Method for analyzing component difference between Maotai-flavor Daqu and Daqu-making wheat by using non-targeted metabonomics

Abstract

The invention discloses a method for analyzing component difference between Maotai-flavor yeast and yeast-making wheat by using non-targeted metabonomics. The method comprises the following steps: S1, pre-treating a sample; s2, detecting each sample by adopting the combination of an ultra-high performance liquid chromatograph and a quadrupole time-of-flight mass spectrometer; s3, acquiring original data, performing peak extraction, peak alignment and normalization by adopting metabonomics data analysis software, and evaluating the overall difference and intra-group variation among sample groups by adopting principal component analysis; constructing a grouping discrimination model through discriminant analysis of an orthogonal partial least square method; determining differential metabolites of the two groups of samples by taking variable projection importance (VIP) greater than 1 and statistical significance P less than 0.01 as a primary screening standard and combining a secondary screening standard that the difference multiple log2FC greater than 2 is up-regulated and the difference multiple log2FC less than-2 is down-regulated; s4, performing compound structure and category annotation on the screened differential metabolites through a metabolome database; and performing pathway enrichment analysis by adopting a statistical test method, and determining a core metabolic pathway enriched by the differential metabolites.

Description

Technical Field

[0001] This invention relates to the field of non-targeted metabolomics analysis technology, specifically to a method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics. Background Technology

[0002] As one of the important aroma types of Chinese baijiu, the unique flavor and quality of Maotai-flavor baijiu depend on the brewing process of Maotai-flavor Daqu (a type of starter culture). Wheat used in Daqu production, as the core raw material, directly determines the saccharification and fermentation capacity and flavor precursor reserves of the Daqu during its production process, thus influencing the final style and quality of the baijiu. In the Maotai-flavor baijiu brewing industry chain, the Daqu production stage is a crucial link between raw materials and finished baijiu. The protein, fat, starch, and other nutrients in the wheat used in Daqu production are not only the core substrates for the growth and reproduction of fermenting microorganisms (such as Gram-positive bacteria and enzyme-producing microorganisms) in Daqu, but also important precursors for the subsequent synthesis of flavor substances in the liquor (such as esters, organic acids, and amino acid derivatives). Therefore, clarifying the component differences and metabolic succession patterns between wheat used in Daqu production and Maotai-flavor Daqu is of significant theoretical and practical importance for optimizing the Daqu production process, stabilizing Daqu quality, and improving the quality of baijiu.

[0003] Currently, research on raw materials for koji making and daqu (a type of starter culture) has made some progress. In existing technologies, scholars such as Wang Yang have explored the relationship between microorganisms and flavor substances in koji-making wheat through microbial community analysis and volatile flavor component detection; Shang Baohua, Li Yingjie, and others have conducted research on the physicochemical indicators (such as protein content and starch gelatinization characteristics) of different wheat varieties, confirming the impact of wheat variety differences on daqu quality and baijiu yield; in addition, some studies have focused on the regulatory effects of process parameters such as temperature and moisture on daqu enzyme activity (such as α-amylase and protease), attempting to improve daqu performance through process optimization. These studies provide basic data for understanding the koji-making process, but still have significant limitations in terms of technical means and research depth, making it difficult to meet the current needs of the sauce-flavored baijiu industry for precise and systematic component analysis.

[0004] Current research focuses primarily on comparing component content or describing microbial community structure, lacking in-depth exploration of the metabolic pathways behind differentially expressed components. Furthermore, existing technologies often employ traditional physicochemical analysis methods (such as the Kjeldahl method for protein determination, Soxhlet extraction for fat determination, and anthrone colorimetric method for starch determination) or targeted detection techniques (such as gas chromatography-mass spectrometry (GC-MS) for detecting specific volatile components). Traditional physicochemical analysis can only determine the total amount of macronutrients such as moisture, protein, fat, and starch, failing to reflect the micro-metabolites (such as lipid derivatives, amino acid peptides, and vitamin derivatives) generated during the koji-making process. Targeted detection techniques, on the other hand, can only perform quantitative analysis of pre-defined, known metabolites, exhibiting significant blind spots in the detection of unknown intermediate products or novel flavor precursors produced by microbial metabolism during koji-making.

