CN114441669B - Method for identifying models of different Maotai-flavor distilled spirits based on volatile flavor substances - Google Patents
Method for identifying models of different Maotai-flavor distilled spirits based on volatile flavor substances Download PDFInfo
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- 239000000796 flavoring agent Substances 0.000 title claims abstract description 113
- 235000019634 flavors Nutrition 0.000 title claims abstract description 72
- 239000000126 substance Substances 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 38
- 235000015096 spirit Nutrition 0.000 title abstract description 16
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 239000000203 mixture Substances 0.000 claims abstract description 6
- 230000003068 static effect Effects 0.000 claims abstract description 6
- 235000014101 wine Nutrition 0.000 claims description 24
- 238000002290 gas chromatography-mass spectrometry Methods 0.000 claims description 14
- 238000011534 incubation Methods 0.000 claims description 12
- 238000004519 manufacturing process Methods 0.000 claims description 12
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 claims description 8
- 239000007789 gas Substances 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 6
- 235000020068 maotai Nutrition 0.000 claims description 6
- 238000001819 mass spectrum Methods 0.000 claims description 6
- WRMNZCZEMHIOCP-UHFFFAOYSA-N 2-phenylethanol Chemical compound OCCC1=CC=CC=C1 WRMNZCZEMHIOCP-UHFFFAOYSA-N 0.000 claims description 4
- 239000012159 carrier gas Substances 0.000 claims description 4
- HCRBXQFHJMCTLF-ZCFIWIBFSA-N ethyl (2r)-2-methylbutanoate Chemical compound CCOC(=O)[C@H](C)CC HCRBXQFHJMCTLF-ZCFIWIBFSA-N 0.000 claims description 4
- HYBBIBNJHNGZAN-UHFFFAOYSA-N furfural Chemical compound O=CC1=CC=CO1 HYBBIBNJHNGZAN-UHFFFAOYSA-N 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
- ZSIAUFGUXNUGDI-UHFFFAOYSA-N hexan-1-ol Chemical compound CCCCCCO ZSIAUFGUXNUGDI-UHFFFAOYSA-N 0.000 claims description 4
- ZXEKIIBDNHEJCQ-UHFFFAOYSA-N isobutanol Chemical compound CC(C)CO ZXEKIIBDNHEJCQ-UHFFFAOYSA-N 0.000 claims description 4
- 238000000926 separation method Methods 0.000 claims description 4
- 238000000540 analysis of variance Methods 0.000 claims description 3
- 150000001875 compounds Chemical class 0.000 claims description 3
- HNAGHMKIPMKKBB-UHFFFAOYSA-N 1-benzylpyrrolidine-3-carboxamide Chemical compound C1C(C(=O)N)CCN1CC1=CC=CC=C1 HNAGHMKIPMKKBB-UHFFFAOYSA-N 0.000 claims description 2
- OBNCKNCVKJNDBV-UHFFFAOYSA-N butanoic acid ethyl ester Natural products CCCC(=O)OCC OBNCKNCVKJNDBV-UHFFFAOYSA-N 0.000 claims description 2
- 238000003965 capillary gas chromatography Methods 0.000 claims description 2
- 238000007865 diluting Methods 0.000 claims description 2
- 239000001813 ethyl (2R)-2-methylbutanoate Substances 0.000 claims description 2
- 229940090910 ethyl 2-methylbutyrate Drugs 0.000 claims description 2
- 238000012847 principal component analysis method Methods 0.000 claims description 2
- 229920006395 saturated elastomer Polymers 0.000 claims description 2
- 239000011780 sodium chloride Substances 0.000 claims description 2
- LRHPLDYGYMQRHN-UHFFFAOYSA-N N-Butanol Chemical compound CCCCO LRHPLDYGYMQRHN-UHFFFAOYSA-N 0.000 claims 3
- BTANRVKWQNVYAZ-UHFFFAOYSA-N butan-2-ol Chemical compound CCC(C)O BTANRVKWQNVYAZ-UHFFFAOYSA-N 0.000 claims 3
- BDERNNFJNOPAEC-UHFFFAOYSA-N propan-1-ol Chemical compound CCCO BDERNNFJNOPAEC-UHFFFAOYSA-N 0.000 claims 3
- YWHLKYXPLRWGSE-UHFFFAOYSA-N Dimethyl trisulfide Chemical compound CSSSC YWHLKYXPLRWGSE-UHFFFAOYSA-N 0.000 claims 2
- AMQJEAYHLZJPGS-UHFFFAOYSA-N N-Pentanol Chemical compound CCCCCO AMQJEAYHLZJPGS-UHFFFAOYSA-N 0.000 claims 2
- WQOXQRCZOLPYPM-UHFFFAOYSA-N dimethyl disulfide Chemical compound CSSC WQOXQRCZOLPYPM-UHFFFAOYSA-N 0.000 claims 2
- LZCLXQDLBQLTDK-UHFFFAOYSA-N ethyl 2-hydroxypropanoate Chemical compound CCOC(=O)C(C)O LZCLXQDLBQLTDK-UHFFFAOYSA-N 0.000 claims 2
- SHZIWNPUGXLXDT-UHFFFAOYSA-N ethyl hexanoate Chemical compound CCCCCC(=O)OCC SHZIWNPUGXLXDT-UHFFFAOYSA-N 0.000 claims 2
- PPXUHEORWJQRHJ-UHFFFAOYSA-N ethyl isovalerate Chemical compound CCOC(=O)CC(C)C PPXUHEORWJQRHJ-UHFFFAOYSA-N 0.000 claims 2
- MLFHJEHSLIIPHL-UHFFFAOYSA-N isoamyl acetate Chemical compound CC(C)CCOC(C)=O MLFHJEHSLIIPHL-UHFFFAOYSA-N 0.000 claims 2
- PHTQWCKDNZKARW-UHFFFAOYSA-N isoamylol Chemical compound CC(C)CCO PHTQWCKDNZKARW-UHFFFAOYSA-N 0.000 claims 2
- ICMAFTSLXCXHRK-UHFFFAOYSA-N Ethyl pentanoate Chemical compound CCCCC(=O)OCC ICMAFTSLXCXHRK-UHFFFAOYSA-N 0.000 claims 1
- IKHGUXGNUITLKF-XPULMUKRSA-N acetaldehyde Chemical compound [14CH]([14CH3])=O IKHGUXGNUITLKF-XPULMUKRSA-N 0.000 claims 1
- DHKHKXVYLBGOIT-UHFFFAOYSA-N acetaldehyde Diethyl Acetal Natural products CCOC(C)OCC DHKHKXVYLBGOIT-UHFFFAOYSA-N 0.000 claims 1
- 125000002777 acetyl group Chemical class [H]C([H])([H])C(*)=O 0.000 claims 1
- 235000019996 baijiu Nutrition 0.000 claims 1
- WDAXFOBOLVPGLV-UHFFFAOYSA-N ethyl isobutyrate Chemical compound CCOC(=O)C(C)C WDAXFOBOLVPGLV-UHFFFAOYSA-N 0.000 claims 1
- 229940116333 ethyl lactate Drugs 0.000 claims 1
- 229940117955 isoamyl acetate Drugs 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 12
- 238000005516 engineering process Methods 0.000 abstract description 2
- 239000004615 ingredient Substances 0.000 abstract description 2
- 238000004949 mass spectrometry 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
- PTEYJUIKYIKULL-UHFFFAOYSA-N Ethyl pentadecanoate Chemical compound CCCCCCCCCCCCCCC(=O)OCC PTEYJUIKYIKULL-UHFFFAOYSA-N 0.000 description 6
- 238000002790 cross-validation Methods 0.000 description 6
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 4
- XIRNKXNNONJFQO-UHFFFAOYSA-N ethyl hexadecanoate Chemical compound CCCCCCCCCCCCCCCC(=O)OCC XIRNKXNNONJFQO-UHFFFAOYSA-N 0.000 description 4
- AOGQPLXWSUTHQB-UHFFFAOYSA-N hexyl acetate Chemical compound CCCCCCOC(C)=O AOGQPLXWSUTHQB-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
- 239000000243 solution Substances 0.000 description 3
- 238000010200 validation analysis Methods 0.000 description 3
- 230000001476 alcoholic effect Effects 0.000 description 2
- 239000007864 aqueous solution Substances 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- MTZQAGJQAFMTAQ-UHFFFAOYSA-N ethyl benzoate Chemical compound CCOC(=O)C1=CC=CC=C1 MTZQAGJQAFMTAQ-UHFFFAOYSA-N 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
- 229940067592 ethyl palmitate Drugs 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000012634 fragment Substances 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- -1 2-nonone Chemical compound 0.000 description 1
- NIXOWILDQLNWCW-UHFFFAOYSA-M Acrylate Chemical compound [O-]C(=O)C=C NIXOWILDQLNWCW-UHFFFAOYSA-M 0.000 description 1
- JELGPLUONQGOHF-UHFFFAOYSA-N Hexadec-9-ensaeure-ethylester Natural products CCCCCCC=CCCCCCCCC(=O)OCC JELGPLUONQGOHF-UHFFFAOYSA-N 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- JELGPLUONQGOHF-MDZDMXLPSA-N ethyl 9-hexadecenoate Chemical compound CCCCCC\C=C\CCCCCCCC(=O)OCC JELGPLUONQGOHF-MDZDMXLPSA-N 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 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
