A method for regulating fermentation quality of physalis alkekengi based on metabolic pathway analysis

Abstract

The application discloses a kind of based on metabolic pathway analysis's Iresine heratensis fermentation quality regulation method, it is related to food biotechnology field, including, the sample of different varieties Iresine heratensis is obtained, carries out non-targeted metabolomics analysis, obtains metabolite spectrum data;The metabolite spectrum data carries out difference analysis, constructs key difference metabolite dataset;Metabolic pathway enrichment analysis is carried out to the key difference metabolite dataset, determines core metabolic pathway;Regulate the process parameter in Iresine heratensis fermentation process, obtain the fermentation product after regulation;The quality of the fermentation product after regulation is verified, feedback optimization the process parameter, form closed loop quality regulation method;The application is determined core metabolic pathway by constructing key difference metabolite dataset and enrichment analysis, realizes from traditional experience type fermentation process optimization to the change of accurate regulation based on metabolic mechanism, solves the problem that fermentation quality in prior art is greatly blind, target is not clear.

Description

Technical Field

[0001] This invention relates to the field of food biotechnology, specifically to a method for regulating the fermentation quality of wild marmalade based on metabolic pathway analysis. Background Technology

[0002] Ground cherry (Physalis pubescens L.), a small berry resource with both nutritional and health benefits, has received widespread attention in the development of functional foods in recent years. With the continuous growth in consumer demand for natural and healthy products, the planting area of ​​ground cherry has been expanding year by year, and the development of its deep-processed products, especially fermented products, has become an important direction for extending the industrial chain and increasing added value. Fermentation technology can not only extend the shelf life of ground cherry but also generate new active ingredients through microbial metabolic transformation, improving the flavor and functional properties of the product.

[0003] Currently, research on the fermentation process of wild marmalade mainly focuses on traditional methods such as single-factor experiments and response surface methodology. These methods examine the effects of parameters like fermentation time, temperature, and inoculum size on apparent indicators such as total acidity and sensory scores to determine optimal process conditions. While these methods can improve product quality to some extent, they are essentially empirical optimizations based on a "black box" model. They lack a systematic understanding of the underlying metabolic mechanisms during fermentation, leading to vague targets for process control, significant blind spots in quality improvement, and difficulty in achieving targeted enrichment of active ingredients and precise enhancement of functional activities.

[0004] Therefore, how to reveal the molecular mechanism of the formation of the quality of wild physalis and establish a method for precise regulation of the fermentation process from the metabolic level has become a technical problem that urgently needs to be solved in this field.

[0005] In response to this problem, this application proposes a method for regulating the fermentation quality of wild marmalade based on metabolic pathway analysis. Summary of the Invention

[0006] The purpose of this invention is to provide a method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis, in order to solve the problems of empirical optimization based on the "black box" model in the prior art, which lacks a systematic understanding of the internal metabolic mechanisms in the fermentation process, resulting in vague targets for process control, blind improvement of quality, and difficulty in achieving targeted enrichment of product efficacy components and precise enhancement of functional activity.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis includes:

[0009] Samples of different varieties of Physalis pubescens were obtained and non-targeted metabolomics analysis was performed to obtain metabolite spectral data;

[0010] Differential analysis was performed on the metabolite spectrum data to identify key differential metabolites that distinguish varieties, and a dataset of key differential metabolites was constructed.

[0011] Metabolic pathway enrichment analysis was performed on the key differential metabolite dataset to identify the core metabolic pathways affecting the quality characteristics of wild physalis.

[0012] Based on the key nodes in the core metabolic pathway, the process parameters in the fermentation process of wild marmalade are adjusted to obtain the regulated fermentation product.

[0013] The quality of the post-fermentation product is verified, and the process parameters are optimized to form a closed-loop quality control method.

[0014] Furthermore, the process involves obtaining samples of different varieties of Physalis alkekengi and performing non-targeted metabolomics analysis to obtain metabolite spectral data, including:

[0015] Metabolites were extracted from lyophilized Physalis alkekengi powder. Data acquisition of the extract was performed using ultra-high performance liquid chromatography-high resolution mass spectrometry. By searching and comparing with a mass spectrometry database, a wide range of endogenous metabolites in the Physalis alkekengi sample were identified to generate metabolite spectral data containing the types of metabolites and their relative contents.

[0016] Furthermore, the differential analysis of the metabolite spectrum data, screening out key differential metabolites that distinguish varieties, and constructing a key differential metabolite dataset includes:

[0017] Principal component analysis and partial least squares discriminant analysis were used to perform multivariate statistical analysis on the metabolite spectral data. The key differential metabolites were identified by using variable importance projection values ​​greater than 1 and statistical significance differences less than or equal to 0.05 as screening criteria, combined with the fold changes of metabolites among different varieties. The classification effect was verified by hierarchical cluster analysis, and finally integrated to form the dataset of key differential metabolites.