[0005] In summary, existing technologies for analyzing the differences in components between wheat used in koji making and soy sauce-flavored koji have three major problems: incomplete detection coverage, insufficient mechanism analysis, and inconsistent methodological standards. Therefore, developing a method that can comprehensively, accurately, and systematically analyze the differences in components between soy sauce-flavored koji and wheat used in koji making has become a key requirement for solving the current bottlenecks in optimizing the koji-making process and improving the quality of soy sauce-flavored baijiu. Summary of the Invention

[0006] The present invention aims to provide a method for analyzing the differences in components between soy sauce-flavored koji and the wheat used for koji production using non-targeted metabolomics, which can comprehensively, accurately, and systematically analyze the differences in components between soy sauce-flavored koji and the wheat used for koji production.

[0007] To achieve the above objectives, this application provides the following technical solution: A method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in its production using non-targeted metabolomics includes: S1. Sample pretreatment: The wheat and soy sauce-flavored koji samples were pulverized and then divided into quarters using the quartering method. They were stored separately in a low-temperature environment. A mixed extract of organic solvent and water was added, and the samples were vortexed, ultrasonicated in an ice-water bath, and then the supernatant was concentrated and dried under vacuum after centrifugation to obtain dried products. The dried products were then added to a complex solution of acetonitrile and water, vortexed, ultrasonicated in a water bath, and centrifuged. The supernatant was collected to obtain the samples. S2. Quality control sample testing: Each sample was tested using an ultra-high performance liquid chromatograph coupled with a quadrupole time-of-flight mass spectrometer. S3. Data Processing and Differential Metabolite Screening: Raw data were acquired, and peak extraction, alignment, and normalization were performed using metabolomics data analysis software. Principal component analysis was used to assess the overall differences between sample groups and the variability within groups. A grouping discriminant model was constructed using orthogonal partial least squares discriminant analysis. The primary screening criteria were variable projection importance (VIP) > 1 and statistical significance P < 0.01, combined with the secondary screening criteria of log2FC > 2 for upregulation and log2FC < -2 for downregulation, to determine the differential metabolites between the two groups of samples. S4. Metabolic Pathway Annotation: The screened differentially metabolites were annotated with compound structures and categories using a metabolomics database; pathway enrichment analysis was performed using statistical tests, with the resultant value being logarithmic. 10 Using (P) > 1.3 as the standard, the core metabolic pathways for the enrichment of differential metabolites were identified.

[0008] As a preferred method, weigh 40-60 mg of the pulverized sample, add 800-1200 μL of a mixed extraction solution consisting of a polar organic solvent and water, vortex for 20-40 s, grind at a frequency of 40-50 Hz for 8-12 min, and sonicate in an ice-water bath for 8-12 min; after standing at -25℃ to -15℃ for 0.8-1.2 h, centrifuge at 3-5℃ and 10000-14000 r / min for 12-18 min, and take 400-600 μL of the supernatant for vacuum concentration and drying; add 140-180 μL of a complex solution consisting of acetonitrile and water in a volume ratio of 1:1 to the dried product, vortex for 20-40 s, sonicate in an ice-water bath for 8-12 min, and then centrifuge at 3-5℃ and 10000-14000 r / min for 12-18 min, and take 100-140 μL of the supernatant for detection.

[0009] As a preferred method, a reversed-phase C18 column was used. Mobile phase A was an aqueous solution containing 0.08%-0.12% formic acid, and mobile phase B was an acetonitrile solution containing 0.08%-0.12% formic acid. The injection volume was 0.8-1.2 μL, the flow rate was 350-450 μL / min, and a gradient elution program was used: initially maintaining 1%-3% mobile phase B, then linearly increasing to 95%-100% mobile phase B, and finally returning to the initial mobile phase ratio; The spectrometer was used with an electrospray ionization source, and detection was performed in both positive and negative ion modes. The capillary voltage was 2300-2700V in positive ion mode and -1800V to -2200V in negative ion mode. The cone voltage was 28-32V, the ion source temperature was 90-110℃, the desolvation gas temperature was 480-520℃, the backflush gas flow rate was 45-55L / h, the desolvation gas flow rate was 750-850L / h, and the scanning range was 40-1300m / z.