- 235000020097 white wine Nutrition 0.000 description 1
Classifications
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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
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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
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
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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
- G01N30/62—Detectors specially adapted therefor
- G01N30/72—Mass spectrometers
-
- 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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- 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)
- Seasonings (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The application relates to the technical field of white spirit ingredient detection, in particular to a method for identifying models of different Maotai-flavor white spirits based on volatile flavor substances. Based on the static headspace-temperature programming sample inlet-gas mass spectrometry technology, the flavor composition of different Maotai-flavor white spirits is analyzed, and a discriminant analysis method is adopted to establish different Maotai-flavor white spirits identification models, so that Maotai-flavor white spirits with different grades and different producing areas can be accurately distinguished, the average predictive compliance rate is more than 95%, and a new thought and method are provided for quality identification of the Maotai-flavor white spirits.
Description
Technical Field
The application relates to the technical field of white spirit ingredient detection, in particular to a method for identifying models of different Maotai-flavor white spirits based on volatile flavor substances.
Background
In recent years, with popularization of healthy consumption concepts and promotion of consumption structure upgrading, maotai-flavor liquor is more and more popular with consumers, the market is continuously heated, a lot of liquor enterprises start to develop Maotai-flavor products, and various capital are also crowded into the Maotai-liquor field. However, the unique production process of the Maotai-flavor liquor results in extremely complex flavor, and the current evaluation of the quality grade of the Maotai-flavor liquor only depends on sensory evaluation of professional wine tasters, and a relatively objective instrument discrimination analysis method is lacked, so that the fish-dragon products on the market are mixed, the judgment of consumers is difficult, and various false wines and the like are often produced on the market to be good. Therefore, developing accurate identification methods of different Maotai-flavor distilled spirits (different grades and different producing areas) has important significance for the true and false identification and standardized management of the Maotai-flavor distilled spirits.
The experiment adopts a static headspace-temperature programming sample inlet-gas mass spectrometry technology to analyze volatile flavor substances of different Maotai-flavor white spirits, establishes identification models of different Maotai-flavor white spirits based on a principal component analysis method and a discriminant analysis method, and provides a new thought and method for quality identification of Maotai-flavor white spirits.
Disclosure of Invention
In one aspect, the application provides a method for identifying different Maotai-flavor distilled spirits based on the composition of volatile flavor substances, comprising the following steps:
s1, detecting the content of flavor substances in a wine sample;
s2, determining key flavor substances;
determining key flavors includes the steps of:
s2.1, the peak shape of an extracted ion chromatographic peak of the selected flavor substances is good, and the baseline separation degree is more than or equal to 1.5;
s2.2, the peak area of the quantitative ion extraction chromatograph of the selected substances is more than 0.01% of the total peak area;
s2.3, the average prejudgement coincidence rate of a judging model established based on the content of the flavor substances on different types of samples is more than 80 percent;
s2.4 repeating the steps S2.1-S2.3 to determine 20-30 key flavor substances;
s3, establishing an identification model;
s4, verifying the identification model.
In some embodiments, in the step S2, after the step S2.1, the method further includes the steps of: the quantitative ion extraction chromatographic peak area of the selected substances is more than 0.01% of the total peak area.
In some embodiments, step S1 comprises an 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, injecting a static headspace gas;
s1.4, 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 1:1-9.
In some embodiments, in the step S1.2, the headspace incubation condition is 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 sample volume is 1-3ml.
In some embodiments, in step S1.4, the GC conditions are the use of a capillary gas chromatography column having a specification of DB-WAX 30m 0.25mm 0.25 μm; the carrier gas is helium with the flow rate of 0.8-1.5 mL/min.
In some embodiments, in the step S1.4, the mass spectrum condition is EI, the ion source temperature is 230 ℃, the mass spectrum scanning adopts a full scanning mode and an ion scanning mode, and the scanning range is 33 amu to 350amu.
In some embodiments, the peak area or semi-quantitative content of key flavor is used as a variable in the identification model.
Drawings
Fig. 1 is a total ion flow diagram of the detection of volatile flavor substances in Maotai-flavor liquor by adopting a static headspace-temperature programmed sample inlet-filler liner-gas chromatography-mass spectrometry combination method.
FIG. 2 is a graph showing identification models of different grades of Maotai-flavor liquor based on 20 key flavor contents.
Fig. 3 is a total ion flow diagram of the detection of volatile flavor substances in Maotai-flavor liquor by adopting a static headspace-temperature programmed sample inlet-no-filler liner-gas chromatography-mass spectrometry combination method.
Fig. 4 is a graph showing identification models of Maotai-flavor liquor in different producing areas based on 22 key flavor substance contents.
Detailed Description
The technical solution of the present application is further illustrated by the following specific examples, which do not represent limitations on the scope of protection of the present application. Some insubstantial modifications and adaptations of the application based on the inventive concept by others remain within the scope of the application.
Example 1 identification method of Maotai-flavor liquor of different grades based on volatile flavor composition
A method for identifying different grades of Maotai-flavor liquor based on volatile flavor substance composition comprises the steps of extraction of flavor substances in liquor samples, gas chromatography-mass spectrometry analysis, determination of key flavor substances, establishment of identification models, verification of unknown samples and the like.
The condition of the step of extracting the wine-like flavor substance is that 1mL of wine-like and 2mL of saturated NaCl aqueous solution are added into a 20mL sample bottle, the alcoholic strength of the wine-like is diluted to 18%, the headspace condition is that the incubation temperature is 50 ℃, the incubation time is 15min, the sample injection amount is 1mL, the quantitative loop temperature is 90 ℃, and the transmission line temperature is 140 ℃.
The conditions of the gas chromatography-mass spectrometry combined analysis step are as follows: a capillary gas chromatographic column is adopted, and the specification is DB-WAX 30m multiplied by 0.25mm multiplied by 0.25 mu m; the carrier gas is helium, and the flow rate is 1mL/min; the sample inlet is a temperature programming sample inlet, the sample inlet mode is a solvent emptying mode, the liner tube is a glass liner tube filled with TENAX, the temperature program is 50 ℃ for 5min, 720 ℃/min to 300 ℃ for 10min, and then 3 ℃/min to 200 ℃; the temperature programming of the column box is 40 ℃ for 0min, the temperature is raised to 230 ℃ for 15min at 5 ℃/min, and the running time is 53min; 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 gas chromatography-mass spectrometry analysis step, the flavor substances in the wine samples are separated by a gas chromatograph, then enter a mass spectrometer for analysis, and the detected flavor substances are qualitatively analyzed 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 substances detected in wine samples using HS-PTV-GC-MS
Note that: the first fragment ion in the quantitative qualitative fragment ion in the table is the quantitative ion for peak area calculation of the species.
The flavor substance spectrograms of the different grades of Maotai-flavor wine samples A-C are collected, the flavor substance spectrograms of each type of the wine samples are collected according to the conditions, the number of each type of the samples is not less than 10, and the quality grades of the products are A & gtB & gtC in sequence (A is aged Maotai wine, B is common Maotai wine, and C is Maotai-flavor series wine).
The key flavor substance determination means that the key flavor substance is selected according to the following principle: (1) The peak shape of the extracted ion chromatographic peak of the substance is good, and the baseline separation degree is more than or equal to 1.5; (2) The quantitative ion extraction chromatographic peak area of the substance is more than 0.01% of the total peak area; (3) There was a very significant difference between the flavour contents of the different wine samples using the ANOVA method (P value less than 0.01); (4) The average prejudgement coincidence rate of the discrimination model established based on the content of the flavor substances on different types of samples is more than 80 percent.
On the basis of meeting the conditions, gradually reducing the quantity of the flavor substances for establishing the discrimination model, and finally determining 20 compounds as important flavor substances of the different-grade Maotai-flavor white spirit, wherein the important flavor substances are ethyl butyrate, ethyl 2-methylbutyrate, isobutanol, hexyl acetate and hexyl acetate respectivelyPropyl acrylate, ethyl heptanoate, n-hexanol, 2-nonone, acetic acid, furfural, ethyl decanoate, ethyl benzoate, phenethyl alcohol, ethyl tetradecanoate, ethyl pentadecanoate 1, ethyl pentadecanoate 2, ethyl pentadecanoate 3, ethyl palmitate 1, ethyl palmitate 2, and ethyl 9-hexadecenoate, respectively, and the above flavor substances were noted as: c (C) 1 、C 2 ……C 19 、C 20 。
Based on the peak areas of the above 20 flavors, two discriminant functions F1 (formula 1) and F2 (formula 2) are obtained by using, for example, SPSS software, and the characteristic roots are 57.025 and 19.295, respectively, and can explain 74.73% and 25.27% of variance variables of the flavors, respectively, and the recognition model is shown in fig. 2. The formula of the discriminant function is equal to the intercept + the substance coefficients times their peak areas. In this model, different grades of Maotai-flavor liquor A, B and C are well distinguished, and the class centers are (18.132,1.927), (-1.542, -4.783) and (-4.423,4.386), respectively.
F1 = -21.172+1.06e-08 xc1+5.02E-07 xc2+2.72E-06 xc3-5.60E-07 xc4+1.32E-07 xc5+1.66E-07 xc6-5.74E-07 xc7-8.99E-06 xc8+2.31E-07 xc9+3.30E-07 xc10+8.90E-07 xc11+2.55E-06 xc12+3.42E-06 xc13+1.80E-06 xc14-4.62E-06 xc15+1.38E-06 xc16+5.82E-06 xc17+3.44E-05 xc18-2.02E-07 xc19-2.26E-05 xc20 (formula 1)
F2 = -7.683+2.96E-08 XC1-6.24E-07 XC2+2.13E-07 XC3+1.17E-06 XC4+8.85E-07 XC 5-2.12E-07 XC6+1.24E-06 XC7+1.93E-06 XC8-7.38E-07 XC9-3.52E-07 XC10+8.43E-07 XC11+1.61E-05 XC12+5.86E-06 XC 13-1.37E-07 XC14+3.55E-06 XC 15-9.72E-07 XC 16-5.41E-06 XC 17-6.14E-05 XC 18+3.84E-07 XC 19+2.90E-05 XC 20 (formula 2)
C1-C20 in the formula are peak areas of the corresponding flavor substances, respectively.
Collecting the flavor substance spectrogram of the unknown wine sample by the same method to obtain the peak area of C1-C20, calculating the discriminant functions F1 and F2 according to the formulas 1 and 2, and then calculating the distance from the flavor substance spectrogram to the center point of the A, B, C class, wherein the class with the shortest distance is the pre-judging class of the unknown wine sample.
The method is adopted to carry out result verification on the identification model. As a result, the cross-validation accuracy of 93 training samples of 3-grade Maotai-flavor liquor was 100% (see Table 2 for results), and the prediction results of 23 prediction samples were all correct, with 100% compliance (see Table 3 for results). Wherein the training samples are data for modeling, wherein the total number of the A samples is 13, the total number of the B samples is 41, and the total number of the C samples is 39. The cross-validation means that 1 wine sample is extracted from the 93 training samples as a prediction sample, the remaining 92 samples are modeled as training samples, and then the category of the extracted samples is predicted, and whether the result accords with the category is checked. The prediction samples here refer to other samples out of the above 93 modeling training samples, and total 23 samples, wherein the total number of samples of class a is 3, the total number of samples of class B is 10, and the total number of samples of class C is 10.
Table 2 cross-validation results for training samples
Table 3 validation results of predicted samples
Example 2 Maotai-flavor liquor identification method based on different production areas of volatile flavor composition
A Maotai-flavor liquor identification method based on different production areas composed of volatile flavor substances comprises the steps of liquor-like flavor substance extraction, gas chromatography-mass spectrometry analysis, key flavor substance determination, identification model establishment, unknown sample verification analysis and the like.
The condition of the step of extracting the wine-like flavor substance is that 0.5mL of wine-like substance, 1.5mL of saturated NaCl aqueous solution and 3 mu L of internal standard solution (tertiary amyl alcohol ethanol solution with the concentration of 1860 mg/L) are added into a 20mL sample bottle, the preparation method is that 0.093g of tertiary amyl alcohol standard substance (Sigma-Adrich) is weighed, 99.999% ethanol (ACS reagent) is used for fixing the volume to 50 mL), and the alcoholic strength of the wine-like substance is diluted to 13.25% and is capped. Headspace conditions: the incubation temperature was 70 ℃, the incubation time was 10min, the sample injection amount was 3mL, the quantitative loop temperature was 100 ℃, and the transmission line temperature was 140 ℃.
The conditions of the gas chromatography-mass spectrometry combined analysis step are as follows: a capillary gas chromatographic column is adopted, and the specification is DB-WAX 30m multiplied by 0.25mm multiplied by 0.25 mu m; helium is used as carrier gas, and the flow rate is 1.5mL/min; the sample inlet is a temperature programming sample inlet, the temperature of the sample inlet is 230 ℃, the split ratio is 5:1, and the liner tube is a glass liner tube without filler. The temperature programming 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 gas chromatography-mass spectrometry analysis step, the flavor substances in the wine 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 white wine, wherein 11.16 is the concentration of the internal standard tertiary amyl alcohol in the wine, and the unit is mg/L.
The method is characterized in that the flavor substance spectrograms of the Maotai-flavor liquor samples P1-P3 in different producing areas are collected, the flavor substance spectrograms of the liquor samples of each type are collected according to the conditions, the number of the samples of each type is not less than 10, wherein the P1 producing area is a geographical protection range producing area of Maotai liquor, the P2 producing area is other producing areas of the Maotai town, the P3 producing area is other producing areas, and the number of the samples is 24, 28 and 14 respectively.
The key flavor substance determination means that the key flavor substance is selected according to the following principle: (1) The peak shape of the extracted ion chromatographic peak of the substance is good, and the baseline separation degree is more than or equal to 1.5; (2) There was a very significant difference (P value less than 0.05) between the flavour contents of the different wine samples using the ANOVA method; (3) The average prejudgement coincidence rate of the discrimination model established based on the content of the flavor substances on different types of samples is more than 80 percent.
On the basis of meeting the conditions, gradually reducing the quantity of flavor substances for establishing a discrimination model, and finally determining 22 compounds as different producing areasThe flavor substances modeled by the Maotai-flavor liquor are respectively marked as: c (C) 1 、 C 2 ……C 21 、C 22 See table 4.
TABLE 4 flavoring substances for Maotai-flavor liquor modeling in different producing regions
Based on the semi-quantitative contents of the 22 flavor substances, the discriminant analysis is carried out to obtain two discriminant functions F1 (formula 3) and F2 (formula 4), wherein the characteristic roots are 19.274 and 7.596 respectively, and 71.73% and 28.27% of variance variables of the flavor substances can be explained respectively, and the recognition model is shown in figure 4. The formula of the discriminant function is equal to the intercept + the substance coefficients times their peak areas. In the model, different plant areas of Maotai-flavor distilled spirit P1, P2 and P3 are well distinguished, and category center points are (-4.803,1.897), (0.720, -3.104) and (6.793,2.957) respectively.
F1 = -5.198+0.001 xc1+0.016xc2+0.0002 xc3-0.004 xc4+0.010 xc5+0.027 xc6+0.006 xc7-0.239 xc8+0.099 xc9-0.010 xc10-0.071xc11-0.057 xc12+0.025 xc13+0.051xc14+0.001 xc15+0.001 xc16-0.020 xc17+ 6.360 xc18+ 4.381 xc19-2.149 xc20+0.017xc21-10.004 xc 22 (formula 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 the flavor substance spectrogram of the unknown wine sample by the same method to obtain the semi-quantitative content of C1-C22, calculating the discriminant functions F1 and F2 according to formulas 3 and 4, and then calculating the distances from the discriminant functions F1 and F2 to the center points of the P1, P2 and P3 categories, wherein the category with the shortest distance is the pre-judging category of the unknown wine sample.
The method is adopted to carry out result verification on the identification model. As a result, the average coincidence rate of the cross validation of 66 training samples of the Maotai-flavor liquor of 3 producing areas is 89.39% (the result is shown in Table 5), and the average coincidence rate of the prediction samples of 44 is 96% (the result is shown in Table 6), wherein the prediction coincidence rates of the P1 and P3 producing areas are 100%.
Training samples are data used for modeling, where there are 24P 1 samples, 28P 2 samples, and 14P 3 samples. The cross validation is to extract 1 wine sample from the 66 training samples as a prediction sample, model the rest 65 samples as training samples, predict the category of the extracted samples, perform poor validation 66 times, and check whether the result accords with the standard. The predicted samples here refer to the other samples out of 66 modeling training samples, 44 total, with 16P 1 production zone samples, 25P 2 samples, and 3P 3 samples.
TABLE 5 Cross-validation results of training samples for different producing zones
TABLE 6 validation results of predicted samples for different zones
The identification model of different Maotai-flavor distilled spirits based on the volatile flavor substances can be suitable for accurately identifying the Maotai-flavor distilled spirits with different grades and different producing areas.
It is important to note here that the foregoing embodiments are limited to the particular embodiments disclosed as the best mode contemplated for carrying out the application, and that these embodiments are presented in a manner that will enable one of ordinary skill in the art to more fully understand the principles and operation of the application and are not intended to limit the application in any way nor to limit the scope of the application in any way any one of ordinary skill in the art to which it pertains.
Claims (10)
1. A method for identifying different Maotai-flavor baijiu based on the composition of volatile flavor substances, comprising the steps of:
s1, detecting the content of flavor substances in a wine sample;
s2, determining key flavor substances;
determining key flavors includes the steps of:
s2.1, the peak shape of an extracted ion chromatographic peak of the selected flavor substances is good, and the baseline separation degree is more than or equal to 1.5;
s2.2 has extremely obvious difference between the contents of the same flavor substances of different wine samples by adopting an ANOVA method, and accords with the P value less than 0.01;
s2.3, the average prejudgement coincidence rate of a judging model established based on the content of the flavor substances on different types of samples is more than 80 percent;
s2.4 repeating the steps S2.1-S2.3 to determine key flavor substances;
s3, establishing an identification model; the identification model is based on a principal component analysis method;
a method for identifying Maotai-flavor liquor based on different production areas composed of volatile flavor substances; the P1 production area is a geographical protection range production area of the Maotai liquor, the P2 production area is other production areas of the Maotai town, and the P3 production area is other production areas;
22 compounds were identified as modeled flavors for the Maotai-flavor liquor in different production areas, respectively acetal, 2-butanol, 1-propanol, 1-diethoxy-3-methylbutane, 2-methylpropanol, 1-butanol, isoamyl alcohol, 1-pentanol, 1-hexanol, furfural, phenethyl alcohol, ethyl butyrate, isoamyl acetate, ethyl caproate, ethyl lactate, acetaldehyde, ethyl 2-methylpropionate, ethyl 2-methylbutyrate, dimethyl disulfide, ethyl isovalerate, ethyl valerate, dimethyl trisulfide, and these were identified as: c (C) 1 、C 2 ……C 21 、C 22 The method comprises the steps of carrying out a first treatment on the surface of the Obtaining 2 discriminant functions F1 and F2:
F1＝-5.198+0.001×C1+0.016×C2+0.0002×C3-0.004×C4+0.010×C5+
0.027×U6+0.006×U7-0.239×U8+0.099×U9-0.010×U10-0.071×C11-0.057×
C12+0.025×C13+0.051×C14+0.001×C15+0.001×C16-0.020×C17+6.360×C18+4.381×C19-2.149×C20+0.017×C21-10.004×C22
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
Equation 4
Collecting the flavor substance spectrogram of an unknown wine sample by the same method to obtain semi-quantitative content of C1-C22, calculating discriminant functions F1 and F2 according to formulas 3 and 4, and calculating distances from the discriminant functions F1 and F2 to the center points of P1, P2 and P3 categories, wherein the category with the shortest distance is the pre-judging category of the unknown wine sample;
s4, verifying the identification model.
2. The method according to claim 1, wherein in the step S2, after the step S2.1, the method further comprises the steps of:
the quantitative ion extraction chromatographic peak area of the selected substances is more than 0.01% of the total peak area.
3. The method of claim 1, wherein step S1 comprises an HS-PTV-GC-MS detection method.
4. The method of claim 3, wherein the HS-PTV-GC-MS detection method comprises the steps of:
s1.1, diluting a sample;
s1.2, headspace incubation;
s1.3, injecting a static headspace gas;
s1.4, GC-MS analysis.
5. The method according to claim 4, wherein in the step S1.1, the sample is diluted with a saturated aqueous sodium chloride solution.
6. The method of claim 5, wherein the volume ratio of sample to saturated sodium chloride is 1:1-9.
7. The method according to claim 4, wherein in the step S1.2, the headspace incubation condition is an incubation temperature of 45-80 ℃, an incubation time of 5-30min, and a quantitative loop temperature of 80-130 ℃.
8. The method according to claim 4, wherein in the step S1.3, the sample volume is 1-3ml.
9. The method according to claim 4, wherein in the step S1.4, the GC condition is the use of a capillary gas chromatography column having a specification of DB-WAX 30 m.times.0.25 mm.times.0.25 μm; the carrier gas is helium with the flow rate of 0.8-1.5 mL/min.
10. The method of claim 4, wherein in the step S1.4, the mass spectrum condition is EI, the ion source temperature is 230 ℃, the mass spectrum scanning adopts full scanning and ion scanning modes, and the scanning range is 33 amu to 350amu.
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