Method for identifying camel milk and counterfeit products thereof and application of camel milk and counterfeit products thereof
Through gas chromatography-mass spectrometry combined with stoichiometric method, combined with partial least squares method and principal component analysis discriminant classification model, the accuracy and efficiency of camel milk identification in the existing technology are solved, and the camel milk and its High accuracy, fast and reliable identification of counterfeit products.
Patent Information
- Application Number
- CN202411326935.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2025-02-18
AI Technical Summary
The prior art is difficult to accurately, quickly and reliably identify camel milk and its counterfeit products, especially in the process of heating dairy products, which leads to a decrease in detection sensitivity and accuracy.
The composition data of short-chain fatty acids were obtained through gas chromatography-mass spectrometry combined technology, and the partial least squares method and principal component analysis discriminant classification model were used for discriminant analysis to achieve accurate identification of camel milk and its counterfeit products.
It realizes high accuracy identification of camel milk and its counterfeit products, simplifies the inspection process, reduces the inspection cost, is suitable for large-scale sampling inspection, and has strong traceability.
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Figure CN119470679A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of food inspection, in particular to a method for identifying camel milk and its counterfeit products (Holstein cow milk, buffalo milk, goat milk, sheep milk). Background Art
[0002] Camel milk is one of the important sources of milk in desert and semi-desert areas and is known as "desert gold". Camel milk is rich in nutritional value and easy to digest. Compared with common ruminant milk such as cow's milk and goat's milk, camel milk has the characteristics of low sugar, low cholesterol, high minerals, high vitamins and high concentration of insulin, and its nutritional value is significantly higher than common commercially available milk products. Camel milk production is low, and its production is affected by factors such as breed, nutritional status, and lactation stage. Therefore, the market price is much higher than that of ordinary cow's milk and goat's milk. In order to pursue high profits, some unscrupulous merchants use low-priced dairy products to impersonate high-priced camel milk for sale, which seriously damages the rights and interests of consumers and fair competition in the market. Therefore, establishing an accurate, fast and reliable camel milk identification method is of great significance to ensuring the quality of camel milk and the safety of dairy products.
[0003] At present, the method of identifying counterfeit camel milk mainly focuses on the detection of protein differences. However, the heating process of dairy products is highly destructive to proteins, resulting in a decrease in the sensitivity and accuracy of detection methods based on protein differences, and prone to false positive results. In addition, DNA differences based on substances have certain thermal stability and species differences, but DNA testing is time-consuming and expensive, with a high application threshold, and cannot be adapted to large-scale sampling tests. Therefore, it is of great practical significance to establish a more accurate and efficient detection method for distinguishing low-priced cow's milk and goat's milk from high-priced camel milk. Summary of the invention
[0004] The purpose of the present invention is to provide a method and application for identifying camel milk and its counterfeits. The method obtains the composition data of short-chain fatty acids in raw milk samples by gas chromatography-mass spectrometry, performs discriminant analysis by partial least squares (PLS), and further establishes a principal component analysis discriminant classification (PCA-CLASS) model to achieve accurate identification of camel milk and its common counterfeits.
[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:
[0006] A method for identifying camel milk and its counterfeit products, wherein the counterfeit products are Holstein cow milk, buffalo milk, goat milk, and sheep milk;
[0007] The following steps are involved:
[0008] 1) Gas chromatography-mass spectrometry was used to obtain the short-chain fatty acid data of Holstein cow milk, buffalo milk, goat milk, sheep milk and camel milk, and preliminary preprocessing was performed;
[0009] 2) Based on the composition data of each component of short-chain fatty acids, a partial least squares discriminant analysis model was constructed using chemometric software to determine the unknown samples;
[0010] 3) Using chemometric software, a PCA-CLASS discriminant analysis model is constructed, and unknown samples are judged based on the PCA-CLASS discriminant analysis model.
[0011] Among them, the camel milk and fake camel milk (Holstein cow milk, buffalo milk, goat milk, sheep milk) in the present invention all refer to the normal milk liquid produced by dairy animals in the normal lactation period.
