Volatile matter detection method for baijiu base liquor of different rounds and flavor difference analysis method for baijiu

Abstract

The invention provides a volatile matter detection method for different rounds of baijiu base liquor and a flavor difference analysis method for baijiu. The volatile matter detection method for different rounds of baijiu base liquor comprises the following steps: S01, taking different rounds of baijiu base liquor, and correspondingly preparing a plurality of different rounds of baijiu samples by adopting a closed volatilization balance mode; s02, respectively detecting volatile matters in the plurality of different rounds of white spirit samples by adopting a gas chromatograph-ion mobility spectrometry combined instrument to obtain detection data, and S03, carrying out data processing and qualitative analysis on the detection data corresponding to the different rounds of white spirit samples in S20 by adopting VOCal software to obtain the volatile matters in the plurality of different rounds of white spirit samples. Two-dimensional spectrograms of the volatile matters in the white spirit samples in different rounds, difference spectrograms of the volatile matters in the white spirit samples in different rounds and fingerprint spectrums of the volatile matters in the white spirit samples in different rounds are obtained. Complex volatile component information is presented in the form of visual images and data, the visualization degree is high, identification of different rounds of base liquor of Baijiu is facilitated, and rapid detection of flavor quality of different rounds of base liquor is realized.

Description

Technical Field

[0001] This application relates to the field of liquor testing technology, and in particular to a method for detecting volatiles in liquor base liquor from different batches and a method for analyzing the flavor differences of liquor. Background Technology

[0002] Maotai-flavor baijiu is renowned worldwide for its unique flavor characterized by "prominent Maotai aroma, elegant and delicate taste, mellow body, and long-lasting fragrance even in an empty glass." Its unique brewing process is the core of this flavor. The production cycle of Maotai-flavor baijiu lasts a full year, strictly adhering to the "12987" process: one production cycle per year, two rounds of feeding, nine rounds of steaming and cooking, eight rounds of fermentation, and seven rounds of distillation. In this process, seven rounds of fermentation in stacks and fermentation in cellars are interconnected, with each round having different fermentation conditions and microbial metabolic activities, resulting in seven distinct base baijiu. These base baijiu not only differ in flavor but also in chemical composition, with each round containing a unique combination of aromatic substances. In-depth research into the volatile composition and changes of base baijiu from different rounds is crucial for revealing the chemical nature of the fermentation process, optimizing brewing techniques, and ensuring the quality of the final product.

[0003] In the brewing process of Maotai-flavor baijiu, the collection of base liquor from rounds 1 to 7 takes approximately one year. Due to the large span of brewing time, the environmental conditions, microbial metabolic activities, and chemical reaction processes of the base liquor vary from round to round, resulting in significant differences in the content and composition of its flavor compounds. In actual production, if the changes in flavor compounds of the base liquor from different rounds cannot be understood in a timely manner, it is impossible to optimize and adjust subsequent brewing processes based on this information, which may lead to unstable product quality and affect the company's economic benefits.

[0004] However, the complexity of volatiles in the baijiu matrix presents a significant challenge to component analysis. Baijiu contains a wide variety of volatiles, exhibiting significant differences in content, chemical properties, polarity, boiling point, solubility, and volatility. These volatiles encompass various compounds such as alcohols, esters, aldehydes, acids, and ketones, which interact and influence each other to collectively constitute the unique flavor system of baijiu. The vast differences in the properties of these volatiles make accurate and comprehensive analysis extremely difficult.

[0005] Currently, chromatographic analysis methods play an important role in the component detection of baijiu (Chinese liquor), mainly including gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and liquid chromatography (LC). However, these methods generally suffer from cumbersome sample pretreatment, which is time-consuming, labor-intensive, and prone to introducing errors that affect the accuracy of the analytical results. They also fail to meet the requirements for efficient and rapid detection and cannot provide timely feedback of analytical results. Summary of the Invention

[0006] The first aspect of this application provides a method for detecting volatiles in base liquor from different distillation batches, including:

[0007] S01: Baijiu base liquor from different batches was used to prepare multiple baijiu samples from different batches using a closed evaporation equilibrium method.

