Method for identifying breast muscles of Hetian chicken and elephant hole chicken based on metabonomics
Differential metabolites of the breast muscles of Hetian frogs and Xiangdong chickens were screened through metabolomics and ultra-high performance liquid chromatography-mass spectrometry, solving the problem of strong subjectivity of traditional identification methods, achieving efficient and accurate identification results, and having the application potential of food traceability and quality control.
Patent Information
- Application Number
- CN202510371387.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-27
- Publication Date
- 2025-07-11
AI Technical Summary
Traditional methods are difficult to accurately distinguish between the muscles of the river frog and the elephant cave chicken, and are highly subjective and not accurate enough.
Metabolomics technology combined with ultra-high performance liquid chromatography-mass spectrometry technology was used to screen differential metabolites and potential characteristic markers through principal component analysis and orthogonal partial least squares analysis, and identify them using ultra-high performance liquid chromatography-mass spectrometry detection.
It has achieved efficient and accurate identification of the muscles of Hetian Frog and Xiangdong Chicken, and has a wide range of application prospects, especially in food traceability and quality control.
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Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of chicken breast muscle variety identification, and particularly to a method for identifying the breast muscles of Hetian chickens and Xiangdong chickens based on metabolomics. Background Art
[0002] With the increasing requirements of consumers for food safety and meat quality, the identification and quality control of poultry varieties have become more and more important. Hetian chickens and Xiangdong chickens are local special chicken breeds in China. Due to their different growth environments and feeding methods, there are certain differences in their meat quality, flavor and nutritional components. Traditional identification methods mainly rely on sensory evaluation, which is highly subjective and difficult to accurately distinguish. Therefore, the use of modern metabolomics technology to analyze the differences in metabolites in chicken meat provides a more accurate and efficient identification method. Summary of the Invention
[0003] The purpose of the present invention is to provide a method for identifying the breast muscles of Hetian chickens and Xiangdong chickens based on metabolomics. Through ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) technology and combined with high-precision statistical analysis, it can efficiently and accurately complete the variety identification of the breast muscles of Hetian chickens and Xiangdong chickens, and has a wide range of application prospects, especially in food traceability, quality control and variety identification.
[0004] In order to achieve the above-mentioned invention purpose, the present invention provides the following technical solutions:
[0005] The present invention provides a method for identifying the breast muscles of Hetian chickens and Xiangdong chickens based on metabolomics, which is characterized by including the following steps:
[0006] (1) Extract the test samples to obtain metabolite extracts and collect metabolomics data;
[0007] (2) Perform principal component analysis and orthogonal partial least squares analysis on the metabolomics data in step (1), and screen out differential metabolites and potential characteristic markers;
[0008] (3) Use the potential characteristic markers screened in step (2) to perform ultra-high performance liquid chromatography-mass spectrometry detection and identification on the test samples.
[0009] Preferably, differential metabolites are screened with a variable importance > 1, a significance level < 0.05, and a fold change FC of the metabolite expression levels between Hetian chicken and Xiangdong chicken breast muscle samples greater than 1.5 or less than 0.67 as the threshold values; differential metabolites with |Log2FC| greater than 2.5 of the metabolite extracts are used as potential characteristic markers.
[0010] Preferably, the obtained data after detection is preprocessed to obtain the relative peak areas and retention times of all metabolites.
[0011] Preferably, the preprocessing includes baseline correction, peak extraction, peak correction, standardization, and normalization, filtering peaks with a missing rate > 50% in the sample, filling blank values with KNN, and correcting peak areas using the SVR method.
[0012] Preferably, the conditions for ultra-high performance liquid chromatography in the detection process are as follows: mobile phase A is an aqueous solution of 0.1% formic acid, and mobile phase B is an acetonitrile solution of 0.1% formic acid; the injection volume is 4 μL, the flow rate is 0.4 mL / min, and the column temperature is 40 °C.
[0013] Preferably, the gradient elution conditions are as follows: initially 5% B, rising to 20% within 2 min, rising to 60% from 2 - 5 min, rising to 99% from 5 - 6 min and holding for 1.5 min, dropping to 5% B within 0.1 min and equilibrating for 2.4 min.
[0014] Preferably, the mass spectrometry conditions in the detection process are as follows: the mass scanning ranges of MS1 and MS2 are 75 - 1000 Da, and the resolution is 35000; scanning in positive and negative ion modes, the ionization voltage in the positive ion mode is 3500 V, and the ionization voltage in the negative ion mode is 3200 V; the sheath gas flow rate is 30 Arb; the auxiliary gas flow rate is 5 Arb; the temperature of the ion transfer tube is 320 °C; the atomization temperature is 300 °C; the collision energy step is 50 V.
[0015] By adopting the above technical solutions, the present invention has the following beneficial effects:
[0016] The technical solution of the present invention uses non-targeted metabolomics technology to screen out differential biomarker metabolites for identifying the breast muscles of Hetian chickens and Xiangdong chickens, screens out differential metabolites (the greater the fold difference, the more significant the differential expression) and potential characteristic markers by comparing the expression levels of metabolites in the two samples, and then combines ultra-high performance liquid chromatography-mass spectrometry technology for analysis and identification. This method clearly reveals the metabolite differences between the breast muscles of Hetian chickens and Xiangdong chickens, can effectively distinguish these two kinds of chicken; this technical method is advanced, the test results are reliable, has a wide application prospect, and provides an efficient and accurate tool for food traceability, quality control, and variety identification. Description of the Drawings
[0017] Figure 1 is the correlation coefficient graph between QC samples ( Figure 1 where A in represents the correlation analysis graph of quality control samples in the positive ion mode, and B represents the correlation analysis graph of quality control samples in the negative ion mode);
[0018] Figure 2 is the PCA score graph of breast muscle samples of Hetian chickens and Xiangdong chickens;
[0019] Figure 3It is the OPLS-DA score plot of the breast muscle samples of Hetian chickens and Xiangdong chickens;
[0020] Figure 4 It is the OPLS-DA statistical chart of the breast muscle samples of Hetian chickens and Xiangdong chickens. Specific implementation manners
[0021] The present invention provides a method for identifying the breast muscles of Hetian chickens and Xiangdong chickens based on metabolomics, which is characterized by including the following steps:
[0022] (1) Extract the sample to be tested to obtain a metabolite extract, and collect metabolomics data;
[0023] (2) Perform principal component analysis and orthogonal partial least squares analysis on the metabolomics data in step (1), and screen out differential metabolites and potential characteristic markers;
[0024] (3) Use the potential characteristic markers screened in step (2) to perform ultra-high performance liquid chromatography-mass spectrometry detection on the sample to be tested, and identify it.
[0025] In the present invention, the sample to be tested is extracted to obtain polar and non-polar metabolite extracts, and metabolomics data is collected for the extracts.
[0026] In the present invention, principal component analysis and orthogonal partial least squares chemometric analysis are performed on the metabolomics data, and differential metabolites and potential characteristic markers are screened out. Whether there are differences between the two groups of samples is judged through the principal component analysis, and the potential characteristic markers of the sample to be tested are determined through the orthogonal partial least squares analysis.
[0027] In the present invention, differential metabolites are screened with the variable importance in the projection (VIP)>1, significance level (P)<0.05, and the fold change (FC) of the metabolite expression levels between the breast muscle samples of Hetian chickens and Xiangdong chickens being greater than 1.5 or the fold change (FC) being less than 0.67 as the threshold value; differential metabolites with |Log2FC| of the metabolite extract greater than 2.5 are used as potential characteristic markers.
[0028] In the present invention, after preprocessing the detected data, the relative peak areas and retention times of all metabolites are obtained.
[0029] In the present invention, the preprocessing includes baseline correction, peak extraction, peak correction, standardization and normalization. Peaks with a missing rate > 50% in the sample are filtered, and blank values are filled by KNN. The peak area is corrected by the SVR method. The corrected and screened peaks are used for metabolite identification by retrieving the self-built laboratory database, integrating public libraries, prediction libraries and the metDNA method. Finally, substances with a comprehensive identification score above 0.5 and a CV value of the QC sample less than 0.3 are extracted, and then positive and negative mode merging is performed (retaining the substance with the highest qualitative level and the smallest CV value).
[0030] In the present invention, the conditions of the ultra-high performance liquid chromatography in the detection process are as follows: mobile phase A is an aqueous solution of 0.1% formic acid, and mobile phase B is an acetonitrile solution of 0.1% formic acid; the injection volume is 4 μL, the flow rate is 0.4 mL / min, and the column temperature is 40 °C. The gradient elution conditions of the present invention are: initially 5% B, rising to 20% within 2 min, rising to 60% from 2 - 5 min, rising to 99% from 5 - 6 min and maintaining for 1.5 min, dropping to 5% B within 0.1 min and equilibrating for 2.4 min.
[0031] In the present invention, the mass spectrometry conditions in the detection process are as follows: the mass scanning ranges of MS1 and MS2 are 75 - 1000 Da, and the resolution is 35000; positive and negative ion mode scanning, the ionization voltage in the positive ion mode is 3500 V, and the ionization voltage in the negative ion mode is 3200 V; the sheath gas flow rate is 30 Arb; the auxiliary gas flow rate is 5 Arb; the temperature of the ion transfer tube is 320 °C; the atomization temperature is 300 °C; the collision energy step is 50 V.
[0032] The technical solutions provided by the present invention are described in detail below in conjunction with embodiments, but they should not be construed as limiting the protection scope of the present invention.
[0033] Example 1
[0034] The breast muscle samples of Hetian chickens and Xiangdong chickens were collected from the animal feeding laboratory of Longyan University. Hetian chicken breast muscle samples (120 days old) and Xiangdong chicken breast muscle samples (120 days old) were randomly selected and fed under the same feeding management conditions and nutritional levels. After sampling, the breast muscle samples were placed in a refrigerator at 80 °C for standby.
[0035] Sample preparation: Take out the sample from the -80°C refrigerator and thaw it on ice (all subsequent operations are carried out on ice); use liquid nitrogen to grind it evenly, weigh 20 mg (±1 mg) of the sample and transfer it to a centrifuge tube with the corresponding number; add 400 μL of 70% methanol aqueous solution containing internal standard, shake at 1500 r / min for 5 min, and let it stand on ice for 15 min; centrifuge at 12000 r / min for 10 min at 4°C, take 300 μL of the supernatant and transfer it to a new centrifuge tube, and let it stand at -20°C for 30 min; perform secondary centrifugation, centrifuge at 12000 r / min for 3 min at 4°C, collect 200 μL of the supernatant and transfer it to an injection vial for LC-MS analysis.
[0036] The instrument used in the experiment is a Vanquish ultra-high performance liquid chromatograph (Massachusetts, USA), and the equipped chromatographic column is a Waters ACQUITY Premier HSS T3 Column (1.8 μm, 2.1 mm × 100 mm). Liquid phase conditions: Mobile phase A is 0.1% formic acid aqueous solution; mobile phase B is 0.1% formic acid acetonitrile solution. Elution concentration of mobile phase B: Initially 5% B, increase to 20% within 2 min, increase to 60% from 2 - 5 min, increase to 99% from 5 - 6 min and hold for 1.5 min, decrease to 5% B within 0.1 min and equilibrate for 2.4 min. Injection volume is 4 μL, flow rate is 0.4 mL / min, and column temperature is 40°C.
[0037] The mass spectrometer used is a Q Exactive HF-X mass spectrometer (Massachusetts, USA). Mass spectrometry conditions: The mass scanning range of MS1 and MS2 is 75 - 1000 Da, and the resolution is 35000; positive and negative ion mode scanning, the ionization voltage in positive ion mode is 3500 V, and the ionization voltage in negative ion mode is 3200 V; the sheath gas flow rate is 30 Arb; the auxiliary gas flow rate is 5 Arb; the ion transfer tube temperature is 320°C; the atomization temperature is 300°C; the collision energy step is 50 V.
[0038] The raw data from the mass spectrometer was converted to the mzML format using ProteoWizard. The XCMS program was used for peak extraction, alignment, and retention time correction. Peaks with a missing rate > 50% in each group of samples were filtered, and blank values were filled using KNN. The SVR method was used to correct the peak areas. The corrected and filtered peaks were used for metabolite identification by searching the in-house database, integrating public libraries, prediction libraries, and the metDNA method. Finally, substances with a comprehensive identification score above 0.5 and a CV value of QC samples less than 0.3 were extracted, and then positive and negative mode merging was performed (retaining the substance with the highest qualitative level and the smallest CV value). Statistical analyses such as principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on the metabolite data, and the VIP value of each metabolite was calculated to screen out significantly different metabolites. The t-test was used to analyze the significant differences in metabolites between the breast muscles of Hetian chickens and Xiangdong chickens (P < 0.05), and the Fold Change (FC) of metabolites was calculated. Generally, metabolites with a variable importance for projection (VIP) > 1 and a significance level of univariate analysis of variance test (t-test) < 0.05 were considered differential metabolites.
[0039] According to Figure 1 Principal component analysis was performed on the metabolite data to determine whether there were differences between the two groups of samples. The correlation coefficients between QC samples were all greater than 0.99 and close to 1, indicating good instrument stability and reliable data. To understand the variability between and within sample groups, PCA analysis was performed on the test sample data. According to Figure 2 As shown, there was a clear separation of metabolites between the breast muscle samples of Hetian chickens and Xiangdong chickens, indicating significant metabolite differences between the two groups of samples.
[0040] Subsequently, orthogonal partial least squares analysis was performed on the breast muscle samples of Hetian chickens and Xiangdong chickens to establish an OPLS-DA model ( Figure 3 and Figure 4 as shown). In the model, R 2 X(cum) and R 2 Y(cum) respectively represent the explanatory ability of the model for the X and Y matrices, Q 2 Y(cum) represents the predictive ability of the model. When R 2 X is smaller, R 2 Y and Q 2 Y are larger and close to 1, the model is more stable and reliable. According to Figure 4 It can be seen that for the breast muscles of Hetian chickens and Xiangdong chickens, R 2 Y = 0.99, Q 2 Y = 0.68. When Q 2 > 0.5, the model can be considered effective. Q 2When it is >0.9, it is an excellent model. The results show that the model has high quality and strong reliability in screening differential metabolites. Based on the OPLS-DA model, the screening criteria for differential metabolites are VIP>1, P<0.05, and the ratio of the expression levels of metabolites between the breast muscles of Hetian chickens and Xiangdong chickens is greater than 1.5 or less than 0.67 (i.e., the fold change FC>1.5 or FC<0.67). It was observed that 59 metabolites were up-regulated and 139 metabolites were down-regulated in the breast muscle samples of Xiangdong chickens. At the same time, in order to streamline the number of differential metabolites, the fold change of each differential metabolite in the two groups of samples of the breast muscles of Hetian chickens and Xiangdong chickens was compared. Taking Log2 as the base, the calculation results were sorted, and differential metabolites with |Log2FC| greater than 2.5, a total of 11 substances, were selected as biological candidate markers (potential characteristic markers), and the results are shown in Table 1.
[0041] Table 1 Potential characteristic markers of the breast muscles of Hetian chickens and Xiangdong chickens
[0042]
[0043] It can be seen from Table 1 that 5 metabolites were significantly highly expressed in the breast muscles of Xiangdong chickens, and 6 metabolites were significantly highly expressed in the breast muscles of Hetian chickens, which can clearly distinguish the breast muscles of Hetian chickens and Xiangdong chickens.
[0044] In summary, it can be seen that the technical solution of the present invention uses non-targeted metabolomics combined with ultra-high performance liquid chromatography-mass spectrometry technology for analysis and identification. By comparing the expression levels of metabolites in two samples, differential metabolites and potential characteristic markers can be effectively screened out, clearly revealing the metabolite differences between the breast muscles of Hetian chickens and Xiangdong chickens, and being able to effectively distinguish these two kinds of chickens; this technical method is advanced and the test results are reliable, and it has good application prospects in food traceability, quality control, variety identification, etc.
[0045] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can also be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.
Claims
1. A method for identifying the breast muscles of Hetian chickens and Xiangdong chickens based on metabolomics, characterized in that, It includes the following steps: (1) Extract the test sample to obtain a metabolite extract, and collect metabolomics data; (2) Perform principal component analysis and orthogonal partial least squares analysis on the metabolomics data in step (1), and screen out differential metabolites and potential characteristic markers; (3) Use the potential characteristic markers screened in step (2) to perform ultra-high performance liquid chromatography-mass spectrometry detection and identification on the test sample; Taking variable importance > 1, significance level < 0.05, and the difference multiple FC of metabolite expression levels between Hetian chicken and Xiangdong chicken breast muscle samples greater than 1.5 or less than 0.67 as the threshold value to screen differential metabolites; Taking differential metabolites with |Log2FC| of the metabolite extract greater than 2.5 as potential characteristic markers.
2. The method according to claim 1, wherein After preprocessing the detected data, obtain the relative peak areas and retention times of all metabolites.
3. The method according to claim 2, wherein The preprocessing includes baseline correction, peak extraction, peak correction, standardization and normalization, filtering peaks with a missing rate > 50% in the sample, filling blank values with KNN, and correcting the peak areas using the SVR method.
4. The method according to claim 1, characterized in that The conditions of the ultra-high performance liquid chromatography in the detection process are as follows: mobile phase A is 0.1% formic acid aqueous solution, and mobile phase B is 0.1% formic acid acetonitrile solution; the injection volume is 4 μL, the flow rate is 0.4 mL / min, and the column temperature is 40 °C; The gradient elution conditions are: initially 5% B, rising to 20% within 2 min, rising to 60% from 2 - 5 min, rising to 99% from 5 - 6 min and holding for 1.5 min, dropping to 5% B within 0.1 min and equilibrating for 2.4 min.
5. The method according to claim 1, characterized in that, The mass spectrometry conditions in the detection process are: the mass scanning ranges of MS1 and MS2 are 75 - 1000 Da, and the resolution is 35000; positive and negative ion mode scanning, the ionization voltage in the positive ion mode is 3500 V, and the ionization voltage in the negative ion mode is 3200 V; the sheath gas flow rate is 30 Arb; the auxiliary gas flow rate is 5 Arb; the ion transfer tube temperature is 320 °C; the atomization temperature is 300 °C; the collision energy step size is 50 V.