavor, and the relationship between the trend of fermentation parameter changes and yeast metabolic activity; The relationship between yeast metabolic activity and fermentation performance is uniformly labeled as activity monitoring data.

[0027] Specifically, not only the morphology and quantity of yeast were monitored, but also the live and dead cells were distinguished by methylene blue staining technology, thus providing comprehensive information about yeast biomass. Through subsequent fermentation capacity tests, metabolic activity tests and correlation analysis, this program systematically explored the relationship between yeast metabolic activity and fermentation performance, providing rich data support for in-depth understanding of the beer fermentation process. The morphology and quantity of yeast cells were detected using an optical microscope, combined with methylene blue staining technology, which can accurately distinguish live and dead cells, thereby ensuring the accuracy of yeast biomass determination. Real-time monitoring of fermentation parameters (such as carbon dioxide release, sugar concentration and alcohol concentration) and recording of fermentation parameter change trends at different time points improve the reliability of fermentation process monitoring. Through a series of standardized operating procedures and test methods, efficient monitoring of yeast metabolic activity during beer fermentation is achieved. The obtained data (such as biomass, fermentation parameters, metabolic activity test results, etc.) can be directly used for correlation analysis, providing a practical basis for quality control and process optimization in the beer production process. It combines a variety of modern biological and biochemical techniques (such as optical microscopy observation, methylene blue staining, biomass conversion method, liquid chromatography detection, etc.), reflecting the rigor and innovation of scientific research. By correlating and analyzing the relationship between yeast metabolic activity and fermentation performance, a new perspective and idea is provided for the study of the mechanism of beer fermentation process. The activity monitoring data can not only be used to evaluate the fermentation capacity of current beer fermentation samples, but also provide scientific guidance for future beer production process improvements and new product development. Using microscopes and methylene blue staining technology, living and dead cells can be accurately distinguished and counted, which improves the accuracy of biomass determination. Through real-time monitoring of carbon dioxide release, sugar concentration and alcohol concentration during fermentation, the progress and status of fermentation can be understood in a timely manner, which is convenient for process adjustment. It not only monitors the number and morphology of yeast cells, but also includes comprehensive tests of fermentation capacity and metabolic activities, such as sugar metabolism, alcohol production and flavor compound generation, etc., to analyze the fermentation performance of yeast from multiple angles. Through correlation analysis, various data such as biomass, fermentation parameters, metabolic activities, etc. are integrated to obtain the relationship between yeast metabolic activity and fermentation performance, providing a scientific basis for optimizing the fermentation process. Various important parameters and relationships of yeast in the fermentation process are taken into account, including yeast quantity, vitality, morphology, biomass, sugar metabolism ability, alcohol tolerance, flavor compound production, etc., to form a systematic and complete monitoring method. Through in-depth research on the relationship between metabolic activity and fermentation performance, the key factors affecting fermentation efficiency and product quality can be found out, thereby optimizing the use of yeast and fermentation conditions, and improving the quality and flavor of beer. The combination of multiple detection technologies and analytical methods has improved the depth of understanding of yeast metabolic activity and fermentation process, and provided new ideas for the improvement of beer brewing process.

[0028] Specifically, the timing of alcohol concentration sampling is adjusted, and also includes: Extract the alcohol concentration after each alcohol concentration sampling; comparing the alcohol concentration with a preset alcohol concentration threshold; When the alcohol concentration exceeds the preset alcohol concentration threshold, the alcohol concentration parameter, yeast concentration parameter, sugar concentration parameter, fermentation temperature and fermentation pH value corresponding to each alcohol concentration test of the sample in the sugar metabolism test after fermentation are retrieved; Obtaining a first duration adjustment coefficient using an alcohol concentration parameter, a yeast concentration parameter, and a sugar concentration parameter; The first duration adjustment coefficient is obtained by the following formula: ; Among them, S 01 represents the first time adjustment coefficient; n represents the number of alcohol concentration tests of the sample after fermentation in the sugar metabolism test; C i represents the alcohol concentration parameter corresponding to the i-th alcohol concentration test; C i+1 represents the alcohol concentration parameter corresponding to the alcohol concentration test for the i+1th time; C ni+1 represents the yeast concentration parameter corresponding to the alcohol concentration test for the i+1th time; C mi+1 represents the sugar concentration parameter corresponding to the alcohol concentration test for the i+1th time; C bi+1 Indicates the standard deviation of the alcohol concentration parameter corresponding to the i+1th alcohol concentration test; Comparing the first duration adjustment coefficient with a preset adjustment coefficient threshold; When the first duration adjustment coefficient is lower than the preset adjustment coefficient threshold, there is no need to adjust the timing duration; When the first duration adjustment coefficient exceeds a preset adjustment coefficient threshold, the timing duration is adjusted.

