Method for identifying freshness of different parts of pork and application

Abstract

The invention discloses a method for identifying freshness of different parts of pork and application. The method comprises the following steps: firstly, constructing a PCA-LDA model for identifying fresh pork and frozen-thawed pork, wherein the PCA-LDA model comprises the following steps: respectively obtaining mass spectrum fingerprint data of the fresh pork and the frozen-thawed pork; and based on a principal component analysis method and a linear discriminant analysis method, constructing a PCA-LDA model by using the mass spectrum fingerprint data of the fresh pork and the mass spectrum fingerprint data of the frozen and thawed pork. Secondly, a pork sample to be detected is identified on the basis of the PCA-LDA model, the result shows that the identification accuracy reaches 99% or above, the identification efficiency is high, the single-time determination identification speed is 6 seconds per sample, fresh pork and frozen-thawed pork at different parts can be rapidly and accurately distinguished, and the method has an extremely good application prospect in pork product identification.

Description

Technical Field

[0001] The invention relates to the technical field of food identification, and in particular to a method for identifying the freshness of different parts of pork and its application. Background Art

[0002] During the transportation, storage and processing of pork, temperature fluctuations or multiple freeze-thaw cycles may lead to muscle tissue damage, lipid oxidation and protein degradation, which may affect the taste, generate potential harmful substances and increase food safety risks. In particular, when frozen-thawed meat is placed in room temperature or high temperature environments, microbial reproduction and chemical reactions are further intensified, and its quality deterioration and the generation of risk substances become more and more significant. Therefore, the rapid, efficient and accurate identification of pork freshness has become an urgent problem to be solved by pork-related companies in raw material quality control.

[0003] At present, traditional pork quality evaluation is mostly focused on sensory testing, physical and chemical index determination and other methods, but these methods have defects such as strong subjectivity, low detection efficiency and insufficient precision, which makes it difficult to meet the needs of enterprises for fast, efficient and reliable identification technology. In recent years, with the rapid development of analytical detection technology, mass spectrometry technology has become one of the important tools for food safety detection with its high resolution, high sensitivity and excellent qualitative and quantitative capabilities. Especially in the development of high-resolution mass spectrometry technology, the application of full scan and multi-level scanning modes can achieve comprehensive detection of target objects, non-target objects and unknown compounds, providing a more comprehensive and accurate analytical means for meat quality identification. Related studies have shown that in the identification of pork freshness, frozen-thawed meat will produce risk substances due to its oxidative degradation and microbial metabolism caused by repeated freezing and thawing. These substances not only reflect the quality changes of frozen-thawed meat, but also provide clear targets for pork freshness identification. However, due to the complex pork matrix, components such as fat and protein will cause interference to the test results such as "matrix effect", "false positive" and "false negative", which puts higher requirements on the sensitivity and stability of the analytical method.

[0004] Based on this, how to effectively identify frozen-thawed pork and fresh pork from different parts has become one of the main technical problems that need to be solved urgently. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a PCA-LDA model construction method for identifying fresh pork and frozen-thawed pork, the construction method is simple, and the constructed model can be used to quickly identify the freshness of different parts of pigs, and the identification accuracy rate reaches more than 99%.

[0006] The invention also proposes a model for identifying the freshness of pork.

[0007] The invention also provides a method for identifying fresh pork and frozen-thawed pork.

[0008] The invention also provides a method for identifying the freshness of different parts of pork.

[0009] The present invention also proposes an application of the above-mentioned PCA-LDA model construction method, the above-mentioned model for identifying the freshness of pork, the above-mentioned method for identifying fresh pork and frozen-thawed pork, or the above-mentioned method for identifying the freshness of different parts of pork in pork identification.

[0010] A first aspect of the present invention provides a method for constructing a PCA-LDA model for identifying fresh pork and frozen-thawed pork, comprising: S1. Obtaining mass spectrometry fingerprint data of fresh pork and frozen-thawed pork respectively; S2. Based on principal component analysis and linear discriminant analysis, a PCA-LDA model is constructed using the mass spectrum fingerprint data of the fresh pork and the mass spectrum fingerprint data of the frozen-thawed pork.

