CN112415118A - Method for identifying true and false composite finished white spirit - Google Patents
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- 239000002131 composite material Substances 0.000 title claims abstract description 23
- 235000014101 wine Nutrition 0.000 claims abstract description 39
- 150000001875 compounds Chemical class 0.000 claims abstract description 37
- 239000000796 flavoring agent Substances 0.000 claims abstract description 27
- 235000019634 flavors Nutrition 0.000 claims abstract description 23
- 238000010238 partial least squares regression Methods 0.000 claims abstract description 18
- 238000004817 gas chromatography Methods 0.000 claims abstract description 10
- 238000010813 internal standard method Methods 0.000 claims abstract description 8
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- 238000010183 spectrum analysis Methods 0.000 claims abstract description 6
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- 230000009286 beneficial effect Effects 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The invention discloses a method for identifying true and false composite finished white spirit, which comprises the following steps: (1) performing full spectrum analysis on the compound white spirit by adopting gas chromatography to obtain a fingerprint spectrum; (2) further adopting an internal standard method to carry out quantitative determination on the chromatographic peak of the true and false composite finished white spirit; (3) importing the measured data into SIMCA14.1 software to establish a working set, and carrying out standardization and centralization pretreatment on the data; (4) establishing a partial least squares regression equation model of the contents of the true and false wine and the flavor characteristic components by adopting a partial least squares regression multivariate statistical method; (5) verifying the regression model by adopting a cross verification method, and evaluating the fitting effect; (6) and introducing the sample to be detected into a model to form a prediction set, and predicting the authenticity of the sample by adopting partial least squares regression discriminant analysis. The method can provide visual evidence for identifying true and false wine, and has the advantages of high accuracy and reliable result.
Description
Technical Field
The invention belongs to the technical field of finished liquor authenticity identification and analysis, and particularly discloses an identification method of composite finished liquor true and false wine prepared by combining fragrant, strong-flavor and Maotai-flavor liquor
Background
The composite finished white spirit ingeniously fuses faint scent type, strong scent type and Maotai-flavor type white spirit according to a certain proportion, is elegant in fragrance, harmonious in fragrance, sweet and rich, unique in flavor, becomes a popular product in the white spirit market in China, and is deeply loved by consumers. With the increasing market share of the composite finished white spirit, imitation fake white spirit begins to appear on the market, which not only infringes the rights and interests of consumers, but also seriously harms the brand image of products and influences the healthy development of the composite white spirit. A simple, convenient and quick true and false wine identification method is established by scientific and technical means, and has important significance for maintaining the brand image of products, ensuring the safety and rights and interests of consumers and standardizing the white spirit industry and market development.
With the rapid development of science and technology, people are also always exploring new methods for identifying true and false wine products. Li Shi utilizes single photon ionization time-of-flight mass spectrometry and combines Principal Component Analysis (PCA), establishes a method for rapidly and accurately identifying white spirit and strong wine, and has important application value for rapidly screening false and inferior wine products [ Li Shi, electrospray extraction and single photon ionization mass spectrometry are used for rapidly analyzing true and false wine [ D ]. Jiangxi: Donghua university of Physician, 2013 ]. The method is characterized in that a prediction model is established by using the Butterdaly and combining an electronic tongue technology with multivariate statistical analysis, and obvious difference between real wine and fake wine in delicate flavor is found [ application of the Butterdaly electronic tongue technology in identification of real and fake Yili aged wine [ J ]. food industry science and technology, 2017 ]. Liujiafei and the like explore 2 simple, accurate and quick non-targeted detection methods by using ultra-high performance liquid chromatography-tandem high-resolution mass spectrometry and an ultraviolet-visible spectrophotometer in combination with Principal Component Analysis (PCA), and provide more technical supports for identifying true and false couchgrass platforms [ Liujiafei, non-targeted detection technology for identifying couchgrass platform wine true and false [ J ]. food safety quality detection bulletin, 2019 ]. The Sunjizhen utilizes a capillary column gas chromatography to carry out component analysis on the white spirit, and fingerprint similarity calculation software is used for establishing a standard fingerprint, so that the white spirit is effectively distinguished [ the Sunjizhen. "fingerprint" technology is applied to the quality evaluation of white spirit products [ J ]. brewing technology, 2005 ]. The Shejian utilizes traditional Chinese medicine chromatogram analysis and data management system software to identify and analyze the wine, establishes a gas chromatography fingerprint spectrum method of the red wine, and can quickly and effectively identify the true and false of the wine [ the Shejian gas chromatography fingerprint spectrum method quickly identifies the true and false of the red wine [ J ]. brewing technology, 2012 ]. The Chinese patent application with the application number of 202010241399.8 discloses an identification method for the storage years of fragrant scent type finished white spirit bottles. However, the disclosed white spirit authenticity identification technology mainly focuses on typical white spirits with strong flavor, Maotai flavor, fen-flavor and the like, and authenticity prediction of compound finished white spirits is not reported.
Disclosure of Invention
The invention aims to provide an identification method of composite finished liquor true and false, which is characterized in that main flavor characteristic components in the composite finished liquor are measured by a direct sample injection gas chromatography internal standard method, Partial Least square discriminant Analysis (PLS-DA) is adopted, a PLS-DA model of the authenticity and flavor component content of the composite finished liquor is established, and a test sample is introduced into the model to predict the authenticity of a liquor sample to be tested.
The technical scheme provided by the invention is as follows:
the method for identifying the true and false wine of the compound finished white spirit comprises the following steps:
9) performing full spectrum analysis on the compound finished white spirit by adopting gas chromatography to obtain a fingerprint spectrum;
10) quantitatively measuring the chromatographic peak of the true and false composite finished white spirit by adopting an internal standard method;
11) carrying out data preprocessing on the measurement result obtained in the step 2) to obtain a data set;
12) classifying the data in the data set into a working set and a predicted value respectively;
13) establishing a partial least square regression model by using the authenticity of the composite finished white spirit in the working set and the content of the flavor components obtained in the step 3) by adopting a partial least square regression multivariate statistical method;
14) evaluating the partial least squares regression model by cross validation method, and determining the fraction R of variable Y explained by each component in the model2Y (cum) and the fraction Q of the predicted variable Y based on the cross-validated model2(cum) evaluating cumulative interpretability and cross-validation of the model;
15) performing significance test on the regression coefficient of the partial least squares regression model by using a displacement test (Permutation test) method, wherein the displacement times are 200 times, and p is less than 0.05;
16) and (5) importing the data in the prediction set into the partial least squares regression model in the step 5) to predict the authenticity of the sample.
As a preferred technical scheme of the invention, in the step 2), the content of the undefined chromatographic peak in the compound finished white spirit is determined by adopting a semi-quantitative method.
In a preferred embodiment of the present invention, in step 3), the data preprocessing is performed by using SIMCA14.1 software for normalization and centering.
As a preferred technical scheme of the invention, in the step 4), when the partial least square regression model is established, the content of the flavor characteristic components is used as an independent variable X matrix, and the authenticity of the corresponding compound finished white spirit is used as a dependent variable Y matrix.
As a preferred technical scheme of the invention, in the step 4), a component with a variable accumulated contribution rate of more than 89% is selected as a main component to establish a partial least squares regression model.
As a preferred embodiment of the present invention, in step 5), R is2Intercept at Y-axis<0.4，Q2Intercept at Y-axis<0.05, indicating that no overfitting of the model occurred; the closer the correlation coefficient is to 1, the smaller the error between the predicted value and the actual value of the model.
Compared with the prior art, the invention has the beneficial effects that:
(1) performing full spectrum analysis on the compound finished white spirit, and performing semi-quantitative analysis on the indeterminate indexes by adopting an internal standard method;
(2) the metabonomics technical method is innovatively transferred and applied to the identification of the true and false composite finished white spirit;
(3) establishing a partial least squares regression equation model of the contents of true and false wine and flavor characteristic components of the compound finished white wine by adopting partial least squares discriminant analysis (PLS-DA);
(4) evaluating the effectiveness and the rationality of the PLS-DA model by adopting a cross validation method;
(5) performing predictive analysis on the authenticity of the compound finished product white spirit on the random sample through predictive analysis;
(6) the method is applied to authenticity identification of the compound finished product white spirit, has the advantages of objectivity, accuracy and reliability, can provide visual and visual evidences, and has sufficient persuasion.
Drawings
FIG. 1 is a finger print of the compound finished product spirit real wine in example 1;
FIG. 2 is a finger print of the pseudo wine of the compound finished product of the white spirit in example 1;
FIG. 3 is a score chart of a PLS-DA model (M1) of the compound finished white spirit in example 1;
FIG. 4 is a PLS-DA validation model of compound finished spirit in example 1;
FIG. 5 is A score chart of A composite finished product liquor true and false liquor prediction model (PS-A) in example 1;
FIG. 6 is a score chart of a prediction model (PS-B) for true and false 4 random wines in example 2.
Detailed Description
It should be noted that, in the present application, features of embodiments and embodiments may be combined with each other without conflict, and technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the specific real-time mode, the method for identifying the true and false wine of the compound finished white spirit comprises the following steps:
1) performing full spectrum analysis on the compound finished white spirit by adopting gas chromatography to obtain a fingerprint spectrum;
2) measuring the content of chromatographic peak in the compound finished white spirit by adopting a gas chromatography internal standard method;
3) importing the measurement result obtained in the step 2) into SIMCA14.1 software to establish engineering and data sets;
4) carrying out data standardization and centralization on the data set, and classifying the data set into a working set and a prediction set;
5) setting engineering attributes for the working set, selecting a PLS-DA model, and dividing the sample into a T group (true wine) and an F group (false wine) according to the authenticity of the composite finished product white wine to form an unmatched model;
6) automatically fitting the model, and obtaining an optimal PLS-DA model by the system according to the cross validity index;
7) fraction R of variable Y explained by each component in model2Y (cum) and the fraction Q of the predicted variable Y based on the cross-validated model2(cum) evaluating cumulative interpretability and cross-validation of the model;
8) identifying the special points through a scatter diagram of the main components; if there are special points, then a refit is needed.
9) And (3) performing significance test on the regression coefficient of the partial least squares regression model by adopting a displacement test (Permutation test) method on the fitted model, wherein the displacement times are 200 times, and p is less than 0.05.
In the step 2), the content of the flavor characteristic components in the compound finished white spirit is determined by referring to the national standard GB/T10345-2007 'white spirit analysis method', and other non-fixed components are determined by adopting a semi-quantitative method.
In the step 5), when a partial least square regression model is established, the content of the flavor characteristic components is used as an independent variable X matrix, and the authenticity of the corresponding compound finished white spirit is used as a dependent variable Y matrix.
And 6), selecting components with the accumulated contribution rate of the variables being more than 89% as main components to establish a partial least squares regression model.
In step 7), the standard deviation is corrected<1，R2Intercept at Y-axis<0.4，Q2Intercept at Y-axis<0.05, indicating that no overfitting of the model occurred; the closer the correlation coefficient is to 1, the smaller the error between the predicted value and the actual value of the model.
Example 1
1. Materials and methods
1.1 materials and apparatus
40 compound finished product white spirit samples, wherein include: establishing a model working set by 18 compound finished product spirit true wines (sample names are sequentially T-1 and T-2 … … Y-18) and 18 compound finished product spirit fake wines (sample names are sequentially Y-1 and Y-2 … … Y-18); and the other 4 composite white spirit samples are used as A prediction set (PS-A), and the sample names are PS-1, PS-2, PS-9 and PS-13 respectively.
7890 gas chromatograph (Agilent technologies, Inc., USA), split/no split sample inlet and FID detector; CP-Wax 57CB capillary chromatography column (50 m.times.0.25 mm, 0.25 μm, Agilent J & W, USA); FA2004 ten thousandth balance (shanghai seminaceae).
1.2 methods
1.2.1 content determination of flavor characteristic components of white spirit
Accurately transferring 1mL of wine sample into a 2mL sample bottle, adding 10 mu L of internal standard use solution, and determining by adopting a direct injection gas chromatography; further adopting an internal standard method to determine the content of the flavor characteristic components in the sample, setting the quantitative correction factor of the non-fixed components as 1, and adopting the following specific calculation formula:
in the formula: ciContent of the i-th compound, mg/L; a. theiPeak area of the i-th compound; a. the0Peak area of internal standard; f. ofiThe ith compoundA quantitative correction factor for the substance; c0Content of internal standard substance, mg/L.
1.2.2 data processing
And (3) importing the detection data into SIMCA14.1 software to establish engineering and data sets, and carrying out data standardization and centralized preprocessing.
1.2.3 partial least squares regression model building
The method comprises the steps of concentrating work consisting of 36 compound finished product white spirit samples, taking the content (mg/L) of main flavor components as an independent variable X matrix, taking the authenticity of the corresponding compound finished product white spirit samples as a dependent variable Y matrix, establishing a PLS-DA model, and automatically fitting the model (M1).
1.2.4 partial least squares regression model evaluation
Carrying out specific point identification on the main component scatter diagram, wherein all points are required to be within a 95% confidence interval; if the specific points exist, the model needs to be fitted again; and evaluating the model by adopting a cross verification method, wherein the replacement times are 200.
1.2.5 sample prediction
A prediction set (PS-A) consisting of 4 samples to be tested is introduced into A PLS-DA model (M1) for authenticity prediction analysis.
2. Results and discussion
2.1 flavor component content analysis
Performing full spectrum analysis on the compound finished product white spirit by adopting a gas chromatography method to obtain a fingerprint spectrum of the compound finished product white spirit true and false as shown in figures 1 and 2; the content of flavor characteristic components in the sample is further determined by adopting an internal standard method, the total peak number of the flavor characteristic components in the composite finished white spirit is 56, 34 compounds are determined qualitatively by a standard product, 22 compounds are determined not to be determined, and descriptive statistical results are shown in table 1.
TABLE 1 descriptive statistics of flavor component content (mg/L)
2.2 partial least squares regression model
Taking the contents (mg/L) of main flavor components in 36 compound finished product white spirit samples as independent variable X matrixes and the authenticity of corresponding compound finished product white spirit samples as dependent variable Y matrixes, establishing a PLS-DA model (M1), wherein the parameter information of the model (M1) is shown in Table 2, and the PLS-DA score chart is shown in FIG 3.
Note: SIMCA14.1 software (Umetrics AB, Umea, Sweden) was used for data processing, model construction, and the like.
TABLE 2 PLSR model (M1) parameter List of Compound finished spirit
As can be seen from Table 2, the 2 principal components 2 add up to 0.999, Q, which can explain the Y matrix information2(cum) is 0.998, fitting parameter R2Y (cum) and Q2All are close to 1, which shows that the model (M1) has extremely high interpretability and cross validity, and the sample authenticity has obvious linear relation with the interpretation variable, thereby proving the reasonability and accuracy of the model (M1).
The composite finished product white spirit true and false wine is well distinguished on a PLS-DA score chart (figure 3), the true wine is concentrated in the second quadrant and the third quadrant, the false wine is concentrated in the first quadrant and the fourth quadrant, the clustering effect is good, all samples are within a 95% confidence interval, no special point exists, and the model (M1) is good in fitting effect and does not need to be changed.
2.3 partial least squares regression model evaluation results
The model (M1) was evaluated using a cross-validation method, the validation results are shown in table 3. As can be seen from Table 3, the correlation coefficient R of the model (M1)2Is 0.96, Q2Is 0.94; r2Has a regression line intercept of 0.133, Q2Section of regression line ofThe distance is-0.375 and less than 0.05. All left-hand Qs in the validation model (FIG. 4)2The value is lower than the rightmost origin, all R on the left2The value is lower than the original value on the right, and the verification result shows that the model (M1) has good interpretability and cross validity.
TABLE 3 PLS-DA model (M1) validation results
2.4 sample Authenticity prediction results
A prediction set (PS-A) consisting of 4 samples to be tested is introduced into A PLS-DA model (M1) for prediction, and the prediction results are shown in Table 4 and FIG. 5.
Table 44 Compound finished product liquor sample true and false liquor prediction results
As can be seen from FIG. 5, PS-1 and PS-2 were clustered with true wine group and PS-9 and PS-13 with fake wine group in 4 samples; as can be seen from Table 4, the coincidence degrees of PS-1 and PS-2 with real wine are 0.92 and 0.97, respectively, and are close to 1; the degrees of coincidence between PS-9 and PS-13 and the fake wine are 1.02 and 1.04, respectively, and are close to 1. The result shows that the samples PS-1 and PS-2 in the prediction set (PS-A) are true wine, and the samples PS-9 and PS-13 are fake wine; the predicted results were consistent with the sensory evaluation results.
Example 2
1. Materials and methods
4 composite finished product liquor samples (sample numbers are PS-24, PS-25, PS-42 and PS-43 respectively) are randomly provided to form a prediction set (PS-B) for true and false prediction, and the steps of quantitative analysis of the samples are the same as those in example 1. The flavor component measurement data is represented in tabular form with rows representing observations and columns representing variables.
SIMCA14.1 software was started, the PLS-DA model (M1) of example 1 was introduced, and the prediction set (PS-B) was introduced to predict the results, as shown in Table 5 and FIG. 6.
TABLE 54 prediction results of true and false wine for random white spirit samples
As can be seen from FIG. 6, PS-24 and PS-25 were published in the second and third quadrants and PS-42 and PS-43 were distributed in the first and fourth quadrants in 4 samples; from Table 5, it can be seen that the degrees of coincidence between PS-24 and PS-25 and the real wine are 0.90 and 0.98, respectively, which are close to 1; the degrees of coincidence between PS-9 and PS-13 and the pseudo-wine are 0.99 and 1.01, respectively, and are close to 1. The result shows that the samples PS-24 and PS-25 in the prediction set (PS-B) are true wine, and the samples PS-42 and PS-43 are false wine; the predicted results were consistent with the sensory evaluation results.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. The method for identifying the true and false wine of the compound finished white spirit is characterized by comprising the following steps:
1) performing full spectrum analysis on the compound finished white spirit by adopting gas chromatography to obtain a fingerprint spectrum;
2) quantitatively measuring the chromatographic peak of the true and false composite finished white spirit by adopting an internal standard method;
3) carrying out data preprocessing on the measurement result obtained in the step 2) to obtain a data set;
4) and classifying the data in the data set into a working set and a predicted value respectively.
5) Establishing a partial least square regression model by using the authenticity of the composite finished white spirit in the working set and the content of the flavor components obtained in the step 3) by adopting a partial least square regression multivariate statistical method;
6) evaluating the partial least squares regression model by cross validation method, and determining the fraction R of variable Y explained by each component in the model2Y (cum) and the fraction Q of the predicted variable Y based on the cross-validated model2(cum), the cumulative interpretability and cross-validation of the evaluation model.
7) And (3) carrying out significance test on the regression coefficient of the partial least squares regression model by using a displacement test (Permutation test) method, wherein the displacement times are 200 times, and p is less than 0.05.
8) And (5) importing the data in the prediction set into the PLSR model in the step 5) to predict the authenticity of the sample.
2. The method according to claim 1, wherein in step 2), the content of the undefined chromatographic peak in the composite finished white spirit is determined by a semi-quantitative method.
3. The method of claim 1, wherein in step 3), the data preprocessing is standardized and centralized using SIMCA14.1 software.
4. The method according to claim 1, wherein in the step 4), when the partial least squares regression model is established, the content of the flavor characteristic components is used as an independent variable X matrix, and the authenticity of the corresponding compound finished white spirit is used as a dependent variable Y matrix.
5. The method according to claim 1, wherein in the step 4), a component with a variable cumulative contribution rate of more than 89% is selected as a main component to establish a partial least squares regression model.
6. The method of claims 1-5, wherein in step 5), R is2Intercept at Y-axis<0.4，Q2Intercept at Y-axis<0.05, indicating that no overfitting of the model occurred; the closer the correlation coefficient is to 1, the smaller the error between the predicted value and the actual value of the model.
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| 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 |
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