[0012] Wherein, in the step (1), the camel milk and the counterfeit product are raw milk samples collected from healthy dairy animals on a pasture, and the raw milk is normal milk. Pre-treatment of Holstein cow milk, buffalo milk, goat milk, sheep milk and camel milk is carried out in the following specific method:
[0013] (1) Thaw the frozen raw milk sample in a 37°C water bath, stir to avoid foaming, take 1 mL of the raw milk sample, and add 50 μL of anteiso C6:0 internal standard solution (2500 μg / mL);
[0014] (2) Add 3 mL of isopropanol / hexane (2 / 1, v / v) solution to the sample, vortex and centrifuge at 12000 g for 10 min at 4°C. Transfer the upper lipid extract except the protein layer into a heat-resistant glass test tube;
[0015] (3) Add 2 mL of NaOH isopropanol solution (2%) to the extracted lipid extract, seal it and heat it in a 50°C water bath for 30 min;
[0016] (4) After cooling, open the lid and add 2 mL of hydrochloric acid isopropanol solution (10%). After sealing, heat in an 80°C water bath for 150 min. After cooling on ice, open the lid and add 10 mL of ultrapure water along the wall of the heat-resistant glass test tube to make the interface between the aqueous phase and the organic phase clear;
[0017] (5) After derivatization, the sample was extracted with two steps of n-hexane (1.5 mL + 2.5 mL) and then measured by gas chromatography-mass spectrometry.
[0018] The conditions for collecting data by gas chromatography-mass spectrometry are as follows:
[0019] The gas chromatograph used in the experiment was a 7890A GC (Agilent), and the chromatographic column was a DB-1 capillary column (30m×250μm×0.25μm, Agilent) filled with non-polar 100% dimethylpolysiloxane; the gas chromatographic parameters were: injection volume was 1μL, split ratio was 10:1; carrier gas was ultra-high purity He, 99.999%; flow rate was constant at 1mL / min, and the total running time was 23min; the injection port temperature was 250°C, and the oven temperature was programmed and set as follows: the initial temperature was 50°C for 5min, then the temperature was increased to 110°C at 10°C / min, and finally the temperature was increased to 280°C at 80°C / min and maintained for 9.875min;
[0020] The mass spectrometer used in the experiment was 5975C MS (Agilent), using the selected ion monitoring mode; the mass spectrometer parameters were: the transfer line temperature was 250°C, the quadrupole temperature was 150°C, the MS ion source temperature was 230°C; the ionization energy was 70 eV, and the solvent delay time was 1.7 min;
[0021] Among them, the mass spectrum information of short-chain fatty acids based on gas chromatography-mass spectrometry is as follows:
[0022]
[0023] Wherein, IS stands for internal standard, anteiso C6:0.
[0024] The collected short-chain fatty acid data were pre-processed by removing the peak area with a signal-to-noise ratio lower than 10 times, removing the short-chain fatty acid content value below the quantitative limit, and performing Z-SCORE standardization on the data. The specific method is:
[0025] include:
[0026] 1) The mass spectrometry data processing software Agilent MSD ChemStation E.02.02.1431 was used to automatically integrate the detected short-chain fatty acids, and the data with a signal-to-noise ratio lower than 10 times were manually eliminated to obtain the short-chain fatty acid peak area data of all raw milk samples;
[0027] 2) Importing the peak area data of all extracted short-chain fatty acids into the data preprocessing software Excel, calculating the content of individual short-chain fatty acids according to the internal standard method, and eliminating the data below the quantitative limit. The short-chain fatty acid content is expressed as g / 100g TSCFA;
[0028] 3) Z-SCORE standardization of data;
[0029] 4) Import data after standardization
[0030] The data were imported into the data processing software SCIMA 14.1 for visual analysis. The data after Z-SCORE standardization were imported into the chemometrics software SIMCA14.1 for processing, and a PLS-DA discrimination model for genuine and fake camel milk was preliminarily established.
[0031] The chemical dosage software used in step (2) and step (3) is SIMCA software, and the preferred SIMCA software is SIMCA 14.1 software.
[0032] Wherein, the unknown samples in step (2) and step (3) are authentic camel milk samples and blind samples of counterfeit camel milk.
[0033] Wherein, in the step (2), after the PLS-DA model is established, the identification of camel milk and common counterfeit camel milk by the PLS-DA model is judged by the PLS-DA 3D score graph. The established PLS-DA is used to predict blind samples. The substance to be identified falls in the dark gray area for camel milk, and the remaining light gray area is counterfeit camel milk. In addition, after the PLS-DA model is established, the model is also verified. The PLS-DA model verification of the present invention evaluates the accuracy of the model by the area (AUG) of the receiver operating characteristic curve (ROC), and evaluates whether the model is overfitting by the Permutations permutation test.
[0034] Wherein, in the step (3), the genuine camel milk samples and other common camel milk counterfeits are divided into a "training set" and a "prediction set". The training set is a known classification sample of the model. The discrimination model of genuine and fake camel milk is established based on the setting of the training set. The data of genuine camel milk samples are selected as the training set as the real group, and the counterfeit sample data is set as the training set counterfeit group. A PCA-CLASS model is established, and the reliability of the PCA-CLASS model is verified by the prediction set.
[0035] Compared with the prior art, the outstanding effects of the present invention are:
[0036] (1) High accuracy: By combining gas chromatography-mass spectrometry with chemometric methods, the composition characteristics of short-chain fatty acids in dairy products can be accurately detected, enabling accurate identification of camel milk and its counterfeits.
[0037] (2) Simple and fast: Compared with immunological methods such as protein or DNA, the short-chain fatty acid content determination method of this method is simpler and can be completed in 4-6 hours, with high detection efficiency. In addition, accurate discrimination of real camel milk and counterfeit camel milk can be achieved through mathematical and statistical methods such as partial least squares analysis and discriminant models.
[0038] (3) Strong applicability: The technical threshold for determining the content of short-chain fatty acids in the present invention is low, the cost is low, and it is suitable for large-scale sampling and testing. In addition, the method of the present invention can not only be used to identify counterfeit camel milk, but also provide basic information for the practical application of short-chain fatty acids in milk and further research on milk fat.
[0039] (4) Strong traceability: The identification method of the present invention can be used to make judgments based on data, thus achieving sample-free judgments on historical data, literature data, etc.
[0040] The identification method and application of camel milk and its counterfeit products described in the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 In the figure, (a) and (b) are the PLS-DA model score graphs.
[0042] Figure 2 VIP diagram of the PLS-DA model.
[0043] Figure 3 This is the ROC diagram of the PLS-DA model.
[0044] Figure 4 In the figure, (a) to (e) are the PLS-DA model Permutation verification diagrams.
[0045] Figure 5 In the figure, (a) and (b) are the PLS-DA model prediction data score graphs.
[0046] Figure 6 In the figure, (a) and (b) are the DModX PS histograms of the PCA-CLASS model training set.
[0047] Figure 7 This is the ROC diagram of the PCA-CLASS model training set.
[0048] Figure 8 In the figure, (a) and (b) are the DModX PS histograms of the PCA-CLASS model prediction set.
[0049] Fig. 9 Cooman's plot for the PCA-CLASS model prediction set.
[0050] Fig.10 This is the ROC diagram of the PCA-CLASS model prediction set. DETAILED DESCRIPTION
[0051] The present invention will be described in detail below in conjunction with the examples, but it will be appreciated by those skilled in the art that the following examples are only used to illustrate the present invention and should not be used to limit the scope of the present invention. If no specific conditions are specified in the examples, the conditions are carried out according to conventional conditions or the conditions recommended by the manufacturer. The reagents or instruments used are conventional products that can be obtained commercially if the manufacturer is specified.
[0052] A method for identifying camel milk and its counterfeit products comprises the following steps:
[0053] (1) Preparation of milk samples
[0054] The collected camel milk and common counterfeit camel milk samples were derivatized by isopropyl esterification, including centrifugation, saponification, acid-catalyzed esterification, extraction and other processes to obtain the test solution.
[0055] (2) Data collection and preprocessing
[0056] Camel milk and common counterfeit camel milk samples were detected by gas chromatography-mass spectrometry to collect raw data; the raw data were automatically integrated and manually integrated to obtain the peak areas of all short-chain fatty acids, and the content of short-chain fatty acids was calculated, and the content was g / 100g TSCFA.
[0057] (3) Multivariate statistical data analysis and model building
[0058] The obtained short-chain fatty acid content results were subjected to supervised partial least squares analysis (PLS-DA) and a PCA-CLASS discriminant analysis model was constructed.
[0059] Example 1
[0060] The partial least squares analysis (PLS-DA) model was used to identify authentic camel milk samples and counterfeit camel milk samples.
[0061] 1. Experimental methods
[0062] (1) Raw milk sample collection
[0063] The sampling conditions and sample quantities of each raw milk sample are as follows:
[0064] Table 1 Raw milk sampling
[0065]
[0066] (2) Raw milk sample pretreatment
[0067] 1) The frozen raw milk sample was thawed in a 37°C water bath, stirred to avoid foaming, 1 mL of the raw milk sample was transferred, and 50 μL of anteiso C6:0 internal standard solution (2500 μg / mL) was added.
[0068] 2) Add 3 mL of isopropanol / n-hexane (2 / 1, v / v) solution to the sample, vortex and centrifuge at 12000 g for 10 min at 4°C, and transfer the upper lipid extract except the protein layer into a heat-resistant glass test tube.
[0069] 3) Add 2 mL of NaOH isopropanol solution (2%) to the extracted lipid extract, seal the tube and heat it in a 50° C. water bath for 30 min.
[0070] 4) After cooling, open the lid, add 2 mL of hydrochloric acid isopropanol solution (10%), seal and heat in a water bath at 80°C for 150 min. After cooling on ice, open the lid and add 10 mL of ultrapure water along the wall of the heat-resistant glass test tube to make the interface between the aqueous phase and the organic phase clear.
[0071] 5) After derivatization, the sample was extracted with two steps of n-hexane (1.5 mL + 2.5 mL) and then determined by gas chromatography-mass spectrometry.
[0072] (3) Collection of short-chain fatty acid data
[0073] The milk samples were tested by gas chromatography-mass spectrometry.
[0074] The gas chromatograph used in the experiment was a 7890A GC (Agilent), and the chromatographic column was a DB-1 capillary column (30 m×250 μm×0.25 μm, Agilent) filled with non-polar 100% dimethylpolysiloxane. The gas chromatographic parameters were: injection volume 1 μL, split ratio 10:1. The carrier gas was ultra-high purity He (99.999%), and the flow rate was constant at 1 mL min -1 The total running time was 23 min. The injection port temperature was 250 °C, and the oven temperature was programmed as follows: the initial temperature was 50 °C for 5 min, then increased at 10 °C min -1 The temperature was raised to 110℃ and then 80℃·min -1 The temperature was raised to 280°C and maintained for 9.875 min.
[0075] The mass spectrometer used in the experiment was 5975C MS (Agilent), using the selected ion monitoring mode. The mass spectrometer parameters were: the transmission line temperature was 250°C, the quadrupole temperature was 150°C, the MS ion source temperature was 230°C, the ionization energy was 70 eV, and the solvent delay time was 1.7 min.
[0076] Table 2 Mass spectrometry information of short-chain fatty acids based on gas chromatography-mass spectrometry
[0077]
[0078] Note: IS, stands for internal standard, anteiso C6:0.
[0079] (4) Processing of short-chain fatty acid data
[0080] The mass spectrometry data processing software Agilent MSD ChemStation E.02.02.1431 was used to automatically integrate the detected short-chain fatty acids, and the data with a signal-to-noise ratio lower than 10 times were manually eliminated to obtain the short-chain fatty acid peak area data of all raw milk samples. The peak area data of all extracted short-chain fatty acids were imported into the data preprocessing software Excel to calculate the content of individual short-chain fatty acids, and the data below the quantitative limit were manually eliminated. The content was expressed as g / 100gTSCFA. Finally, the data was standardized by Z-SCORE and imported into the chemometrics software SIMCA14.1 for processing, and a PLS-DA discrimination model for genuine and fake camel milk was preliminarily established.
[0081] 2. Model multivariate statistical analysis verification results
[0082] As an important means of multivariate statistical analysis, supervised partial least squares analysis (PLS-DA) was applied to the identification of camel milk and common counterfeit camel milk.
[0083] like Figure 1 As shown in the figure, on the PLS-DA score graph, each group has a high degree of aggregation in the score graph, indicating that the pretreatment and instrument status are good, and the analysis method is reliable and accurate; secondly, the cumulative explanation of the difference of the first five components exceeds 78%, and there is a good separation degree, indicating that PLS-DA has a good ability to distinguish camel milk from common counterfeit camel milk (Holstein cow milk, buffalo milk, goat milk, sheep milk), and there are obvious differences between camel milk and common counterfeit camel milk, which can be well distinguished by PLS-DA. R2X and R2Y represent the model's explanatory power for the X and Y matrices, respectively, and Q2Y represents the model's predictive power. The closer R2Y and Q2Y are to 1, the more stable and reliable the model is. In the PLS-DA model, the R2Y and Q2Y calculated by the principal component are 0.815 and 0.787, respectively, indicating that the model is stable and reliable, with good predictive power (R2Y>0.5, Q2Y>0.5).
[0084] like Figure 2 As shown, the biological characteristic substances with a VIP>1 for distinguishing camel milk and counterfeit camel milk are anteiso C5:0, iso C4:0 and C6:0.
[0085] like Figure 3As shown in the figure, the receiver operating characteristic curve (ROC) was obtained by cross-validation, and the area under the curve (AUC) was used to estimate the accuracy of each model. The accuracy of both the genuine camel milk samples and the counterfeit camel milk samples under the PLS-DA model was >0.998, indicating that the model has good discrimination ability, high prediction accuracy, and can accurately identify counterfeit camel milk products.
[0086] like Figure 4 As shown in the Permutation (overfitting) verification diagram, the PLS-DA model predicts that camel milk and counterfeit camel milk (Holstein cow milk, buffalo milk, goat milk, sheep milk) all meet the requirements that the rightmost point is always the corresponding point after permutation on the left, the intercept of R2Y does not exceed 0.3-0.4, and the intercept of Q2Y does not exceed 0.05, so the established PLS-DA model does not overfit.
[0087] The PLS-DA model has a good ability to distinguish camel milk from common counterfeit camel milk (Holstein cow milk, buffalo milk, goat milk, and sheep milk). There are obvious differences between camel milk and common counterfeit camel milk, which can be well distinguished by PLS-DA.
[0088] 3. Model prediction of unknown milk samples
[0089] (1) Blind samples of camel milk (authentic group) and counterfeit samples (Holstein milk, buffalo milk, goat milk, and sheep milk, counterfeit group) were collected for model prediction. The results of the blind samples are shown in Table 3. The GC-MS acquisition method and data processing method were the same as above.
[0090] Table 3 Prediction of raw milk sampling
[0091]
[0092] (2) Use gas chromatography-mass spectrometry to monitor the unknown milk sample blind sample, calculate the content of short-chain fatty acids, and use this model to distinguish, and then color it according to the actual category of the blind sample. Figure 5 As shown, samples falling in the dark grey circle area are camel milk samples, and samples falling in other light grey areas are counterfeit camel milk samples. However, only a preliminary conclusion can be drawn as to which category of counterfeit the samples belong to, which is prone to misjudgment and cannot be used to predict which category of counterfeit the counterfeit samples belong to.
[0093] Example 2
[0094] The PCA-CLASS discriminant model was further used to identify genuine camel milk samples and counterfeit camel milk samples.
[0095] 1. Experimental methods
[0096] (1) The PCA-CLASS model was used for the establishment and prediction of the final model. The preparation of camel milk samples (real group) and fake samples (Holstein milk, buffalo milk, goat milk, sheep milk, fake group) of the training set and the prediction set, the gas chromatography-mass spectrometry acquisition method, and the data processing method were the same as in Example 1.
[0097] (2) The data after Z-SCORE standardization were imported into the chemometrics software SIMCA 14.1 for processing. A PCA-CLASS discrimination model for authentic and fake camel milk was initially established based on the training group, and the model was used for prediction based on the prediction group.
[0098] 2. Model verification results
[0099] like Figure 6 As shown in (a), the DModX values of all fake camel milk samples in the training set are greater than Dcrit (0.05) in the real group model, and they are not the same as the real camel milk samples in the real group, so they are fake camel milk samples. Figure 6 As shown in (b), the DModX values of all the real camel milk sample data in the training set in the fake group model are greater than Dcrit (0.05), and they are not the same as the fake camel milk samples in the fake group. They are real camel milk samples, which is consistent with the actual situation.
[0100] like Figure 7 As shown in the figure, the ROC curve shows that the PCA-CLASS model predicts the accuracy of both genuine camel milk samples and counterfeit camel milk samples to be 1, indicating that the model has good discrimination ability, high prediction accuracy, and can accurately identify counterfeit camel milk products.
[0101] 3. Model prediction results
[0102] like Figure 8 As shown in (a), the DModX values of all fake camel milk sample data in the prediction set in the real group model are greater than Dcrit (0.05). It can be preliminarily determined that these samples are not of the same type as the real camel milk samples in the real group and are fake camel milk samples, which is consistent with the actual situation. Figure 8 As shown in (b), the DModX values of all the real camel milk sample data in the prediction set in the fake group model are greater than Dcrit (0.05). It can be preliminarily determined that these samples are not the same as the fake camel milk samples in the fake group, but are real camel milk samples, which is consistent with the actual situation.
[0103] like Fig. 9As shown, in Cooman's figure, the horizontal axis is the distance from the predicted sample point to the real sample model in the prediction set, and the vertical axis is the distance to the fake sample model in the prediction set. The dark gray data points of the real camel milk samples in the prediction set are distributed on the red line of the X-axis DCrit (0.05) and are judged as real samples. Only one is higher than the red line of the X-axis DCrit (0.05) and is misjudged. The light gray data points of the fake camel milk samples in the prediction set are distributed on the red line of the Y-axis DCrit (0.05) and are judged as fake samples. Only two light gray points are higher than the red line of the Y-axis DCrit (0.05) and the red line of the X-axis DCrit (0.05), and are also judged as fake samples. The prediction results are highly consistent with the actual results. Therefore, this model can be used to distinguish between real camel milk samples and fake samples that are not camel milk, and has a high accuracy rate, but it cannot be used to predict which type of fake samples belong to.
[0104] like Fig.10 As shown in the figure, the ROC curve shows that the PCA-CLASS model predicts the accuracy of authentic camel milk samples>0.9583, and the accuracy of predicting counterfeit camel milk samples reaches 1, which means that the model has good discrimination ability, high prediction accuracy, and can accurately identify counterfeit camel milk products.
[0105] The prediction set misclassification table shown in Table 4 shows that the prediction accuracy of the PCA-CLASS model established in the present invention for genuine camel milk samples reaches 91.67%, the prediction accuracy for counterfeit camel milk samples reaches 100%, and the total prediction accuracy can reach 98.15%.
[0106] Table 4 Prediction set misclassification table
[0107]
[0108] In summary, the present invention provides an effective method for identifying camel milk and its common counterfeits based on short-chain fatty acid characteristic analysis, which has the advantages of high accuracy, simple operation, strong applicability, strong traceability, etc., and is of great significance for ensuring fair competition in the dairy market and the rights and interests of consumers.
[0109] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.
Claims
1. A method for identifying camel milk and its counterfeit products, characterized in that: The counterfeit products described are Holstein cow milk, buffalo milk, goat milk, and sheep milk; The following steps are involved: 1) Gas chromatography-mass spectrometry was used to obtain the short-chain fatty acid data of Holstein cow milk, buffalo milk, goat milk, sheep milk and camel milk, and preliminary preprocessing was performed; 2) Based on the composition data of each component of short-chain fatty acids, a partial least squares discriminant analysis model was constructed using chemometric software to determine the unknown samples; 3) Using chemometric software, a PCA-CLASS discriminant analysis model is constructed, and unknown samples are judged based on the PCA-CLASS discriminant analysis model.
2. The method for identifying camel milk and its counterfeit products according to claim 1, characterized in that: In the step (1), Holstein cow milk, buffalo milk, goat milk, sheep milk and camel milk are pre-treated, and the specific method is as follows: (1) Thaw the frozen raw milk sample in a 37°C water bath, stir to avoid foaming, remove the raw milk sample, and add anteiso C6:0 internal standard solution; (2) Add isopropanol / hexane solution to the sample, vortex and centrifuge at 12000g at 4°C, and transfer the upper lipid extract except the protein layer into a test tube; (3) Add NaOH isopropanol solution to the extracted lipid extract, seal it and heat it in a 50°C water bath; (4) After cooling, open the lid, add hydrochloric acid isopropanol solution, seal and heat in a water bath at 80°C; after cooling on ice, open the lid and add ultrapure water along the wall of the test tube to make the interface between the aqueous phase and the organic phase obvious; (5) After derivatization, the sample was extracted with n-hexane in two steps and then analyzed by gas chromatography-mass spectrometry.
3. The method for identifying camel milk and its counterfeit products according to claim 1, characterized in that: In the step (1), the conditions for collecting data by gas chromatography-mass spectrometry are: The chromatographic column of the gas chromatography was a DB-1 capillary column filled with non-polar 100% dimethylpolysiloxane; the gas chromatography parameters were: injection volume was 1 μL, split ratio was 10:1; carrier gas was ultra-high purity He, 99.999%; flow rate was constant at 1 mL / min, and the total running time was 23 min; the injection port temperature was 250 °C, and the oven temperature was programmed and set as follows: the initial temperature was 50 °C for 5 min, then increased to 110 °C at 10 °C / min, and finally increased to 280 °C at 80 °C / min and maintained for 9.875 min; The mass spectrometer used the selected ion monitoring mode; the mass spectrometer parameters were: the transfer line temperature was 250°C, the quadrupole temperature was 150°C, the MS ion source temperature was 230°C; the ionization energy was 70 eV, and the solvent delay time was 1.7 min; Among them, the mass spectrum information of short-chain fatty acids based on gas chromatography-mass spectrometry is as follows: Wherein, IS stands for internal standard, anteiso C6:
0.
4. The method for identifying camel milk and its counterfeit products according to claim 1, characterized in that: In the step (1), the collected short-chain fatty acid data is preliminarily preprocessed, including eliminating peak areas with a signal-to-noise ratio lower than 10 times, eliminating short-chain fatty acid content values below the quantitative limit, and performing Z-SCORE standardization on the data.
5. The method for identifying camel milk and its counterfeit products according to claim 4, characterized in that: The unknown samples in step (2) and step (3) are authentic camel milk samples and blind samples of counterfeit camel milk.
6. The method for identifying camel milk and its counterfeit products according to claim 5, characterized in that: The chemical dosage software used in step (2) and step (3) is SIMCA software.
7. The method for identifying camel milk and its counterfeit products according to claim 6, characterized in that: In the step (3), the real camel milk sample data is selected as the real group of the training set, and the fake sample data is selected as the fake group of the training set. The PCA-CLASS discriminant analysis model of the real and fake camel milk is established based on the setting of the training set.
8. Use of the identification method according to any one of claims 1 to 7 in identifying camel milk and its counterfeits.