[0018] Furthermore, the metabolic pathway enrichment analysis performed on the key differential metabolite dataset identifies the core metabolic pathways affecting the quality characteristics of *Physalis alkekengi*, including:

[0019] The dataset of key differential metabolites was mapped to the KEGG metabolic pathway database. Enrichment and topology analysis of metabolic pathways were performed using the MetPA database to identify metabolic pathways with significant enrichment of key differential metabolites and high influence factors. These pathways were then used as the core metabolic pathways that determine the inherent quality characteristics of Physalis alkekengi.

[0020] Furthermore, the process parameters during the fermentation of *Physalis alkekengi* are adjusted according to key nodes in the core metabolic pathway to obtain the adjusted fermentation product, including:

[0021] The process parameters for regulating the fermentation of *Physalis alkekengi* are used to obtain the regulated fermentation product. Specifically, this involves optimizing the fermentation process parameters, including inoculum size, fermentation temperature, fermentation time, and initial sugar content, through single-factor and response surface methodology experiments, targeting the rate-limiting enzymes or key metabolites in the core metabolic pathway. This promotes the metabolic flow towards the synthesis of target quality components, thereby obtaining the regulated fermentation product.

[0022] Furthermore, the optimization includes the following fermentation process parameters: inoculum size, fermentation temperature, fermentation time, and initial sugar content:

[0023] Using sensory scores and total acidity as response values, a four-factor, three-level response surface methodology was applied to establish a regression model between process parameters and fermentation product quality. The optimal combination of process parameters was then obtained through model analysis.

[0024] Furthermore, the method for quality control, which verifies the quality of the post-fermentation product and optimizes the process parameters to form a closed loop, includes:

[0025] The total phenol and total flavonoid content and in vitro antioxidant activity of the fermentation products after regulation were determined. At the same time, the dynamic changes of key active ingredients and flavor substances were analyzed by targeted or flavor metabolomics. The verification results were compared with the key differential metabolite dataset and core metabolic pathways. The optimization direction and range of process parameters were adjusted according to the comparison results.

[0026] Compared with existing technologies, this invention provides a method for regulating the fermentation quality of *Physalis alkekengi* based on metabolic pathway analysis. This invention constructs a dataset of key differential metabolites and uses enrichment analysis to identify core metabolic pathways, achieving a shift from traditional empirical fermentation process optimization to precise regulation based on metabolic mechanisms. This solves the technical problems of blindly improving fermentation quality and unclear targets in existing technologies, significantly improving the enrichment efficiency of target functional components in fermentation products and the targeted controllability of product quality. By feeding back the fermentation product quality verification results to the core metabolic pathways for comparison and optimization, this invention establishes a closed-loop linkage mechanism between metabolic analysis and process control, overcoming the limitations of optimizing single process parameters and achieving source regulation of the intrinsic quality of fermentation products and enhancement of functional activity at the molecular level. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0028] Figure 1 The flowchart illustrates a method for regulating the fermentation quality of wild marmalade based on metabolic pathway analysis, as provided in this embodiment of the invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] As attached Figure 1 As shown:

[0031] Example 1:

[0032] Quality control of *Phyllostachys martensii* fermentation based on flavonoid biosynthesis pathway:

[0033] This embodiment implements a method for controlling the fermentation quality of wild marmalade based on metabolic pathway analysis according to the following steps:

[0034] Step 1: Obtain samples of different varieties of Physalis alkekengi and perform non-targeted metabolomics analysis to obtain metabolite profile data.

[0035] Mature fruits of three main cultivated Physalis varieties in Heilongjiang Province, namely “Longsuanjiang No. 1” (variety A), “Longsuanjiang No. 3” (variety B) and “Golden Fruit” (variety C), were collected. 500g of each fruit was taken, and after removing the sepals, the fruits were immediately frozen with liquid nitrogen and stored in an ultra-low temperature freezer at -80℃.

[0036] Sample pretreatment: Weigh 100 mg of each variety of lyophilized *Physalis alkekengi* powder, add 1 mL of 70% methanol solution (containing internal standard 2-chloro-L-phenylalanine, concentration 0.3 mg / mL), vortex mix for 30 s, ultrasonically extract at room temperature for 15 min, centrifuge at 12000 rpm for 10 min at 4℃, and filter the supernatant through a 0.22 μm organic filter membrane to obtain the test solution.

[0037] Chromatographic conditions: A Waters ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 1.8 μm) was used at a column temperature of 40 °C. The injection volume was 2 μL, and the flow rate was 0.4 mL / min. Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was 0.1% formic acid acetonitrile solution. Gradient elution program: 0–2 min, 5% B; 2–6 min, 5%–30% B; 6–9 min, 30%–50% B; 9–12 min, 50%–80% B; 12–14 min, 80%–100% B; 14–16 min, 100% B; 16–16.1 min, 100%–5% B; 16.1–20 min, 5% B.

[0038] Mass spectrometry conditions: Thermo Q Exactive high-resolution mass spectrometry was used, with electrospray ionization source in both positive and negative ion modes for detection. Spray voltages were 3.8 kV (positive ions) and 3.2 kV (negative ions), capillary temperature was 320 °C, sheath gas flow rate was 40 arb, and auxiliary gas flow rate was 10 arb. The scan range was 70–1050 m / z, with a resolution of 70,000.

