Intelligent control method for high-precision fermentation and purification of stachydrine

Abstract

The application discloses a high-precision intelligent control and adjustment method for stachydrine fermentation and purification, and relates to the field of biological fermentation process control. The method comprises the following steps: obtaining a stachydrine mixed solution in a first sugar content interval; inoculating Eurotium cristatum at a first preset temperature after sterilization to start fermentation and obtain an initial fermentation liquor; monitoring the content ratio change of stachydrine and non-target sugars in real time through HPLC to obtain sugar content monitoring data; introducing a membrane separation device for selective dialysis to obtain membrane permeation rate monitoring data; performing dynamic evaluation on stachydrine enrichment according to the sugar content monitoring data and the membrane permeation rate monitoring data, generating a dynamic adjustment strategy for fermentation conditions based on the evaluation results; and performing fermentation process adjustment control. The method solves the technical problem that the existing stachydrine fermentation and purification cannot realize real-time and accurate monitoring and dynamic adjustment of fermentation conditions, resulting in low fermentation efficiency and low purity, and achieves the technical effect of improving the fermentation efficiency and purity of stachydrine through intelligent control of the fermentation process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological fermentation process control, in particular to an intelligent control adjustment method for high-precision fermentation and purification of stachydrine. BACKGROUND

[0002] As a functional oligosaccharide, stachydrine has important application value in the fields of food, medicine, etc., and high-precision fermentation and purification of stachydrine is crucial to improve product quality and market competitiveness. At present, the main method to solve the problem of stachydrine fermentation and purification is to realize the enrichment and purification of stachydrine through traditional fermentation process combined with subsequent separation and purification steps such as chromatographic separation. However, due to the lack of real-time and accurate monitoring of the change of sugar substances in the fermentation process, it is difficult to dynamically adjust the fermentation conditions, resulting in low fermentation efficiency and low purity of stachydrine, which cannot meet the market demand for high-purity stachydrine.

[0003] At present, in the related technology, the stachydrine fermentation and purification cannot be real-time and accurate monitoring and dynamically adjusted, resulting in low fermentation efficiency and low purity. SUMMARY

[0004] The present application provides an intelligent control adjustment method for high-precision fermentation and purification of stachydrine, which adopts the technical means of real-time monitoring of the content ratio change of sugar substances in the fermentation broth by HPLC, obtaining membrane permeability monitoring data by using membrane separation equipment, and dynamically evaluating stachydrine enrichment based on these monitoring data, and then generating a dynamic adjustment strategy for fermentation conditions, etc., to solve the technical problems of low fermentation efficiency and low purity caused by the inability to real-time and accurate monitoring and dynamically adjusting the fermentation conditions in the existing stachydrine fermentation and purification, and achieve the technical effect of improving the fermentation efficiency and purity of stachydrine by intelligent control of the fermentation process.

[0005] The present application provides an intelligent control adjustment method for high-precision fermentation and purification of stachydrine, which includes: obtaining a stachydrine mixed solution in a first sugar content interval, the stachydrine mixed solution containing stachydrine and non-target sugar that can be degraded by Eurotium cristatum; sterilizing the stachydrine mixed solution and inoculating Eurotium cristatum at a first preset temperature to start fermentation, obtaining an initial fermentation broth; real-time monitoring the content ratio change of stachydrine and non-target sugar in the initial fermentation broth by HPLC to obtain sugar content monitoring data; introducing a membrane separation equipment to selectively dialyze the initial fermentation broth to obtain membrane permeability monitoring data, including stachydrine interception concentration change rate and membrane flux change value; dynamically evaluating stachydrine enrichment according to the sugar content monitoring data and the membrane permeability monitoring data, generating a dynamic adjustment strategy for fermentation conditions based on the evaluation result; and adjusting and controlling the fermentation process according to the dynamic adjustment strategy for fermentation conditions.

[0006] In a possible implementation, the water-soluble sugar enrichment dynamic evaluation is performed according to the sugar content monitoring data and the membrane permeability monitoring data, a fermentation condition dynamic adjustment strategy is generated based on an evaluation result, and the following processing is performed: water-soluble sugar real-time purity data is calculated according to the sugar content monitoring data; a correlation coefficient of a water-soluble sugar interception concentration change rate and a membrane flux change value is calculated according to the membrane permeability monitoring data; the water-soluble sugar real-time purity data and the correlation coefficient are fused to obtain a comprehensive purity evaluation value; a purity change trend is analyzed according to the comprehensive purity evaluation value to obtain a water-soluble sugar real-time purity improvement rate; and if the water-soluble sugar real-time purity improvement rate is less than or equal to a preset improvement rate threshold, a fermentation condition dynamic adjustment strategy is generated.

[0007] In a possible implementation, if the water-soluble sugar real-time purity improvement rate is less than or equal to a preset improvement rate threshold, a fermentation condition dynamic adjustment strategy is generated, and the following processing is performed: an attribution analysis is performed on a purity change trend of water-soluble sugar to identify a key limiting sugar class in the non-target sugar class; a fermentation condition optimization model is constructed based on degradation characteristics of Eurotium cristatum on non-target sugar classes; and a fermentation condition optimization model is used to optimize fermentation conditions based on the key limiting sugar class to generate a fermentation condition dynamic adjustment strategy.

[0008] In a possible implementation, the attribution analysis is performed on the purity change trend of water-soluble sugar to identify the key limiting sugar class in the non-target sugar class, and the following processing is performed: non-target sugar class real-time purity data is calculated according to the sugar content monitoring data; a purity change trend is analyzed according to the non-target sugar class real-time purity data to obtain a non-target sugar class real-time purity decline rate; and the non-target sugar class real-time purity decline rate is arranged in ascending order, and a non-target sugar class ranked first is determined as the key limiting sugar class.

[0009] In a possible implementation, the fermentation condition optimization model is constructed based on degradation characteristics of Eurotium cristatum on non-target sugar classes, and the following processing is performed: degradation kinetics parameters of Eurotium cristatum on each type of non-target sugar class under different fermentation conditions are determined through a pre-experiment, a first response relationship, a second response relationship, and an Nth response relationship are established, where N is the number of types of non-target sugar classes; a first fermentation condition optimization branch, a second fermentation condition optimization branch, and an Nth fermentation condition optimization branch are constructed based on the first response relationship, the second response relationship, and the Nth response relationship; a cross-branch coordinated control mechanism is constructed based on a degradation kinetics coupling relationship between the optimization branches; and the first fermentation condition optimization branch, the second fermentation condition optimization branch, and the Nth fermentation condition optimization branch and the cross-branch coordinated control mechanism are integrated to construct the fermentation condition optimization model.

[0010] In a possible implementation, the fermentation condition optimization based on the key limiting sugar is performed by the fermentation condition optimization model to generate a fermentation condition dynamic adjustment strategy, and the following processing is performed: based on the key limiting sugar, a Mth fermentation condition optimization branch is determined, where the Mth fermentation condition optimization branch is a branch corresponding to the key limiting sugar, and M is greater than or equal to 1 and less than or equal to N; the Mth fermentation condition optimization branch performs fermentation condition optimization according to an Mth response relationship, and outputs a fermentation condition dynamic adjustment initial strategy; according to the cross-branch coordinated control mechanism, the fermentation condition dynamic adjustment initial strategy is input into the remaining N-1 branches for simulation prediction, the degradation rate change amplitudes of the branches are calculated, and if the predicted degradation rate drop amplitude of any N-1 branch is greater than or equal to a preset amplitude threshold, a constraint condition is generated; based on the constraint condition, the fermentation condition dynamic adjustment initial strategy is optimized and adjusted until the predicted degradation rate drop amplitude of any N-1 branch is less than the preset amplitude threshold, and a fermentation condition dynamic adjustment strategy is generated.

[0011] In a possible implementation, the content ratio change of stachyose and non-target sugars in the initial fermentation broth is monitored in real time by HPLC to obtain sugar content monitoring data, and the following processing is performed: an HPLC online detection system is arranged in the fermentation tank, the HPLC online detection system includes a micro pump, an online filter and a detector; the micro pump extracts the initial fermentation broth at a preset time interval and a preset flow rate to obtain a fermentation broth sample; the fermentation broth sample passes through the online filter to remove solids to obtain a standard fermentation broth sample; and the detector detects the content ratio of stachyose and non-target sugars in the standard fermentation broth sample to generate sugar content monitoring data.

[0012] In a possible implementation, the initial fermentation broth is selectively dialyzed by introducing a membrane separation device to obtain membrane permeation rate monitoring data, and the following processing is performed: a membrane separation device is arranged in a fermentation tank body circulation pipeline, the membrane separation device includes a hollow fiber membrane assembly, a peristaltic pump, a first detector and a second detector, where the first detector is arranged in a retentate side circulation pipeline, and the second detector is arranged in a permeate side outlet; the peristaltic pump pumps the initial fermentation broth into the membrane separation device at a preset flow rate, and the initial fermentation broth is selectively dialyzed by the hollow fiber membrane assembly to obtain a retentate and a permeate; the stachyose concentration data of the retentate are acquired in real time by the first detector, and the flow data of the permeate are acquired in real time by the second detector; and the stachyose retention concentration change rate and the membrane flux change value are calculated according to the stachyose concentration data and the flow data, respectively.

[0013] In a possible implementation, the fermentation process adjustment control according to the fermentation condition dynamic adjustment strategy includes the following processing: a multi-parameter coupled sensor is arranged in the fermentation tank; the multi-parameter coupled sensor monitors the temperature, dissolved oxygen and stirring speed in the fermentation tank in real time to obtain real-time fermentation condition monitoring data; and the fermentation condition in the fermentation tank is adjusted by a PID controller according to the fermentation condition dynamic adjustment strategy and the real-time fermentation condition monitoring data, to perform the fermentation process adjustment control.

[0014] In a possible implementation, after the fermentation process adjustment control according to the fermentation condition dynamic adjustment strategy, the following processing is further performed: when the stachydrine purity in the sugar content monitoring data is greater than or equal to a first preset purity, and the purity of non-target sugars is less than or equal to a second preset purity, the fermentation is terminated to obtain a high-purity fermentation liquor; after sterilization processing of the high-purity fermentation liquor, the system temperature is controlled to be less than or equal to a second preset temperature, and the active carbon is used for precipitation and decolorization processing to obtain a first purified liquid; the first purified liquid is subjected to impurity and sterilization removal processing by an ultrafiltration membrane to obtain a second purified liquid; the second purified liquid is subjected to desalination and impurity removal processing by a nanofiltration membrane and then concentrated to a second brix interval to obtain a concentrated fermentation liquor; and after spray drying processing of the concentrated fermentation liquor, a stachydrine dry powder with a stachydrine purity greater than or equal to a third preset purity is obtained.

[0015] The intelligent control adjustment method for stachydrine high-precision fermentation and purification provided in the present application first obtains a stachydrine mixed liquor in a first brix interval, the stachydrine mixed liquor containing stachydrine and non-target sugars that can be degraded by Eurotium cristatum; then the stachydrine mixed liquor is sterilized, and Eurotium cristatum is inoculated at a first preset temperature to start fermentation, to obtain an initial fermentation liquor; then the content ratio change of stachydrine and non-target sugars in the initial fermentation liquor is monitored in real time by HPLC to obtain sugar content monitoring data, and the initial fermentation liquor is subjected to selective dialysis by a membrane separation device to obtain membrane permeation rate monitoring data, including stachydrine interception concentration change rate and membrane flux change value; then the stachydrine enrichment dynamic evaluation is performed according to the sugar content monitoring data and the membrane permeation rate monitoring data, the fermentation condition dynamic adjustment strategy is generated based on the evaluation result, and finally the fermentation process adjustment control is performed according to the fermentation condition dynamic adjustment strategy. The technical effect of improving the stachydrine fermentation efficiency and purity by intelligent control of the fermentation process is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application will be briefly introduced below. In the present application, flowcharts are used to illustrate the operations performed by the method according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 The flowchart of the intelligent control adjustment method for high-precision fermentation and purification of stachydrine provided by the embodiments of the present application is shown.

[0018] Figure 2 The flowchart of generating a dynamic adjustment strategy for fermentation conditions in the intelligent control adjustment method for high-precision fermentation and purification of stachydrine provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0019] The foregoing description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described as follows.

[0020] In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application.

[0021] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The term "first\second" is only to distinguish similar objects, and does not represent a specific order of the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art in the technical field of the present application. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0022] The embodiments of the present application provide an intelligent control adjustment method for high-precision fermentation and purification of stachydrine, asFigure 1 The method comprises the following steps:

[0023] In step S100, a stachyose mixed solution in a first sugar content interval is obtained, wherein the stachyose mixed solution comprises stachyose and non-target sugars that can be degraded by Eurotium cristatum.

[0024] Specifically, the stachyose mixed solution in the first sugar content interval is obtained by a raw material blending system. The raw material blending system is used to mix stachyose concentrated juice and pure water, and adjust the sugar content to the first sugar content interval. The first sugar content interval refers to the sugar content range of the stachyose mixed solution, which is set to about 10°Brix, and specifically can be 9°Brix to 11°Brix. This interval is optimized to ensure that Eurotium cristatum has the best fermentation efficiency and the ability to selectively degrade non-target sugars in the subsequent fermentation process. A sugar content detection device (refractometer or online sugar content sensor, etc.) is used to monitor the sugar content of the stachyose mixed solution in real time. The stachyose mixed solution is a solution comprising stachyose and non-target sugars (fructose, sucrose, raffinose, manninotriose, etc.) that can be degraded by Eurotium cristatum. Eurotium cristatum is a microorganism that can achieve stachyose enrichment by degrading non-target sugars during fermentation.

[0025] The specific operation steps can be as follows: stachyose concentrated juice with a sugar content of 50°Brix is added to a fermenter, an appropriate amount of pure water is added, and the system is diluted to a sugar content of about 10°Brix after stirring. A refractometer is used to detect the sugar content after dilution to ensure that it is about 10°Brix.

[0026] For example, 500 g of stachyose concentrated juice with a content of 60% (mass percentage of stachyose in total sugar) (initial sugar content of 50°Brix) is added to a 5 L fermenter, 2000 g of pure water is added, and after stirring, the sugar content is adjusted to 10°Brix by a refractometer.

[0027] In step S200, the stachyose mixed solution is sterilized, and Eurotium cristatum is inoculated at a first preset temperature to start fermentation to obtain an initial fermentation broth.

[0028] Specifically, the heating and cooling system of the fermenter is used to sterilize the stachyose mixed solution with adjusted sugar content at a high temperature (for example, 121°C for 15 minutes). After sterilization, the system is cooled to a first preset temperature by the cooling system. The first preset temperature refers to the pre-set fermentation starting temperature to ensure the best growth and metabolic activity of Eurotium cristatum during fermentation. This temperature is optimized through experiments and can ensure that the strain can quickly adapt to the environment and start fermentation after inoculation. For example, 30°C. When the temperature of the system reaches the first preset temperature, an automatic inoculation device is used to inoculate the Eurotium cristatum strain (for example, 200 mL of cultured bacterial solution) to start the fermentation process.

[0029] For example, the diluted stachyose mixture is sterilized at 121℃ for 15 minutes. After sterilization is completed, the temperature of the fermentation broth is reduced to 30℃ by a cooling system. At this time, the temperature sensor monitors the temperature of the fermentation broth in real time to ensure that it is stable at 30℃. Then, 200 mL of the cultured Eurotium cristatum is inoculated to start fermentation.

[0030] Step S300, the content ratio changes of stachyose and non-target sugars in the initial fermentation broth are monitored in real time by HPLC to obtain sugar content monitoring data.

[0031] Specifically, the stachyose and non-target sugars in the fermentation broth are monitored in real time by using a high-performance liquid chromatograph (HPLC), which is an analytical instrument for separating and detecting sugar components in the fermentation broth. The HPLC data are automatically collected and analyzed by a chromatographic workstation software to obtain the sugar content monitoring data.

[0032] The specific operation steps can be: after 60 minutes of fermentation, the fermentation tank is regularly sampled (for example, once every 20 minutes), and the sample is injected into the HPLC system. The HPLC system separates and detects stachyose and non-target sugars (fructose, sucrose, raffinose, and manninotriose) according to the set chromatographic conditions. The chromatographic workstation software automatically records the detection results and calculates the content ratio changes of each sugar.

[0033] In one possible implementation, the step S300 of monitoring the content ratio changes of stachyose and non-target sugars in the initial fermentation broth in real time by HPLC to obtain sugar content monitoring data further includes a step S310 of setting an HPLC online detection system in the fermentation tank, the HPLC online detection system including a micro pump, an online filter, and a detector. Specifically, the HPLC online detection system is installed in the fermentation tank, and includes a micro pump, an online filter, and a detector for real-time sampling and detection of the fermentation broth. It is ensured that the micro pump, the online filter, and the detector are correctly connected, and the HPLC system is calibrated to ensure the accuracy of the detection results.

[0034] Step S320, the micro pump extracts the initial fermentation broth at a preset time interval and a preset flow rate to obtain a fermentation broth sample. Specifically, the time interval and the flow rate of the micro pump are set, and the micro pump extracts the fermentation broth sample according to the set parameters. For example, the micro pump extracts 1 mL of the fermentation broth sample every 20 minutes.

[0035] Step S330, the fermentation broth sample is filtered through the online filter to obtain a standard fermentation broth sample. Specifically, the extracted fermentation broth sample is filtered through the online filter to remove solid impurities, and a filtered standard fermentation broth sample is obtained. For example, the fermentation broth sample is filtered through a 0.45 μm online filter to remove solid impurities, and a standard fermentation broth sample is obtained.

[0036] Step S340, the detector detects the content ratio of stachyose and non-target sugars in the standard fermentation broth sample, and generates sugar content monitoring data. Specifically, the standard fermentation broth sample is injected into the HPLC detector, and the HPLC system separates and detects each sugar component according to the set chromatographic conditions. The chromatographic workstation software automatically records the detection results and calculates the content ratio of each sugar. For example, the HPLC detector detects at a wavelength of 254 nm, the mobile phase is methanol-water (volume ratio 70:30), and the flow rate is 1.0 mL / min. The detection results show that the content of stachyose gradually increases, and the content of non-target sugars gradually decreases. This implementation can monitor the content ratio of stachyose and non-target sugars in the fermentation broth in real time through the HPLC online detection system, thereby achieving precise control of the fermentation process. This real-time monitoring method can timely detect abnormal conditions in the fermentation process, and dynamically adjust the fermentation conditions according to the monitoring data to ensure efficient fermentation.

[0037] Step S400, introducing a membrane separation device to selectively dialyze the initial fermentation broth to obtain membrane permeability monitoring data, including stachyose rejection concentration change rate and membrane flux change value.

[0038] Specifically, the fermentation broth is selectively dialyzed using an ultrafiltration or nanofiltration membrane device, and the membrane flux and stachyose rejection concentration change rate are monitored in real time by a flow meter and a concentration sensor. Specifically, the fermentation broth is introduced into the membrane separation device, and the appropriate operating pressure is set. The membrane flux is monitored in real time by a flow meter, and the stachyose rejection concentration is monitored by a concentration sensor. The change value of the membrane flux and the change rate of the stachyose rejection concentration are recorded, and the membrane permeability monitoring data is generated for subsequent dynamic evaluation. For example, the fermentation broth is selectively dialyzed through a 500 dalton nanofiltration membrane, and the operating pressure is set to 0.5 MPa.

[0039] The stachyose interception concentration refers to the concentration of stachyose upstream of the membrane (interception side) in the membrane separation process. The change rate refers to the amount of change of the stachyose interception concentration per unit time, reflecting the enrichment efficiency of stachyose in the membrane separation process. During the membrane separation process, the concentration of stachyose upstream of the membrane is monitored in real time by a concentration sensor, and the concentration value of stachyose is recorded every certain time. The change rate of the stachyose interception concentration is calculated by dividing the concentration difference between adjacent time points by the time interval. By monitoring the change rate of the stachyose interception concentration, the enrichment of stachyose can be understood in real time, providing data support for subsequent dynamic evaluation.

[0040] The membrane flux refers to the volume of liquid passing through a unit membrane area per unit time, expressed in L / m 2 h. The change value refers to the amount of change of the membrane flux per unit time, reflecting the permeation of non-target sugars in the membrane separation process. During the membrane separation process, the liquid flow through the membrane is monitored in real time by a flow meter, and the flow value is recorded every certain time. The membrane flux is calculated according to the flow and membrane area, and the change value of the membrane flux is calculated by dividing the membrane flux difference between adjacent time points by the time interval. By monitoring the change value of the membrane flux, the degradation and permeation of non-target sugars can be understood in real time. The larger the change value of the membrane flux, the more non-target sugars are degraded and permeated, and the better the enrichment effect.

[0041] In a possible implementation, the membrane separation device is used for selective dialysis of the initial fermentation broth, and membrane permeation monitoring data is obtained, and step S400 further includes step S410. A membrane separation device is arranged in the fermentation tank out-of-tank circulation pipeline, and the membrane separation device includes a hollow fiber membrane assembly, a peristaltic pump, a first detector and a second detector. Specifically, the hollow fiber membrane assembly is installed in the fermentation tank out-of-tank circulation pipeline, so that the fermentation broth can be selectively dialyzed through the membrane assembly. The peristaltic pump is connected between the fermentation tank and the membrane assembly, and is used to pump the fermentation broth into the membrane separation device at a preset flow rate. The first detector is arranged in the retentate circulation pipeline, and is used to monitor the stachyose concentration of the retentate in real time. The second detector is arranged at the permeate side outlet, and is used to monitor the flow rate of the permeate in real time. The membrane separation device is installed in the fermentation tank out-of-tank circulation pipeline, so that the hollow fiber membrane assembly, the peristaltic pump, the first detector and the second detector are correctly connected. The detectors are calibrated to ensure the accuracy of the monitoring data. The hollow fiber membrane assembly is composed of many small hollow fibers, which have a microporous structure allowing small molecules (such as non-target sugar degradation products) to permeate, while large molecules (such as stachyose) are retained. The peristaltic pump pushes the liquid flow by squeezing the hose, and the flow rate can be controlled by adjusting the rotation speed of the pump. The first detector uses an optical or electrochemical method to measure the stachyose concentration in the retentate in real time. The second detector uses a flow sensor to measure the flow rate of the permeate in real time.

[0042] In step S420, the peristaltic pump pumps the initial fermentation broth into the membrane separation device at a preset flow rate, and selectively dialyzes the initial fermentation broth through the hollow fiber membrane assembly to obtain retentate and permeate. Specifically, the flow rate of the peristaltic pump is set (for example, 100 mL / min), and the peristaltic pump pumps the initial fermentation broth into the hollow fiber membrane assembly at the set flow rate. The fermentation broth passes through the hollow fiber membrane assembly, and the non-target sugar degradation products of the Eurotium cristatum are permeated through the membrane, and the stachyose is retained.

[0043] In step S430, the stachyose concentration data of the retentate is obtained in real time by the first detector, and the flow rate data of the permeate is obtained in real time by the second detector. Specifically, the first detector monitors the stachyose concentration of the retentate in real time, and the second detector monitors the flow rate of the permeate in real time. The data of the stachyose concentration and the flow rate of the permeate are recorded.

[0044] Step S440, the stachydrine concentration data and the flow data are used to calculate the stachydrine retention concentration change rate and the membrane flux change value, respectively. Specifically, the stachydrine retention concentration change rate is calculated according to the difference in stachydrine concentration between adjacent time points divided by the time interval. The membrane flux change value is calculated according to the difference in permeate flow between adjacent time points divided by the time interval. This implementation mode can monitor the membrane flux and the stachydrine retention concentration change rate in real time through the membrane separation device, and in combination with the HPLC data, the enrichment of stachydrine and the degradation of non-target sugars in the fermentation process can be comprehensively monitored, thereby providing more comprehensive data support for dynamic evaluation of stachydrine enrichment, so as to optimize the fermentation conditions and improve the enrichment efficiency and purity of stachydrine.

[0045] Step S500, the stachydrine enrichment dynamic evaluation is performed according to the sugar content monitoring data and the membrane permeability monitoring data, and a fermentation condition dynamic adjustment strategy is generated based on the evaluation result.

[0046] Specifically, the HPLC monitoring data and the membrane permeability monitoring data are processed and analyzed by using a computer control system. Based on a preset evaluation algorithm, the stachydrine enrichment effect is dynamically evaluated. According to the evaluation result, a fermentation condition adjustment strategy is automatically generated, for example, adjusting the fermentation temperature, stirring speed or dissolved oxygen content, etc.

[0047] As shown in Figure 2 In one possible implementation, the stachydrine enrichment dynamic evaluation is performed according to the sugar content monitoring data and the membrane permeability monitoring data, and a fermentation condition dynamic adjustment strategy is generated based on the evaluation result, step S500 further includes step S510, the stachydrine real-time purity data is calculated according to the sugar content monitoring data. Specifically, the content data of stachydrine and non-target sugars are obtained from the HPLC detector, and the real-time purity of stachydrine is calculated, the formula is: stachydrine real-time purity = stachydrine content / (stachydrine content + total content of non-target sugars).

[0048] Step S520, the correlation coefficient of the stachydrine retention concentration change rate and the membrane flux change value is calculated according to the membrane permeability monitoring data. Specifically, the stachydrine retention concentration change rate and the membrane flux change value are obtained from the membrane separation device, and the correlation coefficient is calculated using a statistical method, the formula is: correlation coefficient = Cov (X, Y) / (σ X σ Y ), wherein X is the stachydrine retention concentration change rate, Y is the membrane flux change value, Cov (X, Y) is the covariance, σ X and σ YThe correlation coefficient reflects the correlation between the change rate of stachydrine interception concentration and the change value of membrane flux. A higher correlation coefficient indicates a stronger positive or negative correlation between the two. As a supplementary indicator for purity evaluation, if the correlation coefficient is high, it means that the change in membrane flux can better reflect the stachydrine interception, thereby enhancing the reliability of purity evaluation.

[0049] In step S530, the stachydrine real-time purity data is fused with the correlation coefficient to obtain a comprehensive purity evaluation value. Specifically, the stachydrine real-time purity data and the correlation coefficient are jointly analyzed, a multi-dimensional dynamic evaluation strategy is adopted, and a more reliable comprehensive purity evaluation value is generated. Specifically, according to the size of the correlation coefficient, the reliability weight of the stachydrine real-time purity data is adjusted to generate the comprehensive purity evaluation value. For example, if the correlation coefficient > 0.9 indicates that the membrane separation process is highly matched with the fermentation degradation, the HPLC data reliability is improved, and the calculation formula of the comprehensive purity evaluation value may be, for example: comprehensive purity evaluation value = stachydrine real-time purity data x 1.05 (slightly up, enhance confidence); if the correlation coefficient < 0.5 indicates that there may be deviations in the membrane separation or fermentation process (such as a decrease in strain activity), the HPLC data reliability is reduced, and the calculation formula of the comprehensive purity evaluation value may be, for example: comprehensive purity evaluation value = stachydrine real-time purity data x 0.9 (weight reduction); if 0.5 ≤ correlation coefficient ≤ 0.9 indicates a normal range, the calculation formula of the comprehensive purity evaluation value may be, for example: comprehensive purity evaluation value = stachydrine real-time purity data.

[0050] In step S540, a purity change trend analysis is performed according to the comprehensive purity evaluation value to obtain a stachydrine real-time purity improvement rate. Specifically, through time series analysis, the change trend of stachydrine purity is calculated, and the calculation formula of the stachydrine real-time purity improvement rate is: stachydrine real-time purity improvement rate = (current comprehensive purity evaluation value - last time point comprehensive purity evaluation value) / last time point comprehensive purity evaluation value x 100%.

[0051] In step S550, if the stachydrine real-time purity improvement rate is less than or equal to a preset improvement rate threshold, a fermentation condition dynamic adjustment strategy is generated. Specifically, a preset purity improvement rate threshold is set, and the stachydrine real-time purity improvement rate is compared with the threshold. If the stachydrine real-time purity improvement rate is less than or equal to the threshold, a fermentation condition dynamic adjustment strategy is generated. This implementation provides a more accurate and reliable stachydrine purity evaluation method by combining HPLC data and membrane permeability monitoring data. By dynamically correcting purity data, real-time monitoring of purity change trend, and adjusting fermentation conditions as needed, the enrichment efficiency and purity of stachydrine can be significantly improved, and the stability and repeatability of the fermentation process can be enhanced.

[0052] In a possible implementation, when the real-time purity improvement rate of stachyose is less than or equal to a preset improvement rate threshold, a fermentation condition dynamic adjustment strategy is generated, and step S550 further includes step S551 of performing attribution analysis on a purity change trend of stachyose to identify a key limiting sugar in the non-target sugars. Specifically, by using a statistical analysis method, a key factor affecting the purity change of stachyose is identified, and it is determined which non-target sugar has a significant impact on the purity improvement of stachyose. For example, it is assumed that it is found through attribution analysis that the content change of fructose has a significant impact on the purity improvement of stachyose, and then fructose is identified as the key limiting sugar.

[0053] Step S552, based on the degradation characteristics of Eurotium cristatum on the non-target sugars, a fermentation condition optimization model is constructed. Specifically, the degradation rates of Eurotium cristatum on fructose, sucrose, raffinose and manninotriose are determined through experiments, and a fermentation condition optimization model is constructed according to the degradation characteristics, the input of the model is the key limiting sugar, and the output is the optimized fermentation condition.

[0054] Step S553, based on the key limiting sugar, the fermentation condition optimization model is used to optimize the fermentation condition, and a fermentation condition dynamic adjustment strategy is generated. Specifically, using the fermentation condition optimization model, the optimal fermentation condition is calculated according to the information of the key limiting sugar. According to the model output, a fermentation condition dynamic adjustment strategy is generated, such as adjusting the fermentation temperature or the aeration amount. This implementation identifies the key limiting sugar which has a significant impact on the purity improvement of stachyose through attribution analysis, which can help to more accurately adjust the fermentation condition and improve the purity improvement efficiency.

[0055] In a possible implementation, the attribution analysis on the purity change trend of stachyose to identify the key limiting sugar in the non-target sugars includes step S5511 of calculating non-target sugar real-time purity data according to the sugar content monitoring data. Specifically, the content data of stachyose and each non-target sugar is obtained from the HPLC detector, the real-time purity of each non-target sugar is calculated, and the formula is: non-target sugar real-time purity data = non-target sugar content / (stachyose content + total content of non-target sugars).

[0056] Step S5512, according to the non-target sugar real-time purity data, the purity change trend is analyzed to obtain the real-time purity decrease rate of the non-target sugar. Specifically, by using time series analysis, the change trend of the purity of each non-target sugar is calculated. The real-time purity decrease rate of the non-target sugar is calculated, and the formula is: non-target sugar real-time purity decrease rate = (last time point non-target sugar real-time purity data - current non-target sugar real-time purity data) / last time point non-target sugar real-time purity data x 100%.

[0057] Step S5513, the real-time purity decrease rate of the non-target saccharides is arranged in ascending order, and the non-target saccharide ranked first is determined as the key limiting saccharide. Specifically, the real-time purity decrease rates of all non-target saccharides are arranged in ascending order, and the non-target saccharide ranked first is determined as the key limiting saccharide. For example, after arranging the real-time purity decrease rates of fructose, sucrose, raffinose and manninotriose in ascending order, it is found that the real-time purity decrease rate of fructose is the lowest and ranks first, and therefore fructose is determined as the key limiting saccharide. This implementation mode can quantify the degradation rate of each non-target saccharide by analyzing the real-time purity decrease rate of the non-target saccharide, thereby clearly identifying the non-target saccharide with the slowest degradation rate, and then optimizing the fermentation conditions in a targeted manner, reducing unnecessary adjustments, and improving the efficiency of improving the purity of stachydrine.

[0058] In a possible implementation mode, the fermentation condition optimization model is constructed based on the degradation characteristics of Eurotium cristatum on non-target saccharides, and step S552 further includes step S5521 of determining the degradation kinetics parameters of Eurotium cristatum on each type of non-target saccharide under different fermentation conditions through pre-experiments, establishing a first response relationship, a second response relationship, and an Nth response relationship, where N is the number of types of non-target saccharides. Specifically, pre-experiments of different fermentation conditions are designed, such as temperature (30℃, 32℃, 34℃), stirring speed (100rpm, 150rpm, 200rpm), and dissolved oxygen content (20%, 30%, 40%). Under each condition, the degradation rate and efficiency of Eurotium cristatum on each non-target saccharide are determined. According to the experimental data, the degradation kinetics model of each non-target saccharide, i.e., the response relationship, is established.

[0059] Step S5522, based on the first response relationship, the second response relationship, and the Nth response relationship, a first fermentation condition optimization branch, a second fermentation condition optimization branch, and an Nth fermentation condition optimization branch are constructed. Specifically, the degradation kinetics model of each non-target saccharide is analyzed to determine the degradation rate under different conditions. According to the experimental data, the fermentation condition optimization branch of each non-target saccharide is constructed.

[0060] Step S5523, based on the degradation kinetics coupling relationship between each optimization branch, a cross-branch coordinated control mechanism is constructed. Specifically, by analyzing the mutual influence relationship between the degradation kinetics parameters of Eurotium cristatum on different non-target saccharides, a multi-target synergistic regulation mechanism is established. First, the synergistic or antagonistic relationship between the saccharide degradation conditions is identified (for example, increasing the temperature may promote the degradation of fructose but inhibit the degradation of raffinose), and then a dynamic weight distribution algorithm is constructed to dynamically adjust the priority of the fermentation conditions according to the real-time monitored saccharide degradation rate, so as to ensure that the removal efficiency of other saccharides is not significantly affected due to the optimization of the degradation of a certain saccharide.

[0061] For example, when the system identifies fructose as the key limiting sugar, the initial strategy is to increase the temperature to 32°C to accelerate its degradation. But the monitoring finds that the raffinose degradation rate drops by more than 20% as a result, at which point the coordination mechanism is activated: the temperature is adjusted back to 30°C, while the stirring speed is increased from 100 rpm to 150 rpm and the dissolved oxygen level is maintained at 30%. This adjustment keeps the fructose degradation rate at 90% of the optimal level, while the raffinose degradation rate returns to 95% of the initial value, achieving a multi-objective balance.

[0062] Step S5524, integrate the first fermentation condition optimization branch, the second fermentation condition optimization branch to the Nth fermentation condition optimization branch and the cross-branch coordination control mechanism to build a fermentation condition optimization model. Specifically, the independent optimization branch of each non-target sugar is systematically integrated with the cross-branch coordination control mechanism to build a global optimization model. This model takes the identification results of key limiting sugars and the degradation kinetics parameters of each sugar as inputs, calculates the optimal fermentation condition combination through a multi-objective optimization algorithm (such as the weighted summation method), and outputs specific adjustment schemes including temperature, dissolved oxygen level, stirring speed, etc., to achieve the optimal control strategy for the current fermentation state. This implementation method can intelligently balance the degradation needs of different sugars by building a fermentation condition optimization model that includes a cross-branch coordination control mechanism, prioritizing the processing of key limiting sugars while avoiding significant impacts on the degradation efficiency of other non-target sugars, thereby improving the overall efficiency of stachydrine enrichment.

[0063] In one possible implementation, the fermentation condition optimization model is based on the key limiting sugar to generate a dynamic adjustment strategy for fermentation conditions, and step S553 further includes step S5531 of determining an Mth fermentation condition optimization branch based on the key limiting sugar, where the Mth fermentation condition optimization branch is a one-to-one corresponding branch of the key limiting sugar, and M is greater than or equal to 1 and less than or equal to N. Specifically, according to the identified key limiting sugar (fructose), the corresponding Mth branch (e.g., fructose fermentation condition optimization branch) is selected from the N fermentation condition optimization branches that have been built. Each branch strictly corresponds to a non-target sugar, ensuring targeted optimization. For example, when fructose is identified as the key limiting sugar, the "fructose fermentation condition optimization branch" is automatically selected as the Mth branch, which contains all the kinetic parameters and response relationships of fructose degradation.

[0064] Step S5532, the Mth fermentation condition optimization branch performs fermentation condition optimization according to the Mth response relationship, and outputs a fermentation condition dynamic adjustment initial strategy. Specifically, the selected Mth fermentation condition optimization branch calculates the optimal fermentation condition based on its exclusive response relationship (the kinetic model established in step S5521). For example, the fructose fermentation condition optimization branch outputs the initial strategy: “increase the temperature to 32°C and adjust the stirring speed to 120 rpm” under the current fermentation state (temperature 30°C, dissolved oxygen 20%) according to the pre-experiment data, which is expected to increase the fructose degradation rate by 25%.

[0065] Step S5533, according to the cross-branch coordination control mechanism, the fermentation condition dynamic adjustment initial strategy is input into the remaining N-1 branches for simulation prediction, the degradation rate change amplitude of each branch is calculated, and a constraint condition is generated if the predicted degradation rate of any N-1 branch decreases by more than or equal to a preset amplitude threshold. Specifically, the fermentation condition dynamic adjustment initial strategy is input into the remaining N-1 branches (such as sucrose, raffinose, and mannose triose branches) for simulation. The predicted degradation rate change is calculated through the response relationship of each branch. If the predicted degradation rate of any branch decreases by ≥ a preset threshold (such as 10%), a constraint condition is generated. For example, the fermentation condition dynamic adjustment initial strategy causes the predicted degradation rate of the raffinose branch to decrease by 15% (exceeding the 10% threshold), and the system generates the constraint condition: “the temperature adjustment amplitude cannot exceed +1.5°C”.

[0066] Step S5534, based on the constraint condition, the fermentation condition dynamic adjustment initial strategy is optimized and adjusted until the predicted degradation rate of any N-1 branch decreases by less than the preset amplitude threshold, and a fermentation condition dynamic adjustment strategy is generated. Specifically, the fermentation condition dynamic adjustment initial strategy is re-optimized under the constraint condition. Through iterative calculation, an adjustment scheme that meets the degradation requirements of all branches is found. For example: first round of optimization: temperature +2°C (initial) → raffinose rate decreases by 15% (violation); second round of optimization: temperature +1.5°C → raffinose rate decreases by 8% (qualified), but fructose rate only increases by 18%; final strategy: temperature +1.5°C and dissolved oxygen +5%, which makes the fructose rate increase by 22% and the raffinose rate decrease by only 5%. This implementation mode realizes the global optimal control of the stachydrine purification process through multi-branch collaborative optimization, while ensuring the maximum degradation efficiency of the key limiting sugar (if sugar), intelligently constraining the negative impact on the degradation of other non-target sugars, and achieving the global optimal control of the stachydrine purification process.

[0067] Step S600, according to the fermentation condition dynamic adjustment strategy, the fermentation process is adjusted and controlled.

[0068] Specifically, the temperature and other parameters of the fermenter are automatically adjusted using a PLC (programmable logic controller) or a DCS (distributed control system). The actuator includes a heater, a cooler, a stirrer, a ventilation valve, etc. The PLC is a device for industrial automation control, which can control the operating parameters of the fermenter according to a preset program; the DCS (distributed control system) is a system for complex industrial process control, which can centrally monitor and decentralized control multiple devices. For example, the PLC receives the fermentation condition dynamic adjustment strategy, according to which the PLC controls the heater or cooler to adjust the temperature of the fermenter to a set value, and / or controls the pH adjustment pump to add acid or alkali to adjust the pH to a set value, and / or adjusts the ventilation valve to adjust the ventilation amount to a set value, and / or controls the stirrer to a set speed to ensure uniform mixing of the fermentation broth.

[0069] In one possible implementation, the fermentation process adjustment control according to the fermentation condition dynamic adjustment strategy further includes step S610 of disposing a multi-parameter coupled sensor in the fermenter. Specifically, an integrated multi-parameter coupled sensor is deployed inside the fermenter, which adopts a modular design and can synchronously collect key parameters such as temperature, dissolved oxygen (DO), and stirring speed, and transmit data to the control system in real time through an industrial bus (such as RS485 or PROFIBUS). For example, an integrated sensor with a PT100 temperature probe, an optical dissolved oxygen electrode, and a Hall effect tachometer is installed at a height of 1 / 3 from the bottom of the tank to ensure representative measurement, wherein the temperature measurement range is 0-50℃, the accuracy is ±0.1℃, the dissolved oxygen measurement range is 0%-100% air saturation, the accuracy is ±0.5%, and the stirring speed measurement range is 0-300rpm, the accuracy is ±1rpm.

[0070] In step S620, the multi-parameter coupled sensor monitors the temperature, dissolved oxygen, and stirring speed in the fermenter in real time to obtain real-time fermentation condition monitoring data. Specifically, the multi-parameter coupled sensor continuously collects data at a frequency of 1Hz, and generates structured real-time monitoring data after signal conditioning. The control system packs the temperature, DO, and stirring speed data into a unified data frame through timestamp alignment technology. For example, at a certain moment, the data frame shows: temperature 30.2℃, DO 25.3%, stirring speed 105rpm, which deviates from the target values (temperature 32℃, DO 30%, 120rpm) of the fermentation condition dynamic adjustment strategy. The data flow is as follows: sensor→signal converter→PLC→data frame format: [timestamp][temperature][DO][speed].

[0071] At step S630, the fermentation condition dynamic adjustment strategy and the real-time fermentation condition monitoring data are used to adjust the fermentation conditions in the fermentation tank by a PID controller to perform fermentation process adjustment control. Specifically, a three-channel independent PID controller is used to process the deviation signals of temperature, DO, and stirring speed respectively. The PID controller calculates the output control quantity according to the difference between the target value of the fermentation condition dynamic adjustment strategy and the real-time fermentation condition monitoring data. For example, for temperature control, the PID output adjusts the duty cycle (0%~100%) of the heating jacket, and when the measured temperature is 30.2℃ and the target temperature is 32℃, the output heating power is 75%; for DO control, the PID adjusts the opening degree of the air inlet valve and the stirring compensation, and when the measured DO is 25.3% and the target DO is 30%, the electromagnetic valve is opened to 60% and the stirring is increased to 115 rpm; when the DO response lags, the feedforward control is started to increase the stirring speed 10 seconds in advance. If the sensor data suddenly changes (for example, the DO suddenly drops by 10%), the Kalman filter algorithm is triggered to remove noise, and the redundant sensor verification is started at the same time to ensure the control reliability. This implementation mode realizes real-time and accurate regulation and control of the fermentation conditions through the closed-loop linkage of multi-parameter coupling sensing and intelligent PID control, and improves the fermentation stability and product consistency.

[0072] In a possible implementation, after the fermentation process adjustment control according to the fermentation condition dynamic adjustment strategy, the method further includes: when the stachydrine purity in the sugar content monitoring data is greater than or equal to a first preset purity, and the purity of non-target sugars is less than or equal to a second preset purity, terminating the fermentation to obtain a high-purity fermentation broth; controlling the system temperature to be less than or equal to a second preset temperature after sterilizing the high-purity fermentation broth, performing decolorization treatment by activated carbon to obtain a first purified liquid; performing impurity removal and sterilization treatment on the first purified liquid by an ultrafiltration membrane to obtain a second purified liquid; performing desalting and impurity removal treatment on the second purified liquid by a nanofiltration membrane, and then concentrating the second purified liquid to a second brix interval to obtain a concentrated fermentation broth; and performing spray drying treatment on the concentrated fermentation broth to obtain stachydrine dry powder with a stachydrine purity greater than or equal to a third preset purity.

[0073] Specifically, the system detects that the preset purity threshold is reached to terminate the fermentation. The first preset purity refers to the minimum target percentage of stachydrine in the total sugar content, which is determined through preliminary experiments and can ensure the economy and feasibility of the subsequent purification process (for example, set to 97%). The second preset purity refers to the maximum allowed residual percentage of non-target sugars in the total sugar, which is set according to the final product specification (for example, ≤0.5%). When the HPLC monitoring shows that the stachydrine purity is ≥ the first preset purity and the purity of non-target sugars is ≤ the second preset purity, the fermentation is immediately terminated. The control system records this time as the optimal harvest point, and automatically closes the stirring and aeration, and starts the subsequent processing program.

[0074] The fermentation system is sterilized. The second preset temperature refers to the maximum safe temperature that the cooling process needs to reach after sterilization, which needs to maintain product stability and avoid the degradation of heat-sensitive components (for example, set to 45°C). For example, a short-time high-temperature sterilization process is used, which is maintained at 90°C (allowing a ±2°C fluctuation) for 30-50 seconds, and then immediately cooled to ≤ the second preset temperature. This process parameter is determined through microbial validation tests to ensure sterilization effect while maximizing the retention of active ingredients.

[0075] The sterilized system is subjected to decolorization purification. The first purified liquid refers to the intermediate product obtained after decolorization treatment. During treatment, food-grade activated carbon is added at a ratio of 0.1-0.5% weight / volume (i.e. 0.1-0.5g / 100mL) (preferably 0.4%), stirred below the second preset temperature for 20-40 minutes (preferably 30 minutes), and the carbon residue is removed by a pre-filter after decolorization. For example, after adding 0.2% activated carbon for 30 minutes, the color value of a certain batch is reduced from 1500IU to below 50IU, meeting the first purified liquid standard.

[0076] The first purified liquid is subjected to membrane separation treatment to obtain the second purified liquid. The ultrafiltration membrane refers to a separation membrane with a molecular weight cutoff of 1-100kDa (preferably 10kDa). The operation is controlled at a pressure of 0.1-0.5MPa (preferably 0.3MPa) and a temperature of 20-40°C. Through this step, macromolecular impurities and microorganisms are removed, and the turbidity of the system is reduced to below 0.1NTU, meeting the requirements of the second purified liquid.

[0077] The second purified liquid is concentrated and purified. The second sugar concentration range refers to the target concentration range of the final concentrated liquid (for example, 20-30°Brix, preferably 25°Brix). A nanofiltration membrane with a molecular weight cutoff of 200-800Da (preferably 500Da) is used, and the operation is carried out at a pressure of 0.3-0.7MPa (preferably 0.5MPa). This step can simultaneously achieve the effects of desalination, removal of impurities, and concentration, for example, when a certain batch is concentrated to 25.3°Brix, the conductivity is reduced to below 50μS / cm.

[0078] The concentrated fermentation broth is dried to obtain the final product. The third preset purity refers to the minimum purity standard of the stachydrine dry powder (for example, ≥95%, preferably ≥96%). During spray drying, the inlet temperature is controlled at 160-200°C (preferably 180°C), and the outlet temperature is controlled at 70-80°C (preferably 75°C). The moisture content of the obtained dry powder is ≤5% (preferably ≤3.5%), for example, a certain batch of stachydrine dry powder with a purity of 96.3% is obtained. This implementation realizes efficient conversion from fermentation broth to high-purity stachydrine dry powder by intelligently controlling the fermentation endpoint and optimizing the multi-stage purification process, ensuring the efficiency and stability of the entire process.

[0079] The embodiments of the present application adopt the technical means of monitoring the change of the content ratio of sugar substances in the fermentation liquor in real time by HPLC, obtaining the membrane permeation rate monitoring data by using the membrane separation equipment, and performing dynamic evaluation of stachydrine enrichment based on the monitoring data, and further generating a dynamic adjustment strategy of fermentation conditions, thereby solving the technical problems of low fermentation efficiency and low purity caused by the inability to monitor in real time and dynamically adjust the fermentation conditions in the existing stachydrine fermentation and purification, and achieving the technical effects of improving the fermentation efficiency and purity of stachydrine by intelligently controlling the fermentation process.

[0080] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps described in the present application can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. A method for intelligent control adjustment of high-precision purification of stachydrine fermentation, characterized in that, The method comprises: obtaining a stachyose mixed solution of a first sugar content interval, the stachyose mixed solution containing stachyose and non-target sugars degradable by Eurotium cristatum; after sterilizing the stachyose mixed solution, inoculating Eurotium cristatum at a first preset temperature to start fermentation, and obtaining an initial fermentation broth; real-time monitoring of the content ratio change of stachyose and non-target sugars in the initial fermentation broth by HPLC to obtain sugar content monitoring data; introducing a membrane separation device to selectively dialyze the initial fermentation broth to obtain membrane permeation rate monitoring data, including stachyose retention concentration change rate and membrane flux change value; based on the sugar content monitoring data and the membrane permeation rate monitoring data, performing dynamic evaluation of stachyose enrichment, and generating a dynamic adjustment strategy for fermentation conditions based on the evaluation results; based on the dynamic adjustment strategy for fermentation conditions, performing fermentation process adjustment control; based on the sugar content monitoring data and the membrane permeation rate monitoring data, performing dynamic evaluation of stachyose enrichment, and generating a dynamic adjustment strategy for fermentation conditions based on the evaluation results, comprising: calculating stachyose real-time purity data from the sugar content monitoring data; calculating a correlation coefficient of stachyose retention concentration change rate and membrane flux change value from the membrane permeation rate monitoring data; fusing the stachyose real-time purity data and the correlation coefficient to obtain a comprehensive purity evaluation value; based on the comprehensive purity evaluation value, performing purity change trend analysis to obtain a stachyose real-time purity improvement rate; if the stachyose real-time purity improvement rate is less than or equal to a preset improvement rate threshold, generating a dynamic adjustment strategy for fermentation conditions; if the stachyose real-time purity improvement rate is less than or equal to a preset improvement rate threshold, generating a dynamic adjustment strategy for fermentation conditions, comprising: performing attribution analysis on the purity change trend of stachyose to identify key limiting sugars in the non-target sugars; based on the degradation characteristics of Eurotium cristatum on non-target sugars, constructing a fermentation condition optimization model; based on the key limiting sugars, performing fermentation condition optimization through the fermentation condition optimization model to generate a dynamic adjustment strategy for fermentation conditions; based on the degradation characteristics of Eurotium cristatum on non-target sugars, constructing a fermentation condition optimization model, comprising: determining the degradation kinetic parameters of each type of non-target sugar by Eurotium cristatum under different fermentation conditions through pre-experiments to establish a first response relationship, a second response relationship, and an Nth response relationship, where N is the number of types of non-target sugars; based on the first response relationship, the second response relationship, and the Nth response relationship, constructing a first fermentation condition optimization branch, a second fermentation condition optimization branch, and an Nth fermentation condition optimization branch; based on the degradation kinetic coupling relationship between each optimization branch, constructing a cross-branch coordinated control mechanism; integrating the first fermentation condition optimization branch, the second fermentation condition optimization branch, and the Nth fermentation condition optimization branch, and the cross-branch coordinated control mechanism to construct a fermentation condition optimization model.

2. The intelligent control adjustment method for high-precision fermentation and purification of stachydrine according to claim 1, characterized in that, the attribution analysis on the purity change trend of stachyose to identify key limiting sugars in the non-target sugars, comprising: calculating non-target sugar real-time purity data from the sugar content monitoring data; According to the non-target saccharide real-time purity data, a purity change trend analysis is performed to obtain a non-target saccharide real-time purity decline rate; The non-target saccharide real-time purity decline rate is arranged in ascending order, and a non-target saccharide ranked first is determined as a key limiting saccharide.

3. The intelligent control adjustment method for high-precision fermentation and purification of stachydrine according to claim 1, characterized in that, Based on the key limiting saccharide, fermentation condition optimization is performed through the fermentation condition optimization model to generate a fermentation condition dynamic adjustment strategy, including: Based on the key limiting saccharide, an Mth fermentation condition optimization branch is determined, wherein the Mth fermentation condition optimization branch is a branch corresponding to the key limiting saccharide one by one, and M is greater than or equal to 1 and less than or equal to N; The Mth fermentation condition optimization branch performs fermentation condition optimization according to an Mth response relationship, and outputs a fermentation condition dynamic adjustment initial strategy; According to the cross-branch coordination control mechanism, the fermentation condition dynamic adjustment initial strategy is input into the remaining N-1 branches for simulation prediction, the degradation rate change amplitudes of the branches are calculated, and a constraint condition is generated if the predicted degradation rate decline amplitude of any N-1 branch is greater than or equal to a preset amplitude threshold; Based on the constraint condition, the fermentation condition dynamic adjustment initial strategy is optimized and adjusted until the predicted degradation rate decline amplitude of any N-1 branch is less than the preset amplitude threshold, and a fermentation condition dynamic adjustment strategy is generated.

4. The intelligent control adjustment method for high-precision fermentation and purification of stachydrine according to claim 1, characterized in that, The content ratio change of stachyose and non-target saccharides in the initial fermentation broth is monitored in real time through HPLC to obtain saccharide content monitoring data, including: An HPLC online detection system is arranged in the fermentation tank, and the HPLC online detection system includes a micro pump, an online filter and a detector; The micro pump extracts the initial fermentation broth at a preset time interval and a preset flow rate to obtain a fermentation broth sample; The fermentation broth sample is subjected to solid impurity removal through the online filter to obtain a standard fermentation broth sample; The detector detects the content ratio of stachyose and non-target saccharides in the standard fermentation broth sample to generate saccharide content monitoring data.

5. The intelligent control adjustment method for high-precision fermentation and purification of stachydrine according to claim 1, characterized in that, The initial fermentation broth is subjected to selective dialysis through the membrane separation equipment to obtain membrane permeation rate monitoring data, including: A membrane separation equipment is arranged in a fermentation tank body circulation pipeline, and the membrane separation equipment includes a hollow fiber membrane assembly, a peristaltic pump, a first detector and a second detector, wherein the first detector is arranged in a retention side circulation pipeline, and the second detector is arranged in a permeation side outlet; The peristaltic pump pumps the initial fermentation broth into the membrane separation equipment at a preset flow rate, and the initial fermentation broth is subjected to selective dialysis through the hollow fiber membrane assembly to obtain a retentate and a permeate; The stachyose concentration data of the retentate are acquired in real time through the first detector, and the flow data of the permeate are acquired in real time through the second detector; The stachyose retention concentration change rate and the membrane flux change value are calculated according to the stachyose concentration data and the flow data, respectively.

6. The intelligent control adjustment method for high-precision fermentation and purification of stachydrine according to claim 1, characterized in that, The fermentation process is adjusted and controlled according to the fermentation condition dynamic adjustment strategy, including: A multi-parameter coupled sensor is arranged in the fermentation tank; The multi-parameter coupled sensor monitors the temperature, the dissolved oxygen and the stirring speed in the fermentation tank in real time to obtain real-time fermentation condition monitoring data; According to the fermentation condition dynamic adjustment strategy and the real-time fermentation condition monitoring data, the fermentation conditions in the fermentation tank are adjusted by a PID controller to perform fermentation process adjustment control.

7. The intelligent control adjustment method for high-precision fermentation and purification of stachydrine according to claim 1, characterized in that, After the fermentation process adjustment control according to the fermentation condition dynamic adjustment strategy, the method further includes: When the stachydrine purity in the sugar content monitoring data is greater than or equal to a first preset purity, and the non-target sugar purity is less than or equal to a second preset purity, the fermentation is terminated to obtain a high-purity fermentation liquor; After sterilization treatment on the high-purity fermentation liquor, the system temperature is controlled to be less than or equal to a second preset temperature, and the active carbon is used for precipitation and decolorization treatment to obtain a first purified liquor; The first purified liquor is subjected to impurity and bacteria removal treatment by an ultrafiltration membrane to obtain a second purified liquor; The second purified liquor is subjected to desalination and impurity removal treatment by a nanofiltration membrane, and then concentrated to a second brix interval to obtain a concentrated fermentation liquor; After spray drying treatment on the concentrated fermentation liquor, a stachydrine dry powder with a stachydrine purity greater than or equal to a third preset purity is obtained.

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