CN114441669A - Method for identifying models of different Maotai-flavor liquor based on volatile flavor substance composition - Google Patents

Method for identifying models of different Maotai-flavor liquor based on volatile flavor substance composition Download PDF

Info

Publication number
CN114441669A
CN114441669A CN202111527437.7A CN202111527437A CN114441669A CN 114441669 A CN114441669 A CN 114441669A CN 202111527437 A CN202111527437 A CN 202111527437A CN 114441669 A CN114441669 A CN 114441669A
Authority
CN
China
Prior art keywords
flavor
maotai
sample
different
liquor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111527437.7A
Other languages
Chinese (zh)
Other versions
CN114441669B (en
Inventor
王莉
杨婧
倪德让
杨帆
王和玉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Kweichow Moutai Co Ltd
Original Assignee
Kweichow Moutai Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Kweichow Moutai Co Ltd filed Critical Kweichow Moutai Co Ltd
Priority to CN202111527437.7A priority Critical patent/CN114441669B/en
Publication of CN114441669A publication Critical patent/CN114441669A/en
Application granted granted Critical
Publication of CN114441669B publication Critical patent/CN114441669B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02Column chromatography
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02Column chromatography
    • G01N30/04Preparation or injection of sample to be analysed
    • G01N30/06Preparation
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02Column chromatography
    • G01N30/62Detectors specially adapted therefor
    • G01N30/72Mass spectrometers
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02Column chromatography
    • G01N30/86Signal analysis
    • G01N30/8675Evaluation, i.e. decoding of the signal into analytical information
    • G01N30/8679Target compound analysis, i.e. whereby a limited number of peaks is analysed

Landscapes

  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Library & Information Science (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
  • Seasonings (AREA)

Abstract

The invention relates to the technical field of liquor component detection, in particular to a method for identifying different Maotai-flavor liquor identification models based on volatile flavor substance composition. The flavor components of different Maotai-flavor liquor are analyzed based on a static headspace-temperature programmed injection port-gas chromatography-mass spectrometry technology, different Maotai-flavor liquor identification models are established by adopting a discrimination analysis method, Maotai-flavor liquor of different grades and different production areas can be accurately discriminated, the average prejudgment coincidence rate is over 95%, and a new thought and method are provided for quality discrimination of Maotai-flavor liquor.

Description

Method for identifying models of different Maotai-flavor liquor based on volatile flavor substance composition
Technical Field
The invention relates to the technical field of liquor component detection, in particular to a method for identifying different sauce flavor liquor identification models based on volatile flavor substance composition.
Background
In recent years, along with the promotion of health consumption concepts and the drive of upgrading of consumption structures, Maotai-flavor liquor is more and more popular with consumers, the market is continuously heated, many liquor enterprises begin to develop Maotai-flavor types, and all ways of capital are also bee-owned and enter the field of Maotai liquor. However, due to the unique production process of the Maotai-flavor liquor, the flavor of the Maotai-flavor liquor is extremely complex, the evaluation on the quality grade of the Maotai-flavor liquor only depends on the sensory evaluation of professional wine tasters at present, and a relatively objective instrument discrimination and analysis method is lacked, so that the phenomena that the products in the market are mixed, the consumer is difficult to judge, various fake liquors are frequently produced in the market, and the like are not good enough are caused. Therefore, the development of accurate identification methods for different Maotai-flavor liquor (different grades and different production areas) has important significance for true and false identification and standardized management of Maotai-flavor liquor.
According to the experiment, the static headspace-temperature programmed injection port-gas chromatography-mass spectrometry technology is adopted to analyze the volatile flavor substances of different Maotai-flavor white spirits, and identification models of different Maotai-flavor white spirits are established based on a principal component analysis method and a discriminant analysis method, so that a new idea and a new method are provided for quality identification of the Maotai-flavor white spirits.
Disclosure of Invention
In one aspect, the application provides a method for identifying different Maotai-flavor liquor based on the composition of volatile flavor substances, which comprises the following steps:
s1, detecting the content of flavor substances in the wine sample;
s2 identifying key flavors;
determining key flavors comprises the steps of:
s2.1, the selected flavor substances have good peak shape of the extracted ion chromatogram, and the base line separation degree is more than or equal to 1.5;
s2.2, the peak area of the quantitative ion extraction chromatographic peak of the selected substance is more than 0.01 percent of the total peak area;
s2.3, the average pre-judgment coincidence rate of the discrimination model established based on the flavor substance content to different types of samples is more than 80 percent;
s2.4, repeating the steps S2.1-S2.3, and determining 20-30 key flavor substances;
s3, establishing an identification model;
s4 verifies the recognition model.
In some embodiments, the step S2, after the step S2.1, further includes the following steps: the peak area of the quantitative ion extraction chromatographic peak of the selected substance is more than 0.01 percent of the total peak area.
In some embodiments, step S1 includes a HS-PTV-GC-MS detection method.
In some embodiments, the method comprises the steps of:
s1.1, diluting a sample;
s1.2, headspace incubation;
s1.3, carrying out static headspace gas sample injection;
s1.4, and GC-MS analysis.
In some embodiments, in step S1.1, the sample is diluted with a saturated aqueous sodium chloride solution; in some embodiments, the volume ratio of the sample to saturated sodium chloride is 1:1 to 9.
In some embodiments, in step S1.2, the headspace incubation conditions are an incubation temperature of 45-80 ℃, an incubation time of 5-30min, and a quantitative loop temperature of 80-130 ℃.
In some embodiments, in step S1.3, the injection volume is 1-3 ml.
In some embodiments, the GC conditions in step S1.4 are a capillary gas chromatography column with a specification of DB-WAX 30m x 0.25mm x 0.25 μm; the carrier gas is helium, and the flow rate is 0.8-1.5 mL/min.
In some embodiments, in step S1.4, the mass spectrometry conditions are EI, the ion source temperature is 230 ℃, and the mass spectrometry scan adopts full scan and ion scan modes, and the scan range is 33 to 350 amu.
In some embodiments, the peak area or semi-quantitative content of key flavor substances is used as a variable in the recognition model.
Drawings
FIG. 1 is a total ion flow diagram for detecting volatile flavor substances in the Maotai-flavor liquor by adopting a static headspace-temperature programming sample inlet-filler liner tube-gas chromatography-mass spectrometry combined method.
FIG. 2 is a different-grade Maotai-flavor liquor identification model established based on the content of 20 key flavor substances in the invention.
FIG. 3 is a total ion flow diagram for detecting volatile flavor substances in Maotai-flavor liquor by adopting a static headspace-temperature programming sample inlet-filler-free liner tube-gas chromatography-mass spectrometry combined method.
FIG. 4 is a Maotai-flavor liquor identification model established in different production areas based on the content of 22 key flavor substances in the invention.
Detailed Description
The technical solutions of the present invention are further illustrated by the following specific examples, which do not represent limitations to the scope of the present invention. Insubstantial modifications and adaptations of the present invention by others of the concepts fall within the scope of the invention.
Example 1 identification method of Maotai-flavor liquor of different grades based on volatile flavor substance composition
A method for identifying a model of Maotai-flavor liquor with different grades based on volatile flavor substance composition comprises the steps of extraction of flavor substances in a liquor sample, gas chromatography-mass spectrometry combined analysis, determination of key flavor substances, establishment of an identification model, verification of an unknown sample and the like.
The method comprises the steps of adding 1mL of wine sample and 2mL of saturated NaCl aqueous solution into a 20mL sample bottle, diluting the alcoholic strength of the wine sample to 18%, under the headspace condition that the incubation temperature is 50 ℃, the incubation time is 15min, the sample volume is 1mL, the quantitative loop temperature is 90 ℃, and the transmission line temperature is 140 ℃.
The gas chromatography-mass spectrometry combined analysis step conditions are as follows: adopting a capillary gas chromatographic column with the specification of DB-WAX 30m multiplied by 0.25mm multiplied by 0.25 mu m; the carrier gas is helium, and the flow rate is 1 mL/min; the sample inlet is a temperature programmed sample inlet, the sample inlet mode is a solvent emptying mode, the liner tube is a glass liner tube of a TENAX filler, the temperature program is 50 ℃ for 5min, 720 ℃/min to 300 ℃ for 10min, and then 3 ℃/min to 200 ℃; the temperature program of the column box is 40 ℃ for 0min, the temperature is increased to 230 ℃ for 15min at the speed of 5 ℃/min, and the running time is 53 min; the mass spectrum conditions are as follows: the ionization mode is EI, the ion source temperature is 230 ℃, the scanning mode is a full scanning mode, and the scanning range is 33-350 amu.
