Raw material blood sugar reducing method in lycopene production process

Abstract

The invention relates to the technical field, and provides a raw material blood glucose reducing method in a lycopene production process, which combines resin adsorption and fermentation technologies, and remarkably improves the blood glucose reducing effect and the production efficiency by monitoring and optimizing the blood glucose reducing process in real time. The method comprises the following specific steps: substituting tomato clear juice powder data into a resin adsorption sugar-reducing model, determining the type and quantity of required resin, carrying out mixed adsorption, setting, monitoring and adjusting fermentation parameters in real time to obtain an optimal sugar-reducing effect, carrying out sterilization, drying and quality detection after fermentation, and finally comparing actual quality with predicted quality to obtain a sugar-reducing result. According to the method, the blood glucose reducing model is continuously optimized, the blood glucose reducing effect and the production stability are improved, the production efficiency is improved, the product is more suitable for diabetics, in addition, the method has the self-learning and optimizing capacity, and the blood glucose reducing effect can be continuously optimized along with accumulation of production data.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic power generation regulating electric variables, and in particular to a raw material sugar reduction method in a lycopene production process. Background Art

[0002] Lycopene is a red pigment, a type of carotenoid. Lycopene is a natural pigment contained in plants, mainly found in the ripe fruit of tomatoes, a plant of the Solanaceae family, and also found in red plant fruits such as watermelon and grapefruit. The method for detecting soluble sugars includes using a chromatographic column to separate the soluble sugars in the sample, and determining the sugar content by detecting the peak area or concentration after separation. This method has good separation effect and high accuracy, but requires professional equipment and technology.

[0003] The fermentation sugar-lowering experiment aims to reduce the sugar content in the raw materials through the fermentation process, explore the impact of fermentation on the sugar-lowering effect, and provide an experimental basis for the subsequent development of low-sugar foods or beverages. During the fermentation process, microorganisms can use the sugars in the raw materials for growth and metabolism, thereby converting sugars into other substances, such as alcohol, carbon dioxide, etc. By selecting suitable microorganisms and fermentation conditions, the sugar content in the raw materials can be effectively reduced. In the lycopene production process, raw material sugar reduction does not directly target lycopene itself for sugar-lowering treatment, but indirectly makes the final product lycopene more suitable for diabetic patients or people who need to control blood sugar by selecting and processing low-sugar raw materials and optimizing the production process.

[0004] In the actual lycopene production process, the sugar content of the raw materials is reduced so that the produced lycopene can be reduced in sugar. Since the sugar reduction step involves fermentation and separation, when mass-producing reduced-sugar lycopene, it is necessary to monitor the quality of the entire process of sugar reduction of the raw materials required for lycopene production. For this purpose, a method for reducing the sugar content of raw materials in the lycopene production process has been developed. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for reducing the sugar content of raw materials in a lycopene production process, which solves the problem that the whole process of reducing the sugar content of raw materials required for the production of lycopene cannot be quality-controlled during the batch production of reduced-sugar lycopene because the steps of reducing the sugar content involve fermentation and separation.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides a raw material sugar reduction method in a lycopene production process, comprising:

[0008] Step S101, obtaining raw data of tomato juice powder, the raw data of tomato juice powder including soluble sugar content, water content data, and nutrient component data;

[0009] Step S102, substituting the raw data of the tomato juice powder into a pre-constructed resin adsorption glucose reduction analysis model, calculating the type and amount of resin required for the resin adsorption glucose reduction, adding the resin to the tomato juice powder and mixing the mixture according to the type and amount of resin required for the resin adsorption glucose reduction, and performing adsorption on the mixture by the resin. When the resin is adsorbing, the change of the soluble sugar content in the mixture is monitored in real time to determine the optimal adsorption time;

[0010] Step S103, setting fermentation parameters, which include temperature, humidity and pH value. During the fermentation process, the change of soluble sugar content is monitored in real time, and the fermentation parameters are adjusted according to the preset fermentation blood sugar reduction control model to obtain the optimal blood sugar reduction effect parameters;

[0011] Step S104, after the fermentation is completed, sterilization and drying are performed to obtain the processed tomato juice powder, and quality testing is performed on the processed tomato juice powder to obtain the quality test result of the processed tomato juice powder;

[0012] Step S105, substitute the original data of tomato juice powder, fermentation parameters and the original data of tomato juice powder into the preset quality detection model to obtain the expected quality data, compare the expected quality data with the quality detection result of the processed tomato juice powder to obtain the quality comparison result, compare the quality comparison result with the preset standard value to obtain the quality error, use the mass error to optimize the resin adsorption glucose reduction analysis model and the preset fermentation glucose reduction control model, and use the optimized mass error to optimize the resin adsorption glucose reduction analysis model and the preset fermentation glucose reduction control model to obtain the updated optimal glucose reduction effect parameters and the updated optimal adsorption time, and deploy the optimized resin adsorption glucose reduction analysis model, the optimized preset fermentation glucose reduction control model, the updated optimal glucose reduction effect parameters and the updated optimal adsorption time to the production system to reduce the glucose content of lycopene production raw materials.

[0013] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S101:

[0014] The controller sends instructions to the data acquisition module to collect the original data of the tomato juice powder;

[0015] After receiving the instruction, the data acquisition module uses sensors and analytical instruments to measure the soluble sugar content, water content and nutritional data of the tomato juice powder;

[0016] The data acquisition module sends the measured soluble sugar content, water content and nutritional content data of the tomato juice powder back to the controller;

[0017] Sensors and analytical instruments measure:

[0018] Brix meter: used to measure the soluble sugar content;

[0019] Moisture meter: used to measure the moisture content in a substance;

[0020] Spectrometer: used to quickly analyze various chemical components in food, including water, sugar, and nutrients such as protein and fat;

[0021] Chromatography: High performance liquid chromatography is used to determine chemical components, including vitamins, minerals and nutrients;

[0022] Electrochemical sensor: used to measure sugars.

[0023] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S102:

[0024] The controller substitutes the raw data obtained in step S101 into a pre-built resin adsorption glucose reduction analysis model;

[0025] Determine the required resin type and quantity through resin adsorption sugar reduction analysis model calculation;

[0026] The controller sends a command to the resin adding device, and the resin adding device adds the required type and amount of resin to the tomato juice powder to mix the resin with the tomato juice powder;

[0027] During the mixing process, the controller monitors the changes in the soluble sugar content in the mixture in real time through monitoring equipment to determine the optimal adsorption time.

[0028] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S103:

[0029] The controller initially sets fermentation parameters, including temperature, humidity, and pH;

[0030] At the beginning of the fermentation process, the controller monitors the changes in the soluble sugar content in real time. According to the preset fermentation blood sugar reduction control model, the controller generates a fermentation parameter data set, and uses the parameters in the generated fermentation parameter data set to adjust the temperature, humidity and pH value in turn to obtain the fermentation parameter corresponding blood sugar reduction effect data set, and selects the data with the best fermentation effect from the fermentation parameter corresponding blood sugar reduction effect data set as the best blood sugar reduction effect parameter.

[0031] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S104:

[0032] After the fermentation is completed, a fermentation product is obtained, and the controller sends instructions to the sterilization equipment and the drying equipment, and the fermentation product is sterilized and dried by the sterilization equipment and the drying equipment;

[0033] After the sterilization and drying system executes the instructions and completes the sterilization and drying of the fermentation product, the processed tomato juice powder is obtained;

[0034] The controller sends instructions to the quality detection module to perform quality detection on the processed tomato juice powder.

[0035] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S105:

[0036] The controller substitutes the raw data in step S101, the fermentation parameters in step S103 and the processed quality inspection results into a preset quality inspection model;

[0037] The estimated quality data is calculated through the preset quality inspection model, including soluble sugar content, water content and nutritional components;

[0038] The controller compares the expected quality data with the actual quality test results to obtain a quality comparison result, which includes a soluble sugar content comparison result, a water content comparison result, and a nutrient component comparison result;

[0039] The controller obtains the preset standard values, which include the standard value of soluble sugar content, the standard value of water content and the standard value of nutrients.

[0040] If the data in the quality comparison result is higher than the preset standard value, a quality failure warning message will be generated.

[0041] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S105:

[0042] Collect the mass error data in the process of resin adsorption and glucose reduction, including experimental condition data, raw data and mass error. The experimental condition data includes the resin type, quantity and adsorption time, and the raw data includes the soluble sugar content, water content and nutritional components.

[0043] The mass error data of the resin adsorption glucose reduction process was used to verify the machine learning model resin adsorption glucose reduction analysis model, and the optimized resin adsorption glucose reduction analysis model was obtained.

[0044] Furthermore, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S105:

[0045] Collect quality error data in fermentation experiments, as well as corresponding fermentation parameters and hypoglycemic effect data. Fermentation parameters include temperature, humidity and pH value.

[0046] The preset fermentation glucose-lowering control model is verified using the quality error data in the fermentation experiment, as well as the corresponding fermentation parameters and glucose-lowering effect data, to obtain an optimized preset fermentation glucose-lowering control model.

[0047] The present invention provides a method for reducing the sugar content of raw materials in a lycopene production process, and its beneficial effects are mainly reflected in the following aspects:

[0048] Improved blood sugar lowering effect and production efficiency: Through the combination of resin adsorption and fermentation blood sugar lowering technology, as well as the application of real-time monitoring and quality detection models, the present invention achieves precise control and optimization of the entire blood sugar lowering process. This not only improves the blood sugar lowering effect, but also improves production efficiency, making the produced blood sugar lowering lycopene more suitable for specific populations, especially diabetic patients.

[0049] High degree of automation and intelligence: The method of the present invention reduces human intervention, improves production efficiency and product consistency. Through automation and intelligence, each step can be carried out according to the preset standards, thereby reducing the risk of human error and improper operation.

[0050] Possessing self-learning and adaptive capabilities: Through the quality comparison and optimization links, the present invention continuously verifies and adjusts the model and parameters, so that the system has the ability to self-learn and adapt. This means that as time goes by and production data accumulates, the system can continuously optimize the blood sugar reduction effect and production efficiency, ensuring the steady improvement of product quality.

[0051] In summary, the present invention not only improves the hypoglycemic effect and production efficiency, but also improves the applicability and consistency of the product by accurately controlling the entire hypoglycemic process. At the same time, its automation and intelligent characteristics as well as its self-learning and adaptability make this method have broad application prospects and market potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0055] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0056] See also Figure 1 The present invention provides a raw material sugar reduction method in a lycopene production process, comprising:

[0057] Step S101, obtaining raw data of tomato juice powder, the raw data of tomato juice powder including soluble sugar content, water content data, and nutrient component data;

[0058] The raw data of tomato juice powder obtained in step S101 include soluble sugar content, water content data and nutrient composition data. First, they are substituted into the pre-built resin adsorption sugar reduction analysis model as input data to calculate the required resin type and quantity; second, during the fermentation process, these data help to monitor and control the changes in soluble sugar content in real time to obtain the best sugar reduction effect; finally, in the quality evaluation and optimization stage, the raw data is compared with the quality test results to evaluate the sugar reduction effect, and the optimization model is used to ensure that the production system can continuously output products that meet quality standards. Through these measures, the produced lycopene products can have consistent quality and nutritional value.

[0059] Step S102, substituting the raw data of the tomato juice powder into a pre-constructed resin adsorption glucose reduction analysis model, calculating the type and amount of resin required for the resin adsorption glucose reduction, adding the resin to the tomato juice powder and mixing the mixture according to the type and amount of resin required for the resin adsorption glucose reduction, and performing adsorption on the mixture by the resin. When the resin is adsorbing, the change of the soluble sugar content in the mixture is monitored in real time to determine the optimal adsorption time;

[0060] Step S102 involves the precise calculation of the type and quantity of resin, and the subsequent resin adsorption process. First, the raw data of the tomato juice powder (including soluble sugar content, water content and nutrient content data) is substituted into the pre-built resin adsorption glucose reduction analysis model. The resin adsorption glucose reduction analysis model will calculate the type and quantity of resin required to achieve the predetermined glucose reduction effect based on the input data through internal algorithms and logic.

[0061] When the calculation results are obtained, the system will send instructions to the resin adding device to automatically add the required type and amount of resin to the tomato juice powder and mix them to form a mixture. Next, the resin begins to absorb soluble sugars in the mixture. In this process, the resin captures and fixes the sugars, thereby achieving a blood sugar lowering effect.

[0062] During the resin adsorption process, the system monitors the changes in the soluble sugar content in the mixture in real time, usually by detecting the sugar concentration in the mixture. The monitoring equipment transmits real-time data to the control system, which processes and analyzes the data to understand the efficiency and progress of resin adsorption. By analyzing the changing trend of the soluble sugar content, the system can determine when the best adsorption effect is achieved, and this time point is the best adsorption time.

[0063] In addition, if the adsorption effect is poor or the speed is too slow during the adsorption process, the system can also optimize the resin type, quantity or mixing conditions according to these real-time data to improve the sugar reduction efficiency. In this way, step S102 ensures the accuracy and efficiency of the resin adsorption sugar reduction process through precise calculation and real-time monitoring.

[0064] Step S103, setting fermentation parameters, which include temperature, humidity and pH value. During the fermentation process, the change of soluble sugar content is monitored in real time, and the fermentation parameters are adjusted according to the preset fermentation blood sugar reduction control model to obtain the optimal blood sugar reduction effect parameters;

[0065] In step S103, the fermentation parameters are set and the soluble sugar content is monitored in real time, and the parameters are adjusted in combination with the preset fermentation glucose reduction control model to obtain the best glucose reduction effect. The specific steps are as follows:

[0066] Initialization parameters: The controller first sets the fermentation parameters based on empirical values ​​or pre-experimental data. These parameters usually include temperature, humidity and pH value.

[0067] Real-time monitoring: After the fermentation process begins, the controller will monitor the changes in the soluble sugar content in the mixture in real time.

[0068] Model control: Based on the preset fermentation sugar reduction control model, the controller generates a series of fermentation parameter data sets. These parameter sets are used to adjust fermentation conditions such as temperature, humidity and pH.

[0069] Adjusting parameters: The controller uses the parameters in these data sets to adjust the fermentation conditions in sequence, and observes and records the changes in soluble sugar content after each adjustment.

[0070] Effect evaluation: By comparing the blood sugar reduction effects under different parameter combinations, the controller generates a blood sugar reduction effect data set corresponding to the fermentation parameters.

[0071] Screening the best parameters: In the data set of glucose-lowering effects corresponding to fermentation parameters, screen out the data that leads to the best glucose-lowering effect. This set of data is the best glucose-lowering effect parameters.

[0072] Model feedback: After fermentation is completed, in addition to using the optimal blood sugar lowering effect parameters for production, the data from the actual fermentation process (including fermentation parameters, blood sugar lowering effect, etc.) will also be fed back to the preset fermentation blood sugar lowering control model.

[0073] Model optimization: Use this data to verify and optimize the model to improve its accuracy and efficiency in future fermentation processes.

[0074] Through the above steps, the method of the present invention can ensure the best blood sugar lowering effect during the fermentation process, while ensuring the monitorability and repeatability of the entire process.

[0075] Step S104, after the fermentation is completed, sterilization and drying are performed to obtain the processed tomato juice powder, and quality testing is performed on the processed tomato juice powder to obtain the quality test result of the processed tomato juice powder;

[0076] In step S104, after the fermentation is completed, the fermentation product will be sterilized and dried. The specific process is as follows:

[0077] Sterilization method: The controller sends instructions to the sterilization equipment, which sterilizes the fermentation product. The sterilization method here may include high-temperature steam sterilization, ultraviolet sterilization or other physical or chemical sterilization methods, but the specific method is not detailed in the document.

[0078] After the sterilization treatment is completed, the microorganisms in the fermentation products are effectively killed and meet the hygiene standards, providing guarantee for subsequent processing and preservation.

[0079] Drying method: The controller sends instructions to the drying equipment, which dries the sterilized fermentation product. Drying methods may include hot air drying, vacuum drying, etc., the purpose of which is to remove excess moisture from the product and improve the stability and shelf life of the product.

[0080] After the drying process is completed, the processed tomato juice powder is obtained, and its water content is significantly reduced, meeting the requirements of subsequent processing or storage.