[0039] Data processing: Peak alignment, peak extraction, and deconvolution were performed using Compound Discoverer 3.1 software. Metabolites were identified using the mzCloud, ChemSpider, and HMDB databases. A total of 876 metabolites were identified, including 124 metabolites specific to variety A, 98 metabolites specific to variety B, and 112 metabolites specific to variety C. Metabolite spectral data containing the types and relative amounts of metabolites were obtained.

[0040] Step 2: Perform differential analysis on the metabolite spectrum data, screen out the key differential metabolites that distinguish varieties, and construct a dataset of key differential metabolites.

[0041] Multivariate statistical analysis was performed using SIMCA-P 14.1 software. Principal component analysis showed clear separation among varieties A, B, and C, with cumulative contribution rates of R²X = 0.856 and Q² = 0.792 for the three principal components. Partial least squares discriminant analysis (PSDA) model parameters were R²Y = 0.934 and Q² = 0.887, and the model was not overfitted after 200 permutation tests.

[0042] Using the variable importance projection value (VIP) > 1.0, t-test p < 0.05, and fold change (|fold change| ≥ 2) as screening criteria, 156 differentially expressed metabolites were identified in the variety A vs B comparison group (89 upregulated and 67 downregulated), 187 differentially expressed metabolites were identified in the variety A vs C comparison group (102 upregulated and 85 downregulated), and 143 differentially expressed metabolites were identified in the variety B vs C comparison group (71 upregulated and 72 downregulated).

[0043] Differential metabolites present in all three comparison groups were selected, and 72 key differential metabolites were identified through volcano plot analysis. Hierarchical clustering heatmaps showed that these 72 metabolites could completely distinguish the three varieties, and the clustering pattern was consistent with the phylogenetic relationship of the varieties. A dataset of key differential metabolites was constructed, including metabolite name, molecular formula, mass spectrometry information, relative content, and VIP value.

[0044] Step 3: Perform metabolic pathway enrichment analysis on the key differential metabolite dataset to identify the core metabolic pathways affecting the quality characteristics of wild physalis.

[0045] Seventy-two key differentially expressed metabolites were imported into the MetaboAnalyst 5.0 platform, and metabolic pathway enrichment and topology analyses were performed based on the KEGG database. A total of 38 metabolic pathways were enriched, among which 5 pathways had an impact value >0.1 and p <0.05: flavonoid and flavonol biosynthesis pathway (impact value 0.32, p=0.002), phenylpropane biosynthesis pathway (impact value 0.28, p=0.004), flavonoid biosynthesis pathway (impact value 0.24, p=0.008), tricarboxylic acid cycle (impact value 0.18, p=0.015), and amino acid metabolism pathway (impact value 0.12, p=0.023).

[0046] Further analysis revealed that the metabolites (naringenin, dihydroquercetin, and quercetin) corresponding to key enzymes (phenylalanine ammonia-lyase, chalcone synthase, and flavanone-3-hydroxylase) in the flavonoid and flavonol biosynthesis pathway showed the most significant differences among the three varieties (VIP>1.8, p<0.001), and were highly positively correlated with total flavonoid content (variety A 12.36 mg / g, variety B 8.94 mg / g, variety C 15.27 mg / g) and antioxidant activity (DPPH scavenging rate: variety A 78.6%, variety B 62.3%, variety C 85.2%) (r>0.85). Therefore, the flavonoid and flavonol biosynthesis pathway was identified as the core metabolic pathway.

[0047] Step 4: Based on the key nodes in the core metabolic pathway, adjust the process parameters during the fermentation of *Physalis alkekengi* to obtain the regulated fermentation product.

[0048] To target key nodes in the biosynthetic pathways of flavonoids and flavonols—chalcone synthase and naringenin—a regulatory strategy was designed: to promote the release of bound flavonoid glycosides and the conversion of flavonoid aglycones by optimizing fermentation conditions.

[0049] Variety C (Golden Berry), which has the highest content of key differential metabolites, was selected as raw material. After washing, it was blanched for 30 seconds, crushed and pulped, and 0.1% pectinase was added for enzymatic hydrolysis at 50℃ for 90 minutes. The initial sugar content was adjusted to 18%, and a mixed strain of Lactobacillus plantarum and Streptococcus thermophilus (1:1 ratio) was inoculated at a rate of 4%.

[0050] Based on the results of single-factor preliminary experiments, response surface methodology was used to optimize four factors at three levels: fermentation time (4, 6, 8 days), fermentation temperature (32, 37, 42℃), initial sugar content (14, 18, 22%), and strain ratio (1:2, 1:1, 2:1). The total flavonoid content and the relative content of naringenin were used as response values.

[0051] The optimal process parameters were obtained as follows: fermentation time 6.5 days, fermentation temperature 35℃, initial sugar content 18%, and inoculum ratio 1:1. Fermentation was carried out under these conditions to obtain the regulated fermentation product F-PC-1, which showed a total flavonoid content of 19.84 mg / g and a relative naringin content 3.2 times higher than that of the unfermented raw material.

