Organic acid metabolic markers for distinguishing wild and farmed four major carps and their uses

Abstract

The present application relates to the technical field of identification or confirmation of the four major carps, and specifically to organic acid metabolic markers and uses for distinguishing wild and farmed four major carps. The metabolic markers include 1-methyl-L-histidine, D-glucosamine, 4-methyl-2-pyrrolidine carboxylic acid, L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, 6-methylpiperidine-2-carboxylic acid, N-benzyloxycarbonyl-D-proline, morpholine-4-acetic acid, imidazole-4-formamide, 1,3-dicyclohexylurea and at least one of β-ureidoisobutyric acid. Metabolic markers can be accurately used to distinguish wild and farmed four major carps, provide scientific basis and technical support for relevant law enforcement departments, strengthen the supervision and crackdown on illegal fishing in the Yangtze River Basin, reduce overfishing of wild resources, and thus provide a technical basis for better management and protection of four major carp resources.

Description

Technical Field

[0001] The present application relates to the technical field of identification or confirmation of the four major carps, and specifically to organic acid metabolic markers and uses for identifying the four major carps from the wild and farmed varieties. Background Art

[0002] Black carp, grass carp, silver carp and bighead carp, these four major carps are important aquaculture species in my country and are mainly distributed in the Yangtze River Basin. Summary of the invention

[0003] Aiming at the problem that the biological traits of wild and farmed four major carps are similar and the identification technology is lacking, this application adopts ultra-high throughput liquid chromatography-mass spectrometry technology for the first time to collect muscle samples of different wild and farmed four major carp populations, conduct metabolomics analysis, explore the common differences and changes of metabolites of wild and farmed four major carps, and screen out the metabolic markers of organic acids: 1-methyl-L-histidine (CAS: 332-80-9), D-aminoglucosidic acid (CAS: 3646-68-2), 4-methyl-2-pyrrolidinecarboxylic acid (CAS: 3005-85-4), L-prolyl-L-phenyl Alanine (CAS: 13589-02-1), ornithine (CAS: 70-26-8), hexadecanoylethanolamide (CAS: 544-31-0), 6-methylpiperidine-2-carboxylic acid (CAS: 99571-58-1), N-benzyloxycarbonyl-D-proline (CAS: 3160-46-1), morpholin-4-acetic acid (CAS: 3235-69-6), imidazole-4-carboxamide (CAS: 26832-08-6), 1,3-dicyclohexylurea (CAS: 2387-23-7), β-ureidoisobutyric acid (CAS: 2905-86-4). The results of liquid chromatography-mass spectrometry were used for statistical and logistic regression analysis, and a logistic regression analysis model was obtained. The source of the test samples was determined according to the logistic regression model, which can accurately distinguish wild and farmed species, and has good sensitivity and specificity. In addition, the metabolic markers and uses provided in this application can provide scientific basis and technical support for relevant law enforcement departments, strengthen the supervision and crackdown on illegal fishing in the Yangtze River Basin, reduce overfishing of wild resources, and provide a technical foundation for better management and protection of the four major carp resources.

[0004] To this end, the embodiments of the present application disclose at least the following technical solutions:

[0005] In a first aspect, the embodiments disclose a metabolic marker comprising at least one of 1-methyl-L-histidine, D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, 6-methylpiperidine-2-carboxylic acid, N-benzyloxycarbonyl-D-proline, morpholin-4-acetic acid, imidazole-4-carboxamide, 1,3-dicyclohexylurea and B-ureidoisobutyric acid.

[0006] In an embodiment of the first aspect, the metabolic marker comprises at least one of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-ureidoisobutyric acid.

[0007] In an embodiment of the first aspect, the metabolic marker comprises at least one of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-ylacetic acid.

[0008] In an embodiment of the first aspect, the metabolic marker comprises at least one of L-prolyl-L-phenylalanine, ornithine, palmitoylethanolamide, morpholin-4-ylacetic acid and imidazole-4-carboxamide.