[0011] The PCA-LDA model construction method according to the embodiment of the present invention has at least the following beneficial effects: The PCA-LDA model construction method for identifying fresh pork and frozen-thawed pork of the present invention is simple, does not require sample preparation, and can obtain analytical data by in-situ analysis for model construction. In addition, the present invention can achieve rapid and accurate differentiation of fresh pork and frozen-thawed pork from different parts by constructing a pork identification model, which helps to greatly improve the efficiency of production quality control and reduce food safety risks caused by frozen-thawed meat.

[0012] In addition, fingerprint data is collected in the construction method of the present invention. Compared with high-resolution mass spectrometry data that focuses on precise molecular analysis, mass spectrometry fingerprint data pays more attention to the overall characteristics and quality control of the sample, which helps to achieve accurate detection of fresh pork and frozen-thawed pork.

[0013] In some embodiments of the present invention, the fresh pork includes fresh pork leg and fresh pork loin.

[0014] In some embodiments of the present invention, the frozen-thawed pork includes frozen-thawed pork leg and frozen-thawed pork loin.

[0015] In some embodiments of the present invention, the pork leg meat includes pork front leg meat and pork hind leg meat.

[0016] In some embodiments of the present invention, the pig breeds include backcross binary breeds and Muyuan binary breeds.

[0017] In some embodiments of the present invention, the method for obtaining the mass spectral fingerprint data includes rapid evaporation ionization-high resolution mass spectrometry.

[0018] The rapid evaporation ionization-high resolution mass spectrometry (REIMS-HRMS) method has the advantages of non-destructive analysis and real-time detection. It can quickly collect ionization signals generated by pork samples from different parts during the freeze-thaw process, providing technical support for the rapid identification of pork freshness.

[0019] In some embodiments of the present invention, the rapid evaporation ionization-high resolution mass spectrometry comprises: The fresh pork and / or the frozen-thawed pork are sampled using a rapid evaporation ionization device to obtain aerosol, which is then analyzed using a high-resolution mass spectrometer.

[0020] In some embodiments of the present invention, the mass spectrometry detection parameters of the rapid evaporation ionization-high resolution mass spectrometry are set to: The modes were negative ion mode and high sensitivity mode, the scanning mass range was 100~1200 m / z; the scanning time was 0.4~0.6 seconds / scan, and the flow rate of the accurate mass calibration solution was 180~220μL / min.

[0021] In some embodiments of the present invention, the accurate mass calibration solution is a 300-500 ppb leucine enkephalin solution.

[0022] In some embodiments of the present invention, the frozen-thawed pork is thawed before sampling.

[0023] In some embodiments of the present invention, the PCA-LDA model is constructed using an abstract model generator.

[0024] In some embodiments of the present invention, before constructing the PCA-LDA model, the mass spectrum fingerprint data is corrected using Lockmass accurate mass number.

[0025] A second aspect of the present invention provides a model for identifying the freshness of pork, which is obtained by using the PCA-LDA model construction method described in any one of the first aspects.

[0026] The model according to the embodiment of the present invention has at least the following beneficial effects: the model constructed by the present invention can be used for identification to quickly identify the freshness of different parts of pigs, wherein the time for 6 parallel measurements of each pork sample is less than 1.2 minutes, the single measurement identification speed is 6 seconds / sample, and the identification accuracy rate is over 99%.

[0027] A third aspect of the present invention provides a method for identifying fresh pork and frozen-thawed pork, comprising: The mass spectrum fingerprint data of the pork sample to be tested is obtained, and the mass spectrum fingerprint data is imported into the model for identifying the freshness of pork described in the second aspect for classification and identification.

[0028] In some embodiments of the present invention, the method for acquiring the mass spectrum fingerprint data of the pork sample to be tested includes rapid evaporation ionization-high resolution mass spectrometry, which is specifically described in the first aspect.

[0029] A fourth aspect of the present invention provides a method for identifying the freshness of different parts of pork, comprising: The mass spectrum fingerprint data of the pork sample to be tested is obtained, and the mass spectrum fingerprint data is imported into the model constructed by the PCA-LDA model construction method described in the first aspect for classification and identification.

[0030] In some embodiments of the present invention, the fresh pork and the frozen-thawed pork may come from different parts.

[0031] In some embodiments of the present invention, the method for acquiring the mass spectrum fingerprint data of the pork sample to be tested includes rapid evaporation ionization-high resolution mass spectrometry, which is specifically described in the first aspect.

[0032] The fifth aspect of the present invention provides the application of the PCA-LDA model construction method as described in any one of the first aspect, the model for identifying the freshness of pork as described in the second aspect, the method for identifying fresh pork and frozen-thawed pork as described in any one of the third aspect, or the method for identifying the freshness of different parts of pork as described in the fourth aspect in pork identification.

[0033] In some embodiments of the present invention, the pork identification includes identification of different pork parts, fresh pork and frozen-thaw pork.

[0034] Other features and advantages of the present invention will be set forth in the description which follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein: Figure 1 The pork samples according to the present invention are classified by type and gender; Figure 2 This is a flow chart of rapid evaporation ionization-high resolution mass spectrometry analysis and statistical modeling according to an embodiment of the present invention; Figure 3 This is the chromatogram of 6 parallel determinations of fresh pork samples of the present invention; Figure 4 This is the chromatogram of 6 parallel measurements of frozen-thawed pork samples of the present invention; Figure 5 This is the mass spectrometry fingerprint of the fresh pork sample of the present invention; Figure 6 The mass spectrometry fingerprint of the frozen-thawed pork sample of the present invention; Figure 7This is a prediction result diagram based on the visualized PCA-LDA model of the present invention; Figure 8 This is a cross-validation result diagram of the PCA-LDA model of the present invention; Fig. 9 This is a graph showing the results of six blind sample measurements of the present invention; Fig.10 This is a blind sample freshness identification result diagram of the present invention, wherein red represents fresh leg meat, navy blue represents frozen-thawed leg meat, yellow represents fresh spine fat, and light blue represents frozen-thawed spine fat. DETAILED DESCRIPTION

[0036] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0037] The words "preferably", "more preferably", etc. in the present invention refer to embodiments of the present invention that may provide certain beneficial effects in certain circumstances. However, other embodiments may also be preferred under the same circumstances or other circumstances. In addition, the description of one or more preferred embodiments does not imply that other embodiments are not applicable, nor is it intended to exclude other embodiments from the scope of the present invention.

[0038] When a numerical range is disclosed herein, the above range is considered to be continuous and includes the minimum and maximum values ​​of the range, as well as every value between such minimum and maximum values. Further, when a range refers to an integer, every integer between the minimum and maximum values ​​of the range is included. In addition, when multiple ranges are provided to describe features or characteristics, the ranges can be combined. In other words, unless otherwise indicated, all ranges disclosed herein should be understood to include any and all subranges included therein.

[0039] In the description of the present invention, the reference term "and / or" includes all and any combinations of one or more of the associated listed items.

[0040] In the description of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0041] If the specific conditions are not specified in the examples, the experiments were carried out under conventional conditions or conditions recommended by the manufacturer. If the manufacturers of the reagents or instruments are not specified, they are all conventional products that can be purchased commercially.

[0042] Example 1: Construction of PCA-LDA model for identifying fresh pork and frozen-thawed pork This embodiment provides a PCA-LDA model construction method for identifying fresh pork and frozen-thawed pork, which specifically includes the following contents.

[0043] 1. Test Materials 1. Reagents Isopropyl alcohol (chromatographically pure, Fisher, USA); sodium hydroxide (analytical pure, J&K, China); formic acid (chromatographically pure, J&K, China); leucine enkephalin (purity > 99%, Waters, USA). Unless otherwise specified, water is first-grade water as specified in GB / T 6682.

[0044] 2. Instruments (1) Rapid evaporation ionization-high resolution mass spectrometry (REIMS-QToF) (Waters, USA), or similar rapid evaporation ionization-high resolution mass spectrometer.

[0045] (2) Rapid evaporation ionization equipment settings: In all experiments, electrosurgical cutting was performed using an Erbe VIO50C electrosurgical generator (Erbe Elektromedizin GmbH, Tuebingen, Germany). The generator was set to “autocut” mode with a power output of 30 W.

[0046] (3) Rapid evaporation ionization-high resolution mass spectrometry system configuration: The REIMS source was connected to an Erbe 20321-028 monopolar electrosurgeon (Erbe Elektromedizin GmbH, Tuebingen, Germany) using a 3-m long, 15-mm diameter ultraflexible tubing (for exhaust / ventilation). The REIMS source was orthogonally coupled to a quadrupole time-of-flight mass spectrometer (Waters, Wilmslow, Manchester, UK).

[0047] 2. Sample Collection and Preparation 60 pork samples were purchased from the local pork market, from 20 pigs, covering two breeds: backcross binary and Muyuan binary. Among them, there were 6 backcross binary samples and 14 Muyuan binary samples. The origin distribution of the samples and the proportion of their breeds and genders are shown in Tables 1 and Figure 1 .

[0048] Table 1: Origin distribution of samples

[0049] The samples were divided into three parts: front leg, hind leg and back fat for further analysis. The gender distribution was 8 boars and 12 sows. The samples were classified, stored and labeled according to the standardized process.

[0050] Each sample was divided into two treatment groups: fresh group and freeze-thaw group, and experiments were carried out separately. The fresh group collected data on the day the sample was received, while the freeze-thaw group simulated actual storage and transportation conditions and collected data after two complete freeze-thaw cycles. The freeze-thaw process includes: freezing at -20℃ → completely thawing at room temperature of 22-25℃ → repeating a freeze-thaw cycle.

[0051] 3. Sample Testing 1. Instrument calibration (1) Preparation of calibration fluid: The preparation method of high-resolution mass spectrometry mass axis calibration solution (0.5 mM sodium formate) is as follows: Take 1 mL of 0.1 M sodium hydroxide, add 900 μL of ultrapure water and 100 μL of formic acid in sequence, and mix well. Use 90:10 isopropanol / water solution to make up to 20 mL, and ultrasonicate for 5 minutes to obtain 5 mM sodium formate solution. Take 2 mL of the above 5 mM sodium formate solution, and then use 90:10 isopropanol / water solution to make up to 20 mL, and ultrasonicate for 5 minutes to obtain 0.5 mM sodium formate calibration solution.

[0052] (2) Mass spectrometer calibration method: Prior to sample analysis, the mass spectrometer was calibrated by infusing 0.5 mM sodium formate calibration solution (90% isopropanol) at a flow rate of 20 μL / min, and the mass resolution was set to 15,000 (full width half maximum, FWHM at m / z 600). During calibration, the heater bias was set to 40 V and the cone voltage was set to 60 V.

[0053] 2. Mass spectrometry analysis (1) Preparation of accurate mass calibration solution: Lockmass accurate mass calibration-400ppb Leucine Enkephalin preparation method is as follows: Using a 5 mL pipette, add 7.5 mL of water to the 3 mg leucine enkephalin bottle, cap the bottle and shake well. Ultrasonicate for 5 minutes to obtain a 400 ng / μL leucine enkephalin aqueous solution. Using a 200 μL pipette, transfer 50 μL of the 400 ng / μL leucine enkephalin aqueous solution to a 20 mL volumetric flask, dilute to 20 mL with 50:50 acetonitrile / water solution (containing 0.1% formic acid), and ultrasonicate for 5 minutes to obtain a 1 ng / μL leucine enkephalin solution. Using a 1000 μL pipette, transfer 8000 μL of the 1 ng / μL leucine enkephalin to another 20 mL volumetric flask, dilute to 20 mL with isopropyl alcohol (IPA), and ultrasonicate for 5 minutes to obtain the final 400 ppb leucine enkephalin solution.

[0054] (2) Mass spectrometry analysis parameter settings: The samples were analyzed by rapid evaporation ionization-high resolution mass spectrometry in negative ion mode and high sensitivity mode, with a scan mass range of 100–1200 m / z and a scan time of 0.5 s / scan. Accurate mass calibration was achieved by infusing a lock mass solution at a continuous flow rate of 200 μL / min of a leucine enkephalin solution (m / z 554.2615, 2 ng / μL, dissolved in isopropanol) through a Waters Acquity UPLC I-class system (Waters, Milford, MA, USA).

[0055] 4. Data Collection and Modeling Analysis 1. Data Collection Fresh pork samples were directly analyzed in situ using the iKnife technology (i.e., an electrosurgical knife coupled with rapid evaporative ionization-high resolution mass spectrometry (REIMS-HRMS)), and frozen-thawed pork samples were analyzed in situ after thawing to obtain mass spectrometry fingerprint data.

[0056] Specifically, the sample was cut multiple times (thermocoagulated) using a rapid evaporation ionization device. Depending on the sample size, multiple unique tissue sampling points (burning points) were operated to generate surgical smoke (aerosol). The number of sampling points depended on the specific size of the sample. The generated aerosol was analyzed by a high-resolution mass spectrometer (negative ion mode) and operated as described above for mass spectrometry. The instrument was calibrated daily using sodium formate solution. The accurate mass calibration solution (leucine enkephalin solution) was injected as the solvent matrix at a flow rate of 0.2 mL / min.

[0057] Figure 2 It is a rapid evaporation ionization-high-resolution mass spectrometry in-situ analysis and high-resolution mass spectrometry modeling process, which demonstrates the analysis process through iKnife in-situ analysis, high-resolution mass spectrometry data acquisition, fingerprint spectrum data acquisition and modeling identification.

[0058] 2. Data processing and modeling analysis The raw data were collected by MassLynx v4.2 (SCN966 & SCN1010) software (Waters, Wilmslow, Manchester, UK), and the mass spectral fingerprint was obtained.

[0059] Figure 3 and Figure 4 These are the total ion current TIC chromatograms obtained in the REIMS working method, which are chromatograms of six parallel measurements of fresh pork samples and frozen-thawed pork samples.

[0060] Figure 5 and Figure 6 The mass spectrometry fingerprints of fresh pork samples and frozen-thawed pork samples show significant differences in the characteristic signals in the range of m / z 200–300 Da (such as m / z 279.3, 281.3, 283.2) and m / z 600–900 Da (such as m / z 594.3, 697.5, 742.5) between fresh and frozen-thawed samples. The signal intensity and chemical composition complexity of fresh samples are higher, while the signals of frozen-thawed samples are more single. In fresh meat samples, free fatty acids (such as m / z 279.3, 281.3) and phospholipids (such as m / z 594.3, 697.5) are more significant, reflecting the integrity of cell membranes and the activity of lipid metabolism. In frozen-thawed meat samples, these signals are weakened or absent, indicating that the freezing and thawing process may have destroyed the cell membrane structure and aggravated lipid oxidation. The fingerprint of fresh samples shows more high-intensity ion peaks, indicating the diversity and high concentration of metabolites. In the fingerprint spectra of freeze-thaw samples, the number of ion peaks decreased and their intensities decreased, indicating that the freeze-thaw treatment led to the degradation or disappearance of metabolites.

[0061] Based on the above mass spectral fingerprint data, the Abstract Model Builder (AMX) v1.0.1563.0 (Waters Research Centre, Budapest, Hungary) was used for analysis. In AMX, the data were corrected by Lockmass accurate mass, and the visual PCA-LDA discriminant model of fresh meat and frozen-thawed meat was constructed based on the principal component analysis (PCA) and linear discriminant analysis (LDA) methods with four groups of data: "fresh leg meat (including fresh front leg meat and fresh hind leg meat)", "fresh spine", "frozen-thawed leg meat (including frozen-thawed front leg meat and frozen-thawed hind leg meat)", and "frozen-thawed spine".

[0062] Figure 7 The LDA classification distribution of different pork samples based on the visual PCA-LDA discriminant model is shown, which shows that it can be effectively classified with a classification accuracy of 100%.

[0063] Example 2: Visualization of PCA-LDA Discriminant Model Cross-Validation 720 pork samples were collected (from Pinggu District, Beijing, Gucheng County, Hengshui City, Hebei Province, Nangong City, Xingtai City, Hebei Province, Xian County, Cangzhou City, Hebei Province, Guantao County, Handan City, Hebei Province, Jizhou District, Hengshui City, Hebei Province, Shenzhen City, Hengshui City, Hebei Province, Guangzong County, Xingtai City, Hebei Province, Nanhe County, Xingtai City, Hebei Province, Xinhe County, Xingtai City, Hebei Province, including backcross dual-purpose pigs and Muyuan dual-purpose pigs), including 240 fresh leg meat (including 120 front leg meat and 120 hind leg meat), 120 fresh spine fat, 240 frozen-thawed leg meat (including 120 front leg meat and 120 hind leg meat), and 120 frozen-thawed spine fat. The samples were measured using the above-mentioned mass spectrometry analysis conditions, and the pork samples of different parts and freshness were accurately distinguished based on the fresh meat and frozen-thaw meat PCA-LDA model constructed above.

[0064] The cross validation results are as follows Figure 8 As shown, the classification accuracy is 100%, which shows that the visualized PCA-LDA discrimination model constructed by the present invention has excellent detection accuracy.

[0065] Example 3: Blind Sample Testing 40 pork samples were collected, and the samples were measured using the mass spectrometry analysis conditions of Example 1 above, and were detected based on the PCA-LDA model of fresh meat and frozen-thawed meat constructed above.

[0066] The blind sample verification results are as follows Fig. 9 and Fig.10As shown, in the blind sample test, the PCA-LDA model constructed by the present invention exhibits a high recognition ability, and the classification accuracy is above 99%. The recognition probabilities of fresh spine fat and frozen-thawed spine fat are 99.39% and 99.38%, respectively. The distribution of fresh meat and frozen-thawed meat is clearly distinguished, and no significant error occurs. The reliability and accuracy of the model were further verified by mass spectrometry fingerprinting, with an identification time of 6s / sample. The distribution characteristics of key metabolites in the sample are highly consistent with the model prediction results, further illustrating the practical applicability and robustness of the model.

[0067] In summary, the present invention provides a method and application for identifying the freshness of different parts of pork. The present invention constructs a visual PCA-LDA discriminant model by combining mass spectrometry analysis and statistical modeling technology, and successfully realizes efficient identification of pork freshness and processing status based on the visual PCA-LDA discriminant model, with a model classification accuracy of more than 99%. Blind sample verification further verifies the reliability and practical application ability of the model. As a fast and accurate pork quality control tool, it can provide an important reference for the freshness detection of other meats and processed foods.

[0068] The above is a detailed description of the embodiments of the present invention, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the purpose of the present invention. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

Claims

1. A PCA-LDA model construction method for identifying fresh pork and frozen-thawed pork, characterized in that: include: S1. Obtaining mass spectrometry fingerprint data of fresh pork and frozen-thawed pork respectively; S2. Based on principal component analysis and linear discriminant analysis, a PCA-LDA model is constructed using the mass spectrum fingerprint data of the fresh pork and the mass spectrum fingerprint data of the frozen-thawed pork.

2. The PCA-LDA model construction method according to claim 1, characterized in that: The fresh pork includes fresh pork leg and fresh pork loin; And / or, the frozen-thawed pork includes frozen-thawed pork leg meat and frozen-thawed pork loin.

3. The PCA-LDA model construction method according to claim 2, characterized in that: The method for obtaining the mass spectrum fingerprint data includes rapid evaporation ionization-high resolution mass spectrometry.

4. The PCA-LDA model construction method according to claim 3, characterized in that: The mass spectrometry detection parameters of the rapid evaporation ionization-high resolution mass spectrometry are set as follows: The modes are negative ion mode and high sensitivity mode, the scanning mass range is 100~1200 m / z; the scanning time is 0.4~0.6 seconds / scan, and the flow rate of the accurate mass calibration solution is 180~220μL / min; Preferably, the accurate mass calibration solution is a 300-500 ppb leucine enkephalin solution.

5. The PCA-LDA model construction method according to any one of claims 1 to 4, characterized in that: The PCA-LDA model is constructed using an abstract model generator.

6. A model for identifying the freshness of pork, characterized in that: The method for constructing the PCA-LDA model is adopted according to any one of claims 1 to 5.

7. A method for identifying fresh pork and frozen-thawed pork, characterized in that: include: The mass spectrum fingerprint data of the pork sample to be tested is obtained, and the mass spectrum fingerprint data is imported into the model for identifying the freshness of pork as described in claim 6 for classification and identification.

8. The method according to claim 7, characterized in that The method for obtaining mass spectrum fingerprint data of the pork sample to be tested includes rapid evaporation ionization-high resolution mass spectrometry.

9. A method for identifying the freshness of different parts of pork, characterized in that: include: The mass spectrum fingerprint data of the pork sample to be tested is obtained, and the mass spectrum fingerprint data is imported into the model constructed by the PCA-LDA model construction method described in any one of claims 2 to 5 for classification and identification.

10. Use of the PCA-LDA model construction method according to any one of claims 1 to 5, the model for identifying the freshness of pork according to claim 6, the method for identifying fresh pork and frozen-thawed pork according to any one of claims 7 to 8, or the method for identifying the freshness of different parts of pork according to claim 9 in pork identification.

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