A method for screening DES using ANN and COSMO-RS and extracting oil tea polysaccharides based on the DES combined with a double enzyme method

Abstract

The present invention provides a method, device, medium and program product for screening DES using ANN and COSMO‑RS and extracting oil tea polysaccharides based on the DES combined with a dual enzyme method, relating to the field of biological extraction technology. Based on the decomposition model of P‑CL and the chemical potential information of DESs, this application uses COSMO‑RS to predict the relative solubility of P‑CL in DESs, and uses ANN to predict the pH of DESs to screen out the most promising DES combination. For the first time, a composite extraction system of dual enzyme and DES extraction was proposed, and the composite system was optimized using bioinformatics methods such as COSMO‑RS and ANN. This study proposes a treatment method for complex extraction environments, which has broad application prospects in the extraction of natural products.

Description

Technical Field

[0001] The present invention relates to the field of biological extraction technology, and more specifically to a method, device, medium and program product for screening DES using ANN and COSMO-RS and extracting oil tea polysaccharides based on the DES in combination with a double enzyme method. Background Art

[0002] Camellia oleifera polysaccharides (P-CL) are important bioactive compounds with antioxidant, antitumor, and lipid-lowering properties. P-CL can reduce ROS levels, further improving the survival rate of Caenorhabditis elegans under abiotic stress. Therefore, a technology for efficiently extracting active substances from byproducts would benefit the development of the camellia oleifera industry. Recent advances over the past two decades have shown that traditional hot water extraction methods are often combined with multiple techniques, including enzyme extraction, ultrasonic extraction, and microwave extraction, to isolate natural polysaccharides. The yield of Armillaria mellea polysaccharides extracted using enzyme-assisted ultrasonic extraction was 1.64 times and 1.21 times higher than that obtained using enzyme-assisted and ultrasonic extraction methods, respectively.

[0003] In recent years, deep eutectic solvents (DES) have become a preferred alternative to organic solvents in natural product extraction due to their economical and recyclable properties. DES are synthesized from a hydrogen bond donor (HBD) and a hydrogen bond acceptor (HBA) at a specific temperature. The physical properties of DES are influenced by the type, molar ratio, and content of the HBA and HBD components. Its most important characteristic is that its melting point is lower than that of each component, making DES largely liquid at room temperature. Camellia oleifera polysaccharides are primarily present in the cell walls. Traditional hot water extraction struggles to effectively disrupt these walls, limiting their efficiency. Enzymes such as cellulase can help disrupt the cell walls and increase polysaccharide yield. Lentinan was extracted using cellulase, papain, and pectinase, achieving a yield of 15.65%. However, DES and enzyme-assisted extraction methods are rarely used in conjunction with plant polysaccharide extraction because DES contains salts that inactivate cellulases and proteases during extraction. Currently, there is a lack of research methods that simultaneously address the relationship between DES and enzymes and the effect of DES on polysaccharide extraction efficiency. In this study, artificial neural networks (ANN) and a conductor-like screening model for realistic solvents (COSMO-RS) were used to screen the pH and solubility of DES to create a suitable liquid environment for cellulase and protease. Summary of the Invention

[0004] The present invention aims to address at least one of the technical problems existing in the prior art. To this end, the present invention provides a method, apparatus, medium, and program product for screening DES using ANN and COSMO-RS, and extracting polysaccharides from camellia oleifera using a dual-enzyme method based on the DES. The present method utilizes a deep eutectic solvent dual-enzyme system (DES-dEAE) to enable simultaneous enzymatic hydrolysis and DES extraction.

[0005] In a first aspect, the present application discloses a method for screening DES using ANN and COSMO-RS, the method comprising:

[0006] 101, construct a database consisting of sugar, HBA, HBD and DES molecules;

[0007] 102. Analyze the DES molecule using COSMO-RS software to obtain molecular descriptors of the corresponding DES molecule and molecular descriptors of sugars (denoted as Sσ-profiles);

[0008] 103. Inputting the molecular descriptor of the DES molecule into the ANN model to obtain a pH prediction value of the corresponding DES molecule; determining the optimal pH range of the enzyme type used for extracting plant polysaccharides; comparing the pH prediction value with the optimal pH range of the dual-enzyme crossover, and selecting the DES molecule whose pH prediction value is within the optimal pH range as the first candidate DES;

[0009] 104, predicting the activity coefficient of the sugar in the DES using COSMO-RS software based on the molecular descriptor of the sugar, and screening a DES with a low activity coefficient as a second candidate DES;

[0010] 105 , taking the intersection of the first candidate DES and the second candidate DES as the optimal DES; if there is no intersection, taking the DES closest to the first candidate DES and the second candidate DES as the optimal DES.

[0011] In some embodiments, the optimal DES is at least 1;

[0012] Optionally, when the plant polysaccharide is camellia oleifera polysaccharide, the optimal DES is composed of choline chloride (ChCl) and sorbitol (Sor);

[0013] Optionally, the DES molecule is created by a pair of corresponding HBA and HBD molecules in a 1:1 molar ratio.

[0014] In some embodiments, the method for obtaining molecular descriptors in 102 includes: extracting the σ-profile and σ-surface of HBAs and HBDs within the range of ±0.025e / Å as "prf" data using COSMO-RS software, and converting the σ-profile data into molecular descriptors by calculating the integral of the area under the σ-profile curve, which is recorded as Sσ-profiles.

[0015] In some embodiments, the method for constructing the ANN model in 103 includes: obtaining a training data set; the training data range includes temperature, water content, molar ratio, and all measured pressures are 1.01 bar; the σ-profiles of HBA and HBD are divided into 8 regions and the areas of the corresponding regions are integrated; the training data set is input into the nonlinear model constructed by the ANN, and a pH prediction value is output;

[0016] Optionally, the nonlinear model includes 9 input layers (8 discrete Sσ-profiles and temperature), 3 hidden layers and an output layer.

[0017] In some embodiments, between 101 and 102, the method further includes: solving the DES molecule in a water environment at a first temperature (0°C) to obtain a solution result, saving the solution result in a COSMO file format for subsequent calculations; and importing the COSMO file into COSMO-RS software for prediction.

[0018] The second aspect of the present application discloses a method for extracting oil tea polysaccharides based on DES combined with a dual enzyme method, the method comprising:

[0019] 201, drying and crushing the oil tea leaves to obtain oil tea leaf powder;

[0020] 202. Extracting the camellia oleifera powder using an enzyme-assisted extraction method comprising cellulase, protease, and the optimal DES described in the first aspect of the present application to obtain a camellia oleifera polysaccharide extract;

[0021] 203, subjecting the oil tea leaf polysaccharide extract to alcohol precipitation, collecting the precipitate, and freeze-drying to obtain oil tea leaf polysaccharide;

[0022] Optionally, the optimal DES consists of choline chloride and sorbitol.

