Method for identifying repeatedly frozen and thawed meat products by screening differential markers based on lipidomics

Abstract

The invention discloses a method for identifying a repeatedly frozen and thawed meat product by screening differential markers based on lipidomics. The method comprises the following steps: pre-treating a sample; carrying out non-targeted component determination by an LC-MS / MS method; performing data preprocessing and multivariate statistical analysis; screening differential markers by taking variable importance projection indexes and the like as screening indexes; and carrying out targeted quantitative analysis on the key differential marker to confirm the reliability of identifying the repeated freezing and thawing of the meat product by using the key differential marker as a biomarker. By adopting the method, 55 differential markers are successfully screened, key biomarkers LPC (18: 1 / 0: 0) and myristic acid are determined, and the key biomarkers can be used for identifying repeated freezing and thawing of meat products through targeted quantitative analysis.

Description

Technical Field

[0001] This invention belongs to the field of food inspection technology, specifically relating to a method for identifying repeatedly frozen and thawed meat products based on lipidomics screening of differential biomarkers. Background Technology

[0002] Freezing has become a popular method of meat preservation, playing a vital role in maintaining meat quality and safety and serving as an important technology for ensuring a secure meat supply chain. However, temperature fluctuations during storage, transportation, or consumption can lead to repeated freeze-thaw cycles.

[0003] During freeze-thaw cycles, ice crystals form, melt, and regenerate, and cell membranes rupture, causing microstructural damage that leads to a significant decline in nutritional and quality characteristics, such as color deterioration, reduced water retention, and oxidation and hydrolysis of lipids and proteins. This can even shorten the expected shelf life of meat products achieved by freezing technology. Traditional quality deterioration indicators typically rely on a combined analysis of multiple indicators, including volatile basic nitrogen, thiobarbituric acid reactant values, and sensory evaluation. However, this approach has low sensitivity, struggles to capture early molecular changes during freeze-thaw cycles, and is cumbersome, complex, and inefficient. Especially in the stage of ≤3 freeze-thaw cycles, traditional indicators are insufficient for identification.

[0004] In recent years, lipidomics based on LC-MS / MS technology has emerged as a method with high separation capability, high throughput, and high resolution.

[0005] Currently, there are no reports on the application of differential biomarkers based on lipidomics screening in the identification of repeatedly frozen and thawed meat products. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments.

[0007] In view of the problems existing in the above and / or prior art, the present invention is proposed.

[0008] Therefore, the purpose of this invention is to overcome the shortcomings of the prior art and provide a method for identifying repeatedly frozen and thawed meat products based on lipidomics screening of differential biomarkers.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for identifying repeatedly frozen and thawed meat products based on lipidomics screening of differential biomarkers, comprising,

[0010] Differential biomarkers for identifying repeatedly frozen and thawed meat products were screened based on lipidomics. These differential biomarkers included LPC (0:0 / 18:1) and myristic acid.

[0011] After freeze-thaw treatment, the differential markers of frozen-thawed meat products were quantitatively determined by LC-MS / MS, and the number of freeze-thaw cycles of the frozen-thawed meat products was identified based on the measurement data.

[0012] As a preferred embodiment of the method described in this invention, the step of screening for differential biomarkers in repeatedly frozen-thawed meat products based on lipidomics includes,

[0013] Samples with different freeze-thaw cycles were homogenized with methyl tert-butyl ether-methanol solution, centrifuged, and the supernatant was collected, freeze-dried, redissolved with isopropanol-acetonitrile solution, filtered through a membrane, and then loaded for testing.

[0014] LC-MS / MS was used to perform extensive non-targeted lipidomics analysis on the samples to obtain information on their composition.

[0015] The raw data of the material composition information are preprocessed and subjected to multivariate statistical analysis;

[0016] Differential biomarkers were screened and identified using variable importance projection indices, which included P-value, VIP value, and fold difference (FC). The screening criteria for lipid composition data were: P < 0.05, VIP ≥ 1, and FC ≥ 2 or ≤ 0.5.

[0017] In a preferred embodiment of the method described in this invention, LC-MS / MS is used to quantitatively determine the differential markers in the samples, wherein...

[0018] Chromatographic conditions: ChromCore AQ C18 column, column size 2.1mm×100.0mm×1.8μm; mobile phase A for liquid chromatography was 10mM ammonium acetate-acetonitrile aqueous solution; mobile phase B was 10mM ammonium acetate-acetonitrile-isopropanol solution.

[0019] The analytical method parameters were set as follows: injection volume 2 μL, flow rate 0.3 mL / min; gradient elution program: 0-1 min 35% B; 1-2 min 30-40% B; 10-15 min 80-95% B; 15-18 min 95-100% B; 15-20 min 95-100% B; 16-18 min 30-40% B.

[0020] Mass spectrometry parameter settings: mass scan range 50-1200 m / z, ion source spray voltage for positive and negative ion modes respectively, ion source temperature 120℃, desolvation gas temperature 450-480℃; cone gas flow rate 50-80 L / h, desolvation gas flow rate 750-800 L / h, nebulizer gas pressure 6.5 bar; cone voltage 40 V, collision energy dynamic range 20-45 eV;

[0021] Leucine enkephalin was used as a locked mass reference, and mass axis calibration was performed every 30 seconds.

[0022] In a preferred embodiment of the method described in this invention, the methyl tert-butyl ether-methanol solution has a volume ratio of 3:1.

[0023] In a preferred embodiment of the method described in this invention, the isopropanol-acetonitrile solution has a volume ratio of 1:1.

[0024] In a preferred embodiment of the method described in this invention, the filter membrane is subjected to sample loading and testing, wherein the particle size of the filter membrane is 0.22 μm.

[0025] As a preferred embodiment of the method described in this invention, the preprocessing involves extracting meat quality spectral data with different freeze-thaw cycles using Progenesis QI software, and performing preprocessing on the data including baseline filtering, peak identification, integration, retention time correction, and peak alignment.

[0026] As a preferred embodiment of the method described in this invention, the multivariate statistical analysis involves importing the preprocessed data into SIMCA 14.1 software, and using PCA to analyze the similarity and differences of samples, characterizing sample differences and excluding outliers.

[0027] OPLS-DA analysis was then performed to reduce background interference and obtain VIP values.

[0028] Beneficial effects of this invention:

[0029] This invention provides a method for identifying repeatedly frozen and thawed meat products by screening differential biomarkers based on lipidomics. This method uses non-targeted and targeted chromatography-mass spectrometry to discover biomarkers of quality changes in repeatedly frozen and thawed meat products, enabling highly sensitive and efficient identification of these products. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0031] Figure 1The schematic diagram of the distribution of lipid metabolites and their number in the embodiments of the present invention is shown in the figure. AB is a schematic diagram of the distribution of lipid types and numbers, CD is a PCA score diagram of substances between groups, and EH is a volcano diagram of differential compounds between groups.

[0032] Figure 2 This is a Venn diagram of commonly observed differentially expressed metabolites in lipidomics screening in this embodiment of the invention.

[0033] Figure 3 This is a graph showing the trend of changes in the content of differentially metabolites in lipidomics in embodiments of the present invention;

[0034] Figure 4 This is a Spearman correlation coefficient analysis diagram of lipidomics in an embodiment of the present invention;

[0035] Figure 5 This is a graph showing the trend of changes in the content of key lipidomics biomarkers in an embodiment of the present invention.

[0036] Figure 6 This is a graph showing the number of metabolites identified under different extraction conditions in Comparative Example 1 of the present invention. Detailed Implementation

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the examples in the specification.

[0038] The embodiments of this application provide a method for screening differential biomarkers based on lipidomics to identify repeatedly frozen and thawed meat products.

[0039] Example 1

[0040] 1. Sample pretreatment

[0041] Sample Preparation: Fresh pig hind legs were used in the experiment. After removing visible connective tissue, the meat was minced using a meat grinder at a lean-to-fat ratio of 2:8, and then formed into meatballs using a round mold (3cm in diameter and 1cm in thickness). The temperature was maintained at approximately 4℃ throughout the preparation process. The samples were randomly divided into FT0, FT1, FT2, FT3, and FT5 groups, representing samples that underwent 0, 1, 2, 3, and 5 freeze-thaw cycles, respectively. Eight samples were stored in each group in a freezer. Except for the FT0 group, the samples in the other groups were immediately flash-frozen at -30℃ to -20℃, then frozen at -20±0.5℃ for 24 hours, and then thawed at 4℃ for 12 hours until the core temperature of the sample reached 2℃, which was recorded as one freeze-thaw cycle (FT1). The above operation was repeated to complete 2, 3, and 5 freeze-thaw cycles.

[0042] Lipid extraction: 50 mg of sample was added to a 2 mL centrifuge tube, along with 1 mL of MTBE-methanol extractant (3:1, v / v) pre-cooled to -20 °C and two steel balls (3 mm). The mixture was homogenized using a tissue homogenizer (60 Hz, twice, 60 seconds each time). Then, 250 μL of ice water was added, and the homogenization was repeated twice under the same conditions. After standing at -20 °C for 5 hours, the homogenized sample was centrifuged at 12000 rpm for 15 minutes at 4 °C. 250 μL of the supernatant was dried using a solvent evaporator and stored at -80 °C for later use. Before analysis, the sample was reconstituted with 400 μL of isopropanol-acetonitrile solution (1:1, v / v), filtered through a 0.22 μm filter membrane, and transferred to a brown LC / MS vial. Quality control (QC) samples were prepared by mixing equal volumes of all test samples. One QC sample was inserted for every five samples tested to monitor system stability.

[0043] 2. Determination of non-targeted components

[0044] Metabolomics analysis was performed using an ACQUITY I-Class plus / Synapt XS ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry system (Waters Corporation, USA). The QTOF mass spectrometry was conducted using an electrospray ionization (ESI) source in both positive and negative ion modes. Mass spectrometry data were collected in MS^E-independent acquisition (DIA) mode, alternating between low-energy (precursor ion) and high-energy (full fragment) scans. The reliability of the monitoring data and the stability of the system were evaluated by monitoring the retention time shift and relative standard deviation of peak areas for quality control (QC) samples and internal standards.

[0045] Metabolite separation was performed using a ChromCore AQ C18 column (2.1 mm × 100.0 mm × 1.8 μm). The mobile phase A for liquid chromatography was 10 mM ammonium acetate-acetonitrile aqueous solution, and the mobile phase B was 10 mM ammonium acetate-acetonitrile-isopropanol solution. The analytical parameters were set as follows: injection volume 2 μL, flow rate 0.3 mL / min; gradient elution program: 0–1 min 35% B; 1–2 min 30–40% B; 10–15 min 80–95% B; 15–18 min 95–100% B; 15–20 min 95–100% B; 16–18 min 30–40% B. Mass spectrometry parameter settings: mass scan range 50-1200 m / z, ion source spray voltage for both positive and negative ion modes, ion source temperature 120℃, desolvation gas temperature 450-480℃; cone gas flow rate 50-80 L / h, desolvation gas flow rate 750-800 L / h, nebulizer gas pressure 6.5 bar; cone voltage 40 V, collision energy dynamic range 20-45 eV. Leucine enkephalin was used as the locked mass reference, and mass axis calibration was performed every 30 seconds.

[0046] 3. Data Processing

[0047] The raw mass spectrometry data were processed using Progenesis QI software, including baseline filtering, peak identification, integration, retention time correction, and peak alignment, yielding a data matrix containing retention time, mass-to-charge ratio, and peak intensity. Based on the relative standard deviation (>30%) of the QC samples, raw data with significant deviations were discarded. Peak area data with missing values ​​≤30% in a single group or all groups were retained, and the missing values ​​in the raw data were simulated to be filled with one-fifth of the detection limit. The corrected peaks were then used to retrieve lipidomics identification information from the Lipid Maps library.

[0048] 4. Data Result Mining

[0049] Metabolic molecules in repeatedly frozen-thawed pork mince were analyzed using positive and negative ionization modes. For example... Figure 1 As shown in AB, a total of 1053 metabolites were identified in repeatedly frozen and thawed pork mince, including 534 positive ion metabolites and 519 negative ion metabolites. PCA analysis was used to observe the differences in metabolites among the groups. Figure 1 As shown in CD. A volcano plot is created by combining the p-value and fold change (FC) of the samples. The distribution of upregulated metabolites (red dots) and downregulated metabolites (blue dots) can be visually observed from the differential metabolite volcano plot. Figure 1 EH). The number of differentially expressed metabolites is shown in Table 1, with P < 0.05 and FC ≥ 2 or ≤ 0.5 as the criteria.

[0050] Table 1. Comparison of metabolite counts among groups with different freeze-thaw cycles.

[0051]

[0052] like Figure 2 As shown in Table 2, based on OPLS-DA criteria of P < 0.05, FC ≥ 2 or ≤ 0.5, VIP ≥ 1, and Venn diagram screening, 55 characteristic differentially expressed metabolites were found in the four groups FT0 vs FT1, FT0 vs FT2, FT0 vs FT3, and FT0 vs FT5, including 8 downregulated and 47 upregulated differentially expressed metabolites. The trends in the content of these differentially expressed metabolites were visualized using a heatmap. Figure 3 Spearman correlation coefficient analysis was performed on the top 30 differentially metabolites (based on VIP values). Figure 4 These characteristic metabolites could serve as potential biomarkers for freeze-thaw cycles.

[0053] Table 2. Commonly observed characteristic metabolites among groups

[0054]

[0055]

[0056]

[0057] 5. Data Result Validation

[0058] The top 10 differentially metabolites with the highest VIP scores were lysophosphatidylcholine (LPC) (0:0 / 18:1(9E)), LPE (O-16:0 / 0:0), TG (14:1 / 18:1 / 18:3), myristic acid, Cer (m18:1 / 24:1), LPE (20:2 / 0:0), DG (14:0 / 20:4 / 0:0), LPS (O-26:1 / 0:0), Cer (t18:1(6OH) / 27:0), and PE (O-16:0 / 20:5).

[0059] Example 2

[0060] 1. Targeted quantitative analysis of representative biomarkers

[0061] Targeted quantitative analysis was performed using ultra-high performance liquid chromatography-triple quadrupole mass spectrometry (Waters Corporation, USA). Metabolite separation was performed using a ChromCore AQ C18 column (2.1 mm × 100.0 mm × 1.8 μm). The mobile phase for lysophosphatidylcholine (LPC) was 0:0 / 18:1. Mobile phase A was methanol solution, and mobile phase B was 125 mM formic acid aqueous solution. The analytical parameters were set as follows: injection volume 2 μL, flow rate 0.3 mL / min; gradient elution program: 0 min, 90% A; 0.5 min, 90% A; 2.0 min, 100% A; 5.0 min, 100% A; 5.5 min, 90% A.

[0062] For myristic acid liquid chromatography, mobile phase A was 10 mM ammonium acetate-acetonitrile aqueous solution, and mobile phase B was 10 mM ammonium acetate-acetonitrile-isopropanol solution. The analytical parameters were set as follows: injection volume 2 μL, flow rate 0.3 mL / min; gradient elution program: 0-1 min 35% B; 1-2 min 30-40% B; 10-15 min 80-95% B; 15-18 min 95-100% B; 15-20 min 95-100% B; 16-18 min 30-40% B.

[0063] Mass spectrometry parameter settings: mass scan range 50-1200 m / z, ion source spray voltage for positive and negative ion modes respectively, ion source temperature 120℃, desolvation gas temperature 450-480℃; cone gas flow rate 50-80 L / h, desolvation gas flow rate 750-800 L / h, nebulizer gas pressure 6.5 bar; cone voltage 40 V, collision energy dynamic range 20-45 eV.

[0064] Leucine enkephalin was used as a locked mass reference, and mass axis calibration was performed every 30 seconds.

[0065] 2. Data Results Analysis

[0066] The results show that the trend of their content in meatballs changes as follows: Figure 5 As shown, with the increase of repeated freeze-thaw cycles, the content of LPC (0:0 / 18:1) and myristic acid gradually increases, which can achieve identification after only one freeze-thaw cycle.

[0067] Comparative Example 1

[0068] 1. Sample preparation

[0069] Fresh pig hind legs were used in the experiment. Visible connective tissue was removed, and the meat was minced using a meat grinder at a lean-to-fat ratio of 2:8. The minced meat was then shaped into meatballs using a round mold (3cm in diameter, 1cm thick). The temperature was maintained at approximately 4℃ throughout the preparation process. Samples were randomly divided into four groups: FT0, FT1, FT2, FT3, and FT5, representing samples that underwent 0, 1, 2, 3, and 5 freeze-thaw cycles, respectively. Eight samples were stored in each group in a freezer. Except for the FT0 group, the samples in the other groups were immediately flash-frozen from -30℃ to -20℃, then frozen at -20±0.5℃ for 24 hours, and then thawed at 4℃ for 12 hours until the core temperature of the sample reached 2℃. This was recorded as one freeze-thaw cycle (FT1). The above operation was repeated to complete 2, 3, and 5 freeze-thaw cycles.

[0070] 2. Optimization of extraction conditions

[0071] Take 50 mg of sample and two steel balls (3 mm) and add them to a 2 mL centrifuge tube. Optimize the extraction conditions as follows:

[0072] a. Extract with methanol:acetonitrile:water = 2:2:1, homogenize for 1 min, repeat 4 times, vortex for 1 min, centrifuge, and collect the supernatant;

[0073] b. Extract with 1.2 mL of isopropanol, homogenize for 1 min, repeat 4 times, vortex for 1 min, centrifuge, and collect the supernatant;

[0074] c. Extract with 1.2 mL of MTBE, homogenize for 1 min, repeat 4 times, vortex for 1 min each time, centrifuge, and collect the supernatant.

[0075] d. MTBE:MeOH = 3:11 mL, homogenize twice, add 250 μl of water, homogenize twice more, vortex for 1 min, centrifuge at high speed, and then take the supernatant;

[0076] e. Add 360 μL of water and MTBE:MeOH = 5:1960 μL, homogenize together for 1 min, repeat 4 times, vortex for 1 min, centrifuge, and collect the supernatant.

[0077] f. Methanol:water = 6:5440μL, homogenize twice, add 800μL MTBE, homogenize twice more, vortex for 1 min, centrifuge at high speed, and then take the supernatant;

[0078] g. Methanol:water = 2:5525μL, add 750μl of MTBE, homogenize together for 1 min, repeat 4 times, vortex for 1 min, centrifuge, and collect the supernatant;

[0079] h. Chloroform: Methanol = 2:11 mL, homogenize twice, add 250 μL of water, homogenize twice more, vortex for 1 min, centrifuge, and collect the supernatant;

[0080] 2. Determination of non-targeted components

[0081] Metabolomics analysis was performed using an ACQUITY I-Class plus / Synapt XS ultra-high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry system (Waters Corporation, USA). The QTOF mass spectrometry was conducted using an electrospray ionization (ESI) source in both positive and negative ion modes. Mass spectrometry data were collected in MS^E-independent acquisition (DIA) mode, with alternating low-energy (precursor ion) and high-energy (full fragment) scans. The reliability of the monitoring data and the stability of the system were evaluated by monitoring the retention time shift and relative standard deviation of peak areas for quality control (QC) samples and internal standards.

[0082] Metabolite separation was performed using a ChromCore AQ C18 column (2.1 mm × 100.0 mm × 1.8 μm). The mobile phase A for liquid chromatography was 10 mM ammonium acetate-acetonitrile aqueous solution, and the mobile phase B was 10 mM ammonium acetate-acetonitrile-isopropanol solution. The analytical parameters were set as follows: injection volume 2 μL, flow rate 0.3 mL / min; gradient elution program: 0–1 min 35% B; 1–2 min 30–40% B; 10–15 min 80–95% B; 15–18 min 95–100% B; 15–20 min 95–100% B; 16–18 min 30–40% B. Mass spectrometry parameter settings: mass scan range 50-1200 m / z, ion source spray voltage for both positive and negative ion modes, ion source temperature 120℃, desolvation gas temperature 450-480℃; cone gas flow rate 50-80 L / h, desolvation gas flow rate 750-800 L / h, nebulizer gas pressure 6.5 bar; cone voltage 40 V, collision energy dynamic range 20-45 eV. Leucine enkephalin was used as the locked mass reference, and mass axis calibration was performed every 30 seconds.

[0083] 3. Data Processing

[0084] The raw mass spectrometry data were processed using Progenesis QI software, including baseline filtering, peak identification, integration, retention time correction, and peak alignment, yielding a data matrix containing retention time, mass-to-charge ratio, and peak intensity. Based on the relative standard deviation (>30%) of the QC samples, raw data with significant deviations were discarded. Peak area data with missing values ​​≤30% in a single group or all groups were retained, and the missing values ​​in the raw data were simulated to be filled with one-fifth of the detection limit. The corrected peaks were then used to retrieve lipidomics identification information from the Lipid Maps library.

[0085] 3. Data Analysis

[0086] Metabolic molecules of repeatedly frozen-thawed pork mince were analyzed using positive and negative ionization modes. The number of metabolites identified under different extraction conditions is as follows: Figure 6 As shown, condition d detected the most metabolites, and therefore this condition was selected as the optimal extraction condition.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the present invention.

Claims

1. A method for identifying repeatedly frozen-thawed meat products based on lipidomics screening of differential biomarkers, characterized in that: include, Differential biomarkers for identifying repeatedly frozen and thawed meat products were screened based on lipidomics. After freeze-thaw treatment, the differential markers of frozen-thawed meat products were quantitatively determined by LC-MS / MS, and the number of freeze-thaw cycles of the frozen-thawed meat products was identified based on the measurement data.

2. The method as described in claim 1, characterized in that: The differential markers include lysophosphatidylcholine (LPC) (0:0 / 18:1) and myristic acid.

3. The method as described in claim 1, characterized in that: The differential biomarkers for identifying repeatedly frozen and thawed meat products, screened using lipidomics, include: Samples with different freeze-thaw cycles were homogenized with methyl tert-butyl ether-methanol solution, centrifuged, and the supernatant was collected, freeze-dried, redissolved with isopropanol-acetonitrile solution, filtered through a membrane, and then loaded for testing. LC-MS / MS was used to perform extensive non-targeted lipidomics analysis on the samples to obtain information on their composition. The raw data of the material composition information are preprocessed and subjected to multivariate statistical analysis; Differential biomarkers were screened and identified using variable importance projection indices, which included P-value, variable projection importance VIP value, and fold difference FC. The screening conditions for lipid composition data were: P < 0.05, VIP ≥ 1, and FC ≥ 2 or ≤ 0.

5.

4. The method as described in claim 2, characterized in that: The differential markers in the samples were quantitatively determined using LC-MS / MS, wherein... Chromatographic conditions: ChromCore AQ C18 column, column size 2.1mm×100.0mm×1.8μm; mobile phase A for liquid chromatography was 10mM ammonium acetate-acetonitrile aqueous solution; mobile phase B was 10mM ammonium acetate-acetonitrile-isopropanol solution. The analytical method parameters were set as follows: injection volume 2 μL, flow rate 0.3 mL / min; gradient elution program: 0-1 min 35% B; 1-2 min 30-40% B; 10-15 min 80-95% B; 15-18 min 95-100% B; 15-20 min 95-100% B; 16-18 min 30-40% B. Mass spectrometry parameter settings: mass scan range 50-1200 m / z, ion source spray voltage for positive and negative ion modes respectively, ion source temperature 120℃, desolvation gas temperature 450-480℃; cone gas flow rate 50-80 L / h, desolvation gas flow rate 750-800 L / h, nebulizer gas pressure 6.5 bar; cone voltage 40 V, collision energy dynamic range 20-45 eV; Leucine enkephalin was used as a locked mass reference, and mass axis calibration was performed every 30 seconds.

5. The method as described in claim 2, characterized in that: The methyl tert-butyl ether-methanol solution has a volume ratio of 3:

1.

6. The method as described in claim 2, characterized in that: The isopropanol-acetonitrile solution has a volume ratio of 1:

1.

7. The method as described in claim 2, characterized in that: The filter membrane was subjected to sample loading and testing, wherein the particle size of the filter membrane was 0.22 μm.

8. The method as described in claim 2, characterized in that: The preprocessing involved extracting meat quality spectral data with different freeze-thaw cycles using Progenesis QI software, and performing preprocessing on the data including baseline filtering, peak identification, integration, retention time correction, and peak alignment.

9. The method as described in claim 1 or 8, characterized in that: The multivariate statistical analysis involves importing the preprocessed data into SIMCA. The software 14.1 uses PCA to analyze sample similarity and differences, characterize sample differences, and exclude outliers. OPLS-DA analysis was then performed to reduce background interference and obtain VIP values.

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