A method for regulating microbial community in fermentation process of soybean nuggets

Abstract

The application provides a kind of fermented soybean microbial community regulation method, it is related to microbial inoculation technical field, including the following steps: fermented soybean old tank water inoculation process, select multiple from different fermentation batches of old tank water as candidate old tank water;Collect the physicochemical adaptability index of each candidate old tank water;Construct physicochemical adaptability index calculation function, obtain physicochemical adaptability index;Collect the historical performance characteristic parameters of old tank water;Calculate historical reliability index;Set physicochemical adaptability threshold and historical reliability threshold, build comparison decision mechanism.The application avoids the risk of inoculation failure caused by single index reaching the standard and another dimension existing hidden trouble, improves the scientificity of old tank water screening and inoculation success rate.Can reasonably weaken the interference of extreme abnormal value on evaluation result, make screening result more robust and reliable, realize the fine, standardization and microbial community quantization regulation of old tank water inoculation suitability.

Description

Technical Field

[0001] This invention relates to the field of microbial inoculation technology, specifically to a method for regulating the microbial community during the fermentation process of green beans. Background Technology

[0002] In the microbial community regulation method during green bean fermentation, the old fermentation water, as an inoculation substrate rich in functional microorganisms such as lactic acid bacteria and yeast left over from the previous batch of fermentation, directly determines the start-up rate and dominant microbial succession direction of the next batch of fermentation by its microbial community composition and metabolic activity. By screening out old fermentation water that meets both taste suitability and historical reliability standards and using it to prepare fermentation broth, it is possible to ensure that the target microbial community in the fermentation system quickly establishes population dominance after inoculation and drives the biotransformation process of the substrate in a targeted manner. At the same time, it effectively inhibits the growth of miscellaneous bacteria and maintains the acid-base and salt-sugar balance of the fermentation environment, thereby making the microbial community regulation of the subsequent batches of fermentation process more stable and reliable.

[0003] In the prior art, Chinese Patent Publication No. CN105274035A discloses a method for targeted screening of probiotic lactic acid bacteria based on a kimchi fermentation system. This technology includes: 1) targeted screening of *Lactococcus lactis* spp. at low salt and room temperature; 2) targeted screening of *Weissella* spp. at low salt and low temperature; 3) targeted screening of *Leuconostoc mesenteriae* spp. at medium to low temperature and low salt; and 4) targeted screening of *Lactobacillus plantarum* spp. at high salt and room temperature. The above technology, based on probiotics screened through a kimchi fermentation system, has high safety and can be directly used in health products related to human health. It utilizes the changing patterns of the bacterial structure during kimchi fermentation to design different fermentation conditions and target the screening of probiotic lactic acid bacteria spp. at specific fermentation stages under these conditions. The method is relatively simple and avoids extensive separation and purification work. It avoids blindly selecting the desired probiotics in the complex bacterial structure of kimchi fermentation systems, and the targeted-screened probiotic spp. can be directly applied according to their respective probiotic functions.

[0004] However, the existing technologies mentioned above rely solely on empirical judgment or a single pH index to screen aged jar water, which makes it difficult to comprehensively reflect the overall suitability of aged jar water in multiple taste dimensions such as acidity, saltiness, and sweetness. They lack a systematic quantitative integration of multiple physicochemical indicators, resulting in insufficient objectivity and accuracy. At the same time, conventional methods often only focus on the current physicochemical state of aged jar water, without taking into account important historical performance such as successful fermentation records accumulated during long-term use, idle time, and abnormal fermentation accidents. This makes it impossible to effectively identify aged jar water that is currently acceptable but has a high historical risk, leading to a one-sided and lagging screening decision.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for regulating the microbial community during the fermentation process of green beans, thereby addressing the problems mentioned in the background section. This invention avoids the risk of inoculation failure caused by achieving a single indicator while harboring hidden dangers in another dimension, thus significantly improving the scientific rigor and success rate of screening with fermented bean water. It can reasonably reduce the interference of extreme outliers on the evaluation results, making the screening results more robust and reliable. It achieves refined, standardized, and quantifiable regulation of the suitability of fermented bean water inoculation, providing a more reliable technical guarantee for the stable and targeted regulation of the microbial community during the green bean fermentation process.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for regulating the microbial community during green bean fermentation includes the following steps: S1: In the inoculation process of old jar water for green bean fermentation, multiple samples of old jar water from different fermentation batches are selected as candidate old jar water; new salt and sugar water for subsequent inoculation in the current green bean fermentation process is prepared, and the physicochemical adaptability indicators of each candidate old jar water are collected. The physicochemical adaptability indicators include pH, total acid content, salt concentration and reducing sugar content. S2: Preprocess the physicochemical adaptability indicators collected in step S1. The preprocessing includes outlier removal and data normalization. Construct a physicochemical adaptability index calculation function, substitute each preprocessed physicochemical adaptability indicator into the physicochemical adaptability index calculation function, and obtain the physicochemical adaptability index, which is used to characterize the taste state of the old jar water. S3: Collect historical performance characteristic parameters of the old jar water, including the cumulative number of successful fermentation times, the number of idle days since the most recent fermentation, and the number of abnormal fermentation times. Preprocess the historical performance characteristic parameters. S4: Construct a historical reliability index calculation function based on the preset weights of historical performance characteristic parameters, and calculate the historical reliability index by weighting the preprocessed historical performance characteristic parameters; S5: Set a physicochemical adaptability threshold and a historical reliability threshold, and construct a comparison decision mechanism. The comparison decision mechanism is used to compare the physicochemical adaptability index obtained in S2 with the physicochemical adaptability threshold, and to compare the historical reliability index obtained in S4 with the historical reliability threshold. Based on the output of the comparison decision mechanism, determine whether the corresponding candidate old jar water can be mixed with new salt sugar water for inoculation to regulate the microbial community of green bean fermentation.