[0029] The technical effect of the above technical solution is: the solution monitors the alcohol concentration in real time, and automatically determines whether the sampling timing duration needs to be adjusted according to the change of alcohol concentration and the comparison with the preset threshold. This intelligent adjustment mechanism can flexibly respond to the actual situation in the fermentation process and optimize the management of the fermentation process. Improve fermentation efficiency and product quality: When the alcohol concentration exceeds the preset threshold, the solution will further analyze multiple key parameters in the fermentation process (such as alcohol concentration, yeast concentration, sugar concentration, fermentation temperature and fermentation pH value), and calculate the first duration adjustment coefficient through these parameters. This step helps to accurately identify the key factors affecting the fermentation efficiency, and adjust the sampling timing duration accordingly, so as to improve the fermentation efficiency and ensure product quality. By accurately adjusting the sampling timing duration, too frequent or too sparse sampling can be avoided, thereby reducing the waste of manpower, material resources and time. This is particularly important for large-scale industrial production, which can significantly reduce production costs. The solution calculates the first duration adjustment coefficient by introducing a mathematical formula, and adjusts the timing duration based on the coefficient, making the entire production process more controllable and predictable. This helps manufacturers better plan production plans and improve production efficiency. Since the solution is based on real-time monitoring and data analysis, it has strong adaptability. Whether under different fermentation conditions or facing different types of fermentation raw materials, the solution can be flexibly adjusted according to actual conditions to ensure the stability and efficiency of the fermentation process.

[0030] On the other hand, the scheme can quickly identify abnormal changes in alcohol concentration during the fermentation process by monitoring the alcohol concentration in real time and comparing it with the preset threshold. Once the alcohol concentration exceeds the threshold, subsequent data retrieval and analysis are immediately performed to achieve rapid adjustment of the timing duration. This rapid response mechanism helps to correct deviations in the fermentation process in a timely manner and ensure the stability and controllability of the fermentation process. By using multiple parameters such as alcohol concentration, yeast concentration and sugar concentration to calculate the first duration adjustment coefficient, the scheme can more comprehensively reflect the actual situation of the fermentation process. This multi-dimensional data analysis method helps to improve the accuracy of adjusting the timing duration, so that the adjusted timing duration is more in line with the actual needs of the fermentation process. The scheme not only takes into account the changes in alcohol concentration, but also introduces multiple influencing factors such as yeast concentration, sugar fermentation temperature and fermentation pH value. This enables the scheme to maintain strong adaptability and robustness when facing various complex and uncertain fermentation concentrations and conditions. Even under abnormal or extreme conditions, the fermentation process can be kept stable by adjusting the timing duration. By accurately adjusting the timing duration, the scheme can avoid unnecessary sampling and testing operations, thereby saving manpower, material and time resources. At the same time, since the adjusted timing duration is more in line with the actual needs of the fermentation process, the fermentation efficiency and product quality can be improved, and the production cost can be further reduced. The design of this solution has certain scalability and flexibility. With the continuous advancement of fermentation technology and the discovery of new influencing factors, more parameters and variables can be easily introduced to improve and adjust the solution. This enables the solution to adapt to the changes and development needs of the fermentation process in the future.

[0031] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in fast response speed, high adjustment accuracy, strong robustness, high resource utilization, scalability and flexibility. These effects jointly promote the optimization and upgrading of the fermentation process and provide a strong guarantee for the production efficiency and product quality of the enterprise. At the same time, this technical solution improves the efficiency and quality of the fermentation process through an intelligent and precise adjustment mechanism, reduces production costs, and enhances the controllability and predictability of the production process, which has important practical application value.

