A method and apparatus for discriminating meat adulteration

Abstract

The embodiment of the present application provides a kind of method and device for identifying meat adulteration, which comprises: obtaining first sample, second sample and third sample, the third sample is obtained by mixing the first sample and the second sample;Prepare the taste nucleotide extraction solution of first sample, second sample, third sample and test sample respectively;Obtain the first group HPLC chromatogram of first sample, the second group HPLC chromatogram of second sample, the third group HPLC chromatogram of third sample and the fourth group HPLC chromatogram of test sample;Obtain the common peak data of first group HPLC chromatogram, second group HPLC chromatogram, third group HPLC chromatogram and fourth HPLC chromatogram, and obtain the recognition result by chemical pattern recognition to common peak data;According to the recognition result, whether the test sample is adulterated is identified.The method can realize the effective supervision and control of meat adulteration.

Description

Technical Field

[0001] This invention relates to the field of food safety testing, and specifically to a method and apparatus for identifying adulterated meat. Background Technology

[0002] Beef and mutton are highly nutritious and delicious, making them popular with consumers. In recent years, the price of beef and mutton has been rising continuously. In order to obtain greater profits, some unscrupulous vendors have been adulterating beef and mutton with cheap meat sources (such as pork and duck) or directly pressing them to pass them off as high-quality beef and mutton.

[0003] Currently, the main methods for identifying adulterated meat include sensory methods, immunoassays, PCR, spectrometry, and mass spectrometry. Sensory methods are simple, do not require complex testing equipment, and are low in cost, but they require highly skilled personnel and have relatively low accuracy. Immunoassays have high specificity and do not require large instruments, but they are susceptible to external interference, so their application is not widespread. PCR methods have high sensitivity, high specificity, and high resolution, but they are relatively complex, require sophisticated equipment, and are expensive. Spectrometry has the advantages of being non-destructive, rapid, and portable, but its application in meat adulteration detection is currently limited, and related data is scarce. Mass spectrometry has high accuracy and high sensitivity, but the instruments used are expensive, and it requires a high level of technical skill from the operators.

[0004] Different species of meat have distinct flavors, and nucleotides are the main contributing precursors to meat flavor, such as cytidine monophosphate, uridine monophosphate, guanylic acid, inosine monophosphate, adenosine monophosphate, hypoxanthine, and inosine. Studies have shown that the proportion of flavor-enhancing nucleotides varies among different species of meat, and this difference can serve as a basis for identifying adulteration. Fingerprinting and chemical pattern recognition technologies are rapidly developing analytical methods that have achieved significant results in food and pharmaceutical analysis. Fingerprinting comprehensively reflects the chemical composition information of a sample, while chemical pattern recognition technology can efficiently process and analyze this information, making it useful for adulteration identification. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for identifying adulterated meat, which can effectively monitor and control adulteration of meat.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for identifying adulterated meat, the method comprising: obtaining a first sample, a second sample and a third sample, wherein the first sample is a pure meat sample with the same nominal composition as the meat product to be tested, the second sample is a pure meat sample with the same possible adulterant composition, and the third sample is obtained by mixing the first sample and the second sample;

[0007] Flavor nucleotide extraction solutions were prepared for the first sample, the second sample, the third sample, and the sample to be tested, respectively.

[0008] Obtain the HPLC chromatogram of the flavor nucleotide extract solution, wherein the HPLC chromatogram of the flavor nucleotide includes the first set of HPLC chromatograms of the first sample, the second set of HPLC chromatograms of the second sample, the third set of HPLC chromatograms of the third sample, and the fourth set of HPLC chromatograms of the sample to be tested.

[0009] Acquire the common peak data of the first group of HPLC spectra, the second group of HPLC spectra, the third group of HPLC spectra, and the fourth group of HPLC spectra, and perform chemical pattern recognition on the common peak data to obtain the recognition result;

[0010] The identification results are used to determine whether the sample to be tested is adulterated. Optionally, the chemical pattern recognition includes cluster analysis and principal component analysis.

[0011] Optionally, identifying whether the sample to be tested is adulterated based on the identification result includes:

[0012] Based on the identification results, if the sample to be tested and the first sample are clustered together, it is determined that the sample to be tested is not adulterated and belongs to the first sample.

[0013] If the sample to be tested clusters with the second or third sample, it is determined that the sample to be tested contains a certain amount of the second sample.

[0014] Optionally, the method further includes:

[0015] A reference fingerprint chromatogram is generated based on the first set of HPLC chromatograms. The first set of HPLC chromatograms, the second set of HPLC chromatograms, and the third set of HPLC chromatograms are compared with the reference fingerprint chromatogram to determine the similarity.

[0016] The adulteration amount of the sample to be tested is determined based on the similarity and the adulteration amount of the second sample in the third sample.

[0017] Optionally, determining the adulteration amount of the sample to be tested based on the similarity and the adulteration amount of the second sample in the third sample includes:

[0018] A polynomial fitting was performed on the similarity and the amount of adulteration in the second sample in the third sample to obtain the fitting equation;

[0019] The adulteration amount of the sample to be tested is obtained based on the fitting equation.

[0020] Optionally, the third sample is obtained by mixing the first sample and the second sample in a series of gradient proportions.

[0021] Optionally, the method further includes:

[0022] Before calculating the similarity, flavor nucleotide fingerprints are constructed from the first set of HPLC spectra, the second set of HPLC spectra, and the third set of HPLC spectra to identify common peaks.

[0023] Optionally, the preparation of the flavor nucleotide extraction solutions for the first sample, the second sample, the third sample, and the sample to be tested, respectively, includes:

[0024] After grinding and pulverizing the first sample, the second sample, the third sample, and the sample to be tested, respectively, perchloric acid solution was added to homogenize the mixture, and centrifugation was performed to obtain the supernatant of the first sample, the second sample, the third sample, and the sample to be tested.

[0025] After adjusting the pH of the supernatant of the first sample, the second sample, the third sample, and the sample to be tested to neutral, the solution was diluted with water to obtain the flavor nucleotide extraction solution of the first sample, the second sample, the third sample, and the sample to be tested.

[0026] Optionally, obtaining the HPLC chromatogram of the flavor nucleotides in the flavor nucleotide extract solution includes:

[0027] The flavor nucleotide extract solution was separated by high performance liquid chromatography and detected by ultraviolet detector to obtain the HPLC chromatogram of flavor nucleotides.

[0028] On the other hand, the present invention also proposes an apparatus for identifying adulterated meat, the apparatus comprising:

[0029] The acquisition module is used to acquire a first sample, a second sample, and a third sample. The first sample is a pure meat sample with the same nominal composition as the meat product to be tested. The second sample is a pure meat sample with the same possible adulterant composition. The third sample is obtained by mixing the first sample and the second sample.

[0030] The first processing module is used to prepare flavor nucleotide extraction solutions for the first sample, the second sample, the third sample, and the sample to be tested, respectively.

[0031] The second processing module is used to obtain the HPLC chromatogram of the flavor nucleotide extract solution, wherein the HPLC chromatogram of the flavor nucleotide includes the first set of HPLC chromatograms of the first sample, the second set of HPLC chromatograms of the second sample, the third set of HPLC chromatograms of the third sample, and the fourth set of HPLC chromatograms of the sample to be tested.

[0032] The third processing module is used to acquire the common peak data of the first group of HPLC spectra, the second group of HPLC spectra, the third group of HPLC spectra and the fourth group of HPLC spectra, and to perform chemical pattern recognition on the common peak data to obtain the recognition result;

[0033] The fourth processing module is used to identify whether the sample to be tested is adulterated based on the identification result.

[0034] A method for identifying adulterated meat according to the present invention includes: obtaining a first sample, a second sample, and a third sample, wherein the first sample is a pure meat sample with the same nominal composition as the meat to be tested, the second sample is a pure meat sample with the same possible adulterant composition, and the third sample is obtained by mixing the first sample and the second sample; preparing flavor nucleotide extraction solutions of the first sample, the second sample, the third sample, and the sample to be tested respectively; obtaining HPLC chromatograms of the flavor nucleotide extraction solutions, wherein the HPLC chromatograms include a first set of HPLC chromatograms of the first sample, a second set of HPLC chromatograms of the second sample, a third set of HPLC chromatograms of the third sample, and a fourth set of HPLC chromatograms of the sample to be tested; obtaining common peak data of the first set of HPLC chromatograms, the second set of HPLC chromatograms, the third set of HPLC chromatograms, and the fourth set of HPLC chromatograms, performing chemical pattern recognition on the common peak data to obtain an identification result; and identifying whether the sample to be tested is adulterated based on the identification result. This invention establishes an HPLC fingerprint of flavor nucleotides, performs similarity analysis and chemical pattern recognition analysis, and combines the fitting equation between similarity and adulteration amount to identify and quantify meat adulteration, providing certain data and references for meat product quality control and evaluation, and realizing effective supervision and control of meat adulteration.

[0035] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0037] Figure 1 This is a flowchart illustrating a method for identifying adulterated meat according to the present invention;

[0038] Figure 2A-2C This is a schematic diagram of the superimposed fingerprint spectra of mutton and pork samples, the superimposed fingerprint spectra of beef and duck samples, and the standard solution spectra of the present invention.

[0039] Figures 3A-3B This is a schematic diagram of the HCA results of mutton and pork related samples, and beef and duck related samples of the present invention;

[0040] Figures 4A-4B This is a schematic diagram of the PCA score of mutton and pork related samples, and beef and duck related samples of the present invention;

[0041] Figure 5 This is a schematic diagram of the relationship between similarity and adulteration amount in an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram of a device for identifying adulterated meat according to the present invention.

[0043] Explanation of reference numerals in the attached figures

[0044] 100 - A device for identifying adulterated meat;

[0045] 200 - Acquisition Module;

[0046] 300 - First Processing Module;

[0047] 400 - Second Processing Module;

[0048] 500 - Third Processing Module;

[0049] 600 - Fourth processing module. Detailed Implementation

[0050] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0051] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0052] Example 1

[0053] Figure 1 This is a flowchart illustrating a method for identifying adulterated meat according to the present invention, as shown below. Figure 1 As shown, a method for identifying adulterated meat according to the present invention includes:

[0054] Step S101 involves obtaining a first sample, a second sample, and a third sample. The first sample is a pure meat sample with the same nominal composition as the meat product to be tested. The second sample is a pure meat sample with the same possible adulterant components. The third sample is obtained by mixing the first sample and the second sample.

[0055] According to one specific implementation method, two types of pure meat are prepared and labeled as the first sample and the second sample, respectively. The first sample and the second sample are then adulterated in different proportions to obtain a third sample. The pure meat can be pork, mutton, beef, duck, etc. The third sample includes multiple adulterated samples with different adulteration ratios, which are preset. Specifically, the adulteration can be carried out according to the mass ratio of the first sample to the second sample of 3:1, 2:1, 1:1, 1:2, and 1:3, respectively.

[0056] Step S102 involves preparing flavor nucleotide extraction solutions for the first sample, the second sample, the third sample, and the sample to be tested, respectively.

[0057] According to a specific embodiment, the preparation of flavor nucleotide extraction solutions for the first sample, the second sample, the third sample, and the sample to be tested includes: grinding and pulverizing the first sample, the second sample, the third sample, and the sample to be tested, adding perchloric acid solution to homogenize, centrifuging to obtain supernatants of the first sample, the second sample, the third sample, and the sample to be tested; adjusting the pH of the supernatants of the first sample, the second sample, the third sample, and the sample to be tested to neutral, and then making up to volume with water to obtain flavor nucleotide extraction solutions for the first sample, the second sample, the third sample, and the sample to be tested.

[0058] Specifically, the preparation of the sample flavor nucleotide extraction solution includes the following steps: take an appropriate amount of sample and grind it with liquid nitrogen, weigh an appropriate amount, add 5% perchloric acid solution to homogenize, centrifuge and collect the supernatant, homogenize 3 times, combine the supernatants, adjust the pH to neutral with sodium hydroxide solution, make up to volume with water, take an appropriate amount and filter it through a filter membrane to obtain the sample flavor nucleotide extraction solution.

[0059] Step S103 is to obtain the HPLC chromatogram of the flavor nucleotides in the flavor nucleotide extraction solution. The HPLC chromatogram of the flavor nucleotides includes the first set of HPLC chromatograms of the first sample, the second set of HPLC chromatograms of the second sample, the third set of HPLC chromatograms of the third sample, and the fourth set of HPLC chromatograms of the sample to be tested.

[0060] According to one specific embodiment, obtaining the HPLC chromatogram of the flavor nucleotide extract solution includes: separating the flavor nucleotide extract solution by high performance liquid chromatography and detecting it with an ultraviolet detector to obtain the HPLC chromatogram of the flavor nucleotide.

[0061] Specifically, the high-performance liquid chromatography (HPLC) separation and ultraviolet (UV) detector detection conditions are as follows: Column: ChromCore AQC18, 250 mm × 4.6 mm, 5 μm, or equivalent column; Column temperature: 40 °C; Injection volume: 10 μL; Flow rate: 1 mL / min; Mobile phase: A is 50 mmol / L potassium dihydrogen phosphate aqueous solution (pH 5.4), B is 30% methanol; Gradient elution: 0–14 min, 100% A; 14–15 min, 100%–0% A; 15–19 min, 0% A; 19–20 min, 0%–100% A; 20–26 min, 100% A; Data acquisition time: 2–22 min; Detection wavelength: 254 nm.

[0062] Step S104 involves obtaining common peak data from the first group of HPLC spectra, the second group of HPLC spectra, the third group of HPLC spectra, and the fourth group of HPLC spectra, and performing chemical pattern recognition on the common peak data to obtain the recognition results. The chemical pattern recognition includes cluster analysis and principal component analysis.

[0063] According to one specific implementation method, the first set of HPLC chromatograms, the second set of HPLC chromatograms, the third set of HPLC chromatograms, and the fourth set of HPLC chromatograms are imported into a traditional Chinese medicine chromatographic fingerprint similarity evaluation system in CDF format. The time window width in the system is set to 0.1 min, and common peaks are identified after multi-point correction.

[0064] The present invention has at least 15 common peaks (including: 5'-guanosine monophosphate, 5'-inosine monophosphate, hypoxanthine, 5'-adenosine monophosphate and inosine).

[0065] Step S105 is to identify whether the sample to be tested is adulterated based on the identification result.

[0066] According to one specific implementation, the chemical pattern recognition includes cluster analysis and principal component analysis.

[0067] Cluster analysis is a process of grouping and classifying large amounts of data based on the qualitative or quantitative characteristics of the experimental data to understand the inherent structure of the dataset and to describe each dataset. Its main idea is to use multivariate statistical values ​​to quantitatively determine the relationships between data, classifying them so that individuals within the same category have relatively small differences and relatively high similarity, while individuals in different categories have large differences and low similarity. Cluster analysis automatically classifies data based on its characteristics. Different clustering methods are often combined to leverage their strengths and compensate for their weaknesses; for example, hierarchical clustering and K-means clustering are frequently used together. First, hierarchical clustering is used to determine the number of categories and screen for outliers. Second, after removing outliers, the remaining samples are re-hierarchically clustered to obtain cluster centers for each category. Finally, the cluster centers obtained from hierarchical clustering are used as the initial classification centers for K-means clustering to obtain the final clustering results. Hierarchical clustering and K-means clustering can only analyze variables of a single type, i.e., categorical or continuous variables. If the data contains both categorical and continuous variables, a second-order clustering method can be used, which can analyze both categorical and continuous variables simultaneously.

[0068] Principal component analysis is an analytical method that studies how to explain the variance and covariance structure of multivariates using a few principal components. It identifies a small number of principal components that retain as much information as possible about the original variables and are uncorrelated with each other. It can be presented as a method within factor analysis.

[0069] According to a specific implementation, the step of identifying whether the sample to be tested is adulterated based on the identification result includes: if the sample to be tested clusters with the first sample, it is determined that the sample to be tested is not adulterated and belongs to the first sample; if the sample to be tested clusters with the second pure meat sample or the third sample, it is determined that a certain amount of the second pure meat sample has been added to the sample to be tested.

[0070] The method further includes: generating a reference fingerprint spectrum based on the first set of HPLC spectra; comparing the first set of HPLC spectra, the second set of HPLC spectra, and the third set of HPLC spectra with the reference fingerprint spectrum to determine the similarity; and determining the adulteration amount of the sample to be tested based on the similarity and the adulteration amount of the second sample in the third sample.

[0071] Determining the adulteration amount of the test sample based on the similarity and the adulteration amount of the second sample in the third sample includes: performing a polynomial fitting on the similarity and the adulteration amount of the second sample in the third sample to obtain a fitting equation; and obtaining the adulteration amount of the test sample based on the fitting equation. The third sample is obtained by mixing the first sample and the second sample in different proportions. The relationship between the similarity and the adulteration amount is obtained using a polynomial fitting method, and the correlation coefficient is ≥0.99.

[0072] The method further includes: before calculating the similarity, constructing flavor nucleotide fingerprints from the first set of HPLC spectra, the second set of HPLC spectra, and the third set of HPLC spectra to identify common peaks.

[0073] This invention establishes an HPLC fingerprint of flavor nucleotides, performs similarity analysis and chemical pattern recognition analysis, and combines the fitting equation between similarity and adulteration amount to identify and quantify meat adulteration, providing certain data and references for meat product quality control and evaluation.

[0074] Example 2

[0075] The materials and reagents required for this embodiment include: 5 batches of pure mutton (labeled M-1 to M-5), 5 batches of pure pork (labeled P-1 to P-5), 4 batches of pure beef (labeled B-1 to B-4), and 4 batches of pure duck meat (labeled D-1 to D-4) samples; 2 batches of unknown mutton and 3 batches of unknown beef samples; standard inosine (CAS 58-63-9, purity 99.4%), disodium 5'-inosine (CAS 4691-65-0, purity 99.9%), disodium 5'-guanylate (CAS 5550-12-9, purity 92.5%), 5'-adenosine (CAS 61-19-8, purity 98.9%), hypoxanthine (CAS 58-63-9, purity 98.9%), and guanosine (CAS 5550-12-9, purity 92.5%). 68-94-0 (purity 99.9%); methanol, chromatographic grade, potassium dihydrogen phosphate, dipotassium hydrogen phosphate, analytical grade, perchloric acid (purity 70-72%), sodium hydroxide and water.

[0076] Specific implementation methods include:

[0077] Preparation of standard solutions: Weigh appropriate amounts of standard substances disodium 5'-guanylate, disodium 5'-inosine, hypoxanthine, 5'-adenosine, and inosine, dissolve them in water, and dilute to volume. Prepare standard stock solutions with concentrations of 1000, 1000, 400, 1000, and 1000 μg / mL, respectively, and store at 4℃. Take appropriate amounts of standard stock solutions, dilute them with water, and dilute to volume to prepare mixed standard working solutions with concentrations of 20, 160, 20, 20, and 80 μg / mL, respectively, and store at 4℃.

[0078] Sample preparation: Pure mutton, pure pork, pure beef and pure duck samples: Take an appropriate amount of sample, grind and crush it with liquid nitrogen and store it in a -80℃ refrigerator for later use.

[0079] Simulated adulterated mutton samples: Take appropriate amounts of pure mutton and pure pork samples, mix them evenly at mass ratios of 3:1, 2:1, 1:1, 1:2, and 1:3, add liquid nitrogen to grind and pulverize, and store in a -80℃ refrigerator for later use. They are labeled as M+P(3:1), M+P(2:1), M+P(1:1), M+P(1:2), and M+P(1:3), respectively. The adulteration amounts of pork in the mutton are 25%, 33%, 50%, 67%, and 75%, respectively.

[0080] Simulated adulterated beef samples: Take appropriate amounts of pure beef and pure duck meat samples, mix them evenly at mass ratios of 3:1, 2:1, 1:1, 1:2, and 1:3, add liquid nitrogen to grind and pulverize, and store in a -80℃ refrigerator for later use. They are labeled as B+D (3:1), B+D (2:1), B+D (1:1), B+D (1:2), and B+D (1:3), respectively. The adulteration amounts of duck meat in the beef are 25%, 33%, 50%, 67%, and 75%, respectively.

[0081] Sample extraction: Weigh 2g of sample and place it in a 50mL centrifuge tube. Homogenize the sample three times with 20mL of 5% perchloric acid solution. Centrifuge the homogenate at 4000r / min for 10min at 4℃. Transfer the supernatant to a 50mL beaker. Combine the supernatants and adjust the pH to about 7 with 5mol / L sodium hydroxide solution. Transfer the solution to a 50mL volumetric flask, dilute to volume with water, shake well, and filter through a filter membrane for analysis.

[0082] Instrument conditions: Column: ChromCore AQC18 (250 mm × 4.6 mm, 5 μm); Column temperature: 40 ℃; Injection volume: 10 μL; Flow rate: 1 mL / min; Mobile phase: A is 50 mmol / L potassium dihydrogen phosphate aqueous solution (pH 5.4), B is 30% methanol; Gradient elution: 0–14 min, 100% A; 14–15 min, 100%–0% A; 15–19 min, 0% A; 19–20 min, 0%–100% A; 20–26 min, 100% A; Data acquisition time: 2–22 min; Detection wavelength: 254 nm.

[0083] Fingerprint spectroscopy methodology investigation: Weigh the same batch of samples, process them according to the sample extraction method, analyze them according to the instrument conditions, inject the samples six times consecutively, select the seventh peak as the reference peak, and calculate the relative peak area and relative retention time of the common peaks, and calculate the RSD.

[0084] Six samples from the same batch were weighed, processed according to the sample extraction method, analyzed according to the instrument conditions, and peak number seven was selected as the reference peak. The relative peak area and relative retention time of the common peaks were calculated, and the RSD was calculated.

[0085] Weigh the same batch of samples, process them according to the sample extraction method, analyze them according to the instrument conditions, and detect them at different time points of 0, 2, 4, 8, 12 and 24 hours. Select peak number 7 as the reference peak, and calculate the relative peak area and relative retention time of the common peaks, and calculate the RSD.

[0086] Fingerprint analysis: Each sample was processed according to the sample extraction method, analyzed according to the instrument conditions, and the spectrum was collected and imported into the Chinese herbal chromatographic fingerprint similarity evaluation system to establish fingerprint spectrum, identify common peaks, and perform similarity analysis.

[0087] The common peak correlation data of each sample were imported into SIMCA software for cluster analysis and principal component analysis.

[0088] Results and analysis of this embodiment: The fingerprinting methodology results show that the RSD of the relative retention time and relative peak area of ​​each common peak in the precision, repeatability and stability tests are all less than 3%, indicating that the precision, repeatability and stability are good and meet the technical requirements of fingerprinting research.

[0089] The chromatograms of mutton and pork samples, as well as those of beef and duck samples, were imported into the Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System in CDF format. Using the chromatograms of samples M-1 and B-1 as reference spectra, a time window width of 0.1 min was set. After multi-point correction and mark peak matching, the superimposed fingerprint spectra are shown below. Figure 2A and Figure 2B Nineteen common peaks were identified (peak numbers 1–20), among which peaks 6, 7, 12, 15, and 19 were compared with the standard sample spectrum (e.g., Figure 2C The results were compared with those shown in the figure and confirmed to be 5'-guanosine monophosphate, 5'-inosine monophosphate, hypoxanthine, 5'-adenosine monophosphate and inosine, respectively.

[0090] The mutton samples (M-1 to M-5) were used to generate mutton control fingerprint profiles using the median method (e.g., Figure 2A In the middle R-1), beef samples (B-1 to B-4) were used to generate beef control fingerprint profiles using the median method (e.g., R-1). Figure 2BThe similarity scores were compared between R-2 and R-1 for mutton and pork samples, and between R-2 for beef and duck samples. Table 1 shows the similarity evaluation results for each sample. The results indicate that the similarity of mutton samples (M-1 to M-5) ranged from 0.971 to 0.999, the similarity of pork samples (P-1 to P-5) ranged from 0.900 to 0.920, and the similarity of mixed mutton and pork samples (M+P(3:1), M+P(2:1), M+P(1:1), M+P(1:2), M+P(1:3)) was [not specified in the original text]. The similarity scores ranged from 0.934 to 0.977, and gradually decreased with decreasing proportions of mutton. For beef samples (B-1 to B-4), the similarity ranged from 0.958 to 0.999; for duck samples (D-1 to D-4), it ranged from 0.690 to 0.871; and for mixed beef and duck samples (B+D(3:1), B+D(2:1), B+D(1:1), B+D(1:2), B+D(1:3)), the similarity ranged from 0.914 to 0.983, with the similarity decreasing gradually with decreasing proportions of beef. These results indicate significant interspecific differences in flavor nucleotides between mutton and pork, and between beef and duck. Therefore, similarity can be used as an evaluation index to assess adulteration with pork in mutton and with duck in beef.

[0091] Table 1:

[0092]

[0093]

[0094] Common peak data from mutton and pork samples, as well as beef and duck samples, were imported into SIMCA software. Hierarchical clustering was performed using the ward mode for distance calculation and size for classification. The results are as follows: Figure 3A and 3B As shown. When the cluster distance is 10, the mutton samples (M-1~M-5) can be distinguished from the pork samples (P-1~P-5) and the mixed samples of mutton and pork (M+P(3:1), M+P(2:1), M+P(1:1), M+P(1:2), M+P(1:3)). When the cluster distance is 20, the beef samples (B-1~B-4) can be distinguished from the duck samples (D-1~D-4) and the mixed samples of beef and duck (B+D(3:1), B+D(2:1), B+D(1:1), B+D(1:2), B+D(1:3)).

[0095] PCA employs unsupervised pattern recognition, selecting the first two PCs for differentiation. The results are shown below. Figure 4A and 4B Analysis results of mutton and pork (e.g.) Figure 4AAs shown in the figure, the samples were divided into three categories: mutton samples (M-1 to M-5) in one category, pork samples (P-1 to P-5) in another category, and mixed mutton and pork samples (M+P(3:1), M+P(2:1), M+P(1:1), M+P(1:2), M+P(1:3)) in another category; the analysis results for beef and duck (as shown in the figure) were also analyzed. Figure 4B The data shows that the samples are divided into three categories: beef samples (B-1 to B-4) are in one category, duck samples (D-1 to D-4) are in another category, and mixed samples of beef and duck (B+D(3:1), B+D(2:1), B+D(1:1), B+D(1:2), B+D(1:3)) are in another category.

[0096] By combining the adulteration amounts of pork in mutton and duck in beef with their corresponding similarities, we fitted and plotted the relationship curves between similarity and adulteration amount, and obtained the corresponding fitting equations as follows: y1 = 0.000005x1 2 -0.0003x1+0.9866(R1 2 =0.9937) and y2 =0.00002x2 2 +0.0003x2+0.984(R2 2 =0.9961), such as Figure 5 As shown. From Figure 5 As can be seen, the similarity between mutton and pork, and between beef and duck, is inversely correlated with the amount of adulteration (correlation coefficients are all ≥0.99), meaning that as the amount of adulteration increases, the similarity decreases. Therefore, adulteration can be identified in mutton and beef by combining fingerprint similarity analysis with cluster analysis and principal component analysis, and the amount of adulteration can be further quantified based on the fitted equation.

[0097] Based on the above methods, unknown mutton and beef samples purchased from the market were tested and compared with the results of real-time fluorescence quantitative PCR (Table 2). The results showed that the adulterated meat source identification results of the two methods were consistent. The adulteration amount of each sample was analyzed using the method of this study, and the results were 34-66%.

[0098] Table 2

[0099]

[0100] Example 3

[0101] like Figure 6As shown, the present invention also proposes an apparatus 100 for identifying adulterated meat. The apparatus includes: an acquisition module 200 for acquiring a first sample, a second sample, and a third sample, wherein the first sample is a pure meat sample with the same nominal composition as the meat to be tested, the second sample is a pure meat sample with the same possible adulterant components, and the third sample is obtained by mixing the first and second samples; a first processing module 300 for preparing flavor nucleotide extraction solutions of the first sample, the second sample, the third sample, and the sample to be tested, respectively; a second processing module 400 for acquiring HPLC chromatograms of the flavor nucleotide extraction solutions, wherein the HPLC chromatograms include a first set of HPLC chromatograms of the first sample, a second set of HPLC chromatograms of the second sample, a third set of HPLC chromatograms of the third sample, and a fourth set of HPLC chromatograms of the sample to be tested; a third processing module 500 for acquiring common peak data of the first set of HPLC chromatograms, the second set of HPLC chromatograms, the third set of HPLC chromatograms, and the fourth HPLC chromatogram, and performing chemical pattern recognition on the common peak data to obtain an identification result; and a fourth processing module 600 for identifying whether the sample to be tested is adulterated based on the identification result. This device identifies and quantifies meat adulteration by establishing HPLC fingerprints of flavor nucleotides, performing similarity analysis and chemical pattern recognition analysis, and combining the fitting equation between similarity and adulteration amount, providing certain data and references for meat product quality control and evaluation.

[0102] A method for identifying adulterated meat according to the present invention includes: obtaining a first sample, a second sample, and a third sample, wherein the first sample is a pure meat sample with the same nominal composition as the meat to be tested, the second sample is a pure meat sample with the same possible adulterant composition, and the third sample is obtained by mixing the first sample and the second sample; preparing flavor nucleotide extraction solutions of the first sample, the second sample, the third sample, and the sample to be tested respectively; obtaining HPLC chromatograms of the flavor nucleotide extraction solutions, wherein the HPLC chromatograms include a first set of HPLC chromatograms of the first sample, a second set of HPLC chromatograms of the second sample, a third set of HPLC chromatograms of the third sample, and a fourth set of HPLC chromatograms of the sample to be tested; obtaining common peak data of the first set of HPLC chromatograms, the second set of HPLC chromatograms, the third set of HPLC chromatograms, and the fourth set of HPLC chromatograms, performing chemical pattern recognition on the common peak data to obtain an identification result; and identifying whether the sample to be tested is adulterated based on the identification result. This invention establishes an HPLC fingerprint of flavor nucleotides, performs similarity analysis and chemical pattern recognition analysis, and combines the fitting equation between similarity and adulteration amount to identify and quantify meat adulteration, providing certain data and references for meat product quality control and evaluation, and realizing effective supervision and control of meat adulteration.

[0103] On the other hand, embodiments of the present invention provide a storage medium on which a program is stored, which, when executed by a processor, implements the method for identifying adulterated meat.

[0104] This invention provides a processor for running a program, wherein the program executes the method for identifying adulterated meat during runtime.

[0105] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring a first sample, a second sample, and a third sample; the first sample is a pure meat sample with the same nominal composition as the meat to be tested; the second sample is a pure meat sample with the same possible adulterant components; and the third sample is obtained by mixing the first and second samples. Flavor nucleotide extraction solutions are prepared for the first, second, third, and test samples, respectively. Flavor nucleotide HPLC spectra of the flavor nucleotide extraction solutions are obtained, including a first set of HPLC spectra for the first sample, a second set of HPLC spectra for the second sample, a third set of HPLC spectra for the third sample, and a fourth set of HPLC spectra for the test sample. Common peak data of the first, second, third, and fourth HPLC spectra are obtained; chemical pattern recognition is performed on the common peak data to obtain an identification result; and the test sample is identified as adulterated based on the identification result. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0106] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: obtaining a first sample, a second sample, and a third sample, wherein the first sample is a pure meat sample with the same nominal composition as the meat product to be tested, the second sample is a pure meat sample with the same possible adulterant composition, and the third sample is obtained by mixing the first sample and the second sample; preparing flavor nucleotide extraction solutions of the first sample, the second sample, the third sample, and the sample to be tested, respectively; obtaining HPLC chromatograms of the flavor nucleotide extraction solutions, wherein the HPLC chromatograms of the flavor nucleotides include a first set of HPLC chromatograms of the first sample, a second set of HPLC chromatograms of the second sample, a third set of HPLC chromatograms of the third sample, and a fourth set of HPLC chromatograms of the sample to be tested; obtaining common peak data of the first set of HPLC chromatograms, the second set of HPLC chromatograms, the third set of HPLC chromatograms, and the fourth set of HPLC chromatograms, performing chemical pattern recognition on the common peak data to obtain an identification result; and identifying whether the sample to be tested is adulterated based on the identification result.

[0107] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0112] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0113] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0114] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0115] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for identifying adulterated meat, characterized in that, The method includes: Obtain a first sample, a second sample, and a third sample. The first sample is a pure meat sample with the same nominal composition as the meat product to be tested. The second sample is a pure meat sample with the same possible adulterant components. The third sample is obtained by mixing the first sample and the second sample. The identification of meat adulteration is to identify the adulteration of mutton with pork and / or beef with duck. Flavor nucleotide extraction solutions were prepared for the first sample, the second sample, the third sample, and the sample to be tested, respectively. Obtain the HPLC chromatogram of the flavor nucleotide extract solution, wherein the HPLC chromatogram of the flavor nucleotide includes the first set of HPLC chromatograms of the first sample, the second set of HPLC chromatograms of the second sample, the third set of HPLC chromatograms of the third sample, and the fourth set of HPLC chromatograms of the sample to be tested. Acquire the common peak data of the first group of HPLC spectra, the second group of HPLC spectra, the third group of HPLC spectra, and the fourth group of HPLC spectra, and perform chemical pattern recognition on the common peak data to obtain the recognition result; Based on the identification results, determine whether the sample to be tested is adulterated; The preparation of flavor nucleotide extraction solutions for the first sample, second sample, third sample, and test sample, respectively, includes: After grinding and pulverizing the first sample, the second sample, the third sample, and the sample to be tested, respectively, perchloric acid solution was added to homogenize the mixture, and centrifugation was performed to obtain the supernatant of the first sample, the second sample, the third sample, and the sample to be tested. After adjusting the pH of the supernatant of the first sample, the second sample, the third sample and the sample to be tested to neutral, the solution was diluted with water to obtain the flavor nucleotide extraction solution of the first sample, the second sample, the third sample and the sample to be tested. The process of obtaining the HPLC chromatogram of the flavor nucleotide extract solution includes: The flavor nucleotide extract solution was separated by high performance liquid chromatography and detected by ultraviolet detector to obtain the HPLC chromatogram of flavor nucleotides; The high-performance liquid chromatography (HPLC) separation and ultraviolet (UV) detector detection conditions are as follows: Column: ChromCore AQC18, 250 mm × 4.6 mm, 5 μm; Column temperature: 40 ℃; Injection volume: 10 μL; Flow rate: 1 mL / min; Mobile phase: A is 50 mmol / L potassium dihydrogen phosphate aqueous solution, pH 5.4, B is 30% methanol; Gradient elution: 0–14 min, 100% A; 14–15 min, 100%–0% A; 15–19 min, 0% A; 19–20 min, 0%–100% A; 20–26 min, 100% A; Data acquisition time: 2–22 min; Detection wavelength: 254 nm.

2. The method according to claim 1, characterized in that, The chemical pattern recognition includes cluster analysis and principal component analysis.

3. The method according to claim 1, characterized in that, The step of identifying whether the sample to be tested is adulterated based on the identification result includes: Based on the identification results, if the sample to be tested and the first sample are clustered together, it is determined that the sample to be tested is not adulterated and belongs to the first sample. If the sample to be tested clusters with the second or third sample, it is determined that the sample to be tested contains a certain amount of the second sample.

4. The method according to claim 1, characterized in that, The method also includes: A reference fingerprint chromatogram is generated based on the first set of HPLC chromatograms. The first set of HPLC chromatograms, the second set of HPLC chromatograms, and the third set of HPLC chromatograms are compared with the reference fingerprint chromatogram to determine the similarity. The adulteration amount of the sample to be tested is determined based on the similarity and the adulteration amount of the second sample in the third sample.

5. The method according to claim 4, characterized in that, The step of determining the adulteration amount of the sample to be tested based on the similarity and the adulteration amount of the second sample in the third sample includes: A polynomial fitting was performed on the similarity and the amount of adulteration in the second sample in the third sample to obtain the fitting equation; The adulteration amount of the sample to be tested is obtained based on the fitting equation.

6. The method according to claim 4, characterized in that, The third sample is obtained by mixing the first sample and the second sample in a series of gradient proportions.

7. The method according to claim 4, characterized in that, The method also includes: Before calculating the similarity, flavor nucleotide fingerprints are constructed from the first set of HPLC spectra, the second set of HPLC spectra, and the third set of HPLC spectra to identify common peaks.

8. A device for identifying adulterated meat, characterized in that, The device includes: The acquisition module is used to acquire a first sample, a second sample, and a third sample. The first sample is a pure meat sample with the same nominal composition as the meat product to be tested. The second sample is a pure meat sample with the same possible adulterant composition. The third sample is obtained by mixing the first sample and the second sample. The identification of meat adulteration is to identify the adulteration of mutton with pork and / or beef with duck. The first processing module is used to prepare flavor nucleotide extraction solutions for the first sample, the second sample, the third sample, and the sample to be tested, respectively. The second processing module is used to obtain the HPLC chromatogram of the flavor nucleotide extract solution, wherein the HPLC chromatogram of the flavor nucleotide includes the first set of HPLC chromatograms of the first sample, the second set of HPLC chromatograms of the second sample, the third set of HPLC chromatograms of the third sample, and the fourth set of HPLC chromatograms of the sample to be tested. The third processing module is used to acquire the common peak data of the first group of HPLC spectra, the second group of HPLC spectra, the third group of HPLC spectra and the fourth group of HPLC spectra, and to perform chemical pattern recognition on the common peak data to obtain the recognition result; The fourth processing module is used to identify whether the sample to be tested is adulterated based on the identification result; The preparation of flavor nucleotide extraction solutions for the first sample, second sample, third sample, and test sample, respectively, includes: After grinding and pulverizing the first sample, the second sample, the third sample, and the sample to be tested, respectively, perchloric acid solution was added to homogenize the mixture, and centrifugation was performed to obtain the supernatant of the first sample, the second sample, the third sample, and the sample to be tested. After adjusting the pH of the supernatant of the first sample, the second sample, the third sample and the sample to be tested to neutral, the solution was diluted with water to obtain the flavor nucleotide extraction solution of the first sample, the second sample, the third sample and the sample to be tested. The process of obtaining the HPLC chromatogram of the flavor nucleotide extract solution includes: The flavor nucleotide extract solution was separated by high performance liquid chromatography and detected by ultraviolet detector to obtain the HPLC chromatogram of flavor nucleotides; The high-performance liquid chromatography (HPLC) separation and ultraviolet (UV) detector detection conditions are as follows: Column: ChromCore AQC18, 250 mm × 4.6 mm, 5 μm; Column temperature: 40 ℃; Injection volume: 10 μL; Flow rate: 1 mL / min; Mobile phase: A is 50 mmol / L potassium dihydrogen phosphate aqueous solution, pH 5.4, B is 30% methanol; Gradient elution: 0–14 min, 100% A; 14–15 min, 100%–0% A; 15–19 min, 0% A; 19–20 min, 0%–100% A; 20–26 min, 100% A; Data acquisition time: 2–22 min; Detection wavelength: 254 nm.

9. The apparatus according to claim 8, characterized in that, The chemical pattern recognition includes cluster analysis and principal component analysis.

10. The apparatus according to claim 8, characterized in that, The step of identifying whether the sample to be tested is adulterated based on the identification result includes: Based on the identification results, if the sample to be tested and the first sample are clustered together, it is determined that the sample to be tested is not adulterated and belongs to the first sample. If the sample to be tested clusters with the second or third sample, it is determined that the sample to be tested contains a certain amount of the second sample.

Images