A lactic acid bacteria-fermented jujube juice and its application in anti-oxidation and lipid-lowering effects.

Abstract

This invention discloses a lactic acid bacteria-fermented jujube juice and its application in antioxidation and lipid-lowering, belonging to the field of microbial fermentation technology. The preparation of the fermented jujube juice includes the step of fermenting jujube juice using a compound bacteria; the compound bacteria are composed of *Lactobacillus plantarum* LP90 and *Lactobacillus acidophilus* LA85, with an optimal ratio of 1:2; the jujube juice is obtained by juicing pitted jujubes. This invention systematically studies and optimizes the lactic acid bacteria fermentation process of jujube juice, addressing problems such as insufficient functional activity exploration, lack of precise optimization of fermentation process parameters, and low utilization rate of jujube pulp (a byproduct of jujube kernel processing) in existing jujube juice processing. Through strain selection, metabolomics mechanism exploration, and multi-objective process optimization using a BP neural network, the resulting fermented jujube juice exhibits significantly enhanced antioxidant properties and a marked lipid-lowering function.

Description

Technical Field

[0001] This invention relates to the field of microbial fermentation technology, and in particular to a lactic acid bacteria compound fermented jujube juice and its application in anti-oxidation and lipid-lowering. Background Technology

[0002] Sour jujube ( Ziziphus jujuba var. spinosa (Bunge) Hu ex HFChow., as a plant with both medicinal and edible properties, has pulp rich in various bioactive components such as polyphenols, flavonoids, triterpenoids, and dietary fiber, possessing multiple physiological functions including antioxidant activity, lipid-lowering effects, and regulation of intestinal flora. However, jujube pulp is usually used as a byproduct of jujube kernel processing, resulting in low utilization and failing to fully realize its functional value. In recent years, plant-based lactic acid bacteria fermented beverages have become a hot topic in functional food development due to their combination of fruit and vegetable nutrition and probiotic fermentation advantages. Lactic acid bacteria fermentation not only improves the flavor and taste of fruit and vegetable juices but also enhances the bioavailability of active ingredients in raw materials through metabolic transformation, thereby strengthening their functional properties.

[0003] Current research indicates that fermenting fruit and vegetable juices with a single lactic acid bacteria (such as *Lactobacillus plantarum* or *Lactobacillus acidophilus*) can enhance their antioxidant capacity or antibacterial activity. For example, *Lactobacillus plantarum* fermentation can promote the release and conversion of polyphenols, while *Lactobacillus acidophilus* contributes to the formation of antibacterial substances such as organic acids. However, existing technologies are mostly focused on the initial application of single-strain fermentation or strain combinations, and systematic research is lacking on the effects of different lactic acid bacteria blend ratios on the composition of flavonoids, antioxidant activity, and lipid-lowering function in fermentation products. In particular, how the blend ratio of strains affects the transformation pathways of flavonoid components, the changes in metabolites, and the intrinsic relationship between these factors and functional activity during fermentation remains unclear.

[0004] Furthermore, existing fermentation process optimization methods often employ single-factor experiments combined with orthogonal experiments or response surface methodology. These methods have limitations when dealing with complex nonlinear systems involving multiple factors and indicators, failing to comprehensively reflect the interactions between various factors and hindering the comprehensive optimization of multiple functional indicators. Especially when using in vitro lipid-lowering capacity (such as pancreatic lipase inhibition and bile salt binding) as evaluation indicators, the influence mechanism of fermentation conditions (such as inoculum size, temperature, and time) on functional activity is complex, making it difficult for existing optimization methods to efficiently and accurately determine the optimal process parameters.

[0005] Therefore, how to scientifically screen the ratio of lactic acid bacteria to enhance the antioxidant function of jujube juice and systematically optimize the fermentation process to enhance its lipid-lowering activity has become an urgent technical problem to be solved in the field of jujube juice deep processing. Existing technologies have not yet found any research that combines the screening of lactic acid bacteria ratios with flavonoid metabolomics analysis to systematically reveal the mechanism by which fermentation enhances the functional activity of jujube juice; nor have there been any reports of using neural network models to optimize the jujube juice fermentation process for multiple objectives to improve its in vitro lipid-lowering ability. Summary of the Invention

[0006] The purpose of this invention is to provide a lactic acid bacteria compound fermented jujube juice and its application in anti-oxidation and lipid-lowering, so as to solve the problems existing in the prior art. The compound bacteria fermented jujube juice using Lactobacillus plantarum LP90 and Lactobacillus acidophilus LA85 can significantly improve the antioxidant and lipid-lowering functions.

[0007] To achieve the above objectives, the present invention provides the following solution: This invention provides a method for fermenting jujube juice with a compound of lactic acid bacteria to enhance its antioxidant activity. The method includes the step of fermenting jujube juice with a compound of bacteria to obtain fermented jujube juice. The compound of bacteria is composed of Lactobacillus plantarum LP90 and Lactobacillus acidophilus LA85, and the jujube juice is obtained by juicing pitted jujubes.

[0008] Preferably, in the compound bacteria, the mass ratio of Lactobacillus plantarum LP90 to Lactobacillus acidophilus LA85 is (1-3):(1-3).

[0009] Preferably, the mass ratio of Lactobacillus plantarum LP90 to Lactobacillus acidophilus LA85 is 1:2.

[0010] Preferably, the method for preparing the jujube juice includes: pitting jujubes, mixing them with 5-10 times their weight of purified water, pulping them at 30,000-40,000 r / min for 1 min, grinding them for 10-20 min, and filtering them to obtain the jujube juice.

[0011] Preferably, the fermentation conditions are: inoculum size of 1%-5%, fermentation temperature of 30-39℃, and fermentation time of 6-48h.

[0012] Preferably, the fermentation conditions are: an inoculum size of 3%, a fermentation temperature of 37°C, and a fermentation time of 24 hours.

[0013] The present invention also provides a fermented jujube juice, which is prepared using the fermentation method described above.

[0014] This invention also provides the application of the fermented jujube juice in the preparation of antioxidant products. The products can be pharmaceuticals, or health foods or foods that promote antioxidant activity.

[0015] This invention also provides the application of the fermented jujube juice in the preparation of lipid-lowering products. The products can be pharmaceuticals, or health foods or foods that help maintain healthy blood lipid levels.