[0032] Specifically, when the first duration adjustment coefficient exceeds a preset adjustment coefficient threshold, adjusting the timing duration includes: When the first time adjustment coefficient exceeds a preset adjustment coefficient threshold, the fermentation temperature and the fermentation pH value are adjusted; The fermentation temperature and the fermentation pH value are combined with the first time adjustment coefficient to obtain a second time adjustment coefficient; The second duration adjustment coefficient is obtained by the following formula: ; Among them, S02 Represents the second duration adjustment coefficient; S 01 represents the first time adjustment coefficient; n represents the number of alcohol concentration tests of the sample after fermentation in the sugar metabolism test; T i represents the fermentation temperature corresponding to the i-th alcohol concentration test; T i+1 represents the fermentation temperature corresponding to the i+1th alcohol concentration test; P i represents the fermentation pH value corresponding to the alcohol concentration test for the i-th time; P i+1 Indicates the fermentation pH value corresponding to the i+1th alcohol concentration test; The timing duration is adjusted using the second duration adjustment coefficient to obtain an adjusted timing duration, wherein the adjusted timing duration is obtained by the following formula: ; Among them, t x represents the timing duration after adjustment; t represents the timing duration before adjustment; S 02 Represents the second duration adjustment coefficient; S 01 Indicates the first duration adjustment coefficient.

[0033] The technical effect of the above technical solution is: by introducing two additional parameters, fermentation temperature and fermentation pH value, and combining the first duration adjustment coefficient, the second duration adjustment coefficient is calculated, which can more comprehensively reflect the actual situation in the fermentation process. This helps to adjust the timing duration more accurately to adapt to different fermentation conditions, thereby improving the stability and controllability of the entire fermentation process. Since multiple factors affecting the fermentation process (alcohol concentration, yeast concentration, sugar concentration, fermentation temperature and fermentation pH value) are taken into account, the scheme can better cope with various uncertainties and changes. Even under certain abnormal or extreme conditions, the system can maintain the normal operation of the fermentation process by adjusting the timing duration, thereby enhancing the robustness of the system. By accurately adjusting the timing duration, it is possible to ensure that the alcohol concentration is sampled at the right time, thereby more accurately monitoring the fermentation process. This helps to discover and solve problems in a timely manner and avoid unnecessary waste and delays. At the same time, reasonable timing duration adjustment can also optimize the fermentation cycle and improve production efficiency. Accurate timing duration adjustment helps to ensure that key parameters in the fermentation process are monitored and controlled in a timely manner. This helps to reduce the risk of poor fermentation and improve the quality of the final product. For example, by adjusting the timing duration, it is possible to ensure that sampling is performed when yeast activity is highest and sugar concentration is appropriate, thereby obtaining a higher quality alcohol product. By accurately adjusting the timing duration, unnecessary sampling and testing operations can be avoided, thereby reducing energy consumption and costs. This is particularly important for large-scale industrial production and can significantly improve the economic benefits of the enterprise. The solution makes full use of various data in the fermentation process (alcohol concentration, yeast concentration, sugar concentration, fermentation temperature and fermentation pH value), calculates and analyzes through mathematical formulas, and improves the utilization and accuracy of the data. This helps to provide strong support for subsequent fermentation process optimization and decision-making.

[0034] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in improving the accuracy of adjustment, enhancing the robustness of the system, optimizing fermentation efficiency, improving product quality, reducing energy consumption and costs, and improving data utilization. These effects jointly promote the optimization and upgrading of the fermentation process and provide a strong guarantee for the sustainable development of the enterprise.

[0035] In order to solve the problem that the monitoring data cannot be analyzed more effectively in the prior art, thus failing to accurately obtain metabolic activity, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: In step S3, the temperature data and pH value data in the yeast data monitoring process are monitored in real time by using sensors, and the real-time monitoring data is synchronized with the activity monitoring data, including: The sensor is installed before yeast data monitoring of beer fermentation samples; The sensor includes a temperature sensor and a pH sensor. The temperature sensor is installed in the middle of the beer fermentation sample, and the pH sensor is immersed in the beer fermentation sample. Develop a monitoring plan, which includes the frequency and time of data collection; According to the monitoring plan, the yeast data monitoring process of the beer fermentation sample is monitored in real time using sensors; When the real-time monitoring data exceeds the preset monitoring range, an alarm is automatically triggered to remind the staff to adjust the parameters in time to avoid fermentation failure; Integrate the real-time monitoring data with the activity monitoring data to obtain synchronized data, and annotate the synchronized data as comprehensive activity data; Among them, data integration is used to analyze the metabolic activity of yeast and provide a basis for fermentation technology.