[0008] S02: Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect volatiles in multiple batches of baijiu samples from different production runs, and detection data were obtained. The GC-IMS detection conditions included:

[0009] Gas chromatography settings: column was DB-WAX (60m×0.25mm×0.25μm), carrier gas nitrogen flow rate was 1.5mL / min, oven program was as follows: initial temperature 40℃, hold for 5min, then increase to 120℃ at a rate of 4℃ / min, hold for 3min, increase to 200℃ at a rate of 10℃ / min, hold for 5min;

[0010] Ion mobility spectrometry settings: temperature 45℃, drift gas nitrogen with a purity of 99.999%, flow rate 150 mL / min, positive ion mode;

[0011] S03: VOCal software was used to process and qualitatively analyze the detection data of baijiu samples from different batches in S02, and to obtain two-dimensional spectra of volatiles in baijiu samples from different batches, differential spectra of volatiles in baijiu samples from different batches, and fingerprint spectra of volatiles in baijiu samples from different batches.

[0012] In some optional embodiments of the first aspect of this application, the base liquor for different rounds of fermentation is the base liquor extracted seven times during the fermentation process of sauce-flavored liquor.

[0013] In some optional embodiments of the first aspect of this application, the closed-loop evaporation equilibrium method in step S01 includes:

[0014] S011: Dilute the baijiu samples from different batches with ultrapure water at a volume ratio of 10% of the ethanol in the sample preparation solution to obtain diluted baijiu samples from different batches. Then, put the diluted baijiu samples from different batches into sample bottles for headspace sampling. The headspace sampling conditions are as follows: the pre-sampling evaporation equilibrium temperature is 55℃~80℃, the equilibrium time is 10min~30min, the injection volume is 400μL~600μL, and the injection time is 20s~50s.

[0015] In some optional embodiments of the first aspect of this application, a n-ketone with C4 to C9 carbon atoms is used as an external standard compound. Gas chromatography-ion mobility spectrometry detection is performed under the detection conditions of step S20 to obtain calibration data. The detection data is calibrated using the calibration data. The calibrated detection data is then compared with the IMS database in the gas chromatography-ion mobility spectrometer to obtain the qualitative analysis results of volatiles detected by gas chromatography-ion mobility spectrometry for baijiu samples from different batches.

[0016] In some optional embodiments of the first aspect of this application, the qualitative analysis results of volatiles include the name of the volatile compound, retention index, elution time, drift time, and peak intensity.

[0017] In some optional embodiments of the first aspect of this application, in step S03, the two-dimensional spectra of volatiles in baijiu samples from different batches are obtained by normalizing ion migration time and reaction ion peak (RIP) positions.

[0018] In the two-dimensional spectrum of volatiles in the liquor sample, the vertical axis represents the peak elution time of gas chromatography, while the horizontal axis represents the ion migration time of ion migration spectrum.

[0019] Using the two-dimensional spectrum of volatiles in the first batch of baijiu sample as a reference, the baijiu base liquor from each batch was detected by gas chromatography-ion mobility spectrometry to obtain the reference spectrum. The baijiu base liquor from different batches, excluding the first batch, was detected by gas chromatography-ion mobility spectrometry and the signal peaks in the reference spectrum were subtracted to produce the difference spectrum of volatiles in baijiu samples from different batches.

[0020] The second aspect of this application provides a method for analyzing the flavor differences of baijiu (Chinese liquor), including:

[0021] S10: Obtain multiple groups of baijiu samples with different brewing processes. Each group of baijiu samples contains baijiu base liquor from different batches under the corresponding brewing process.

[0022] S20: The volatile matter detection method of different batches of baijiu base liquor in the first aspect of this application is used to detect multiple baijiu sample groups with different brewing processes, and the detection results are obtained. The detection results include the names of volatile compounds and corresponding peak intensities of each baijiu sample in the multiple baijiu sample groups with different brewing processes.

[0023] S30: Using the test results of the base liquor of each batch as the input of the OPLS-DA orthogonal partial least squares discriminant analysis model, the OPLS-DA model is established using SIMCA 14 software and the VIP value corresponding to the volatile matter is calculated. At least based on the relationship between the VIP value corresponding to the volatile matter and the preset VIP threshold, the volatile matter difference markers of the base liquor of each batch in different brewing processes are obtained.

[0024] In some optional embodiments of the second aspect of this application, in step S10, the multiple groups of baijiu samples with different brewing processes include baijiu sample groups with traditional brewing processes and baijiu sample groups with mechanical brewing processes.

[0025] In some optional embodiments of the second aspect of this application, in step S30, multiple preliminary volatile substance difference markers are first obtained based on the relationship between the VIP value corresponding to the volatile substance and the preset VIP threshold. Then, dimers of volatile substances are removed from the multiple preliminary volatile substance difference markers to finally obtain multiple target volatile substances.

