A headspace clustering method for identifying the quality of Maotai-flavor liquor mash

Abstract

This invention discloses a headspace clustering analysis method for Maotai-flavor liquor mash. This method requires no sample pretreatment or large amounts of organic solvents. A certain amount of mash sample is directly weighed and added to a headspace vial. Appropriate headspace conditions are selected, and detection is performed using a PTR photochemical ionization source-time-of-flight mass spectrometer. Cluster analysis allows for the determination of the quality grade of the mash. The method established in this invention can rapidly determine the quality of Maotai-flavor liquor mash, providing a powerful tool for the preliminary assessment of its quality.

Description

Technical Field

[0001] This invention belongs to the field of analytical chemistry instruments, specifically relating to a headspace clustering method for identifying the quality of Maotai-flavor liquor mash. Background Technology

[0002] Traditional methods for judging the quality of fermented mash mainly rely on human sensory analysis. This process presents several challenges: first, it requires a high level of professional knowledge and experience from the evaluators, and different individuals have varying sensory abilities; second, the large number of mash samples results in a huge workload for evaluation. Therefore, it is imperative to develop a mash quality judgment method based on flavor analysis that is suitable for production needs.

[0003] Distillery mash is a complex matrix composed of various organic and inorganic components, mainly including proteins, polysaccharides, extracellular matrix molecules, and microorganisms. Currently, the method of LLME combined with GC-MS has been used to qualitatively identify more than 90 flavor substances in distillery mash and more than 200 flavor substances in daqu (fermentation starter). The components of these substances include alcohols, acids, esters, aldehydes, ketones, furans, pyrazines, aromatics, and phenols. However, quantitative research on the above-mentioned flavor substances in distillery mash still faces challenges. This is because flavor substances are diverse, have significant differences in properties, and are at low concentrations. GC-MS-based detection methods require pretreatment such as purification and enrichment. Enrichment materials or extraction methods have a certain selectivity, which reduces the quantitative coverage of flavor substances. Furthermore, the complex pretreatment process inevitably results in the loss of some samples, affecting the reproducibility of quantitative results. At the same time, the cumbersome pretreatment also leads to excessively long detection time for a single sample, which cannot meet the analytical needs of a large number of samples in the production process. Therefore, it is necessary to develop highly sensitive, rapid, and efficient detection methods for judging the quality of distillery mash fermentation.

[0004] Soft ionization mass spectrometry (Soft Ionization Mass Spectrometry) can obtain molecular or quasi-molecular ions of analytes, offering advantages such as simple spectra and easy resolution. It enables rapid analysis of complex gases with little or no sample pretreatment. The core of Soft Ionization Mass Spectrometry is the soft ionization source. Mainstream soft ionization sources include high-pressure photoionization (HPPI) and proton transfer reaction ionization (PTRCI). Both operate at higher pressures (10² Pa) than the EI source used in GC-MS (10⁻³ Pa), resulting in higher intrasource molecular number density and detection sensitivity in the ppt-ppb range. HPPI is suitable for analyzing aromatic compounds, furans, pyrazines, and phenols with high photoionization cross-sections, while PTRCI exhibits better ionization effects for compounds with high proton affinity, such as alcohols, acids, aldehydes, ketones, and esters.

[0005] In view of this, the present invention discloses a headspace clustering method for identifying the quality of Maotai-flavor liquor mash. This method does not require any pretreatment process or a large amount of organic solvent. A certain amount of mash sample is directly weighed and added to the headspace vial. With appropriate headspace conditions selected, detection is performed using a PTR photochemical ionization source-time-of-flight mass spectrometer. Through cluster analysis, the quality grade of the mash can be determined, providing technical support for the rapid identification of mash fermentation quality. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a headspace clustering method for identifying the quality of Maotai-flavor liquor mash. This method involves directly weighing a certain amount of mash sample and adding it to a headspace vial. Under appropriate headspace conditions, detection is performed using a PTR photochemical ionization source-time-of-flight mass spectrometry (PTR). Cluster analysis then determines the quality grade of the mash. This method requires no sample pretreatment or large amounts of organic solvents and can rapidly classify Maotai-flavor liquor mash into quality grades, providing a powerful tool for the preliminary assessment of Maotai-flavor liquor mash quality.

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

[0008] A headspace clustering method for identifying the quality of fermented mash in Maotai-flavor liquor involves taking 0.5-1 μL of the mash sample to be tested and adding it to the sample introduction device of a headspace thermal desorption-PTR photochemical ionization source-time-of-flight mass spectrometer. Detection conditions are set, and detection is performed using PTR photochemical ionization source-time-of-flight mass spectrometry. The detection data are then used for cluster analysis to obtain the classification results of the mash quality level. This classification is compared with the actual liquor production of the sample to categorize the mash into three levels: superior, medium, and inferior.

[0009] Full-spectrum data were acquired using a rapid PTR photochemical ionization source-time-of-flight mass spectrometer. The headspace sampler equilibrium temperature was 40–80℃; the sampling needle temperature was 60–100℃ with a cleaning time of 0.5–1.0 min; the quantitative loop temperature was 80–150℃ with a volume of 1–5 mL, an injection time of 0.3–1.0 min, and an equilibrium time of 0.01–0.1 min; the transfer tube temperature was 80–180℃. The PTR photochemical ionization source-time-of-flight mass spectrometer conditions were: proton transfer reaction photochemical ionization source, vacuum UV lamp voltage of 1000–2000 V, high-purity helium-water bubbling flow rate of 50–200 mL / min, and ionization zone pressure of 2.0 × 10⁻⁶. 2 ~5.00×10 2 Pa, ion source temperature 150–180℃, acquisition time 1–5 min, MCP voltage 3700–4800 V, detection zone gas pressure 7.00 × 10⁻⁶. -6 ~3.00×10 -4 Pa;

[0010] Import the full-spectrum data acquired by mass spectrometry into cluster analysis software. Set a threshold based on the mass spectrometry background baseline, within the range of 0 to 2000 counts, remove zero data points, and then perform cluster analysis.

[0011] The cluster analysis results were compared with the actual wine production of the samples to obtain the final classification results.

[0012] Superior grade mash has an alcohol yield of >50%, medium grade mash has an alcohol yield of 42% ≤ 50%, and inferior grade mash has an alcohol yield of <42%. Alcohol yield = alcohol yield of mash / amount of mash raw materials.

[0013] The clustering analysis software mentioned is SPSSAU, SAS, etc., and the clustering analysis mode can be Principal Component Analysis-X (PCA-X), Partial Least Squares Discrimination Analysis (PLS-DA), or Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA), etc.

[0014] The headspace thermal desorption-PTR photochemical ionization source-time-of-flight mass spectrometer was used, including a portable gas chromatograph and a photoelectron-induced H3O spectrometer. + Reagent ion chemical ionization source, ion transport system and time-of-flight mass analyzer.

[0015] Compared with the prior art, the advantages of this invention are as follows: The method of this invention does not require any pretreatment process or a large amount of organic solvent to detect the sample. A certain amount of mash sample is directly weighed and added to the sample introduction device of headspace thermal desorption-PTR photochemical ionization source-time-of-flight mass spectrometer for detection and data collection. The quality grade of mash can be determined by cluster analysis software. This method is simple to operate and the detection process is fast, providing a powerful means for the preliminary judgment of the quality of Maotai-flavor liquor mash. Attached Figure Description

[0016] Figure 1 Cluster analysis diagram of different masses of mash in Example 1 was obtained by headspace thermal desorption-PTR photochemical ionization source-time-of-flight mass spectrometry test. Detailed Implementation

[0017] Example 1

[0018] The analysis was performed using headspace thermal desorption-PTR photochemical ionization-time-of-flight mass spectrometry. The headspace sampler equilibrium temperature was 50℃; the sampling needle temperature was 100℃ with a cleaning time of 0.5 min; the quantitative loop temperature was 100℃ with a volume of 1 mL, an injection time of 0.5 min, and an equilibrium time of 0.05 min; the transfer tube temperature was 180℃. The PTR photochemical ionization-time-of-flight mass spectrometry conditions were: proton transfer reaction photochemical ionization source, vacuum UV lamp voltage of 1200 V, high-purity helium-water bubbling flow rate of 100 mL / min, and ionization zone pressure of 2.8 × 10⁻⁶. 2 Pa, ion source temperature 150℃, acquisition time 2 min, MCP voltage 4200V, detection zone gas pressure 5.00×10 -6 Pa;

[0019] Three 0.5 μL samples of fermented mash were taken from each of eight different fermentation tanks from the same batch. These samples were directly injected into the inlet of a PTR photochemical ionization source-time-of-flight mass spectrometer for qualitative and quantitative analysis, and mass spectrometry data were collected. The full-spectrum data of all fermented mash samples acquired by mass spectrometry were imported into the SPSSAU cluster analysis software. A threshold of 1000 counts was set based on the mass spectrometry background baseline, and zero-point data were excluded. The Principal Component Analysis-X (PCA-X) cluster analysis mode was selected for cluster analysis. The analysis results are as follows: Figure 1 As shown, the actual alcohol yields of each mash sample were 516 kg, 541 kg, 480 kg, 470 kg, 454 kg, 461 kg, 345 kg, and 405 kg, respectively, with a mash raw material quantity of 1000 kg. Based on the alcohol yield rate, the high-quality mash yielded 516 kg and 541 kg, the medium-quality mash yielded 480 kg, 470 kg, 454 kg, and 461 kg, and the low-quality mash yielded 345 kg and 405 kg. Figure 1 The clustering analysis results shown are consistent.

Claims

1. A headspace clustering method for identifying the quality of Maotai-flavor liquor mash, characterized in that: Take 0.5-1 μL of the mash sample to be tested and add it to the sample introduction device of the headspace thermal desorption-PTR photochemical ionization source-time-of-flight mass spectrometer. Set the detection conditions and use PTR photochemical ionization source-time-of-flight mass spectrometry to collect data. The detection data are then used for cluster analysis to obtain the classification results of the mash quality level. The results are compared with the actual alcohol production of the sample to classify the mash into three levels: excellent, medium, and poor. The specific method is as follows: (1) Detection conditions and data acquisition: The headspace thermal desorption-PTR photochemical ionization source-time-of-flight mass spectrometer was used for testing. The equilibrium temperature of the headspace sampler was 40-80℃; the temperature of the sampling needle was 60-100℃ and the cleaning time was 0.5-1.0 min; the temperature of the quantitative loop was 80-150℃, the volume was 1-5 mL, the injection time was 0.3-1.0 min and the equilibrium time was 0.01-0.1 min; the temperature of the transfer tube was 80-180℃. The PTR photochemical ionization source-time-of-flight mass spectrometer conditions are as follows: proton transfer reaction photochemical ionization source, vacuum UV lamp voltage 1000–2000 V, high-purity helium-water bubbling flow rate 50–200 mL / min, and ionization region pressure 2.0 × 10⁻⁶. 2 ~5.00× 10 2 Pa, ion source temperature 150–180 ℃, acquisition time 1–5 min, MCP voltage 3700–4800 V, detection zone gas pressure 7.00 × 10⁻⁶ -6 ~3.00 × 10 -4 Pa; (2) PCA cluster analysis: Import the full spectrum data collected by mass spectrometry into the cluster analysis software, set the threshold according to the mass spectrometry background baseline, within 0 to 2000 counts, remove the data points that are zero, set an appropriate cluster analysis mode to perform cluster analysis of the mash samples. (3) The cluster analysis results are compared with the actual wine production of the samples to obtain the final classification results.

2. The headspace clustering method for identifying the quality of Maotai-flavor liquor mash according to claim 1, characterized in that: The clustering analysis software is SPSSAU or SAS, and the clustering analysis mode is principal component analysis, partial least squares regression analysis, or orthogonal partial least squares discriminant analysis.

3. The headspace clustering method for identifying the quality of Maotai-flavor liquor mash according to claim 1, characterized in that: The fast PTR photochemical ionization source-time-of-flight mass spectrometer used includes a headspace sampler and a photoelectron-induced H3O sampler. + Reagent ion chemical ionization source, ion transport system and time-of-flight mass analyzer.

4. The headspace clustering method for identifying the quality of Maotai-flavor liquor mash according to claim 1, characterized in that: Superior grade mash has an alcohol yield of >50%, medium grade mash has an alcohol yield of 42% ≤ 50%, and inferior grade mash has an alcohol yield of <42%. Alcohol yield = alcohol yield of mash / amount of mash raw materials.

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