[0016] The present invention discloses the following beneficial effects: This invention focuses on the systematic research and optimization of the lactic acid bacteria fermentation process for jujube juice. Addressing issues such as insufficient functional activity exploration, lack of precise optimization of fermentation process parameters, and low utilization rate of jujube pulp (a byproduct of jujube kernel processing) in existing jujube juice processing methods, this invention achieves several significant advantages over existing technologies through strain combination screening, metabolomics mechanism exploration, and multi-objective process optimization using a backpropagation neural network. These advantages are detailed below: (1) The optimal ratio of bacterial strains was selected to significantly enhance the antioxidant activity of jujube juice. Existing technologies mostly employ single-strain fermentation of fruit and vegetable juices or simple compound strains, but they lack precise ratio selection tailored to the characteristics of jujube juice, resulting in limited enhancement of the antioxidant activity of the fermented product. This invention, through single-factor experiments with different ratios of *Lactobacillus plantarum* (LP90) and *Lactobacillus acidophilus* (LA85), determined that a 1:2 ratio was the optimal compounding ratio. After 6 hours of fermentation, the flavonoid content of the jujube juice reached 0.9219 mg / ml, representing the peak value for all ratios. Simultaneously, at this compounding ratio, the jujube juice exhibited strong antioxidant activity against DPPH free radicals, hydroxyl free radicals, and ABTS. + The free radical scavenging capacity and total antioxidant capacity (T-AOC) were both optimal, far exceeding the effect of single-strain fermentation. Compared with unfermented jujube juice and traditional single-strain fermented jujube juice, this technology significantly enhanced the antioxidant function of the product through compound fermentation, and clarified the influence of fermentation time on activity, avoiding the loss of active ingredients caused by over-fermentation.

[0017] (2) To reveal the deep metabolic mechanism by which fermentation enhances the activity of jujube juice, providing theoretical support for the development of functional fruit and vegetable juices. Existing research on the activity enhancement mechanism of lactic acid bacteria fermented fruit and vegetable juices largely focuses on physicochemical indicators, lacking precise metabolomics analysis. This invention uses LC-MS / MS to perform quantitative metabolomics analysis on flavonoids in jujube juice before and after fermentation, detecting 39 flavonoids and screening out 16 significantly differentially expressed metabolites. It clarified the pattern of significantly increased flavanols, decreased overall flavonols, and a substantial upregulation of the key chalcone component, naringenin chalcone, after fermentation. Furthermore, it discovered that fermentation induces the production of new functional components such as daidzein and trifolin. Simultaneously, KEGG pathway analysis identified naringenin chalcone and quercetin as key biomarkers, revealing the mechanism by which fermentation enhances the activity of jujube juice by regulating pathways such as flavonoid biosynthesis, flavonoid and flavonol biosynthesis. This addresses the lack of clarity regarding the activity enhancement mechanism of fermented fruit and vegetable juices in existing technologies, providing a replicable theoretical basis for strain selection and process design for similar functional fruit and vegetable juices.

[0018] (3) Achieve precise multi-objective optimization of the jujube juice fermentation lipid-lowering process to enhance the lipid-lowering properties of the product. Existing technologies rarely focus on fermentation processes specifically targeting the lipid-lowering effects of jujube juice, and food process optimization often employs traditional methods such as orthogonal experiments and response surface methodology, which struggle to address complex nonlinear optimization problems involving multiple factors and indicators. This invention uses pancreatic lipase inhibition capacity, glycocholate binding capacity, and taurocholate binding capacity as in vitro lipid-lowering evaluation indicators, selecting inoculum size, fermentation temperature, and fermentation time as influencing factors. A BP neural network model was built using Matlab, and 144 sets of experimental data were trained, validated, and tested. The model's overall R... 2 The coefficient of performance reached 0.98188, indicating an extremely high degree of fit. Furthermore, by combining Pareto optimization to obtain the optimal process parameters for multi-objective balance, compared with traditional optimization methods, this invention achieves accurate prediction and optimization of the lipid-lowering process of jujube juice. Under the optimal process, the pancreatic lipase inhibition rate reached 66.81%, the glycocholate binding rate reached 37.13%, and the taurine binding rate reached 58.43%, significantly improving the lipid-lowering function of jujube juice and filling the technical gap in the lipid-lowering fermentation process of jujube juice.

[0019] (4) Improve the utilization rate of by-products from jujube seed processing to achieve efficient value-added processing of resources. Jujube pulp, a byproduct of jujube kernel processing, generally has a low utilization rate in existing technologies, and its functional components, such as polyphenols, triterpenoids, and dietary fiber, have not been fully developed. This invention uses jujube pulp as raw material to prepare jujube juice and then ferments it with lactic acid bacteria. Through process optimization, it fully explores the antioxidant and lipid-lowering functional activities, transforming a low-value byproduct into a high-value-added functional fermented beverage. This achieves efficient utilization of agricultural processing resources, reduces resource waste in jujube kernel processing, and improves the overall economic benefits of the industry.

[0020] (5) Establish a standardized fermentation process system for jujube juice that is both practical and scalable. The process system established by this invention has clear steps and precise, controllable parameters, making it more suitable for industrial production. It can be directly applied to food processing enterprises and has good prospects for industrialization.

[0021] (6) Provide new methods for food processing optimization and expand the application scenarios of BP neural networks Current food process optimization methods largely rely on traditional experimental approaches, which are inefficient, costly, and ill-suited for complex systems with multiple factors. This invention combines a backpropagation neural network with Pareto optimization and applies it to the optimization of the jujube juice fermentation and lipid-lowering process. It solves the technical challenge of multi-index synergistic optimization, provides a new technical solution for optimizing complex nonlinear processes in the food industry, expands the application scenarios of artificial intelligence algorithms in food processing, and has significant technical reference value. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. 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 effort.

[0023] Figure 1 The effect of different bacterial strain ratios on the physicochemical composition of fermented jujube juice; Figure 2 The effect of different strain ratios on the antioxidant level of fermented jujube juice; Figure 3 PCA score chart; NF: unfermented jujube juice, F: fermented jujube juice; Figure 4 This is a clustering heatmap; the horizontal axis represents the sample name, the vertical axis represents metabolite information, and different colors are used to fill in different values ​​obtained after standardization of different contents. Group represents grouping, and Class represents substance category. Figure 5 This is a KEGG enrichment plot for differential metabolites; A: The vertical axis represents the name of the KEGG metabolic pathway. The numbers in the plot indicate the number of differential metabolites annotated to that pathway. The numbers in parentheses represent the ratio of the number of differential metabolites annotated to that entry to all differential metabolites annotated to KEGG; B: The horizontal axis represents the enrichment factor for each pathway, and the vertical axis represents the pathway name. The color of the dots represents the P-value, with redder dots indicating more significant enrichment. The size of the dots represents the number of differential metabolites enriched. Figure 6 This is a diagram of metabolic pathway networks; Figure 7 This is a diagram of the BP neural network structure. Figure 8 The effects of different factors on the inhibitory capacity of pancreatic lipase; A: inoculum size; B: fermentation temperature; C: fermentation time; Figure 9 The effects of different factors on the binding capacity of glycocholate; A: inoculum size; B: fermentation temperature; C: fermentation time; Figure 10 The effects of different factors on taurine salt binding capacity; A: inoculum size; B: fermentation temperature; C: fermentation time; Figure 11 This is a graph showing the training error of a BP neural network. Figure 12 The graph shows the optimal validation performance of the BP neural network; Figure 13 Comparison of BP neural network predictions with actual values; Figure 14 The fitted curves for the training set (A), validation set (B), test set (C), and all data (D); Figure 15 This is a set of optimal solutions found using Pareto optimization. Detailed Implementation

[0024] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0025] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0026] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0027] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0028] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0029] Example 1: Screening of the optimal compound ratio of jujube juice fermentation strains 1. Main experimental materials are shown in Table 1.

[0030] Table 1 Main experimental materials and reagents 2. Preparation of Jujube Juice After cleaning the jujubes and removing the pits, place them in a blender with 7 times their weight of purified water and blend at 35,000 rpm for 1 minute. Then, transfer them to a colloid mill and grind for 20 minutes to make them finer. Finally, filter the mixture through clean gauze to obtain the jujube juice for later use.

[0031] The freshly squeezed jujube juice obtained through the above steps is pasteurized at 60°C for 30 minutes, then cooled to 35°C and inoculated with bacteria. After fermentation, it is bottled into capped glass bottles and sterilized at 121°C under high pressure for 20 minutes to obtain the finished product.

[0032] 3. Single-factor experiment Lactobacillus plantarum (Lp90) and Lactobacillus acidophilus (LA85) bacterial powders were inoculated into freshly squeezed jujube juice at a 0.4% inoculation rate according to mass ratios of 4:0, 3:1, 2:1, 1:1, 1:2, 1:3, and 0:4. The fermentation temperature was 37℃, and samples were taken at 2h, 4h, 6h, 12h, 24h, 36h, and 48h for determination of flavonoid content, vitamin C content, pH value, titratable acidity, total sugar content, and DPPH free radical, hydroxyl free radical, and ABTS free radical levels. + The total antioxidant capacity of T-AOC was determined to screen for the optimal strain ratio and fermentation time.

[0033] 4. Determination of physicochemical components of jujube juice 4.1 Determination of flavonoid content The determination was performed using spectrophotometry, referring to GB / T20574—2006 "Determination of Total Flavonoid Content in Propolis - Spectrophotometric Colorimetric Method". The procedure was as follows: Take 30g of fermented jujube juice, add 30mL of anhydrous ethanol, stir with a heating magnetic stirrer for 30min, centrifuge at 4℃ and 6000r / min for 10min, take the supernatant and filter it with medium-speed qualitative filter paper, take 4mL of the filtrate and add 1mL of 30% ethanol solution to make up to 5mL, then add 0.3mL of 5% sodium nitrite solution, let stand for 5min, add 0.3mL of 10% aluminum nitrate solution, let stand for 6min, then add 2mL of 1mol / L sodium hydroxide solution, and then make up to 10mL with 30% ethanol solution. After standing for 15min, measure the absorbance at 510nm using a spectrophotometer. A rutin standard curve was plotted with absorbance (y) on the ordinate and the mass concentration (x) of the rutin standard solution on the abscissa. Linear fitting yielded the regression equation: y = 0.2971x - 0.0049, with a correlation coefficient R0. 2 =0.9949. The flavonoid content in the sample was calculated according to the standard regression equation, and the results are expressed in mg / mL.

[0034] 4.2 Determination of Vitamin C Content The vitamin C content of fermented jujube juice was determined by titration, referring to GB5009.86—2016 "National Food Safety Standard - Determination of Ascorbic Acid in Food". The procedure was as follows: Take 5g of fermented jujube juice, add 5g of 20g / L metaphosphoric acid solution, mix well, continue to add 20g / L metaphosphoric acid solution to make up to 100mL, filter with medium-speed qualitative filter paper, take 10mL of filtrate into an Erlenmeyer flask, and titrate with standardized 2,6-dichlorophenolindophenol solution until the solution turns pink and does not fade for 15s. At the same time, a blank control test was performed. The formula for calculating vitamin C content is shown in (1).

[0035] In the formula: V The volume of 2,6-dichlorophenolindophenol solution consumed for titrating the sample, in mL; V 0 The volume of 2,6-dichlorophenolindophenol solution consumed for the titration of the blank, in mL; T The titer of 2,6-dichlorophenolindophenol solution is the number of milligrams of ascorbic acid equivalent to each milliliter of 2,6-dichlorophenolindophenol solution, expressed in mg / mL. A This refers to the dilution factor; m The mass is the sample mass, expressed in grams (g).

[0036] 4.3 Determination of titratable acid content The acid-base indicator titration method was used for determination, referring to GB12456—2021 "National Food Safety Standard for Determination of Titratable Acids in Food". The operation steps were as follows: the fermented jujube juice was filtered with medium-speed qualitative filter paper, 20 mL of filtrate was placed in an Erlenmeyer flask, 2 to 3 drops of prepared phenolphthalein indicator were added, and titrated with 0.1 mol / L sodium hydroxide solution until a faint red color was observed that did not fade for 30 seconds. The amount used was recorded, and a blank control experiment was performed at the same time. The formula for calculating the titratable acid content is shown in (2).

[0037] In the formula: C The concentration of the standard sodium hydroxide solution is expressed in mol / L. V 1 The volume of sodium hydroxide solution consumed in titrating the sample, in mL; V 2 The volume of sodium hydroxide solution consumed for the blank titration, in mL; V 3 The total volume of the sample dilution solution is in mL. V 4 The volume of sample solution taken during titration is in mL; KThe coefficient is used to convert to the appropriate acid, that is, the number of grams of the main acid that 1 mol of sodium hydroxide is equivalent to. Here, the main acid is lactic acid, K=0.090.

[0038] 4.4 Determination of pH value of fermented jujube juice The determination was performed using an automatic potentiometric titrator, referring to GB5009.239—2016 "National Food Safety Standard - Determination of Acidity in Food". The procedure was as follows: the fermented jujube juice was centrifuged at 4℃ and 6000r / min for 10min, the supernatant was filtered through medium-speed qualitative filter paper, and 10mL of the filtrate was used to determine the pH value of the sample at room temperature.

[0039] 4.5 Determination of sugar content in fermented jujube juice The determination was performed using a saccharimeter: Fermented jujube juice was centrifuged at 4℃ and 6000r / min for 10min. The supernatant was filtered through medium-speed qualitative filter paper, and 1-2 drops were added to the saccharimeter. The reading was recorded.

[0040] 5. Determination of antioxidant activity of jujube juice (1) DPPH free radical scavenging capacity determination Fermentation sample solutions of different concentrations (0.5, 1.0, 2.0, 5.0, 10.0, 20.0 mg / mL) were prepared, with ascorbic acid as a positive control. The absorbance was measured at 517 nm. The DPPH free radical scavenging rate was calculated according to formula (3): In the formula: A 0 The absorbance is calculated as 1 mL of DPPH radical solution + 1 mL of double-distilled water. A 1 The absorbance is calculated as 1 mL of DPPH radical solution + 1 mL of sample. A 2 The absorbance is calculated as 1 mL of anhydrous ethanol + 1 mL of sample.

[0041] (2) ABTS + Free radical scavenging capacity determination The ABTS method can be used to determine the antioxidant capacity of both hydrophilic and lipophilic substances, and is the most widely used indirect detection method. + It exhibits a blue-green color, with maximum absorption peaks at 405 nm and 734 nm. The analyte is treated with ABTS. + After free radical scavenging, the antioxidant components in the solution can cause decolorization and a decrease in absorbance at 405 nm. Within a certain range, the change in absorbance is directly proportional to the degree of free radical scavenging. This experiment used an ABTS free radical scavenging ability assay kit to determine the ABTS content of fermented jujube juice. +Free radical scavenging ability was assessed by measuring absorbance at 405 nm using ascorbic acid as a positive control, and ABTS was calculated according to formula (4). + Free radical scavenging rate: In the formula: A 0 The absorbance of the blank tube; A 1 To measure the absorbance of the tube; A 2 The absorbance of the control tube is shown.

[0042] (3) Hydroxyl radical scavenging capacity determination Various free radicals are constantly generated during the metabolic process of life activities. Among them, hydroxyl free radicals are the most active reactive oxygen species in the body, which can mediate many physiological changes. Hydroxyl free radicals act on biomolecules such as proteins, nucleic acids, and lipids in the body, causing damage to cell structure and function, and thus leading to metabolic disorders and diseases. The hydroxyl free radical scavenging capacity is one of the important indicators of the antioxidant capacity of a sample. The hydroxyl free radical scavenging rate was determined by the Fenton colorimetric method using a hydroxyl free radical scavenging rate test kit, and the hydroxyl free radical scavenging rate was calculated according to formula (5): In the formula: A 0 The absorbance of the blank tube; A 1 The absorbance of the undamaged tube; A 2 A3' is the absorbance of the damaged tube; A3' is the absorbance of the control tube. A 3 To measure the absorbance of the tube.

[0043] (4) Determination of total antioxidant capacity (T-AOC) The determination of the total antioxidant level of various antioxidants in the sample, under acidic conditions, reduces Fe... 3+ - Tripyridine triazine (Fe 3+ -TPTZ) produces blue Fe 2+ The TPTZ capacity reflects the total antioxidant capacity. The total antioxidant capacity of fermented jujube juice was determined using a Total Antioxidant Capacity (T-AOC) assay kit (FRAP method), based on Fe... 2+ A standard curve was established using the final concentration (X, μmol / mL) and absorbance value y: y = 14.133x - 0.0899, R0 2 =0.9587, the total antioxidant capacity is calculated according to formula (6): In the formula, V1 The total volume of the reaction is [volume]. V 2 This represents the sample volume.

[0044] 6. Quantitative metabolomics analysis of flavonoids in jujube juice using liquid chromatography-tandem mass spectrometry (LC-MS / MS). (1) Pretreatment before injection Samples were prepared according to the optimal strain ratio and fermentation time selected above. Unfermented jujube juice was used as a control. The changes in the content of various flavonoid components in the jujube juice before and after fermentation were analyzed to explore the potential relationship between changes in substance content and changes in antioxidant levels. After vortexing the sample, 50 μL was transferred, and 10 μL of a 4000 nmol / L internal standard working solution and 500 μL of 70% methanol solution were added. The sample was sonicated for 30 min, centrifuged at 4℃ (12,000 r / min, 5 min), and the supernatant was collected. The sample was filtered through a 0.22 μm filter membrane and stored in a sample vial for LC-MS / MS analysis.

[0045] (2) LC / MS test analysis Chromatographic conditions: Column: Waters ACQUITY UPLC HSS T3 C18 column (1.8 µm, 100 mm × 2.1 mm id); Mobile phase: Phase A was ultrapure water (with 0.05% formic acid), Phase B was acetonitrile (with 0.05% formic acid); Flow rate: 0.35 mL / min; Column temperature: 40℃; Injection volume: 2 μL. Elution gradient: 0 min A / B 90:10 (V / V), 1 min A / B 80:20 (V / V), 9 min 30:70 (V / V), 12.5 min A / B 5:95 (V / V), 13.5 min 5:95 (V / V), 13.6 min 90:10 (V / V), 15 min 90:10 (V / V).

[0046] Mass spectrometry conditions: Electrospray ionization (ESI) source temperature 550℃, mass spectrometry voltage 5500 V in positive ion mode, mass spectrometry voltage -4500 V in negative ion mode, curtain gas (CUR) 35 psi. In the Q-Trap 6500+ liquid chromatography-mass spectrometry system, each ion pair was scanned and detected according to optimized declustering potential (DP) and collision energy (CE).

[0047] (3) Metabolomics data acquisition and analysis The data acquisition instrument system mainly includes ultra-high performance liquid chromatography (UPLC) (ExionLC™ AD, https: / / sciex.com.cn / ) and tandem mass spectrometry (MS / MS) (QTRAP® 6500+, https: / / sciex.com.cn / ).

[0048] The collected data were imported into Compound Discoverer 3.1 database search software for processing, including screening parameters such as retention time and mass-to-charge ratio of metabolites, data preprocessing and pattern recognition analysis, comparison with high-resolution secondary spectral databases mzCloud and mzVault and MassList primary database for metabolite identification.

[0049] Principal component analysis (PCA) was performed on the samples to preliminarily understand the overall metabolic differences among the groups and the magnitude of variability within each group. Metabolite content data were processed using unit variation scaling (UV), and heatmaps were generated using the ComplexHeatmap package in R software. Hierarchical cluster analysis (HCA) was performed on the accumulation patterns of metabolites among different samples. Fold change (FC) was used to screen for differentially expressed metabolites between groups; metabolites with FC ≥ 2 and FC ≤ 0.5 were considered significantly different. The KEGG database was used for annotation, classification, and enrichment analysis of differentially expressed metabolites.

[0050] 7. Statistical Analysis Origin 2021 software and R language were used for data processing and plotting. SPSS 26.0 software was used for significance analysis. P <0.05 indicates a significant difference. Each experiment was repeated three times, and the results are expressed as the mean.

[0051] 8. Results and Analysis 8.1 Results of Single-Factor Experiment (1) Results of physicochemical composition determination of jujube juice like Figure 1As shown, under different strain ratios, the flavonoid content initially increased and then decreased with prolonged fermentation time. The highest flavonoid content (0.9219 mg / mL) was observed in the jujube juice fermented for 6 hours at a strain ratio of 1:2. Both vitamin C content and pH decreased with prolonged fermentation time, with the least vitamin C loss observed during plant-based lactic acid bacteria fermentation. The pH decreased more rapidly at a strain ratio of 1:2. Acidity increased and sugar content decreased with prolonged fermentation time. Overall, a higher proportion of *Lactobacillus acidophilus* resulted in higher acidity and lower sugar content.

[0052] (2) Results of antioxidant activity assay of jujube juice like Figure 2 As shown, the scavenging rates of DPPH and hydroxyl radicals under single-strain fermentation were significantly lower than those under mixed-strain fermentation, with the optimal scavenging ability observed at a ratio of 1:2. Considering both ABTS cations and total antioxidant capacity (T-AOC), a 1:2 ratio also yielded the best scavenging rate. Taking into account both physicochemical indicators and antioxidant levels, the optimal strain ratio is Lactobacillus plantarum (Lp90): Lactobacillus acidophilus (LA85) = 1:2 (mass ratio).

[0053] 8.2 LC-MS / MS Analysis of Quantitative Metabolomics of Flavonoids in Jujube Juice (1) Content and types of flavonoids Table 2 Flavonoid Content and Category Note: NF represents unfermented jujube juice; F represents fermented jujube juice; "-" indicates that the component was not detected.

[0054] As shown in Table 2, the two types of jujube juice, before and after fermentation, contained a total of 39 flavonoids, covering five categories: flavonols, dihydroflavones, flavones, chalcones, and flavanols. In terms of absolute content, rutin (NF and F were 25.34 and 22.28 µmol / L, respectively) was significantly higher than all other substances (generally below 1 µmol / L), making it the most abundant phenolic compound in both samples, accounting for the vast majority of the total. This was followed by naringin and campheneol-3-O-rutin (contents of 0.85 and 0.79 µmol / L), which constituted the second most abundant components besides rutin. Additionally, substances detected only in sample F included: 3-hydroxyflavone, tetramethyl ether flavonoid, apigenin 7-glucoside, luteolin, brown cyanidin, dihydrokaempferol, daidzein, and trifolin. Fermentation may induce or activate the synthetic pathways of these substances, particularly dihydroflavonols, isoflavones, and partially glycosylated flavones.

[0055] Based on the trends observed by substance category, for flavonoids, several flavonoids unique to sample NF (such as geraniol and luteolin) disappeared in group F, replaced by other newly appearing flavonoids (such as luteolin and brown cyanidin) and isoflavones (genistein). This strongly suggests that fermentation altered the downstream pathway of flavonoid synthesis, shifting from synthesizing one group of flavonoids to another group of structurally similar but modified flavonoids. Flavonols showed an overall decreasing trend, with rutin, quercetin, euryponemal glycoside, and isorhamnetin-3-O-glucoside all decreasing. Flavonols unique to group NF (such as isorhamnetin and kaempferol) disappeared in group F. This suggests that fermentation may generally inhibit the biosynthesis of flavonols. Flavanols showed a consistent increasing trend, with catechins and epicatechins significantly increasing, possibly indicating that fermentation promoted the synthesis or accumulation of flavanols. Finally, fermentation with *Lactobacillus plantarum* lowered the pH to below 5, slowing down the polyphenol oxidase (PPO) enzymatic reaction and thus retaining more catechins. The chalcone content of naringenin chalcone increased significantly, and trifolin was newly observed in group F. Chalcones are precursors to flavonoid synthesis, and these changes may reflect the role of fermentation treatment in the upstream pathway of flavonoid synthesis.

[0056] (2) Principal Component Analysis (PCA) like Figure 3 As shown, the metabolites of unfermented jujube juice and fermented jujube juice samples were clearly separated, indicating that fermentation treatment significantly changed the content and types of flavonoids in jujube juice.

[0057] (3) Cluster analysis like Figure 4As shown, the contents of flavonoids in sample F were significantly higher than those in sample NF, including chalcones, flavanols, flavanonols, and isoflavones. In addition, the contents of four flavonoids not belonging to the above categories—flavonol, sinensetin, eriodictyol, and quercetin-3-O-glucuronide—were also increased. In particular, scutellarein tetramethyl ether, apigenin 7-glucoside, jaceosidin, and cynaroside were only detected in group F, indicating that fermentation treatment affected the transformation pathways of certain substances, thereby producing new substances.

[0058] (4) Screening of differential metabolites Using fold change as the screening index, metabolites with FC ≥ 2 and FC ≤ 0.5 were considered significantly different. A total of 16 differentially expressed metabolites were screened, of which 7 were downregulated and 9 were upregulated. Naringenin chalcone and quercetin were substances present in both the NF and F groups; naringenin chalcone was significantly upregulated (FC = 4.415), while quercetin was significantly downregulated (FC = 0.054). Other significantly upregulated components included 3-hydroxyflavone, tetramethyl ether flavone, apigenin 7-glucoside, luteolin, brown cyanidin, dihydrokaempferol, daidzein, and trifolin; significantly downregulated components included isorhamnetin, kaempferol, geraniol, luteolin, 5,7-dihydroxyflavone, and 5-O-norepinephrine.

[0059] (5) Functional annotation and enrichment analysis of differential metabolite KEGG The KEGG (Kyoto Encyclopedia of Genes and Genomes) database helps researchers study genes, expression information, and metabolite levels as a holistic network. Metabolites are annotated and visualized using the KEGG database. The KEGG pathways involved in the differentially metabolites before and after jujube juice fermentation include: flavonoid biosynthesis, metabolic pathways, biosynthesis of secondary metabolites, flavonone and flavonol biosynthesis metabolites, and isoflavone biosynthesis. The KEGG annotation results for significantly differentially metabolites are categorized according to the pathway type in KEGG, such as... Figure 5 As shown in Figure A. Based on the differential metabolite results, KEGG pathway enrichment was performed, and the results are presented as follows. Figure 5 As shown in B.

[0060] The biosynthesis of flavonoids, metabolic pathways, and secondary metabolites all involve six metabolites. Furthermore, the metabolites involved in the biosynthesis of metabolic pathways and secondary metabolites are completely identical. Comparison with the KEGG pathway diagram shows that secondary metabolite biosynthesis is part of the metabolic pathway; therefore, further analysis of the metabolic pathway is unnecessary. The biosynthesis of flavonoids and flavonols involves three metabolites, while the biosynthesis of isoflavones involves only one metabolite: daidzein. Quercetin involves the most metabolic pathways (four), appearing in the biosynthesis of flavonoids, metabolic pathways, secondary metabolites, and both flavonoids and flavonols. Naringenin chalcone is the next most common, appearing in three pathways: flavonoid biosynthesis, metabolic pathways, and secondary metabolite biosynthesis. This indicates a high degree of overlap in the transformation pathways involved in the fermentation of naringenin chalcone and quercetin in jujube juice, suggesting a close connection between the two.

[0061] (6) Metabolic pathway analysis To elucidate the potential links between differential metabolites and pathways, a metabolic pathway network diagram was constructed based on KEGG pathway analysis to illustrate the influence of Lp90 and LA85 fermented jujube juice on the content of flavonoids under optimal compound ratios. Figure 6 Based on this finding, two key biomarkers involved in altering flavonoid composition are naringenin chalcone and quercetin, involving metabolic pathways such as secondary metabolite biosynthesis and its components including flavonoid biosynthesis, flavonoid and flavonol biosynthesis, and isoflavone biosynthesis.

[0062] Naringenin chalcone is a precursor of naringenin, containing a hydroxyl and methoxy group at specific positions. It is a core intermediate in the biosynthetic pathway of flavonoids in plants. It is generated from p-coumaroyl-CoA and malonyl-CoA by chalcone synthase. Subsequently, chalcone isomerase can isomerize it, closing the ring to form naringenin. Compared to naringenin, naringenin chalcone is usually present in very low amounts in natural substances because it is quickly converted into other substances. However, studies have shown that due to its molecular structure, naringenin chalcone may have superior antioxidant capacity (such as free radical scavenging) compared to its closed-ring analogue, naringenin. Furthermore, naringenin chalcone sometimes exhibits stronger anticancer activity than naringenin. Finally, naringenin chalcone also shows great potential in anti-inflammatory, neuroprotective, anti-diabetic, and anti-obesity applications.

[0063] Quercetin is one of the most widely distributed and extensively studied flavonols in the plant kingdom. Its basic structure is a 3-hydroxyflavonoid with multiple phenolic hydroxyl groups, which form the basis of its powerful antioxidant activity. In plants, quercetin typically binds to one or more sugar molecules to form quercetin glycosides (such as rutin and isoquercitrin), which improves its solubility and stability in water. Quercetin possesses an extremely broad range of biological activities, making it a true "all-rounder." In addition to its powerful core antioxidant activity (directly scavenging free radicals), quercetin, like most flavonoids, also exhibits anti-inflammatory, anti-cancer, cardiovascular health protection, immune regulation and anti-allergic effects, and regulation of glucose and lipid metabolism.

[0064] In addition, the newly generated daidzein components in the fermented jujube juice can not only effectively inhibit oxidative stress, but also protect the cardiovascular system, improve lipid metabolism and prevent arteriosclerosis. This provides new research ideas for the continued development of jujube juice with lipid metabolism regulation function.

[0065] The experimental results above indicate that the optimal ratio of *Lactobacillus plantarum* (Lp90) and *Lactobacillus acidophilus* (LA85) in the fermented jujube juice of this invention is 1:2, and the optimal fermentation time is 6 hours. Metabolomics results showed that, using PCA and multivariate cluster analysis, 39 components were detected in the jujube juice. After screening, 16 differentially expressed metabolites were identified, with 9 metabolites significantly upregulated and 7 significantly downregulated. Among these, naringenin chalcone and quercetin were present in both the fermented and unfermented jujube juice. Metabolic pathway analysis revealed that the key metabolic pathways involved in naringenin chalcone and quercetin are flavonoid biosynthesis, flavonoid and flavonol biosynthesis, and isoflavone biosynthesis. In conclusion, the combined lactic acid bacteria fermentation can influence the functional activity of jujube juice in a way closely related to flavonoid and flavonol biosynthesis. Furthermore, the newly produced component daidzein after fermentation provides a new research direction for further development of jujube juice with lipid metabolism-regulating functions. In summary, this experiment, starting from the relationship between functional activity and changes in intrinsic composition, preliminarily elucidates the reasons for the enhanced activity and function of fermented jujube juice from the perspective of metabolomics.

[0066] Example 2: Optimization of Jujube Juice Fermentation Process using BP Neural Network 1. Main experimental materials are shown in Table 3.

[0067] Table 3 Main Experimental Materials 2. Experimental Methods 2.1 Single-factor experiment Three factors were selected: inoculum size (1%, 2%, 3%, 4%, 5%), fermentation temperature (30℃, 31℃, 32℃, 33℃, 34℃, 35℃, 36℃, 37℃, 38℃, 39℃), and fermentation time (0h, 6h, 12h, 18h, 24h, 30h, 36h, 42h, 48h). Single-factor experiments were conducted using in vitro lipid-lowering activity indicators such as pancreatic lipase inhibition capacity, glycocholate binding capacity, and taurocholate binding capacity as evaluation indicators. The principle of controlled variable method was followed. When measuring a single factor, the inoculum size was assumed to be 3%, the fermentation temperature was 37℃, and the fermentation time was 24h.

[0068] 2.2 In vitro lipid-lowering capacity determination (1) Pancreatic lipase inhibition ability Add 50 μL of 50 mmol / L phosphate buffer (pH=8.0) and 50 μL of 10 mg / mL pancreatic lipase solution to 5 mL of jujube juice, then add 50 μL of 0.5 mmol / L 4-nitrobenzene laurate substrate, mix well, and incubate at 37 °C for 20 min. Measure the OD value at 405 nm. Calculate the inhibition rate according to formula (7).

[0069] In the formula: A 1 The blank group (buffer solution replaces the sample); A 2 This is the blank background group (buffer solution replaces the sample, PBS replaces lipase); A 3 For sample groups; A 4 The sample background group (PBS instead of lipase).

[0070] (2) Bile salt binding capacity Add 1.5 mL of jujube juice to a centrifuge tube, then add 1.5 mL (10 mg / mL) of pepsin, 50 μL of concentrated sulfuric acid, and 450 μL of deionized water in sequence. Shake at a constant temperature (37℃, 1 h). Add NaOH solution to adjust the pH of the system to 6.3, then add 2 mL (10 mg / mL) of trypsin. Continue to shake at a constant temperature (37℃, 1 h). Finally, add 2 mL (0.4 mmol / L) of sodium glycocholate solution or 2 mL (0.5 mmol / L) of sodium taurocholate solution. Centrifuge (4000 r / min, 20 min). Take 1 mL of the supernatant and measure the absorbance at 387 nm. Calculate the binding rate of fermented jujube juice to glycocholate and taurocholate, as shown in formula (8).

[0071] In the formula: A 0 This represents the absorbance value of the blank group; A 1 The absorbance values ​​of the sample group; A 2 The absorbance value is for the control group.

[0072] 2.3 Optimization of BP Neural Network Model A BP neural network model built using the Matlab platform employed a transfer function feedforward network and was tested using the backpropagation algorithm. 144 samples were randomly divided into training, validation, and testing groups, representing 70%, 15%, and 15% of the data, respectively. The input layer had three neurons [inoculum size (A), fermentation temperature (B), fermentation time (C)], and the output layer had three neurons [inhibition capacity of pancreatic lipase (Y1), glycocholate binding capacity (Y2), taurocholate binding capacity (Y3)]. The initial layer was connected to the network input, and the input data was mapped to the hidden layer using randomly generated weights. Through forward computation and weight matrix generation, the hidden layer mapped the data to the output layer, obtaining the predicted value. Figure 7 The diagram shown is a BP neural network structure for optimizing the fermentation process of jujube juice, which includes 3 input layers, n hidden layers, and 3 output layers.

[0073] By calculating the error between the predicted and actual values, the process is repeated if the error does not reach the set training error limit for the neural network. After repeated training, the neural network training terminates when the error between the predicted and actual values ​​reaches the error limit or the set number of training iterations. The maximum number of training epochs is 1000, the learning rate is 0.01, and the minimum error for the training objective is 0.001. The number of hidden neurons is varied through repeated experiments until the network performs optimally after training, resulting in a BP neural network model. Finally, Pareto optimization is used to obtain the optimal value of the model. The regression R-value measures the correlation between the output and the objective, with an R-value of 1 indicating a close relationship and 0 indicating a random relationship. RMSE represents the mean squared error.

[0074] 2.4 Statistical Analysis Origin 2021 software was used to process and plot the univariate results, and SPSS 26.0 software was used for significance analysis. P <0.05 indicates a significant difference. A BP neural network model was built using the Matlab platform for data training, validation, and testing, and graphs were generated.

[0075] 3. Results and Analysis 3.1 Results of Single-Factor Experiment Figure 8 China A Figure 9 China A and Figure 10 Figure A shows the effect of inoculum size on the in vitro lipid-lowering activity of fermented jujube juice. Inoculum size, as a key factor in optimizing the fermentation process, directly affects the onset speed, process controllability, and the consistency and flavor of the final product. With increasing inoculum size, the jujube juice's ability to inhibit lipase and bind bile salts initially showed a significant increase (P < 0.05), reaching its peak at a 3% inoculum size (pancreatic lipase inhibition rate 67.17%, glycocholate binding rate 35.35%, taurine binding rate 59.88%), and then significantly decreased (P < 0.05). Generally, a high inoculum size accelerates the fermentation process, creating a high-acidity environment in a short time. However, excessively rapid fermentation can lead to acid stress for the microorganisms and a harsh, monotonous sour taste in the product. In this invention, the excessively high inoculum size may have caused a rapid increase in the total acid content of the jujube juice, affecting the metabolic transformation of flavonoids by *Lactobacillus plantarum*. In addition, different inoculum amounts of Lactobacillus plantarum also have a significant impact on the antioxidant level of fermented fruit juice. The optimal levels of pancreatic lipase inhibition and bile salt binding capacity were observed at an inoculum amount of 3%, which may be the result of the combined effect of the above multiple factors.

[0076] like Figure 8 B, Figure 9China B and Figure 10 As shown in Figure B, as the fermentation temperature gradually increased from 30℃ to 39℃, the inhibition rate of pancreatic lipase and the taurine salt binding rate of jujube juice showed a trend of first increasing and then decreasing. The inhibition rate of pancreatic lipase and the taurine salt binding rate were significantly higher at 37℃ (P < 0.05) than at other fermentation temperatures, at 66.96% and 57.38%, respectively. The peak value of the glycocholate binding rate also appeared at 37℃, but the difference from that at 36℃ was not significant, at 39.29% and 39.04%, respectively. This is mainly related to the optimal growth temperature of the selected strains. The optimal growth temperature of *Lactobacillus acidophilus* is 36-38℃, while *Lactobacillus plantarum* has a wider growth temperature range, typically exhibiting the fastest growth rate and most active metabolism within the 30-37℃ range. Furthermore, the fact that the three lipid-lowering indicators of jujube juice were basically consistent at 35℃ and 39℃ further confirms this point. Simultaneously, fermentation temperature is closely related to enzyme activity, and the ability of the strain to produce β-glucosidase is also a key factor. Flavonoids in plants usually exist in the form of glycosides, which have low bioavailability. Lactobacillus plantarum can hydrolyze flavonoid glycosides by secreting β-glucosidase, converting them into flavonoid aglycones that are more bioactive and easily absorbed.

[0077] like Figure 8 C, Figure 9 C and Figure 10 As shown in Figure C, the inhibition rate of jujube juice on pancreatic lipase and the binding rate of taurine salts remained stable within the first 6 hours of fermentation. From 12 to 24 hours, the inhibitory activity increased significantly. The inhibition rate of pancreatic lipase and the binding rate of taurine salts reached their peak at 24 hours, with corresponding inhibition rates of 66.40% and binding rates of 59.22%. The peak binding rate of glycocholate was 36.29% at 30 hours. After 24 hours of fermentation, the overall situation tended to stabilize. This is because the growth curve of lactic acid bacteria usually enters the logarithmic growth phase between 12 and 48 hours. At this time, the bacterial community rapidly multiplies and produces acid. The surge in the number of bacteria and their high activity effectively promoted the conversion of flavonoids in jujube juice into daidzein, which has lipid-lowering activity. This theory is consistent with the previously mentioned new substance daidzein (which has lipid-lowering function) produced after the fermentation of jujube juice, further corroborating the credibility of the inference. In addition, the active fermentation period of lactic acid bacteria juice is related to the content of fermentable sugars and the initial acidity in the juice. Since jujube itself has a low sugar content and high acidity, the fermentation time is relatively short.

[0078] The single-factor results serve as the original database for training the BP neural network model. To ensure sufficient sample size, each single-factor experiment was repeated six times, resulting in a total of 144 sets of experimental data.

[0079] 3.2 Optimization Results of BP Neural Network In the training of a BP neural network, R 2A larger value indicates a smaller MSE, signifying a better model fit, a smaller error between predicted and actual values, and a more reliable model. Experimental results are as follows: Figure 11 , Figure 12 As shown, the optimal model has 19 hidden layer nodes, corresponding to the maximum average R-value on the test set. 2 The error is 0.99474. The best training performance occurs in the 8th round, with an error of only -0.00473 and a mean squared error (MSE) of 0.0019074. This proves that the model is mature in training. Figure 13 As shown, it represents the fit between the predicted and actual values ​​of the three output indicators. The high overlap of the lines indicates that the neural network has a high degree of fit and the prediction results are relatively accurate, allowing for the prediction of the next output value.

[0080] like Figure 14 As shown, the fitted curves for the training set, validation set, test set, and all data are shown respectively. All datasets converge on a straight line with a slope close to 1, exhibiting a clear linear relationship; where the training set R... 2 =0.97977, validation set R 2 =0.98163, test set R 2 =0.98844, indicating that the dataset has good regression performance. Overall, R... 2 =0.98188, indicating that the BP neural network model has a high degree of fit, which can further ensure the accuracy of the simulation prediction results.

[0081] This invention uses three indicators—pancreatic lipase inhibition rate, glycocholate binding rate, and taurocholate binding rate—as evaluation indicators to jointly indicate in vitro lipid-lowering ability. Therefore, Pareto optimization was used to find an optimal solution. Figure 15 As shown, the optimal outputs predicted by Pareto optimization are: pancreatic lipase inhibition capacity = 66.81%, glycocholate binding capacity = 37.13%, and taurocholate binding capacity = 58.43%. The corresponding optimal fermentation process is a fermentation time of 24 hours, a fermentation temperature of 37°C, and an inoculum size of 3%.

[0082] The experimental results above indicate that, in order to investigate the effects of different inoculum amounts, fermentation times, and fermentation temperatures on the in vitro lipid-lowering ability of jujube juice and to obtain the optimal fermentation process parameters, this invention selected pancreatic lipase inhibition capacity, glycocholate and taurine binding capacity as evaluation indicators. Single-factor experiments were conducted, and a BP neural network model was built using the Matlab platform. 144 sets of data were trained, tested, and validated. Finally, Pareto optimization was used to predict the optimal process parameter output values. The single-factor experimental results showed that when the inoculum amount was 3%, all three in vitro lipid-lowering indicators were significantly higher than at other inoculum amounts. At a fermentation temperature of 37℃, the pancreatic lipase inhibition rate and taurine binding rate were significantly higher than at other temperatures. The peak value of the glycocholate binding rate also appeared at 37℃, but the difference from that at 36℃ was not significant. At a fermentation time of 24 h, the pancreatic lipase inhibition rate and taurine binding rate were the highest, while the peak value of the glycocholate binding rate appeared at 30 h. The BP neural network optimization results showed that the optimal fermentation process parameters were fermentation time of 24 h, fermentation temperature of 37 °C, and inoculum size of 3%. The predicted optimal outputs were: pancreatic lipase inhibition capacity = 66.81%, glycocholate binding capacity = 37.13%, and taurocholate binding capacity = 58.43%.

[0083] In summary, this invention starts with the lipid-lowering activity of fermented jujube juice, using a BP neural network combined with Pareto optimization as an optimization tool to comprehensively analyze three different indicators demonstrating lipid-lowering activity. The complex nonlinear problem involving three factors and three indicators is addressed by continuously adjusting the function parameters to achieve the closest fit to the database, ultimately predicting the input layer data corresponding to the optimal output result. This invention combines a BP neural network model with food processing optimization, providing a new solution for controlling product functionality and quality in the food processing industry, which operates in complex environments and is subject to interference from multiple factors.

[0084] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for fermenting a compound of lactic acid bacteria to enhance the antioxidant activity of jujube juice, characterized in that, The method includes the step of fermenting jujube juice using a compound bacteria to obtain fermented jujube juice; the compound bacteria are composed of Lactobacillus plantarum LP90 and Lactobacillus acidophilus LA85, and the jujube juice is obtained by juicing pitted jujubes.

2. The method for compound fermentation of lactic acid bacteria to enhance the antioxidant activity of jujube juice as described in claim 1, characterized in that, In the compound bacteria, the mass ratio of Lactobacillus plantarum LP90 to Lactobacillus acidophilus LA85 is (1-3):(1-3).

3. The method for compound fermentation of lactic acid bacteria to enhance the antioxidant activity of jujube juice as described in claim 2, characterized in that, The mass ratio of Lactobacillus plantarum LP90 to Lactobacillus acidophilus LA85 was 1:

2.

4. The method for compound fermentation of lactic acid bacteria to enhance the antioxidant activity of jujube juice as described in claim 1, characterized in that, The method for preparing the jujube juice includes: pitting jujubes, mixing them with 5-10 times their weight of purified water, pulping them at 30,000-40,000 r / min for 1 min, grinding them for 10-20 min, and filtering them to obtain the jujube juice.

5. The method for compound fermentation of lactic acid bacteria to enhance the antioxidant activity of jujube juice as described in claim 1, characterized in that, The fermentation conditions are as follows: inoculum size of 1%-5%, fermentation temperature of 30-39℃, and fermentation time of 6-48h.

6. The method for compound fermentation of lactic acid bacteria to enhance the antioxidant activity of jujube juice as described in claim 5, characterized in that, The fermentation conditions are as follows: inoculum size of 3%, fermentation temperature of 37°C, and fermentation time of 24 hours.

7. A fermented jujube juice, characterized in that, The fermented jujube juice is prepared using the fermentation method described in any one of claims 1-6.

8. The application of the fermented jujube juice as described in claim 7 in the preparation of antioxidant products.

9. The application of the fermented jujube juice as described in claim 7 in the preparation of lipid-lowering products.

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