Analysis method for rapidly distinguishing volatile components in different baijiu

Abstract

The invention discloses a non-target analysis technology based on mass spectrum similarity, differential spectrum distribution and time-dependent hierarchical clustering analysis (HCA), is used for analyzing time-dependent mass spectrum data obtained by direct sample introduction of a high-resolution flight time chemical ionization mass spectrometer and realizing rapid identification of volatile components with different white spirit characteristics, and belongs to the technical field of analysis and detection. According to the method, the similarity of volatile components of Baijiu is judged through characteristic included angles between mass spectrograms, and Baijiu with different flavors or different series of Baijiu with the same flavor is qualitatively distinguished; further acquiring species and abundance differences of the volatile components in combination with differential spectrum distribution; the time-dependent HCA method is used for carrying out two-dimensional analysis of time and component latitude on the high-resolution mass spectrum, so that characteristic volatile species of different Baijiu are accurately identified, and efficient and rapid distinguishing and identification of Baijiu brands or aroma types are realized. According to the method, multi-level analysis is carried out on the mass spectrum collected in situ, the mass spectrum difference of different brands or flavors of baijiu is visually displayed, online detection and rapid identification of the volatile components of the baijiu can be achieved, the problem of refined identification of the volatile components of the baijiu is solved, and the method is widely suitable for baijiu quality control, authenticity identification and flavor database construction.

Description

Technical Field

[0001] The present invention belongs to the field of analysis, and relates to an analysis method, specifically, an analysis method for quickly distinguishing liquor brands or flavors and identifying characteristic volatile organic compounds. The method is based on the time-dependent mass spectrum data of liquor volatile components obtained by combining dynamic headspace online purge and high-resolution time-of-flight chemical ionization mass spectrometer, and utilizes mass spectrum similarity analysis, difference spectrum distribution and time-dependent hierarchical clustering analysis (HCA) non-target analysis technology to achieve rapid distinction of different brands or flavors and accurately identify characteristic volatile components. Background Art

[0002] Baijiu is one of the seven major distilled liquors in the world. Its complex process and rich flavor substances give it a unique aroma. At present, Chinese liquor has developed 12 flavor types, including sauce-flavored, strong-flavored, light-flavored, and mixed-flavored. The flavor substances of different flavor types vary significantly in type and content. For example, the main aroma component of strong-flavored liquor is ethyl hexanoate, while the main aroma component of light-flavored liquor is ethyl acetate; while the main aroma substances of sauce-flavored liquor may include 4-ethylguaiacol, pyrazine, and furan / pyran compounds, but there is still controversy. At present, although technologies such as liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and gas chromatography-ion mobility spectrometry (GC-IMS) (such as CN 117517521A and CN 116482246 A) can be used to detect volatile components of liquor, it is difficult to achieve full-component and highly sensitive measurement due to the selectivity of the chromatographic column. In addition, the traditional chromatographic method is complex in qualitative and quantitative analysis and has low time resolution, which limits the rapid differentiation of different liquors. Existing analysis methods, such as principal component analysis (PCA), clustering methods, discriminant analysis and linear regression (CN 115910223 A and CN 113203803A), are mainly used for post-processing data analysis, and are not suitable for rapid analysis of mass spectrometry data in the time and species dimensions. It is difficult to intuitively reflect the differences in the components of different liquors and to achieve online detection and rapid identification of liquor samples. On the other hand, the existing sensory evaluation methods are somewhat subjective and closely related to the experience and physical condition of the wine taster. Therefore, it is urgent to develop a rapid analysis method based on high-resolution mass spectrometry technology that can finely identify the characteristic volatile components of liquor. The existing vacuum ultraviolet photoionization mass spectrometry technology (CN 112946057A) can perform minute-level online detection of 10 volatile components such as acetic acid, n-propanol, and isobutanol in the fermentation tank, but lacks a systematic analysis method for time-dependent mass spectrometry data, making it difficult to achieve rapid differentiation and identification of different liquors. Therefore, establishing an efficient and accurate analysis method for the volatile characteristic components of liquor is of great significance for solving the stability problems of different batches of liquor, finely identifying the characteristic flavor substances of different types of liquor, building a flavor database and deepening the research on liquor flavor chemistry.