[0026] Beneficial effects:

[0027] The first aspect of this application provides a method for detecting volatiles in base liquor from different batches of baijiu (Chinese liquor). This method employs gas chromatography-ion mobility spectrometry (GC-IMS) to detect volatiles in base liquor from different batches. It requires only simple pretreatment of the base liquor, saving time, operational, and equipment costs associated with complex pretreatment steps. Furthermore, this method can generate two-dimensional spectral difference maps and fingerprint maps through contour image visualization and data processing technologies, presenting complex volatile component information in intuitive images and data. This high degree of visualization facilitates the identification of base liquor from different batches. This method can accelerate the detection of flavor quality in base liquor from different batches during baijiu production, promptly identify problems in the production process, provide strong technical support for optimizing brewing processes and ensuring product quality, and promote the development of the baijiu industry towards greater efficiency, precision, and intelligence. In actual production, timely understanding of the changes in flavor substances in base liquor from different batches allows for optimization and adjustment of subsequent brewing processes, potentially improving the stability of baijiu product quality and ensuring the economic benefits of enterprises.

[0028] The flavor difference analysis method for baijiu provided in the second aspect of this application, based on the detection of volatiles in baijiu base liquor from different batches of baijiu samples with various brewing processes, and subsequent processing using the OPLS-DA model in SIMCA 14 software, ultimately obtains volatile difference markers for baijiu base liquor from different batches across different brewing processes. This flavor difference analysis method efficiently and rapidly identifies volatile difference markers between different brewing processes, enabling objective, visual, and accurate analysis of the impact of different brewing processes on the flavor and quality of baijiu. It efficiently and accurately reflects the differences in flavor substances in baijiu under different brewing methods, assesses the quality of mechanical brewing processes, and can guide the improvement of the brewing process for the next batch of baijiu based on the volatile difference markers obtained from the flavor difference analysis method.

[0029] In summary, this application improves the analytical efficiency of dynamic changes of volatiles in base liquor of Maotai-flavor liquor in different batches and processes, and solves the key common problems of traditional methods such as long analysis time and weak visualization of base liquor. It provides technical support for optimizing the mechanized process of Maotai-flavor liquor, accelerating quality monitoring in the liquor production process, and improving the rate of high-quality base liquor. Attached Figure Description

[0030] Figure 1 These are two-dimensional spectra of volatiles obtained by GC-IMS analysis of base wines from seven rounds of different brewing processes in the embodiments of this application.

[0031] Figure 2 These are the GC-IMS differential spectra of volatiles obtained from seven rounds of base wine produced in different brewing processes according to embodiments of this application.

[0032] Figure 3 This is a fingerprint spectrum of volatiles obtained by GC-IMS detection of the base wine from seven rounds of traditional brewing process in the embodiments of this application;

[0033] Figure 4 This is a fingerprint spectrum of volatiles obtained by GC-IMS detection of the base wine from seven rounds of mechanical brewing process in the embodiments of this application;

[0034] Figure 5 This is an OPLS-DA analysis chart of the volatiles of base wine from seven rounds in the conventional brewing process and the mechanical brewing process in the embodiments of this application;

[0035] Figure 6 This is an OPLS-DA model verification diagram of the volatiles of base wine in seven rounds of traditional brewing process and mechanical brewing process in the embodiments of this application;

[0036] Figure 7 This is a bar chart of OPLS-DA analysis VIP values ​​of base liquor volatiles from a certain round in both conventional and mechanical brewing processes according to embodiments of this application. Detailed Implementation

[0037] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the scope of the present application.

[0038] The first aspect of this application provides a method for detecting volatiles in base liquor from different distillation batches, including:

[0039] S01: Baijiu base liquor from different batches was used to prepare multiple baijiu samples from different batches using a closed evaporation equilibrium method.

[0040] S02: Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect volatiles in multiple batches of baijiu samples from different production runs, and detection data were obtained. The GC-IMS detection conditions included:

[0041] Gas chromatography settings: column was DB-WAX (60m×0.25mm×0.25μm), carrier gas nitrogen flow rate was 1.5mL / min, oven program was as follows: initial temperature 40℃, hold for 5min, then increase to 120℃ at a rate of 4℃ / min, hold for 3min, increase to 200℃ at a rate of 10℃ / min, hold for 5min;

[0042] Ion mobility spectrometry settings: temperature 45℃, drift gas nitrogen with a purity of 99.999%, flow rate 150 mL / min, positive ion mode;

[0043] S03: VOCal software was used to process and qualitatively analyze the detection data of baijiu samples from different batches in S20, and to obtain two-dimensional spectra of volatiles in baijiu samples from different batches, differential spectra of volatiles in baijiu samples from different batches, and fingerprint spectra of volatiles in baijiu samples from different batches.

[0044] The first aspect of this application provides a method for detecting volatiles in base liquor from different batches of baijiu (Chinese liquor). This method employs gas chromatography-ion mobility spectrometry (GC-IMS) to detect volatiles in base liquor from different batches. It requires only simple pretreatment of the base liquor, saving time, operational, and equipment costs associated with complex pretreatment steps. Furthermore, this method can generate two-dimensional spectral difference maps and fingerprint maps through contour image visualization and data processing technologies, presenting complex volatile component information in intuitive images and data. This high degree of visualization facilitates the identification of base liquor from different batches. This method can accelerate the detection of flavor quality in base liquor from different batches during baijiu production, promptly identify problems in the production process, provide strong technical support for optimizing brewing processes and ensuring product quality, and promote the development of the baijiu industry towards greater efficiency, precision, and intelligence. In actual production, timely understanding of the changes in flavor substances in base liquor from different batches allows for optimization and adjustment of subsequent brewing processes, potentially improving the stability of baijiu product quality and ensuring the economic benefits of enterprises.

[0045] In some optional embodiments of the first aspect of this application, the base liquor for different rounds of fermentation is the base liquor extracted seven times during the fermentation process of sauce-flavored liquor.

[0046] In some optional embodiments of the first aspect of this application, the closed-loop evaporation equilibrium method in step S01 includes:

[0047] S011: Dilute the baijiu samples from different batches with ultrapure water at a volume ratio of 10% of the ethanol in the sample preparation solution to obtain diluted baijiu samples from different batches. Then, put the diluted baijiu samples from different batches into sample bottles for headspace sampling. The headspace sampling conditions are as follows: the pre-sampling evaporation equilibrium temperature is 55℃~80℃, the equilibrium time is 10min~30min, the injection volume is 400μL~600μL, and the injection time is 20s~50s.

[0048] In some optional embodiments of the first aspect of this application, a n-ketone with C4 to C9 carbon atoms is used as an external standard compound. Gas chromatography-ion mobility spectrometry detection is performed under the detection conditions of step S20 to obtain calibration data. The detection data is calibrated using the calibration data. The calibrated detection data is then compared with the IMS database in the gas chromatography-ion mobility spectrometer to obtain the qualitative analysis results of volatiles detected by gas chromatography-ion mobility spectrometry for baijiu samples from different batches.

[0049] In some optional embodiments of the first aspect of this application, the qualitative analysis results of volatiles include the name of the volatile compound, retention index, elution time, drift time, and peak intensity.

[0050] In some optional embodiments of the first aspect of this application, in step S03, the two-dimensional spectra of volatiles in baijiu samples from different batches are obtained by normalizing ion migration time and reaction ion peak (RIP) positions.

[0051] Using the first batch of baijiu base liquor as a reference, the first batch of baijiu base liquor was detected by gas chromatography-ion mobility spectrometry to obtain a reference spectrum. The baijiu base liquor from different batches other than the first batch was detected by gas chromatography-ion mobility spectrometry and the signal peaks in the reference spectrum were subtracted to produce a difference spectrum of volatiles in baijiu samples from different batches.

[0052] The second aspect of this application provides a method for analyzing the flavor differences of baijiu (Chinese liquor), including:

[0053] S10: Obtain multiple groups of baijiu samples with different brewing processes. Each group of baijiu samples contains baijiu base liquor from different batches under the corresponding brewing process.

[0054] S20: The volatile matter detection method of different batches of baijiu base liquor in the first aspect of this application is used to detect multiple baijiu sample groups with different brewing processes, and the detection results are obtained. The detection results include the names of volatile compounds and corresponding peak intensities of each baijiu sample in the multiple baijiu sample groups with different brewing processes.