[0010] As a preferred option, S2 constructs a grouping discriminant model using orthogonal partial least squares discriminant analysis. The reliability of the model is verified through 180-220 permutation tests. The model fit parameter R is required to be... 2 Y≥0.99, predictive ability parameter Q 2 Y≥0.99, and Q 2 The intercept of the Y-regression line is negative.

[0011] As a preferred embodiment, the principal component analysis correlation coefficient R of the QC sample described in S3 is... 2 >0.98, used to monitor the stability of the entire detection process, when R 2 If the value is ≤0.98, the sample should be retested.

[0012] As a preferred option, the P-value described in S3 is calculated using an independent samples t-test with a confidence level of 99%, ensuring that the false positive rate of differential metabolite screening is ≤5%.

[0013] As a preferred embodiment, the metabolomics database mentioned in S4 includes the Human Metabolomics Database, the Lipid Metabolism Pathway Research Project, and the KEGG database. The differential metabolite classification includes lipids and lipid-like molecules, organic acids and their derivatives, organic heterocyclic compounds, organic oxygenated compounds, benzenes, phenylpropionic acids, and polyketides.

[0014] As a preferred option, the statistical test method described in S4 is Fisher's exact test, and the core metabolic pathways include at least three of the following: cyanoamino acid metabolism, ABC transporter, astrosporin biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, arginine biosynthesis, phenylalanine metabolism, pyruvate metabolism, arachidonic acid metabolism, and glyoxylic acid and dicarboxylic acid metabolism.

[0015] Working principle and beneficial effects of the present invention: This study investigated the differential metabolites and metabolic pathways between wheat used for koji making and soy sauce-flavored koji using physicochemical index determination, non-targeted metabolomics techniques, and multivariate analysis. The moisture content of soy sauce-flavored koji was significantly higher than that of wheat used for koji making, while the protein, fat, and starch contents were significantly lower. Screening was conducted using VIP>1 and P<0.01 as the screening criteria. The results showed that the significantly differential metabolites in each group were mainly lipids and lipid-like molecules, organic acids and their derivatives, organic heterocyclic compounds, and organic oxygen-containing compounds. Further screening using FC revealed that under positive ion mode, esters and other aroma compounds were downregulated between wheat used for koji making and soy sauce-flavored koji, oligosaccharides such as maltoheptaose and D-maltose were downregulated, while sugars such as D-sedoheptulose-7-phosphate and fructose were upregulated. Under negative ion mode, 25-hydroxyvitamin D3 and α-tocotrienol vitamins were upregulated, while melibiose and maltopentose were downregulated. Changes in organic acids and their derivatives were mainly concentrated in amino acids and peptides, with significant shifts in their types and quantities. The key pathways enriched by differential metabolites were cyanoamino acid metabolism, ABC transporter biosynthesis, and astrosporin biosynthesis. Through analysis of the physicochemical properties and metabolites of wheat used for koji making and soy sauce-flavored koji, this study preliminarily explored their differential metabolites and metabolic pathways, providing a theoretical reference for further research on the composition of raw materials for koji making and the metabolite succession during the production of soy sauce-flavored koji. Attached Figure Description

[0016] Figure 1 Correlation plot of positive ion mode samples; Figure 2 Correlation diagram of negative ion mode samples; Figure 3 A diagram showing the positive ion mode of soy sauce-flavored koji and its raw material PCA; Figure 4 A diagram showing the negative ion mode of soy sauce-flavored koji and its raw material PCA; Figure 5 This is the OPLS-DA score graph in positive ion mode; Figure 6 OPLS-DA score graph for negative ion mode; Figure 7 Volcano plot of differential metabolites in positive ion mode; Figure 8 Volcano diagram of differential metabolites in negative ion mode; Figure 9 This refers to the categories of differential metabolites in soy sauce-flavored koji and its raw materials; Figure 10 A bar chart showing the metabolic pathways of positive ion mode soy sauce-flavored koji and its raw material differential metabolites; Figure 11 A bar chart showing the metabolic pathways of negative ion mode soy sauce-flavored koji and its raw material differential metabolites. Detailed Implementation

[0017] The following detailed description illustrates the specific implementation method: 1.1 Sample Preparation Wheat (WT) and soy sauce-flavored koji (YQ) were both collected from the koji-making workshop of a soy sauce-flavored liquor enterprise in Guizhou Province. After the samples were crushed and mixed, they were divided into quarters using the quartering method and stored at 4 ℃ and -80 ℃ for later use.