[0003] The present invention is based on dynamic headspace online purge-high resolution time-of-flight chemical ionization mass spectrometry (application number: 2025101913072), and has achieved real-time online detection of more than 70 volatile components of liquor. On this basis, by coupling high-resolution mass spectrometry similarity analysis, difference spectrum analysis and time-dependent hierarchical clustering analysis (HCA), a non-target analysis method for rapid differentiation and identification of volatile components of different liquors was developed, and characteristic spectra of different liquors were established. This method can be used to identify flavor substances in liquors of different flavors, to distinguish liquors of the same flavor but different processes, and to monitor the quality of different batches of liquor production processes. This invention provides new technical support for liquor quality control and theoretical research on flavor chemistry. Summary of the invention

[0004] In view of the limitations of existing methods in distinguishing the volatile components of different liquors, the present invention aims to provide a rapid and real-time mass spectrometry in situ analysis method to identify the volatile components of liquor, thereby distinguishing liquors of different flavors or liquors of the same flavor but with different processes.

[0005] The present invention proposes an analytical method for quickly distinguishing volatile components of different liquors, the core of which includes a liquor volatile component detection method, high-resolution mass spectrometry similarity analysis, difference spectrum distribution and HCA method.

[0006] The second-level resolution mass spectrometry of the volatile components of liquor was obtained by using the dynamic headspace purge, dilution device and proton transfer reaction time-of-flight mass spectrometer (H 3 O + -PTR-LToF-CIMS) for acquisition.

[0007] Since the concentration difference of volatile components in liquor may cause large fluctuations in data, the present invention adopts the z-score standardization method (Formula 1) to standardize the spectral distribution of the mass spectrum to improve data stability and comparability.

[0008]

[0009] Where y i is the data after standardization, x i is the original mass spectrometry data, In order to evaluate the similarities and differences of volatile components between different liquors, the present invention uses the characteristic angle θ of the mass spectra of different liquors to calculate the mass spectrum similarity of the standardized real-time high-resolution mass spectrometry data. The specific calculation method is shown in the following formula (2):

[0010]

[0011] In the formula, MS a and MS bThe high-resolution mass spectrometry distribution diagrams of two target liquors are shown respectively. In order to quantify the similarity of volatile components between different liquors, the mass spectrometry characteristic angles are divided into the following intervals: 0–5°, excellent consistency; 5–10°, good consistency; 10–15°, more similarity; 15–30°, limited similarity; ≥30°, poor consistency.

[0012] In order to further compare the abundance differences of volatile components of different liquors, the present invention directly performs differential spectrum analysis on the acquired high-resolution mass spectrum distribution. Specifically, by directly performing differential analysis on the mass spectrum results of volatile components of different liquors, the abundance changes of each component in different liquor samples are calculated to obtain the differential spectrum distribution. This method can intuitively reflect the differences in volatile components of different liquors.

[0013] In order to further realize the real-time difference of volatile components of different liquors and the identification of characteristic components, the time-dependent HCA method is used to visualize the time and species of high-time-resolved volatile components, which can effectively distinguish different liquor samples and identify their characteristic components. HCA measures the similarity by calculating the distance between two types of data (Formula 3).

[0014] d(A,B)=∑ t abs(A t -B t ) (3)

[0015] The specific steps are as follows:

[0016] (1) Initial clustering: Find the two data points s and t with the closest distance (most similarity) from all data points and merge them into a new cluster u;

[0017] (2) Update the set: delete s and t from the original data set and add the new cluster u;

[0018] (3) Calculate new distance: Calculate the distance between u and all remaining data points or clusters;

[0019] (4) Iterative merging: Continue to find the data pairs with the minimum distance and merge them into new clusters. The new cluster can be composed of two original measurement points, one measurement point and an existing cluster, or two clusters.