[0081] The quality of the processed tomato juice powder was tested. The specific method and results are as follows:

[0082] The controller sends instructions to the quality inspection module, and the quality inspection module uses a predetermined inspection standard and method to inspect the quality of the tomato juice powder. The inspection items may include soluble sugar content, water content, nutrients (such as protein, fat, vitamins, minerals, etc.) and other possible physical and chemical indicators.

[0083] Instruments that may be used in the testing process include saccharimeter, moisture meter, spectrometer, chromatograph, etc. These instruments can accurately measure various quality indicators of the sample.

[0084] After the quality test is completed, the quality test results of the processed tomato juice powder are obtained. These results will be used to evaluate the overall quality and compliance of the product.

[0085] The test results will be compared with the preset standard values ​​to determine whether the product is qualified. If the test data exceeds the preset standard value, a quality failure warning message will be generated to prompt the producer to take corresponding corrective measures.

[0086] In summary, the sterilization and drying treatment in step S104 are key steps to ensure that the fermentation product meets the hygiene and preservation requirements, and quality testing is a necessary means to evaluate product quality. Through these steps, tomato juice powder that meets the quality standards can be produced and then used in the subsequent lycopene production process.

[0087] Step S105, substitute the original data of tomato juice powder, fermentation parameters and the original data of tomato juice powder into the preset quality detection model to obtain the expected quality data, compare the expected quality data with the quality detection result of the processed tomato juice powder to obtain the quality comparison result, compare the quality comparison result with the preset standard value to obtain the quality error, use the mass error to optimize the resin adsorption glucose reduction analysis model and the preset fermentation glucose reduction control model, and use the optimized mass error to optimize the resin adsorption glucose reduction analysis model and the preset fermentation glucose reduction control model to obtain the updated optimal glucose reduction effect parameters and the updated optimal adsorption time, and deploy the optimized resin adsorption glucose reduction analysis model, the optimized preset fermentation glucose reduction control model, the updated optimal glucose reduction effect parameters and the updated optimal adsorption time to the production system to reduce the glucose content of lycopene production raw materials.

[0088] Step S105 involves substituting various data into the preset model for calculation, comparison, and optimization, and finally deploying the optimized model and parameters to the production system. The following is a detailed description of the steps:

[0089] Substitute data into the quality inspection model data preparation:

[0090] The original data of the tomato juice powder is obtained from step S101, including soluble sugar content, water content data, and nutritional component data.

[0091] The fermentation parameters during the fermentation process are obtained in step S103, including temperature, humidity and pH value.

[0092] Substitution model: Substitute the above two types of data into the preset quality detection model. The quality detection model is established based on previous experimental data and can predict the quality parameters of tomato juice powder after specific treatment (such as fermentation, resin adsorption, etc.).

[0093] Calculate expected quality data: The quality detection model calculates the expected quality data based on the input data, including the soluble sugar content, moisture content and nutritional components after processing.

[0094] Specific implementation methods for comparing actual and expected quality data:

[0095] Obtaining actual quality inspection results: In step S104, the quality of the processed tomato juice powder is inspected to obtain actual quality inspection results.

[0096] Comparison: Compare the expected quality data with the actual quality test results to generate quality comparison results, including soluble sugar content comparison results, moisture content comparison results and nutritional components comparison results.

[0097] Specific implementation method of comparing standard value and generating mass error:

[0098] Obtaining standard values: The controller obtains preset standard values, which include standard values ​​for soluble sugar content, standard values ​​for water content, and standard values ​​for nutrients.

[0099] Generate mass error: Compare the mass comparison results with the preset standard values ​​and calculate the mass error of each parameter.

[0100] Optimize model and parameters: Use the mass error data (including experimental condition data, original data and mass error) during the resin adsorption glucose reduction process to verify and optimize the machine learning model (resin adsorption glucose reduction analysis model).

[0101] At the same time, the quality error data in the fermentation experiment, as well as the corresponding fermentation parameters and blood sugar lowering effect data, are used to verify and optimize the preset fermentation blood sugar lowering control model.

[0102] Parameter update: After model optimization, the model is run again using the new mass error data to obtain updated optimal glucose-lowering effect parameters and updated optimal adsorption time.

[0103] Deploy optimized models and parameters: deploy the optimized resin adsorption sugar reduction analysis model, the optimized preset fermentation sugar reduction control model, the updated optimal sugar reduction effect parameters and the updated optimal adsorption time to the production system.

[0104] These optimized models and parameters will be used to guide the sugar-reducing treatment of raw materials in the actual production process to improve the quality and applicability of lycopene products.

[0105] Through the above steps, the present invention realizes the monitoring and optimization of the whole process of raw material hypoglycemic in the lycopene production process, and ensures the quality and stability of the final product.

[0106] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S101:

[0107] The controller sends instructions to the data acquisition module to collect the original data of the tomato juice powder;

[0108] After receiving the instruction, the data acquisition module uses sensors and analytical instruments to measure the soluble sugar content, water content and nutritional data of the tomato juice powder;

[0109] The data acquisition module sends the measured soluble sugar content, water content and nutritional content data of the tomato juice powder back to the controller;

[0110] Sensors and analytical instruments measure:

[0111] Brix meter: used to measure the soluble sugar content;

[0112] Moisture meter: used to measure the moisture content in a substance;

[0113] Spectrometer: used to quickly analyze various chemical components in food, including water, sugar, and nutrients such as protein and fat;

[0114] Chromatography: High performance liquid chromatography is used to determine chemical components, including vitamins, minerals and nutrients;

[0115] Electrochemical sensor: used to measure sugars.

[0116] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S102:

[0117] The controller substitutes the raw data obtained in step S101 into a pre-built resin adsorption glucose reduction analysis model;

[0118] Determine the required resin type and quantity through resin adsorption sugar reduction analysis model calculation;

[0119] The controller sends a command to the resin adding device, and the resin adding device adds the required type and amount of resin to the tomato juice powder to mix the resin with the tomato juice powder;

[0120] During the mixing process, the controller monitors the changes in the soluble sugar content in the mixture in real time through monitoring equipment to determine the optimal adsorption time.

[0121] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S103:

[0122] The controller initially sets fermentation parameters, including temperature, humidity, and pH;

[0123] At the beginning of the fermentation process, the controller monitors the changes in the soluble sugar content in real time. According to the preset fermentation blood sugar reduction control model, the controller generates a fermentation parameter data set, and uses the parameters in the generated fermentation parameter data set to adjust the temperature, humidity and pH value in turn to obtain the fermentation parameter corresponding blood sugar reduction effect data set, and selects the data with the best fermentation effect from the fermentation parameter corresponding blood sugar reduction effect data set as the best blood sugar reduction effect parameter.

[0124] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S104:

[0125] After the fermentation is completed, a fermentation product is obtained, and the controller sends instructions to the sterilization equipment and the drying equipment, and the fermentation product is sterilized and dried by the sterilization equipment and the drying equipment;

[0126] After the sterilization and drying system executes the instructions and completes the sterilization and drying of the fermentation product, the processed tomato juice powder is obtained;

[0127] The controller sends instructions to the quality detection module to perform quality detection on the processed tomato juice powder.

[0128] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S105:

[0129] The controller substitutes the raw data in step S101, the fermentation parameters in step S103 and the processed quality inspection results into a preset quality inspection model;

[0130] The estimated quality data is calculated through the preset quality inspection model, including soluble sugar content, water content and nutritional components;

[0131] The controller compares the expected quality data with the actual quality test results to obtain a quality comparison result, which includes a soluble sugar content comparison result, a water content comparison result, and a nutrient component comparison result;

[0132] The controller obtains the preset standard values, which include the standard value of soluble sugar content, the standard value of water content and the standard value of nutrients.

[0133] If the data in the quality comparison result is higher than the preset standard value, a quality failure warning message will be generated.

[0134] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S105:

[0135] Collect the mass error data in the process of resin adsorption and glucose reduction, including experimental condition data, raw data and mass error. The experimental condition data includes the resin type, quantity and adsorption time, and the raw data includes the soluble sugar content, water content and nutritional components.

[0136] The mass error data of the resin adsorption glucose reduction process was used to verify the machine learning model resin adsorption glucose reduction analysis model, and the optimized resin adsorption glucose reduction analysis model was obtained.

[0137] Specifically, the raw material sugar reduction method in the lycopene production process provided by the present invention comprises, in step S105:

[0138] Collect quality error data in fermentation experiments, as well as corresponding fermentation parameters and hypoglycemic effect data. Fermentation parameters include temperature, humidity and pH value.

[0139] The preset fermentation glucose-lowering control model is verified using the quality error data in the fermentation experiment, as well as the corresponding fermentation parameters and glucose-lowering effect data, to obtain an optimized preset fermentation glucose-lowering control model.

[0140] The technical solution of the present invention solves the problem that it is difficult to monitor the quality of the whole process of raw material sugar reduction in batch production of sugar-reducing lycopene because the sugar-reducing step involves complex processes such as fermentation and separation by the following methods:

[0141] Establish a data monitoring and analysis system: This method collects the original data of tomato juice powder (including soluble sugar content, water content, nutrients, etc.), and uses a pre-built resin adsorption sugar reduction analysis model and a preset fermentation sugar reduction control model to accurately control the raw material sugar reduction process.

[0142] Real-time monitoring and feedback adjustment: During the resin adsorption and fermentation process, the changes in the soluble sugar content in the mixture are monitored in real time, and the fermentation parameters (such as temperature, humidity, pH value) are dynamically adjusted according to the preset model to achieve the best blood sugar lowering effect.

[0143] Quality test results and optimization cycle: After fermentation, the processed tomato juice powder is quality tested and the test results are compared with the expected quality data to generate quality errors. These error data are used to optimize the sugar reduction analysis model and fermentation control model to achieve continuous improvement of the production system.

[0144] Closed-loop quality control: Through repeated quality testing and model optimization, the quality controllability of the entire process of raw material sugar reduction is ensured. The optimized model and parameters are deployed to the production system to improve the stability and reliability of batch production.

[0145] Automation and intelligence: The entire sugar reduction process is automatically executed by the controller, reducing human intervention and improving production efficiency and product consistency. At the same time, machine learning models are used to verify and optimize data, so that the system has self-learning and adaptive capabilities. In addition to real-time monitoring and feedback adjustments, a quality failure early warning mechanism is also set up to ensure that products that do not meet the standards can be discovered and processed in a timely manner.

[0146] The technical solution of the present invention not only solves the quality control problem in batch production, but also improves the blood sugar lowering effect and production efficiency, making the produced blood sugar lowering lycopene more suitable for diabetic patients or people who need to control blood sugar.

[0147] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for reducing the sugar content of raw materials in a lycopene production process, characterized in that: include: Step S101, obtaining raw data of tomato juice powder, the raw data of tomato juice powder including soluble sugar content, water content data, and nutrient component data; Step S102, substituting the raw data of the tomato juice powder into a pre-constructed resin adsorption glucose reduction analysis model, calculating the type and amount of resin required for the resin adsorption glucose reduction, adding the resin to the tomato juice powder and mixing the mixture according to the type and amount of resin required for the resin adsorption glucose reduction, and performing adsorption on the mixture by the resin. When the resin is adsorbing, the change of the soluble sugar content in the mixture is monitored in real time to determine the optimal adsorption time; Step S103, setting fermentation parameters, which include temperature, humidity and pH value. During the fermentation process, the change of soluble sugar content is monitored in real time, and the fermentation parameters are adjusted according to the preset fermentation blood sugar reduction control model to obtain the optimal blood sugar reduction effect parameters; Step S104, after the fermentation is completed, sterilization and drying are performed to obtain the processed tomato juice powder, and quality testing is performed on the processed tomato juice powder to obtain the quality test result of the processed tomato juice powder; Step S105, substitute the original data of tomato juice powder, fermentation parameters and the original data of tomato juice powder into the preset quality detection model to obtain the expected quality data, compare the expected quality data with the quality detection result of the processed tomato juice powder to obtain the quality comparison result, compare the quality comparison result with the preset standard value to obtain the quality error, use the mass error to optimize the resin adsorption glucose reduction analysis model and the preset fermentation glucose reduction control model, and use the optimized mass error to optimize the resin adsorption glucose reduction analysis model and the preset fermentation glucose reduction control model to obtain the updated optimal glucose reduction effect parameters and the updated optimal adsorption time, and deploy the optimized resin adsorption glucose reduction analysis model, the optimized preset fermentation glucose reduction control model, the updated optimal glucose reduction effect parameters and the updated optimal adsorption time to the production system to reduce the glucose content of lycopene production raw materials.

2. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S101, it includes: The controller sends instructions to the data acquisition module to collect the original data of the tomato juice powder; After receiving the instruction, the data acquisition module uses sensors and analytical instruments to measure the soluble sugar content, water content and nutritional data of the tomato juice powder; The data acquisition module sends the measured soluble sugar content, water content and nutritional content data of the tomato juice powder back to the controller; Sensors and analytical instruments measure: Brix meter: used to measure the soluble sugar content; Moisture meter: used to measure the moisture content in a substance; Spectrometer: used to quickly analyze various chemical components in food, including water, sugar, and nutrients such as protein and fat; Chromatography: High performance liquid chromatography is used to determine chemical components, including vitamins, minerals and nutrients; Electrochemical sensor: used to measure sugars.

3. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S102, it includes: The controller substitutes the raw data obtained in step S101 into a pre-built resin adsorption glucose reduction analysis model; Determine the required resin type and quantity through resin adsorption sugar reduction analysis model calculation; The controller sends a command to the resin adding device, and the resin adding device adds the required type and amount of resin to the tomato juice powder to mix the resin with the tomato juice powder; During the mixing process, the controller monitors the changes in the soluble sugar content in the mixture in real time through monitoring equipment to determine the optimal adsorption time.

4. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S103, it includes: The controller initially sets fermentation parameters, including temperature, humidity, and pH; At the beginning of the fermentation process, the controller monitors the changes in the soluble sugar content in real time. According to the preset fermentation blood sugar reduction control model, the controller generates a fermentation parameter data set, and uses the parameters in the generated fermentation parameter data set to adjust the temperature, humidity and pH value in turn to obtain the fermentation parameter corresponding blood sugar reduction effect data set, and selects the data with the best fermentation effect from the fermentation parameter corresponding blood sugar reduction effect data set as the best blood sugar reduction effect parameter.

5. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S104, it includes: After the fermentation is completed, a fermentation product is obtained, and the controller sends instructions to the sterilization equipment and the drying equipment, and the fermentation product is sterilized and dried by the sterilization equipment and the drying equipment; After the sterilization and drying system executes the instructions and completes the sterilization and drying of the fermentation product, the processed tomato juice powder is obtained; The controller sends instructions to the quality detection module to perform quality detection on the processed tomato juice powder.

6. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S105, it includes: The controller substitutes the raw data in step S101, the fermentation parameters in step S103 and the processed quality inspection results into a preset quality inspection model; The estimated quality data is calculated through the preset quality inspection model, including soluble sugar content, water content and nutritional components; The controller compares the expected quality data with the actual quality test results to obtain a quality comparison result, which includes a soluble sugar content comparison result, a water content comparison result, and a nutrient component comparison result; The controller obtains the preset standard values, which include the standard value of soluble sugar content, the standard value of water content and the standard value of nutrients. If the data in the quality comparison result is higher than the preset standard value, a quality failure warning message will be generated.

7. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S105, it includes: Collect the mass error data in the process of resin adsorption and glucose reduction, including experimental condition data, raw data and mass error. The experimental condition data includes the resin type, quantity and adsorption time, and the raw data includes the soluble sugar content, water content and nutritional components. The mass error data of the resin adsorption glucose reduction process was used to verify the machine learning model resin adsorption glucose reduction analysis model, and the optimized resin adsorption glucose reduction analysis model was obtained.

8. The method for reducing the sugar content of raw materials in the lycopene production process according to claim 1, characterized in that: In step S105, it includes: Collect quality error data in fermentation experiments, as well as corresponding fermentation parameters and hypoglycemic effect data. Fermentation parameters include temperature, humidity and pH value. The preset fermentation glucose-lowering control model is verified using the quality error data in the fermentation experiment, as well as the corresponding fermentation parameters and glucose-lowering effect data, to obtain an optimized preset fermentation glucose-lowering control model.

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