[0036] Specifically, through real-time monitoring of temperature sensors and pH sensors, temperature changes and pH fluctuations during beer fermentation can be quickly captured, thereby achieving instant evaluation of yeast metabolic activity. When the monitoring data exceeds the preset range, an alarm is automatically triggered, allowing operators to respond quickly, adjust fermentation conditions, and avoid potential problems. The sensor is precisely installed in the middle of the beer fermentation sample and immersed in the sample, ensuring the accuracy and representativeness of data collection. Real-time monitoring reduces the errors caused by manual sampling and detection, improves the reliability of data, and integrates real-time monitoring data with activity monitoring data to obtain synchronized comprehensive activity data, which is convenient for subsequent data analysis and processing. This data integration method simplifies the data processing process and improves analysis efficiency. It can be formulated according to actual needs, including data collection frequency and collection time, which makes this method highly flexible and customizable. Operators can adjust the monitoring plan according to different fermentation stages and yeast metabolic characteristics to obtain more accurate data.

[0037] In step S4, the comprehensive activity data is quantitatively and qualitatively analyzed using detection instruments, and the analytical data of the quantitative analysis and qualitative analysis are integrated into components, including: Before conducting quantitative and qualitative analysis, the fermentation samples corresponding to the comprehensive activity data were first processed, including dilution, filtration and centrifugation; The fermented sample after sample treatment is put into atomic absorption spectrometry for quantitative analysis; After quantitative analysis, metabolite concentration data, relative content data, time variation curve data and statistical data of metabolites in the fermentation sample are obtained, wherein the statistical data include standard deviation data and confidence interval data; The fermented sample after sample treatment is put into infrared spectrum for qualitative analysis; After qualitative analysis, metabolite type data, chemical structure data, characteristic peaks and biosynthetic pathway data of metabolites in fermentation samples were obtained; Merge the analytical data obtained from quantitative analysis and qualitative analysis; The integrated components are uniquely coded and labeled, and the unique code includes the chemical formula, retention time and mass spectrum characteristics of the beer fermentation sample; The data after the unique coding label is completed are marked as metabolite data.

[0038] Specifically, before quantitative and qualitative analysis, the fermentation samples were carefully treated by dilution, filtration and centrifugation, which helped to remove impurities and interfering substances in the samples, thereby improving the accuracy and reliability of subsequent analysis. Quantitative analysis by atomic absorption spectroscopy can obtain detailed data such as metabolite types, chemical structures, characteristic peaks and biosynthetic pathways, which are essential for understanding the types and synthetic pathways of yeast metabolites. Qualitative analysis by infrared spectroscopy provides information such as metabolite concentrations, relative contents, time-varying curves and statistical data, which help to evaluate the dynamic changes and overall distribution of metabolites. The combination of quantitative and qualitative analysis makes the data obtained more comprehensive and accurate, which helps to gain a deeper understanding of the metabolic activity of yeast in the beer fermentation process, and provides strong support for optimizing the fermentation process and improving the quality of beer. The data obtained by quantitative and qualitative analysis are combined and labeled by unique coding, which not only improves the data processing efficiency, but also ensures the uniqueness and traceability of the data. This helps to quickly locate and utilize relevant data in subsequent studies, which is not only suitable for the detection of yeast metabolic activity in the beer fermentation process, but can also be extended to other fermentation processes or biological systems as needed. At the same time, by adjusting the sample processing and analysis methods, it can adapt to the needs of different types of yeast and different fermentation conditions.