[0018] Reagents: Petroleum ether, analytical grade, Chengdu Kelong Chemical Co., Ltd.; Ethanol, analytical grade, Shanghai Wokai Biotechnology Co., Ltd.; Hydrochloric acid, analytical grade, Kunshan Jincheng Reagent Co., Ltd.; Sodium hydroxide, analytical grade, Tianjin Kemeio Chemical Reagent Co., Ltd.; Lead acetate, analytical grade, Chengdu Jinshan Chemical Reagent Co., Ltd.; Sodium sulfate, analytical grade, Tianjin Yongda Chemical Reagent Co., Ltd.; Potassium ferrocyanide, analytical grade, Chengdu Kelong Chemical Reagent Factory; Glucose, analytical grade, Tianjin Yongda Chemical Reagent Co., Ltd.; Methanol and acetonitrile, chromatographic grade, Merck GmbH, Germany; L-2-chlorophenylalanine, chromatographic grade, Shanghai Aladdin Biochemical Technology Co., Ltd.; Formic acid, chromatographic grade, HCl Ltd, India.

[0019] 1.2 Instruments and Equipment FA1204N electronic balance, Shanghai Jinghai Instrument Co., Ltd.; DHG-9140A electric heating drying oven, Shanghai-Heng Scientific Instrument Co., Ltd.; HME-700S pulverizer, Hongquan Instrument Co., Ltd.; TD5A medical centrifuge, Yancheng Kaitai Instrument Co., Ltd.; V-5100 UV-Vis spectrophotometer, Shanghai Yuanxi Instrument Co., Ltd.; UPLC Acquity I-Class PLUS ultra-high performance liquid chromatography, Acquity UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm), Waters UPLCXevoG2-XSQTOF high-resolution mass spectrometer (equipped with an electric spray ion (ESI) source): Waters Corporation, USA.

[0020] 1.3 Methods 1.3.1 Nutritional Analysis of Wheat and Soy Sauce Flavor Daqu (a type of starter culture) The contents of moisture, protein, fat, and starch in wheat and soy sauce-flavored koji samples were determined according to the methods described in the literature. (Yang, Zhang Rui, Zhang Liqiang, et al. Correlation study of wheat fineness with physicochemical and flavor components of finished high-temperature koji [J / OL]. Food and Fermentation Industries, 1-10.) 1.3.2 Extraction and Analysis of Metabolites (1) Extraction and analysis of wheat and soy sauce-flavored koji for koji making 50 mg of wheat and daqu (a type of starter culture) samples were ground into powder and added to 1000 μL of a mixture with a volume ratio of methanol:acetonitrile:water = 2:2:1 (V / V). The mixture was vortexed for 30 s. The grinder was set to 45 Hz and ground for 10 min, followed by sonication in an ice-water bath for 10 min. The mixture was then allowed to stand at -20 ℃ for 1 h, centrifuged at 4 ℃ and 12000 r / min for 15 min, and 500 μL of the supernatant was collected and concentrated and dried under vacuum. After drying, 160 μL of an acetonitrile-water aqueous extract (1:1 (V / V)) was added, and the mixture was vortexed for 30 s and sonicated in an ice-water bath for 10 min. The sample was centrifuged at 4 ℃ and 12000 r / min for 15 min. 120 μL of the supernatant was collected into a sample vial for analysis. 10 μL of each sample was mixed to form a quality control sample for analysis.

[0021] (2) Chromatographic conditions Ultra-high performance liquid chromatography (UHPLC) conditions: Mobile phase A: 0.1% formic acid aqueous solution; Mobile phase B: acetonitrile (containing 0.1% formic acid); Injection volume: 1 μL; Flow rate: 400 μL / min. Gradient elution program: 0–0.25 min, 2% mobile phase B; 10–13 min, 98% mobile phase B; 13.1 min, 2% mobile phase B.