[0023] In some embodiments, the method further comprises: the DES is a DES containing a first percentage of water; optionally, the first percentage is 20%-40%, preferably 30%;

[0024] Optionally, the response surface methodology is used to optimize the liquid-to-material ratio, enzyme-to-material ratio, extraction time, and water content of the enzyme-assisted DES extraction method.

[0025] A third aspect of the present application discloses a computer device, comprising: a memory and a processor; the memory is used to store a computer program; and the processor executes the computer program to implement the steps of the above method.

[0026] In a fourth aspect, the present application discloses a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned method when the computer program is executed by a processor.

[0027] In a fifth aspect, the present application discloses a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0028] This application has the following beneficial effects:

[0029] 1. This application innovatively discloses a method for screening DESs using ANN and COSMO-RS. This method uses ANN and COSMO-RS to predict the pH and activity coefficient of the DES based on enzyme properties to identify the most suitable DES. When the plant polysaccharide is Camellia oleifera polysaccharide, screening for a DES suitable for both the enzyme and the polysaccharide within the complex extraction system is challenging. This application employs computer-assisted quantitative structure-property relationship (QSPR) modeling to replace experimental screening, linking the molecular structure of the component with its macroscopic physicochemical properties. ANN predicts the pH of the DES based on the QSPR modeling results. COSMO-RS predicts the activity coefficients of Camellia oleifera polysaccharides and the DES. This study is the first to apply solvent property prediction to screen DESs suitable for a dual-enzyme system (dEAE), identifying the most promising DES combination: sorbitol and choline chloride.

[0030] From a computational simulation perspective, the characteristics of the two enzymes were combined, and the intersection of their optimal pH values ​​was used for predictive calculations. This provided a new approach to predicting complex extraction systems. Specifically, the enzyme characteristics were utilized to computationally identify compatible DESs. Furthermore, DES screening was achieved by combining predictions of the solubility of DESs with those of monosaccharides. The following challenges currently hinder the coexistence of DES with enzymes due to their unique physicochemical properties: ① Excessive solvent polarity or interaction with the hydrogen bond network on the enzyme surface may disrupt the enzyme's native conformation, denaturing the enzyme and thus losing its activity. ② Enzymes are very sensitive to pH. Extreme pH conditions can destabilize the enzyme structure or even irreversibly denature it. Furthermore, high ionic strength can shield the interactions between charged groups on the enzyme surface, further disrupting its conformation. ③ Deep eutectic solvents typically have high viscosity, which limits the mass transfer rate between substrate and enzyme, reducing the efficiency of enzyme-catalyzed reactions. The catalytic activity of an enzyme typically depends on the accessibility of substrate molecules near its active site, and increased viscosity reduces the possibility of substrate molecules diffusing to the enzyme's active site. ④ Enzymes typically have both hydrophobic and hydrophilic regions. Components in deep eutectic solvents may bind to the enzyme's hydrophobic regions, disrupting the enzyme's native conformation and even exposing or blocking the active site, thereby affecting the enzyme's catalytic function. This makes the coexistence of DES with enzymes difficult. Consequently, DES and enzymes have traditionally been treated separately before and after use. This work completed the extraction of camellia oleifera polysaccharides by the simultaneous action of DES and dual enzymes from an experimental perspective.

[0031] 2. This application innovatively combines a selected DES with a dual-enzyme method for extracting Camellia oleifera polysaccharides, or expands upon the use of natural polysaccharides, to achieve simultaneous extraction using DES without disrupting the enzymes. This simplifies the current two-step extraction process using DES and enzymes, which requires first disrupting the cell wall with an enzyme and then extracting with DES.

[0032] 3. Since complex polysaccharides are difficult to use for simulation modeling, this application decomposes polysaccharides into basic modules - monosaccharides for modeling simulation. This method greatly reduces the difficulty of modeling and the requirements for polysaccharide structure analysis.

[0033] 4. Both ANN and COSMO-RS in this application are based on QSPR calculations, and a new DES library was established (the library contains the structures, σ data and Si data of HBA, HBD, DES and sugars, all of which are newly synthesized and calculated).

[0034] In summary, this study used a desaturated ethanol (DES)-dehydrogenase (dEAE) system to extract polysaccharides from Camellia oleifera (L.) oil leaves, which was optimized using ANN and COSMO-RS. Based on the decomposition model of P-Cl and the chemical potential of DES, COSMO-RS was used to predict the relative solubility of P-Cl in DES, and ANN was used to predict the pH of the DES. The most promising DES combination was identified: sorbitol and choline chloride. The ChCl-Sor and cellulase-protease extraction system was then optimized to achieve a solid-liquid ratio of 33:1, an enzyme ratio of 3:1, an extraction time of 15 min, and a DES water content of 40%, resulting in an optimized yield of 21.46 mg / g. Furthermore, a cross-sectional comparison of the composition and activity of polysaccharides extracted from water, DES, and enzyme-assisted DES revealed distinct compositions and activities of polysaccharides extracted from enzyme-assisted DES. This study proposes a hybrid extraction system combining dual enzymes and DES, and optimizes this system using bioinformatics methods such as COSMO-RS and ANN. This study proposed a treatment method for complex extraction environments, which has broad application prospects in the extraction of natural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 This is a schematic diagram of the method flow provided by the first aspect of the embodiment of the present invention;

[0037] Figure 2 is a schematic flow chart of the method provided by the second aspect of an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention;

[0039] Figure 4 is a schematic diagram of the architecture of an exemplary computing device provided by an embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of a storage medium provided by an embodiment of the present invention;

[0041] Figure 6 σ-profile and σ-potential diagrams of the six sugars provided in the embodiments of the present invention, wherein: Figure 6 A is the σ-profile diagram, Figure 6 B is the σ-potential graph;

[0042] Figure 7 σ-surface structure diagrams of full sugar, HBA, HBD and DES provided in the embodiments of the present invention;

[0043] Figure 8 : is the activity coefficient prediction diagram of DES and DES containing 30% water at 50°C provided by the embodiment of the present invention; wherein, Figure 8 A is the predicted activity coefficient of 36 DES, Figure 8 B is the predicted activity coefficient of DES containing 30% water; a, D-mannose; b, galacturonic acid; c, galactose; d, glucose; e, L-rhamnose; f, xylose;

[0044] Figure 9 is a discretized Sσ-profile descriptor represented by 8 segments provided by an embodiment of the present invention; wherein, Figure 9 A is acid, Figure 9 B is alcohol, Figure 9 C stands for sugar, Figure 9 D is acetamide, Figure 9 E is water, ChCl and proline;

[0045] Figure 10 Schematic diagram of the experimental and predicted pH values ​​of the ANN model provided in an embodiment of the present invention; Parity diagram during training; wherein, Figure 10 A is the training set, Figure 10 B is the external test set, Figure 10 C stands for totality;

[0046] Figure 11 Schematic diagram of the pH value of predicted DESs provided by an embodiment of the present invention, wherein the red line represents the optimal pH; the black line represents the appropriate pH range;

[0047] Figure 12 is an infrared spectrum of P-CL provided by an embodiment of the present invention;

[0048] Figure 13 : is the apparent structure diagram of P-CLs analyzed by SEM according to an embodiment of the present invention, wherein: Figure 13 A and B are water-extracted tea oil polysaccharides (P-CL-W), Figure 13 C and D are polysaccharides extracted from tea oil leaves from DES (P-CL-D). Figure 13 E and F are polysaccharides extracted from tea oil by DES-dEAE (P-CL-E);

[0049] Figure 14 This is a monosaccharide composition diagram of P-CL provided in an embodiment of the present invention;

[0050] Figure 15 : is a graph showing the in vitro antioxidant activity results of P-CL at different concentrations provided in the examples of the present invention; wherein, Figure 15 A is the DPPH free radical scavenging ability, Figure 15 B is the ABTS free radical scavenging ability, Figure 15 C is the reducing power of iron ions. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0052] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Figure 1 1 is a flow chart of a method for screening DES using ANN and COSMO-RS according to a first aspect of an embodiment of the present invention. Specifically, the method comprises the following steps:

[0055] 101: Construct a database consisting of sugars, HBAs, HBDs, and DES molecules;

[0056] In some embodiments, the DES molecule is created from corresponding HBA and HBD molecules in a 1:1 molar ratio.

[0057] In some embodiments, between 101 and 102, the method further includes: solving the DES molecule in a water environment at a first temperature (0°C) to obtain a solution result, saving the solution result in a COSMO file format for subsequent calculations; and importing the COSMO file into COSMO-RS software.

[0058] 102: Analyze the DES molecule using COSMO-RS software to obtain a molecular descriptor of the corresponding DES molecule and a molecular descriptor of the sugar (denoted as Sσ-profile);

[0059] In some embodiments, the method further comprises: the structures of monosaccharides, HBAs, and HBDs are 3D structures; optimizing the 3D structures of monosaccharides, HBAs, and HBDs, and using COSMOThermX18 (Dassault Systèmes, Fance) to calculate the σ-profile and σ-surface of each DES molecule.

[0060] In some embodiments, the method for obtaining the molecular descriptor in 102 includes: extracting the σ-profile and σ-surface of HBAs and HBDs within the range of ±0.025e / Å as "prf" data using COSMO-RS software, and converting the σ-profile data into molecular descriptors by calculating the integral of the area under the σ-profile curve, which is recorded as Sσprofiles.

[0061] 103: inputting the molecular descriptor of the DES molecule into the ANN model to obtain a pH prediction value of the corresponding DES molecule; determining an optimal pH range of the enzyme type used for extracting plant polysaccharides; comparing the pH prediction value with the optimal pH range, and selecting the DES molecule whose pH prediction value is within the optimal pH range as a first candidate DES;

[0062] In some embodiments, when the plant polysaccharide is tea leaf polysaccharide, the types of enzymes are cellulase and protease, the optimal pH of protease is 2-5, the optimal pH of cellulase is 4.5-6, and the intersection of the optimal pH of protease and cellulase is the optimal pH; DES with a predicted pH value between 4.5-5 is used as a candidate DES.

[0063] In some embodiments, the method for constructing the ANN model in 103 includes: obtaining a training dataset; the training data range includes temperature (358.15-293.15K), water content (0.18-26.7%), molar ratio (9:1-1:16), and all measured at a pressure of 1.01 bar; processing the HBA and HBD σ-profiles using 8 segments and integrating them across the entire region; inputting the training dataset into a nonlinear model constructed by the ANN, and outputting a pH prediction value. Optionally, the nonlinear model includes 9 input layers, 8 discrete Sσ-profiles and temperature, 3 hidden layers, and an output layer. ANNs are a type of neural network based on a feed-forward strategy. This is so-called because they continuously pass information through nodes until the information reaches the output node. This is also known as the simplest type of neural network.

[0064] 104: predicting the activity coefficient of the sugar in the DESs using COSMO-RS software according to the molecular descriptor of the sugar, and screening a DES with a low activity coefficient as a second candidate DES;

[0065] In some embodiments, COSMO-RS software was used to predict the activity coefficients of six common polyphenols in DESs in infinitely diluted DES at 50°C, and the tests were performed at a water content of 30% (v / v). For any compound, its relative solubility in the solvent is inversely proportional to its activity coefficient in the system, so the higher the activity coefficient of the polysaccharide in the DES, the lower the solubility. The purpose is to find a DES with higher solubility.

[0066] 105: Take the intersection of the first candidate DES and the second candidate DES as the optimal DES; if there is no intersection, take the DES closest to the first candidate DES and the second candidate DES as the optimal DES.

[0067] In some embodiments, the optimal DES is at least 1;

[0068] Optionally, when the plant polysaccharide is camellia oleifera polysaccharide, the optimal DES is composed of choline chloride and sorbitol.

[0069] Figure 2 1 is a flow chart of a method for extracting oil tea polysaccharides based on DES combined with a dual enzyme method according to a first aspect of an embodiment of the present invention. Specifically, the method comprises the following steps:

[0070] 201, drying and crushing the oil tea leaves to obtain oil tea leaf powder;

[0071] In some embodiments, between 201 and 202, the method further comprises: removing pigments, lipids, and saponins from the oil-tea camellia powder using petroleum ether and ethanol by Soxhlet extraction; and then obtaining oil-tea camellia polysaccharides (P-CL) by hot water extraction, DES extraction, and enzyme-assisted DES extraction.

[0072] 202. Extracting the camellia oleifera powder using an enzyme-assisted extraction method comprising cellulase, protease, and the optimal DES described in the first aspect of the present application to obtain a camellia oleifera polysaccharide extract;

[0073] 203, subjecting the oil tea leaf polysaccharide extract to alcohol precipitation, collecting the precipitate, and freeze-drying to obtain oil tea leaf polysaccharide;

[0074] In some embodiments, the optimal DES consists of choline chloride and sorbitol.