[0055] S30: Using the test results of the base liquor of each batch as the input of the OPLS-DA orthogonal partial least squares discriminant analysis model, the OPLS-DA model is established using SIMCA 14 software and the VIP value corresponding to the volatile matter is calculated. At least based on the relationship between the VIP value corresponding to the volatile matter and the preset VIP threshold, the volatile matter difference markers of the base liquor of each batch in different brewing processes are obtained.

[0056] The flavor difference analysis method for baijiu provided in the second aspect of this application, based on the detection of volatiles in baijiu base liquor from different batches of baijiu samples with various brewing processes, and subsequent processing using the OPLS-DA model in SIMCA 14 software, ultimately obtains volatile difference markers for baijiu base liquor from different batches across different brewing processes. This flavor difference analysis method efficiently and rapidly identifies volatile difference markers between different brewing processes, enabling objective, visual, and accurate analysis of the impact of different brewing processes on the flavor and quality of baijiu. It efficiently and accurately reflects the differences in flavor substances in baijiu under different brewing methods, assesses the quality of mechanical brewing processes, and can guide the improvement of the brewing process for the next batch of baijiu based on the volatile difference markers obtained from the flavor difference analysis method.

[0057] In some optional embodiments of the second aspect of this application, in step S10, the multiple groups of baijiu samples with different brewing processes include baijiu sample groups with traditional brewing processes and baijiu sample groups with mechanical brewing processes.

[0058] In some optional embodiments of the second aspect of this application, in step S30, multiple preliminary volatile substance difference markers are first obtained based on the relationship between the VIP value corresponding to the volatile substance and the preset VIP threshold. Then, dimers of volatile substances are removed from the multiple preliminary volatile substance difference markers to finally obtain multiple target volatile substances.

[0060] I. Collection of Base Liquor Samples for Maotai-flavor Baijiu Wheel Series

[0061] The base liquor samples were collected from both the mechanical brewing process (representing mechanical brewing process) and the traditional brewing process (representing traditional brewing process) workshops of a Maotai-flavor liquor enterprise in Zunyi City, Guizhou Province. The base liquor samples were taken from seven batches in 2022.

[0062] The sample numbers and detailed information of the base liquor are shown in Table 1.

[0063] Table 1 Information on Samples of Base Liquor from Different Distillation Rounds for Sauce-Flavored Types

[0064]

[0065] The method for detecting volatiles in different batches of baijiu base liquor provided in the first aspect of this application is used to detect the volatiles in each liquor sample in Table 1. The following operations are performed on baijiu base liquors from different batches in different brewing processes to detect volatiles:

[0066] S01: Prepare multiple liquor samples from different batches using a closed-loop evaporation equilibrium method, taking base liquor from different batches. The closed-loop evaporation equilibrium method in step S01 includes:

[0067] S011: Dilute the baijiu sample with ultrapure water to a volume ratio of 10% of ethanol in the sample preparation solution to obtain baijiu samples from different batches after dilution. Take 1 mL of the diluted baijiu sample and place it in a 20 mL sample bottle, setting up 3 parallel runs. Then, put the baijiu samples from different batches after dilution into the sample bottle for headspace sampling. The headspace sampling conditions are: pre-injection closed evaporation equilibrium temperature of 60℃, equilibrium time of 10 min, injection volume of 500 μL, and injection time of 30 s.

[0068] S02: Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect volatiles in multiple batches of baijiu samples from different production runs, and detection data were obtained. The GC-IMS detection conditions included:

[0069] Gas chromatography (GC) settings: DB-WAX column (60m × 0.25mm × 0.25μm), nitrogen carrier gas flow rate 1.5mL / min, oven program as follows: initial temperature 40℃, hold for 5min, then increase to 120℃ at a rate of 4℃ / min, hold for 3min, increase to 200℃ at a rate of 10℃ / min, hold for 5min; Ion mobility spectrometry (IMS) settings: temperature 45℃, drift gas nitrogen, nitrogen purity 99.999%, flow rate 150mL / min, positive ion mode.

[0070] S03: VOCal software was used to process and qualitatively analyze the detection data of baijiu samples from different batches in S20, and to obtain two-dimensional spectra of volatiles in baijiu samples from different batches, differential spectra of volatiles in baijiu samples from different batches, and fingerprint spectra of volatiles in baijiu samples from different batches.

[0071] like Figure 1 As shown, the GC-IMS two-dimensional spectrum is obtained by normalizing the ion migration time and the position of the reaction ion peak (RIP). Figure 1 Figure A shows the two-dimensional chromatograms of volatiles obtained by GC-IMS analysis of base liquor from seven rounds of traditional brewing, while Figure B shows the two-dimensional chromatograms of volatiles obtained by GC-IMS analysis of base liquor from seven rounds of mechanical brewing. The volatiles detected by GC-IMS are distributed separately to the right of the reaction ion peak (red line). It can be seen that the positions and color intensities of the volatile organic compounds formed in the graphs differ due to variations in retention time, drift time, and ion peak intensity among the base liquors from different rounds. Most volatile compounds have signals within the retention time range of 200-2000 s, but a few have signals within the retention time range of 2200-2400 s. This may be because these compounds are more polar, resulting in longer retention times on polar columns compared to non-polar columns.

[0072] Figure 2 These are the volatile content difference spectra obtained by GC-IMS detection of base wines from seven rounds of different brewing processes in the embodiments of this application. Figure 2 Figure A shows the volatile matter difference spectrum obtained by GC-IMS analysis of the base wine from seven rounds of traditional brewing process, while Figure B shows the volatile matter difference spectrum obtained by GC-IMS analysis of the base wine from seven rounds of mechanical brewing process. Figure 2This allows for a more intuitive display of the changes in volatile compounds in the base liquor across different batches. CT1 and JX1 groups were selected as references, and signal peaks in other spectra were obtained by subtracting the references. The background of the entire spectrum is blue; volatile compounds of the same concentration appear more white. Blue indicates that the concentration of the substance is lower than that of the reference sample, while red indicates that the concentration of the substance is higher than that of the reference sample. The different colored dots appear in different locations, intuitively reflecting the differences in volatile compounds between them; the darker the color, the greater the difference. The fifth, sixth, and seventh batches (CT5, CT6, CT7, JX5, JX6, JX7) show a greater number of red dots, indicating that the concentration of the corresponding volatile compounds is higher than that of the reference sample. In contrast, the background color of the peaks in the second and third batches is lighter, indicating that the concentration of the corresponding volatile compounds is comparable to or slightly higher than that of the reference sample.

[0073] A C4-C9 ketone was used as an external standard compound. Gas chromatography-ion mobility spectrometry (GC-IMS) was performed under the detection conditions described in step S20 to obtain calibration data. The detection data was then calibrated using the calibration data. The calibrated detection data was then compared with the IMS database in the GC-IMS instrument to obtain the qualitative analysis results of volatiles detected by GC-IMS in baijiu samples from different batches.

[0074] By calibrating with external standard compounds (n-ketones, C4–C9), the retention index and migration time of volatile substances were determined, and then compared with the IMS database to achieve qualitative identification of the volatile substances. A total of 48 chromatographic peaks of 38 compounds were identified, and their relevant information is shown in Table 2. Figure 3 and Figure 4 As shown, to observe the changes in volatile compounds among base liquors from different batches, fingerprint spectra of base liquors produced using different processes (CT and JX) were generated. Each row in the fingerprint spectrum represents all signal peaks selected from a single sample, and each column represents the signal peaks of the same volatile compound in different samples. Figure 3 As shown, substances present in higher concentrations during traditional brewing processes include 2-hexanol, 1-propanol, ethyl lactate, ethyl acetate, isoamyl acetate, and 2-methyl-1-propanol.

[0075] like Figure 4As shown, substances present in higher concentrations during mechanical brewing include 1-propanol, ethyl lactate, ethyl acetate, isoamyl acetate, and ethyl butyrate. Compared to traditional processes, the mechanically brewed samples exhibit more shared volatiles across different batches of base spirits. Overall, aside from shared volatiles, the content of other volatiles is lower in the mechanically brewed samples (blue background).

[0076] Table 2. GC-IMS qualitative compound information of samples from different rounds.

[0077]

[0078]

[0079]

[0080]

[0081] Note: M in parentheses represents a monomer, and D represents a dimer.

[0082] The method for detecting volatiles in different batches of baijiu base liquor according to the first aspect of this application was used to detect multiple baijiu sample groups with different brewing processes, and the detection results were obtained. The detection results include the names of volatile compounds and corresponding peak intensities of each baijiu sample in the multiple baijiu sample groups with different brewing processes.

[0083] The test results of the base liquor of each batch of baijiu were used as the input of the OPLS-DA orthogonal partial least squares discriminant analysis model. The OPLS-DA model was established using SIMCA 14 software and the VIP value corresponding to the volatile matter was calculated. At least based on the relationship between the VIP value corresponding to the volatile matter and the preset VIP threshold, the volatile matter difference markers of the base liquor of each batch of baijiu were obtained between different brewing processes.

[0084] The R²X, R²Y, and Q² values ​​in the model are 0.623, 0.983, and 0.98, respectively, demonstrating that the model has good stability and predictive ability. Figure 5 This is an OPLS-DA analysis chart of the volatiles of base wine from seven rounds of brewing in both traditional and mechanical brewing processes according to embodiments of this application. The horizontal axis of the OPLS-DA analysis chart represents the score (Tp) of the major components in the OSC process, thus showing the differences between groups; the vertical axis represents the score (TO) of the orthogonal components in the OSC process, thus showing the differences within groups (differences between samples within the same group). Figure 5As shown, the volatile flavor metabolites (volatiles) of base liquor samples from multiple rounds of traditional brewing processes differ from those from those from multiple rounds of mechanical brewing processes. This indicates a difference between the base liquor sample groups from two different processes, and the two groups can be effectively distinguished. The quality of the discriminant model was evaluated using a permutation test (n=200).

[0085] In the OPLS-DA model, the permutation test plays a crucial role. It involves randomly shuffling the group labels (Y variable) of the experimental and control groups and constructing the corresponding OPLS-DA model multiple times (n=200 times in this embodiment) to obtain the R²X, R²Y, and Q² values ​​of the stochastic model. The Q² value reflects the model's predictive ability for the data variance, i.e., the predictive power; a higher value indicates stronger predictive ability. R²X and R²Y represent the cumulative variance explained by the model for the X and Y matrices, respectively, i.e., the model's interpretability; higher values ​​indicate stronger interpretability and greater model stability and reliability. By observing the graphs of the permutation test results, we can further evaluate the model's performance. Figure 6 In the diagram, the two points at the top right (the actual values) slightly overlap, while the scattered points on the left represent the predicted simulated values. The blue points represent Q2, and the green points represent R2. The intercept of the Q2 regression line is less than 0.05 and is negative, further demonstrating the model's superiority. Therefore, we can conclude that this OPLS-DA model has good predictive ability and interpretability.

[0086] like Figure 6 As shown, the regression line formed by all the blue points Q2 intersects the negative half of the Y-axis, proving the model's effectiveness. Figure 7As shown, the 18 differentially expressed volatile compounds with a VIP > 1 (where 1 is the preset threshold for VIP, and VIP stands for projected variable importance) obtained through screening are, in descending order: isoamyl acetate (M), methyl isovalerate, ethyl butyrate, ethyl formate, isobutyraldehyde, 2-methyl-1-propanol, butyl acetate, ethyl valerate (M), isoamyl acetate (D), ethyl valerate (D), 2-hexanol (D), ethyl hexanoate (D), 2-methylbutyraldehyde, 4-methylpentanol (M), 1-pentanol (M), 2-hexanol (M), and isobutyric acid. Due to the presence of dimers, 15 substances actually have a VIP > 1, indicating that these substances show significant differences between traditional and mechanical processes, serving as markers for volatile differences between different processes. VIP value (projected variable importance) is an indicator used in multivariate statistical analysis to measure the contribution of a variable to a classification model. Its core function is to screen variables that have a significant impact on differences between groups. For example, in PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) models, a higher VIP value indicates that the variable contributes more to distinguishing between groups. Generally, VIP ≥ 1 indicates that the variable's contribution to classification is above average and can be used as a critical value for difference screening.

[0087] In summary, this application improves the analytical efficiency of dynamic changes of volatiles in base liquor of Maotai-flavor liquor in different batches and processes, and solves the key common problems of traditional methods such as long analysis time and weak visualization of base liquor. It provides technical support for optimizing the mechanized process of Maotai-flavor liquor, accelerating quality monitoring in the liquor production process, and improving the rate of high-quality base liquor.

[0088] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for detecting volatiles in base liquor from different distillation batches, characterized in that, include: S01: Baijiu base liquor from different batches was used to prepare multiple baijiu samples from different batches using a closed evaporation equilibrium method. S02: Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect the volatiles in the multiple batches of baijiu samples from different production runs, and detection data were obtained. The GC-IMS detection conditions included: Gas chromatography settings: column was DB-WAX (60m×0.25mm×0.25μm), carrier gas nitrogen flow rate was 1.5mL / min, oven program was as follows: initial temperature 40℃, hold for 5min, then increase to 120℃ at a rate of 4℃ / min, hold for 3min, increase to 200℃ at a rate of 10℃ / min, hold for 5min; Ion mobility spectrometry settings: temperature 45℃, drift gas nitrogen with a purity of 99.999%, flow rate 150 mL / min, positive ion mode; S03: VOCal software was used to process and qualitatively analyze the detection data of baijiu samples from different batches in S02, and to obtain two-dimensional spectra of volatiles in baijiu samples from different batches, differential spectra of volatiles in baijiu samples from different batches, and fingerprint spectra of volatiles in baijiu samples from different batches.

2. The method for detecting volatiles in base liquor from different distillation batches according to claim 1, characterized in that, The base liquor from different batches is the base liquor extracted seven times during the fermentation process of sauce-flavored baijiu.

3. The method for detecting volatiles in base liquor from different distillation batches according to claim 1, characterized in that, The closed-loop volatilization equilibrium method in step S01 includes: S011: Dilute the baijiu samples from different batches with ultrapure water at a volume ratio of 10% of the ethanol in the sample preparation solution to obtain diluted baijiu samples from different batches. Then, put the diluted baijiu samples from different batches into sample bottles for headspace sampling. The headspace sampling conditions are as follows: the pre-sampling evaporation equilibrium temperature is 55℃~80℃, the equilibrium time is 10min~30min, the injection volume is 400μL~600μL, and the injection time is 20s~50s.

4. The method for detecting volatiles in base liquor from different distillation batches according to claim 1, characterized in that, A C4-C9 ketone was used as an external standard compound. Gas chromatography-ion mobility spectrometry (GC-IMS) was performed under the conditions described in step S20 to obtain calibration data. The detection data was then calibrated using the calibration data. The calibrated detection data was then compared with the IMS database in the GC-IMS instrument to obtain the qualitative analysis results of volatiles detected by GC-IMS in the baijiu samples from different batches.

5. The method for detecting volatiles in base liquor from different distillation batches according to claim 4, characterized in that, The qualitative analysis results of the volatiles include the name of the volatile compound, retention index, elution time, drift time, and peak intensity.

6. The method for detecting volatiles in base liquor from different distillation batches according to claim 1, characterized in that, In step S03, the two-dimensional spectra of volatiles in the liquor samples from different batches are obtained by normalizing ion migration time and reaction ion peak (RIP) positions. Using the first batch of baijiu base liquor as a reference, the baijiu base liquor from the first batch is detected by the gas chromatography-ion mobility spectrometry (GC-IMS) instrument to obtain a reference spectrum. The baijiu base liquor from different batches, excluding the first batch, is detected by the GC-IMS instrument, and the signal peaks in the reference spectrum are subtracted to produce a difference spectrum of volatiles in baijiu samples from different batches.

7. A method for analyzing flavor differences in baijiu (Chinese liquor), characterized in that, include: S10: Obtain multiple groups of baijiu samples with different brewing processes. Each group of baijiu samples contains baijiu base liquor from different batches under the corresponding brewing process. S20: The volatile matter detection method of different batches of baijiu base liquor as described in any one of claims 1 to 6 is used to detect the multiple baijiu sample groups with different brewing processes to obtain the detection results. The detection results include the names of volatile compounds and corresponding peak intensities of each baijiu sample in the multiple baijiu sample groups with different brewing processes. S30: Using the test results corresponding to the base liquor of each batch as the input of the OPLS-DA orthogonal partial least squares discriminant analysis model, the OPLS-DA model is established using SIMCA 14 software and the VIP value corresponding to the volatile matter is calculated. At least based on the relationship between the VIP value corresponding to the volatile matter and the preset VIP threshold, the volatile matter difference markers of the base liquor of each batch in different brewing processes are obtained.

8. The method for analyzing flavor differences in baijiu according to claim 7, characterized in that, In step S10, the multiple groups of baijiu samples with different brewing processes include baijiu sample groups made using traditional brewing processes and baijiu sample groups made using mechanical brewing processes.

9. The method for analyzing flavor differences in baijiu according to claim 7, characterized in that, In step S30, multiple preliminary volatile substance difference markers are first obtained based on the relationship between the VIP value corresponding to the volatile substance and the preset VIP threshold. Then, dimers of volatile substances are removed from the multiple preliminary volatile substance difference markers to finally obtain multiple target volatile substances.

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