[0052] Step 5: Verify the quality of the post-fermentation product, and use feedback to optimize the process parameters, forming a closed-loop quality control method.

[0053] Comprehensive quality validation of fermentation product F-PC-1:

[0054] (1) Nutritional composition analysis: The total phenol content was determined by the Folin-Ciocalteu method with gallic acid as the standard. The results showed that the total phenol content of F-PC-1 was 5.68 mg GAE / mL, which was 66.1% higher than that of the unfermented raw material (3.42 mg GAE / mL). The total flavonoid content was determined by the aluminum trichloride colorimetric method with rutin as the standard. The total flavonoid content of F-PC-1 was 19.84 mg RE / mL, which was 60.5% higher than that of the unfermented raw material (12.36 mg RE / mL). The total acid content was determined by the acid-base titration method according to GB / T12456-2021. The total acid content of F-PC-1 was 11.23 g / L, which was 292.7% higher than that of the unfermented raw material (2.86 g / L).

[0055] (2) Antioxidant activity: The DPPH free radical scavenging rate was determined by the DPPH method, and the IC50 of F-PC-1 was [value missing]. 50 The value was 0.86 mg / mL, compared to the unfermented feedstock (IC50). 50 =1.92 mg / mL) increased by 55.2%; ABTS free radical scavenging rate was determined by the ABTS method, and the IC50 of F-PC-1 was 1.92 mg / mL. 50 The value was 0.73 mg / mL, compared to the unfermented feedstock (IC50). 50 =1.68mg / mL) increased by 56.5%; the total antioxidant capacity was determined by the FRAP method, with FeSO4 as the standard, and the FRAP value of F-PC-1 was 2.86mmol / L, which was 85.7% higher than that of the unfermented raw material (1.54mmol / L).

[0056] (3) Metabolomics validation: Targeted metabolomics analysis was performed on F-PC-1 to detect changes in the content of 12 key metabolites in the flavonoid and flavonol biosynthetic pathways. The results showed that the content of naringenin increased by 3.2 times, quercetin by 2.8 times, kaempferol by 2.1 times, and dihydroquercetin by 1.9 times, validating the effectiveness of the regulatory strategy.

[0057] The validation results were compared with the key differential metabolite dataset constructed in step 2. It was found that the metabolite profile of F-PC-1 was highly consistent with the metabolic characteristics of raw material C, and the content of key metabolites was higher than that of the raw material. Based on the comparison results, the fermentation time was fine-tuned from 6.5 days to 6.2 days, and the strain ratio was fine-tuned from 1:1 to 1.2:1, forming an optimized combination of process parameters. After secondary validation, the total flavonoid content under the optimized process parameters was further increased to 20.31 mg / g.

[0058] Example 2:

[0059] Quality regulation of *Phyllostachys pubescens* fermentation based on the phenylpropanone biosynthesis pathway

[0060] This embodiment is implemented according to the following steps:

[0061] Step 1: Obtain samples of different varieties of Physalis alkekengi and perform non-targeted metabolomics analysis to obtain metabolite profile data.

[0062] Three Physalis varieties, namely “Longsuanjiang No. 2” (variety D), “Longsuanjiang No. 4” (variety E) and “Handijiangguo” (variety F), provided by the Heilongjiang Academy of Agricultural Sciences, were selected. 500g of mature fruit were collected from each variety, and the sepals were removed before freezing and storage at -80℃.

[0063] Sample pretreatment was the same as in Example 1. Chromatographic conditions were performed using an Agilent 1290 Infinity II ultra-high performance liquid chromatography system equipped with a ZORBAX Eclipse Plus C18 column (2.1 × 100 mm, 1.8 μm). Mass spectrometry was performed using an Agilent 6546Q-TOF high-resolution mass spectrometer. Ion source parameters were: gas temperature 325 °C, drying gas flow rate 10 L / min, nebulizer pressure 35 psi, sheath gas temperature 375 °C, sheath gas flow rate 11 L / min, capillary voltage 3500 V (positive ions) and 3000 V (negative ions), and fragmentation voltage 135 V.

[0064] Data acquisition was performed using Agilent MassHunter Workstation software, and data processing was performed using Profinder B.10.0 and MPP B.14.9 software. A total of 802 metabolites were identified, including 89 metabolites specific to variety D, 76 metabolites specific to variety E, and 103 metabolites specific to variety F. Metabolite chromatographic data were obtained.

[0065] Step 2: Perform differential analysis on the metabolite spectrum data, screen out the key differential metabolites that distinguish varieties, and construct a dataset of key differential metabolites.

[0066] Principal component analysis and partial least squares discriminant analysis were performed using SIMCA 17.0 software. Model parameters: R2 X=0.823, R 2 Y=0.905, Q 2 =0.869. Using VIP>1.2, p<0.01 and |fold change|≥1.5 as screening criteria, 128 differentially expressed metabolites were screened in the D vs E comparison group, 152 in the D vs F comparison group, and 119 in the E vs F comparison group.

[0067] Volcano plots and Venn analysis identified 58 common differentially expressed metabolites across the three comparison groups, which were designated as key differential metabolites. Hierarchical cluster analysis showed that these 58 metabolites clearly distinguished the three groups. A dataset of key differential metabolites was constructed, including metabolite name, retention time, mass-to-charge ratio, secondary fragmentation, and relative abundance.

[0068] Step 3: Perform metabolic pathway enrichment analysis on the key differential metabolite dataset to identify the core metabolic pathways affecting the quality characteristics of wild physalis.

[0069] MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis, and a total of 32 metabolic pathways were enriched. The influence value of the phenylpropane biosynthesis pathway was 0.31 (p=0.001), the influence value of the flavonoid biosynthesis pathway was 0.27 (p=0.003), the influence value of the flavonoid and flavonol biosynthesis pathway was 0.23 (p=0.006), the influence value of the tyrosine metabolism pathway was 0.15 (p=0.018), and the influence value of the tryptophan metabolism pathway was 0.11 (p=0.027).

[0070] In-depth analysis revealed that key metabolites in the phenylpropane biosynthesis pathway—caffeic acid, p-coumaric acid, ferulic acid, and sinapic acid—differed significantly among the three varieties (VIP>1.5, p<0.005), and were highly correlated with total phenol content (4.86 mg GAE / g for variety D, 3.92 mg GAE / g for variety E, and 5.43 mg GAE / g for variety F) and ABTS free radical scavenging capacity (72.4% for variety D, 58.7% for variety E, and 81.3% for variety F) (r>0.82). Therefore, the phenylpropane biosynthesis pathway was identified as the core metabolic pathway.

[0071] Step 4: Based on the key nodes in the core metabolic pathway, adjust the process parameters during the fermentation of *Physalis alkekengi* to obtain the regulated fermentation product.

[0072] To target the key node in the phenylpropane biosynthesis pathway—phenylalanine ammonia-lyase and its substrate phenylalanine—a regulatory strategy was designed: to promote the accumulation of phenolic acids by adding precursor substances and optimizing fermentation conditions.

[0073] Variety F (cold-region berries), which has the highest content of key differential metabolites, was selected as raw material. After washing, the berries were blanched for 30 seconds, crushed, and pulped. A complex enzyme system of 0.15% pectinase and 0.05% cellulase was added, and the mixture was enzymatically hydrolyzed at 45℃ for 120 minutes. The initial sugar content was adjusted to 20%, and a mixed inoculum of Kluyveromyces marxianus and Lactobacillus plantarum (ratio 1:2) was inoculated at a rate of 5%, with 0.5 g / L of L-phenylalanine added as a precursor.

[0074] A Box-Behnken design was used to optimize four factors at three levels: fermentation time (3, 5, 7 days), fermentation temperature (28, 34, 40℃), initial pH (3.5, 4.0, 4.5), and inoculum ratio (1:1, 1:2, 2:1). Total phenol content and relative p-coumaric acid content were used as response values.

[0075] The optimal process parameters were obtained as follows: fermentation time 5.8 days, fermentation temperature 32℃, initial pH 4.0, and inoculum ratio 1:2. Under these conditions, the regulated fermentation product F-PC-2 was obtained, with a total phenol content of 6.85 mg GAE / mL and a relative p-coumaric acid content 2.6 times higher than that of the unfermented raw material.

[0076] Step 5: Verify the quality of the post-fermentation product, and use feedback to optimize the process parameters, forming a closed-loop quality control method.

[0077] Comprehensive quality validation of fermentation product F-PC-2:

[0078] (1) Nutritional composition analysis: The total phenol content was determined by the Folin-Ciocalteu method. The total phenol content of F-PC-2 was 6.85 mg GAE / mL, which was 66.3% higher than that of the unfermented raw material (4.12 mg GAE / mL); the total flavonoid content was 17.28 mg RE / mL, which was 59.3% higher than that of the unfermented raw material (10.85 mg RE / mL); the total sugar content was determined by the 3,5-dinitrosalicylic acid method. The total sugar content of F-PC-2 was 4.28 g / 100 mL, which was 65.4% lower than that of the unfermented raw material (12.36 g / 100 mL); the total acid content was 10.86 g / L, which was 270.6% higher than that of the unfermented raw material (2.93 g / L).

[0079] (2) Antioxidant activity: DPPH free radical scavenging rate IC 50 The value was 0.79 mg / mL, compared to the unfermented feedstock (IC50). 50 =1.76mg / mL) increased by 55.1%; ABTS free radical scavenging rate IC50 increased by 55.1%; 50 The value was 0.68 mg / mL, compared to the unfermented feedstock (IC50).50 =1.54 mg / mL) increased by 55.8%; the hydroxyl radical scavenging rate was determined by the salicylic acid method, and the scavenging rate of F-PC-2 was 76.8%, which was 59.3% higher than that of the unfermented raw material (48.2%).