[0039] In step S5, the metabolite data is classified according to the classification rules, and the classified metabolite data is subjected to population analysis. After the population analysis, the structure and quantity changes of the yeast population are obtained, including: Retrieving classification rules from a database, the classification rules including chemical properties, molecular structures, biosynthetic pathways and functional attributes of metabolites; Metabolite data were mapped to classification rules using chemical features and mass spectrometry features as mapping basis; After the mapping is completed, the classification data of the metabolite data are obtained; The classification data of metabolite data were statistically analyzed, and the total relative abundance of each classification species was calculated after statistical analysis; Metabolite analysis was performed on beer fermentation samples at different fermentation stages, and the relative abundance of each stage was calculated; The total relative abundance and the relative abundance of each stage are dynamically estimated, and the relative abundance change data are obtained after the dynamic change estimation; The classification data of metabolite data were used to confirm the changes in quantity and distribution under different fermentation conditions, and the yeast population structure was obtained based on the changes in quantity and distribution; The metabolic activity of yeast was inferred based on the relative abundance change data and the yeast population structure; Metabolic activity inference involves retrieving a yeast metabolic network model from a database, mapping the relative abundance change data and yeast population structure into the yeast metabolic network model, and identifying active metabolic pathways and nodes after the mapping is completed; The mapped yeast metabolic network model was used to simulate metabolic flows under different conditions; After the metabolic flow simulation is completed, the metabolic activity data of yeast under different conditions are obtained. The different conditions are various factors that affect the metabolic activity of yeast during beer fermentation, including fermentation stage, temperature, pH value, sugar concentration and yeast population.

[0040] Specifically, a complete and systematic process has been formed from the classification and statistical analysis of metabolites to the analysis of yeast population structure and quantity changes, and then to the inference of yeast metabolic activity. By comprehensively considering multiple dimensions such as the chemical properties, molecular structure, biosynthetic pathways and functional attributes of metabolites, the metabolic activity of yeast in the beer fermentation process can be understood more accurately. The metabolite data are mapped to the classification rules using chemical characteristics and mass spectrometry characteristics as the mapping basis, which improves the accuracy and scientificity of the classification. The total relative abundance of each classification species and the relative abundance at different fermentation stages are calculated through statistical analysis, providing a reliable data basis for dynamic change inference. Not only static metabolite data is paid attention to, but also relative abundance change data is obtained through dynamic change inference, so that the changes in yeast metabolic activity during beer fermentation can be reflected in real time. This helps to timely discover and adjust fermentation conditions and optimize the beer fermentation process. By mapping the relative abundance change data and yeast population structure to the yeast metabolic network model, active metabolic pathways and nodes can be identified, providing strong support for predicting yeast metabolic activity, which helps to guide the optimization and regulation of the beer fermentation process and improve the quality and yield of beer.

[0041] In order to solve the problem in the prior art that there is no effective report generation for the test data, which results in the staff being unable to view the test data in time, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: A system for detecting yeast metabolic activity during beer fermentation, comprising: Metabolic activity test report generation and display unit, used for: Generate a test report based on the metabolic activity data of yeast under different conditions; The test report is generated by summarizing the metabolic activity data of yeast under different conditions. The summarized data include the classification of metabolite data, relative abundance change data, yeast population structure and metabolic flow simulation results; Convert each summary data into graphic data visualization; After visualization conversion, the metabolic activity data of yeast under different conditions were obtained; The visualization data is transmitted to the display terminal for display, and the staff views the visualization data on the display terminal.

[0042] Specifically, it covers many aspects of yeast metabolic activity, including the classification of metabolite data, changes in relative abundance, yeast population structure, and metabolic flow simulation results, thus providing a comprehensive and systematic perspective to evaluate the metabolic activity of yeast under different conditions. Through data aggregation, a large amount of data from different experimental conditions and time points can be integrated to facilitate subsequent analysis and comparison. This helps to identify key change points and trends in yeast metabolic activity, and the visual conversion of graphic data makes complex data sets more intuitive and easy to understand. Staff can quickly obtain key information in the form of charts, images, etc. without having to deeply interpret complex raw data, and transmit the visual data to the display terminal for display, so that staff can view and analyze the data in real time. This helps to promptly identify problems during the beer fermentation process and take corresponding adjustment measures, thereby improving production efficiency and product quality.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0044] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for detecting yeast metabolic activity during beer fermentation, characterized in that: The steps include: S1: Sample data collection: Use a sterile sampler to collect samples of beer at different fermentation stages, and obtain beer fermentation samples after sample collection; S2: Yeast activity monitoring: Use an optical microscope to monitor the yeast data of beer fermentation samples, and annotate the yeast monitoring data as activity monitoring data; S3: Temperature and pH monitoring: Use sensors to monitor the temperature data and pH value data in the yeast data monitoring process in real time, and synchronize the real-time monitoring data with the activity monitoring data to obtain comprehensive activity data after synchronization; S4: Metabolite analysis: Use detection instruments to conduct quantitative and qualitative analysis on the comprehensive activity data, integrate the analytical data of quantitative and qualitative analysis, and obtain metabolite data after component integration; S5: Yeast population analysis: The metabolite data are classified according to the classification rules, and the classified metabolite data are subjected to population analysis. After the population analysis, the structure and quantity changes of the yeast population are obtained, and the metabolic activity of the yeast under different conditions is obtained based on the structure and quantity changes of the yeast population.

2. A method for detecting yeast metabolic activity during beer fermentation according to claim 1, characterized in that: In step S1, a sterile sampler is used to collect samples of beer at different fermentation stages, including: Prepare and inspect the sterile sampler, which includes a sterile syringe and a sterile sampling bottle; Sterile syringes and sterile sampling bottles are sterilized at high temperature before sample collection; After sterilizing the sterile syringe and the sterile sampling bottle at high temperature, the beer sample collection time is confirmed; The sampling time of beer is the sampling time points of the initial fermentation period, the middle fermentation period and the post-fermentation period of beer fermentation; According to the beer sample collection time, a preset volume of sample is extracted using a sterile sampler after high temperature sterilization; The samples collected at different sample collection time points were labeled, including sampling time, fermentation stage, and sampling volume; After the sample labeling is completed, the beer fermentation sample is obtained.

3. A method for detecting yeast metabolic activity during beer fermentation according to claim 2, characterized in that: In step S2, the yeast data of beer fermentation samples is monitored by optical microscopy, including: Before monitoring yeast data of beer fermentation samples, shake the beer fermentation samples first, then extract the beer fermentation samples with a sterile syringe under sterile conditions and drop them on a glass slide; The beer fermentation samples on the slides were examined for morphology and quantity of yeast cells using an optical microscope. The number of yeast cells was distinguished between live cells and dead cells using methylene blue staining technology, and the live cells were counted. The morphology and number of yeast cells were observed by optical microscopy, and the biomass was determined by turbidimetry; The beer fermentation samples after biomass determination were subjected to fermentation capacity test; The fermentation capacity test process is as follows: according to the result of biomass determination, the number of yeast cells in the beer fermentation sample is converted. The conversion method is to prepare a yeast suspension that meets the preset standard volume according to the number of yeast cells, and use turbidimetry to determine the absorbance value of each standard volume of yeast suspension. The standard curve is drawn with the absorbance value as the horizontal axis and the number of yeast cells as the vertical axis. According to the absorbance value of each standard volume of yeast suspension, the corresponding number of yeast cells is found on the standard curve; Inoculate the prepared yeast suspension that meets the preset standard volume into the yeast culture medium to conduct a fermentation experiment; Monitor fermentation parameters during the fermentation process, including carbon dioxide release, sugar concentration and alcohol concentration in the fermentation liquid; At the same time, the fermentation parameter change trends at different time points during the fermentation process are recorded, and the fermentation parameter change trends include the fermentation rate and the final fermentation product yield; Data correlation between biomass and fermentation parameters of beer fermentation samples; After data association, the fermentation capacity data of the beer fermentation samples were obtained.

4. A method for detecting yeast metabolic activity during beer fermentation according to claim 3, characterized in that: The yeast data monitoring of the beer fermentation sample by optical microscopy in step S2 also includes: Metabolic activity testing based on fermentation capacity data of beer fermentation samples; Metabolic activity tests include tests for sugar metabolism, alcohol production, and flavor compound production; Sugar metabolism test is to inoculate the beer fermentation sample that has completed the fermentation capacity test into the culture medium with preset sugar concentration, and monitor the sugar consumption rate in real time; The alcohol production test is to ferment the sample in the sugar metabolism test with yeast and take samples of the alcohol concentration at regular intervals; The flavor compound generation test is to detect the types and concentrations of flavor compounds in the alcohol generation test process using liquid chromatography; The metabolic activity test results were correlated with the fermentation capacity test results, and the relationship between yeast metabolic activity and fermentation performance was obtained after correlation analysis; The relationship between yeast metabolic activity and fermentation performance includes the relationship between yeast quantity and fermentation rate, the relationship between yeast activity and fermentation efficiency, the relationship between yeast morphology and fermentation stability, the relationship between biomass and alcohol yield, the relationship between sugar metabolism ability and fermentation progress, the relationship between alcohol tolerance and alcohol concentration at the end of fermentation, the relationship between flavor compound production and beer flavor, and the relationship between the trend of fermentation parameter changes and yeast metabolic activity; The relationship between yeast metabolic activity and fermentation performance is uniformly labeled as activity monitoring data.

5. A method for detecting yeast metabolic activity during beer fermentation according to claim 4, characterized in that: Adjustments to the timing of alcohol concentration sampling also include: Extract the alcohol concentration after each alcohol concentration sampling; comparing the alcohol concentration with a preset alcohol concentration threshold; When the alcohol concentration exceeds the preset alcohol concentration threshold, the alcohol concentration parameter, yeast concentration parameter, sugar concentration parameter, fermentation temperature and fermentation pH value corresponding to each alcohol concentration test of the sample in the sugar metabolism test after fermentation are retrieved; Obtaining a first duration adjustment coefficient using an alcohol concentration parameter, a yeast concentration parameter, and a sugar concentration parameter; The first duration adjustment coefficient is obtained by the following formula: ； Among them, S 01 represents the first time adjustment coefficient; n represents the number of alcohol concentration tests of the sample after fermentation in the sugar metabolism test; C i represents the alcohol concentration parameter corresponding to the i-th alcohol concentration test; C i+1 represents the alcohol concentration parameter corresponding to the alcohol concentration test for the i+1th time; C ni+1 represents the yeast concentration parameter corresponding to the alcohol concentration test for the i+1th time; C mi+1 represents the sugar concentration parameter corresponding to the alcohol concentration test for the i+1th time; C bi+1 Indicates the standard deviation of the alcohol concentration parameter corresponding to the i+1th alcohol concentration test; Comparing the first duration adjustment coefficient with a preset adjustment coefficient threshold; When the first duration adjustment coefficient is lower than the preset adjustment coefficient threshold, there is no need to adjust the timing duration; When the first duration adjustment coefficient exceeds a preset adjustment coefficient threshold, the timing duration is adjusted.

6. A method for detecting yeast metabolic activity during beer fermentation according to claim 5, characterized in that: When the first duration adjustment coefficient exceeds a preset adjustment coefficient threshold, adjusting the timing duration includes: When the first time adjustment coefficient exceeds a preset adjustment coefficient threshold, the fermentation temperature and the fermentation pH value are adjusted; The fermentation temperature and the fermentation pH value are combined with the first time adjustment coefficient to obtain a second time adjustment coefficient; The second duration adjustment coefficient is obtained by the following formula: ； Among them, S 02 Represents the second duration adjustment coefficient; S 01 represents the first time adjustment coefficient; n represents the number of alcohol concentration tests of the sample after fermentation in the sugar metabolism test; T i represents the fermentation temperature corresponding to the i-th alcohol concentration test; T i+1 represents the fermentation temperature corresponding to the i+1th alcohol concentration test; P i represents the fermentation pH value corresponding to the alcohol concentration test for the i-th time; P i+1 Indicates the fermentation pH value corresponding to the i+1th alcohol concentration test; The timing duration is adjusted using the second duration adjustment coefficient to obtain an adjusted timing duration, wherein the adjusted timing duration is obtained by the following formula: ； Among them, t x represents the timing duration after adjustment; t represents the timing duration before adjustment; S 02 Represents the second duration adjustment coefficient; S 01 Indicates the first duration adjustment coefficient.

7. The method for detecting yeast metabolic activity during beer fermentation according to claim 4, characterized in that: In step S3, the temperature data and pH value data in the yeast data monitoring process are monitored in real time by using sensors, and the real-time monitoring data is synchronized with the activity monitoring data, including: The sensor is installed before yeast data monitoring of beer fermentation samples; The sensor includes a temperature sensor and a pH sensor. The temperature sensor is installed in the middle of the beer fermentation sample, and the pH sensor is immersed in the beer fermentation sample. Develop a monitoring plan, which includes the frequency and time of data collection; According to the monitoring plan, the yeast data monitoring process of the beer fermentation sample is monitored in real time using sensors; When the real-time monitoring data exceeds the preset monitoring range, an alarm is automatically triggered to remind the staff to adjust the parameters in time to avoid fermentation failure; Integrate the real-time monitoring data with the activity monitoring data to obtain synchronized data, and annotate the synchronized data as comprehensive activity data; Among them, data integration is used to analyze the metabolic activity of yeast and provide a basis for fermentation technology.

8. The method for detecting yeast metabolic activity during beer fermentation according to claim 7, characterized in that: In step S4, the comprehensive activity data is quantitatively and qualitatively analyzed using detection instruments, and the analytical data of the quantitative analysis and qualitative analysis are integrated into components, including: Before conducting quantitative and qualitative analysis, the fermentation samples corresponding to the comprehensive activity data were first processed, including dilution, filtration and centrifugation; The fermented sample after sample treatment is put into atomic absorption spectrometry for quantitative analysis; After quantitative analysis, metabolite concentration data, relative content data, time variation curve data and statistical data of metabolites in the fermentation sample are obtained, wherein the statistical data include standard deviation data and confidence interval data; The fermented sample after sample treatment is put into infrared spectrum for qualitative analysis; After qualitative analysis, metabolite type data, chemical structure data, characteristic peaks and biosynthetic pathway data of metabolites in fermentation samples were obtained; Integrate the analytical data obtained from quantitative and qualitative analysis into components; The integrated components are uniquely coded and labeled, and the unique code includes the chemical formula, retention time and mass spectrum characteristics of the beer fermentation sample; The data after the unique coding label is completed are marked as metabolite data.

9. The method for detecting yeast metabolic activity during beer fermentation according to claim 8, characterized in that: In step S5, the metabolite data is classified according to the classification rules, and the classified metabolite data is subjected to population analysis. After the population analysis, the structure and quantity changes of the yeast population are obtained, including: Retrieving classification rules from a database, the classification rules including chemical properties, molecular structures, biosynthetic pathways and functional attributes of metabolites; Metabolite data were mapped to classification rules using chemical features and mass spectrometry features as mapping basis; After the mapping is completed, the classification data of the metabolite data are obtained; The classification data of metabolite data were statistically analyzed, and the total relative abundance of each classification species was calculated after statistical analysis; Metabolite analysis was performed on beer fermentation samples at different fermentation stages, and the relative abundance of each stage was calculated; The total relative abundance and the relative abundance of each stage are dynamically estimated, and the relative abundance change data are obtained after the dynamic change estimation; The classification data of metabolite data were used to confirm the changes in quantity and distribution under different fermentation conditions, and the yeast population structure was obtained based on the changes in quantity and distribution; The metabolic activity of yeast was inferred based on the relative abundance change data and the yeast population structure; Metabolic activity inference involves retrieving a yeast metabolic network model from a database, mapping the relative abundance change data and yeast population structure into the yeast metabolic network model, and identifying active metabolic pathways and nodes after the mapping is completed; The mapped yeast metabolic network model was used to simulate metabolic flows under different conditions; After the metabolic flow simulation is completed, the metabolic activity data of yeast under different conditions are obtained. The different conditions are various factors that affect the metabolic activity of yeast during beer fermentation, including fermentation stage, temperature, pH value, sugar concentration and yeast population.

10. A system for detecting yeast metabolic activity during beer fermentation, used in the method for detecting yeast metabolic activity during beer fermentation as claimed in claim 9, characterized in that: include: Metabolic activity test report generation and display unit, used for: Generate a test report based on the metabolic activity data of yeast under different conditions; The test report is generated by summarizing the metabolic activity data of yeast under different conditions. The summarized data include the classification of metabolite data, relative abundance change data, yeast population structure and metabolic flow simulation results; Convert each summary data into graphic data visualization; After visualization conversion, the metabolic activity data of yeast under different conditions were obtained; The visualization data is transmitted to the display terminal for display, and the staff views the visualization data on the display terminal.

Images