[0008] Furthermore, the specific process for collecting the physicochemical adaptability indicators of each candidate aged jar water in S1 is as follows: a representative sample is taken from each candidate aged jar water, and homogenization and solid-liquid separation are performed first to obtain a clear test solution; pH measurement: The pH value of the test solution is read and recorded based on the principle of hydrogen ion activity measurement; Collection of total acid content: Take a quantitative amount of the test solution, use phenolphthalein as an indicator, and titrate with sodium hydroxide standard solution until it turns slightly red and does not fade for 30 seconds. Calculate the total acid concentration in terms of lactic acid based on the volume of standard alkali consumed. Salt concentration collection: Take another sample of the test solution, use potassium chromate as an indicator, and titrate with silver nitrate standard solution until a brick-red precipitate appears. Obtain the salt content data based on the conversion relationship between the volume of silver nitrate consumed and the equivalent sodium chloride. Collection of reducing sugar content: After removing interfering substances such as proteins, the test solution is reacted with alkaline copper tartrate reagent under controlled conditions. The consumption of standard titration solution is recorded by redox titration or the optical response value is obtained by colorimetric method. The measured value is converted into reducing sugar content data using a pre-calibrated sugar concentration response curve.

[0009] Furthermore, in step S2, a minimum-maximum normalization method is used to uniformly map the physicochemical adaptability index values ​​of pH, total acid content, salt concentration, and reducing sugar content of each candidate aged water to the [0,1] interval. This is used to eliminate the interference of different dimensions and orders of magnitude on the subsequent calculation of the physicochemical adaptability index. The formula used for data normalization is: in, Indicates the first The first candidate of Lao Tan Water The original measured values ​​of the physicochemical adaptability indicators; These correspond to pH, total acid content, salt concentration, and reducing sugar content, respectively. and These represent the first of all candidate old jars of water. The minimum and maximum values ​​of the indicator; These are the normalized values ​​for various physicochemical adaptability indicators.

[0010] Furthermore, the physicochemical adaptability index is calculated using a combination of weighted deviation distance and negative exponent mapping, based on the following formula: in: PAI stands for Physicochemical Adaptability Index. e is a natural constant; D is the weighted Euclidean distance between each physicochemical adaptability index after pretreatment and its corresponding optimal value; The weighted Euclidean distance D quantitatively characterizes the overall deviation between the actual physicochemical characteristics of candidate aged brine and the ideal taste state, using a negative exponential function. Perform mapping.

[0011] Furthermore, the formula for calculating the weighted Euclidean distance D is as follows: in: These are the pH, total acid content, salt concentration, and reducing sugar content after pretreatment, respectively. These are the optimal normalized reference values ​​for each physicochemical adaptability index pre-set according to the ideal taste characteristics of aged jar water; α, β, γ, and δ are the weighting coefficients for pH, total acid content, salt concentration, and reducing sugar content, respectively. Based on the different emphases of the fermentation process on the taste dimensions of acid, salt, and sweetness, the weighting coefficients satisfy γ>β>α>δ>0.

[0012] Furthermore, the process for obtaining the historical performance characteristic parameters in S3 is as follows: The complete usage history of each batch of candidate aged jar water is retrieved, and the completion record of each fermentation event is extracted from the history. The collection process for the cumulative number of successful fermentation is as follows: iterate through all historical fermentation batches of candidate aged jar water, sum up the total number of fermentation batches that are judged to meet the standard, and form the corresponding cumulative number of successful fermentation values ​​of aged jar water. The process for collecting the number of idle days since the last fermentation is as follows: locate the date node when the old jar water was last fermented and sealed, calculate the number of natural days between the date node and the current date for candidate evaluation, and if the old jar water has never been used, assign a fixed number of idle days preset value that is much greater than the upper limit of the normal rotation cycle as the number of idle days since the last fermentation according to the initialization rules. The process for collecting abnormal fermentation counts is as follows: trace back all historical batches of Lao Tan Shui (Old Jar Water) and identify batches marked as fermentation accidents due to severe off-odors, film formation, spoilage, or significant deviations in physicochemical indicators from the safe zone. Calculate the total frequency of these accident batches to form the abnormal fermentation count.