In the step of gas chromatography-mass spectrometry, after being separated by a gas chromatograph, the flavor substances in the wine sample enter a mass spectrometer for analysis, and the detected flavor substances are determined qualitatively based on NIST library 08, and the results are shown in Table 1, and the peak area represents the relative content of the corresponding flavor substances.
TABLE 1 summary of the substances in the wine samples detected by HS-PTV-GC-MS
Figure BDA0003409484550000031
Figure BDA0003409484550000041
Note: the first fragment ion of the quantitative qualitative fragment ions in the table is the quantitative ion and is used for peak area calculation of the substance.
The method comprises the steps of collecting flavor substance spectrograms of the Maotai-flavor wine samples A-C with different grades, and collecting the flavor substance spectrograms of each type of wine sample according to the conditions, wherein the number of each type of sample is not less than 10, and the product quality grade is sequentially A more than B more than C (A is aged Maotai wine, B is common Maotai wine, and C is Maotai series wine).
The key flavor substance determination refers to selection according to the following principle: (1) the substance has good peak shape of extracted ion chromatographic peak, and the base line resolution is more than or equal to 1.5; (2) the peak area of the quantitative ion extraction chromatographic peak of the substance is more than 0.01 percent of the total peak area; (3) there is a very significant difference between the content of flavor substances in different wine samples by ANOVA method (P value is less than 0.01); (4) the average pre-judging coincidence rate of the judging model established based on the content of the flavor substances to different types of samples is more than 80 percent.
On the basis of meeting the conditions, the number of flavor substances for establishing a discrimination model is gradually reduced, and finally 20 compounds serving as important flavor substances of the Maotai-flavor liquor with different grades are determined, wherein the flavor substances are respectively ethyl butyrate, 2-methyl ethyl butyrate, iso-butanol, hexyl acetate, propyl caproate, ethyl heptanoate, n-hexanol, 2-nonaneKetone, acetic acid, furfural, ethyl decanoate, ethyl phenylpropionate, phenethyl alcohol, ethyl tetradecanoate, ethyl pentadecate 1, ethyl pentadecate 2, ethyl pentadecate 3, ethyl hexadecanoate 1, ethyl hexadecanoate 2 and ethyl 9-hexadecanoate, wherein the flavor substances are respectively recorded as: c1、C2……C19、C20
Discrimination analysis is carried out based on the peak areas of the 20 flavor substances, and two discrimination functions F1 (formula 1) and F2 (formula 2) are obtained by using SPSS software, wherein characteristic roots are 57.025 and 19.295 respectively, 74.73% and 25.27% of variance variables of the flavor substances can be respectively explained, and a recognition model is shown in figure 2. The formula of the discriminant function is equal to the intercept + coefficient of each substance multiplied by its peak area. In the model, the Maotai-flavor liquor A, B and C with different grades are well distinguished, and the central points of the categories are (18.132, 1.927), (-1.542, -4.783) and (-4.423, 4.386).
F1 ═ 21.172+1.06E-08 XC 1+5.02E-07 XC 2+2.72E-06 XC 3-5.60E-07 XC 4+ 1.32E-07 XC 5+1.66E-07 XC 6-5.74E-07 XC 7-8.99E-06 XC 8+2.31E-07 XC 9+3.30E-07 XC 10+8.90E-07 XC 11+2.55E-06 XC 12+3.42E-06 XC 13+1.80E-06 XC 14-4.62E-06 XC 15+1.38E-06 XC 16+5.82E-06 XC 17+3.44E-05 XC 18-2.02-07 XC 19.26 XC 26-20 XC (formula 1.05 XC) E-07 XC 468)
F2 ═ 7.683+2.96E-08 XC 1-6.24E-07 XC 2+2.13E-07 XC 3+1.17E-06 XC 4+ 8.85E-07 XC 5-2.12E-07 XC 6+1.24E-06 XC 7+1.93E-06 XC 8-7.38E-07 XC 9-3.52E-07 XC 10+8.43E-07 XC 11+1.61E-05 XC 12+5.86E-06 XC 13-1.37E-07 XC 14+3.55E-06 XC 15-9.72E-07 XC 16-5.41E-06 XC 17-6.14E-05 XC 18+ 3.84E-06 XC 18X 19E-6.11E-6 XC 20 (formula: 3.05 XC 20X C3627E-6E-3627 XC 3627X C3684)
C1-C20 in the formula are respectively the peak areas of the corresponding flavor substances.
Collecting a flavor substance spectrogram of an unknown wine sample by the same method to obtain peak areas of C1-C20, calculating discrimination functions F1 and F2 according to formulas 1 and 2, and calculating the distance between the discrimination functions and the central point of the A, B, C category, wherein the category with the shortest distance is the pre-judgment category of the unknown wine sample.
The method is adopted to carry out result verification on the identification model. The results show that the cross validation accuracy rates of 93 training samples of the 3 grades of Maotai-flavor liquor are all 100% (the results are shown in table 2), and the prediction results of the 23 prediction samples are all correct, and the coincidence rate is 100% (the results are shown in table 3). The training samples are data for modeling, wherein the A-type samples are 13, the B-type samples are 41 and the C-type samples are 39. The cross validation means that 1 wine sample is extracted from the 93 training samples to be used as a prediction sample, the rest 92 samples are used as training samples to be modeled, the category of the extracted sample is predicted, and whether the result is in accordance is checked. The prediction sample refers to other samples which are not in the above 93 modeling training samples, and the number of the samples is 23, wherein the number of the A-type samples is 3, the number of the B-type samples is 10, and the number of the C-type samples is 10.
Table 2 cross-validation results for training samples
Figure BDA0003409484550000051
TABLE 3 validation results of the predicted samples
Figure BDA0003409484550000052
Example 2 Maotai-flavor liquor identification method based on volatile flavor substance composition in different production areas
A method for identifying Maotai-flavor liquor in different production areas based on volatile flavor substance composition comprises the steps of liquor sample flavor substance extraction, gas chromatography-mass spectrometry combined analysis, key flavor substance determination, identification model establishment, unknown sample verification analysis and the like.
The conditions of the wine-like flavor substance extraction step are that 0.5mL of wine sample, 1.5mL of saturated NaCl aqueous solution and 3 muL of internal standard solution (tertiary amyl alcohol ethanol solution, the concentration is 1860 mg/L) are added into a 20mL sample bottle, the preparation method is that 0.093g of tertiary amyl alcohol standard (Sigma-Adrich) is weighed, 99.999% ethanol (ACS reagent) is used for fixing the volume to 50mL, the alcohol content of the wine sample is diluted to 13.25%, and the cap is pressed. Headspace conditions: the incubation temperature is 70 ℃, the incubation time is 10min, the sample volume is 3mL, the quantitative loop temperature is 100 ℃, and the transmission line temperature is 140 ℃.
