CN103235057B - Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds - Google Patents
Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds Download PDFInfo
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Abstract
The invention relates to a method for identifying a white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds, belonging to the technical field of white spirit identification. The method comprises the following steps: (1) setting up an ion abundance mass spectrogram of white spirits of different origin places by using a gas chromatograph mass spectrometer provided with an automatic sampling device; and (2) establishing an ion identification statistical model of the white spirits of different origin places. According to the method, spirit samples of different origin places are analyzed by using headspace solid phase micro-extraction and gas chromatography-mass spectrometry technology (HS-SPME-GC-MS) without analyzing individual compounds in the spectrogram; three-dimensional data is exported through software to obtain an ion abundance mass spectrogram of different spirit samples; and then important characteristic ions are screened out by using stoichiometric methods such as partial least squares-discriminant analysis and gradual linear discriminant analysis, and a neural network model for identifying origin places is established. The invention relates to a novel spirit quality control and origin place protection technology, the operation is simple, the detection sensitivity is high, and the result is visual and reliable; Neural network models for identifying white spirits of different odor types and at different levels can be further established, and even a characteristic ion spectrum library of different white spirits can be built.
Description
Technical field
The present invention relates to a kind of method of differentiating white wine original producton location, particularly relate to a kind of method of using headspace solid-phase microextraction gaschromatographic mass spectrometry technology and stoichiometry statistical method to combine to differentiate white wine original producton location, the special feature of the method is the abundance information by compound ions fragment without resolving compound.
Background technology
White wine is one of traditional product of China, there is long history, diversified brewage process and typical local flavor due to its uniqueness, form different odor types, mainly contained at present giving off a strong fragrance, sauce perfume (or spice), delicate fragrance, meter Xiang, phoenix perfume (or spice), medicine perfume (or spice), fermented soya beans, salted or other wise perfume (or spice), sesame perfume (or spice), hold concurrently perfume, special type and 11 kinds of odor types of white spirit.
Even aromatic white spirit of the same race, because the difference of producing region geographical environment and weather also can cause the difference of liquor flavor, makes trace flavor component and mutual quantity relative ratio relationship difference thereof in wine, and forms different liquor body styles.Along with China joined WTO; the protection of place of origin is also fade-in people's the visual field, and wherein " original producton location " is a geographic name, shows the place of production of product; this place of production has unique geographical environment, weather conditions and traditional special fabrication processes, has determined quality or the feature of this region product.At present, the Liquor-making Enterprises & that several families of China are famous, as: Maotai, five-Grain Liquor etc., all obtained the protection of place of origin, and increasing Liquor-making Enterprises & is in the application protection of place of origin.
At present, the domestic research of differentiating about white wine original producton location is little, to the detection method of white wine composition, mainly contains:
1, vapor-phase chromatography (GC) and gas phase-mass spectrometry (GC-MS)
The most general method of Liquor Analysis is exactly gas chromatography at present, and the material of gas chromatographic analysis is volatile constituent mostly, and the most of aromatic substance in white wine, by cumulative, collaborative effect, works to the flavor quality of white wine.By gas phase-mass spectrometric hyphenated technique, the volatile ingredient in white wine is carried out to qualitative and quantitative analysis, can there is more comprehensive understanding to the local flavor of white wine, but still have the part micro constitutent cannot quantitative and qualitative analysis.Therefore,, by composition qualitative, quantitative in wine being studied to the original producton location of wine, when operating cost, cannot be used for Multi-example and differentiate.
2, liquid phase chromatography
High performance liquid chromatography is applicable to analyzing difficult gasification, not volatile material, as materials such as the organic acid in wine, amino acid, biogenic amines.And these materials are without the local flavor that concerns white wine, so the application of the analysis gas chromatographic technique to white wine composition is more extensive and ripe.
3, near infrared spectroscopy
Near infrared spectrum scanning sample, can obtain the characteristic information of organic molecule hydric group in sample, due to the contained chemical composition difference of variety classes material, the frequency multiplication of hydric group is different from sum of fundamental frequencies vibration frequency, peak position, peak number and the peak of the near infrared collection of illustrative plates forming is different by force, the chemical component difference of sample is larger, and the characteristic difference of collection of illustrative plates is stronger.This method is directly perceived, easy, but helpless for the close sample discriminating of character, sensitivity is low.
The research of wine is mainly concentrated on to grape wine, whiskey and brandy etc. abroad, the original producton location of these wine is identified to had proven technique means.The present invention's direct mass-spectrometric technique used is without separating composition in wine, obtain after gas chromatography-mass spectrum figure, do not need to resolve compound, extract sample message by deriving three-dimensional data collection of ions Abundances, can detect at short notice great amount of samples, build original producton location model of cognition by corresponding Chemical Measurement software analysis data, the stoechiometric process of employing mainly contains principal component analysis, partial least squares analysis, discriminatory analysis, neural network etc. simultaneously.
Given this,, in order to supervise the white wine quality of production and to safeguard white wine market order, the rights and interests of Protection of consumer, invent a kind of white wine original producton location discrimination method imperative.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of method of utilizing gas chromatography-mass spectrum not resolve compound discriminating white wine original producton location, the present invention uses HS-SPME-GC-MS to analyze the white wine wine sample in the different places of production, need not resolve the individualized compound in collection of illustrative plates, and derive three-dimensional data by software, obtain the abundance of ions mass spectrogram of different wine sample, then use offset minimum binary-discriminatory analysis and progressively the stoechiometric process such as linear discriminant analysis filter out key character ion, set up the neural network model that the place of production is differentiated.The present invention has set up a kind of brand-new quality of white spirit control and protection of place of origin method, and simple to operate, detection sensitivity is high, and visual result is reliable.
Technical scheme of the present invention: a kind of method of utilizing gas chromatography-mass spectrum not resolve compound discriminating white wine original producton location, the method comprises the steps:
(1) use the gas chromatograph-mass spectrometer (GCMS) (GC6890N-MSD5975) (Agilent company of the U.S.) of being furnished with solid-phase microextraction automatic sampling apparatus (MPS 2) (German Gerstel company) to set up the abundance of ions mass spectrogram of different places of production white wine
The preparation of a, test sample: the white wine wine sample in the different places of production, need to be first diluted to 10%vol with deionized water, be made into 8 mL solution systems, the wine sample after dilution is saturated with 3 g sodium chloride, is placed in 20 mL head space bottles, and head space bottle seals with silica gel pad;
B, to test sample carry out headspace solid-phase microextraction gaschromatographic mass spectrometry (HS-SPME-GC-MS) analyze:
SPME condition: adopt DVB/CAR/PDMS three phase extraction head preheating 5 min at 40 ℃ of constant temperature, after under same temperature extraction absorption 15 min; After having extracted, extracting head is inserted desorb analyte in gas chromatograph injection port.Because only needing to obtain the whole standard gas figure of wine sample, this technology without resolving compound, therefore, desorption time is made as to 10 min.
Mass spectrum condition: EI ionization source, electronics bombarding energy is 70eV, ion source temperature is 230 ℃; Sweep limit is 35 ~ 350 amu;
C, the chromatogram that above-mentioned test sample is obtained by gas chromatograph are analyzed through NIST 05 mass spectral database (Agilent Technologies Inc.), derive three-dimensional data, obtain different places of production white wine wine sample mass-to-charge ratio
m/zabundance of ions Value Data in 55 ~ 191 scopes, obtains abundance of ions mass spectrogram;
(2) set up the Ion identification statistical model of different places of production white wine
Abundance of ions Value Data described in step c is imported to Chemical Measurement software, carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis, filter out important characteristic ion; Finally set up with the characteristic ion that screening obtains the neural network model that original producton location is differentiated;
Chemical Measurement software is SIMCA-P, IBM SPSS20 and MATLAB software; Wherein offset minimum binary-discriminatory analysis is completed by SIMCA-P, and progressively linear discriminant analysis is completed by IBM SPSS20, and neural network model is set up by MATLAB.
Above-mentioned data analysis step is:
(1) test sample is carried out to HS-SPME-GC-MS analysis, obtain chromatogram;
(2) select to derive mass-to-charge ratio
m/zabundance of ions Value Data in 55 ~ 191 scopes;
(3) by Chemical Measurement software carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis filter out key character ion, set up different white wine original producton location differentiate neural network model.
Beneficial effect of the present invention: the present invention uses HS-SPME-GC-MS to analyze the white wine wine sample in the different places of production, need not resolve the individualized compound in collection of illustrative plates, and derive three-dimensional data by software, obtain the abundance of ions mass spectrogram of different wine sample, then use offset minimum binary-discriminatory analysis and progressively the stoechiometric process such as linear discriminant analysis filter out key character ion, set up the place of production and differentiate neural network model.The present invention has set up a kind of brand-new quality of white spirit control and protection of place of origin method, and simple to operate, detection sensitivity is high, and visual result is reliable.
Accompanying drawing explanation
Fig. 1 Fenyang wine exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: FJ-Fenyang wine
Fig. 2 Lang Jiu exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: LJ-Lang Jiu
Fig. 3 Yanghe River wine exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: the YH-Yanghe River
Fig. 4 white spirit wine exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: LBG-white spirit
Fig. 5 cattle pen mountain wine exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: NLS-cattle pen mountain
The ancient shellfish wine brewed in spring of Fig. 6 exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: GBC-Gu Beichun
Fig. 7 Jian Nan Chun wine exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: JNC-Jian Nan Chun
Fig. 8 Xifeng exists
m/zion collection of illustrative plates in 55 ~ 191 scopes, note: XF-Xifeng
The ion importance ranking figure of offset minimum binary-discriminatory analysis that Fig. 9 aromatic Chinese spirit place of production is differentiated
The original producton location discriminatory analysis result figure of Figure 10 aromatic Chinese spirit
The place of production of the neural network model of Figure 11 aromatic Chinese spirit figure that predicts the outcome
The ion importance ranking figure of offset minimum binary-discriminatory analysis that the multiple white wine of Figure 12 different flavor place of production is differentiated
The original producton location discriminatory analysis result figure of the multiple white wine of Figure 13 different flavor
The place of production of the neural network model of the multiple white wine of Figure 14 different flavor figure that predicts the outcome
Embodiment
Embodiment 1: the original producton location of aromatic Chinese spirit is differentiated
(1) use the gas chromatography mass spectrometer GC 6890N-MSD 5975 that is furnished with automatic sampling apparatus MPS 2 to set up the abundance of ions mass spectrogram of different places of production white wine
The preparation of a, test sample: gather 131 wine samples, wherein 12, Fenyang wine, 42 of white spirits, 35 of Lang Jius, 6, cattle pen mountain, 15, the Yanghe River, 7 of ancient shellfish spring, 6 of Jian Nan Chuns, 8 of Xifengs.The odor type classification of each white wine is respectively, phoenix odor type: Xifeng; Maotai-flavor: Lang Jiu; Laobaigan-flavour: white spirit; Delicate fragrance type: Fenyang wine, cattle pen mountain; Luzhou-flavor: the Yanghe River, Gu Beichun, Jian Nan Chun.
Wine sample is diluted to 10%vol with deionized water, is made into 8 mL solution systems, the wine sample after dilution is saturated with 3 g sodium chloride, is placed in 20 mL head space bottles, and head space bottle seals with silica gel pad.
B, test sample is carried out to HS-SPME-GC-MS analysis.
Instrument: Gerstel company of automatic headspace sampling system MPS 2(Germany); Agilent company of the gas chromatograph-mass spectrometer GC 6890N MSD 5975(U.S.)
SPME condition: adopt DVB/CAR/PDMS three phase extraction head preheating 5 min at 40 ℃ of constant temperature, after under same temperature extraction absorption 15 min; After having extracted, extracting head is inserted desorb analyte in gas chromatograph injection port.Because only needing to obtain the chromatogram of wine sample, this technology without resolving compound, therefore, desorption time is made as to 10 min.
Mass spectrum condition: EI ionization source, electronics bombarding energy is 70eV, ion source temperature is 230 ℃; Sweep limit is 35 ~ 350 amu;
C, chromatogram (being obtained by gas chromatograph) to above-mentioned test sample are analyzed through NIST 05 mass spectral database (Agilent Technologies Inc.), derive three-dimensional data, obtain different places of production white wine wine sample mass-to-charge ratio
m/zabundance of ions Value Data in 55 ~ 191 scopes.Abundance of ions mass spectrogram is shown in respectively Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7 and Fig. 8.
(2) set up the Ion identification statistical model of different places of production white wine
Abundance of ions Value Data described in step c is imported to Chemical Measurement software, carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis, filter out important characteristic ion; Finally set up with the characteristic ion that screening obtains the neural network model that original producton location is differentiated;
Chemical Measurement software is SIMCA-P, IBM SPSS20 and MATLAB software; Wherein offset minimum binary-discriminatory analysis is completed by SIMCA-P, and progressively linear discriminant analysis is completed by IBM SPSS20, and neural network model is set up by MATLAB.
Because abundance of ions value exists obvious order of magnitude difference, therefore, need carry out suitable conversion process to raw data and eliminate the impact of the order of magnitude on result.In the present invention, adopt the method for taking the logarithm to carry out pre-service to data, i.e. log (X+1), formula intermediate value 1 is the validity in order to guarantee numerical value.
Filter out 33 key character ions by offset minimum binary-discriminatory analysis, its ion importance ranking figure is shown in Fig. 9, its value is to be calculated by the quadratic sum of each ion pair offset minimum binary weight, the importance values quadratic sum of all ions equates with ion variable number, so the mean value of ion importance values is 1., filter out 33 ions that ion importance is greater than 1 herein, be respectively
m/z191,190,76,104,149,175,183,176,186,132,59,150,170,174,182,163,92,167,187,147,169,160,140,188,161,113,168,128,166,72,181,151,126, according to ion importance sort descending (corresponding with Fig. 9).
33 ions selecting are further screened to characteristic ion through linear discriminant analysis progressively, be respectively
m/z72,174,183,191 totally 4 characteristic ions, these ions have formed 2 discriminant functions (in table 1).The wine sample discriminant score being obtained by these two discriminant functions, by visual the cluster result of sample (Figure 10).As can be seen from Fig. 10, the wine in three kinds of different places of production of Luzhou-flavor can be good at separately, and the wine in the same place of production is assembled in heaps, and Yanghe River wine is distributed in X positive axis both sides; Jian Nan Chun wine is distributed in fourth quadrant; Ancient shellfish wine brewed in spring is distributed in third quadrant; Classification results is accurate, and discrimination model is carried out to cross validation with leaving-one method, and prediction accuracy reaches 100%.
The differentiation power of table 1 aromatic Chinese spirit discriminant function and the relevant ions of each function
4 characteristic ions that filter out in conjunction with offset minimum binary-discriminatory analysis and progressively linear discriminant analysis method in literary composition are made the input layer of network, and the output layer of network is done in the different places of production of wine sample, build neural network and differentiate model.Occur over-fitting phenomenon for fear of the network building simultaneously, by checking sample, make training set by 70% of all wine samples, 15% makees checking collection, and 15% does test set.In network training process, constantly calculation training error and validation error, if training error reduces, validation error raises, and is indicating that network may start over-fitting, now deconditioning.The model building in the present invention is in the time of iteration 40 times, and validation error reaches minimum.Network the results are shown in Figure 11 to the prediction original producton location of sample, and all wine samples all can correctly be differentiated, rate of accuracy reached 100%.
Embodiment 2: the original producton location of different flavor white wine is differentiated
After the pre-service of abundance of ions Value Data, filter out 61 key character ions through offset minimum binary-discriminatory analysis, its ion importance ranking figure is shown in Figure 12, and 61 ions that the ion importance filtering out is greater than 1, are respectively
m/z181,99,155,100,117,92,106,118,91,103,156,71,169,183,145,72,60,149,96,115,122,152,144,87,88,113,153,189,95,75,105,74,164,166,116,138,167,160,120,58,59,157,114,62,70,73,77,61,146,174,102,190,191,64,90,127,137,139,101,133,141, according to importance sort descending (corresponding with Figure 12).
61 ions selecting are further screened to characteristic ion through linear discriminant analysis progressively, obtain
m/z61,70,71,77,87,91,92,96,99,101,103,106,113,116,117,127,144,146,149,152,153,155,157,166,167,183,191 totally 27 characteristic ions, these ions have formed 7 discriminant functions (in table 2).The variance yields of function 4,5,6,7 is respectively 5.1%, 2.1%, 1.7% and 0.9% as seen from Table 2, little to the contribution rate of differentiating, and can not consider.The wine sample discriminant score being obtained by the first two discriminant function, by visual the cluster result of sample (Figure 13).As can be seen from Fig. 13, the wine in eight kinds of different places of production can be good at separately, and the wine in the same place of production is assembled in heaps, and different flavor also becomes rule to arrange.Two kinds of wine of delicate fragrance type are with being distributed in first quartile, Maotai-flavor Lang Jiu is distributed in third quadrant, Laobaigan-flavour white wine is distributed in fourth quadrant, three kinds of wine of Luzhou-flavor are with being distributed in the second quadrant, phoenix odor type Xifeng is also arranged in this quadrant, and the distance of laobaigan-flavour liquor and delicate fragrance type wine is nearer; As can be seen here, the local flavor of phoenix odor type and rich fragrance wine is more close, and Laobaigan-flavour is closer to delicate fragrance type, and discrimination model is carried out to cross validation with leaving-one method, and prediction accuracy reaches 99.2%.
The differentiation power of the multiple white wine discriminant function of table 2 different flavor and the relevant ions of each function
27 characteristic ions that Wen Zhongyong filters out are made the input layer of network, and the output layer of network is done in the different original producton locations of wine sample, build neural network and differentiate model.Network the results are shown in Figure 14 to the prediction original producton location of sample.As can be seen from Figure 14, all wine samples all can correctly be differentiated, rate of accuracy reached 100%.
Claims (2)
1. utilize gas chromatography-mass spectrum not resolve the method in compound discriminating white wine original producton location, it is characterized in that the method comprises the steps:
(1) use the gas chromatograph-mass spectrometer (GCMS) GC 6890N-MSD 5975 that is furnished with solid-phase microextraction automatic sampling apparatus MPS 2 to set up the abundance of ions mass spectrogram of different places of production white wine
The preparation of a, test sample: the white wine wine sample in the different places of production, need to be first diluted to 10%vol with deionized water, be made into 8 mL solution systems, the wine sample after dilution is saturated with 3 g sodium chloride, is placed in 20 mL head space bottles, and head space bottle seals with silica gel pad;
B, to test sample carry out headspace solid-phase microextraction gaschromatographic mass spectrometry HS-SPME-GC-MS analyze:
SPME condition: adopt DVB/CAR/PDMS three phase extraction head preheating 5 min at 40 ℃ of constant temperature, after under same temperature extraction absorption 15 min; After having extracted, extracting head is inserted desorb analyte in gas chromatograph injection port; Desorption time is made as 10 min;
Mass spectrum condition: EI ionization source, electronics bombarding energy is 70 eV, ion source temperature is 230 ℃; Sweep limit is 35 ~ 350 amu;
The chromatogram that c, above-mentioned test sample are obtained by gas chromatograph is analyzed through NIST 05 mass spectral database Agilent Technologies Inc., derives three-dimensional data, obtains different places of production white wine wine sample mass-to-charge ratio
m/zabundance of ions Value Data in 55 ~ 191 scopes, obtains abundance of ions mass spectrogram;
(2) set up the Ion identification statistical model of different places of production white wine
Abundance of ions Value Data described in step c is imported to Chemical Measurement software, carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis, filter out important characteristic ion; Finally set up with the characteristic ion that screening obtains the neural network model that original producton location is differentiated;
Chemical Measurement software is SIMCA-P, IBM SPSS20 and MATLAB software; Wherein offset minimum binary-discriminatory analysis is completed by SIMCA-P, and progressively linear discriminant analysis is completed by IBM SPSS20, and neural network model is set up by MATLAB;
Described data analysis step is:
(1) test sample is carried out to HS-SPME-GC-MS analysis, obtain chromatogram;
(2) select to derive mass-to-charge ratio
m/zabundance of ions Value Data in 55 ~ 191 scopes;
(3) by Chemical Measurement software carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis filter out key character ion, set up different white wine original producton location differentiate neural network model.
2. according to claim 1ly utilize gas chromatography-mass spectrum not resolve compound to differentiate the method in white wine original producton location, it is characterized in that: only need to obtain wine sample chromatogram and without resolving compound.
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| CN105044198B (en) * | 2015-07-03 | 2018-02-06 | 中国农业大学 | A kind of method based on mineral element fingerprint verification grape wine original producton location |
| CN106770875A (en) * | 2017-03-06 | 2017-05-31 | 江南大学 | The method that grape wine Production area recognition is carried out based on metabolism group research meanses |
| JP6899560B2 (en) * | 2017-05-23 | 2021-07-07 | 株式会社島津製作所 | Mass spectrometric data analyzer and mass spectrometric data analysis program |
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| CN111257436A (en) * | 2018-11-30 | 2020-06-09 | 中国科学院大连化学物理研究所 | Method for identifying specific markers and differential markers of genuine medicinal materials and judging genuine medicinal materials by using mass spectrometry technology |
| CN109781918B (en) * | 2019-01-11 | 2020-05-22 | 浙江大学 | Gas phase ion mobility spectrometry identification method for yellow rice wine produced by different enterprises |
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| CN113361610B (en) * | 2021-06-10 | 2023-01-31 | 北方民族大学 | Intelligent identification method and system for wine production place |
| CN113237977A (en) * | 2021-06-28 | 2021-08-10 | 宜宾五粮液股份有限公司 | Detection method of volatile flavor substances of white spirit |
| CN113311076A (en) * | 2021-07-02 | 2021-08-27 | 上海应用技术大学 | Method for rapidly distinguishing different varieties of rice based on aldehyde compounds |
| CN113917061B (en) * | 2021-10-22 | 2023-07-28 | 贵州大学 | Detection and identification method for volatile substances of Maotai-flavor base wine |
| CN114965973A (en) * | 2022-05-12 | 2022-08-30 | 知里科技(广东)有限公司 | Method for identifying recycled plastic based on instrument detection and analysis technology combined with multiple chemometrics methods and/or machine learning algorithm |
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| CN101762664B (en) * | 2010-01-13 | 2012-05-23 | 江南大学 | Method for measuring volatility phenol compound in white wine |
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