[0013] Furthermore, the preprocessing of historical performance characteristic parameters includes: normalizing the three parameters with different dimensions and distribution characteristics, namely, the cumulative number of successful fermentations, the number of idle days since the most recent fermentation, and the number of abnormal fermentations. For the number of abnormal fermentations, a logarithmic transformation is performed before normalization to reduce the interference of extreme values ​​of high abnormal frequency on subsequent weighted calculations.

[0014] Furthermore, the historical reliability index calculation function is as follows: in: HRI stands for Historical Reliability Index. These are the cumulative number of successful fermentations after pretreatment, the number of idle days since the most recent fermentation, and the number of abnormal fermentations, respectively. It is a very small positive number, used to ensure that the numerator is always greater than zero, and to avoid the exponent being meaningless when the number of successes is zero; These are the penalty weighting coefficients for the number of idle days since the most recent fermentation and the number of abnormal fermentations being greater than zero, respectively, quantifying the weakening effect of the number of idle days since the most recent fermentation and the number of abnormal fermentations on reliability. Adjustments were made based on actual workshop management experience.

[0015] Furthermore, the physicochemical adaptability threshold and the historical reliability threshold are set as follows: and Using historically verified and inoculated aged water as a sample, the physicochemical adaptability index was calculated in reverse to obtain the corresponding distribution sequence. The first quartile of the physicochemical adaptability index distribution sequence was taken as... ; Using aged water that has been used repeatedly for a long time without any abnormal records as a sample, the historical reliability index was calculated. The 10th to 15th percentiles of the empirical distribution were taken and set as the index after verification by production tolerance. .

[0016] Furthermore, the comparison decision mechanism is as follows: the logic of the comparison decision mechanism is: when ,and When the corresponding old jar water is deemed qualified, it indicates that the corresponding candidate old jar water simultaneously meets the dual admission standards of taste suitability and historical reliability, and can be directly used for inoculation with new salt and sugar water for microbial community regulation. when ,and If the corresponding old-style water is deemed unqualified, it indicates that the corresponding candidate old-style water meets the taste index, but its historical reliability is low and it is not adopted. when ,and If the corresponding old-style jarred water is deemed unqualified, it indicates that the historical reliability of the corresponding candidate old-style jarred water meets the requirements, but has deviated from the ideal taste state and is therefore not adopted. when ,and If the corresponding old-style water is deemed unqualified, it indicates that the candidate old-style water does not meet the minimum requirements in terms of both taste suitability and historical reliability, and is therefore not adopted.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention collects multi-dimensional physicochemical indicators and constructs a physicochemical adaptability index combining weighted Euclidean distance and negative exponential mapping, achieving a quantitative and comprehensive evaluation of the taste state of fermented bean water, significantly improving the objectivity and accuracy of the evaluation. Simultaneously, by introducing a historical reliability index encompassing the cumulative number of successful fermentations, the number of idle days since the most recent fermentation, and the number of abnormal fermentations, the long-term performance of the fermented bean water is incorporated into the screening criteria, overcoming the shortcomings of conventional methods that only focus on the current state while ignoring historical usage risks. This invention also employs a dual threshold comparison decision mechanism, requiring candidate fermented bean water to simultaneously meet preset standards in both taste suitability and historical usage reliability before it can be used. This effectively avoids the risk of inoculation failure caused by meeting one indicator while having hidden problems in another dimension, thus significantly improving the scientific nature and success rate of fermented bean water screening. It can reasonably weaken the interference of extreme outliers on the evaluation results, making the screening results more robust and reliable, achieving refined, standardized, and quantifiable control of the inoculation suitability of fermented bean water, and providing a more reliable technical guarantee for the stable and directional domestication of the microbial community in the fermentation process of green beans. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for regulating the microbial community during the fermentation process of green beans according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: Please see Figure 1 The present invention provides the following technical solutions: A method for regulating the microbial community during green bean fermentation is disclosed. This method can be widely applied to vegetable fermentation and processing in bio-agriculture and related industries, and is particularly suitable for the precise screening and inoculation management of old fermentation brine in green bean fermentation. The method for regulating the microbial community during green bean fermentation includes the following steps: S1: In the inoculation process of fermented green beans using aged brine, representative samples need to be selected from multiple different batches of mature aged brine to serve as candidate aged brine for subsequent inoculation, ensuring that the screening scope covers multiple fermentation stages. Simultaneously, for the current batch of green beans' fermentation process, fresh salt and sugar solution is prepared in advance for subsequent inoculation, and multiple physicochemical adaptability indicators are systematically collected for each candidate aged brine. These indicators specifically cover pH, total acid content, salt concentration, and reducing sugar content, comprehensively reflecting the current state of the aged brine in terms of taste. When collecting the physicochemical adaptability indicators of each candidate old-fashioned water, a certain volume of representative sample solution is first accurately measured from each candidate old-fashioned water sample. Then, the sample solution is thoroughly homogenized to ensure that its components are evenly distributed. Then, suspended particles and precipitates are removed through solid-liquid separation to finally obtain a clear and transparent test solution suitable for subsequent testing.

