CN103235057A - 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
- Publication number
- CN103235057A CN103235057A CN2013101541931A CN201310154193A CN103235057A CN 103235057 A CN103235057 A CN 103235057A CN 2013101541931 A CN2013101541931 A CN 2013101541931A CN 201310154193 A CN201310154193 A CN 201310154193A CN 103235057 A CN103235057 A CN 103235057A
- Authority
- CN
- China
- Prior art keywords
- mass
- liquor
- ion
- different
- ions
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
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 gas chromatography-mass spectrum that utilizes and do not resolve the method that compound is differentiated the liquor 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 the liquor original producton location, the special feature of the method is to need not to resolve compound and by the abundance information of compound ions fragment.Belong to liquor authentication technique field.
Background technology
Liquor is one of traditional product of China, long history is arranged, diversified brewage process and typical local flavor because it is unique, form different odor types, mainly contained giving off a strong fragrance, sauce perfume (or spice), delicate fragrance, Mi 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 (or spice), special type and 11 kinds of odor types of white spirit at present.
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 the wine, and forms different wine body styles.Along with China joined WTO; the protection of place of origin also is 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 enterprise that several families of China are famous, as: Maotai, five-Grain Liquor etc., all obtained the protection of place of origin, and increasing liquor enterprise is in the application protection of place of origin.
At present, the domestic research of differentiating about the liquor original producton location seldom, the detection method to the liquor composition mainly contains:
1, vapor-phase chromatography (GC) and gas phase-mass spectrometry (GC-MS)
At present the most general method of liquor analysis is exactly gas chromatography, and the material of gas chromatographic analysis is volatile constituent mostly, the most of aromatic substance in the liquor by add up, collaborative effect, the flavor quality of liquor is worked.By gas phase-mass spectrometric hyphenated technique, the volatile ingredient in the liquor is carried out qualitative and quantitative analysis, more comprehensive understanding can have been arranged the local flavor of liquor, but still had the part micro constitutent can't be qualitative and quantitative.Therefore, by composition qualitative, quantitative in the wine being studied the original producton location of wine, during operating cost, can't be used for various product and differentiate.
2, liquid phase chromatography
High performance liquid chromatography is fit to analyze difficult gasification, not volatile material, as materials such as the organic acid in the wine, amino acid, biogenic amines.And these materials do not have the local flavor that concerns liquor, so, more extensive and ripe to the application of the analysis gas chromatographic technique of liquor composition.
3, near infrared spectroscopy
The near infrared spectrum scanning samples, can obtain the characteristic information that organic molecule in the sample contains hydrogen group, because the contained chemical constitution difference of variety classes material, the frequency multiplication that contains hydrogen group is different with the sum of fundamental frequencies vibration frequency, peak position, peak number and the peak of the near infrared collection of illustrative plates that forms is different by force, the chemical component difference of sample is more big, and the characteristic difference of collection of illustrative plates is more strong.This method is directly perceived, easy, but helpless for the close sample discriminating of character, sensitivity is low.
The research to wine abroad mainly concentrates on grape wine, whiskey and brandy etc., and the original producton location of these wine is identified that the proven technique means have been arranged.The used direct mass-spectrometric technique of the present invention need not to separate composition in the wine, after obtaining gas chromatography-mass spectrum figure, do not need to resolve compound, extract sample message by deriving three-dimensional data collection of ions abundance value, can detect great amount of samples at short notice, simultaneously make up the 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.
Given this, in order to supervise the liquor quality of production and to safeguard the liquor market order, protect consumer's rights and interests, it is imperative to invent a kind of liquor original producton location discrimination method.
Summary of the invention
Technical matters to be solved by this invention provides a kind of gas chromatography-mass spectrum that utilizes and does not resolve the method that compound is differentiated the liquor original producton location, the present invention uses HS-SPME-GC-MS to analyze the liquor wine sample in the different places of production, need not resolve the individualized compound in the collection of illustrative plates, and derive three-dimensional data by software, obtain the abundance of ions mass spectrogram of different wine sample, use then offset minimum binary-discriminatory analysis and progressively stoechiometric process such as linear discriminant analysis filter out the 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 is simple to operate, the detection sensitivity height, and visual result is reliable.
Technical scheme of the present invention: a kind of gas chromatography-mass spectrum that utilizes is not resolved the method that compound is differentiated the liquor original producton location, and this method comprises the steps:
(1) use and to be furnished with solid-phase microextraction automatic sampling apparatus (MPS2) gas chromatograph-mass spectrometer (GCMS) (GC6890N-MSD5975) (U.S. Agilent company) of (German Gerstel company) to set up the abundance of ions mass spectrogram of different places of production liquor
A, the preparation that supplies test agent: the liquor wine sample in the different places of production, need be diluted to 10%vol with deionized water earlier, be made into the 8mL solution system, the wine sample after the dilution is saturated with 3g sodium chloride, places 20mL head space bottle, and the head space bottle seals with silica gel pad;
B, analyze carrying out headspace solid-phase microextraction gaschromatographic mass spectrometry (HS-SPME-GC-MS) for test agent:
The SPME condition: adopt DVB/CAR/PDMS three phase extraction head in 40 ℃ of following preheating 5min of constant temperature, the back is extraction absorption 15min under same temperature; After extraction was finished, extracting head was inserted desorb analyte in the gas chromatograph injection port.The whole head space chromatogram that only need obtain the wine sample owing to present technique need not to resolve compound, therefore, desorption time is made as 10min.
The mass spectrum condition: EI ionization source, electron bombard energy are 70eV, and ion source temperature is 230 ℃; Sweep limit is 35～350amu;
C, analyzed through NIST05 mass spectral database (AgilentTechnologies Inc.) by the chromatogram that gas chromatograph obtains for test agent above-mentioned, derive three-dimensional data, obtain the abundance of ions Value Data in different place of production liquor wine sample mass-to-charge ratio m/z55～191 scopes, get the abundance of ions mass spectrogram;
(2) ion of setting up different places of production liquor is differentiated statistical model
Abundance of ions Value Data described in the step c is imported Chemical Measurement software, carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis, filter out important characteristic ion; The characteristic ion that obtains with screening is set up the neural network model that the original producton location is differentiated at last;
Chemical Measurement software is SIMCA-P, IBM SPSS20 and MATLAB software; Wherein offset minimum binary-discriminatory analysis is finished by SIMCA-P, and progressively linear discriminant analysis is finished by IBM SPSS20, and neural network model is set up by MATLAB.
Above-mentioned data analysis step is:
(1) analyzes carry out HS-SPME-GC-MS for test agent, obtain chromatogram;
(2) select to derive the interior abundance of ions Value Data of mass-to-charge ratio m/z55～191 scopes;
(3) by Chemical Measurement software carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis filter out the key character ion, set up the neural network model that different liquor original producton location is differentiated.
Beneficial effect of the present invention: the present invention uses HS-SPME-GC-MS to analyze the liquor wine sample in the different places of production, need not resolve the individualized compound in the collection of illustrative plates, and derive three-dimensional data by software, obtain the abundance of ions mass spectrogram of different wine sample, use then offset minimum binary-discriminatory analysis and progressively stoechiometric process such as linear discriminant analysis filter out the 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 is simple to operate, the detection sensitivity height, and visual result is reliable.
Description of drawings
The ion collection of illustrative plates of Fig. 1 Fenyang wine in m/z55～191 scopes annotated: FJ-Fenyang wine
The ion collection of illustrative plates of Fig. 2 Lang Jiu in m/z55～191 scopes annotated: the LJ-Lang Jiu
The ion collection of illustrative plates of Fig. 3 Yanghe River wine in m/z55～191 scopes annotated: the YH-Yanghe River
The ion collection of illustrative plates of Fig. 4 white spirit wine in m/z55～191 scopes annotated: the LBG-white spirit
The ion collection of illustrative plates of Fig. 5 cattle pen mountain wine in m/z55～191 scopes annotated: NLS-cattle pen mountain
The ion collection of illustrative plates of the ancient shellfish wine brewed in spring of Fig. 6 in m/z55～191 scopes annotated: GBC-Gu Beichun
The ion collection of illustrative plates of Fig. 7 Jian Nan Chun wine in m/z55～191 scopes annotated: the JNC-Jian Nan Chun
The ion collection of illustrative plates of Fig. 8 Xifeng in m/z55～191 scopes annotated: the 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 of Figure 10 aromatic Chinese spirit is figure as a result
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 liquor of Figure 12 different flavor place of production is differentiated
The original producton location discriminatory analysis of the multiple liquor of Figure 13 different flavor is figure as a result
The place of production of the neural network model of the multiple liquor 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 GC6890N-MSD5975 that is furnished with automatic sampling apparatus MPS2 to set up the abundance of ions mass spectrogram of different places of production liquor
A, the preparation that supplies test agent: gather 131 wine samples, wherein Fenyang wine is 12,42 of white spirits, 35 of Lang Jius, 6 on cattle pen mountain, 15 in the Yanghe River, 7 of ancient shellfish spring, 6 of Jian Nan Chuns, 8 of Xifengs.The odor type classification of each liquor is respectively, phoenix odor type: Xifeng; Maotai-flavor: Lang Jiu; White spirit odor type: white spirit; Delicate fragrance type: Fenyang wine, cattle pen mountain; Luzhou-flavor: the Yanghe River, Gu Beichun, Jian Nan Chun.
The wine sample is diluted to 10%vol with deionized water, is made into the 8mL solution system, the wine sample after the dilution is saturated with 3g sodium chloride, places 20mL head space bottle, and the head space bottle seals with silica gel pad.
B, analyze carrying out HS-SPME-GC-MS for test agent.
Instrument: the German Gerstel of the automatic MPS2(of headspace sampling system company); Gas chromatograph-mass spectrometer (GCMS) GC6890N-MSD5975(U.S. Agilent company)
The SPME condition: adopt DVB/CAR/PDMS three phase extraction head in 40 ℃ of following preheating 5min of constant temperature, the back is extraction absorption 15min under same temperature; After extraction was finished, extracting head was inserted desorb analyte in the gas chromatograph injection port.The chromatogram that only need obtain the wine sample owing to present technique need not to resolve compound, therefore, desorption time is made as 10min.
The mass spectrum condition: EI ionization source, electron bombard energy are 70eV, and ion source temperature is 230 ℃; Sweep limit is 35～350amu;
C, above-mentioned chromatogram (being obtained by gas chromatograph) for test agent is analyzed through NIST05 mass spectral database (Agilent Technologies Inc.), derive three-dimensional data, obtain the abundance of ions Value Data in different place of production liquor wine sample mass-to-charge ratio m/z55～191 scopes.The abundance of ions mass spectrogram is seen Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7 and Fig. 8 respectively.
(2) ion of setting up different places of production liquor is differentiated statistical model
Abundance of ions Value Data described in the step c is imported Chemical Measurement software, carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis, filter out important characteristic ion; The characteristic ion that obtains with screening is set up the neural network model that the original producton location is differentiated at last;
Chemical Measurement software is SIMCA-P, IBM SPSS20 and MATLAB software; Wherein offset minimum binary-discriminatory analysis is finished by SIMCA-P, and progressively linear discriminant analysis is finished by IBM SPSS20, and neural network model is set up by MATLAB.
Because there is tangible order of magnitude difference in the abundance of ions value, therefore, needs that raw data is carried out suitable conversion process and eliminate the order of magnitude to result's influence.Adopt the method take the logarithm that data are carried out pre-service among the present invention, i.e. log (X+1), formula intermediate value 1 is in order to guarantee the validity of numerical value.
Filter out 33 key character ions by offset minimum binary-discriminatory analysis, its ion importance ranking figure sees Fig. 9, its value is that the quadratic sum by each ion pair offset minimum binary weight calculates, the importance values quadratic sum of all ions equates with the ion variable number, so the mean value of ion importance values is 1.Herein, filter out ion importance greater than 33 ions of 1, 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 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 (seeing Table 1).By the wine sample discriminant score of these two discriminant functions acquisitions, with the cluster result of sample visual (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 the fourth quadrant; Ancient shellfish wine brewed in spring is distributed in the third quadrant; Classification results is accurate, and with leaving-one method discrimination model is carried out cross validation, and the 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 the 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, make up neural network and differentiate model.The over-fitting phenomenon occurs for fear of the network that makes up simultaneously, by the checking sample, be about to 70% of all wine samples and make training set, 15% makees the checking collection, and 15% does test set.In the network training process, constantly calculation training sum of errors checking error if training error reduces and the rising of checking error, is indicating that then network may begin over-fitting, stops training this moment.The model that makes up among the present invention is when iteration 40 times, and the checking 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 liquor 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 sees Figure 12, the ion importance that filters out is greater than 61 ions of 1, be 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 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 (seeing Table 2).Function 4,5,6,7 variance yields are respectively 5.1%, 2.1%, 1.7% and 0.9% as seen from Table 2, to the contribution rate differentiated seldom, can not consider.By the wine sample discriminant score of preceding two discriminant functions acquisition, with the cluster result of sample visual (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 the first quartile, the Maotai-flavor Lang Jiu is distributed in the third quadrant, the white spirit aromatic white spirit is distributed in the fourth quadrant, three kinds of wine of Luzhou-flavor are with being distributed in second quadrant, phoenix odor type Xifeng also is arranged in this quadrant, and the distance of white spirit odor type wine and delicate fragrance type wine is nearer; This shows that the local flavor of phoenix odor type and rich fragrance wine is more close, the white spirit odor type is then more close to delicate fragrance type, and with leaving-one method discrimination model carried out cross validation, and the prediction accuracy reaches 99.2%.
The differentiation power of the multiple liquor 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, make up 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. one kind is utilized gas chromatography-mass spectrum not resolve the method that compound is differentiated the liquor original producton location, it is characterized in that this 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 liquor
A, the preparation that supplies test agent: the liquor wine sample in the different places of production, need be diluted to 10%vol with deionized water earlier, be made into 8 mL solution systems, the wine sample after the dilution is saturated with 3 g sodium chloride, places 20 mL head space bottles, and the head space bottle seals with silica gel pad;
B, analyze carrying out headspace solid-phase microextraction gaschromatographic mass spectrometry HS-SPME-GC-MS for test agent:
The SPME condition: adopt DVB/CAR/PDMS three phase extraction head in 40 ℃ of following preheating 5 min of constant temperature, the back is extraction absorption 15 min under same temperature; After extraction was finished, extracting head was inserted desorb analyte in the gas chromatograph injection port; Desorption time is made as 10 min;
The mass spectrum condition: EI ionization source, electron bombard energy are 70 eV, and ion source temperature is 230 ℃; Sweep limit is 35 ~ 350 amu;
C, the above-mentioned test agent that supplies are analyzed through NIST 05 mass spectral database Agilent Technologies Inc. by the chromatogram that gas chromatograph obtains, and derive three-dimensional data, obtain different places of production liquor wine sample mass-to-charge ratio
M/zAbundance of ions Value Data in 55 ~ 191 scopes gets the abundance of ions mass spectrogram;
(2) ion of setting up different places of production liquor is differentiated statistical model
Abundance of ions Value Data described in the step c is imported Chemical Measurement software, carry out offset minimum binary-discriminatory analysis and progressively linear discriminant analysis, filter out important characteristic ion; The characteristic ion that obtains with screening is set up the neural network model that the original producton location is differentiated at last;
Chemical Measurement software is SIMCA-P, IBM SPSS20 and MATLAB software; Wherein offset minimum binary-discriminatory analysis is finished by SIMCA-P, and progressively linear discriminant analysis is finished by IBM SPSS20, and neural network model is set up by MATLAB;
Described data analysis step is:
(1) analyzes carry out HS-SPME-GC-MS for test agent, 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 the key character ion, set up the neural network model that different liquor original producton location is differentiated.
2. the gas chromatography-mass spectrum that utilizes according to claim 1 is not resolved the method that compound is differentiated the liquor original producton location, it is characterized in that: only need obtain the chromatogram of wine sample and need not to resolve compound.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310154193.1A CN103235057B (en) | 2013-04-27 | 2013-04-27 | Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310154193.1A CN103235057B (en) | 2013-04-27 | 2013-04-27 | Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103235057A true CN103235057A (en) | 2013-08-07 |
| CN103235057B CN103235057B (en) | 2014-05-21 |
Family
ID=48883108
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310154193.1A Active CN103235057B (en) | 2013-04-27 | 2013-04-27 | Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103235057B (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105044198A (en) * | 2015-07-03 | 2015-11-11 | 中国农业大学 | Mineral element-based fingerprint identification method used for identifying wine countries of origin |
| 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 |
| CN108414611A (en) * | 2018-06-12 | 2018-08-17 | 福建出入境检验检疫局检验检疫技术中心 | A kind of method in Rapid identification white tea storage time |
| CN108445134A (en) * | 2018-04-20 | 2018-08-24 | 泸州品创科技有限公司 | Alcohol product mirror method for distinguishing |
| CN108931570A (en) * | 2017-05-23 | 2018-12-04 | 株式会社岛津制作所 | Program is used in analytical data of mass spectrum resolver and analytical data of mass spectrum parsing |
| CN109781918A (en) * | 2019-01-11 | 2019-05-21 | 浙江大学 | A kind of gaseous ion migration spectrum discrimination method of the yellow rice wine of difference enterprise production |
| CN110487947A (en) * | 2019-09-19 | 2019-11-22 | 中国农业科学院农产品加工研究所 | Identify the method for hiding pig and its meat products based on chemometrics application |
| CN110632227A (en) * | 2019-09-12 | 2019-12-31 | 中南林业科技大学 | Method for distinguishing Liuyang fermented soya beans, Yangjiang fermented soya beans and Yongchuan fermented soya beans |
| 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 |
| CN112326773A (en) * | 2020-10-16 | 2021-02-05 | 华中科技大学鄂州工业技术研究院 | Method for high-throughput analysis of IgG glycopeptide |
| CN113092570A (en) * | 2021-03-29 | 2021-07-09 | 西华大学 | Method for rapidly measuring quality and picking wine based on in-situ direct mass spectrometry |
| 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 |
| CN113361610A (en) * | 2021-06-10 | 2021-09-07 | 北方民族大学 | Intelligent identification method and system for wine production place |
| CN113917061A (en) * | 2021-10-22 | 2022-01-11 | 贵州大学 | Detection and identification method for volatile substances of Maotai-flavor base liquor |
| 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 |
| CN116403661A (en) * | 2023-04-14 | 2023-07-07 | 中南民族大学 | High-temperature Daqu liquor identification and compound prediction method based on Maillard reaction product fluorescence signal analysis |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101762664A (en) * | 2010-01-13 | 2010-06-30 | 江南大学 | Method for measuring volatility phenol compound in white wine |
| CN101762658A (en) * | 2010-01-13 | 2010-06-30 | 江南大学 | Method for measuring volatility constituent in white wine |
| CN102636588A (en) * | 2012-04-27 | 2012-08-15 | 江南大学 | Method for discriminating white spirits by using quartz crystal oscillator electronic nose |
-
2013
- 2013-04-27 CN CN201310154193.1A patent/CN103235057B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101762664A (en) * | 2010-01-13 | 2010-06-30 | 江南大学 | Method for measuring volatility phenol compound in white wine |
| CN101762658A (en) * | 2010-01-13 | 2010-06-30 | 江南大学 | Method for measuring volatility constituent in white wine |
| CN102636588A (en) * | 2012-04-27 | 2012-08-15 | 江南大学 | Method for discriminating white spirits by using quartz crystal oscillator electronic nose |
Non-Patent Citations (3)
| Title |
|---|
| S.V.DUTRA ET AL: "determination of the geographical origin of brazilian wine by isotope and mineral analysis", 《NNAL BIOANAL CHEM》 * |
| 宋 普等: "白兰地酒龄模型的构建", 《食品科学》 * |
| 罗涛等: "顶空固相微萃取(HSSPME)和气相色谱一质谱(GC—MS)联用分析黄酒中挥发性和半挥发性微量成分", 《酿酒科技》 * |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN105044198A (en) * | 2015-07-03 | 2015-11-11 | 中国农业大学 | Mineral element-based fingerprint identification method used for identifying wine countries of origin |
| 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 |
| CN108931570A (en) * | 2017-05-23 | 2018-12-04 | 株式会社岛津制作所 | Program is used in analytical data of mass spectrum resolver and analytical data of mass spectrum parsing |
| CN108931570B (en) * | 2017-05-23 | 2021-02-19 | 株式会社岛津制作所 | Mass spectrometry data analysis device and program for mass spectrometry data analysis |
| CN108445134B (en) * | 2018-04-20 | 2020-07-17 | 泸州品创科技有限公司 | Wine product identification method |
| CN108445134A (en) * | 2018-04-20 | 2018-08-24 | 泸州品创科技有限公司 | Alcohol product mirror method for distinguishing |
| CN108414611A (en) * | 2018-06-12 | 2018-08-17 | 福建出入境检验检疫局检验检疫技术中心 | A kind of method in Rapid identification white tea storage time |
| 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 |
| CN109781918A (en) * | 2019-01-11 | 2019-05-21 | 浙江大学 | A kind of gaseous ion migration spectrum discrimination method of the yellow rice wine of difference enterprise production |
| CN110632227A (en) * | 2019-09-12 | 2019-12-31 | 中南林业科技大学 | Method for distinguishing Liuyang fermented soya beans, Yangjiang fermented soya beans and Yongchuan fermented soya beans |
| CN110487947A (en) * | 2019-09-19 | 2019-11-22 | 中国农业科学院农产品加工研究所 | Identify the method for hiding pig and its meat products based on chemometrics application |
| WO2021052366A1 (en) * | 2019-09-19 | 2021-03-25 | 中国农业科学院农产品加工研究所 | Chemometrics-based method for analyzing and identifying *** pig and meat product thereof |
| CN112326773B (en) * | 2020-10-16 | 2024-01-12 | 华中科技大学鄂州工业技术研究院 | Method for high-throughput analysis of IgG glycopeptides |
| CN112326773A (en) * | 2020-10-16 | 2021-02-05 | 华中科技大学鄂州工业技术研究院 | Method for high-throughput analysis of IgG glycopeptide |
| CN113092570A (en) * | 2021-03-29 | 2021-07-09 | 西华大学 | Method for rapidly measuring quality and picking wine based on in-situ direct mass spectrometry |
| CN113361610A (en) * | 2021-06-10 | 2021-09-07 | 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 |
| CN113917061A (en) * | 2021-10-22 | 2022-01-11 | 贵州大学 | Detection and identification method for volatile substances of Maotai-flavor base liquor |
| 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 |
| CN116403661A (en) * | 2023-04-14 | 2023-07-07 | 中南民族大学 | High-temperature Daqu liquor identification and compound prediction method based on Maillard reaction product fluorescence signal analysis |
| CN116403661B (en) * | 2023-04-14 | 2023-10-13 | 中南民族大学 | High-temperature Daqu liquor identification and compound prediction method based on Maillard reaction product fluorescence signal analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103235057B (en) | 2014-05-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103235057B (en) | Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds | |
| Causon et al. | Fingerprinting of traditionally produced red wines using liquid chromatography combined with drift tube ion mobility-mass spectrometry | |
| CN106053628B (en) | A kind of method that fast qualitative quantifies fragrance component in tealeaves | |
| Materić et al. | Methods in plant foliar volatile organic compounds research | |
| Pan et al. | Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics | |
| Robinson et al. | Origins of grape and wine aroma. Part 2. Chemical and sensory analysis | |
| CN106841494B (en) | Plant otherness metabolin rapid screening method based on UPLC QTOF | |
| CN103076411A (en) | Analytical method for determining aromatic constituents in tea | |
| Wang et al. | Comparative analysis of essential oil components in Pericarpium Citri Reticulatae Viride and Pericarpium Citri Reticulatae by GC–MS combined with chemometric resolution method | |
| WO2009078362A1 (en) | Method of analyzing isotope ratio of low-concentration ethanol sample | |
| CN111060642A (en) | Method for classifying and identifying tobacco leaves of same variety and different producing areas | |
| CN106885851A (en) | A kind of black tea place of production method of discrimination based on chiral quantitative analysis tech | |
| CN113295797A (en) | Method for rapidly detecting ethyl carbamate in white spirit based on ultra-high performance liquid chromatography combined high-resolution mass spectrometry | |
| Schipilliti et al. | Authentication of citrus volatiles based on carbon isotope ratios | |
| Du et al. | Simultaneous qualitative and quantitative analysis of 28 components in Isodon rubescens by HPLC‐ESI‐MS/MS | |
| CN106053659A (en) | Method for measuring ratio of nicotine carbon, hydrogen and nitrogen stable isotopes in tobacco | |
| CN103293264B (en) | Method for distinguishing quality of yeast | |
| CN107462624A (en) | The rapid assay methods of main ester type compound content in a kind of white wine base liquor | |
| CN112435719A (en) | Method for rapidly searching liquid chromatography separation conditions of compound traditional Chinese medicine analysis based on chromatography retention multivariate statistical model | |
| CN110514757A (en) | A kind of method of Volatile flavor components in fast resolving white wine | |
| CN106018650A (en) | Method for identifying nitrogen-containing compound in kaoliang spirit | |
| CN106198804A (en) | The authentication method of nitrogen-containing compound in a kind of cherry wine | |
| CN102279230A (en) | Method for establishing volatile ingredient fingerprint map of agricultural product and processed product of agricultural product | |
| CN109839457A (en) | Volatile compound carbon stable isotope measuring method in grape wine based on SPME-GC-IRMS | |
| Deng et al. | Quality assessment and origin tracing of Guangdong Liangcha granules using direct mass spectrometry fingerprinting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CP02 | Change in the address of a patent holder |
Address after: 214122 Jiangsu city of Wuxi Province District Liangxi No. 898 South Road 7 layer beam Creek area of food science and Technology Park Patentee after: Jiangnan University Address before: Lihu Avenue Binhu District 214122 Jiangsu city of Wuxi province Jiangnan University No. 1800 Patentee before: Jiangnan University |
|
| CP02 | Change in the address of a patent holder |