[0075] In some embodiments, the method further comprises: the DES is a DES containing a first percentage of water; optionally, the first percentage is 20%-40%, preferably 30%, which can effectively improve the viscosity of the DES and reduce the amount of DES used;

[0076] Optionally, the response surface methodology is used to optimize the liquid-to-material ratio, enzyme-to-material ratio, extraction time, and water content of the enzyme-assisted DES extraction method, with the liquid-to-material ratio being 33:1; the enzyme-to-material ratio being 3:1, the extraction time being 15 min, and the water content being 40%.

[0077] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present invention, such as Figure 3 As shown, the device may include: one or more processors, and one or more memories; wherein the memories store computer-readable codes, and when the computer-readable codes are run by the one or more processors, the method described above may be executed.

[0078] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. It can implement or execute the various methods, operations, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor, including an X86 architecture or an ARM architecture.

[0079] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0080] For example, the method or apparatus according to the embodiment of the present disclosure may also be implemented by Figure 4 The architecture of the computing device 3000 shown in FIG. Figure 4 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage device in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the method provided in the present disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 4 One or more components of a computing device are shown.

[0081] The embodiment of the present invention further provides a computer-readable storage medium, such as Figure 5As shown, it is a schematic diagram of a storage medium provided in an embodiment of the present invention, and computer-readable instructions 4010 are stored on the computer storage medium 4020. When the computer-readable instructions 4010 are executed by the processor, the method according to the embodiment of the present disclosure described with reference to the above figures can be executed. The computer-readable storage medium in the embodiment of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0082] The embodiments of the present disclosure further provide a computer program product or system, including a computer program, which implements the steps of the above method when executed by a processor.

[0083] In some embodiments, this embodiment further discloses a system for screening DES using ANN and COSMO-RS, the system comprising:

[0084] a database construction module, configured to or configured to construct a database consisting of monosaccharides, HBAs, HBDs, and DES molecules;

[0085] a molecular descriptor acquisition module, configured or configured to analyze the DES molecule using COSMO-RS software to obtain molecular descriptors of the corresponding DES molecule and molecular descriptors of sugars (denoted as Sσ-profiles);

[0086] A pH prediction value screening module is used or configured to input the molecular descriptor of the DES molecule into the ANN model to obtain the pH prediction value of the corresponding DES molecule; determine the optimal pH range of the enzyme type used for extracting plant polysaccharides; compare the pH prediction value with the optimal pH range, and select the DES molecule whose pH prediction value is within the optimal pH range as the first candidate DES;

[0087] an activity coefficient screening module, configured to predict the activity coefficient of the sugar in DESs using COSMO-RS software based on the molecular descriptor of the sugar, and screen DES with a high activity coefficient as a second candidate DES;

[0088] The optimal DES acquisition module is used or configured to take the intersection of the first candidate DES and the second candidate DES as the optimal DES; if there is no intersection, take the DES closest to the first candidate DES and the second candidate DES as the optimal DES.

[0089] In some embodiments, this embodiment further discloses a system for extracting oil tea polysaccharides based on DES combined with a dual enzyme method, the system comprising:

[0090] The oil tea leaf crushing processing module is used for or configured to dry and crush the oil tea leaves to obtain oil tea leaf powder;

[0091] a camellia oleifera polysaccharide extract processing module, configured to extract the camellia oleifera powder using an enzyme-assisted extraction method comprising cellulase, protease, and the optimal DES described in the first aspect of the present application to obtain a camellia oleifera polysaccharide extract;

[0092] The oil tea polysaccharide processing module is used for or configured to collect the precipitate after alcohol precipitation of the oil tea polysaccharide extract, and freeze-dry to obtain oil tea polysaccharide. Specific embodiments

[0094] 1. Materials and Methods

[0095] 1.1. Materials and Reagents: Camellia oleifera was collected in Ya'an, Sichuan, China (102.75°, 30.06°) in August 2023. All samples were immediately lyophilized and stored sealed at −20°C for future use. Cellulase, protease, 1-phenyl-3-methyl-5-pyrazolone (PMP), monosaccharide standards (T6-T2000), dextran standards, TPTZ, DPPH, and ABTS were purchased from Sigma-Aldrich Co., Ltd. Choline chloride (ChCl, ≥98%), sorbitol (Sor, ≥98%), proline (Proline, ≥98%), formic acid (FA, ≥98%), propionic acid (PA, ≥98%), lactic acid (LA, ≥98%), acetic acid (AA, ≥98%), malonic acid (MA, ≥98%), 2-propylene glycol (PG, ≥98%), glycerol (Gly, ≥98%), xylitol (Xyl, ≥98%), ethylene glycol (EG, ≥98%), glucose (Glu, ≥98%), fructose (Fru, ≥98%), maltose (Mal, ≥98%), sucrose (Suc, ≥98%), urea (Ure, ≥98%), methylurea (Met, ≥98%), acetamide (Ace, ≥98%), and ethanolamine (Eth, ≥98%) were purchased from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China).

[0096] 1.2. Synthesis of DES: Place HBA and HBD in a 65°C drying oven to remove moisture. Weigh the raw materials in a 1:1 molar ratio and thoroughly mix in an 80°C water bath until a homogeneous, transparent solution is formed. Cool naturally to room temperature and set aside.

[0097] 1.3. Prediction method:

[0098] 1.3.1. Preparation of molecules and molecular descriptors: A molecular database consisting of 6 sugars, 2 HBAs, 18 HBDs and 36 DESs was established. The 3D structures of sugars, HBAs and HBDs were downloaded from the PUBCHEM database in SDF format and constructed into 3D molecules in the software Materials studio2020. DES molecules were created by molecular pairs of corresponding HBA and HBD molecules in a 1:1 molar ratio. The 3D structure was optimized at the density functional level using the DMol3 module, using GGA-BLYP as the generalized gradient approximation and DNP4.4 as the numerical basis set for determining the total energy of the system and solving the self-consistent field. The calculated SCF tolerance was set to 10 -6 The COSMO module is used to solve the molecules in a 0°C water environment, and the results are used in the COSMO file format for subsequent calculations.

[0099] The generated COSMO files were imported into the COSMO-RS software COSMothermX18 (Dassault Systèmes, France). Within the software, the σ-profile and σ-surface data for the HBA and HBD within ±0.025 e / Å were automatically extracted as "prf" data. The σ-profile data were then converted into molecular descriptors, denoted as Sσ-profiles, by calculating the integral of the area under the σ-profile curve. In a sense, the discretization of the σ-profile curve into eight Sσ-profile descriptors was established. The Sσ-profile descriptors were calculated for each HBA and HBD, and the Sσ-profile for the DES was calculated as follows:

[0100]

[0101] Among them S i is the descriptor in region i(e / Å2), x HBA 、x HBD and xH2O are the mole fractions of HBA, HBD, and water, respectively. In this study, the σ-profiles of DES molecular pairs were not directly generated as molecular descriptors. This approach increases the flexibility of DES design, allowing the molar ratios of HBA, HBD, and water to be varied arbitrarily. Furthermore, this approach significantly reduces the workload, allowing the creation of molecular descriptors for a large number of corresponding DESs.

[0102] 1.3.2. COSMO-RS Simulation: The geometry-optimized structures were imported into COSMOThermX18 (Dassault Systèmes, Fance) to calculate the σ-profile and σ-surface of each molecule. For any compound, its solubility in a solvent is inversely proportional to its activity coefficient in the system. Therefore, COSMO-RS was used to predict the activity coefficients of six common polyphenols in an infinitely diluted DES at 50°C and tested at a water content of 30% (v / v).

[0103] 1.3.3. ANN: A feedforward ANN was used to exploit robust nonlinear correlations between descriptors and the pH of the DES. The network consists of multiple processing elements, represented as "neuron nodes." Neurons are interconnected via direct communication activation functions, which contain the information needed to generate the output. The hyperbolic tangent sigmoid activation function for each hidden neuron (Hk) can be calculated as follows:

[0104]

[0105] The tanh activation function converts the Yk values ​​to between -1 and 1. Yk is a linear combination of the inputs associated with hidden neuron k and can be calculated as follows:

[0106]

[0107] Where Wk,input represents the weight coefficient of the link between each input and hidden neuron k, and bk represents the intercept bias of hidden neuron k. The neural network was developed using the Neural Network Toolbox in JMP statistical software. The discretized Sσ-profile descriptor and temperature were selected as inputs, while the pH value of the DES was selected as the output. The learning rate of the network was fixed at 0.1, a squared penalty was used, and a 25% holdout fraction was used for internal cross-validation.

[0108] 1.4 Extraction of Camellia oleifera Polysaccharides: The collected Camellia oleifera leaves were dried in a 65°C oven and then pulverized into powder. Soxhlet extraction with petroleum ether and ethanol was used to remove pigments, lipids, saponins, and other substances from the Camellia oleifera powder. Camellia oleifera polysaccharides (P-CL) were then extracted by hot water extraction, DES extraction, and enzyme-assisted DES extraction (cellulase and protease). The polysaccharide aqueous solution was mixed with Sevag reagent (n-butanol:chloroform = 1:4, v / v) in a 1:3 ratio to remove denatured proteins from the P-CL. These solutions were dialyzed (3500 Da) for 3 days, evaporated, concentrated, and freeze-dried. The freeze-dried samples obtained from water extraction, DES extraction, and enzyme-assisted DES extraction were designated P-CL-W, P-CL-D, and P-CL-E, respectively.

[0109] 1.5. Chemical Composition Determination: Total carbohydrates were determined using the phenol-sulfuric acid method. Protein content was determined using the Coomassie brilliant blue method. Uric acid content was determined using the m-hydroxybiphenyl colorimetric method.

[0110] 1.6. Response surface optimization:

[0111] Single-factor evaluation: Liquid-to-solid ratio (A, 5:1-40:1 mL / g), DES moisture content (B, 25%-65% v / v), sonication time (C, 5-40 min), and PKFs yield were used as evaluation indicators to evaluate the cellulose-to-proteinase ratio (D, 4:1-1:3 mg / mg).

[0112] 1.6.2. Response Surface Optimization: Based on a Box-Behnken design (BBD), Design-Expert 13 software was used to design and analyze a four-factor (AD) and three-level response surface methodology (RSM). The design results are shown in Table 1. Appropriate AD values ​​were selected based on the results of a single-factor experiment using P-CL yield as the response.

[0113] Table 1 Response surface analysis factors and their levels

[0114]

[0115] 1.7. Structural characterization:

[0116] 1.7.1. Fourier Transform Infrared (FT-IR) Analysis: Camellia oleifera polysaccharides were placed in an oven at 60°C for 12 hours to remove moisture. 2 mg of polysaccharide was mixed with KBr, crushed, and pressed into 1.0 mm thick tablets. FT-IR spectrometry was used to analyze the polysaccharides at 400–4000 cm -1 Scan the sample within the range.

[0117] 1.7.2 Monosaccharide Composition Analysis: 5 mg of polysaccharide was dissolved in 3 mL of 3 mol / L trifluoroacetic acid (TFA). The ampoule containing the sample was sealed and placed in a 100°C water bath for 10 h. The hydrolyzate was dried using a vacuum rotary evaporator and washed three times with methanol to remove residual TFA. The cleaved polysaccharide fragments were then dissolved in 3 mL of ddH2O, followed by the addition of 0.5 mol / L PMP reagent and 0.3 mol / L NaOH at 70°C. After 40 min, the excess NaOH was neutralized with 0.3 mol / L HCl, and the aqueous layer was collected with CH3Cl and filtered through a 0.22 μm filter. 10 mg of monosaccharide sample was dissolved in deionized water and diluted to a 1.0 mg / mL monosaccharide standard solution.

[0118] Chromatographic analysis was performed using a ThermoICS5000+ ion chromatography system (ICS5000+, Thermo Fisher Scientific, USA) with a ZorbaxSB-C18 (150 × 4.6 mm) liquid chromatography column and a 20 µL injection volume. Mobile phases A (PBS) and B (acetonitrile, 85:15, v / v) were used at a column temperature of 30°C.

[0119] 1.7.3 Molecular Weight Determination: Weigh 50 mg of each dextran standard and camellia oleifera polysaccharide, dissolve in deionized water, dilute to volume in a 10 mL volumetric flask, and filter through a 0.22 μm filter. This analysis was performed using a Thermo ICS5000+ ion chromatography system (ICS5000+, Thermo Fisher Scientific, USA) using a PLaquagel-OH column (7.5 × 300 μm). The injection volume was 20 μL. The mobile phase was NaCl, and the column temperature was 30°C.

[0120] 1.7.4 Surface morphology: Samples were observed using a scanning electron microscope (SEM; Gemini, Germany) under high vacuum conditions at an accelerating voltage of 5 kV. 145 Images were taken at magnifications of 180x, 500x, and 3000x, respectively.

[0121] 1.8. In vitro antioxidant activity of Camellia oleifera polysaccharides:

[0122] 1.8.1. DPPH Radical Scavenging Ability: Dissolve P-CL in solutions of varying concentrations (0.1-2 mg / mL). Use vitamin C (VC) as a positive control, and deionized water instead of the polysaccharide solution as a blank control. Mix the sugar solution with a 0.2 mmol / L DPPH ethanol solution in a ratio of 1:8. Incubate at room temperature in the dark for 15 minutes, and measure the absorbance of the solution at 517 nm. Calculate the DPPH radical scavenging rate (%) = (A2 - A1) / A2 × 100%; where A1 is the absorbance of the sample group and A2 is the absorbance of the blank control.

[0123] 1.8.2. ABTS Free Radical Scavenging Activity: Dissolve polysaccharide (P-CL) in solutions of varying concentrations (0.1-2 mg / mL). Use VC as a positive control, and deionized water instead of the polysaccharide solution as a blank control. Mix the sugar solution with the pre-prepared ABTS working solution at a ratio of 1:40. After homogenization, incubate the working solution in the dark at room temperature for 10 minutes, and measure the absorbance at 734 nm. Calculate the ABTS free radical scavenging rate (%) as follows: (A2 - A1) / A2 × 100%; where A1 is the absorbance of the sample group and A2 is the absorbance of the blank control group.

[0124] 1.8.3. Total Antioxidant Capacity (FRAP): Dissolve polysaccharide (P-CL) in solutions of varying concentrations (0.1-2 mg / mL). Use VC as a positive control, and deionized water instead of the polysaccharide solution as a blank control. Mix the sugar solution with the pre-prepared FRAP working solution at a ratio of 1:10. Allow the working solution to react uniformly at room temperature in the dark. After 30 minutes, measure the absorbance at 593 nm. Use ferrous sulfate-anhydrous ethanol solution as the standard solution to generate a standard curve. The absorbance corresponding to 1 mmol / L FeSO₄ is defined as the FRAP value.

[0125] 1.9. Statistical Analysis: All experiments were performed in triplicate. Data were analyzed for variance using SPSS (SPSS 29, IBM, USA). One-way ANOVA and Tukey's test were used to test for significant differences (P < 0.05). Graphs were generated using Origin 2024. Data are presented as mean ± SD based on three replicates.

[0126] 2 Results:

[0127] 2.1. Extraction system prediction:

[0128] 2.1.1 Model Construction: The construction of models and molecular descriptors for DES and camellia oleifera polysaccharides is a prerequisite for prediction. The DES molecular model was constructed following the minimum energy principle. HBA and HBD molecules were docked at a fixed molar ratio. In DES systems, different HBA:HBD ratios, such as 1:1, 1:2, and 1:3, can be used, depending on molecular size and the number of hydrogen bond donor and acceptor sites. However, in this study, a 1:1 ratio was selected for all DES configurations because this facilitates comparative analysis of the roles played by HBA and HBD in the DES during COSMO-RS and ANN analysis. This strategy efficiently generated all the desired DES molecules, facilitating subsequent prediction calculations. COSMO-RS describes the distribution of molecular surface polarity and presents it in the form of a σ-surface, a σ-profile, and a σ-potential. The σ-surface is considered a mapping of the σ-profile in space, constructed by assigning each electric field intensity value in the σ-profile to a corresponding position in space. Furthermore, the σ-potential is the integral of the σ-profile and displays the overall electric field intensity response. Sugar σ-profile and σ potential Figure 6 The σ-profile and σ-potential distribution are divided into three regions, namely the hydrogen bond region (σ<-0.0082eÅ-2), the non-polar region (-0.0082eÅ-2<σ<0.0082eÅ-2) and the hydrogen bond acceptor region (σ>0.0082eÅ-2), which correspond to the blue, green and red blocks of the σ-surface, respectively, as shown in Figure 7 As shown. Figure 6 As shown in A, all six sugars are polar, which means they have good solubility in water, so polar DES were selected.

[0129] 2.1.2 COSMO-RS relative solubility prediction results: The activity coefficients of 6 sugar models in 36 DES at 50°C were calculated. The results are as follows: Figure 8 As shown in Figure 2. Since the activity coefficient is inversely proportional to the relative solubility, the higher the activity coefficient of the polysaccharide in DESs, the lower the solubility. Figure 8 As shown in Figure A, the relative solubility of the six monosaccharides in DESs is similar, corresponding to their similar σ curves. It is clearly found that the solubility of monosaccharides in ChCl-based DESs is superior to that in proline-based DESs. Furthermore, amine- and amide-based DESs outperform the other HBD-based DESs, while sugar-based DESs lag behind them. Figure 8 In Example B, the presence of 30% water in the DES system reduces the upper and lower solubility limits, but the impact is minimal. Adding water to DES effectively improves its viscosity and reduces its dosage. From a production perspective, this is both important and economical.

[0130] Overall, because the prediction targets monosaccharides rather than entire polysaccharides, there is still a discrepancy between the predicted results and the actual extraction performance. However, it clearly demonstrates that ChCl-based DESs are a better choice than proline-based DESs. Similarly, amino- and amide-based DESs, as well as sugar-based DESs, are prioritized in subsequent screening.

[0131] 2.1.3 ANN: A dataset of 648 training data points was collected from the literature. The training data ranged from temperature (358.15-293.15K), water content (0.18-26.7%), and molar ratio (9:1-1:16), and all were measured at a pressure of 1.01 bar. In addition, the σ-profiles of HBA and HBD were processed using 8 segments and integrated over the entire region. The results are shown in Figure 2. Figure 9 and as shown in Table 2.

[0132] Table 2 ANN test library;

[0133]

[0134]

[0135] A nonlinear model was constructed using ANN, which had 9 input layers, 8 discrete Sσ-profiles and temperature, and 3 hidden layers (containing 10 neurons) and pH as output. Model ( Figure 10 ) of the regression coefficient (R 2 ) is greater than 0.99, which has good prediction performance. The prepared data is introduced into the model to obtain the pH prediction value of DES in the experiment ( Figure 11 The optimal pH values ​​for the proteases and cellulases used in this experiment are 2-5 and 4.5-6, respectively. Therefore, the pH of the DES used in this experiment should be between 4.5 and 5. The predicted pH for ChCl-Sor is 4.58, which is similar to existing measured results. Furthermore, the pH values ​​for ChCl-Glg, ChCl-Xyl, ChCl-EG, and ChCl-Ace are close to their optimal pH values, which is also consistent with literature.

[0136] Overall, combined with the prediction results of COSMO-RS, DES32 is the most suitable DES for use in a dual-enzyme system.

[0137] 2.2. Comparison of Chemical Composition: Results indicate that the extraction method has a certain influence on the chemical composition of crude polysaccharides from Camellia oleifera, as shown in Table 3. The enzyme-assisted DES extraction method exhibited a high extraction efficiency, extracting 78.27% of carbohydrates. Furthermore, this method significantly reduced the protein content in the polysaccharides, reaching a low of 4.02%. The protein-to-total sugar ratio was only 1:19.47, significantly lower than the ratios of 1:3.35 and 1:3.21 observed with the first two methods. These results demonstrate that the enzyme-assisted DES extraction method outperformed the other two extraction methods in terms of both polysaccharide extraction efficiency and quality.

[0138] Table 3 Chemical composition of P-CL. Different letters in the table indicate significance, P < 0.05.

[0139]

[0140] Structural characterization and analysis of P-CL

[0141] 2.3.1. Structural characterization of P-CL obtained by different extraction methods: Figure 12 As shown in the figure, the polysaccharide of Camellia oleifera has typical infrared spectral characteristics of polysaccharide. -1 There is an OH stretching vibration peak near 2931 cm -1 The CH stretching vibration peak near 1620 cm -1 Asymmetric C=O stretching vibration peak near 1421 cm -1 There is a carboxyl OH vibration absorption peak nearby; the COH and COC glycosidic bond absorption peaks of the pyranose ring are at 1088 cm -1 and 1041 cm -1 The weak absorption peaks of β-glycosidic bond and pyranose ring are located at 600-800 cm -1 Area. 1620 cm -1 The absorption peak near 1620.27 cm corresponds to the main chain vibration of amino acid residues. -1 The absorption peak at shows a smaller vibration amplitude, which indicates a lower protein content.

[0142] SEM results showed that the apparent structures of P-CLs were significantly different. Figure 13 As shown, P-CL-W is an irregular sheet with a hollow core. P-CL-D has denser pores, likely due to DES disrupting the spatial structure of the polysaccharide and reducing aggregation. P-CL-E is a smooth, intact sheet, similar to existing research. The enzyme complex effectively destroys the cell walls of tea oil tea cells, releasing the plant polysaccharides within them and strengthening their structure.

[0143] 2.3.2. Molecular Weight Detection: Based on the different retention times of dextran of varying molecular weight on the chromatographic column, a linear relationship was established between retention time and the logarithm of dextran molecular weight. The molecular weight distribution and composition ratio of the three P-CLs were further calculated using fitted linear equations (Table 4). The results showed that the molecular weight distribution of the three P-CLs ranged from 6 to 106 kDa and was unevenly distributed. Compared with P-CL-W, both P-CL-D and P-CL-E contained additional components with molecular weights of 20 to 30 kDa, accounting for 11.2% and 20.46%, respectively, indicating that P-CL-W has higher purity. Furthermore, the maximum molecular weight of the components extracted by enzyme-assisted DES was lower than that of the components extracted by the other two methods.

[0144] Table 4 Molecular weight and composition ratio of P-CL.

[0145]

[0146] 2.3.3. Monosaccharide composition analysis: as shown in Table 5 and Figure 14 As shown, the P-CL obtained by the three extraction methods is composed of mannose, galactose, glucuronic acid, glucose, xylose, and rhamnose, but the relative content of each monosaccharide varies. P-CL-W has higher levels of xylose, galactose, and glucose. P-CL-D has higher levels of xylose, galactose, and rhamnose. In contrast, P-CL-E has higher contents of xylose, galactose, and glucose. P-CL-W and P-CL-D both have high relative xylose contents, exceeding 40%. In contrast, P-CL-E has lower xylose content but higher glucose content, at 24.17% and 15.60%, respectively. The differences in the relative monosaccharide contents of the three camellia oil polysaccharides may explain the observed differences in their bioactivities.

[0147] Table 5 Monosaccharide composition and relative content of P-CL (%).

[0148]

[0149] Optimization of Extraction Conditions: To further improve polysaccharide extraction yield, we analyzed the effects of liquid-to-solid ratio (A, 1:5-1:50 mL / g), enzyme ratio (B, 4:1–1:3 g), extraction time (C, 5-45 min), and water content (D, 25–65%) on polysaccharide extraction yield. Based on these results, a solid-to-liquid ratio of 1:20-1:40, enzyme ratio of 2:1-4:1, extraction time of 10-20 min, and water content of 25-45% were selected for RSM optimization. Twenty-nine four-factor, three-level BBD experiments were conducted to account for the interaction between the four independent variables and identify the optimal extraction conditions. Second-order polynomial regression and statistical analysis of variance were performed using Design Expert 8.06 software. The causal relationship between the response variable and the four independent variables is described as follows: Y=21.20+1.25A+0.25B+2.333C+2.10D-1.55AB-1.76AC+0.24AD+0.81BC-1.36BD+0.42CD-2.53A 2 -2.63B 2 -1.25C 2 -2.47D 2 ;

[0150] The variance, goodness of fit, and adequacy analysis of the model were summarized. The F value was 32.03, confirming the high significance of the model. The adjusted value of the correlation coefficient (R2) (R 2 adj) and coefficient of variation (CV%) show the goodness of fit. 2 is 0.9697, indicating that the model can explain 96.97% of the total variation, R 2 The adj value was 0.9395, indicating that the model predicted most of the variation in extraction rate. Three-dimensional (3D) response surface plots and two-dimensional (2D) response contour plots are useful tools for visually representing the interaction between two independent variables. Specifically, 3D response surface plots effectively illustrate the relationship between the independent and response variables, while 2D response contour plots provide further insight into the interrelationships between the independent variables and assess the significance of their interactions. Notably, circular response contour plots indicate insignificant interactions between the corresponding variables, while elliptical response contour plots indicate significant interactions.

[0151] 2.5. In vitro antioxidant activity: Natural polysaccharides from plants are considered promising candidates for antioxidant applications. Although their free radical scavenging ability is generally lower than that of small molecule antioxidants such as polyphenols, carotenoids, and vitamins, these biomacromolecules effectively stabilize free radicals by donating hydrogen atoms or transferring electrons, thus exhibiting significant in vitro antioxidant activity. +, FRAP was used to evaluate the in vitro antioxidant effect of P-CL.

[0152] 2.5.1.DPPH scavenging ability of P-CL: Figure 15 As shown in Figure A, all three polysaccharides extracted from tea oil leaves by these three methods exhibited DPPH radical scavenging activity. Their scavenging activity gradually stabilized at a polysaccharide concentration of 0.1 mg / mL. The scavenging rates for P-CL-E were 37.5%, indicating no significant differences in their DPPH radical scavenging abilities. Similar results were found in previous studies, with P-CL-W exhibiting a DPPH radical scavenging activity of 35% at 2 mg / mL.

[0153] 2.5.2. ABTS of P-CL + The cleaning ability: Figure 15 As shown in Figure B, P-CL exhibited excellent ABTS·+ scavenging activity, which was positively correlated with polysaccharide concentration. At a concentration of 2.0 mg / mL, the scavenging rates of tea polysaccharides against ABTS·+ free radicals reached 82.5% (water extraction), 51.3% (DES extraction), and 73.2% (enzyme-assisted DES extraction), respectively. Their IC50 values ​​were 0.829, 2.045, and 1.038 mg / mL, respectively. The results showed that P-CL-W had the greatest ABTS·+ scavenging activity, followed by P-CL-D, and finally P-CL-E. This result may be due to the lowest purity of P-CL-W. The samples may have been contaminated with small molecules with strong antioxidant activity, which increased their ABTS·+ scavenging capacity.

[0154] 2.5.3. Total antioxidant capacity: Compare the iron ion reducing capacity of P-CL, and then convert the total antioxidant capacity of P-CL into FRAP value for comparison. Figure 15 As shown in Figure C, P-CL extracted using all three methods exhibited a certain level of total antioxidant capacity, which increased with increasing polysaccharide concentration. P-CL-W and P-CL-D exhibited similar total antioxidant capacities. Furthermore, at the same concentration, their performance was superior to that of P-CL-E. This trend is closely related to enzyme-assisted extraction. Figure 15 C showed that the iron chelation activity of P-CL-E was unstable and increased proportionally with increasing concentration, indicating that the enzymatically hydrolyzed Camellia oleifera cell walls were unstable. Identifying a more stable enzymatic hydrolysis system has great potential. Overall, P-CL has the potential to be a natural antioxidant.

[0155] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0156] In general, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device. When various aspects of the disclosed embodiments are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0157] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0159] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0160] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0161] The exemplary embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will appreciate that various modifications and combinations may be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for screening DES using ANN and COSMO-RS, characterized in that: The method comprises: 101, construct a database consisting of sugar, HBA, HBD and DES molecules; 102, analyzing the DES molecule using COSMO-RS software to obtain molecular descriptors of the corresponding DES molecule and sugar; 103. Inputting the molecular descriptor of the DES molecule into the ANN model to obtain a pH prediction value of the corresponding DES molecule; determining the optimal pH range of the enzyme type used for extracting plant polysaccharides; comparing the pH prediction value with the optimal pH range, and selecting the DES molecule whose pH prediction value is within the optimal pH range as a first candidate DES; 104, predicting the activity coefficient of the sugar in DESs using COSMO-RS software based on the molecular descriptor of the sugar, and selecting a DES with a high activity coefficient as a second candidate DES; 105 , taking the intersection of the first candidate DES and the second candidate DES as the optimal DES; if there is no intersection, taking the DES closest to the first candidate DES and the second candidate DES as the optimal DES.

2. The method for screening DES using ANN and COSMO-RS according to claim 1, characterized in that: The optimal DES is at least one.

3. The method for screening DES using ANN and COSMO-RS according to claim 2, characterized in that: When the plant polysaccharide is camellia oleifera polysaccharide, the optimal DES is composed of choline chloride and sorbitol.

4. The method for screening DES using ANN and COSMO-RS according to claim 2, characterized in that: The DES molecules are created from corresponding HBA and HBD molecules in a 1:1 molar ratio.

5. The method for screening DES using ANN and COSMO-RS according to claim 1, characterized in that: The method for obtaining the molecular descriptor in 102 includes: extracting the σ-profile and σ-surface of HBAs and HBDs within the range of ±0.025e / Å using COSMO-RS software as "prf" data, and converting the σ-profile data into a molecular descriptor by calculating the integral of the area under the σ-profile curve, which is recorded as Sσ-profile.

6. The method for screening DES using ANN and COSMO-RS according to claim 1, characterized in that: The method for constructing the ANN model in 103 includes: obtaining a training data set; the training data range includes temperature, water content, molar ratio, and all measured pressures are 1.01 bar; the σ-profiles of HBA and HBD are divided into 8 regions and the areas of the corresponding regions are integrated; the training data set is input into the nonlinear model constructed by the ANN, and the pH prediction value is output.

7. The method for screening DES using ANN and COSMO-RS according to claim 6, characterized in that: The nonlinear model includes 9 input layers, 3 hidden layers and an output layer.

8. The method for screening DES using ANN and COSMO-RS according to claim 1, characterized in that: Between 101 and 102, the method further includes: solving the DES molecules in the water environment at the first temperature to obtain a solution result, saving the solution result in a COSMO file format, and importing the COSMO file into COSMO-RS software for prediction.

9. The method for screening DES using ANN and COSMO-RS according to claim 8, characterized in that: The first temperature is 0°C.

10. A method for extracting oil tea polysaccharides based on DES combined with a dual enzyme method, characterized in that: The method comprises: 201, drying and crushing the oil tea leaves to obtain oil tea leaf powder; 202. Extracting the camellia oleifera powder using an enzyme-assisted extraction method comprising cellulase, protease, and the optimal DES according to any one of claims 1 to 9 to obtain a camellia oleifera polysaccharide extract; 203. Subject the oil tea leaf polysaccharide extract to alcohol precipitation, collect the precipitate, and freeze-dry to obtain oil tea leaf polysaccharide.

11. The method for extracting oil tea polysaccharides based on DES combined with double enzyme method according to claim 10, characterized in that: The optimal DES consists of choline chloride and sorbitol.

12. The method for extracting oil tea polysaccharides based on DES combined with dual enzyme method according to claim 10, characterized in that: The method further includes the DES being a DES comprising a first percentage of water.

13. The method for screening DES using ANN and COSMO-RS according to claim 12, characterized in that: The first percentage is 20%-40%.

14. The method for screening DES using ANN and COSMO-RS according to claim 13, characterized in that: The first percentage is 30%.

15. The method for screening DES using ANN and COSMO-RS according to claim 11, characterized in that: The response surface methodology was used to optimize the liquid-to-solid ratio, enzyme-to-solid ratio, extraction time and water content of the enzyme-assisted DES extraction method.

16. A computer device, characterized in that: The device comprises: a memory and a processor; the memory is used to store a computer program; and the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 15.

17. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 15 are implemented.

18. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 15 are implemented.

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