[0080] (3) Flavor compound analysis: Volatile flavor compounds were determined by GC-MS, and a total of 52 volatile components were identified, including 18 esters (accounting for 42.3%), 12 alcohols (accounting for 23.6%), 8 ketones (accounting for 15.2%), and 6 acids (accounting for 8.7%). The contents of key aroma compounds ethyl acetate, ethyl hexanoate, and ethyl octanoate were increased by 1.8-2.3 times compared with traditional fermentation methods.

[0081] (4) In vitro efficacy evaluation: An LPS-induced RAW264.7 macrophage inflammation model was used, and the expression of inflammatory factors IL-6 and TNF-α was detected by ELISA. The results showed that the expression level of IL-6 in the F-PC-2 treatment group (100 μg / mL) was reduced by 52.3% and the expression level of TNF-α was reduced by 47.8% compared with the model group, indicating significant anti-inflammatory activity. An H2O2-induced HepG2 cell oxidative damage model was used, and the level of reactive oxygen species (ROS) was detected by flow cytometry. The ROS level in the F-PC-2 treatment group was reduced by 43.6% compared with the model group.

[0082] Comparing the validation results with the key differential metabolite dataset from step 2 revealed a significant increase in the content of phenylpropanoid compounds in the F-PC-2 metabolite profile, along with the addition of two new phenylpropanoid derivatives. Based on this comparison, the L-phenylalanine addition was adjusted from 0.5 g / L to 0.6 g / L, and the fermentation time was fine-tuned from 5.8 days to 5.5 days, resulting in an optimized combination of process parameters. Secondary validation showed that the optimized total phenol content reached 7.02 mg GAE / mL.

[0083] Comparative example:

[0084] Fermented maize products prepared using traditional empirical optimization methods:

[0085] The fermentation product of wild sourdough was prepared using conventional fermentation technology, without metabolic pathway analysis:

[0086] Raw material processing: Select commercially available ground cherries (the variety is a local mixed variety), wash and blanch for 30 seconds, crush and pulp, add 0.1% pectinase and enzymatically hydrolyze at 50℃ for 90 minutes.

[0087] Fermentation process optimization: Fermentation conditions were optimized using a combination of single-factor experiments and response surface methodology. With an initial sugar content of 18%, and using total acid content as the response value, the fermentation time (4, 5, 6, 7, 8 days), fermentation temperature (30, 35, 40, 45℃), and lactic acid bacteria inoculum size (2%, 3%, 4%, 5%, 6%) were optimized. Analysis using Design-Expert 8.0 software yielded the optimal process parameters: fermentation time 6 days, fermentation temperature 37℃, inoculum size 4%, and initial sugar content 18%.

[0088] Fermentation implementation: The initial sugar content of the juice was adjusted to 18%, and a single strain of Lactobacillus plantarum was inoculated at a rate of 4%. Fermentation was carried out at a constant temperature of 37℃ for 6 days to obtain the control fermentation product CF-1.

[0089] Product Analysis: The total phenol content, determined by the Folin-Ciocalteu method, was 4.23 mg GAE / mL; the total flavonoid content was 13.56 mg RE / mL; the total acid content was 10.67 g / L; and the DPPH free radical scavenging rate IC50 was [not specified]. 50 The value was 1.24 mg / mL, and the IC50 value for ABTS free radical scavenging was 1.24 mg / mL. 50 The value was 1.08 mg / mL.

[0090] The overall effects of Example 1, Example 2, and the comparative example are compared in Table 1 below;

[0091] Table 1

[0092] Indicator Categories Specific indicators Example 1 (F-PC-1) Example 2 (F-PC-2) Comparative Example (CF-1) Data acquisition methods core metabolic pathways Targeted pathways Flavonoid and flavonol biosynthesis Phenylacetane biosynthesis Untargeted KEGG enrichment analysis Key metabolites relative content of naringenin 3.2 times↑ 1.8 times↑ 1.4 times↑ UPLC-Q-TOF-MS / MS Targeted Quantification relative content of p-coumaric acid 2.1 times↑ 2.6 times↑ 1.3 times↑ UPLC-Q-TOF-MS / MS Targeted Quantification Nutritional components Total phenols (mg GAE / mL) 5.68 ± 0.24 6.85 ± 0.31 4.23 ± 0.18 Folin-Ciocalteu colorimetric method, λ=765nm Total flavonoids (mg RE / mL) 19.84 ± 0.86 17.28 ± 0.72 13.56 ± 0.58 <![CDATA[AlCl3 colorimetric method, λ = 510 nm]]> Total acid (g / L) 11.23 ± 0.48 10.86 ± 0.45 10.67 ± 0.43 GB / T 12456-2021 Acid-Base Titration Method Total sugar (g / 100mL) 4.52 ± 0.19 4.28 ± 0.18 5.13 ± 0.22 3,5-Dinitrosalicylic acid colorimetric method, λ=540nm Antioxidant activity <![CDATA[DPPH scavenging rate IC 50 (mg / mL)]]> 0.86 ± 0.04 0.79 ± 0.03 1.24 ± 0.05 DPPH method, λ=517nm <![CDATA[ABTS clearance rate IC 50 (mg / mL)]]> 0.73 ± 0.03 0.68 ± 0.03 1.08 ± 0.04 ABTS method, λ=734nm Total antioxidant capacity (FRAP) (mmol / L) 2.86 ± 0.12 2.61 ± 0.11 1.83 ± 0.08 FRAP method, λ=593nm Flavor substances Total number of volatile components 48 52 35 HS-SPME-GC-MS Ester content 38.6% 42.3% 31.2% GC-MS peak area normalization method Anti-inflammatory activity IL-6 inhibition rate 48.6% 52.3% 32.8% ELISA method, LPS-induced RAW264.7 cells TNF-α inhibition rate 44.2% 47.8% 29.5% ELISA method, LPS-induced RAW264.7 cells Oxidative stress ROS reduction rate 41.5% 43.6% 27.3% DCFH-DA probe, flow cytometry

[0093] Note: "↑" indicates the increase factor compared to unfermented raw materials; "bold" indicates the best value among the same indicators; all data are the mean ± standard deviation of three parallel experiments, and p < 0.05 for the examples compared with the comparative examples.

[0094] Data Acquisition Instructions:

[0095] UPLC-Q-TOF-MS / MS Targeted Quantification: Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry is used to quantitatively analyze target metabolites in MRM mode, and the relative content is calculated using a standard curve.

[0096] Total phenols determination: The Folin-Ciocalteu colorimetric method was used. After the sample reacted with Folin-Ciocalteu reagent, the absorbance was measured at 765 nm. A standard curve was established using gallic acid as a standard.

[0097] Total flavonoids determination: The aluminum trichloride colorimetric method was used. After the sample reacted with AlCl3, the absorbance was measured at 510 nm. A standard curve was established using rutin as a standard.

[0098] Total acid determination: According to GB / T 12456-2021 "Determination of total acid in food" acid-base titration method, phenolphthalein was used as an indicator and NaOH standard solution was used for titration.

[0099] DPPH scavenging rate: After the sample reacted with DPPH ethanol solution for 30 min, the absorbance was measured at 517 nm, and the scavenging rate was calculated as IC50. 50 Value representation.

[0100] ABTS removal rate: ABTS reacts with potassium persulfate to form ABTS. + • Working solution: After adding the sample, react for 6 minutes and then measure the absorbance at 734 nm.

[0101] FRAP method: Under acidic conditions, antioxidants reduce Fe. 3+ -TPTZ is blue Fe 2+ -TPTZ, absorbance was measured at 593 nm, with FeSO4 as the standard.

[0102] HS-SPME-GC-MS: Headspace solid-phase microextraction combined with gas chromatography-mass spectrometry, using a DB-WAX column (30m×0.25mm×0.25μm), temperature program: 40℃ for 3 min, then ramped to 120℃ at 5℃ / min, and then ramped to 230℃ at 8℃ / min for 6 min.

[0103] ELISA method: The cell culture supernatant was processed according to the ELISA kit instructions. The absorbance was measured at 450 nm, and the IL-6 and TNF-α contents were calculated based on the standard curve.

[0104] Flow cytometry: HepG2 cells were subjected to H2O2-induced oxidative damage, then incubated with DCFH-DA probe (10 μM) at 37°C for 30 min. After washing with PBS, the fluorescence intensity was detected by flow cytometry (excitation wavelength 488 nm, emission wavelength 525 nm).

[0105] Technical effect analysis:

[0106] The comparative data shows that, compared with the traditional empirically optimized comparative example, the targeted regulation of the fermentation process based on metabolic pathway analysis in Examples 1 and 2 has significantly improved several key indicators:

[0107] Nutritional components: The total phenol content increased by 34.3% and 61.9% respectively, and the total flavonoid content increased by 46.3% and 27.4% respectively, indicating that the regulation strategy based on the core metabolic pathway can effectively promote the accumulation of phenolic substances.

[0108] Antioxidant activity: DPPH scavenging rate IC 50The values ​​decreased by 30.6% and 36.3% respectively, and the ABTS clearance rate IC 50 The values ​​decreased by 32.4% and 37.0% respectively, while the total antioxidant capacity increased by 56.3% and 42.6% respectively, indicating a significant enhancement in antioxidant performance.

[0109] Flavor quality: The total number of volatile components increased by 37.1% and 48.6% respectively, and the proportion of esters increased by 23.7% and 35.6% respectively, resulting in a significant improvement in flavor quality.

[0110] Functional activity: In terms of anti-inflammatory activity, Example 2 showed that the inhibition rates of IL-6 and TNF-α were increased by 59.5% and 62.0% respectively compared with the control group; in terms of anti-oxidative stress, the reduction rate of reactive oxygen species was increased by 52.0% and 59.7% respectively, and the functional activity was significantly enhanced.

[0111] Example 1 and Example 2 target different core metabolic pathways (flavonoid and flavonol biosynthesis pathway and phenylpropane biosynthesis pathway), and each has its own advantages in specific indicators, but both are significantly better than the comparative examples that do not perform metabolic pathway analysis, thus verifying the technical effect of the method of the present invention.

[0112] As can be seen from the above, this invention, by constructing a dataset of key differential metabolites and identifying core metabolic pathways through enrichment analysis, has achieved a shift from traditional empirical fermentation process optimization to precise regulation based on metabolic mechanisms. This solves the technical problems of blind improvement in fermentation quality and unclear targets in existing technologies, and significantly improves the enrichment efficiency of target active ingredients in fermentation products and the directional controllability of product quality.

[0113] This invention establishes a closed-loop linkage mechanism between metabolic analysis and process control by feeding back the quality verification results of fermentation products to the core metabolic pathway for comparison and optimization. This overcomes the limitations of optimizing a single process parameter and achieves source control of the intrinsic quality of fermentation products and enhancement of functional activity at the molecular level.

[0114] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis, characterized in that, include: Samples of different varieties of Physalis pubescens were obtained and non-targeted metabolomics analysis was performed to obtain metabolite spectral data; Differential analysis was performed on the metabolite spectrum data to identify key differential metabolites that distinguish varieties, and a dataset of key differential metabolites was constructed. Metabolic pathway enrichment analysis was performed on the key differential metabolite dataset to identify the core metabolic pathways affecting the quality characteristics of wild physalis. Based on the key nodes in the core metabolic pathway, the process parameters in the fermentation process of wild marmalade are adjusted to obtain the regulated fermentation product. The quality of the post-fermentation product is verified, and the process parameters are optimized to form a closed-loop quality control method.

2. The method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis according to claim 1, characterized in that, The process involves obtaining samples from different varieties of Physalis alkekengi, performing non-targeted metabolomics analysis, and obtaining metabolite profile data, including: Metabolites were extracted from lyophilized Physalis alkekengi powder. Data acquisition of the extract was performed using ultra-high performance liquid chromatography-high resolution mass spectrometry. By searching and comparing with a mass spectrometry database, a wide range of endogenous metabolites in the Physalis alkekengi sample were identified to generate metabolite spectral data containing the types of metabolites and their relative contents.

3. The method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis according to claim 1, characterized in that, The differential analysis of the metabolite spectrum data is performed to screen out key differential metabolites that distinguish varieties, and a key differential metabolite dataset is constructed, including: Principal component analysis and partial least squares discriminant analysis were used to perform multivariate statistical analysis on the metabolite spectral data. The key differential metabolites were identified by using variable importance projection values ​​greater than 1 and statistical significance differences less than or equal to 0.05 as screening criteria, combined with the fold changes of metabolites among different varieties. The classification effect was verified by hierarchical cluster analysis, and finally integrated to form the dataset of key differential metabolites.

4. The method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis according to claim 1, characterized in that, The metabolic pathway enrichment analysis performed on the key differential metabolite dataset identified the core metabolic pathways affecting the quality characteristics of wild groundcherry, including: The dataset of key differential metabolites was mapped to the KEGG metabolic pathway database. Enrichment and topology analysis of metabolic pathways were performed using the MetPA database to identify metabolic pathways with significant enrichment of key differential metabolites and high influence factors. These pathways were then used as the core metabolic pathways that determine the inherent quality characteristics of Physalis alkekengi.

5. The method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis according to claim 1, characterized in that, The process parameters during the fermentation of Physalis alkekengi are adjusted according to key nodes in the core metabolic pathway to obtain the adjusted fermentation products, including: The process parameters for regulating the fermentation of *Physalis alkekengi* are used to obtain the regulated fermentation product. Specifically, this involves optimizing the fermentation process parameters, including inoculum size, fermentation temperature, fermentation time, and initial sugar content, through single-factor and response surface methodology experiments, targeting the rate-limiting enzymes or key metabolites in the core metabolic pathway. This promotes the metabolic flow towards the synthesis of target quality components, thereby obtaining the regulated fermentation product.

6. The method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis according to claim 5, characterized in that, The optimization includes fermentation process parameters such as inoculum size, fermentation temperature, fermentation time, and initial sugar content, including: Using sensory scores and total acidity as response values, a four-factor, three-level response surface methodology was applied to establish a regression model between process parameters and fermentation product quality. The optimal combination of process parameters was then obtained through model analysis.

7. The method for quality control of *Physalis alkekengi* fermentation based on metabolic pathway analysis according to claim 1, characterized in that, The method for quality control, which verifies the quality of the post-fermentation product and optimizes the process parameters to form a closed loop, includes: The total phenol and total flavonoid content and in vitro antioxidant activity of the fermentation products after regulation were determined. At the same time, the dynamic changes of key active ingredients and flavor substances were analyzed by targeted or flavor metabolomics. The verification results were compared with the key differential metabolite dataset and core metabolic pathways. The optimization direction and range of process parameters were adjusted according to the comparison results.

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