[0020] The distance between the new cluster u and any measurement or cluster in the set v is calculated as follows:

[0021]

[0022] The present invention uses mass spectrum similarity analysis, difference spectrum distribution and time-dependent hierarchical cluster analysis (HCA) methods to perform non-target analysis on the mass spectra of volatile components of liquor obtained by high-resolution mass spectrometry. By comparing the similarity of mass spectrum distribution diagrams, qualitative differentiation of liquor can be achieved; difference spectrum distribution can intuitively reflect the differences in volatile components of different liquors, which is helpful for in-depth analysis of characteristic components of liquors with different or same aroma types; further, based on the analysis of time-dependent HCA in the time dimension and species dimension, the characteristic volatile components of different liquor samples can be accurately identified, providing a scientific basis for liquor quality identification, process optimization and flavor analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a high-resolution mass spectrum of different series of volatile components of a certain liquor.

[0024] Figure 2 This is the difference spectrum of high-resolution mass spectra of volatile components of two series of liquors.

[0025] Figure 3 Fingerprint characteristics of volatile components in liquor identified by HCA method. DETAILED DESCRIPTION

[0026] In order to make the purpose, analysis method and advantages of the embodiments of the present invention clearer, the analysis method in the embodiments of the present invention will be clearly and completely described below in combination with the implementation of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The high-resolution mass spectrum distribution similarity analysis and time-dependent HCA hierarchical analysis of the present invention are characterized by:

[0028] The present invention utilizes the high-resolution mass spectrometry online analysis and detection system developed by the inventor in the early stage to perform online detection of the volatile organic components of liquor, and obtain a high-resolution mass spectrometry data set with second-level time and resolution. For the volatile components of liquors of different flavors or different series of the same flavor, formula 1 is used to standardize the data to improve the stability and comparability of the data and reduce the influence of outliers. Subsequently, the standardized mass spectrum distribution data is subjected to similarity analysis, and the similarity of the volatile components of different liquors is qualitatively identified based on the characteristic angle method. On this basis, the high-resolution mass spectrometry data of different liquors are subjected to differential spectrum analysis. By calculating the differential spectrum data of two liquors, the abundance difference of the same components is intuitively presented, thereby realizing the scientific distinction of liquor categories and the accurate analysis of component characteristics.

[0029] The time-dependent HCA method is used to perform a two-dimensional analysis of the volatile components of liquor in terms of time and species. In the species dimension, HCA is used to evaluate the specific differences in the volatile components of different liquors; in the time dimension, HCA presents the visual difference characteristics of the volatile components of liquor through the temporal distribution of multiple species. When HCA calculates the distance between paired species, the data in the species dimension is first standardized, and then the Euclidean distance is calculated based on Formula 5.

[0030]

[0031] Furthermore, according to Formula 6, the error sum of squares (ESS) is used as an accurate indicator to measure the distance and information loss between pairs of species. During the analysis, the hierarchical clustering results between species are iteratively calculated.

[0032] ESS(C)=∑ x∈C (xm x ) T (xm x ) (6)

[0033] Where C is the distance matrix of all paired sample data, m x is the mean of the sample points in C, and x is the distance between the initial two species. By calculating the degree of aggregation of volatile species of different liquors, the smaller the ESS value, the higher the degree of clustering between species. The present invention iteratively calculates the Euclidean distance between the merged species to ensure that the increment of the sum of the ESS before and after the merger is minimized, thereby optimizing the clustering process and improving the accurate identification of the volatile components of different liquor bodies.

[0034] The present invention combines mass spectrometry similarity analysis, difference spectrum distribution and time-dependent HCA methods to perform non-target analysis on the volatile component spectrum of liquor detected by high-resolution mass spectrometry, thereby realizing qualitative identification and similarity determination of volatile components of liquor. This method not only supports visualization of mass spectrometry data with high time resolution, but also can accurately distinguish the differences and characteristic components of volatile components of different liquors. Through dual analysis in the time dimension and the species dimension, the present invention can efficiently distinguish different types of liquors, as well as common or specific volatile components in the same type of liquor. Compared with traditional cluster analysis or dimensionality reduction analysis methods, this method has the advantages of in-situ analysis, refined identification and rapid analysis, and overcomes the limitations of traditional methods in terms of real-time performance and recognition accuracy.

[0035] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0036] Example 1

[0037] In view of the analytical method and application of the present invention for quickly distinguishing the volatile components of different liquors, the composition spectrum of four series of liquor samples (W1-W4) of a domestic Luzhou-flavor liquor was measured and real-time online monitoring was performed using dynamic headspace online purge and high-resolution time-of-flight chemical ionization mass spectrometry. Figure 1 The composition spectrum of volatile organic components of a certain Luzhou-flavor liquor in the W1-W4 series. The results show that there are obvious differences in the volatile components and their abundances of different series of liquors, especially the composition differences between W1, W2 and W3, W4 are more significant. Table 1 calculates the characteristic angle (θ) between the W1-W4 series of liquors based on the mass spectrometry similarity method. The results show that the θ between W3 and W4 is between 0-5°, indicating that the volatile component spectra of the two are highly similar; the θ between the other series of liquors is greater than 30°, indicating that there are significant differences in their component spectra. Figure 2 The high-resolution mass spectrometry difference distribution of W1 and W2 series liquors is shown. It can be observed intuitively that the species distribution and abundance of their volatile components are significantly different, which further verifies the efficiency and accuracy of this method in the analysis of volatile components of liquor.

[0038] The time-dependent HCA method was used to conduct an in-depth analysis of the changes in the volatile components of the W1-W4 series of liquors to reveal the species differences in the time dimension. Table 2 lists the 71 volatile components detected in the W1-W4 series of liquors and numbers them one by one as the input target components of the HCA method. Figure 3The HCA clustering results of the W1-W4 series of liquors are shown, which intuitively shows the species changes of each series of liquors in the time dimension, and can effectively distinguish different liquor samples. The results are consistent with the characteristic angle analysis, which further verifies the reliability of this method. In order to further identify the volatile characteristic species of different liquors, this study divides them into 6 categories according to their basic characteristics (labels 1-6 in the figure). The analysis results are as follows: Categories 1, 2, 3 and 4 dominate the changes in volatile components of the W1-W4 series of liquors. For example, the contents of methanol, acetone, butyl octanoate, phenol, etc. in W1 and W2 were significantly higher than those in W3 and W4 (p<0.01); the content of phenylethanol in W4 was higher than that in W3 (p<0.01); the content of cyclopentanol and butyl crotonate in W3 and W4 was higher than that in W1 (p<0.05); the content of propylene glycol, phenylethyl butyrate, and ethyl lactate in W4 was higher than that in W1, W2, and W3 (p<0.01); the content of cyclopentanol, butylene glycol, and octanol in W2 was significantly higher than that in W1, W3, and W4 (p<0.01). In addition, the abundance of ethyl hexanoate, the main aroma component of Luzhou-flavor liquor, was similar in W1-W4, which further confirmed the accuracy and reliability of the HCA method in distinguishing and identifying the volatile components of the same aroma liquor. There is no significant difference in the abundance of category 5 components (pyruvic acid, amyl alcohol, ethyl formate, tetrahydrofuran) in the W1-W4 series of liquors, indicating that this type of species remains relatively stable in different series. The present invention successfully reveals the characteristics of volatile components in the time dimension and species dimension of different series of Luzhou-flavor liquors by combining the HCA method with mass spectrometry similarity analysis, and verifies the scientificity and practicality of this method in liquor flavor analysis, quality identification and process optimization.

[0039] Table 1 Mass spectra similarity of W1-W4 series liquors (θ)

[0040]

[0041] Table 2 Volatile components of liquor

[0042]

[0043]

[0044]

Claims

1. A method for quickly distinguishing volatile components in different liquors, characterized in that: The following steps are involved: (1) Volatile component detection: Based on dynamic headspace online purge and high-resolution time-of-flight chemical ionization mass spectrometry, the time-dependent high-resolution mass spectrum distribution map of the volatile components was successfully obtained; (2) Mass spectrum similarity analysis: The obtained high-resolution mass spectrometry signals are subjected to z-score normalization to improve the stability and comparability of mass spectrometry signal data and effectively reduce the impact of outliers. Then, the standardized liquor mass spectrum distribution diagram (MS a and MS b ). By formula The angle θ is calculated to qualitatively distinguish whether the volatile components of different liquors are similar. (3) Difference spectrum distribution: By directly subtracting the mass spectrometry results of the volatile components of different liquor samples, the abundance changes of each component in different liquor samples are calculated, and then the difference spectrum distribution is obtained. This method can intuitively reflect the differences in volatile components of different liquors, and then reveal the unique differences in volatile substance composition and aroma characteristics between different liquors. (4) Time-dependent HCA analysis method: Based on the volatile components identified by high-resolution mass spectrometry and time two-dimensional data, the following steps are used to identify the characteristic volatile components of liquor. a) Euclidean distance calculation, through the formula Calculate the Euclidean distance between species of liquor (a and b) and obtain the distance matrix (C) of all liquor volatile components; b) Initial distance setting and mean calculation: Set the initial distance (x) between the two volatile components and take the mean of the sample points in C (m x ), as the basis for further calculations; c) Error sum of squares (ESS) calculation: by formula ESS(C)=∑ x∈C (x-m x ) T (x-m x ) The ESS of volatile components in different liquors was calculated as an accurate indicator to measure the distance and information loss between volatile components of different liquors. d) Analysis of polymerization degree: By calculating the polymerization degree of volatile species in different liquor samples, the smaller the ESS value, the higher the degree of clustering between species and the more similar the species are. e) Iterative calculation to optimize clustering: By iteratively calculating the Euclidean distance between the merged species, the increment of the sum of ESS before and after the merger is ensured to be minimal, thereby optimizing the clustering process and improving the accurate identification of volatile components of different liquor entities.

2. The method according to claim 1, characterized in that: The liquor volatile component detection technology does not require preprocessing, can monitor the dynamic changes of volatile components in real time, provide real-time mass spectrometry data, and ensure rapid dynamic detection of liquor volatile components.

3. The method according to claim 1, characterized in that Non-target analysis of high-resolution mass spectra and signal data was performed using mass spectral similarity analysis, difference spectrum distribution and time-dependent HCA methods to obtain more accurate volatile component information and conduct detailed classification and characteristic component identification.

4. The method according to claim 1, characterized in that Based on the mass spectrometry similarity results, difference spectrum results and HCA hierarchical clustering results, the similarities and differences in the distribution of volatile components of liquor can be qualitatively identified, and the characteristic aroma components and common components of different liquors can be identified, thereby effectively distinguishing the aroma and brand characteristics of liquor.

5. The method according to claim 1, characterized in that When evaluating mass spectral similarity, the high-resolution mass spectral distribution data needs to be standardized using the z-score method, thereby effectively avoiding the influence of outliers in volatile components and improving the stability and comparability of the analysis results.

6. The method according to claim 1, characterized in that The time-dependent HCA method uses time-dependent high-resolution mass spectrometry data as input, adds time dimension disturbance, achieves visually intuitive distinction, and improves the accuracy and effectiveness of traditional cluster analysis.

7. The method according to claim 1, characterized in that The HCA method uses Euclidean distance and error square sum as key parameters in the analysis to further optimize the clustering analysis of volatile components of liquor and ensure the accuracy and effectiveness of the clustering process.

8. Application of the analytical method according to any one of claims 1 to 7 in liquor production quality control, authenticity identification and flavor database construction.

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