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 PDFInfo
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- 239000000796 flavoring agent Substances 0.000 title claims abstract description 105
- 239000000126 substance Substances 0.000 title claims abstract description 70
- 235000019634 flavors Nutrition 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 34
- 239000000203 mixture Substances 0.000 title claims abstract description 11
- 238000002290 gas chromatography-mass spectrometry Methods 0.000 claims abstract description 15
- 230000003068 static effect Effects 0.000 claims abstract description 6
- 238000001514 detection method Methods 0.000 claims abstract description 5
- 238000011534 incubation Methods 0.000 claims description 12
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 7
- 239000007789 gas Substances 0.000 claims description 6
- 239000012159 carrier gas Substances 0.000 claims description 4
- 239000001307 helium Substances 0.000 claims description 4
- 229910052734 helium Inorganic materials 0.000 claims description 4
- SWQJXJOGLNCZEY-UHFFFAOYSA-N helium atom Chemical compound [He] SWQJXJOGLNCZEY-UHFFFAOYSA-N 0.000 claims description 4
- 238000004949 mass spectrometry Methods 0.000 claims description 4
- 238000000540 analysis of variance Methods 0.000 claims description 3
- 239000007864 aqueous solution Substances 0.000 claims description 3
- 238000007865 diluting Methods 0.000 claims description 3
- 238000003965 capillary gas chromatography Methods 0.000 claims description 2
- 229920006395 saturated elastomer Polymers 0.000 claims description 2
- 238000000926 separation method Methods 0.000 claims description 2
- 239000011780 sodium chloride Substances 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 19
- 238000004458 analytical method Methods 0.000 abstract description 11
- 238000002347 injection Methods 0.000 abstract description 4
- 239000007924 injection Substances 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 2
- 150000002500 ions Chemical class 0.000 description 15
- 238000012549 training Methods 0.000 description 12
- MSXVEPNJUHWQHW-UHFFFAOYSA-N 2-methylbutan-2-ol Chemical compound CCC(C)(C)O MSXVEPNJUHWQHW-UHFFFAOYSA-N 0.000 description 6
- 238000002790 cross-validation Methods 0.000 description 6
- 235000020068 maotai Nutrition 0.000 description 6
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- 125000001495 ethyl group Chemical group [H]C([H])([H])C([H])([H])* 0.000 description 4
- XIRNKXNNONJFQO-UHFFFAOYSA-N ethyl hexadecanoate Chemical compound CCCCCCCCCCCCCCCC(=O)OCC XIRNKXNNONJFQO-UHFFFAOYSA-N 0.000 description 4
- 238000012795 verification Methods 0.000 description 4
- QTBSBXVTEAMEQO-UHFFFAOYSA-N Acetic acid Chemical compound CC(O)=O QTBSBXVTEAMEQO-UHFFFAOYSA-N 0.000 description 3
- 239000000945 filler Substances 0.000 description 3
- 235000015096 spirit Nutrition 0.000 description 3
- 238000010200 validation analysis Methods 0.000 description 3
- WRMNZCZEMHIOCP-UHFFFAOYSA-N 2-phenylethanol Chemical compound OCCC1=CC=CC=C1 WRMNZCZEMHIOCP-UHFFFAOYSA-N 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- RGXWDWUGBIJHDO-UHFFFAOYSA-N ethyl decanoate Chemical compound CCCCCCCCCC(=O)OCC RGXWDWUGBIJHDO-UHFFFAOYSA-N 0.000 description 2
- TVQGDYNRXLTQAP-UHFFFAOYSA-N ethyl heptanoate Chemical compound CCCCCCC(=O)OCC TVQGDYNRXLTQAP-UHFFFAOYSA-N 0.000 description 2
- MMKRHZKQPFCLLS-UHFFFAOYSA-N ethyl myristate Chemical compound CCCCCCCCCCCCCC(=O)OCC MMKRHZKQPFCLLS-UHFFFAOYSA-N 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000012634 fragment Substances 0.000 description 2
- HYBBIBNJHNGZAN-UHFFFAOYSA-N furfural Chemical compound O=CC1=CC=CO1 HYBBIBNJHNGZAN-UHFFFAOYSA-N 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- ZSIAUFGUXNUGDI-UHFFFAOYSA-N hexan-1-ol Chemical compound CCCCCCO ZSIAUFGUXNUGDI-UHFFFAOYSA-N 0.000 description 2
- AOGQPLXWSUTHQB-UHFFFAOYSA-N hexyl acetate Chemical compound CCCCCCOC(C)=O AOGQPLXWSUTHQB-UHFFFAOYSA-N 0.000 description 2
- ZXEKIIBDNHEJCQ-UHFFFAOYSA-N isobutanol Chemical compound CC(C)CO ZXEKIIBDNHEJCQ-UHFFFAOYSA-N 0.000 description 2
- 238000001819 mass spectrum Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- HNAGHMKIPMKKBB-UHFFFAOYSA-N 1-benzylpyrrolidine-3-carboxamide Chemical compound C1C(C(=O)N)CCN1CC1=CC=CC=C1 HNAGHMKIPMKKBB-UHFFFAOYSA-N 0.000 description 1
- -1 2-nonaneKetone Chemical compound 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000001476 alcoholic effect Effects 0.000 description 1
- OBNCKNCVKJNDBV-UHFFFAOYSA-N butanoic acid ethyl ester Natural products CCCC(=O)OCC OBNCKNCVKJNDBV-UHFFFAOYSA-N 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012850 discrimination method Methods 0.000 description 1
- UTUVIKZNQWNGIM-UHFFFAOYSA-N ethyl 2-phenylpropanoate Chemical compound CCOC(=O)C(C)C1=CC=CC=C1 UTUVIKZNQWNGIM-UHFFFAOYSA-N 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- HUAZGNHGCJGYNP-UHFFFAOYSA-N propyl butyrate Chemical compound CCCOC(=O)CCC HUAZGNHGCJGYNP-UHFFFAOYSA-N 0.000 description 1
- HTUIWRWYYVBCFT-UHFFFAOYSA-N propyl hexanoate Chemical compound CCCCCC(=O)OCCC HTUIWRWYYVBCFT-UHFFFAOYSA-N 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 235000015067 sauces Nutrition 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 239000012086 standard solution Substances 0.000 description 1
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- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
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- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
- G01N30/72—Mass spectrometers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
- G01N30/8679—Target compound analysis, i.e. whereby a limited number of peaks is analysed
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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
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
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
TABLE 3 validation results of the predicted samples
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,
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
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
TABLE 6 validation of prediction samples for different producing areas
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.
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