[0022] Mass spectrometry conditions: Detection was performed in positive and negative ion modes using an ESI ion source. In positive ion mode, capillary voltage: 2500 V; in negative ion mode, capillary voltage: -2000 V; cone voltage: 30 V; ion source temperature: 100 ℃; desolvation gas temperature: 500 ℃; backflush gas flow rate: 50 L / h; desolvation gas flow rate: 800 L / h; mass-to-nucleus ratio: 50~1200 m / z.

[0023] 1.3.3 Data Processing The physicochemical indicators of wheat used for koji making and soy sauce-flavored koji were obtained by using the average of three valid parallel test data sets. The results are expressed as (mean ± standard deviation). Statistical analysis was performed using SPSS 22.0 software. A p-value < 0.05 was considered statistically significant between the physicochemical indicator data sets. Raw data were obtained using MassLynx V4.2, and peak extraction and alignment were performed using Progenesis QI software. Compounds were annotated using the Kyoto Genome and Encyclopedia of Genetics database (https: / / www.genome.jp / kegg / pathway.html), the Human Metabolome Database (HMDB) (https: / / hmdb.ca / metabolites), and the Lipid Metabolites and Pathways Strategy (LipidMaps) database (http: / / www.lipidmaps.org / ). Based on multidimensional statistical analysis data, principal component analysis was performed using prcomp 3.6.1, and orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed using ropls 1.6.2. Differential metabolites were screened by calculating the p-values ​​of differences among compounds using the value importance in projection (VIP) of the sample groupings and the t-test.

[0024] 2 Results and Analysis 2.1 Physicochemical Analysis of Wheat and Soy Sauce Flavor Daqu (Kigarette Starter) The physicochemical properties of the wheat used for koji making and the soy sauce-flavored koji were determined, focusing on four indicators: moisture, protein, fat, and starch content, which provide essential nutrients for microorganisms. The moisture content of the soy sauce-flavored koji was significantly higher than that of the wheat used for koji making. A certain amount of water is added during the koji-making process to ensure the necessary moisture for microbial growth under high-temperature conditions. Except for moisture content, the protein, fat, and starch contents of the soy sauce-flavored koji were significantly lower than those of the wheat used for koji making. During fermentation, these three types of substances are transformed or decomposed into amino acids, fermentable sugars, and fatty acids—energy and flavor compounds—under the action of microorganisms and enzymes.

[0025] Table 1. Detection results of physicochemical properties of the samples

[0026] 2.2 Non-targeted metabolomics analysis 2.2.1 Sample repeatability correlation Spearman's rank correlation coefficient (R²) is an indicator for assessing the correlation of biological replications. The closer R² is to 1, the higher the correlation coefficient between relative groups, indicating a stronger correlation between parallel samples. This can verify the reliability of results regarding differential metabolites in samples. Figure 1 and Figure 2 As shown, the correlation between wheat and koji samples under both positive and negative ion modes was greater than 0.914, indicating good sample stability, strong correlation between parallel samples, and good homogeneity.

[0027] 2.2.2 Principal Component Analysis Principal component analysis of wheat and soy sauce-flavored koji samples can reflect the overall differences between sample groups and the magnitude of variability within each group. Figure 3 and Figure 4 It can be seen that the contribution rates of the two principal components differ under both positive and negative ion modes. Specifically, the contribution rate of the first principal component is 82.35% under positive ion mode and 86.42% under negative ion mode; the contribution rate of the second principal component is 4.27% under positive ion mode and 2.95% under negative ion mode. The cumulative contribution rates of different components under the same mode are 86.62% and 89.37%, respectively. Under both positive and negative ion modes, the distance between the wheat and soy sauce-flavored koji samples is relatively large, indicating significant differences in the metabolites contained in the two.