The gas chromatography-mass spectrometry combined analysis step conditions are as follows: adopting a capillary gas chromatographic column with the specification of DB-WAX 30m multiplied by 0.25mm multiplied by 0.25 mu m; helium is taken as carrier gas, and the flow rate is 1.5 mL/min; the sample inlet is a temperature programmed sample inlet, the temperature of the sample inlet is 230 ℃, the split ratio is 5:1, and the liner tube is a non-filler glass liner tube. The temperature raising program of the column box is 40 ℃ for 1min, the temperature is raised to 42.5 ℃ at 0.5 ℃/min, then the temperature is raised to 220 ℃ at 35 ℃/min for 7min, and the running time is 18.07 min; the mass spectrum conditions are as follows: the ionization mode is EI, the ion source temperature is 230 ℃, the scanning mode is a full scanning mode, and the scanning range is 33-350 amu.
In the step of gas chromatography-mass spectrometry, the flavor substances in the liquor sample are separated by a gas chromatograph, and then enter a mass spectrometer for qualitative and quantitative analysis to obtain the content of volatile flavor substances in the liquor,
Figure BDA0003409484550000061
Figure BDA0003409484550000062
wherein 11.16 is the concentration of the internal standard tertiary amyl alcohol in the wine, and the unit is mg/L.
The method comprises the steps of collecting flavor substance spectrograms of sauced wine samples P1-P3 in different production areas, and collecting the flavor substance spectrograms of each type of wine sample according to the conditions, wherein the number of each type of sample is not less than 10, the P1 production area is a Maotai wine geographical protection range production area, the P2 production area is other Maotai town production areas, the P3 production area is other production areas, and the number of the samples is respectively 24, 28 and 14.
The key flavor substance determination refers to selection according to the following principle: (1) the substance has good peak shape of extracted ion chromatographic peak, and the base line resolution is more than or equal to 1.5; (2) there was a very significant difference between the content of flavour in different wine samples using the ANOVA method (P value less than 0.05); (3) the average pre-judgment coincidence rate of the judgment model established based on the content of the flavor substances to different types of samples is more than 80 percent.
On the basis of meeting the conditions, the number of flavor substances for establishing a discrimination model is gradually reduced, and finally 22 chemical compounds are determinedThe substances are used as flavor substances for modeling the Maotai-flavor liquor in different production areas, and the substances are respectively recorded as: c1、 C2……C21、C22See table 4.
TABLE 4 flavor substances for modeling Maotai-flavor liquor in different production areas
Figure BDA0003409484550000071
Discriminant analysis was performed based on the semi-quantitative content of the above 22 flavors to obtain two discriminant functions F1 (formula 3) and F2 (formula 4), characteristic roots were 19.274 and 7.596, respectively, which can account for 71.73% and 28.27% of variance variables of the flavors, respectively, and the recognition model is shown in fig. 4. The formula of the discriminant function is equal to the intercept + coefficient of each substance multiplied by its peak area. In the model, Maotai-flavor liquor P1, P2 and P3 in different factories are well distinguished, and the category center points are (-4.803, 1.897), (0.720, -3.104) and (6.793, 2.957).
F1 ═ 5.198+0.001 XC 1+0.016 XC 2+0.0002 XC 3-0.004 XC 4+0.010 XC 5+0.027 XC 6+0.006 XC 7-0.239 XC 8+0.099 XC 9-0.010 XC 10-0.071 XC 11-0.057 XC 12+ 0.025 XC 13+0.051 XC 14+0.001 XC 15+0.001 XC 16-0.020 XC 17+6.360 XC 18+4.381 XC 19-2.149 XC 20+0.017 XC 21-10.004 XC 22 (equation 3)
F2 ═ 2.903+0.007 × C1+0.016 × C2+0.00004 × C3+0.002 × C4-0.013 × C5+0.045 × C6-0.006 × C7-0.011 × C8+0.151 × C9-0.001 × C10-0.210 × C11-0.030 × C12-0.233 × C13+0.060 × C14+0.002 × C15- -0.002 × C16+0.016 × C17-4.282 × C18-0.960 × C19+1.723 × C20+0.526 × C21-19.694 × C22 (formula 4)
Collecting a flavor substance spectrogram of an unknown wine sample by the same method to obtain semi-quantitative content of C1-C22, calculating discrimination functions F1 and F2 according to formulas 3 and 4, and calculating distances from the discrimination functions to central points of P1, P2 and P3 categories, wherein the category with the shortest distance is the pre-discrimination category of the unknown wine sample.
The method is adopted to carry out result verification on the identification model. The results show that the average cross-validation compliance rate of 66 training samples in 3 production areas of Maotai-flavor liquor is 89.39% (the results are shown in Table 5), the average prediction compliance rate of 44 prediction samples is 96% (the results are shown in Table 6), and the prediction compliance rates of P1 and P3 production areas are 100%.
Training samples were data used for modeling, with 24 total P1 samples, 28 total P2 samples, and 14 total P3 samples. The cross validation refers to that 1 wine sample is extracted from the above 66 training samples to be used as a prediction sample, the rest 65 samples are used as training samples to be modeled, the category of the extracted sample is predicted, the poor validation is carried out for 66 times, and whether the result is in accordance is checked. The prediction sample refers to other samples which are not in the 66 modeling training samples, and the total number of the samples is 44, wherein the number of the P1 producing area samples is 16, the number of the P2 samples is 25, and the number of the P3 samples is 3.
TABLE 5 Cross-validation results of training samples for different production zones
Figure BDA0003409484550000081
TABLE 6 validation of prediction samples for different producing areas
Figure BDA0003409484550000082
The different Maotai-flavor liquor identification models based on the volatile flavor substance composition can be suitable for accurately identifying Maotai-flavor liquor of different grades and different production areas.
It should be noted that the above embodiments are only for further illustration and description of the technical solutions of the present invention, so that those skilled in the art can more accurately understand the inventive idea and the operation solutions of the present invention, and do not further limit the present invention, and that the modifications made by those skilled in the art without outstanding substantive features and remarkable progress are all within the protection scope of the present invention.

