CN113203803A - Method for identifying white spirit storage time through multivariate linear stepwise regression - Google Patents
Method for identifying white spirit storage time through multivariate linear stepwise regression Download PDFInfo
- Publication number
- CN113203803A CN113203803A CN202110348728.3A CN202110348728A CN113203803A CN 113203803 A CN113203803 A CN 113203803A CN 202110348728 A CN202110348728 A CN 202110348728A CN 113203803 A CN113203803 A CN 113203803A
- Authority
- CN
- China
- Prior art keywords
- white spirit
- storage time
- identifying
- stepwise regression
- steps
- 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
Classifications
-
- 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
-
- 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
-
- 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
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- 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
- G01N2030/022—Column chromatography characterised by the kind of separation mechanism
- G01N2030/025—Gas chromatography
-
- 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
- G01N2030/062—Preparation extracting sample from raw material
-
- 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
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
- G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Crystallography & Structural Chemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computing Systems (AREA)
- Theoretical Computer Science (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a method for identifying white spirit storage time by multivariate linear stepwise regression, belonging to the technical field of biological information. The invention provides a method for identifying the white spirit storage time by multiple linear stepwise regression, which can simply, practically and quickly identify the white spirit storage time. The method can better identify the storage time of the white spirit, has the discrimination accuracy basically reaching more than 80 percent, and has the advantages of high speed, high automation degree, high accuracy and the like.
Description
Technical Field
The invention belongs to the technical field of biological information, and particularly relates to a method for temporarily identifying the storage time of different white spirits by using a white spirit volatile component fingerprint as a data base, establishing a mathematical model by adopting a multiple linear stepwise regression idea and fitting a linear regression equation.
Background
The annual white spirit is white spirit stored for a certain time, and is widely favored by consumers due to soft and mellow mouthfeel. Since the concept of the primary pulp of the year is firstly proposed, the wine of the year conforms to the market demand and quickly becomes an important component of the market of the wine. While the market of the liquor is vigorously developed, the liquor faces various challenges, such as counterfeiting, faking, secondary and good phenomena are reported, and the development of corresponding identification technology is urgently needed. At present, the problem of vintage counterfeiting of white spirit is caused by imperfect detection methods of vintage white spirit and lack of relevant industrial standards. In order to maintain the benefits of consumers and standardize the management order of the annual liquor market, the development of a scientific method capable of quickly and accurately identifying annual liquor is urgently needed.
In the field of the research of the liquor year supervision and identification technology, no applicable national standard exists at present, and the main identification technology proposed by researchers comprises the following steps: the slow occupation provides a method for identifying the volatility coefficient of the spirit year, and the spirit year identification is realized by constructing a functional relation between the spirit storage life and the volatile matter content of the spirit year. According to the Yangtao et Al, the content variation relationship of Al, Fe, Cu and other metal ions in the wine in different years, the relationship between the viscosity of wine body and the storage time of white spirit, and the relationship between trace amount of conjugated unsaturated double bond molecules in white spirit and the storage time of the wine in different years are utilized to identify the wine in multiple aspects. The Qin Renwei proposes that the relationship between carbon-14 decay rate and the storage time of the yearly wine is used for identifying and determining the production years of the yearly wine. The research methods provide various identification schemes for liquor identification in the year, but the methods either need professional large-scale instruments or have complicated analysis steps and long analysis time.
In addition, white spirit is a complex system, volatile components are affected by many factors, and therefore, aged information is often buried in noisy backgrounds. At present, infrared spectrum, fluorescence spectrum, Raman spectrum or electrochemical methods commonly used for liquor year identification judge the liquor age through a data set of whole volatile components, and are difficult to eliminate the interference of noise. Therefore, there is still a large research space for scientific statistical analysis, quantitative association of specific marker compounds. At present, the market of liquor for years is increasingly huge, the number of samples to be detected is increasingly increased, and how to develop a simple and practical rapid detection and identification technology becomes a new urgent need.
Disclosure of Invention
In order to simply, practically and quickly judge the white spirit storage time, the invention provides a method for identifying the white spirit storage time by multiple linear stepwise regression.
More specifically, the method for identifying the storage time of the white spirit by the multivariate linear stepwise regression comprises the following steps:
A. sample preparation: taking white spirit with different storage times as samples to be detected, reducing the alcoholic strength of the white spirit samples to be below 10 vol% by adopting ultrapure water, and simultaneously adding sodium chloride and an internal standard substance to obtain samples to be detected;
B. volatile compound extraction: b, using a headspace solid phase microextraction method to extract volatile compounds from the sample to be detected obtained in the step A through an extraction head;
C. collecting a fingerprint spectrum: after desorption of the extraction head at the sample inlet, acquiring volatile component fingerprint information by adopting GC-MS (gas chromatography-Mass spectrometer) to obtain retention time, matching fraction and peak area of the volatile compound;
D. the method comprises the steps of performing qualitative determination through retention time and matching fraction of volatile compounds, performing normalization processing on peak areas of the volatile compounds, performing multiple stepwise regression analysis through the corrected peak areas, establishing a multiple linear stepwise regression mathematical model to obtain a fitted linear regression equation, and identifying white spirit storage time through the fitted linear regression equation.
In the method for identifying the white spirit storage time through the multivariate linear stepwise regression, in the step A, the specific operation of sample preparation is as follows: taking white spirit with different storage times as samples to be detected, reducing the alcohol content of the white spirit samples to 5-10% vol by adopting ultrapure water, placing 4-8 mL into a sample injection bottle, adding 0.1-0.3 g/mL of sodium chloride until the solution is saturated, and adding an internal standard substance accounting for 1-2% of the volume fraction of the system to obtain the samples to be detected.
In the method for identifying the white spirit storage time by the multivariate linear stepwise regression, in the step A, the internal standard substance is tertiary amyl alcohol; the concentration of the internal standard substance is 8.05 g/L.
In the method for identifying the white spirit storage time by the multivariate linear stepwise regression, in the step B, parameters of headspace solid-phase microextraction are as follows: balancing at 40-60 ℃ for 3-10 min, and extracting at 40-60 ℃ for 20-80 min.
In the method for identifying the white spirit storage time through the multiple linear stepwise regression, in the step C, the extraction head desorbs for 1-10 min at the temperature of 240-260 ℃ of the injection port.
In the step C, the GC analysis conditions are as follows: a60 m × 0.25mm × 0.50 μm TG-WAXMS capillary gas chromatography column was used, the carrier gas was high-purity helium gas, the flow rate was 1.0mL/min, the split ratio: 20: 1, temperature programming is as follows: the temperature is maintained at 50 ℃ for 2min, the temperature is raised to 145 ℃ at 3 ℃/min, then the temperature is raised to 230 ℃ at 15 ℃/min and maintained for 3min, and the temperature of the injection port is maintained at 250 ℃.
In the step C, the MS analysis conditions are as follows: transmission line temperature 200 ℃, ion source temperature 260 ℃, scanning mass range m/z: 33-350 amu, ionization mode: EI +; electron energy: 70 eV.
Further, in the method for identifying the white spirit storage time by multiple linear stepwise regression, when the sample to be tested is white spirit with the storage time of 0, 2, 4, 6, 9, 12, 15, 17, 21 and 24 months in the step a, the fitted linear regression equation obtained in the step D is as follows: ethyl oleate-0.08 propyl acetate +0.026 undecanol-0.189 + 1-butanol +0.782 + 2-hexanoic acid-phenethyl ester +0.022 + 1-methylene-1H-indene-0.117 furfuryl hexanoate-0.324 ethyl palmitate-0.17 isobutyraldehyde-0.115 (3Z) -3-decen-1-ol acetate, identified by fitting a linear regression equation to the white spirit storage Time; wherein the Time unit is month.
The invention has the following effects:
the method has the advantages of simple processing steps of the liquor samples of the liquor years, convenient operation and suitability for processing and screening large-scale samples; the gas chromatography-mass spectrometer has the advantages of stable and mature technology, high precision of instrument analysis, small error among samples, high repeatability, reliable result and large analysis flux; according to the method, a mathematical model is established through a multiple linear stepwise regression idea, a linear regression equation is fitted, the identification of the white spirit storage time can be better carried out, the discrimination accuracy basically reaches more than 80%, and the method has the advantages of high speed, high automation degree, high accuracy and the like.
Detailed Description
Specifically, the method for identifying the white spirit storage time by the multivariate linear stepwise regression comprises the following steps:
A. sample preparation: taking white spirit with different storage times as samples to be detected, reducing the alcoholic strength of the white spirit samples to be below 10 vol% by adopting ultrapure water, and simultaneously adding sodium chloride and an internal standard substance to obtain samples to be detected;
B. volatile compound extraction: b, using a headspace solid phase microextraction method to extract volatile compounds from the sample to be detected obtained in the step A through an extraction head;
C. collecting a fingerprint spectrum: after desorption of the extraction head at the sample inlet, acquiring volatile component fingerprint information by adopting GC-MS (gas chromatography-Mass spectrometer) to obtain retention time, matching fraction and peak area of the volatile compound;
D. the method comprises the steps of performing qualitative determination through retention time and matching fraction of volatile compounds, performing normalization processing on peak areas of the volatile compounds, performing multiple stepwise regression analysis through the corrected peak areas, establishing a multiple linear stepwise regression mathematical model to obtain a fitted linear regression equation, and identifying white spirit storage time through the fitted linear regression equation.
In the invention, the white spirits with different storage times are used as samples to be detected, the number of the samples to be detected with different storage times is not less than 5, and meanwhile, the samples to be detected with different storage times are generally taken by taking months or years as units because the accuracy of the storage time of days is too high and is difficult to detect.
In the invention, after volatile component fingerprint information is collected by GC-MS, the qualitative determination is carried out on the substance according to the comparison of the ion fragment and retention time of the compound with a database, the substance is determined, and the semi-quantitative determination can be carried out on the substance only by an internal standard substance. The data collected by the GC-MS comprise retention time, matching fraction and peak area, and the data are original data for establishing a multiple linear stepwise regression mathematical model.
Wherein, in the above data, the retention time and the matching score are used to qualify the substance, determine that the substance: the matching score is obtained by comparing a substance defined by mass spectrum fragments with a substance of a spectrum library standard, generally, the substance with the matching score of more than 80% is considered to be correctly matched, then, the retention index is obtained by comparing the retention time of the substance with the matching score of more than 80% with the retention time of a standard compound of the substance, and the substance is considered to be correctly matched if the error of the retention index is not more than 30%. Therefore, the white spirit substances with different storage times are determined through the retention time and the matching fraction.
The peak area is the content of each substance in the reaction white spirit, and the invention performs semi-quantification on each substance because the invention does not (or does not need) perform standard quantification on an external standard curve, but only adds an internal standard substance.
In step D of the method, in order to enable the detection result to be more accurate, the log10 values of all peak areas of all corrected samples are subjected to multiple stepwise regression analysis by adopting a linear regression distribution method in SPSS, a multiple linear stepwise regression mathematical model is established, variable screening is automatically carried out, important variables and equation coefficients are obtained, and a fitting linear regression equation is obtained; the step-by-step method combines the advantages of the forward method and the backward method, after each new independent variable is introduced forwards, the substituted independent variable is recalculated to check whether the new independent variable has the value continuously remained in the equation, and the introduction and the elimination of the independent variable are alternately carried out according to the value until no new independent variable can be introduced or eliminated.
In the step D of the method, after a fitting linear regression equation is obtained, the log10 value of the correction peak area of the important volatile compounds in the white spirit with unknown storage time is substituted into the equation, and the storage time of the white spirit can be measured.
In step A of the method, the concrete operations of sample preparation are as follows: taking white spirit with different storage times as samples to be detected, reducing the alcohol content of the white spirit samples to 5-10% vol by adopting ultrapure water, placing 4-8 mL into a sample injection bottle, adding 0.1-0.3 g/mL of sodium chloride until the solution is saturated, and adding an internal standard substance accounting for 1-2% of the volume fraction of the system to obtain the samples to be detected.
In the step A of the method, tertiary amyl alcohol with the concentration of 8.05g/L is used as an internal standard substance to accurately semi-quantitatively identify compounds in the white spirit, which greatly contribute to the identification of the storage time.
In step B of the method, parameters of headspace solid-phase microextraction are as follows: balancing at 40-60 ℃ for 3-10 min, and extracting at 40-60 ℃ for 20-80 min, so as to avoid the extraction time from being too long and cause the extraction effect to be poor; in the step C, the extraction head desorbs for 1-10 min at the temperature of 240-260 ℃ of the sample inlet.
In step C of the method, the GC analysis conditions are as follows: a60 m × 0.25mm × 0.50 μm TG-WAXMS capillary gas chromatography column was used, the carrier gas was high-purity helium gas, the flow rate was 1.0mL/min, the split ratio: 20: 1, temperature programming is as follows: the temperature is maintained at 50 ℃ for 2min, the temperature is raised to 145 ℃ at 3 ℃/min, then the temperature is raised to 230 ℃ at 15 ℃/min and maintained for 3min, and the temperature of the injection port is maintained at 250 ℃.
In step C of the method, MS analysis conditions are as follows: transmission line temperature 200 ℃, ion source temperature 260 ℃, scanning mass range m/z: 33-350 amu, ionization mode: EI +; electron energy: 70 eV.
The method adopts GC-MS to obtain the volatile flavor component fingerprints of the white spirit in different storage time, uses the volatile flavor component fingerprints of the white spirit as a data base, adopts a multiple linear stepwise regression idea to establish a mathematical model, and adopts a fitted linear regression equation to identify the storage time of the white spirit, wherein the accuracy basically reaches more than 80%.
More specifically, in the method for identifying the white spirit storage time through multiple linear stepwise regression, when the sample to be tested is white spirit with the storage time of 0, 2, 4, 6, 9, 12, 15, 17, 21 and 24 months in step a, the fitted linear regression equation obtained in step D is as follows: ethyl oleate-0.08 propyl acetate +0.026 undecanol-0.189 + 1-butanol +0.782 + 2-hexanoic acid-phenethyl ester +0.022 + 1-methylene-1H-indene-0.117 furfuryl hexanoate-0.324 ethyl palmitate-0.17 isobutyraldehyde-0.115 (3Z) -3-decen-1-ol acetate, identified by fitting a linear regression equation to the white spirit storage Time; wherein the Time unit is month.
In addition, tests show that when the fitted linear regression equation obtained in the step D is applied to identify the storage time of the white spirit, the accuracy can reach more than 80% when the storage time of a sample to be detected does not exceed twice the storage time of a wine sample of the fitted equation; after the time is twice that of the fitted equation wine sample, the accuracy is reduced, but the accuracy is still kept above 60%, and compared with the prior art, the method has the advantages that the white wine storage time can be reflected to a certain extent. When the odor type of the fitting equation wine sample is different from that of the to-be-detected sample, the storage time of the to-be-detected sample and the fitting equation wine sample has similar influence on the result, but the odor type of the fitting equation wine sample has certain influence on the result accuracy, the accuracy is reduced due to the odor type difference, and the accuracy can be basically guaranteed to reach more than 80%.
Based on the reasons, in actual operation, an appropriate fitting equation wine sample can be selected according to the estimated storage time of the sample to be detected so as to ensure the accuracy of the result.
The present invention will be described in detail with reference to examples; the following examples are given as examples of the present invention and are not intended to limit the scope of the present invention. Various changes and modifications can be made to the invention without departing from the spirit and scope of the invention, and all such changes and modifications are intended to be within the scope of the invention.
Example 1
The method for temporarily identifying the white spirit storage time by multivariate linear stepwise regression comprises the following steps:
A. selecting and pretreating a white spirit sample: taking Luzhou-flavor liquor samples (sequentially numbered B1-B10) with the storage time of 0 month, 2 months, 4 months, 6 months, 9 months, 12 months, 15 months, 17 months, 21 months and 24 months respectively, and parallelly detecting each sample point for 6 times; the sample is reduced to 8% vol with ultrapure water, 4mL of the reduced sample is placed in a 20mL sample bottle, and 10. mu.L of 8.05g/L internal standard tert-amyl alcohol and 0.8g NaCl are added.
B. Volatile compound extraction: step A the sample vial was equilibrated in a water bath at 50 ℃ for 5min, after which the SPME extraction head (50/30 Xm DVB/CAR/PDMS) was inserted into the headspace of the sample vial and adsorbed at 50 ℃ for 40 min.
C. Collecting a fingerprint spectrum: taking out the adsorbed extraction head, inserting into a gas chromatography sample inlet, desorbing at 250 deg.C for 3min, and collecting fingerprint information by GC-MS to obtain retention time, matching fraction and peak area of volatile compounds (98 compounds are detected in all samples);
gas chromatography conditions:
a60 m × 0.25mm × 0.50 μm TG-WAXMS capillary gas chromatography column was used, the carrier gas was high-purity helium gas, the flow rate was 1.0mL/min, the split ratio: 20: 1, temperature programming is as follows: the temperature is maintained at 50 ℃ for 2min, the temperature is raised to 145 ℃ at 3 ℃/min, then the temperature is raised to 230 ℃ at 15 ℃/min and maintained for 3min, and the temperature of the injection port is maintained at 250 ℃.
Mass spectrometry conditions:
transmission line temperature 200 ℃, ion source temperature 260 ℃, scanning mass range m/z: 33-350 amu, ionization mode: EI +; electron energy: 70 eV.
D. C, determining the 98 volatile compounds by determining the retention time and the matching fraction of the 98 volatile compounds obtained in the step C;
after the peak area of the volatile compound obtained in the step C is subjected to normalization treatment, performing multiple stepwise regression analysis on all corrected peak area log10 data, and establishing a multiple linear stepwise regression mathematical model (the model fitting goodness is shown in Table 1), so as to obtain a fitting linear regression equation: t (month) ═ 0.478 a1-0.08 a2+0.026 A3-0.189 a4+0.782 a5+0.022 a6-0.117 a7-0.324 A8-0.17 a9-0.115 a10 (R) (R8-0.17 a9-0.115 a1020.996); the corrected peak areas (log10) of the important volatile compounds B1-B10 in Table 2 were substituted into the equation to temporarily identify (verify) the storage time of the white wine, and the results are shown in Table 3.
TABLE 1 important volatile compounds and their equation coefficients
| Material numbering | Name of substance | Coefficient of equation |
| A1 | Oleic acid ethyl ester | 0.478 |
| A2 | N-propyl acetate | -0.08 |
| A3 | Undecanol | 0.026 |
| A4 | 1-Butanol | -0.189 |
| A5 | Hexanoic acid-2-phenylethyl ester | 0.782 |
| A6 | 1-methylene-1H-indenes | 0.022 |
| A7 | Hexanoic acid furfuryl ester | -0.117 |
| A8 | Hexadecanoic acid ethyl ester | -0.324 |
| A9 | Isobutyraldehyde | -0.17 |
| A10 | (3Z) -3-decen-1-ol acetate | -0.115 |
Corrected peak area (log10) of important volatile Compounds in tables 2B 1-B10
TABLE 3B 1-B10 identification of storage time
| Sample numbering | Actual storage time (moon) | Model prediction time (moon) |
| B1 | 0 | 0 |
| B2 | 2 | 1.8 |
| B3 | 4 | 3.6 |
| B4 | 6 | 5.8 |
| B5 | 9 | 8.6 |
| B6 | 12 | 11.3 |
| B7 | 15 | 16.2 |
| B8 | 17 | 16.9 |
| B9 | 21 | 21.3 |
| B10 | 24 | 24.3 |
Example 2
Two portions of the Luzhou-flavor liquor samples with the storage time of 1 year, 2 years, 3 years, 4 years and 5 years are respectively taken for analysis (sequentially numbered as C1-C10), the rest analysis operations are consistent with those of example 1, the correction peak areas of important volatile compounds in the samples are shown in table 4, the model established in example 1 and a fitting linear regression equation are adopted to judge the storage time of the liquor in 1 year to 5 years, and the results are shown in table 5.
Corrected peak area (log10) of important volatile Compounds in tables 4C 1 to C10
| A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | A9 | A10 | |
| C1 | 6.62 | 1.28 | 0.25 | 2.45 | 10.26 | 0.39 | 2.68 | 4.13 | 5.21 | 0.87 |
| C2 | 6.94 | 1.27 | 0.36 | 3.11 | 9.12 | 0.13 | 3.53 | 4.27 | 4.68 | 1.58 |
| C3 | 7.04 | 2.11 | 0.53 | 2.88 | 23.75 | 0.83 | 4.22 | 5.01 | 5.07 | 2.39 |
| C4 | 7.80 | 1.37 | 0.47 | 3.24 | 25.80 | 0.71 | 3.79 | 4.89 | 4.21 | 4.02 |
| C5 | 6.62 | 1.24 | 0.31 | 2.69 | 49.42 | 0.63 | 3.62 | 4.36 | 5.75 | 3.58 |
| C6 | 6.05 | 2.06 | 0.14 | 2.87 | 41.29 | 0.49 | 3.72 | 3.96 | 3.88 | 0.97 |
| C7 | 5.48 | 1.42 | 0.27 | 3.03 | 53.46 | 0.53 | 2.99 | 4.03 | 4.91 | 2.32 |
| C8 | 7.45 | 1.33 | 0.33 | 2.91 | 51.50 | 0.74 | 2.72 | 4.87 | 4.83 | 4.21 |
| C9 | 7.25 | 2.07 | 0.72 | 2.85 | 64.36 | 0.69 | 3.93 | 4.31 | 4.36 | 4.58 |
| C10 | 7.28 | 1.88 | 0.43 | 2.87 | 67.19 | 1.06 | 3.54 | 4.87 | 5.34 | 4.03 |
TABLE 5 identification of C1-C10 storage time
Example 3
Two portions of the Maotai-flavor liquor samples with the storage time of 10 months, 20 months, 30 months and 40 months respectively are taken for analysis (sequentially numbered as D1-D8), the rest analysis operations are consistent with those of the example 1, the correction peak areas of the important volatile compounds in the samples are shown in the table 6, the model and the fitting linear regression equation established in the example 1 are adopted to judge the storage time of the Maotai-flavor liquor with the storage time of 10 months to 40 months, and the results are shown in the table 7.
Corrected peak area (log10) of important volatile Compounds in tables 6D 1 to D10
| A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | A9 | A10 | |
| D1 | 4.58 | 0.21 | 0.05 | 2.31 | 9.39 | 1.36 | 4.36 | 1.36 | 4.33 | 5.37 |
| D2 | 7.21 | 0.15 | 0.18 | 2.68 | 9.60 | 1.87 | 2.39 | 5.83 | 3.69 | 4.20 |
| D3 | 5.18 | 0.22 | 0.29 | 3.31 | 21.00 | 0.42 | 4.01 | 4.36 | 2.57 | 3.05 |
| D4 | 4.39 | 0.14 | 0.33 | 4.19 | 22.04 | 0.57 | 3.24 | 2.58 | 3.77 | 4.27 |
| D5 | 5.94 | 0.63 | 0.42 | 2.37 | 34.97 | 1.06 | 3.19 | 4.32 | 2.08 | 2.58 |
| D6 | 3.57 | 0.34 | 0.07 | 2.66 | 35.55 | 2.22 | 1.87 | 3.66 | 2.36 | 3.69 |
| D7 | 6.85 | 0.28 | 0.42 | 2.97 | 43.28 | 4.19 | 1.11 | 6.07 | 3.87 | 4.21 |
| D8 | 7.04 | 0.19 | 0.37 | 3.12 | 47.32 | 3.51 | 1.28 | 5.23 | 5.68 | 2.99 |
TABLE 7D 1-D8 identification of storage time
Claims (9)
1. The method for identifying the white spirit storage time by multivariate linear stepwise regression is characterized by comprising the following steps of: and (3) acquiring the volatile component fingerprint spectrums of the white spirit in different storage time by adopting GC-MS, establishing a multiple linear stepwise regression mathematical model, and fitting a linear regression equation to identify the storage time of the different white spirit.
2. The method for identifying the storage time of the white spirit by the multivariate linear stepwise regression according to claim 1, wherein the method comprises the following steps: the method comprises the following steps:
A. sample preparation: taking white spirit with different storage times as samples to be detected, reducing the alcoholic strength of the white spirit samples to be below 10 vol% by adopting ultrapure water, and simultaneously adding sodium chloride and an internal standard substance to obtain samples to be detected;
B. volatile compound extraction: b, using a headspace solid phase microextraction method to extract volatile compounds from the sample to be detected obtained in the step A through an extraction head;
C. collecting a fingerprint spectrum: after desorption of the extraction head at the sample inlet, acquiring volatile component fingerprint information by adopting GC-MS (gas chromatography-Mass spectrometer) to obtain retention time, matching fraction and peak area of the volatile compound;
D. the method comprises the steps of performing qualitative determination through retention time and matching fraction of volatile compounds, performing normalization processing on peak areas of the volatile compounds, performing multiple stepwise regression analysis through the corrected peak areas, establishing a multiple linear stepwise regression mathematical model to obtain a fitted linear regression equation, and identifying white spirit storage time through the fitted linear regression equation.
3. The method for identifying the storage time of the white spirit by the multivariate linear stepwise regression according to claim 2, wherein the method comprises the following steps: in the step A, the concrete operations of sample preparation are as follows: taking white spirit with different storage times as samples to be detected, reducing the alcohol content of the white spirit samples to 5-10% vol by adopting ultrapure water, placing 4-8 mL into a sample injection bottle, adding sodium chloride according to the concentration of 0.1-0.3 g/mL until saturation, and adding an internal standard substance accounting for 1-2% of the volume fraction of the system to obtain the samples to be detected.
4. The method for identifying the storage time of the white spirit by the multivariate linear stepwise regression according to claim 3, wherein the method comprises the following steps: in the step A, the internal standard substance is tert-amyl alcohol; the concentration of the internal standard substance is 8.05 g/L.
5. The method for temporarily identifying the storage time of the white spirit by the multivariate linear stepwise regression according to claim 2, wherein the method comprises the following steps: in the step B, parameters of headspace solid phase microextraction are as follows: balancing at 40-60 ℃ for 3-10 min, and extracting at 40-60 ℃ for 20-80 min.
6. The method for identifying the storage time of the white spirit by the multivariate linear stepwise regression according to claim 2, wherein the method comprises the following steps: in the step C, the extraction head desorbs for 1-10 min at the temperature of 240-260 ℃ of the sample inlet.
7. The method for identifying the storage time of the white spirit through the multivariate linear stepwise regression according to any one of claims 2 to 6, wherein the method comprises the following steps: in step C, the GC analysis conditions were: a60 m × 0.25mm × 0.50 μm TG-WAXMS capillary gas chromatography column was used, the carrier gas was high-purity helium gas, the flow rate was 1.0mL/min, the split ratio: 20: 1, temperature programming is as follows: the temperature is maintained at 50 ℃ for 2min, the temperature is raised to 145 ℃ at 3 ℃/min, then the temperature is raised to 230 ℃ at 15 ℃/min and maintained for 3min, and the temperature of the injection port is maintained at 250 ℃.
8. The method for identifying the storage time of the white spirit through the multivariate linear stepwise regression according to any one of claims 2 to 6, wherein the method comprises the following steps: in step C, the MS analysis conditions are as follows: transmission line temperature 200 ℃, ion source temperature 260 ℃, scanning mass range m/z: 33-350 amu, ionization mode: EI +; electron energy: 70 eV.
9. The method for identifying the storage time of the white spirit through the multivariate linear stepwise regression according to any one of claims 2 to 8, wherein the method comprises the following steps: when the sample to be detected in the step A is the white spirit stored for 0, 2, 4, 6, 9, 12, 15, 17, 21 and 24 months, the fitted linear regression equation obtained in the step D is as follows: ethyl oleate-0.08 propyl acetate +0.026 undecanol-0.189 + 1-butanol +0.782 + 2-hexanoic acid-phenethyl ester +0.022 + 1-methylene-1H-indene-0.117 furfuryl hexanoate-0.324 ethyl palmitate-0.17 isobutyraldehyde-0.115 (3Z) -3-decen-1-ol acetate, identified by fitting a linear regression equation to the white spirit storage Time; wherein the Time unit is month.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110348728.3A CN113203803B (en) | 2021-03-31 | 2021-03-31 | Method for identifying white spirit storage time by multiple linear stepwise regression |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110348728.3A CN113203803B (en) | 2021-03-31 | 2021-03-31 | Method for identifying white spirit storage time by multiple linear stepwise regression |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113203803A true CN113203803A (en) | 2021-08-03 |
| CN113203803B CN113203803B (en) | 2023-06-06 |
Family
ID=77025880
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110348728.3A Active CN113203803B (en) | 2021-03-31 | 2021-03-31 | Method for identifying white spirit storage time by multiple linear stepwise regression |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113203803B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114062556A (en) * | 2021-11-19 | 2022-02-18 | 泸州老窖集团有限责任公司 | Carbon isotope composition detection method for white spirit flavor substances |
| CN115078573A (en) * | 2022-06-09 | 2022-09-20 | 江苏洋河酒厂股份有限公司 | Method for predicting quality grade of soft type white spirit base liquor |
| WO2023040391A1 (en) * | 2021-09-18 | 2023-03-23 | 天地壹号饮料股份有限公司 | Method for determining content of ethanol in wines on basis of gas chromatographic method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070288117A1 (en) * | 2006-06-07 | 2007-12-13 | Shimadzu Corporation | Taste analyzing apparatus |
| CN111562240A (en) * | 2020-05-29 | 2020-08-21 | 江南大学 | Chinese liquor year wine detector and method |
-
2021
- 2021-03-31 CN CN202110348728.3A patent/CN113203803B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070288117A1 (en) * | 2006-06-07 | 2007-12-13 | Shimadzu Corporation | Taste analyzing apparatus |
| CN111562240A (en) * | 2020-05-29 | 2020-08-21 | 江南大学 | Chinese liquor year wine detector and method |
Non-Patent Citations (4)
| Title |
|---|
| GUOHUI LI等: "Determination and formation of Ethyl Carbamate in Chinese spirits", 《FOOD CONTROL》 * |
| 孟维一等: "HS-SPME结合GC-O-MS技术分析不同大曲中的香气活性化合物", 《食品工业科技》 * |
| 罗涛等: "顶空固相微萃取(HS-SPME）和气相色谱-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》 * |
| 马燕红等: "清香型白酒酒龄鉴别的方法研究", 《食品科学》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023040391A1 (en) * | 2021-09-18 | 2023-03-23 | 天地壹号饮料股份有限公司 | Method for determining content of ethanol in wines on basis of gas chromatographic method |
| CN114062556A (en) * | 2021-11-19 | 2022-02-18 | 泸州老窖集团有限责任公司 | Carbon isotope composition detection method for white spirit flavor substances |
| CN114062556B (en) * | 2021-11-19 | 2023-10-31 | 泸州老窖集团有限责任公司 | Carbon isotope composition detection method for white spirit flavor substances |
| CN115078573A (en) * | 2022-06-09 | 2022-09-20 | 江苏洋河酒厂股份有限公司 | Method for predicting quality grade of soft type white spirit base liquor |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113203803B (en) | 2023-06-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113203803B (en) | Method for identifying white spirit storage time by multiple linear stepwise regression | |
| CN113406251B (en) | Method for predicting storage years of white spirit | |
| CN103235057A (en) | Method for identifying white spirit origin place by using gas phase chromatography-mass spectrometry without analyzing compounds | |
| JP2007512538A (en) | Method and apparatus for deconvolution of a convolved spectrum | |
| CN111060642A (en) | Method for classifying and identifying tobacco leaves of same variety and different producing areas | |
| CN111521722A (en) | Method for identifying storage years of fragrant odor type finished product white spirit bottles | |
| CN112578046A (en) | Method for rapidly identifying mango varieties based on gas chromatography-ion mobility spectrometry technology | |
| CN105319308A (en) | Gas chromatography/mass spectrometry analysis apparatus of various compositions of white spirit, and analysis method thereof | |
| CN101065662A (en) | Method for determination of organic hydroperoxides | |
| CN113237977A (en) | Detection method of volatile flavor substances of white spirit | |
| Leis et al. | Analysis of Varietal Thiols in Sauvignon Blanc Wines—Optimization of a Solid-Phase Extraction Gas Chromatography Tandem Mass Spectrometry Method | |
| US4529879A (en) | Process for the determination of isotopes by mass spectrometry | |
| Huang et al. | Methanol concentration as a preceding eliminative marker for the authentication of Scotch Whiskies in Taiwan | |
| Cvačka et al. | Computer‐assisted interpretation of atmospheric pressure chemical ionization mass spectra of triacylglycerols | |
| CN114217002B (en) | Method for detecting contents of chamomile azulene and sabinene in chamomile essential oil | |
| CN109839457A (en) | Volatile compound carbon stable isotope measuring method in grape wine based on SPME-GC-IRMS | |
| CN111402961B (en) | Multi-species GC-MS endogenous metabolite database and establishment method thereof | |
| CN113376280A (en) | Method for simultaneously detecting 94 amino acids in urine sample | |
| CN113960198A (en) | Method for detecting impurity gas in electronic special gas based on gas chromatography-mass spectrometry | |
| CN117649897B (en) | Method for predicting gas production of soy sauce in shelf life | |
| CN112362768A (en) | Method for measuring content of Berol 185 in sample by using liquid chromatography tandem mass spectrometry | |
| CN113834886B (en) | Detection method for rapidly and accurately quantifying nitrogen-oxygen heterocyclic compounds in green tea and evaluation method for dryness degree of green tea | |
| CN110118832B (en) | Construction method and application of fingerprint of organic acid and volatile aroma components of Shanxi mature vinegar | |
| CN118655247A (en) | Method for constructing true and false wine distinguishing model and true and false wine distinguishing method | |
| CN112067714B (en) | Method for determining peanut oil essence in peanut oil |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |