Method for component analysis of sauce-aroma baijiu on basis of untargeted metabonomics technology, and use thereof

Abstract

A method for component analysis of sauce-aroma Baijiu on the basis of untargeted metabonomics technology, and the use thereof. The analysis method comprises the following steps: (1) sampling, involving: taking a sauce-aroma Baijiu sample as a sample to be subjected to detection, without requiring any sample pretreatment; (2) detection and analysis of said sample on the basis of an ultra-high performance liquid chromatography-quadrupole Orbitrap mass spectrometry technique, wherein after all the components in said sample are separated by means of ultra-high performance liquid chromatography, the components directly enter a mass spectrometer for determination; and (3) qualitative analysis, involving: performing qualitative analysis on the components in said sample on the basis of the data obtained from the determination in step (2), wherein the chromatographic column of the ultra-high performance liquid chromatography is InfinityLab Poroshell HPH-C18 (2.1×100 mm, 1.9 μm). The provided method can achieve more comprehensive and more accurate analysis of components in sauce-aroma Baijiu, and provides a brand-new development direction for detailed analysis of chemical components in Baijiu.

Description

A sauce-flavor liquor component analysis method based on non-targeted metabolomics technology and application TECHNICAL FIELD

[0001] The present application belongs to the technical field of detection and analysis of sauce-flavor liquor, and particularly relates to a sauce-flavor liquor component analysis method based on non-targeted metabolomics technology and application. BACKGROUND

[0002] Liquor is composed of ethanol, water and trace components, among which the proportion of ethanol and water is 98%-99%, and the remaining 1%-2% is trace components, and it is these trace components that determine the flavor and style of liquor. According to different sensory characteristics, liquor is mainly divided into sauce-flavor liquor, clear-flavor liquor, strong-flavor liquor and rice-flavor liquor. For a long time, component analysis has been the focus of liquor flavor research. According to literature reports, 1088, 817, 518 and 542 compounds have been identified from sauce-flavor, strong-flavor, clear-flavor and rice-flavor liquor respectively, but the analysis of components in liquor still needs further research.

[0003] At the same time, the production process of sauce-flavor liquor can be described as "12987", i.e. one-year production cycle, twice feeding, nine times cooking, eight times fermentation and seven times taking liquor. Seven different rounds of sauce-flavor liquor correspond to seven times of taking liquor, each with its own characteristics. At present, the discrimination of different rounds of sauce-flavor liquor is mostly based on sensory evaluation principles. In addition, researchers have carried out research on the difference in flavor compounds of different rounds of sauce-flavor liquor for volatile components and non-volatile components respectively. The research on the components of different rounds of sauce-flavor liquor mostly focuses on the analysis of a certain type of compound and physicochemical characteristics, and has not yet achieved comprehensive analysis of organic components in different rounds of sauce-flavor liquor.

[0004] Therefore, in order to more comprehensively analyze the components in sauce-flavor liquor and further more accurately mine the difference in specific components or common components in each round of sauce-flavor liquor, this application aims to provide a method for comprehensive analysis of components in sauce-flavor liquor based on non-targeted metabolomics technology, and a method for discriminating different rounds of sauce-flavor liquor and analyzing the dynamic changes of components in different rounds of sauce-flavor liquor base liquor based on the analysis method. SUMMARY

[0005] One of the purposes of the present application is to provide a method for comprehensive analysis of components in sauce-flavor liquor based on non-targeted metabolomics technology.

[0006] The second purpose of the present application is to provide a method for discriminating different rounds of sauce-flavor liquor base liquor.

[0007] The third object of the present application is to provide a dynamic change analysis method for components of different batches of Jiangxiang Baijiu base liquor.

[0008] Currently, the detection and analysis of trace components in Baijiu are mostly performed by GC-MS technology. However, the traditional GC-MS technology focuses on identifying volatile molecular components and is difficult to determine polar and non-volatile molecular components in Baijiu. In view of the technical difficulties such as the current incomplete analysis of chemical components of different batches of Jiangxiang Baijiu and the large amount of lost molecular information, the present application introduces a high-resolution mass spectrometer. The mass analyzer of ultra-high performance liquid chromatography-quadrupole-electric orbit field ion trap mass spectrometer (UPLC-Q-Exactive Orbitrap-MS) is a quadrupole and Orbitrap (electric field orbit ion trap). The quadrupole can pre-screen ions in QExactive, and its basic working principle is as follows: the electric field is formed by applying voltage on the four electrode rods, so that the ions to be tested become harmonic ions and are smoothly detected by the quadrupole (while other non-tested ions become unstable and cannot be smoothly detected by the quadrupole); Orbitrap separates ions of different m / z by the difference in the z-direction movement frequency of different m / z, which can realize high-resolution function.

[0009] According to the first aspect of the present application, the present application provides a Jiangxiang Baijiu component analysis method based on non-targeted metabolomics technology, which comprises the following steps:

[0010] (1) Sampling: taking Jiangxiang Baijiu sample as the sample to be tested without sample pretreatment;

[0011] (2) Detection: detecting and analyzing the sample to be tested based on ultra-high performance liquid chromatography-quadrupole-electric orbit field ion trap mass spectrometry technology, and all components in the sample to be tested are separated by ultra-high performance liquid chromatography and then directly determined by mass spectrometer;

[0012] (3) Qualitative analysis: qualitatively analyzing the components in the sample to be tested based on the data obtained in step (2);

[0013] In some embodiments of the present application, the chromatographic column of the ultra-high performance liquid chromatography is InfinityLab Poroshell HPH-C18 (2.1x100mm, 1.9μm).

[0014] In some embodiments of the present application, the mobile phase of the ultra-high performance liquid chromatography is: ultrapure water as mobile phase A, and 100% methanol as mobile phase B.

[0015] In some embodiments of the application, the gradient elution method of the ultra-high performance liquid chromatography comprises: adjusting the initial proportion of mobile phase B phase to 9-11%, maintaining for 2-3 min; linearly increasing B phase from 9-11% to 100% B by 17-18 min; maintaining 100% B for the next 1.5-2.5 min, and then linearly decreasing B phase from 100% to 9-11% by 0.3-0.6 min, and maintaining 0.05-0.2 min to balance the column pressure.

[0016] In some embodiments of the application, the chromatographic conditions of the ultra-high performance liquid chromatography comprise: the column oven temperature is 38-42°C; the temperature of the autosampler is 3-5°C; the injection volume is 2-4 μL; and the flow rate is 0.2-0.4 mL / min.

[0017] In some embodiments of the application, the mass spectrometry conditions comprise: scan mode: Full MS / dd-MS2, scan range: 100-1500 m / z, electrospray ionization source: ESI+ and / or ESI-; wherein the parameter settings of the positive ion acquisition mode are: HESI parameter settings: spray voltage: +3.0-+4.0 kV; capillary temperature: 240-260°C; sheath gas flow rate: 47-48 Arb; auxiliary gas flow: 10-12 Arb; auxiliary gas heater temperature: 320-370°C; collision energy: 5-13, 15-25, 25-35 eV; preferably, the spray voltage is: +3.3-+3.8 kV; preferably, the capillary temperature: 245-255°C; preferably, the auxiliary gas heater temperature: 340-360°C; preferably, the collision energy is: 8-12, 18-22, 28-32 eV.

[0018] In some embodiments of the application, the parameter settings of the negative ion acquisition mode are: HESI parameter settings: spray voltage: -1.5--2.5 kV; capillary temperature: 240-260°C; sheath gas flow rate: 47-448 Arb; auxiliary gas flow: 10-12 Arb; auxiliary gas heater temperature: 320-370°C; collision energy: 15-25, 25-35, 35-45 eV; preferably, the spray voltage is: -1.8--2.2 kV; preferably, the capillary temperature: 245-255°C; preferably, the auxiliary gas heater temperature: 340-350°C; preferably, the collision energy is: 18-22, 28-32, 38-42 eV.

[0019] In some embodiments of the present application, the qualitative analysis of the components in the sample to be tested based on the data obtained in step (2) comprises the following steps: 1) importing the data in step (2) into Compound Discoverer 3.3, establishing a workflow and performing data analysis, and further matching with the database to determine the compounds with a confidence level of 2 / 3; 2) based on the database m / z clouds, analyzing the matching of the molecular ion mass-to-charge ratio, the secondary mass spectrum fragment ion and the database, and analyzing whether the retention time conforms to the chromatographic retention rule, to determine the compounds with a confidence level of 1; and obtaining the molecular formula information of the components in the sample to be tested.

[0020] In some embodiments of the present application, the qualitative analysis of the components in the sample to be tested based on the data obtained in step (2) further comprises the following steps:

[0021] 3) based on the molecular formula information of the corresponding components obtained in step 2), preliminarily judging the attribution category of the compounds, and then analyzing the mass-to-charge ratio, the fragment abundance ratio and the isotope abundance ratio, to deduce the structure and element composition of the substance, i.e., to obtain the specific compound information in the corresponding liquor sample.

[0022] In some embodiments of the present application, the qualitative analysis of the components in the sample to be tested based on the data obtained in step (2) further comprises the following steps: 3) based on the molecular formula information of the corresponding components obtained in step 2), analyzing the number of compounds containing different element compositions.

[0023] In some embodiments of the present application, the qualitative analysis of the components in the sample to be tested based on the data obtained in step (2) further comprises the following steps:

[0024] 3) based on the molecular formula information of the corresponding components obtained in step 2), preliminarily judging the attribution category of the compounds, and then analyzing the mass-to-charge ratio, the fragment abundance ratio and the isotope abundance ratio, to deduce the structure and element composition of the substance, i.e., to obtain the specific compound information in the corresponding liquor sample; and 3) based on the molecular formula information of the corresponding components obtained in step 2), analyzing the number of compounds containing different element compositions.

[0025] In some embodiments of the present application, the analysis of the number of compounds containing different element compositions comprises:

[0026] extracting different element compositions in different compounds;

[0027] extracting the number of different elements in different compounds;

[0028] Based on the extracted different element compositions in different compounds and the corresponding number of different elements, the compounds containing different element compositions are analyzed.

[0029] In some embodiments of the present application, the different element compositions in the compounds containing different element compositions include COH, COHN, COHNP, COHS, CHN, COHNS.

[0030] In some embodiments of the present application, the different elements in the step of extracting different element compositions in different compounds include at least one of C, O, H, N, P, S, F, Cl, and I.

[0031] In some embodiments of the present application, the analysis method further comprises:

[0032] (4) Quantitative analysis of components in the sample to be tested: the components in the sample to be tested are quantified according to the ratio of the peak area to the concentration of the extracted ion chromatogram of the sample to be tested and the standard sample primary spectrum, and the quantitative result is obtained.

[0033] According to the second aspect of the present application, the present application also provides a method for discriminating different batches of Jiang-flavor base liquor, which comprises the following steps:

[0034] 1) obtaining component information in the different batches of Jiang-flavor base liquor based on the analysis method of any one of the first aspect of the present application;

[0035] 2) analyzing the different components in the different batches of Jiang-flavor base liquor;

[0036] 3) discriminating the different batches of Jiang-flavor base liquor based on the different components.

[0037] In some embodiments of the present application, in step 2), the analysis of the different components in the different batches of Jiang-flavor base liquor comprises the following steps: analyzing the component information obtained in step 1) based on at least one of univariate statistics, multivariate statistics, and difference analysis method, and screening the different components.

[0038] In some embodiments of the present application, the screening standard of the different components includes at least one of a P-value threshold p < 0.05, a variable difference contribution degree VIP ≥ 1, and a difference fold Fc > 2.

[0039] In some embodiments of the present application, the univariate statistical analysis is t-test analysis, the multivariate statistical analysis is orthogonal partial least squares (OPLS) algorithm, and the difference analysis is fold change method.

[0040] According to a third aspect of the present application, the present application provides a dynamic change analysis method of components in different batches of Jiang-flavor base liquor, which comprises the following steps:

[0041] I. obtaining component information in different batches of Jiang-flavor base liquor based on the analysis method according to any one of the first aspect of the present application;

[0042] II. screening common components in different batches of Jiang-flavor base liquor based on the component information;

[0043] III. classifying the common components screened in the above step based on H / C and O / C in the molecular formula of the common components, saturation and oxidation, or volatility;

[0044] IV. analyzing the change trend of the common components in different batches of Jiang-flavor base liquor based on the classification results obtained in step III.

[0045] In some embodiments of the present application, in step III, the classification of the common components screened in the above step based on H / C ratio and O / C ratio in the molecular formula of the common components comprises the following steps:

[0046] if 1.5≤H / C≤2 and 0≤O / C<0.3 in the molecular formula of the common components, the corresponding common component is classified as a lipid compound; if 1.5≤H / C≤2.2 and 0.3≤O / C<0.67 in the molecular formula of the common components, the corresponding common component is classified as a peptide compound; if 1.5≤H / C≤2.2 and 0.67≤O / C≤1.2 in the molecular formula of the common components, the corresponding common component is classified as a carbohydrate compound; if 0.7≤H / C<1.5 and 0≤O / C<0.1 in the molecular formula of the common components, the corresponding common component is classified as an unsaturated hydrocarbon compound; if 0.7≤H / C<1.5 and 0.1≤O / C<0.67 in the molecular formula of the common components, the corresponding common component is classified as a lignin compound; if 0≤H / C<1.5 and 0.67≤O / C≤1.2 in the molecular formula of the common components, the corresponding common component is classified as a tannin compound; if 0.2≤H / C<0.7 and 0≤O / C≤0.67 in the molecular formula of the common components, the corresponding common component is classified as a condensed ring aromatic hydrocarbon compound.

[0047] In some embodiments of the present application, in the classification of the common components screened in the above step based on at least one parameter of saturation, oxidation, H / C ratio or O / C ratio in the molecular formula of the common components, the common components are classified into 7 types of compounds, including lipids, peptides, carbohydrate compounds, unsaturated hydrocarbons, lignin compounds, tannin compounds and condensed ring aromatic hydrocarbons.

[0048] In some embodiments of the present application, the method for obtaining the H / C ratio and the O / C ratio in the common component formula comprises the following steps: extracting the number of C, H and O elements in the common component formula, and calculating the H / C ratio and the O / C ratio according to the extracted number of C, H and O elements.

[0049] In some embodiments of the present application, the function used for extracting the number of C elements in the common component formula is:

[0050] "=IF(MID(B2, FIND("C", B2)+1, 1)="","", (MID(B2, FIND("C", B2)+1, 2)))".

[0051] The function used for extracting the number of O elements in the common component formula is:

[0052] "=IF(MID(B2, FIND("O", B2)+1, 1)="","", (MID(B2, FIND("O", B2)+1, 2)))".

[0053] The function used for extracting the number of H elements in the common component formula is:

[0054] "=IF(MID(B2, FIND("H", B2)+1, 1)="","", (MID(B2, FIND("H", B2)+1, 2)))".

[0055] In some embodiments of the present application, in step three, classifying the common components screened in step two based on the saturation and oxidation degree in the common component formula comprises the following steps:

[0056] extracting the number of C, H, O, N and S elements in the common component formula, calculating the (DBE-O) / C and NOSC values, and classifying the common components based on the calculated (DBE-O) / C and NOSC values.

[0057] In some embodiments of the present application, the formula for extracting the number of C, H, O, N, and S elements in the molecular formula of the common component is: "=IF(MID(B2, FIND("C", B2)+1, 1)="", 0, (MID(B2, FIND("C", B2)+1, 2))), =IF(MID(B2, FIND("H", B2)+1, 1)="", 0, (MID(B2, FIND("H", B2)+1, 2))), =IF(MID(B2, FIND("O", B2)+1, 1)="", 0, (MID(B2, FIND("O", B2)+1, 2))), =IF(MID(B2, FIND("N", B2)+1, 1)="", 0, (MID(B2, FIND("N", B2)+1, 2))), =IF(MID(B2, FIND("S", B2)+1, 1)="", 0, (MID(B2, FIND("S", B2)+1, 2)))".

[0058] In some embodiments of the present application, the formula for calculating the NOSC value is: NOSC = 4 - (4*N C +N H -2*N O -3*N N -2*N S ) / N C ; and the formula for calculating (DBE-O) / C is: (DBE-O) / C = ((2*N C +N N -N H +2) / 2-N O ) / N C ; wherein N C represents the number of C elements, N N represents the number of N elements, N H represents the number of H elements, N O represents the number of O elements, and N S represents the number of S elements.

[0059] In some embodiments of the present application, the classification of the common component based on the calculated (DBE-O) / C and NOSC values comprises the following steps:

[0060] When NOSC<0, (DBE-0) / C>0, the corresponding common component is classified as Unsaturated-reduced (URC), i.e. unsaturated reduced component; when NOSC<0, (DBE-0) / C<0, the corresponding common component is classified as saturated-reduced (SRC), i.e. saturated reduced component; NOSC>0, (DBE-0) / C>0, the corresponding common component is classified as Unsaturated-oxidized (UOC), i.e. unsaturated oxidized component; NOSC>0, (DBE-0) / C<0, the corresponding common component is classified as SATURATED-OXIDIZED (SOC), i.e. saturated oxidized component; NOSC=0, the corresponding common component is classified as neutral state (NSC), i.e. neutral component.

[0061] In some embodiments of the present application, in step three, classifying the common components screened above based on volatility in the molecular formula of the common components comprises the following steps:

[0062] Extracting the number of C, H, O, N, S elements in the molecular formula of the common components, calculating the Co value; based on the calculated Co value, classifying the common components.

[0063] In some embodiments of the present application, the formula for extracting the number of C, H, O, N, S elements in the molecular formula of the common components is: "=IF(MID(B2, FIND("C", B2)+1, 1)=""; 0, (MID(B2, FIND("C", B2)+1, 2))), =IF(MID(B2, FIND("H", B2)+1, 1)=""; 0, (MID(B2, FIND("H", B2)+1, 2))), =IF(MID(B2, FIND("O", B2)+1, 1)=""; 0, (MID(B2, FIND("O", B2)+1, 2))), =IF(MID(B2, FIND("N", B2)+1, 1)=""; 0, (MID(B2, FIND("N", B2)+1, 2))), =IF(MID(B2, FIND("S", B2)+1, 1)=""; 0, (MID(B2, FIND("S", B2)+1, 2)))".

[0064] In some embodiments of the present application, the formula for calculating the Co value is: Log10Co=(n c o -n c )b c -n o b o-2*n c n o / (n c +n o )*b co -n N b N -n S b S ; where N in the formula C N represents the number of elements in C. N N represents the number of elements. H N represents the number of elements in H. O N represents the number of O elements. S n represents the number of elements in S; in the formula... c o b represents the reference carbon. c This represents the contribution of the C atom to log10Co, b o b represents the contribution of the O atom to log10Co. co b represents the non-ideality of the carbon-oxygen relationship. N b represents the contribution of the N atom to log10Co. S This represents the contribution of the S atom to log10Co.

[0065] In some embodiments of this application, classifying the common components based on the calculated Co value includes the following steps:

[0066] When C o >3×10 6 μg·m -3 The corresponding common component is classified as Volatile organic compounds; when 300 <C o <3×10 6 μg·m -3 The corresponding common component classification is Intermediate volatility organic components, which are medium volatile components; when 0.3 <C o <300μg·m -3 The corresponding common component is classified as a semivolatile organic compound, i.e., a semi-volatile component; when 3 × 10 - 4 <C o <0.3μg·m -3 The corresponding common component classification is Low-volatile organic components, that is, components with low volatility when C o <3×10 -4 μg·m- 3 If the corresponding common component is classified as Extremely low-volatile organic compouds, it is classified as an extremely low-volatile organic compound.

[0067] In some embodiments of the present application, based on the classification results obtained in step three, the analysis of the change trend of the common components in the different batches of Jiang-flavor base liquor comprises: based on the classification results obtained in step (3), the common components in the different batches of Jiang-flavor base liquor are classified into multiple categories of compounds, and based on the change of the response values of the multiple categories of compounds in the different batches of Jiang-flavor base liquor, the change trend results of the common components in the different batches of Jiang-flavor base liquor are obtained.

[0068] According to the fourth aspect of the present application, the present application also provides an application of the analysis method according to any one of the first aspect of the present application in the field of liquor making. The analysis method according to any one of the first aspect of the present application or the discrimination method according to any one of the second aspect of the present application or the application of the analysis method according to any one of the first aspect of the present application in the field of liquor making.

[0069] Compared with the prior art, the present application has the following beneficial effects:

[0070] The present application provides a method for analyzing different batches of Jiang-flavor base liquor, a method for further discriminating different batches of Jiang-flavor base liquor based on the analysis results obtained by the analysis method, and a method for further analyzing the dynamic changes of the components of different batches of Jiang-flavor base liquor based on the analysis results obtained by the analysis method. Based on the ultra-high performance liquid chromatography-quadrupole-electrostatic orbit field ion trap mass spectrometry technology provided by the present application, more comprehensive analysis of the components in different batches of Jiang-flavor base liquor can be achieved, and based on the obtained analysis results, the discrimination of different batches of Jiang-flavor base liquor and the dynamic change analysis method of the components of different batches of Jiang-flavor base liquor are further realized. Compared with the prior art, the present application provides a method for qualitatively analyzing the components in Jiang-flavor base liquor, which can detect more components, improves the efficiency of analyzing Jiang-flavor liquor samples, and further realizes the differentiation of different batches of Jiang-flavor base liquor. BRIEF DESCRIPTION OF DRAWINGS

[0071] FIG. 1 is a total ion current diagram of the components in the test liquor sample in Example 1 of the present application in the positive ion mode;

[0072] FIG. 2 is a total ion current diagram of the components in the test liquor sample in Example 1 of the present application in the negative ion mode;

[0073] FIG. 3 is the number of different element component molecules in the test liquor sample in Example 1 of the present application;

[0074] Figure 4 is the corresponding area of different element composition molecules in the wine sample to be tested in Example 1 of the present application;

[0075] Figure 5 is the number proportion of different element composition molecules in the wine sample to be tested in Example 1 of the present application;

[0076] Figure 6 is the response intensity proportion of different element composition molecules in the wine sample to be tested in Example 1 of the present application;

[0077] Figure 7 is the relative molecular mass distribution proportion in the wine sample to be tested in Example 1 of the present application;

[0078] Figure 8 is the number of signal molecules under different liquid phase conditions of different chromatographic columns in positive ion mode in the comparative example of the present application;

[0079] Figure 9 is the number of signal molecules under different mass spectrometry conditions of C18 column in positive ion mode in the comparative example of the present application;

[0080] Figure 10 is the number of signal molecules under different mass spectrometry conditions and different mass spectrometry conditions in negative ion mode in the comparative example of the present application;

[0081] Figure 11 is the number of molecular signal detected by the present application in different aroma type wines in the comparative example of the present application;

[0082] Figure 12 is the molecular signal intensity detected by the present application in different aroma type wines in the comparative example of the present application;

[0083] Figure 13 is the number of different element composition molecules of UPLC-QTOF-MS detected in Maotai-flavor liquor samples in the comparative example of the present application;

[0084] Figure 14 is the base peak chart of different rounds of Maotai-flavor liquor in Example 2 of the present application;

[0085] Figure 15 is the heat map of organic components of different rounds of Maotai-flavor liquor in Example 2 of the present application;

[0086] Figure 16 is the number of unique components of different rounds of Maotai-flavor liquor in Example 2 of the present application;

[0087] Figure 17 is the distribution of unique components of different rounds of Maotai-flavor liquor in Example 2 of the present application;

[0088] Figure 18 is the discrimination result of different rounds of Maotai-flavor base liquor in Example 2 of the present application;

[0089] Figure 19 is the discrimination result of different rounds of Maotai-flavor base liquor in Comparative Example 4 of the present application;

[0090] Figure 20 is the discrimination result of different rounds of Maotai-flavor base liquor in Comparative Example 5 of the present application;

[0091] FIG. 21 is an example graph of the qualitative analysis of components in the Jiangxiang Baijiu base liquor (2,3,5,6-tetramethylpyrazine) according to Example 1 of the present application;

[0092] FIG. 22 is an example graph of the qualitative analysis of components in the Jiangxiang Baijiu base liquor (4-hydroxybenzaldehyde) according to Example 1 of the present application;

[0093] FIG. 23 is the clustering heat map analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 3 of the present application;

[0094] FIG. 24 is the correlation analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 3 of the present application;

[0095] FIG. 25 is the change trend analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 3 of the present application;

[0096] FIG. 26 is the change trend analysis graph of ester compounds in the Jiangxiang Baijiu base liquor of each round (rounds 1 to 7) according to Example 3 of the present application;

[0097] FIG. 27 is the clustering heat map analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 4 of the present application;

[0098] FIG. 28 is the correlation analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 4 of the present application;

[0099] FIG. 29 is the change trend analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 4 of the present application;

[0100] FIG. 30 is the change trend analysis graph of unsaturated reducing compounds in the Jiangxiang Baijiu base liquor of each round (rounds 1 to 7) according to Example 4 of the present application;

[0101] FIG. 31 is the clustering heat map analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 5 of the present application;

[0102] FIG. 32 is the correlation analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 5 of the present application;

[0103] FIG. 33 is the change trend analysis result of common components in the Jiangxiang Baijiu base liquor of each round according to Example 5 of the present application;

[0104] FIG. 34 is the change trend analysis graph of low-volatility compounds in the Jiangxiang Baijiu base liquor of each round (rounds 1 to 7) according to Example 5 of the present application. DETAILED DESCRIPTION

[0105] The technical solutions of the present application are further illustrated below through specific examples, which do not represent a limitation on the protection scope of the present application. Some non-essential modifications and adjustments made by others according to the concept of the present application still belong to the protection scope of the present application.

[0106] The ultra-high performance liquid chromatograph used in the embodiments of the present application is of the model Ultimate 3000 (Thermo Fisher Scientific, USA), and the mass spectrometer used is Q-Exactive plus (Thermo Fisher Scientific, USA).

[0107] The reagents used in the present application are: methanol, chromatographically pure.

[0108] Example 1

[0109] The present embodiment provides a method for analyzing components in Jiangxiang Baijiu (liquor), which comprises the following steps:

[0110] (1) Sampling: 1 mL of Jiangxiang Baijiu (liquor) stock solution is taken as the sample to be tested, without sample pretreatment;

[0111] (2) Detection: The sample to be tested in step (1) is detected and analyzed by using the ultra-high performance liquid chromatography-quadrupole-electrostatic orbit field ion trap-mass spectrometry (UHPLC-Q Exactive MS) technology. All components in the sample to be tested are separated by the ultra-high performance liquid chromatography and then directly determined by the mass spectrometry:

[0112] The analysis conditions of the ultra-high performance liquid chromatograph used are as follows:

[0113] The chromatographic column is InfinityLab Poroshell HPH-C18 (2.1×100 mm, 1.9 μm, Agilent, USA), the column oven parameter is set to 40℃, the automatic sampler temperature is set to 4℃, the injection volume is 3 μL, the flow rate is 0.3 mL / min, and the gradient elution mode is as follows: ultrapure water is used as the mobile phase A, and 100% methanol is used as the mobile phase B. The specific gradient elution method is shown in Table 1: the initial proportion of B is 10%, which is maintained for 2.5 min; then B is linearly increased from 10% to 100% B through 17.5 min, i.e., B is 100% at 20 min; 100% B is maintained for the next 2 min, and then B is linearly decreased from 100% to 10% B (initial condition) through 0.5 min, and maintained for 0.1 min to balance the column pressure;

[0114] Table 1 Gradient conditions of mobile phase

[0115] The analysis conditions of the Q Exactive mass spectrometer are as follows:

[0116] Scan mode: Full MS / dd-MS 2 Scan range: 100-1500 m / z, Electrospray ionization source: ESI+, ESI - ;

[0117] The parameters of mass spectrometry positive ion acquisition mode (ESI + ) are set as follows: HESI parameter setting: spray voltage: +3.5 kV; capillary temperature: 250℃; sheath gas flow rate: 47.5 Arb; auxiliary gas flow: 11.25 Arb; auxiliary combustion gas heater temperature: 350℃; collision energy: 10, 20, 30 eV;

[0118] The parameters of mass spectrometry negative ion acquisition mode (ESI - ) are set as follows: HESI parameter setting: spray voltage: -2.0 kV; capillary temperature: 250℃; sheath gas flow rate: 47.5 Arb; auxiliary gas flow: 11.25 Arb; auxiliary combustion gas heater temperature: 350℃; collision energy: 20, 30, 40 eV;

[0119] (3) Data processing: 1) Import the data in step (2) into Compound Discoverer 3.3, establish workflow (select spectra—align retention times-detect compounds-group compounds-assign compounds annotations) and perform data analysis, further match and determine the compounds with a confidence level of 2 / 3 by comparing ChemBank, METLIN, KEGG, ChemSpider, PubChem, Human Metabolome Database, ChemSpiderman, NIST database; 2) Based on the database m / z clouds, by analyzing the matching of molecular ion mass-to-charge ratio, secondary mass spectrum fragment ions and the database, and analyzing whether the retention time conforms to the chromatographic retention rule, further determine the compounds with a confidence level of 1; obtain 5838 molecular formulas; 3) Based on the molecular formula information of the corresponding components obtained in step 2), preliminarily judge the attribution category of the compounds, and then analyze the mass-to-charge ratio, fragment abundance ratio and isotope abundance ratio, so as to deduce the structure and element composition of the substance; obtain the specific compound component information in the corresponding liquor sample;

[0120] For example: qualitative analysis of 2,3,5,6-tetramethylpyrazine and 4-hydroxybenzaldehyde, the original data is imported into Compound Discoverer 3.3, a workflow is established and data analysis is performed, the ChemSpider database is used for matching and it is determined that the compound is a compound with a confidence level of 2; based on the database m / z clouds, the molecular ion mass-to-charge ratio and the secondary mass spectrum fragment ion of 2,3,5,6-tetramethylpyrazine and 4-hydroxybenzaldehyde are matched with the database, and the retention time conforms to the chromatographic retention rule, and it is determined that it is a compound with a confidence level of 1; based on the molecular formula information of the obtained corresponding component, the attribution category of the compound is preliminarily judged, and the mass-to-charge ratio, fragment abundance ratio and isotope abundance ratio are analyzed, so as to deduce the structure and element composition of the substance, that is, the specific compound information in the corresponding liquor sample is obtained, which can be seen in Table 2, FIG. 21 and FIG. 22:

[0121] Table 2

[0122] (4) Data visualization expression:

[0123] Based on the molecular formula in (3), the formula:

[0124] “=CONCATENATE(IF(ISERR(FIND(“C”, $F2)), “”, “C”), IF(ISERR(FIND(“O”, $F2)), “”, “O”), IF(ISERR(FIND(“H”, $F2)), “”, “H”), IF(ISERR(FIND(“N”, $F2)), “”, “N”), IF(ISERR(FIND(“P”, $F2)), “”, “P”), IF(ISERR(FIND(“S”, $F2)), “”, “S”), IF(ISERR(FIND(“F”, $F2)), “”, “F”), IF(ISERR(FIND(“Cl”, $F2)), “”, “Cl”), IF(ISERR(FIND(“I”, $F2)), “”, “I”))” can extract the element composition of different compounds at the same time, and the extracted elements specifically include C, O, H, N, P, S, F, Cl and I;

[0125] Further, components / molecules with COH, COHN, COHNP, COHS, CHN and COHNS as element compositions are extracted from the liquor sample, so as to analyze the compounds in the liquor from the perspective of element composition, which is as follows:

[0126] The number of C elements in the liquor sample is extracted based on the formula "=IF(MID(B2, FIND("C", B2)+1, 1)="", 0, (MID(B2, FIND("C", B2)+1, 2)))"; the number of H elements in the liquor sample is extracted based on the formula "=IF(MID(B2, FIND("H", B2)+1, 1)="", 0, (MID(B2, FIND("H", B2)+1, 2)))"; the number of high O elements in the liquor sample is extracted based on the formula "=IF(MID(B2, FIND("O", B2)+1, 1)="", 0, (MID(B2, FIND("O", B2)+1, 2)))"; the number of N elements in the liquor sample is extracted based on the formula "=IF(MID(B2, FIND("N", B2)+1, 1)="", 0, (MID(B2, FIND("N", B2)+1, 2)))"; the number of S elements in the liquor sample is extracted based on the formula "=IF(MID(B2, FIND("S", B2)+1, 1)="", 0, (MID(B2, FIND("S", B2)+1, 2)))"; and the number of P elements in the liquor sample is extracted based on the formula "=IF(MID(B2, FIND("P", B2)+1, 1)="", 0, (MID(B2, FIND("P", B2)+1, 2)))";

[0127] The components in the liquor sample to be tested are detected and analyzed by the above method. The total ion chromatogram of the components in the sample to be tested obtained in the positive ion mode is shown in FIG. 1, and the total ion chromatogram of the components in the sample to be tested obtained in the negative ion mode is shown in FIG. 2, and a total of 14500 molecular signals are detected. Based on the method in Example 1, the component information in the liquor sample to be tested is collected by the positive ion mode and the negative ion mode respectively, and all the original data mass spectrum files obtained are imported into Compound Discoverer 3.3 software for analysis. The software automatically distinguishes the positive and negative ion modes according to the mass spectrum information of different substance signals, and performs qualitative analysis. After extracting the corresponding elements and the number of elements, the results are shown in FIGS. 3-7. FIG. 3 is the number of molecules of different elements in the liquor sample to be tested, FIG. 4 is the response area corresponding to the molecules of different elements in the liquor sample to be tested, FIG. 5 is the proportion of the number of molecules of different elements in the liquor sample to be tested, FIG. 6 is the response intensity proportion of the molecules of different elements in the liquor sample to be tested based on the existing element distribution, and FIG. 7 is the result of dividing the components in the liquor sample to be tested based on the molecular mass distribution and calculating the proportion.

[0128] As can be seen from FIG. 3 and FIG. 5, the number of molecules detected from the to-be-tested liquor sample is 5838 molecular formulas, among which the molecules with COH as the element composition are the main molecules, 2449 molecules are detected, accounting for 41.9% of the total number, followed by COHN, 1212 molecules are detected, accounting for 20.8% of the total number; the remaining main molecule types detected are COHNP, COHS, CHN, COHNS and other molecules; as can be seen from FIG. 4 and FIG. 6, the response intensity of the molecules with COH as the element composition is the highest, the response is 1.40x10 11 , accounting for 57.0% of the total amount; followed by COHN, the response is 2.45x10 10 , accounting for 10.0% of the total amount; the response intensity of the remaining molecule types is COHNP (8.1%), COHS (7.3%), CHN (7.1%), COHNS (6.3%) and other molecules (4.3%) in turn; in addition, as can be seen from FIG. 7, the relative molecular mass distribution of the components in the to-be-tested liquor sample is extensive, the number of molecule signals with molecular weight of 150-250 Da is the most, which is 2268, accounting for 38.8%, and the proportion of molecules with molecular weight greater than 450 Da is the least, which is only 6.0%.

[0129] Comparative Example 1

[0130] I. Explore the influence of different chromatographic parameters on the detection results under positive ion mode

[0131] Based on the method of Example 1, 12 kinds of methods for detecting components in Jiangxiang Baijiu samples under different liquid chromatography conditions formed by 3 different liquid chromatography columns were compared, and the results are shown in FIG. 8. Among them, the 3 different liquid chromatography columns are as follows:

[0132] (1) InfinityLab Poroshell HPH-C18 (2.1x100mm, 1.9um, Agilent, USA);

[0133] (2) Acquity HSS T3 (50mmx2.1mm, 1.8um, Waters, USA);

[0134] (3) Poroshell 120 PFP (150mmx2.1mm, 2.7um, Agilent, USA);

[0135] The 12 kinds of methods for detecting components in Jiangxiang Baijiu under different liquid chromatography conditions formed by the three kinds of chromatographic columns are shown in Table 3:

[0136] Table 3

[0137] The conditions of "Gradient 1" in Table 3 are shown in Table 4, and the conditions of "Gradient 2" in Table 3 are shown in Table 5:

[0138] Table 4 Flow elution method - "Gradient 1"

[0139] Table 5 Flow elution method - "Gradient 2"

[0140] According to the results shown in Figure 8, when using ultrapure water containing 0.1% formic acid as mobile phase A and 100% methanol as mobile phase B, the number of signal molecules of the Acquity HSS T3 (50mmx2.1mm, 1.8μm, Waters, USA) column was the highest, and the number of signal molecules of the Acquity HSS T3 (50mmx2.1mm, 1.8μm, Waters, USA) column was also higher when using "Gradient 2" flow elution method for elution. At the same time, when using ultrapure water as mobile phase A and 100% methanol as mobile phase B, the number of signal molecules of the InfinityLab Poroshell HPH-C18 (2.1x100mm, 1.9μm, Agilent, USA) column was the highest when using "Gradient 1" flow elution method for elution, and the number of signal molecules of the InfinityLab Poroshell HPH-C18 (2.1x100mm, 1.9μm, Agilent, USA) column was also higher when using "Gradient 1" flow elution method for elution compared with "Gradient 2" flow elution method, and the number of signal molecules of the InfinityLab Poroshell HPH-C18 (2.1x100mm, 1.9μm, Agilent, USA) column was also the highest when using ultrapure water as mobile phase A and 100% methanol as mobile phase B and using "Gradient 1" flow elution method for elution, and the number of signal molecules was 8500, 7800 for T3 column and 6100 for PFP column.

[0141] II. Explore the influence of different chromatography and mass spectrometry conditions on the detection results of C18 column in positive ion mode

[0142] Based on the method in Example 1, with InfinityLab Poroshell HPH-C18 (2.1x100mm, 1.9pm, Agilent, USA) as the chromatographic column, the flow phase, spray voltage conditions and collision energy conditions were changed respectively, and the influence of different chromatography and mass spectrometry conditions on the detection results of C18 column in positive ion mode was explored. The specific group settings are as follows:

[0143] (1) C18+LC1+MeOH / H2O (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, ultrapure water as mobile phase A, 100% methanol as mobile phase B), collision energy condition is 10, 20, 30eV, spray voltage condition is 3kv;

[0144] (2) C18+LC1+MeOH / H2O (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, ultrapure water as mobile phase A, 100% methanol as mobile phase B), collision energy condition is 10, 20, 30eV, spray voltage condition is 3.5kv;

[0145] (3) C18+LC1+MeOH / H2O (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, ultrapure water as mobile phase A, 100% methanol as mobile phase B), collision energy condition is 10, 20, 30eV, spray voltage condition is 4kv;

[0146] (4) C18+LC1+MeOH / H2O (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, ultrapure water as mobile phase A, 100% methanol as mobile phase B), spray voltage condition is 3.5kv, collision energy condition is 10, 20, 30eV;

[0147] (5) C18+LC1+MeOH / H2O (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, ultrapure water as mobile phase A, 100% methanol as mobile phase B), spray voltage condition is 3.5kv, collision energy condition is 20, 30, 40eV;

[0148] (6) C18+LC1+MeOH / H2O (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, ultrapure water as mobile phase A, 100% methanol as mobile phase B), spray voltage condition is 3.5kv, collision energy condition is 30, 40, 50eV;

[0149] (7) C18+LC1+MeOH / H2O+0.1%FA (i.e. C18 as chromatographic column, "Gradient 1" as elution mode, 0.1% formic acid-containing ultrapure water as mobile phase A, 100% methanol as mobile phase B), spray voltage: 3KV; collision energy: 10, 20, 30eV;

[0150] (8) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 3KV; collision energy: 20, 30, 40 eV;

[0151] (9) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 3KV; collision energy: 30, 40, 50 eV;

[0152] (10) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 3.5KV; collision energy: 10, 20, 30 eV;

[0153] (11) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 3.5KV; collision energy: 20, 30, 40 eV;

[0154] (12) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 3.5KV; collision energy: 30, 40, 50 eV;

[0155] (13) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 4KV; collision energy: 10, 20, 30 eV;

[0156] (14) C18 + LC1 + MeOH / H2O + 0.1% FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid in ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), spray voltage: 4KV; collision energy: 20, 30, 40 eV;

[0157] (15) C18+LC1+MeOH / H2O+0.1%FA (i.e., using C18 as the chromatographic column, "gradient 1" as the elution mode, ultrapure water containing 0.1% formic acid as the mobile phase A, and 100% methanol as the mobile phase B), spray voltage: 4KV; collision energy: 30, 40, 50eV.

[0158] The results are shown in Figure 9. According to the results in Figure 9, the C18 column, under the chromatographic conditions of "gradient 1" elution mode, ultrapure water as mobile phase A, and 100% methanol as mobile phase B, with collision energies of 10, 20, and 30 eV and a spray voltage of 3.5 kV, achieved the highest number of signal molecules, specifically 8786. Furthermore, under the same collision energy and spray voltage conditions, this method resulted in a higher number of detected signal molecules compared to using ultrapure water containing 0.1% formic acid as mobile phase A and 100% methanol as mobile phase B.

[0159] III. Investigating the effects of different chromatographic and mass spectrometric parameters on detection results in negative ion mode.

[0160] Based on the method of Example 1, the effects of different chromatographic parameters and different mass spectrometry parameters on the detection results in negative ion mode were investigated. The specific group settings are as follows:

[0161] (1) Using T3+LC2+MeOH / H2O+0.1%FA (i.e., using T3 as the column, "gradient 2" as the elution mode, ultrapure water containing 0.1% formic acid as the mobile phase A, and 100% methanol as the mobile phase B) as the chromatographic conditions, the following spray voltage and collision energy conditions were set: 2KV+10, 20, 30eV, 2KV+20, 30, 40eV, 2KV+30, 40, 50eV, 2.5KV+10, 20, 30eV, 2.5KV+20, 30, 40eV, 2.5KV+30, 40, 50eV, 3KV+10, 20, 30eV, 3KV+20, 30, 40eV, 3KV+30, 40, 50eV;

[0162] (2) Using T3+LC1+MeOH / H2O (i.e., using T3 as the column, "gradient 1" as the elution mode, ultrapure water as the mobile phase A, and 100% methanol as the mobile phase B) as the chromatographic conditions, the following spray voltage and collision energy conditions were set: 2KV+10, 20, 30eV, 2KV+20, 30, 40eV, 2KV+30, 40, 50eV, 2.5KV+10, 20, 30eV, 2.5KV+20, 30, 40eV, 2.5KV+30, 40, 50eV, 3KV+10, 20, 30eV, 3KV+20, 30, 40eV, 3KV+30, 40, 50eV;

[0163] (3) Using PFP+LC2+MeOH / H2O+0.1%FA (i.e., using PFP as the column, "gradient 2" as the elution mode, ultrapure water containing 0.1% formic acid as the mobile phase A, and 100% methanol as the mobile phase B) as the chromatographic conditions, the following spray voltage and collision energy conditions were set: 2KV+10, 20, 30eV, 2KV+20, 30, 40eV, 2KV+30, 40, 50eV, 2.5KV+10, 20, 30eV, 2.5KV+20, 30, 40eV, 2.5KV+30, 40, 50eV, 3KV+10, 20, 30eV, 3KV+20, 30, 40eV, 3KV+30, 40, 50eV;

[0164] (4) Using PFP+LC2+MeOH / H2O (i.e., using PFP as the column, "gradient 2" as the elution mode, ultrapure water as the mobile phase A, and 100% methanol as the mobile phase B) as the chromatographic conditions, the following spray voltage and collision energy conditions were set: 2KV+10, 20, 30eV, 2KV+20, 30, 40eV, 2KV+30, 40, 50eV, 2.5KV+10, 20, 30eV, 2.5KV+20, 30, 40eV, 2.5KV+30, 40, 50eV, 3KV+10, 20, 30eV, 3KV+20, 30, 40eV, 3KV+30, 40, 50eV;

[0165] (5) C18+LC1+MeOH / H2O+0.1%FA (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, 0.1% formic acid ultrapure water as the mobile phase A, 100% methanol as the mobile phase B) as the chromatographic conditions, the following groups of spray voltage and collision energy conditions are set: 2KV+10, 20, 30eV, 2KV+20, 30, 40eV, 2KV+30, 40, 50eV, 2.5KV+10, 20, 30eV, 2.5KV+20, 30, 40eV, 2.5KV+30, 40, 50eV, 3KV+10, 20, 30eV, 3KV+20, 30, 40eV, 3KV+30, 40, 50eV;

[0166] (6) C18+LC1+MeOH / H2O (i.e. C18 as the chromatographic column, "Gradient 1" as the elution mode, ultrapure water as the mobile phase A, 100% methanol as the mobile phase B) as the chromatographic conditions, the following groups of spray voltage and collision energy conditions are set: 2KV+10, 20, 30eV, 2KV+20, 30, 40eV, 2KV+30, 40, 50eV, 2.5KV+10, 20, 30eV, 2.5KV+20, 30, 40eV, 2.5KV+30, 40, 50eV, 3KV+10, 20, 30eV, 3KV+20, 30, 40eV, 3KV+30, 40, 50eV.

[0167] As shown in Figure 10, according to the results shown in Figure 10, under the chromatographic conditions of PFP+LC2+MeOH / H2O+0.1%FA (i.e. PFP as the chromatographic column, "Gradient 2" as the elution mode, 0.1% formic acid ultrapure water as the mobile phase A, 100% methanol as the mobile phase B), the spray voltage is 2.5KV, and the collision energy is 10, 20, 30eV, so that the sample of the liquor to be tested can achieve the maximum number of signal molecules in the negative ion mode. That is, the separation performance of the PFP column is best when the inorganic phase is 0.1% formic acid ultrapure water, and 8471 signal molecules can be detected when the spray voltage is 2.5KV and the collision energy is 10, 20, 30eV. The C18 column is second, and the separation performance is best when the inorganic phase is 0.1% formic acid ultrapure water, and 6643 signal molecules can be detected when the spray voltage is 3.0KV and the collision energy is 10, 20, 30eV. And the separation performance of the chromatographic column in the positive and negative ion modes, that is, the number of molecular signals detected in the sample of the liquor to be tested in the positive and negative ion modes, ultimately verified that the separation performance of C18 is more stable. Among them, the number of molecular signals detected by C18 in the positive and negative ion modes is 15429, the number of molecular signals detected by the PFP column in the positive and negative ion modes is 14571, and the number of molecular signals detected by the T3 column in the positive and negative ion modes is 13900.

[0168] Comparative Example 2

[0169] Based on the method in Example 1, the components in the samples of three different types of baijiu (Jiangxiang, Nongxiang and Qingxiang) were detected and analyzed respectively, and the original data mass spectrum data positive and negative ion mode files obtained by detection were imported into Compound Discoverer 3.3 software for analysis. The software distinguished the positive and negative ion modes according to the mass spectrum information of different substances signals. The data obtained by software analysis and database comparison was in the form of mixed positive and negative ion modes. The results are shown in Figures 11-12.

[0170] As shown in Figure 12, in terms of the number of molecular signals, the number of molecular signals of Jiangxiang baijiu was 7588, which was much higher than that of Nongxiang baijiu and Qingxiang baijiu, and the number of molecular signals of Nongxiang baijiu and Qingxiang baijiu was 3818 and 3754 respectively. As shown in Figure 11, from the perspective of molecular response intensity, the molecular response intensity of Jiangxiang baijiu was the highest, reaching 3.4 x 10 11 , while the molecular response intensity of Nongxiang baijiu and Qingxiang baijiu was 1.6 x 10 11 and 7.7 x 10 10 respectively.

[0171] From the perspective of molecular element composition, the number of molecular signals with COH as the element composition in Jiangxiang baijiu was 3168, while the number of COH molecular signals in Nongxiang baijiu and Qingxiang baijiu was 1622 and 1603 respectively. The molecular response intensity in Jiangxiang baijiu was greater than that in Nongxiang baijiu and Qingxiang baijiu. For N-containing molecules, 1705 molecular signals with COHN as the element composition were detected in Jiangxiang baijiu, while only 483 and 614 molecular signals were detected in Nongxiang baijiu and Qingxiang baijiu. The COHN molecular signal intensity in Jiangxiang baijiu was 4.1 x 10 10 , while the COHN molecular signal intensity in Nongxiang baijiu and Qingxiang baijiu was 1.1 x 10 10 and 6.4 x 10 9 respectively.

[0172] Comparative Example 3

[0173] This comparative example is based on Example 1 and provides a method for detecting and analyzing the components in a sample of Jiangxiang baijiu, which is different from Example 1 in that the comparative example uses UPLC-QTOF-MS high-resolution mass spectrometry. The specific steps are as follows:

[0174] (1) Sampling: same as Example 1; (2) detection: using UPLC-QTOF-MS high-resolution mass spectrometry to detect the sample to be tested; wherein the analysis conditions of the chromatograph are as follows: the chromatographic column is selected as: HSS T3 C18 (1.8μm, 2.1×50mm, Waters), temperature 40℃, injection volume 3μL, flow rate maintained at 0.3mL / min; gradient elution mode: mobile phase A was water containing formic acid (0.01%, v / v), and mobile phase B was acetonitrile; elution gradient: initial gradient of 2%B within 1.0 min, gradient of 25%B within 2.5 min, gradient of 40%B within 3 min, gradient of 70%B within 10.0 min, and gradient of 100%B within 11.0 min; mass spectrometry analysis conditions were as follows: data acquisition was performed in TOF MS mode, capillary voltage 1.0kV, cone voltage 4.0V, ion source temperature 120℃, desolvation gas temperature 450℃, cone gas flow rate 50L / h, desolvation flow rate 800L / h, scan range 50~120m / z;

[0175] (3) Data processing: The original data of each group were analyzed using Progenesis QI 2.3 software (Waters). Samples with detection signals three times larger than blank samples were extracted as target peaks. Then, database comparison was performed to screen the molecules. The results are shown in Figure 13.

[0176] As can be seen, the analysis of Maotai-flavor liquor using UPLC-QTOF-MS high-resolution mass spectrometry yielded 994 molecular signals. The main molecular types included COH, COHN, COHNS, COHNP, COHS, COHP, CHN, and CHNS, with COHN exhibiting the most signals (357). The total number of molecular signals and molecular types are fewer than those obtained in this application. Compared to UPLC-QTOF-MS high-resolution mass spectrometry, this application yields a higher number of molecular signals and a greater variety of molecular signals with different elemental compositions. Therefore, this method offers higher mass spectrometry resolution and higher mass accuracy.

[0177] Example 2

[0178] This embodiment, based on Embodiment 1, provides a method for identifying base liquor of sauce-flavored baijiu from different batches. The specific steps are as follows:

[0179] (1) Obtaining component information in base liquor samples from different batches: Same as in Example 1, based on the detection method in Example 1, the component information in base liquor samples from different batches is analyzed to obtain component information in base liquor samples from different batches. Among them, the base peak diagram of the base liquor samples from different batches is shown in Figure 14, and the component heat map of the base liquor samples from different batches is finally obtained as shown in Figure 15.

[0180] (2) Mining different round difference organic components: using univariate statistics, multivariate statistics and difference analysis step (1) to obtain the component information in different round base liquor samples; The specific is as follows:

[0181] First, n rounds of base liquor is Rn group, and the rest of the rounds of base liquor is classified as Rn_R group, and the difference significance p value between Rn and Rn_R groups is analyzed by t test to screen the difference organic components; Second, the organic components of Rn group and Rn_R group are analyzed by using orthogonal partial least squares algorithm analysis (OPLS-DA), and the OPLS-DA-Rn model is constructed; Then the distribution of the difference components of Rn group and Rn_R group is analyzed by using volcano plot, and the difference fold Fc value is obtained at this time; According to the p value in t test, the VIP value of OPLS-DA-Rn model and the difference fold Fc value in volcano plot analysis, the characteristic components of the nth round base liquor are obtained. Among them, the components with p<0.05, VIP≥1 and Fc>2 are the specific components for distinguishing the organic components of the nth round and other rounds of base liquor;

[0182] (3) Visualization of difference organic components: the specific components of each round of Jiangxiang Baijiu obtained by screening in step (2) are visualized, as shown in Figure 16, the number of specific components of each round of base liquor is 259, 92, 28, 180, 174, 341 and 365, respectively, and further visualization of the difference components in each round of Jiangxiang Baijiu is carried out by using vk plot (as shown in Figure 17);

[0183] (4) Discrimination: the difference components in each round of Jiangxiang Baijiu obtained in step (3) can be further used to discriminate the round of Jiangxiang Baijiu sample. As shown in Figure 18, based on PLS-DA analysis, discrimination analysis of one to seven rounds of base liquor can be realized, and the accuracy of discrimination analysis is high.

[0184] Comparative Example 4

[0185] This comparative example is based on example 2, which provides a method for discriminating different rounds of Jiangxiang Baijiu base liquor. The difference between example 2 is that in step (2) of mining different round difference organic components, p<0.05, VIP≥1 and Fc>3 are used as the standard for screening, and the specific steps are as follows:

[0186] (1) Obtain component information in base liquor samples of different rounds: same as in Example 2; (2) Mine different rounds of difference organic components: screening is performed with p < 0.05, VIP≥1, and Fc>3 as the standard, and the rest is the same as in Example 2; (3) Visual expression of difference organic components: the method is the same as in Example 2, and finally the number of difference components of base liquor of each round is obtained as 142, 42, 25, 86, 96, 140, and 211; (4) Discrimination: the method is the same as in Example 2, and the difference components obtained based on step (3) are used to discriminate the round to which the Maotai-flavor liquor sample belongs, and the discrimination result is shown in FIG. 19.

[0187] According to the result shown in FIG. 19, compared with Example 2, the discrimination accuracy of base liquor of each round of Maotai-flavor liquor is obviously reduced.

[0188] Comparative Example 5

[0189] The comparative example 5 is based on Example 2, and provides a method for discriminating base liquor of different rounds of Maotai-flavor liquor, which is different from Example 1 in that in step (1), the method for obtaining component information in base liquor samples of different rounds is different. In step (1), the comparative example adopts a conventional GC-MS method to collect component information in base liquor samples of different rounds, and then based on the same methods of steps (2), (3), and (4) in Example 2, discriminant analysis is performed on the round of base liquor of Maotai-flavor liquor. The result is shown in FIG. 20. According to the result shown in FIG. 20, based on the component information in base liquor samples of different rounds collected by the conventional GC-MS method, based on the same discriminant analysis method of Example 2, it is difficult to accurately distinguish the round of base liquor of Maotai-flavor liquor.

[0190] Example 3

[0191] The example is based on Example 1, and provides a method for analyzing dynamic changes of components of base liquor of different rounds of Maotai-flavor liquor, which comprises the following steps:

[0192] (1) Sampling: same as Example 1; (2) Detection: same as Example 1; (3) Data processing and compound qualification: 1) import the data in step (2) into Compound Discoverer 3.3, establish workflow (select spectra—align retention times-detect compounds-group compounds-assign compounds annotations) and perform data analysis, further match and determine the compounds with a confidence level of 2 / 3 by comparing ChemBank, METLIN, KEGG, ChemSpider, PubChem, Human Metabolome Database, ChemSpiderman, NIST database; 2) based on database m / z clouds, by analyzing the matching of molecular ion mass-to-charge ratio, secondary mass spectrum fragment ions and database, and analyzing whether the retention time conforms to the chromatographic retention rule, to determine the compounds with a confidence level of 1; after this analysis step, a total of 3958 molecules / molecular formula were finally obtained, of which the number of molecules in the liquor samples in the first to seventh rounds were: 2949, 3164, 3130, 3349, 3310, 3404, 3265; 3) at the same time, based on the molecular formula information obtained in step 2) of each liquor sample, further preliminarily judge the attribution category of the compound, and then analyze the mass-to-charge ratio, fragment abundance ratio and isotope abundance ratio, so as to deduce the structure and element composition of the substance, and the specific compound information in the corresponding base liquor of Maotai-flavor liquor in different rounds can be obtained; (4) screen the common components in the base liquor samples of Maotai-flavor liquor in different rounds, the specific method is: select organic components with response area higher than 10 5 , based on Upset / Venn diagram analysis, finally 2084 common components are screened from the 3958 detected molecules; (5) classify the screened common components, as follows:

[0193] Extract the specific elements (C, H, O) and composition number in the molecular formula of the screened common components, the specific function is as follows: first, based on the formula:

[0194] "=CONCATENATE(IF(ISERR(FIND("C", $F2)), "", "C"), IF(ISERR(FIND("O", $F2)), "", "O"), IF(ISERR(FIND("H", $F2)), "", "H"))" can extract different element compositions of different compounds, and the extracted elements include C, O, and H; then, based on the formula "=IF(MID(B2, FIND("C", B2) + 1, 1) = "", 0, (MID(B2, FIND("C", B2) + 1, 2)))", the number of C elements in the liquor sample is extracted; based on the formula "=IF(MID(B2, FIND("H", B2) + 1, 1) = "", 0, (MID(B2, FIND("H", B2) + 1, 2)))", the number of H elements in the liquor sample is extracted; based on the formula "=IF(MID(B2, FIND("O", B2) + 1, 1) = "", 0, (MID(B2, FIND("O", B2) + 1, 2)))", the number of high-content O elements in the liquor is extracted;

[0195] According to the number of C, H, and O elements in the common component molecular formula obtained by extraction, the H / C and O / C ratios are calculated, and the common components obtained by the above screening are classified based on the corresponding H / C and O / C ratios. The classification basis and results are shown in Table 6:

[0196] Table 6 Component division basis

[0197] According to the classification basis and classification results shown in Table 6, the organic components of each round of Maotai-flavor liquor are divided into 7 categories, namely, lipids, peptides, carbohydrates, unsaturated hydrocarbons, lignin, tannins, and condensed ring aromatic hydrocarbons;

[0198] (6) Based on the results of classifying the common components obtained by the above screening based on H / C and O / C ratios, the change trend of the common components is analyzed, as follows:

[0199] Firstly, the above-mentioned 7 categories of common components in each round of Maotai-flavor liquor base liquor are subjected to cluster heat map analysis, and the results are shown in Figure 23. According to the results shown in Figure 23, the common components of the first to seventh rounds show a strong correlation, and the first round of Maotai-flavor liquor and the remaining six rounds of Maotai-flavor liquor have large component differences and are divided into two categories; the second and third rounds of Maotai-flavor liquor can be clustered into one category, which is consistent with the flavor characteristics; the fourth and fifth rounds of Maotai-flavor liquor can be clustered into one category;

[0200] Further correlation analysis was performed on the 7 common components of different rounds of Jiang-flavor liquor. Specifically, the data were imported into the origin software and Pearson correlation analysis was performed using CorrelationPlot.opx to obtain the results shown in FIG. 24. According to the results shown in FIG. 24, the common components of the first to seventh rounds showed strong correlation.

[0201] Based on the response value changes of the 7 types of organic components obtained by classifying the common components, the change trends of the 7 types of organic components in the base liquor of the first to seventh rounds were analyzed, and the results are shown in FIG. 25. According to the results shown in FIG. 25, the content of lipid compounds in the second to fourth rounds of Jiang-flavor liquor was 5.33E+10, 5.34E+10, and 5.36E+10, respectively, and the change trend was small. The response value of lipid compounds in the fifth to seventh rounds of Jiang-flavor liquor first increased and then decreased. With the increase of rounds, the response values of peptide, carbohydrate, lignin, tannin, and condensed ring aromatic compounds all showed a trend of first increasing and then decreasing, with the difference that the response values of peptide, carbohydrate, lignin, and tannin compounds in the fourth round of Jiang-flavor liquor were the highest, the response value of concentrated aromatic compounds in the fifth round of Jiang-flavor liquor was the highest, and the response value of unsaturated carbon hydrogen compounds showed an increasing trend.

[0202] Meanwhile, taking lipid compounds as an example, based on the data processing in step (3) and the qualitative method in step 3) in the compound qualitative step, the number of lipid compounds in the base liquor of different rounds of liquor was qualitatively obtained as follows: 98 ester compounds were qualitatively detected in the base liquor of the first round of Jiang-flavor liquor, 97 ester compounds were qualitatively detected in the base liquor of the second round of Jiang-flavor liquor, 101 ester compounds were qualitatively detected in the base liquor of the third round of Jiang-flavor liquor, 105 ester compounds were qualitatively detected in the base liquor of the fourth round of Jiang-flavor liquor, 103 ester compounds were qualitatively detected in the base liquor of the fifth round of Jiang-flavor liquor, 99 ester compounds were qualitatively detected in the base liquor of the sixth round of Jiang-flavor liquor, and 104 ester compounds were qualitatively detected in the base liquor of the seventh round of Jiang-flavor liquor.

[0203] Based on the different rounds of liquor base liquor lipid compounds obtained by the above qualitative, the same method as in step (4) was used to screen the common ester compounds in the different rounds of Maotai-flavor liquor base liquor samples. Through the above screening method, 93 common lipid compounds were finally screened. Based on the response value changes of the screened common lipid compounds in each round of base liquor samples, the change trend of the screened common lipid compounds in the first to seventh rounds of base liquor was further analyzed, and the results are shown in Figure 26. According to the results shown in Figure 26, the content of lipid compounds reached the highest in the sixth round, and in the first to sixth rounds, the content of lipid compounds gradually increased with the increase of rounds, reached the highest in the sixth round, and decreased in the seventh round. That is, through the above comparison of the qualitative results, the analysis results obtained by classifying the common components screened based on H / C and O / C ratio are consistent with the results of common component change trend analysis based on the qualitative results of different rounds of Maotai-flavor liquor components, that is, the classification analysis of common components in different rounds of liquor samples based on the above classification method can further analyze the change trend of common components in different rounds of Maotai-flavor liquor from the perspective of element composition.

[0204] Example 4

[0205] Based on Examples 1 and 3, the present embodiment provides a dynamic change analysis method for components of different rounds of Maotai-flavor liquor base liquor, which comprises the following steps:

[0206] (1) Sampling: same as Example 1; (2) Detection: same as Example 1; (3) Data processing and compound qualification: same as Example 3; (4) Screening common components in different rounds of Maotai-flavor liquor base liquor samples: same as Example 3; (5) Classifying the screened common components based on saturation and oxidation, the specific classification steps are as follows: Extract the number of C, H, O, N, and S elements, and the extraction formula is “=IF(MID(B2, FIND(“C”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“C”, B2)+1, 2))), =IF(MID(B2, FIND(“H”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“H”, B2)+1, 2))), =IF(MID(B2, FIND(“O”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“O”, B2)+1, 2))), =IF(MID(B2, FIND(“N”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“N”, B2)+1, 2))), =IF(MID(B2, FIND(“S”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“S”, B2)+1, 2)))”; calculate (DBE-O) / C and NOSC values through the formula, and the specific formula is:

[0207] NOSC = 4 - (4 * N C + N H - 2 * N O - 3 * N N - 2 * N S ) / N C , (DBE-O) / C = ((2 * N C + N N - N H + 2) / 2 - N O ) / N C

[0208] wherein, "N" in the formula corresponds to the number of different elements;

[0209] The classification and results obtained based on the above classification method are shown in Table 7:

[0210] Table 7

[0211] (6) Based on the results of the classification of the common components obtained by screening based on the saturation and oxidation degree, the change trend of the common components was analyzed, which is as follows:

[0212] Firstly, the above-mentioned 5 types of common components in each round of Jiang-flavor liquor base liquor, specifically: saturated-reduced (SRC), saturated-oxidized (SOC), unsaturated-reduced (URC), unsaturated-oxidized (UOC), neutral state (NSC), and the clustering heat map analysis of the 5 types of common components was carried out, and the results are shown in Figure 27. According to the results shown in Figure 27, the common components of the first to seventh rounds obtained based on this classification method also show a strong correlation relationship. The components of the first round of Jiang-flavor liquor and the remaining six rounds of Jiang-flavor liquor are quite different and are divided into two categories. However, the difference is that based on this classification method, the third and fourth rounds of Jiang-flavor liquor can be clustered into one category; the sixth and seventh rounds of Jiang-flavor liquor can be clustered into one category;

[0213] Further correlation analysis of the 5 types of common components of different rounds of Jiang-flavor liquor was carried out, and the specific method was as follows: using origin software, importing data, using CorrelationPlot.opx, and carrying out Pearson correlation analysis to obtain the results shown in Figure 28. According to the results shown in Figure 28, the common components of the first to seventh rounds also show a strong correlation relationship;

[0214] Based on the response value changes of the 5 categories of organic components obtained by classifying the common components, the change trends of the above-mentioned 5 categories of organic components in the base liquor of 1 to 7 rounds were analyzed, and the results are shown in Figure 29. According to the results shown in Figure 29, except for the unsaturated reducing components, the contents of the remaining components in the third, fourth and fifth rounds of liquor are higher than those in the remaining rounds. Based on this, it can be inferred that the contents of unsaturated oxidizing components, saturated oxidizing components, saturated reducing components and neutral components are related to the taste of the base liquor. The higher the content, the more mellow and full-bodied the liquor is. In addition, it is speculated that with the change of rounds, more unsaturated oxidizing components are converted into unsaturated reducing components.

[0215] At the same time, based on the data processing of step (3) above and the qualitative method of step 3) in the compound qualitative step, the compounds in different rounds of base liquor were qualitatively obtained. Taking unsaturated reducing compounds as an example: 244 were qualitatively detected in the first round of Jiangxiang Baijiu base liquor, 246 in the second round, 250 in the third round, 255 in the fourth round, 253 in the fifth round, 253 in the sixth round, and 255 in the seventh round.

[0216] Based on the above-mentioned qualitative results of the lipids in different rounds of base liquor, the same method as in step (4) was used to screen the common unsaturated reducing compounds in different rounds of Jiangxiang Baijiu base liquor samples. After the above-mentioned screening method, 243 were finally screened. Based on the response value changes of the screened common unsaturated reducing compounds in each round of base liquor sample, the change trend of the screened common unsaturated reducing compounds in 1 to 7 rounds of base liquor was further analyzed, and the results are shown in Figure 30. According to the results shown in Figure 30, the unsaturated reducing components as a whole showed an upward trend with the change of rounds. That is, through the above-mentioned comparison of qualitative results, the analysis results obtained by classifying the screened common components based on the degree of saturation and oxidation are consistent with the results of the change trend analysis of common components based on the qualitative results of different rounds of Jiangxiang Baijiu components. That is, based on the above-mentioned classification method for classifying and analyzing the common components in different rounds of liquor samples, the change trend of the common components in different rounds of Jiangxiang Baijiu can be further analyzed from the perspective of elemental composition.

[0217] Example 5

[0218] This example is based on Example 1 and provides a method for dynamic change analysis of components in different rounds of Jiangxiang Baijiu base liquor, comprising the following steps:

[0219] (1) Sampling: same as Example 1; (2) Detection: same as Example 1; (3) Data processing and compound qualification: same as Example 3; (4) Screening common components in different rounds of Jiang-flavor Baijiu base liquor samples: same as Example 3; (5) Classification of the screened common components, as follows: the number of C, H, O, N and S elements was extracted, and the extraction formula was “=IF(MID(B2, FIND(“C”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“C”, B2)+1, 2))), =IF(MID(B2, FIND(“H”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“H”, B2)+1, 2))), =IF(MID(B2, FIND(“O”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“O”, B2)+1, 2))), =IF(MID(B2, FIND(“N”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“N”, B2)+1, 2))), =IF(MID(B2, FIND(“S”, B2)+1, 1) = “”, 0, (MID(B2, FIND(“S”, B2)+1, 2)))”, the Co value was calculated by formula, and the specific formula was:

[0220] Log10Co = (n c o -n c b c -n o b o -2*n c n o / (n c +n o )*b co -n N b N -n S b S , wherein the specific information of n c o , b c , b o , b co , b N , b S in the formula is shown in Table 8:

[0221] Table 8

[0222] The classification results obtained based on the above classification method are shown in Table 9:

[0223] Table 9

[0224] (6) Based on the volatility of the common components obtained by screening, the results of the classification were analyzed for the change trend of the common components, as follows:

[0225] Firstly, the above-mentioned 5 types of common components (volatile components, medium volatile components, semi-volatile components, low volatile components, and extremely low volatile components) in each round of Jiang-flavor base liquor were subjected to cluster heat map analysis, and the results are shown in Figure 31. According to the results shown in Figure 31, similarly, the components of the first round of Jiang-flavor base liquor and the remaining six rounds of Jiang-flavor base liquor were significantly different and were classified into two categories; but differently, the second and third rounds of Jiang-flavor base liquor could be clustered into one category; the fourth and fifth rounds of Jiang-flavor base liquor could be clustered into one category; as can be seen from Figure 31, the contents of medium volatile components and semi-volatile components were significantly higher than those of the remaining 3 types of components, and the contents of medium volatile components in the fourth to sixth rounds of Jiang-flavor base liquor were higher than those in the remaining rounds, and the contents of medium volatile components in the second, third, fifth and sixth rounds of Jiang-flavor base liquor were higher than those in the remaining rounds;

[0226] Further correlation analysis of the 5 types of common components of different rounds of Jiang-flavor base liquor was performed, specifically as follows: using origin software, the data was imported, and CorrelationPlot.opx was used for Pearson correlation analysis, and the results are shown in Figure 32. According to the results shown in Figure 32, the common components of the first to seventh rounds also showed a strong correlation;

[0227] Based on the response value changes of the 5 types of organic components obtained by classifying the common components, the change trends of the above-mentioned 5 types of organic components in the first to seventh rounds of base liquor were analyzed, and the results are shown in Figure 33. According to the results shown in Figure 33, the contents of IVOC and LVOC showed a trend of first increasing and then decreasing, the content of VOC showed a trend of increasing, the content of SVOC showed a fluctuating change, i.e., first increasing, then decreasing, then increasing, and then decreasing, and the content of ELVOC showed no obvious change.

[0228] In addition, based on this method, it was found that compounds with high boiling points (boiling point > 100℃) can also be volatile compounds, such as benzyl alcohol (boiling point 205℃) which is a volatile compound, and phenylpropanol (boiling point 119-121℃) which is a medium volatile compound.

[0229] Meanwhile, based on the data processing in step (3) above and the qualitative mode in step 3) in the compound qualitative step, the compounds in the different rounds of base liquor were qualitatively obtained. Taking low volatile compounds as an example: 55 low volatile compounds were qualitatively obtained in the first round of base liquor, 57 low volatile compounds were qualitatively obtained in the second round of base liquor, 57 low volatile compounds were qualitatively obtained in the third round of base liquor, 57 low volatile compounds were qualitatively obtained in the fourth round of base liquor, 65 low volatile compounds were qualitatively obtained in the fifth round of base liquor, 58 low volatile compounds were qualitatively obtained in the sixth round of base liquor, and 60 low volatile compounds were qualitatively obtained in the seventh round of base liquor.

[0230] Based on the above-mentioned qualitative results of low volatile compounds in different rounds of base liquor, the same method in step (4) was used to screen the common ester compounds in different rounds of base liquor samples. After the above-mentioned screening method, 55 common ester compounds were finally screened. Based on the response value changes of the screened common low volatile compounds in each round of base liquor sample, the change trend of the screened common low volatile compounds in the first to seventh rounds of base liquor was further analyzed. The results are shown in Figure 34. According to the results shown in Figure 34, the response area of the common low volatile components showed a trend of first increasing and then decreasing with the change of rounds. That is, through the above-mentioned comparison of qualitative results, the analysis results obtained by classifying the screened common components based on volatility are consistent with the results of change trend analysis of common components based on the qualitative results of different rounds of Maotai-flavor liquor components. That is, based on the above-mentioned classification method for classifying and analyzing common components in different rounds of liquor samples, the change trend of common components in different rounds of Maotai-flavor liquor can be further analyzed from the perspective of elemental composition.

Claims

1. A method for component analysis of Maotai-flavor liquor based on non-targeted metabolomics technology, characterized in that, The analytical method includes the following steps: (1) Sampling: Take a sample of Maotai-flavor liquor as the sample to be tested. No sample pretreatment is required. (2) Detection: The sample to be tested is detected and analyzed based on ultra-high performance liquid chromatography-quadrupole electrostatic orbital field ion trap mass spectrometry. All components in the sample to be tested are directly entered into the mass spectrometer for determination after separation by ultra-high performance liquid chromatography. (3) Qualitative analysis: Based on the data obtained in step (2), qualitative analysis is performed on the components in the sample to be tested; The ultra-high performance liquid chromatography column used is an InfinityLab Poroshell HPH-C18 (2.1×100mm, 1.9μm).

2. The analytical method according to claim 1, characterized in that, The mobile phase of the ultra-high performance liquid chromatography is: ultrapure water as mobile phase A and 100% methanol as mobile phase B. Preferably, the gradient elution method for ultra-high performance liquid chromatography includes: adjusting the initial proportion of mobile phase B to 9-11% and holding for 2-3 min; linearly increasing phase B from 9-11% to 100% B over 17-18 min; maintaining 100% B for the next 1.5-2.5 min; then linearly decreasing phase B from 100% to 9-11% B over 0.3-0.6 min and holding for 0.05-0.2 min to balance the column pressure; Preferably, the chromatographic conditions for the ultra-high performance liquid chromatography include: column oven temperature of 38-42℃; autosampler temperature of 3-5℃; injection volume of 2-4μL; and flow rate of 0.2-0.4mL / min.

3. The analytical method according to claim 1, characterized in that, The mass spectrometry conditions include: Scan mode: Full MS / dd-MS 2 Scan range: 100-1500 m / z; Electrospray ionization source: ESI+ and / or ESI - ; Preferably, the parameters for the positive ion collection mode are set as follows: HESI parameter settings: spray voltage: +3.0-+4.0kV; capillary temperature: 240-260℃; sheath flow rate: 47-48Arb; auxiliary gas flow rate: 10-12Arb; auxiliary gas heater temperature: 320-370℃; collision energy: 5-13, 15-25, 25-35eV; Preferably, the parameters for the negative ion collection mode are set as follows: HESI parameter settings: spray voltage: -1.5--2.5kV; capillary temperature: 240-260℃; sheath flow rate: 47-448Arb; auxiliary gas flow rate: 10-12Arb; auxiliary gas heater temperature: 320-370℃; collision energy: 15-25, 25-35, 35-45eV.

4. The analytical method according to claim 1, characterized in that, The qualitative analysis of the components in the sample to be tested based on the data obtained in step (2) includes the following steps: Import the data from step (2) into Compound Discoverer 3.3, establish a workflow and perform data parsing, and further identify compounds with a confidence level of 2 / 3 by comparing and matching the database. Based on the m / z clouds database, the molecular ion mass-to-charge ratio, the matching of secondary mass spectrometry fragment ions with the database, and whether the retention time conforms to the chromatographic retention rules are analyzed to identify compounds with a confidence level of 1; the molecular formula information of the components in the sample to be tested is obtained. Based on the obtained molecular formula information of the corresponding components, the category of the compound is initially determined. Then, the mass-to-charge ratio, fragment abundance ratio and isotope abundance ratio are analyzed to infer the structure and elemental composition of the substance, that is, to obtain the specific compound information in the corresponding liquor sample; and / or, based on the obtained molecular formula information of the corresponding components, the number of compounds containing different elemental compositions is analyzed.

5. The analytical method according to claim 4, characterized in that, The analysis of the number of compounds containing different elemental compositions includes: Extracting different elemental compositions from different compounds; Extracting the quantities of different elements from different compounds; Based on the different elemental compositions and the corresponding quantities of different elements in the extracted compounds, the compounds containing different elemental compositions were analyzed; Preferably, the different elemental compositions in the compound containing different elemental compositions include at least one of COH, COHN, COHNP, COHS, CHN, and COHNS; the different elements in the step of extracting the different elemental compositions in the different compounds include at least one of C, O, H, N, P, S, F, Cl, and I.

6. The analytical method as described in claim 1, characterized in that the analytical method further includes: (4) Quantitative analysis of the components in the sample to be tested: The components in the sample to be tested are quantified according to the ratio of peak area to concentration of the primary spectrum of the sample to be tested and the standard and the extracted ion chromatogram, and the quantitative results are obtained.

7. A method for identifying base liquor of sauce-flavored baijiu from different distillation batches, characterized in that, Includes the following steps: 1) Obtain component information of the base liquor of the different batches of Maotai-flavor liquor based on the method described in any one of claims 1-6; 2) Analyze the different components in the base liquor of the Maotai-flavor Baijiu from different batches; 3) Based on the differences in the components, the base liquors of the different batches of Maotai-flavor liquor are identified.

8. The discrimination method as described in claim 7, characterized in that, In step 2), the analysis of the differential components in the base liquor of the sauce-flavored baijiu from different batches includes the following steps: analyzing the component information obtained in step 1) based on at least one of univariate statistics, multivariate statistics, and differential analysis methods, and screening the differential components; Preferably, the criteria for screening the differential components include at least one of the following: p-value threshold p < 0.05, variable difference contribution VIP ≥ 1, and difference fold Fc > 2; Preferably, the univariate statistical analysis is a t-test; the multivariate statistical analysis is an orthogonal partial least squares algorithm; and the difference analysis is a multiple change method.

9. A method for analyzing the dynamic changes of base liquor components in different batches of Maotai-flavor liquor, characterized in that, The analytical method includes the following steps: I. Obtaining component information of the base liquor of the different batches of Maotai-flavor liquor based on the method described in any one of claims 1-6; II. Screening common components in the base liquor of the different batches of Maotai-flavor liquor based on the aforementioned component information; 3. Classify the common components obtained from the screening based on the H / C and O / C ratios, saturation and oxidation degree, or volatility in the molecular formula of the common components; IV. Based on the classification results obtained in step three, the changing trends of common components in the base liquor of Maotai-flavor liquor from different batches are analyzed.

10. The method as described in claim 9, characterized in that, Step three involves classifying the common components obtained from the screening process based on the H / C ratio and O / C ratio in the molecular formula of the common components, including the following steps: If the molecular formula of a common component has a ratio of 1.5 ≤ H / C ≤ 2 and 0 ≤ O / C < 0.3, then the corresponding common component is classified as a lipid compound; if the molecular formula of a common component has a ratio of 1.5 ≤ H / C ≤ 2.2 and 0.3 ≤ O / C < 0.67, then the corresponding common component is classified as a peptide compound; if the molecular formula of a common component has a ratio of 1.5 ≤ H / C ≤ 2.2 and 0.67 ≤ O / C ≤ 1.2, then the corresponding common component is classified as a carbohydrate compound; if the molecular formula of a common component has a ratio of 0.7 ≤ H / C < 1.5 and 0 ≤ O / C < 0.3, then the corresponding common component is classified as a lipid compound. If the ratio is less than 0.1, the corresponding common component is classified as an unsaturated hydrocarbon compound; if the molecular formula of the common component has 0.7 ≤ H / C < 1.5 and 0.1 ≤ O / C < 0.67, the corresponding common component is classified as a lignin compound; if the molecular formula of the common component has 0 ≤ H / C < 1.5 and 0.67 ≤ O / C ≤ 1.2, the corresponding common component is classified as a tannin compound; if the molecular formula of the common component has 0.2 ≤ H / C < 0.7 and 0 ≤ O / C ≤ 0.67, the corresponding common component is classified as a polycyclic aromatic hydrocarbon compound. Preferably, the method for obtaining the H / C ratio and O / C ratio in the common component molecular formula includes: extracting the compositional quantities of C, H, and O elements in the common component molecular formula, and calculating the H / C ratio and O / C ratio in the common component molecular formula based on the extracted quantities of C, H, and O elements.

11. The analytical method as described in claim 9, characterized in that, Step three involves classifying the common components obtained from the screening process based on their saturation and oxidation degree in the molecular formulas of the common components. This includes the following steps: Extract the number of C, H, O, N, and S elements in the molecular formula of the common components, and calculate the (DBE-O) / C and NOSC values; classify the common components based on the calculated (DBE-O) / C and NOSC values. Preferably, classifying the common components based on the calculated (DBE-O) / C and NOSC values ​​includes the following steps: If NOSC<0, (DBE-0) / C>0, then the corresponding common component is classified as an unsaturated reducing component; If NOSC < 0 and (DBE-O) / C < 0, then the corresponding common component is classified as a saturated reducing component. If NOSC>0 and (DBE-O) / C>0, then the corresponding common component is classified as an unsaturated oxidizing component. If NOSC>0 and (DBE-O) / C<0, then the corresponding common component is classified as a saturated oxidizing component. If NOSC = 0, then the common components of the pair are classified as neutral components. Preferably, the formula for calculating the NOSC value is: NOSC = 4 - (4 * N) C +N H -2*N O -3*N N -2*N S ) / N C The formula for calculating (DBE-O) / C is: (DBE-O) / C = ((2*N) / C) C +N N -N H +2) / 2-N O ) / N C In the formula, N C N represents the number of elements in C. N N represents the number of elements in N. H N represents the number of elements in H. O N represents the number of O elements. S This indicates the number of elements in S.

12. The analytical method as described in claim 9, characterized in that, Step three, classifying the common components obtained from the screening based on the volatility in the molecular formula of the common components, includes the following steps: Extract the number of C, H, O, N, and S elements in the molecular formula of the common components and calculate the Co value; classify the common components based on the calculated Co value; Preferably, classifying the common components based on the calculated Co value includes the following steps: C o >3×10 6 μg·m -3 The corresponding common components are classified as volatile organic compounds. 300 <C o <3×10 6 μg·m -3 The corresponding common components are classified as medium volatile components; 0.3 <C o <300μg·m -3 If so, the corresponding common component is classified as a semi-volatile component; 3×10 -4 <C o <0.3μg·m -3 The corresponding common components are classified as low-volatility components. C o <3×10 -4 μg·m -3 The corresponding common components are classified as extremely low volatility components; Preferably, the formula for calculating the Co value is: Log10Co=(n c o -n c )b c -n o b o -2*n c n o / (n c +n o )*b co -n N b N -n S b S ;In the formula, N C N represents the number of elements in C. N N represents the number of elements in N. H N represents the number of elements in H. O N represents the number of O elements. S n represents the number of elements in S. c o b represents the reference carbon. c Indicates the contribution of C atoms to log10Co, b o b represents the contribution of the O atom to log10Co. co b represents the non-ideality of the carbon-oxygen relationship. N b represents the contribution of the N atom to log10Co. S This represents the contribution of the S atom to log10Co.

13. The analytical method as described in claim 9, characterized in that, the analysis of the changing trends of common components in the base liquor of the different batches of Maotai-flavor liquor based on the classification results obtained in step three includes: Based on the classification results obtained in step three, the common components in the base liquor of Maotai-flavor liquor from different batches are divided into multiple categories of compounds. Based on the changes in the response values ​​of the multiple categories of compounds obtained from the classification in the base liquor of Maotai-flavor liquor from each batch, the trend results of the changes of the common components in the base liquor of Maotai-flavor liquor from each batch are obtained.

14. The application of an analytical method as described in any one of claims 1-6, or a discrimination method as described in any one of claims 7-8, or an analytical method as described in any one of claims 9-13, in the field of brewing.

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