Claims (9)

1. A method for identifying different Maotai-flavor liquor based on the composition of volatile flavor substances is characterized by comprising the following steps:
s1, detecting the content of flavor substances in the wine sample;
s2 identifying key flavors;
determining key flavors comprises the steps of:
s2.1, the selected flavor substances have good peak shape of the extracted ion chromatogram, and the base line separation degree is more than or equal to 1.5;
s2.2, the content of the same flavor substance in different wine samples by adopting an ANOVA method has very obvious difference, and the P value is less than 0.01;
s2.3, the average pre-judgment coincidence rate of the discrimination model established based on the flavor substance content to different types of samples is more than 80 percent;
s2.4, repeating the steps S2.1-S23, and determining 20-30 key flavor substances;
s3, establishing an identification model;
s4 verifies the recognition model.
2. The method of claim 1, wherein in step S2, after step S2.1, further comprising the steps of:
the peak area of the quantitative ion extraction chromatographic peak of the selected substance is more than 0.01 percent of the total peak area.
3. The method of claim 1, wherein step S1 includes HS-PTV-GC-MS detection method;
preferably, the method comprises the steps of:
s1.1, diluting a sample;
s1.2, headspace incubation;
s1.3, static headspace gas sample introduction;
s1.4, and GC-MS analysis.
4. The method of claim 3, wherein in step S1.1, the sample is diluted with a saturated aqueous solution of sodium chloride;
preferably, the volume ratio of the sample to the saturated sodium chloride is 1: 1-9.
5. The method of claim 3, wherein in step S1.2, the headspace incubation conditions are an incubation temperature of 45-80 ℃, an incubation time of 5-30min, and a quantitative loop temperature of 80-130 ℃.
6. The method of claim 3, wherein in step S1.3, the sample volume is 1-3 ml.
7. The detection method according to claim 3, wherein in the step S1.4, the GC conditions are a capillary gas chromatography column with a specification of DB-WAX 30m x 0.25mm x 0.25 μm; the carrier gas is helium, and the flow rate is 0.8-1.5 mL/min.
8. The method of claim 3, wherein in step S1.4, the mass spectrometry conditions are EI, the ion source temperature is 230 ℃, and the mass spectrometry scan adopts a full scan and an ion scan mode, and the scan range is 33-350 amu.
9. The method of claim 1, wherein peak area or semi-quantitative content of key flavor substances is used as a variable in the recognition model.
CN202111527437.7A 2021-12-14 2021-12-14 Method for identifying models of different Maotai-flavor distilled spirits based on volatile flavor substances Active CN114441669B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111527437.7A CN114441669B (en) 2021-12-14 2021-12-14 Method for identifying models of different Maotai-flavor distilled spirits based on volatile flavor substances