[0022] The pH value is determined using the principle of hydrogen ion activity measurement. The pretreated test solution is placed on a calibrated pH meter, and a stable pH value is directly read and recorded. This value directly reflects the acidity or alkalinity of the old jar water.

[0023] For the determination of total acid content, a separate quantitative sample of the test solution is taken, phenolphthalein is added as an indicator, and a sodium hydroxide standard solution of known concentration is slowly titrated. During the titration process, the endpoint is determined by an automatic potentiometric titrator in the microbial detection and analysis instrument. The titration endpoint is reached when the solution turns slightly red and does not fade for 30 seconds. Based on the volume of standard alkaline solution consumed, the total acid concentration in terms of lactic acid is calculated according to the lactic acid conversion factor, thus reflecting the total amount of titratable acid in the old jar water.

[0024] To determine the salt concentration, take another sample of the test solution, add potassium chromate indicator, and perform precipitation titration using silver nitrate standard solution. When a brick-red precipitate appears in the solution, it indicates that the chloride has been completely precipitated. This titration can also be performed quickly using a chloride ion selective electrode or an automatic titrator in a microbial detection and analysis instrument. Based on the equivalent conversion relationship between the volume of silver nitrate consumed and sodium chloride, the salt content in the test solution can be calculated.

[0025] The determination of reducing sugar content requires pretreatment of the test solution to remove impurities such as proteins that may interfere with the colorimetric or titration reactions. Then, the purified sample solution is reacted with alkaline copper tartrate reagent at a specified temperature and reaction time. The amount of standard solution consumed is recorded by redox titration, or the corresponding optical response value is obtained by colorimetric method. Alternatively, a biosensor or biochemical sensor based on the principle of glucose oxidase can be used to directly read the reducing sugar concentration. Finally, the concentration-response value relationship curve obtained in advance through standard sugar solution calibration is used to accurately convert the measured response value into reducing sugar content data, characterizing the residual level of fermentable sugars in the fermented water.

[0026] S2: Preprocessing is performed on the physicochemical adaptability indicators collected in step S1. This preprocessing includes two steps: outlier removal and data normalization. Outlier removal aims to identify and eliminate significant outliers caused by measurement errors, sample inhomogeneity, or recording bias, ensuring that subsequent analysis is based on valid and reliable measurement results. Data normalization is used to standardize the dimensions and orders of magnitude of each indicator, avoiding unreasonable bias in subsequent index calculations due to differences in the original numerical ranges. After completing the above preprocessing, a calculation function for the physicochemical adaptability index is further constructed. The preprocessed values ​​of each physicochemical adaptability indicator are sequentially substituted into this function, and the physicochemical adaptability index corresponding to each candidate aged jar water is obtained through function calculation, in order to quantitatively evaluate its suitability at the taste level.

[0027] The specific normalization process employs a minimum-maximum method. For each candidate aged water sample—pH, total acid content, salt concentration, and reducing sugar content—the original measured values ​​are linearly scaled according to the minimum and maximum values ​​of their respective indicators, ensuring that all transformed values ​​fall within the closed interval [0,1]. This mapping method effectively eliminates the dimensional effects caused by different units and significant variations in the range of variation between different indicators, while preserving the relative magnitudes of the original data within each indicator. The formula used for data normalization is: in, Indicates the first The first candidate of Lao Tan Water The original measured values ​​of the physicochemical adaptability indicators; These correspond to pH, total acid content, salt concentration, and reducing sugar content, respectively. and These represent the first of all candidate old jars of water. The minimum and maximum values ​​of the indicator; These are the normalized values ​​for various physicochemical adaptability indicators.

[0028] The physicochemical adaptability index is calculated using a combination of weighted deviation distance and negative exponent mapping, based on the following formula: in: PAI stands for Physicochemical Adaptability Index. e is a natural constant; D is the weighted Euclidean distance between each physicochemical adaptability index after pretreatment and its corresponding optimal value; PAI and D show a strict negative correlation. Due to the use of a negative exponential function, the larger D is, the smaller PAI is; conversely, the smaller D is, the larger PAI is. This mapping transforms the deviation between actual physicochemical characteristics and the ideal taste state into a dimensionless score between 0 and 1. When D approaches 0, PAI approaches 1, indicating that the physicochemical state of the candidate aged jar water perfectly matches the ideal expectation; as D continuously increases, PAI decreases rapidly in an exponential decay manner, but remains greater than 0, reflecting the continuous change characteristic that the greater the deviation, the worse the adaptability. The weighted Euclidean distance D quantitatively characterizes the overall deviation between the actual physicochemical characteristics of candidate aged brine and the ideal taste state, using a negative exponential function. Perform mapping.

[0029] The formula for calculating the weighted Euclidean distance D is: in: These are the pH, total acid content, salt concentration, and reducing sugar content after pretreatment. The deviation for each item is the square of the actual normalized value minus the optimal normalized reference value. Therefore, the deviation term is a non-negative number regardless of whether the actual value is higher or lower than the optimal value.

[0030] These are the optimal normalized reference values ​​for each physicochemical adaptability index, pre-set according to the ideal taste characteristics of aged jarred water. These reference values ​​are all positioned at the physicochemical levels corresponding to the ideal taste state. The closer the actual value is to the reference value, the smaller the deviation term and the lower D; the farther the actual value deviates from the reference value, the larger the deviation term and the higher D. The reference value itself does not participate in the determination of positive or negative correlation; it only exists as an ideal anchor point with zero deviation, and all deviations are measured with this anchor point as the origin. α, β, γ, and δ are the weighting coefficients for pH, total acid content, salt concentration, and reducing sugar content, respectively. In the green bean fermentation system, salt concentration directly determines the osmotic pressure environment and is the most rigid threshold for inhibiting spoilage bacteria and screening beneficial lactic acid bacteria. Even a small deviation can lead to fermentation failure; therefore, their weights are crucial. The highest total acid content reflects the fermentation maturity and acid-producing capacity of aged jar water, and is a direct indicator of lactic acid bacteria activity and the system's buffering capacity; its importance is second only to salinity. Secondly, while pH is intuitive, it fluctuates with changes in total acid and buffering agents, and exhibits a certain degree of collinearity with total acid; therefore, its independent weighting is not appropriate. Slightly lower Reducing sugars, serving as a continuous carbon source reserve for fermentation, have a relatively weaker impact on the initial inoculation microbial activity compared to the other three factors, and can be flexibly replenished by the sugars in subsequent fresh salt-sugar solutions. Therefore, in this embodiment, the weight coefficients satisfy γ>β>α>δ>0, as the weights are minimized.

[0031] S3: After calculating the physicochemical adaptability indicators, it is also necessary to collect performance characteristic parameters for each candidate aged water sample during its historical use to assess its reliability accumulated over long-term use. These historical performance characteristic parameters specifically cover three aspects: the cumulative number of successful fermentations, the number of idle days since the most recent fermentation, and the number of abnormal fermentations. After obtaining these parameters, they need to be preprocessed to eliminate differences in the units and distribution characteristics of the original data, laying the foundation for the subsequent calculation of the historical reliability index.

[0032] The specific collection process for various historical performance characteristic parameters is as follows: First, retrieve the complete usage history of each batch of candidate aged jar water. This record should detail the start and end times of each fermentation, the fermentation process status, and the final judgment conclusion. Based on this, extract the judgment result at the end of each fermentation event from the historical record. For the collection of the cumulative number of successful fermentations, it is necessary to traverse all historical fermentation batches of the aged jar water, identify each batch that has been judged to meet the fermentation quality standards, and sum up the number of these qualified batches to finally obtain the cumulative number of successful fermentations of the aged jar water.

[0033] To determine the number of idle days since the most recent fermentation, first locate the specific date when the old jar of water last completed fermentation and was removed from the jar and sealed. Then, using that date as the starting point, calculate the number of calendar days between that date and the current date of candidate evaluation. This period is the number of idle days. If the old jar of water has never been used, there is no actual removal date. In this case, a preset fixed number of idle days is assigned to it according to the initialization rules. This preset value is much larger than the upper limit of the rotation cycle of old jar of water during normal use, so as to reasonably reflect its lack of recent active use records in subsequent evaluations.

[0034] For the collection of abnormal fermentation times, all historical batches of the old jar water were traced back, and each batch was checked one by one to see if there were any serious off-odors, film growth, spoilage, or serious deviations in physicochemical indicators from the safe range during the fermentation process. All batches that were clearly marked as fermentation accidents were included in the statistics, and the total frequency of these accident batches was finally summarized to form the abnormal fermentation times of the old jar water.

[0035] After collecting the three historical performance characteristic parameters mentioned above, these parameters underwent unified preprocessing. Since the cumulative number of successful fermentations, the number of idle days since the most recent fermentation, and the number of abnormal fermentations belong to different dimensions and have different numerical distribution characteristics, they need to be normalized separately to ensure comparability on a numerical scale. Especially for the parameter of abnormal fermentation numbers, since some old fermented water may have multiple accident records, extremely high values ​​can easily have an excessive impact on subsequent weighted calculations. Therefore, before normalization, a logarithmic transformation is required to compress the fluctuations caused by extremely high values, ensuring that the preprocessed data can smoothly participate in the weighted calculation of the historical reliability index.

[0036] S4: Construct a historical reliability index calculation function based on the preset weights of historical performance characteristic parameters, and calculate the historical reliability index by weighting the preprocessed historical performance characteristic parameters; The historical reliability index calculation function is as follows: in: HRI stands for Historical Reliability Index; the constant term 1 in the denominator serves as a benchmark anchor, ensuring that when both the number of idle days and the number of abnormal fermentations are zero, the HRI equals [value missing]. .

[0037] These are the cumulative number of successful fermentations after pretreatment, the number of idle days since the most recent fermentation, and the number of abnormal fermentations, respectively. HRI and cumulative number of successful fermentations They show a positive correlation. The larger the value, the more qualified fermentation batches the old jar water has accumulated in its historical use, and the more reliable its experience and performance stability are. Under the same denominator conditions, the HRI will be higher accordingly. It is negatively correlated with HRI. The greater the number of days since the last fermentation, the longer the old jar water has been idle, and the vitality and population structure of its internal microbial community may degenerate, which will have a negative impact on the reliability of subsequent inoculation.

[0038] It is negatively correlated with HRI. The higher the number of abnormal fermentation times, the more frequent the accidents have occurred in the history of the old jar water, and the more adverse records there are such as film formation, spoilage, severe odor, or serious deviation of physicochemical indicators from the safe range. The reliability risk increases accordingly, which significantly weakens the trust in subsequent inoculation.

[0039] It is a very small positive number, used to ensure that the numerator is always greater than zero, and to avoid the exponent being meaningless when the number of successes is zero; These represent the penalty weighting coefficients set for the number of idle days since the most recent fermentation and the number of abnormal fermentations, respectively. Both coefficients are positive. This is specifically designed to quantify the weakening effect of vacancy days on the reliability of aged water. The longer the vacancy period, the more significant the potential decrease in the activity and stability of the microbial community within the aged water, and the greater the weakening effect. This is used to quantify the negative impact of abnormal fermentation on reliability. The more abnormal fermentation records there are, the higher the potential risks associated with the old water in its historical use, and the greater the degree of degradation in reliability. In this embodiment, the penalty is flexibly adjusted based on experience data from actual production management in the workshop. It is set after comprehensive consideration based on factors such as the correlation between past fermentation success rate and idle time, tolerance for accident records, and process fluctuations caused by the current season or raw material characteristics. This ensures that the penalty can reasonably reflect the actual risk without excessively inhibiting the selection of normal aged water.

[0040] S5: After calculating the physicochemical adaptability index and historical reliability index, corresponding judgment thresholds are set for these two indices, namely the physicochemical adaptability threshold and the historical reliability threshold, serving as two entry barriers to measure whether candidate aged brine is qualified. Based on this, a complete comparison and decision-making mechanism is further constructed. The core function of this mechanism is to compare the physicochemical adaptability index obtained in step S2 with the set physicochemical adaptability thresholds, and simultaneously compare the historical reliability index obtained in step S4 with the historical reliability thresholds. Through this dual comparison, the decision-making mechanism can comprehensively evaluate whether each candidate aged brine meets the minimum requirements in both taste suitability and historical reliability, and provide a clear qualification / disqualification judgment based on the comparison results. Finally, based on this judgment result, it is determined whether the candidate aged brine is qualified to be mixed with freshly prepared salt and sugar water for subsequent green bean fermentation inoculation, thereby achieving targeted regulation of the microbial community in the fermentation system, ensuring stable fermentation process and improving the quality of the finished product.

[0041] The physicochemical adaptability threshold and the historical reliability threshold are set as follows: and Using historically qualified aged jar water that had passed both sensory evaluation and actual inoculation verification as reference samples, the physicochemical adaptability index of each sample was calculated in reverse. The results were then compiled into a distribution sequence, and statistical quantile analysis was performed on this sequence. The first quartile of the physicochemical adaptability index distribution sequence was taken as the reference value. ; Then, old-fashioned bottled water that has been used repeatedly in production for a long time without any abnormal records is selected as a reference sample. The historical reliability index of these samples is calculated, and the corresponding empirical distribution is obtained. Then, an appropriate value is selected from the 10th to 15th percentile of this distribution. The tolerance of this value is then verified in combination with actual production to confirm that it can reasonably distinguish between reliable and unreliable old-fashioned bottled water. Finally, it is set as [the appropriate value]. .

[0042] The comparison decision mechanism is as follows: The comparison logic of the comparison decision mechanism is as follows: when ,and When the corresponding old jar water is deemed qualified, it means that the old jar water meets or exceeds the set entry threshold in terms of both taste suitability and historical reliability. Since both dimensions meet the requirements at the same time, it can be directly mixed with freshly prepared salt and sugar water for inoculation, thereby achieving effective regulation of the microbial community. when ,and If the corresponding old jar water is deemed unqualified, it means that although the current taste index of the old jar water is within the acceptable range, the lack of reliability exposed in its historical usage records makes it untrustworthy for subsequent inoculation and therefore it is not adopted. when ,and If the corresponding old-fashioned water is deemed unqualified, it indicates that although the old-fashioned water has performed stably and reliably in historical use, its current taste characteristics have deviated from the ideal state and cannot provide a suitable fermentation environment for the new salt and sugar water; therefore, it will not be used. when ,and If the water is deemed unqualified, it indicates that the water fails to meet the minimum requirements in both taste suitability and historical reliability, thus falling under the category of double non-compliance and not meeting the basic conditions for subsequent inoculation, and is therefore excluded.

[0043] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0044] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0045] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0046] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for regulating the microbial community during the fermentation process of green beans, characterized in that, Includes the following steps: S1: In the inoculation process of old jar water for green bean fermentation, multiple samples of old jar water from different fermentation batches are selected as candidate old jar water; new salt and sugar water for subsequent inoculation in the current green bean fermentation process is prepared, and the physicochemical adaptability indicators of each candidate old jar water are collected. The physicochemical adaptability indicators include pH, total acid content, salt concentration and reducing sugar content. S2: Preprocess the physicochemical adaptability indicators collected in step S1. The preprocessing includes outlier removal and data normalization. Construct a physicochemical adaptability index calculation function, substitute each preprocessed physicochemical adaptability indicator into the physicochemical adaptability index calculation function, and obtain the physicochemical adaptability index, which is used to characterize the taste state of the old jar water. S3: Collect historical performance characteristic parameters of the old jar water, including the cumulative number of successful fermentation times, the number of idle days since the most recent fermentation, and the number of abnormal fermentation times. Preprocess the historical performance characteristic parameters. S4: Construct a historical reliability index calculation function based on the preset weights of historical performance characteristic parameters, and calculate the historical reliability index by weighting the preprocessed historical performance characteristic parameters; S5: Set a physicochemical adaptability threshold and a historical reliability threshold, and construct a comparison decision mechanism. The comparison decision mechanism is used to compare the physicochemical adaptability index obtained in S2 with the physicochemical adaptability threshold, and to compare the historical reliability index obtained in S4 with the historical reliability threshold. Based on the output of the comparison decision mechanism, determine whether the corresponding candidate old jar water can be mixed with new salt sugar water for inoculation to regulate the microbial community of green bean fermentation.

2. The method for regulating the microbial community during the fermentation process of green beans according to claim 1, characterized in that: The specific process for collecting the physicochemical adaptability indicators of each candidate old jar water in S1 is as follows: a representative sample is taken from each candidate old jar water sample, and homogenization and solid-liquid separation are performed first to obtain a clear test solution. pH measurement: The pH value of the test solution is read and recorded based on the principle of hydrogen ion activity measurement; Collection of total acid content: Take a quantitative amount of the test solution, use phenolphthalein as an indicator, and titrate with sodium hydroxide standard solution until it turns slightly red and does not fade for 30 seconds. Calculate the total acid concentration in terms of lactic acid based on the volume of standard alkali consumed. Salt concentration collection: Take another sample of the test solution, use potassium chromate as an indicator, and titrate with silver nitrate standard solution until a brick-red precipitate appears. Obtain the salt content data based on the conversion relationship between the volume of silver nitrate consumed and the equivalent sodium chloride. Collection of reducing sugar content: After removing interfering substances such as proteins, the test solution is reacted with alkaline copper tartrate reagent under controlled conditions. The consumption of standard titration solution is recorded by redox titration or the optical response value is obtained by colorimetric method. The measured value is converted into reducing sugar content data using a pre-calibrated sugar concentration response curve.

3. The method for regulating the microbial community during the fermentation process of green beans according to claim 1, characterized in that: In step S2, a minimum-maximum normalization method is used to uniformly map the physicochemical adaptability index values ​​of pH, total acid content, salt concentration, and reducing sugar content of each candidate aged water to the [0,1] interval. This is used to eliminate the interference of different dimensions and orders of magnitude on the subsequent calculation of the physicochemical adaptability index. The formula used for data normalization is: in, Indicates the first The first candidate of Lao Tan Water The original measured values ​​of the physicochemical adaptability indicators; These correspond to pH, total acid content, salt concentration, and reducing sugar content, respectively. and These represent the first of all candidate old jars of water. The minimum and maximum values ​​of the indicator; These are the normalized values ​​for various physicochemical adaptability indicators.

4. The method for regulating the microbial community during the fermentation process of green beans according to claim 3, characterized in that: The physicochemical adaptability index is calculated using a combination of weighted deviation distance and negative index mapping, based on the following formula: in: PAI stands for Physicochemical Adaptability Index. e is a natural constant; D is the weighted Euclidean distance between each physicochemical adaptability index after pretreatment and its corresponding optimal value; The weighted Euclidean distance D quantitatively characterizes the overall deviation between the actual physicochemical characteristics of candidate aged brine and the ideal taste state, using a negative exponential function. Perform mapping.

5. The method for regulating the microbial community during the fermentation process of green beans according to claim 4, characterized in that: The formula for calculating the weighted Euclidean distance D is as follows: in: These are the pH, total acid content, salt concentration, and reducing sugar content after pretreatment, respectively. These are the optimal normalized reference values ​​for each physicochemical adaptability index pre-set according to the ideal taste characteristics of aged jar water; α, β, γ, and δ are the weighting coefficients for pH, total acid content, salt concentration, and reducing sugar content, respectively. Based on the different emphases of the fermentation process on the taste dimensions of acid, salt, and sweetness, the weighting coefficients satisfy γ>β>α>δ>0.

6. The method for regulating the microbial community during the fermentation process of green beans according to claim 1, characterized in that: The process for obtaining the historical performance characteristic parameters in S3 is as follows: The complete usage history of each batch of candidate aged jar water is retrieved, and the completion record of each fermentation event is extracted from the history. The collection process for the cumulative number of successful fermentation is as follows: iterate through all historical fermentation batches of candidate aged jar water, sum up the total number of fermentation batches that are judged to meet the standard, and form the corresponding cumulative number of successful fermentation values ​​of aged jar water. The process for collecting the number of idle days since the last fermentation is as follows: locate the date node when the old jar water was last fermented and sealed, calculate the number of natural days between the date node and the current date for candidate evaluation, and if the old jar water has never been used, assign a fixed number of idle days preset value that is much greater than the upper limit of the normal rotation cycle as the number of idle days since the last fermentation according to the initialization rules. The process for collecting abnormal fermentation counts is as follows: trace back all historical batches of Lao Tan Shui (Old Jar Water) and identify batches marked as fermentation accidents due to severe off-odors, film formation, spoilage, or significant deviations in physicochemical indicators from the safe zone. Calculate the total frequency of these accident batches to form the abnormal fermentation count.

7. The method for regulating the microbial community during the fermentation process of green beans according to claim 6, characterized in that: The preprocessing of historical performance characteristic parameters includes: normalizing the three parameters with different dimensions and distribution characteristics, namely, the cumulative number of successful fermentations, the number of idle days since the most recent fermentation, and the number of abnormal fermentations. For the number of abnormal fermentations, a logarithmic transformation is performed before normalization to reduce the interference of extreme values ​​of high abnormal frequency on subsequent weighted calculations.

8. The method for regulating the microbial community during the fermentation process of green beans according to claim 4, characterized in that: The historical reliability index calculation function is as follows: in: HRI stands for Historical Reliability Index. These are the cumulative number of successful fermentations after pretreatment, the number of idle days since the most recent fermentation, and the number of abnormal fermentations, respectively. It is a very small positive number, used to ensure that the numerator is always greater than zero, and to avoid the exponent being meaningless when the number of successes is zero; These are the penalty weighting coefficients for the number of idle days since the most recent fermentation and the number of abnormal fermentations being greater than zero, respectively, quantifying the weakening effect of the number of idle days since the most recent fermentation and the number of abnormal fermentations on reliability. Adjustments were made based on actual workshop management experience.

9. The method for regulating the microbial community during the fermentation process of green beans according to claim 8, characterized in that: The physicochemical adaptability threshold and the historical reliability threshold are set as follows: and Using historically verified and inoculated aged water as a sample, the physicochemical adaptability index was calculated in reverse to obtain the corresponding distribution sequence. The first quartile of the physicochemical adaptability index distribution sequence was taken as... ; Using aged water that has been used repeatedly for a long time without any abnormal records as a sample, the historical reliability index was calculated. The 10th to 15th percentiles of the empirical distribution were taken and set as the index after verification by production tolerance. .

10. The method for regulating the microbial community during the fermentation process of green beans according to claim 9, characterized in that: The comparison decision mechanism is as follows: The logic of the comparison decision mechanism is as follows: when ,and When the corresponding old jar water is deemed qualified, it indicates that the corresponding candidate old jar water simultaneously meets the dual admission standards of taste suitability and historical reliability, and can be directly used for inoculation with new salt and sugar water for microbial community regulation. when ,and If the corresponding old-style water is deemed unqualified, it indicates that the corresponding candidate old-style water meets the taste index, but its historical reliability is low and it is not adopted. when ,and If the corresponding old-style jarred water is deemed unqualified, it indicates that the historical reliability of the corresponding candidate old-style jarred water meets the requirements, but has deviated from the ideal taste state and is therefore not adopted. when ,and If the corresponding old-style water is deemed unqualified, it indicates that the candidate old-style water does not meet the minimum requirements in terms of both taste suitability and historical reliability, and is therefore not adopted.

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