[0028] 2.2.3 OPLS-DA Analysis OPLS-DA can maximize inter-group differences, which is beneficial for identifying differentially expressed metabolites. For example... Figure 5 and Figure 6 The OPLS-DA score plots for wheat used in koji making and soy sauce-flavored koji are shown. According to the OPLS-DA model, to1 represents the intra-group differential component, and the longitudinal distance between sample points is proportional to the intra-group differential. The percentage in parentheses represents the proportion of this component in the total variance. R2Y and Q2Y are the prediction parameters for evaluating the model. R2Y represents the output of the established model, i.e., the sample grouping matrix, and Q2Y represents whether the established model can distinguish the correct sample grouping through metabolic expression levels. The closer the R2Y and Q2Y values ​​are to 1, the more stable and reliable the model is, indicating that the model can be used to screen differential metabolites between samples. In positive ion mode, the inter-group differential component is 86%, the intra-group differential component is 1%, R2Y is 1, and Q2Y is 0.999; in negative ion mode, the inter-group differential component is 88%, the intra-group differential component is 3%, and both R2Y and Q2Y are 1. In summary, the established OPLS-DA model is considered an excellent model and can be used to screen differential metabolites in wheat used in koji making and soy sauce-flavored koji.

[0029] This experiment performed a total of 200 permutation tests to ensure the reliability of the OPLS-DA model. First, the sample groups were randomly shuffled. Then, OPLS-DA modeling was performed based on the new permutation groups. Finally, R²Y and Q²Y were calculated, and the modeling results were plotted. Figure 4 The dashed lines in the figure represent the regression lines fitted for R2Y and Q2Y, respectively, with positive slopes indicating a meaningful model. The red and blue dots represent the R2Y and Q2Y of the model after Y permutation, respectively. The blue dots are generally above the red dots, indicating that the detected values ​​are lower than the true values, suggesting good independence between the training and test sets. Differential metabolites can be screened based on VIP values. The Q2Y regression line intercept is negative, indicating that the established OPLS-DA model does not exhibit overfitting.

[0030] 2.2.4 Volcano Plot Analysis of Differential Metabolites Differential metabolites in the samples were obtained by combining one-way T-tests and the comprehensive coefficient of variation method, and the trends of differential metabolite changes between groups were shown. The screening condition was P < 0.05. Volcano plot ( Figure 7 , Figure 8 The study revealed the overall trend of differences in metabolite content between wheat used for koji making and soy sauce-flavored koji. Under positive ion mode, 1323 differential metabolites were screened from wheat used for koji making and soy sauce-flavored koji. Among them, 931 differential metabolites were upregulated and 392 differential metabolites were downregulated in soy sauce-flavored koji. Under negative ion mode, a total of 870 differential metabolites were screened. Among them, 569 were upregulated and 301 were downregulated.

[0031] 2.2.5 Screening, classification and identification of differential metabolites Differential metabolites were screened using the criteria of VIP≥1 and P<0.01. The results of matching differential metabolites with the HMDB database were plotted as follows. Figure 9 According to the Super Class classification, lipids and lipid-like molecules account for 17.19% of the differential metabolites of wheat and soy sauce-flavored koji, organic acids and their derivatives account for 10.99%, organic heterocyclic compounds account for 8.66%, organic oxygenated compounds account for 5.88%, benzenes account for 4.47%, and phenylpropionic acid and polyketides account for 2.64%.

[0032] Due to the large number of differentially expressed metabolites, univariate fold-change (FC) analysis was performed. A larger log2FC value indicates more significant upregulation, while a smaller log2FC value indicates more significant downregulation. In positive ion mode, the main upregulated lipids and lipid-like molecules in wheat koji production compared to soy sauce-flavored koji were 9,10-dihydroxystearate (log2FC=31.27) and 3-methylglutarylcarnitine (log2FC=31.21), while the main downregulated were methyl 3-octanoate (log2FC=-9.08) and methyl jasmonic acid (log2FC=-8.69). The main upregulated organic acids and their derivatives were leucyl isoleucine (log2FC=33.79), threonyl tyrosine (log2FC=31.18), and L-ornithine (log2FC=29.82). Downregulation mainly involved L-homocysteine ​​(log2FC=-9.97) and N-acetylasparagine (log2FC=-7.52). Among organic oxygen-containing compounds, upregulation mainly involved D-sedoheptulose-7-phosphate (log2FC=30.02) and fructose (log2FC=29.46), while downregulation mainly involved maltheptaose (log2FC=-8.95) and D-maltose (log2FC=-8.90). Under negative ion mode, among lipids and lipid-like molecules, upregulation mainly involved 25-hydroxyvitamin D3 (log2FC=35.44) and α-tocotrienol (log2FC=33.03), while downregulation mainly involved carvacrol (log2FC=-18.92) and glycerophosphatidylethanolamine (log2FC=-12.64). The main upregulated organic acids and their derivatives were N-lactyl-phenylalanine (log2FC=35.84), D-phenylalanine (log2FC=35.68), and N-octanoyl-L-homoserine lactone (log2FC=35.62). The main downregulated compound was fructosyl-lysine (log2FC=-13.71). Among organic oxygen-containing compounds, the main upregulated compounds were nicotinamide-β-riboside (log2FC=30.44), while the main downregulated compounds were melibiose (log2FC=-6.91) and maltopentose (log2FC=-6.34).

[0033] Studies have shown that methyl 3-octanoate and methyl jasmonic acid are aroma compounds in liquor, with methyl jasmonic acid possessing a jasmine-like fragrance. 25-hydroxyvitamin D3 is a derivative of vitamin D3, while α-tocotrienols are fat-soluble vitamins found in grains such as wheat, possessing important antioxidant activities. D-sedoheptulose-7-phosphate is a precursor for cell wall components synthesized by Gram-positive bacteria, which are important components of the microbial community in Daqu (a type of starter culture). Fructose is a crucial material basis for the growth, metabolism, and flavor formation of Daqu microorganisms; it exists in the raw materials for Baijiu brewing, Daqu, and Baijiu itself, with some sugars being consumed during the growth and metabolism of numerous microorganisms in Daqu. In the Daqu-making wheat and sauce-flavored Daqu groups, the content of oligosaccharides such as maltodextrose and D-maltose increases. Starch is broken down into oligosaccharides under the action of α-amylase, and the presence of oligosaccharides also promotes the production of α-amylase. Oligosaccharides are further broken down into glucose, entering the sugar metabolism pathway, and can also produce saturated and unsaturated fatty acids. Within the wheat and soy sauce-flavored koji group, the changes in organic acids and their derivatives are mainly concentrated in amino acids and peptides, with significant changes in their types and quantities.

[0034] 2.2.6 Analysis of Differential Metabolite Metabolic Pathways The KEGG IDs of differentially metabolites were matched with the KEGG database to obtain the metabolic pathways involved by the differentially metabolites. Figure 10 , 11 In positive ion mode, the main metabolic pathways enriched by differential metabolites were cyanoamino acid metabolism; ABC transporters; ubiquinone and other terpenoid quinone biosynthesis; arginine biosynthesis; and phenylalanine metabolism. In negative ion mode, the main metabolic pathways enriched by differential metabolites were staurosporine biosynthesis; cyanoamino acid metabolism; pyruvate metabolism; arachidonic acid metabolism; and glyoxylate and dicarboxylate metabolism. A -log10(p) > 1.3 was considered an important metabolic pathway, and the results indicate that cyanoamino acid metabolism, ABC transporters, and staurosporine biosynthesis are important pathways for the enrichment of differential metabolites.

[0035] It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solution of this invention. These modifications and improvements should also be considered within the scope of protection of this invention, and will not affect the effectiveness of the invention or the practicality of the patent. The scope of protection claimed in this application shall be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in its production using non-targeted metabolomics, characterized in that, Includes the following steps: S1. Sample pretreatment: The wheat and soy sauce-flavored koji samples were pulverized and then divided into quarters using the quartering method. They were stored separately in a low-temperature environment. A mixed extract of organic solvent and water was added, and the samples were vortexed, ultrasonicated in an ice-water bath, and then the supernatant was concentrated and dried under vacuum after centrifugation to obtain dried products. The dried products were then added to a complex solution of acetonitrile and water, vortexed, ultrasonicated in a water bath, and centrifuged. The supernatant was collected to obtain the samples. S2. Quality control sample testing: Each sample was tested using an ultra-high performance liquid chromatograph coupled with a quadrupole time-of-flight mass spectrometer. S3. Data Processing and Differential Metabolite Screening: Obtain raw data, perform peak extraction, peak alignment and normalization using metabolomics data analysis software, and use principal component analysis to evaluate the overall differences between sample groups and the variability within groups; construct a grouping discriminant model through orthogonal partial least squares discriminant analysis. The primary screening criteria were variable projection importance (VIP) > 1 and statistical significance P < 0.

01. The secondary screening criteria were log2FC > 2 for upregulation and log2FC < -2 for downregulation. The differential metabolites between the two groups of samples were then determined. S4. Metabolic Pathway Annotation: The screened differentially metabolites were annotated with compound structures and categories using a metabolomics database; pathway enrichment analysis was performed using statistical tests, with the resultant value being logarithmic. 10 Using (P) > 1.3 as the standard, the core metabolic pathways for the enrichment of differential metabolites were identified.

2. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics as described in claim 1, characterized in that: Weigh 40-60 mg of the pulverized sample, add 800-1200 μL of a mixed extraction solution consisting of a polar organic solvent and water, vortex for 20-40 s, grind at a frequency of 40-50 Hz for 8-12 min, and sonicate in an ice-water bath for 8-12 min; after standing at -25℃ to -15℃ for 0.8-1.2 h, centrifuge at 3-5℃ and 10000-14000 r / min for 12-18 min, and take 400-600 μL of the supernatant for vacuum concentration and drying; add 140-180 μL of a complex solution consisting of acetonitrile and water in a 1:1 volume ratio to the dried material, vortex for 20-40 s, sonicate in an ice-water bath for 8-12 min, and then centrifuge at 3-5℃ and 10000-14000 r / min for 12-18 min, and take 100-140 μL of the supernatant for detection.

3. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics as described in claim 2, characterized in that: A reversed-phase C18 column was used. Mobile phase A was an aqueous solution containing 0.08%-0.12% formic acid, and mobile phase B was an acetonitrile solution containing 0.08%-0.12% formic acid. The injection volume was 0.8-1.2 μL, and the flow rate was 350-450 μL / min. A gradient elution program was used: initially maintaining 1%-3% mobile phase B, then linearly increasing to 95%-100% mobile phase B, and finally returning to the initial mobile phase ratio. Mass spectrometry was performed using an electrochromatographic method. The spray ion source was used for detection in both positive and negative ion modes. The capillary voltage was 2300-2700V in positive ion mode and -1800V to -2200V in negative ion mode. The cone voltage was 28-32V. The ion source temperature was 90-110℃, the desolvation gas temperature was 480-520℃, the backflushing gas flow rate was 45-55L / h, the desolvation gas flow rate was 750-850L / h, and the scanning range was 40-1300m / z.

4. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used for koji production using non-targeted metabolomics as described in claim 3, characterized in that: S2 constructs a grouping discriminant model using orthogonal partial least squares discriminant analysis. The reliability of the model is verified through 180-220 permutation tests. The model fit parameter R is required to be... 2 Y≥0.99, predictive ability parameter Q 2 Y≥0.99, and Q 2 The intercept of the Y-regression line is negative.

5. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics according to claim 4, characterized in that: The principal component analysis correlation coefficient R of the QC sample mentioned in S3 2 >0.98, used to monitor the stability of the entire detection process, when R 2 If the value is ≤0.98, the sample should be retested.

6. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics according to claim 5, characterized in that: The P-value described in S3 is calculated using an independent samples t-test with a confidence level of 99%, ensuring that the false positive rate of differential metabolite screening is ≤5%.

7. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics according to claim 6, characterized in that, The metabolomics database mentioned in S4 includes the Human Metabolomics Database, the Lipid Metabolism Pathway Research Project, and the KEGG database. The differential metabolite classification includes lipids and lipid-like molecules, organic acids and their derivatives, organic heterocyclic compounds, organic oxygenated compounds, benzenes, phenylpropionic acids, and polyketides.

8. The method for analyzing the differences in components between soy sauce-flavored koji and the wheat used in koji production using non-targeted metabolomics according to claim 7, characterized in that, The statistical test method described in S4 is Fisher's exact test. The core metabolic pathways include at least three of the following: cyanoamino acid metabolism, ABC transporter, astrosporin biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, arginine biosynthesis, phenylalanine metabolism, pyruvate metabolism, arachidonic acid metabolism, and glyoxylic acid and dicarboxylic acid metabolism.

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