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111527437.7A CN114441669B (en) 2021-12-14 2021-12-14 Method for identifying models of different Maotai-flavor distilled spirits based on volatile flavor substances

Publications (2)

Publication Number Publication Date
CN114441669A true CN114441669A (en) 2022-05-06
CN114441669B CN114441669B (en) 2023-10-27

Family

ID=81364240

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111527437.7A Active CN114441669B (en) 2021-12-14 2021-12-14 Method for identifying models of different Maotai-flavor distilled spirits based on volatile flavor substances

Country Status (1)

Country Link
CN (1) CN114441669B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115047129A (en) * 2022-06-16 2022-09-13 贵州茅台酒股份有限公司 Sorghum variety identification method based on volatile substance composition characteristics
CN115598276A (en) * 2022-10-27 2023-01-13 江南大学(Cn) Method for judging quality grade of Maotai-flavor liquor based on content and proportion of volatile compounds

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106153771A (en) * 2015-08-27 2016-11-23 泸州品创科技有限公司 The stage division of a kind of Chinese liquor base liquor and application
CN108445134A (en) * 2018-04-20 2018-08-24 泸州品创科技有限公司 Alcohol product mirror method for distinguishing
CN111521722A (en) * 2020-03-31 2020-08-11 中国食品发酵工业研究院有限公司 Method for identifying storage years of fragrant odor type finished product white spirit bottles
CN111579691A (en) * 2020-06-16 2020-08-25 茅台学院 Method for identifying Maotai-flavor liquor

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106153771A (en) * 2015-08-27 2016-11-23 泸州品创科技有限公司 The stage division of a kind of Chinese liquor base liquor and application
CN108445134A (en) * 2018-04-20 2018-08-24 泸州品创科技有限公司 Alcohol product mirror method for distinguishing
CN111521722A (en) * 2020-03-31 2020-08-11 中国食品发酵工业研究院有限公司 Method for identifying storage years of fragrant odor type finished product white spirit bottles
CN111579691A (en) * 2020-06-16 2020-08-25 茅台学院 Method for identifying Maotai-flavor liquor

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
ANA C. PEREIRA ET AL: "Analysis and assessment of Madeira wine ageing over an extended time period through GC–MS and chemometric analysis" *
任红波;: "白酒中香味物质的顶空―气相色谱/质谱联用分析" *
唐平 等: "赤水河流域不同地区酱香型白酒风味化合物分析" *
张晓婕 等: "不同工艺酱香型白酒挥发性物质差异分析" *
王辉 等: "基于电子鼻对不同香型白酒的快速识别和分类" *
黄治国 等: "模糊模型识别方法在浓香型白酒酒质评价中的应用研究" *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115047129A (en) * 2022-06-16 2022-09-13 贵州茅台酒股份有限公司 Sorghum variety identification method based on volatile substance composition characteristics
CN115047129B (en) * 2022-06-16 2023-07-04 贵州茅台酒股份有限公司 Sorghum variety identification method based on volatile matter composition characteristics
CN115598276A (en) * 2022-10-27 2023-01-13 江南大学(Cn) Method for judging quality grade of Maotai-flavor liquor based on content and proportion of volatile compounds

Also Published As

Publication number Publication date
CN114441669B (en) 2023-10-27

Similar Documents

Publication Publication Date Title
CN114441669A (en) Method for identifying models of different Maotai-flavor liquor based on volatile flavor substance composition
Belmonte-Sánchez et al. Rum classification using fingerprinting analysis of volatile fraction by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry
Pedersen et al. Quantitative analysis of geraniol, nerol, linalool, and α-terpineol in wine
CN109781918B (en) Gas phase ion mobility spectrometry identification method for yellow rice wine produced by different enterprises
Pereira et al. Aroma ageing trends in GC/MS profiles of liqueur wines
CN113203803B (en) Method for identifying white spirit storage time by multiple linear stepwise regression
Li et al. Vintage analysis of Chinese Baijiu by GC and 1H NMR combined with multivariable analysis
CN111521722A (en) Method for identifying storage years of fragrant odor type finished product white spirit bottles
CN105319308A (en) Gas chromatography/mass spectrometry analysis apparatus of various compositions of white spirit, and analysis method thereof
CN113917061B (en) Detection and identification method for volatile substances of Maotai-flavor base wine
Malfondet et al. Discrimination of French wine brandy origin by PTR-MS headspace analysis using ethanol ionization and sensory assessment
Ceballos-Magana et al. Quantitation of twelve metals in tequila and mezcal spirits as authenticity parameters
CN110514757A (en) A kind of method of Volatile flavor components in fast resolving white wine
Uttl et al. Critical assessment of chemometric models employed for varietal authentication of wine based on UHPLC-HRMS data
CN107462624B (en) Method for rapidly determining content of main ester compounds in base liquor of white spirit
CN111948191B (en) Multi-light-source Raman spectrum analysis method and application thereof
Huang et al. Methanol concentration as a preceding eliminative marker for the authentication of Scotch Whiskies in Taiwan
CN109164180B (en) Method for distinguishing Mark of Masuria cheese identity based on decision tree extraction features
Xu et al. Characterizing bourbon whiskey via the combination of LC-MS and GC–MS based molecular fingerprinting
Wang et al. GC/MS-based untargeted metabolomics reveals the differential metabolites for discriminating vintage of Chenxiang-type baijiu
CN114137138A (en) Volatile component-based resolution method for Sichuan Pixian broad bean paste
CN114113360B (en) Method for distinguishing aftertaste of Maotai-flavor liquor based on hard-volatile organic acid
Huang et al. Improvement of principal component analysis (PCA) by using log-transformed fermentation congeners for the authentication of Scotch whiskies
CN112067714B (en) Method for determining peanut oil essence in peanut oil
Zhang et al. A practical method based on gas chromatography–mass spectrometry combined with chemometrics for the identification of Moutai liquor

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant