A method for separating and purifying rosmarinic acid and a control system thereof

Abstract

The present application relates to the field of separation and purification control, and particularly relates to a method for separating and purifying rosmarinic acid and a control system thereof, which analyzes the time of anomaly based on time series data of the conductivity, controls a capture module and a crystallization device to cooperatively perform a precursor capture action, acquires precursor features, and includes interframe pixel difference features and optical flow motion vectors, calculates a precursor anomaly representation parameter based on the precursor features, determines an anomaly tendency based on the precursor anomaly representation parameter, and controls a crystallization parameter of the crystallization device. The present application captures precursor features of an abnormal burst crystallization in a crystallization process in a specific manner, timely intervenes in and adjusts process parameters, reduces abnormal crystallization in a separation and purification process, and improves separation and purification stability and quality.

Description

Technical Field

[0001] This invention relates to the field of separation and purification control, and in particular to a method for separating and purifying rosmarinic acid and its control system. Background Technology

[0002] Rosmarinic acid is a water-soluble polyphenolic compound widely found in plants of the Lamiaceae family, such as perilla, salvia miltiorrhiza, and rosemary. It possesses excellent antioxidant, anti-inflammatory, antibacterial, and neuroprotective activities, and has broad application prospects in the food, cosmetic, pharmaceutical, and feed additive fields. Perilla, a traditional Chinese medicinal and edible plant, is rich in rosmarinic acid in its stems and leaves, making it an ideal raw material for the industrial production of this compound. However, the efficient and economical separation and purification of high-purity (e.g., above 90%) rosmarinic acid crystals from the complex extracts of perilla, along with precise control of the crystallization process, remains a key challenge for industrialization.

[0003] For example, Chinese Patent Publication No. CN106542999A discloses a separation technique for natural products, specifically a purification method for rosmarinic acid. Its key features include: first, dissolving crude rosmarinic acid in an alkaline buffer solution and filtering to obtain a sample solution; then loading the sample solution onto a boric acid affinity column, rinsing the column with an alkaline buffer solution, followed by elution with an acidic buffer solution to obtain an affinity eluent; next, loading the affinity eluent onto a macroporous resin column, rinsing the column with purified water, followed by elution with an ethanol-water solution to obtain an ethanol-water eluent; finally, drying the ethanol-water eluent under reduced pressure to obtain the purified rosmarinic acid product. This method for separating and purifying rosmarinic acid is simple, requires no toxic or harmful organic solvents, has high purification efficiency, and produces a high-purity product, making it suitable for industrial production.

[0004] However, the following problems still exist in the existing technology.

[0005] In existing technologies, real-time monitoring and control of the crystallization process are not considered, making it impossible to determine the precursors of explosive crystallization. This can easily lead to crystallization anomalies and result in poor quality of the separated and purified crystal products. Summary of the Invention

[0006] Therefore, the present invention provides a method for separating and purifying rosmarinic acid and its control system, in order to overcome the problems in the prior art that do not consider real-time monitoring and control of the crystallization process, cannot determine the precursors of explosive crystallization, are prone to crystallization abnormalities, and result in poor quality of the separated and purified crystal products.

[0007] To achieve the above objectives, in one aspect, the present invention provides a control system for the separation and purification of rosmarinic acid, comprising:

[0008] The capture module includes an image capture unit for capturing image data of the crystallization liquid surface within the crystallization apparatus and a conductivity capture unit for obtaining the conductivity within the crystallization solution.

[0009] A precursor acquisition module, connected to the capture module, is used to analyze the anomaly time based on the time-series data of the conductivity, control the capture module and the crystallization device to coordinate the execution of precursor capture actions, and acquire image data collected within the corresponding time domain segment.

[0010] The precursor feature analysis module, connected to the anomaly analysis module, is used to analyze precursor features based on the image data, including performing inter-frame comparison based on the image data, parsing inter-frame pixel difference features, extracting the optical flow motion vector of the pixel points in the pixel features, and determining the flow diffusion feature value based on the diffusion of the optical flow motion vector.

[0011] An anomaly analysis module, connected to the precursor analysis module, is used to calculate precursor anomaly characterization parameters based on precursor characteristics, determine anomaly tendency based on the precursor anomaly characterization parameters, and control the crystallization parameters of the crystallization device, including...

[0012] Based on the aforementioned precursor anomaly characterization parameters, the temperature is adjusted, the adjusted conductivity is collected, the effectiveness of the adjustment is analyzed, and it is determined whether to terminate the adjustment.

[0013] The precursor capture action includes slowing down the stirring mechanism, controlling the illumination probe of the image capture unit, and acquiring image data of the crystallization liquid surface.

[0014] Furthermore, the shooting angle of the illumination probe is at a predetermined angle to the surface of the crystallization liquid, and the illumination width of the illumination probe must be less than the maximum width of the crystallization liquid surface.

[0015] Furthermore, the precursor acquisition module is used to analyze the anomaly time based on the time-series data of the conductivity, including:

[0016] Used to obtain the rate of change of electrical conductivity over several cycles;

[0017] If the rate of change is greater than the predetermined rate of change threshold, then it is determined that there is an abnormal moment within the corresponding period.

[0018] Furthermore, the precursor feature analysis module is used to perform inter-frame comparison based on image data and analyze inter-frame pixel difference features, including,

[0019] Extract pixel features between several adjacent image frames, including saturation and brightness;

[0020] Determine the differences in pixel features between adjacent image frames, including the saturation difference ratio and the brightness difference ratio;

[0021] This is used to determine the mean saturation difference ratio and the mean brightness difference ratio of several adjacent image frames, in order to calculate the inter-frame pixel difference features.

[0022] Furthermore, the precursor feature analysis module is used to perform inter-frame comparison based on image data, extract the optical flow motion vector of pixel points from pixel features, and determine the flow diffusion feature value based on the diffusivity of the optical flow motion vector, including:

[0023] Used to determine the target feature contour in several adjacent image frames based on temporally arranged image data, determine the edge pixels of the target feature contour, and determine the optical flow vector of the edge pixels in the adjacent image frames;

[0024] Used to determine the position vector of each edge pixel relative to the center of the target feature contour, and to determine the angle between the optical flow vector and the position vector corresponding to each edge pixel;

[0025] The mean angle of the edge pixels is used to solve for the mean angle value, and the mean angle value is determined as the flow diffusion feature value.

[0026] Furthermore, the anomaly analysis module calculates precursor anomaly characterization parameters based on precursor features, including:

[0027] Used to obtain precursor features, including inter-frame pixel difference features and flow diffusion feature values;

[0028] This is used to calculate the ratio of inter-frame pixel difference features to a preset inter-frame pixel difference threshold, thereby obtaining the factors influencing inter-frame differences.

[0029] This is used to calculate the ratio of the flow diffusion threshold to the flow diffusion characteristic value, thereby obtaining the factors influencing flow diffusion;

[0030] This is used to sum the weighted values ​​of inter-frame difference influencing factors and flow diffusion influencing factors to obtain the precursor variation characterization parameters.

[0031] Furthermore, the mutation analysis module determines the mutation tendency based on the precursor mutation characterization parameters, including:

[0032] The precursor mutation characterization parameters are compared with preset precursor mutation characterization parameter threshold values;

[0033] If the precursor mutation characterization parameter is greater than or equal to the preset precursor mutation characterization parameter threshold value, it is determined that there is a mutation tendency.

[0034] Furthermore, the anomaly analysis module adjusts the temperature based on the precursor anomaly characterization parameters, including:

[0035] The reduction in temperature is positively correlated with the parameters characterizing the precursor mutations.

[0036] Furthermore, the anomaly analysis module collects the adjusted conductivity, analyzes the effectiveness of the adjustment, and determines whether to terminate the adjustment, including...

[0037] The rate of change of the conductivity within the period is determined. If the rate of change returns to the preset rate of change threshold range, the adjustment is determined to be effective, and the adjustment is terminated.

[0038] On the other hand, the present invention also provides a separation and purification method for a separation and purification control system for rosmarinic acid, comprising,

[0039] Step S1: Add the decolorized concentrated dried product containing perilla to an ethanol solution, heat to dissolve, filter, and place the filtrate into a crystallization device for cooling and crystallization.

[0040] Step S2: Collect image data of the crystallization liquid surface and the electrical conductivity of the crystallization solution within the crystallization apparatus;

[0041] Step S3: Based on the time-series data of the conductivity, analyze the anomaly time, control the capture module and the crystallization device to perform the precursor capture action in coordination, and acquire the image data collected in the corresponding time domain segment;

[0042] Step S4, analyzing precursor features based on the image data, includes: performing inter-frame comparison based on the image data, analyzing inter-frame pixel difference features, extracting the optical flow motion vector of the pixel points in the pixel features, and determining the flow diffusion feature value based on the diffusion of the optical flow motion vector.

[0043] Step S5: Calculate precursor mutation characterization parameters based on precursor characteristics, determine mutation tendency based on the precursor mutation characterization parameters, and control the crystallization parameters of the crystallization device, including...

[0044] Based on the aforementioned precursor anomaly characterization parameters, the temperature is adjusted, the adjusted conductivity is collected, and the effectiveness of the adjustment is analyzed to determine whether to terminate the adjustment.

[0045] Compared with existing technologies, this invention analyzes the anomaly time based on the conductivity-based time-series data, controls the capture module and crystallization device to collaboratively perform precursor capture actions, obtains precursor features, including inter-frame pixel difference features and optical flow motion vectors, calculates precursor anomaly characterization parameters based on the precursor features, determines the anomaly tendency based on the precursor anomaly characterization parameters, and controls the crystallization parameters of the crystallization device. This invention captures the precursor features of explosive crystallization anomalies during the crystallization process in a specific way, intervenes in a timely manner to adjust process parameters, reduces crystallization anomalies during separation and purification, and improves the stability and quality of separation and purification.

[0046] In particular, this invention analyzes the anomaly moments based on conductivity time-series data, triggering a precursor capture action through potential conductivity anomalies. During the precursor capture action, by slowing down stirring and irradiating the interface of the crystallization solution with an illumination probe, crystallization features are stimulated to appear in the image data, thereby facilitating the capture of early anomaly precursors. This provides data support for timely analysis of anomaly precursors, enabling timely intervention and adjustment of process parameters, reducing crystallization anomalies during separation and purification, and improving the stability and quality of separation and purification.

[0047] In particular, this invention analyzes precursor features through image data. The acquisition of these precursor features employs a specific method—precursor capture—making crystallization characteristics more prominent and easier to capture in images. In practice, before explosive crystallization, nucleation is typically triggered by one or more burst points in these regions. Molecular clusters first reach critical sizes in these hotspot areas, forming the first batch of stable crystal nuclei. These newly formed, high-concentration nucleus / molecular cluster regions are then carried away by the fluid and act as "seeds" in the flow, inducing a chain reaction. Visually, this resembles fog spreading from a point. Furthermore, before explosive crystallization, small nuclei may be generated and aggregate, visually resembling fog, causing rapid changes in the pixel characteristics of the image data. Based on this, this invention considers capturing the precursor features of explosive crystallization by examining inter-frame pixel difference characteristics and flow diffusion characteristics, thereby intervening in and adjusting process parameters in a timely manner to reduce crystallization anomalies during separation and purification, and improving the stability and quality of separation and purification.

[0048] In particular, this invention determines the tendency of anomalies by using precursor anomaly characterization parameters. The calculation of precursor anomaly characterization parameters includes inter-frame pixel difference features and flow diffusion feature values, which characterize the precursor phenomenon of burst crystallization. The larger the value, the higher the anomaly tendency. Based on this, timely intervention and adjustment can be carried out to avoid intervention after burst crystallization occurs, thereby reducing the anomaly of crystallization during the separation and purification process and improving the stability and quality of separation and purification. Attached Figure Description

[0049] Figure 1 A schematic diagram of the rosmarinic acid separation and purification control system according to an embodiment of the invention;

[0050] Figure 2 This is a logic block diagram illustrating the anomaly moment in an embodiment of the invention.

[0051] Figure 3 A logic block diagram for determining the mutation tendency in an embodiment of the invention;

[0052] Figure 4 This is a logic block diagram illustrating whether to terminate the adjustment in an embodiment of the invention. Detailed Implementation

[0053] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0054] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0055] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0056] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0057] Please see Figure 1 The diagram shown is a schematic diagram of the rosmarinic acid separation and purification control system according to an embodiment of the present invention. The rosmarinic acid separation and purification control system according to an embodiment of the present invention includes:

[0058] The capture module includes an image capture unit for capturing image data of the crystallization liquid surface within the crystallization apparatus and a conductivity capture unit for obtaining the conductivity within the crystallization solution.

[0059] A precursor acquisition module, connected to the capture module, is used to analyze the anomaly time based on the time-series data of the conductivity, control the capture module and the crystallization device to coordinate the execution of precursor capture actions, and acquire image data collected within the corresponding time domain segment.

[0060] The precursor feature analysis module, connected to the anomaly analysis module, is used to analyze precursor features based on the image data, including performing inter-frame comparison based on the image data, parsing inter-frame pixel difference features, extracting the optical flow motion vector of the pixel points in the pixel features, and determining the flow diffusion feature value based on the diffusion of the optical flow motion vector.

[0061] An anomaly analysis module, connected to the precursor analysis module, is used to calculate precursor anomaly characterization parameters based on precursor characteristics, determine anomaly tendency based on the precursor anomaly characterization parameters, and control the crystallization parameters of the crystallization device, including...

[0062] Based on the aforementioned precursor anomaly characterization parameters, the temperature is adjusted, the adjusted conductivity is collected, the effectiveness of the adjustment is analyzed, and it is determined whether to terminate the adjustment.

[0063] The precursor capture action includes slowing down the stirring mechanism, controlling the illumination probe of the image capture unit, and acquiring image data of the crystallization liquid surface.

[0064] Specifically, there are no restrictions on the specific structure of the capture module. The image capture unit can be an industrial CCD camera, which can be equipped with an illumination probe to illuminate the surface of the crystal liquid with a specific illumination width. The conductivity capture unit can be a conductivity sensor with anti-corrosion function. These are all existing technologies and will not be described in detail.

[0065] Specifically, there are no restrictions on the structure of the precursor acquisition module, the precursor feature analysis module, and the anomaly analysis module. They can be composed of logic components or combinations of logic components, including field-programmable processors, computers, or microprocessors in computers.

[0066] Specifically, the specific structure of the crystallization device is not limited. In practice, it is a cooling crystallization device. The crystallization device is usually equipped with a stirring mechanism. The stirring mechanism needs to be able to adjust the stirring rate. Those skilled in the art can select the corresponding cooling crystallization device according to their needs, which will not be elaborated here.

[0067] Specifically, the shooting angle of the illumination probe is at a predetermined angle to the surface of the crystallization liquid, and the illumination width of the illumination probe must be less than the maximum width of the crystallization liquid surface.

[0068] Specifically, please refer to Figure 2 As shown, it is a logic block diagram of the analysis of anomaly moments according to an embodiment of the invention. The precursor acquisition module is used to analyze the anomaly moments based on the time-series data of the conductivity, including:

[0069] Used to obtain the rate of change of electrical conductivity over several cycles;

[0070] If the rate of change is greater than the predetermined rate of change threshold, then it is determined that there is an abnormal moment within the corresponding period.

[0071] Specifically, the rate of change threshold is obtained through prior experiments. The average rate of change of conductivity during several cycles in the entire crystallization process is predetermined under the condition that there are no abnormalities in crystallization. The rate of change threshold characterizes the situation where the rate of change deviates from the constant value. In practice, a predetermined multiple of the average rate of change is set as the rate of change threshold. The predetermined multiple is selected within the range [1.25, 1.5]. In practice, it is preferably 1.35.

[0072] This invention analyzes the anomaly moments based on time-series data of conductivity. It triggers a precursor capture action by detecting potential anomalies in conductivity. During the precursor capture action, the stirring is slowed down and the interface of the crystallization solution is irradiated by an illumination probe to stimulate the crystallization characteristics to appear in the image data. This facilitates the capture of early anomaly precursors, provides data support for timely analysis of anomaly precursors, and enables timely intervention to adjust process parameters, reduce crystallization anomalies during separation and purification, and improve the stability and quality of separation and purification.

[0073] Specifically, the precursor feature analysis module is used to perform inter-frame comparison based on image data and analyze inter-frame pixel difference features, including,

[0074] Extract pixel features between several adjacent image frames, including saturation and brightness;

[0075] Determine the differences in pixel features between adjacent image frames, including the saturation difference ratio and the brightness difference ratio;

[0076] This is used to determine the mean saturation difference ratio and the mean brightness difference ratio of several adjacent image frames, in order to calculate the inter-frame pixel difference features.

[0077] In practice, the mean of the saturation difference ratio and the mean of the brightness difference ratio are calculated, and this mean is used as the inter-frame pixel difference feature.

[0078] The difference ratio between two values ​​is the ratio of the absolute difference between the two values ​​to the mean of the two values.

[0079] Specifically, the precursor feature analysis module is used to perform inter-frame comparison based on image data, extract the optical flow motion vector of each pixel in the pixel features, and determine the flow diffusion feature value based on the diffusivity of the optical flow motion vector, including:

[0080] Used to determine the target feature contour in several adjacent image frames based on temporally arranged image data, determine the edge pixels of the target feature contour, and determine the optical flow vector of the edge pixels in the adjacent image frames;

[0081] Used to determine the position vector of each edge pixel relative to the center of the target feature contour, and to determine the angle between the optical flow vector and the position vector corresponding to each edge pixel;

[0082] The mean angle of the edge pixels is used to solve for the mean angle value, and the mean angle value is determined as the flow diffusion feature value.

[0083] In practice, there is no limitation on the method for determining the optical flow vector. The optical flow analysis can be implemented using any suitable method known in the art, such as including but not limited to the Lucas-Kanade algorithm, the Farneback algorithm, or optical flow networks based on deep learning (such as RAFT), etc., which will not be elaborated further.

[0084] In practice, the position vector is directed from the center of the target feature contour to the edge pixel.

[0085] The essence of radial movement is that the motion vector field diverges outward from the center point. The key to quantification is to analyze the consistency between the motion direction of each pixel and its position relative to the center point. Therefore, the smaller the mean angle, the stronger the consistency and the stronger the diffusion from the center outward.

[0086] Specifically, the mutation analysis module calculates precursor mutation characterization parameters based on precursor features, including:

[0087] Used to obtain precursor features, including inter-frame pixel difference features and flow diffusion feature values;

[0088] This is used to calculate the ratio of inter-frame pixel difference features to a preset inter-frame pixel difference threshold, thereby obtaining the factors influencing inter-frame differences.

[0089] This is used to calculate the ratio of the flow diffusion threshold to the flow diffusion characteristic value, thereby obtaining the factors influencing flow diffusion;

[0090] This is used to sum the weighted values ​​of inter-frame difference influencing factors and flow diffusion influencing factors to obtain the precursor variation characterization parameters.

[0091] In practice, the inter-frame pixel difference threshold was obtained experimentally. Specifically, several image data without abnormal crystallization processes were recorded, and the maximum value of the inter-frame pixel difference feature was recorded as the inter-frame pixel difference threshold.

[0092] In practice, several image data of the liquid surface before the burst crystallization can be obtained in advance, and the mean value of the flow diffusion characteristic value can be determined. This mean value is used as the flow diffusion threshold.

[0093] In practice, to comprehensively consider the influencing factors of inter-frame differences and flow diffusion, the weights in the weighted summation were all set to 0.5.

[0094] In practice, the target feature contour is obtained through image clustering. The image data is clustered to determine several clusters, and each cluster is identified as a target feature, thereby recognizing the target feature contour.

[0095] Specifically, please refer to Figure 3 As shown, it is a logical block diagram of determining the mutation tendency according to an embodiment of the invention. The mutation analysis module determines the mutation tendency based on the precursor mutation characterization parameters, including:

[0096] The precursor mutation characterization parameters are compared with preset precursor mutation characterization parameter threshold values;

[0097] If the precursor mutation characterization parameter is greater than or equal to the preset precursor mutation characterization parameter threshold value, it is determined that there is a mutation tendency.

[0098] In practice, the purpose of setting threshold values ​​for precursor mutation characterization parameters is to determine the mutation boundary. Based on this, the precursor mutation characterization parameters are pre-calculated when the inter-frame pixel difference feature is equal to the preset inter-frame pixel difference threshold and when the flow diffusion threshold is equal to the flow diffusion feature value. The product of the precursor mutation characterization parameter and the error coefficient is solved. The purpose of the error coefficient is to appropriately amplify the precursor mutation characterization parameter. The error coefficient is selected in the interval [1.15, 1.3], preferably 1.2.

[0099] This invention analyzes precursor features through image data. The acquisition of these precursor features employs a specific method—precursor capture—to make crystallization characteristics more prominent and easier to capture in images. In practice, before explosive crystallization, nucleation is typically triggered by one or more burst points in these regions. Molecular clusters first reach critical sizes in these hotspot areas, forming the first batch of stable crystal nuclei. These newly formed, high-concentration nucleus / molecular cluster regions are then carried away by the fluid and act as "seeds" in the flow, inducing a chain reaction. Visually, this resembles fog spreading from a point. Furthermore, before explosive crystallization, tiny nuclei may be generated and aggregate, visually resembling fog, causing rapid changes in the pixel characteristics of the image data. Based on this, this invention considers capturing the precursor features of explosive crystallization by examining inter-frame pixel differences and flow diffusion characteristics, thereby intervening in and adjusting process parameters in a timely manner to reduce crystallization anomalies during separation and purification, and improving the stability and quality of separation and purification.

[0100] Specifically, the anomaly analysis module adjusts the temperature based on the precursor anomaly characterization parameters, including:

[0101] The reduction in temperature is positively correlated with the parameters characterizing the precursor mutations.

[0102] In implementation, a base temperature reduction is set. To avoid excessive temperature reduction, the base temperature reduction is between 10% and 30% of the initial temperature, preferably 20%. Simultaneously, an adjustment coefficient is determined. The adjustment coefficient is the ratio of the precursor mutation characterization parameter to the preset precursor mutation characterization parameter threshold value. The reduction is set as the product of the base temperature reduction and the adjustment coefficient. Thus, the reduction is positively correlated with the precursor mutation characterization parameter.

[0103] Specifically, please refer to Figure 4 As shown, this is a logic block diagram for determining whether to terminate the adjustment according to an embodiment of the invention. The anomaly analysis module collects the adjusted conductivity, analyzes the effectiveness of the adjustment, and determines whether to terminate the adjustment, including...

[0104] The rate of change of the conductivity within the period is determined. If the rate of change returns to the preset rate of change threshold range, the adjustment is determined to be effective, and the adjustment is terminated.

[0105] In practice, the threshold range of the rate of change is predetermined. The rate of change of conductivity during several cycles in the process of crystallization without abnormality is recorded in advance, the normal distribution of the rate of change is solved, and the 95% confidence interval is determined. The 95% confidence interval is used as the threshold range of the rate of change.

[0106] This invention determines the tendency of anomalies by using precursor anomaly characterization parameters. The calculation of precursor anomaly characterization parameters includes inter-frame pixel difference features and flow diffusion feature values, which characterize the precursor phenomenon of burst crystallization. The larger the value, the higher the anomaly tendency. Based on this, timely intervention and adjustment can be carried out to avoid intervention after burst crystallization occurs, thereby reducing crystallization anomalies during separation and purification and improving the stability and quality of separation and purification.

[0107] It also provides separation and purification methods for a separation and purification control system for rosmarinic acid, including,

[0108] Step S1: Add the decolorized concentrated dried product containing perilla to an ethanol solution, heat to dissolve, filter, and place the filtrate into a crystallization device for cooling and crystallization.

[0109] Step S2: Collect image data of the crystallization liquid surface and the electrical conductivity of the crystallization solution within the crystallization apparatus;

[0110] Step S3: Based on the time-series data of the conductivity, analyze the anomaly time, control the capture module and the crystallization device to perform the precursor capture action in coordination, and acquire the image data collected in the corresponding time domain segment;

[0111] Step S4, analyzing precursor features based on the image data, includes: performing inter-frame comparison based on the image data, analyzing inter-frame pixel difference features, extracting the optical flow motion vector of the pixel points in the pixel features, and determining the flow diffusion feature value based on the diffusion of the optical flow motion vector.

[0112] Step S5: Calculate precursor mutation characterization parameters based on precursor characteristics, determine mutation tendency based on the precursor mutation characterization parameters, and control the crystallization parameters of the crystallization device, including...

[0113] Based on the aforementioned precursor anomaly characterization parameters, the temperature is adjusted, the adjusted conductivity is collected, and the effectiveness of the adjustment is analyzed to determine whether to terminate the adjustment.

[0114] In practice, the decolorized, concentrated, and dried product containing perilla is obtained through preliminary steps, specifically including:

[0115] Step S101: Weigh the perilla leaves, extract them by reflux with an aqueous solution, and concentrate the extract to obtain perilla concentrate.

[0116] Step S102: Treat the perilla concentrate with ethanol solvent to remove impurities, separate the ethanol solution, concentrate to remove ethanol and obtain alcohol precipitate concentrate;

[0117] Step S103: Adjust the pH of the alcohol precipitation concentrate to acidic, extract with ethyl acetate, separate the ethyl acetate layer, concentrate and recover the ethyl acetate, and obtain concentrated paste I;

[0118] Step S104: Concentrated paste I is extracted using a mixed solvent, and the mixed solvent layer is separated and concentrated to obtain concentrated paste II;

[0119] Step S105: Add water to concentrated paste II and stir to dissolve. Add activated carbon and stir to decolorize. Separate to obtain decolorized solution and concentrate to obtain decolorized concentrated dried substance.

[0120] Multiple crystallization processes are required for cooling crystallization to improve product purity.

[0121] In step S101, the amount of water added as the extraction solvent is 8-15 times that of the perilla medicinal material, the extraction time is 60-120 min, and the extraction is performed twice; during vacuum concentration, the temperature is 60-80℃, the vacuum degree is -0.07MPa to -0.09MPa, and the density of the concentrate is 1.05-1.10g / ml.

[0122] In step S102, the ethanol solvent is used to remove impurities, and the concentration of ethanol precipitation should reach more than 80%, and the density of the concentrated ethanol precipitation solution should be 1.01-1.05 g / ml.

[0123] In step S103, the pH is adjusted to acidic by using at least one of hydrochloric acid, sulfuric acid, phosphoric acid, acetic acid, and citric acid, with a pH of 1-4.5; the amount of ethyl acetate used is the ratio of medicinal material to volume = 1:0.5-1.

[0124] In step S104, the mixed solvent is two or more of the following: petroleum ether, n-hexane, cyclohexane, n-butanol, isopropanol, dichloromethane, butyl acetate, propyl acetate, and ethyl acetate. The amount used is the ratio of medicinal material to volume = 1:0.2-1.

[0125] In step S105, the water added is in a ratio of medicinal material to volume of 1:0.2-0.5, the activated carbon dosage is 0.1-0.5% of the medicinal material, the decolorization temperature is 50-70℃, the concentration temperature is 60-80℃, and the vacuum degree is -0.07MPa to -0.09MPa.

[0126] The amount of ethanol added is based on the weight to volume ratio of the decolorized, concentrated, and dried material, which is 1:1-2. The dissolution temperature is 50-70℃.

[0127] During crystallization, the crystallization temperature is around 4℃ and the time is 24-48 hours.

[0128] Example 1,

[0129] Provide complete data on the crystallization process, specifically including:

[0130] (1) Weigh 1 kg of perilla medicinal material, add water and reflux to extract twice, 10 L each time, extract for 60 min, filter and separate, combine the two extracts, and vacuum concentrate at 70℃ to obtain about 600 g of perilla concentrate.

[0131] (2) Slowly add 3L of 95% ethanol for alcohol precipitation to remove impurities. After the ethanol is completely added, let it stand for 1 hour, filter and separate to obtain alcohol precipitate. Concentrate the alcohol solution until there is no alcohol smell to obtain about 500g of alcohol precipitate concentrate.

[0132] (3) Adjust the pH to about 4.0 with phosphoric acid, add 500 ml of ethyl acetate and shake to extract for 30 min. After standing and separating the layers, separate the ethyl acetate layer and concentrate under vacuum at 50℃ to obtain about 80 g of concentrated paste.

[0133] (4) Add 200ml of mixed solvent (petroleum ether: isopropanol = 1:1) to the concentrated paste, then add 100ml of water and shake to extract for 30min. After standing and separating the layers, separate the mixed solvent layer and concentrate to obtain about 50g of concentrated paste.

[0134] (5) Add 200ml of water to the concentrated paste and stir to dissolve. Then add 2g of activated carbon and stir to decolorize for 30min at 50℃. Filter to separate the decolorized solution and vacuum concentrate at 70℃ to obtain about 28g of concentrated dried perilla material.

[0135] (6) Add about 25g of concentrated dried material to 50ml of ethanol solution and stir to dissolve at 70℃. Cool to room temperature and crystallize at 4℃ for 24h. Filter to separate the crystallized material and dry in an oven at 60℃ for 6h to obtain about 16g of the first crystallized dried material.

[0136] (7) First crystallization and drying of the substance: The crystalline substance was dissolved in 100 ml of water at 60°C by stirring. After cooling to room temperature, it was placed at 4°C for 12 h to crystallize. The crystalline substance was filtered and separated. It was dried in an oven at 60°C for 6 h to obtain approximately 9.8 g of crystalline dried substance with a content of 92%.

[0137] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A control system for the separation and purification of rosmarinic acid, characterized in that, include: The capture module includes an image capture unit for capturing image data of the crystallization liquid surface within the crystallization apparatus and a conductivity capture unit for obtaining the conductivity within the crystallization solution. A precursor acquisition module, connected to the capture module, is used to analyze the anomaly time based on the time-series data of the conductivity, control the capture module and the crystallization device to coordinate the execution of precursor capture actions, and acquire image data collected within the corresponding time domain segment. A precursor feature analysis module, which is connected to the anomaly analysis module, is used to analyze precursor features based on the image data, including performing inter-frame comparison based on the image data, parsing inter-frame pixel difference features, extracting the optical flow motion vector of the pixel points in the pixel features, and determining the flow diffusion feature value based on the diffusion of the optical flow motion vector. An anomaly analysis module, connected to the precursor characteristic analysis module, is used to calculate precursor anomaly characterization parameters based on precursor characteristics, determine anomaly tendency based on the precursor anomaly characterization parameters, and control the crystallization parameters of the crystallization device, including... Based on the aforementioned precursor anomaly characterization parameters, the temperature is adjusted, the adjusted conductivity is collected, the effectiveness of the adjustment is analyzed, and it is determined whether to terminate the adjustment. The precursor capture action includes slowing down the stirring mechanism, controlling the illumination probe of the image capture unit, and acquiring image data of the crystallization liquid surface.

2. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The shooting angle of the illumination probe is at a predetermined angle to the surface of the crystallizing liquid, and the illumination width of the illumination probe must be less than the maximum width of the crystallizing liquid surface.

3. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The precursor acquisition module is used to analyze the anomaly moments based on the time-series data of the conductivity, including... Used to obtain the rate of change of electrical conductivity over several cycles; If the rate of change is greater than the predetermined rate of change threshold, then it is determined that there is an abnormal moment within the corresponding period.

4. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The precursor feature analysis module is used to perform inter-frame comparison based on image data and analyze inter-frame pixel difference features, including, Extract pixel features between several adjacent image frames, including saturation and brightness; Determine the differences in pixel features between adjacent image frames, including the saturation difference ratio and the brightness difference ratio; This is used to determine the mean saturation difference ratio and the mean brightness difference ratio of several adjacent image frames, in order to calculate the inter-frame pixel difference features.

5. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The precursor feature analysis module is used to perform inter-frame comparison based on image data, extract the optical flow motion vector of each pixel in the pixel features, and determine the flow diffusion feature value based on the diffusivity of the optical flow motion vector, including: Used to determine the target feature contour in several adjacent image frames based on temporally arranged image data, determine the edge pixels of the target feature contour, and determine the optical flow vector of the edge pixels in the adjacent image frames; Used to determine the position vector of each edge pixel relative to the center of the target feature contour, and to determine the angle between the optical flow vector and the position vector corresponding to each edge pixel; The mean angle of the edge pixels is used to solve for the mean angle value, and the mean angle value is determined as the flow diffusion feature value.

6. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The mutation analysis module calculates precursor mutation characterization parameters based on precursor features, including... Used to obtain precursor features, including inter-frame pixel difference features and flow diffusion feature values; This is used to calculate the ratio of inter-frame pixel difference features to a preset inter-frame pixel difference threshold, thereby obtaining the factors influencing inter-frame differences. This is used to calculate the ratio of the flow diffusion threshold to the flow diffusion characteristic value, thereby obtaining the factors influencing flow diffusion; This is used to sum the weighted values ​​of inter-frame difference influencing factors and flow diffusion influencing factors to obtain the precursor variation characterization parameters.

7. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The mutation analysis module determines the mutation tendency based on the precursor mutation characterization parameters, including... The precursor mutation characterization parameters are compared with preset precursor mutation characterization parameter threshold values; If the precursor mutation characterization parameter is greater than or equal to the preset precursor mutation characterization parameter threshold value, it is determined that there is a mutation tendency.

8. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The anomaly analysis module adjusts the temperature based on the precursor anomaly characterization parameters. include, The reduction in temperature is positively correlated with the parameters characterizing the precursor mutations.

9. The rosmarinic acid separation and purification control system according to claim 1, characterized in that, The anomaly analysis module collects the adjusted conductivity, analyzes the effectiveness of the adjustment, and determines whether to terminate the adjustment. The rate of change of the conductivity within the period is determined. If the rate of change returns to the preset rate of change threshold range, the adjustment is determined to be effective, and the adjustment is terminated.

10. A separation and purification method applied to the separation and purification control system of rosmarinic acid according to any one of claims 1-9, characterized in that, include, Step S1: Add the decolorized concentrated dried product containing perilla to an ethanol solution, heat to dissolve, filter, and place the filtrate into a crystallization device for cooling and crystallization. Step S2: Collect image data of the crystallization liquid surface and the electrical conductivity of the crystallization solution within the crystallization apparatus; Step S3: Based on the time-series data of the conductivity, analyze the anomaly time, control the capture module and the crystallization device to perform the precursor capture action in coordination, and acquire the image data collected in the corresponding time domain segment; Step S4, analyzing precursor features based on the image data, includes: performing inter-frame comparison based on the image data, analyzing inter-frame pixel difference features, extracting the optical flow motion vector of the pixel points in the pixel features, and determining the flow diffusion feature value based on the diffusion of the optical flow motion vector. Step S5: Calculate precursor mutation characterization parameters based on precursor characteristics, determine mutation tendency based on the precursor mutation characterization parameters, and control the crystallization parameters of the crystallization device, including... Based on the aforementioned precursor anomaly characterization parameters, the temperature is adjusted, the adjusted conductivity is collected, the effectiveness of the adjustment is analyzed, and it is determined whether to terminate the adjustment.

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