[0009] In an embodiment of the first aspect, the metabolic marker comprises at least one of D-glucosamine, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline.

[0010] In a second aspect, the embodiments disclose a metabolic marker for distinguishing wild black carp from farmed black carp, which contains at least one of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-ureidoisobutyric acid.

[0011] In an embodiment of the second aspect, the metabolite markers comprise 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-ureidoisobutyric acid.

[0012] In a third aspect, the embodiments disclose a metabolic marker for distinguishing wild grass carp from farmed grass carp, which contains at least one of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid.

[0013] In an embodiment of the third aspect, the metabolic markers comprise 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-ylacetic acid.

[0014] In a fourth aspect, the embodiments disclose a metabolic marker for distinguishing wild silver carp from farmed silver carp, which contains at least one of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholin-4-acetic acid and imidazole-4-carboxamide.

[0015] In an embodiment of the fourth aspect, the metabolic markers comprise L-prolyl-L-phenylalanine, ornithine, palmitoylethanolamide, morpholin-4-ylacetic acid and imidazole-4-carboxamide.

[0016] In a fifth aspect, the embodiments disclose a metabolic marker for distinguishing wild bighead carp from farmed bighead carp, which contains at least one of D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline.

[0017] In an embodiment of the fifth aspect, the metabolic markers comprise D-glucosamine, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline.

[0018] In a sixth aspect, the embodiments disclose a reagent composition comprising a reagent for detecting the metabolic marker described in any one of the first to fifth aspects.

[0019] In an embodiment of the sixth aspect, the reagent comprises a reagent for extracting metabolites from a test sample. In some embodiments, the reagent comprises steel beads and a methanol-water solution (4:1, v / v).

[0020] In the seventh aspect, the embodiments disclose the metabolite markers described in any one of the first to fifth aspects and / or the use of the metabolite markers described in the first to fifth aspects for distinguishing wild and farmed four major carps.

[0021] In an eighth aspect, an embodiment discloses a method for distinguishing wild black carp from farmed black carp.

[0022] In an embodiment of the eighth aspect, the method includes a method for testing the content of each metabolite marker in the metabolite marker described in the second aspect in a test sample. In some embodiments, the test sample is an upper back muscle sample of black carp. In some embodiments, each metabolite marker includes 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and B-ureidoisobutyric acid.

[0023] In an embodiment of the eighth aspect, the method further includes: obtaining a first logistic regression model and a first threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the first logistic regression model to obtain a first wild probability; and determining whether the test sample is the wild black carp or the farmed black carp based on the magnitude of the first wild probability and the first threshold. In some embodiments, the first logistic regression model is: X=1.384×A-1.253×B+7.954×C-9.283×D+20.067; first wild probability=1 / (1+e －X); wherein A, B, C, and D are the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and B-ureidoisobutyric acid, respectively, and the first wild probability represents the probability that the test sample is wild black carp. In some embodiments, if the first wild probability is greater than the first threshold value, the test sample is wild black carp. In some embodiments, if the first wild probability is less than the first threshold value, the test sample is farmed black carp.

[0024] In an embodiment of the eighth aspect, the step of determining the first threshold value includes: using the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and B-ureidoisobutyric acid of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the first wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the first wild probability values, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each first wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the first wild probability corresponding to the maximum Youden index is the first threshold value. In some embodiments, the first threshold value is 0.4996.

[0025] In a ninth aspect, an embodiment discloses a method for distinguishing wild grass carp from farmed grass carp.

[0026] In an embodiment of the ninth aspect, the method comprises a method for testing the content of each metabolite marker in the metabolite markers described in the third aspect in the test sample. In some embodiments, the test sample is an upper back muscle sample of grass carp. In some embodiments, each metabolite marker comprises 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and morpholin-4-acetic acid.

[0027] In an embodiment of the ninth aspect, the method further includes: obtaining a second logistic regression model and a second threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the second logistic regression model to obtain a second wild probability; and determining whether the test sample is the wild grass carp or the farmed grass carp based on the magnitude of the second wild probability and the second threshold. In some embodiments, the second logistic regression model is: X=-4.442×A-34.516×B-1.251×C-32.472×D+595.011; second wild probability=1 / (1+e －X); wherein A, B, C, and D are the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamide, and morpholine-4-acetic acid, respectively, and the second wild probability represents the probability that the test sample is a wild grass carp. In some embodiments, if the second wild probability is greater than the second threshold value, the test sample is a wild grass carp. In some embodiments, if the second wild probability is less than the second threshold value, the test sample is a farmed grass carp.

[0028] In an embodiment of the ninth aspect, the step of determining the second threshold value includes: using the standardized values ​​of 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide, and morpholine-4-acetic acid of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the second wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the second wild probability values, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each second wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the second wild probability corresponding to the maximum Youden index is the second threshold value. In some embodiments, the second threshold value is 0.5038.

[0029] In a tenth aspect, an embodiment discloses a method for distinguishing wild silver carp from farmed silver carp.

[0030] In an embodiment of the tenth aspect, the method includes a method for testing the content of each metabolite marker in the metabolite marker described in the fourth aspect in a test sample. In some embodiments, the test sample is an upper back muscle sample of silver carp. In some embodiments, each metabolite marker includes L-prolyl-L-phenylalanine, ornithine, palmitoylethanolamide, morpholin-4-acetic acid, and imidazole-4-carboxamide.

[0031] In an embodiment of the tenth aspect, the method further includes: obtaining a third logistic regression model and a third threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the third logistic regression model to obtain a third wild probability; and determining whether the test sample is the wild silver carp or the farmed silver carp based on the third wild probability and the third threshold. In some embodiments, the third logistic regression model is: X=3.645×A-4.261×B+0.349×C-13.196×D-22.73×E+293.016; third wild probability=1 / (1+e －X); wherein A, B, C, D, and E are the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-acetic acid, and imidazole-4-carboxamide, respectively, and the third wild probability represents the probability that the test sample is wild silver carp. In some embodiments, if the third wild probability is greater than the third threshold value, the test sample is wild silver carp. In some embodiments, if the third wild probability is less than the third threshold value, the test sample is farmed silver carp.

[0032] In an embodiment of the tenth aspect, the step of determining the third threshold value includes: using the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, palmitoylethanolamide, morpholine-4-acetic acid, and imidazole-4-carboxamide of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the third wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the third wild probability values, and drawing an ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each third wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the third wild probability corresponding to the maximum Youden index is the third threshold value. In some embodiments, the third threshold value is 0.3817.

[0033] In an eleventh aspect, the embodiment discloses a method for distinguishing wild bighead carp from farmed bighead carp.

[0034] In an embodiment of the eleventh aspect, the method includes a method for testing the content of each metabolite marker in the metabolite markers described in the fifth aspect in the test sample. In some embodiments, the test sample is an upper back muscle sample of bighead carp. In some embodiments, the each metabolite marker includes D-aminoglucosamine, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline.

[0035] In an embodiment of the eleventh aspect, the method further includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple standardized values ​​of the contents of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the fourth logistic regression model to obtain a fourth wild probability; determining whether the test sample is the wild bighead carp or the farmed bighead carp based on the size of the fourth wild probability and the fourth threshold. In some embodiments, the fourth logistic regression model is: X = -17.948×A-18.644×B-13.235×C+0.897×D+6.003×E+330.941; fourth wild probability = 1 / (1+e －X); wherein A, B, C, D, and E are standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline, respectively, and the fourth wild probability represents the probability that the test sample is a wild bighead carp. In some embodiments, if the fourth wild probability is greater than the fourth threshold value, the test sample is a wild bighead carp. In some embodiments, if the fourth wild probability is less than the fourth threshold value, the test sample is a farmed bighead carp.

[0036] In the embodiment of the eleventh aspect, the step of determining the fourth threshold value includes: using the standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline of the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain a plurality of the fourth wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each of the fourth wild probability values, and drawing an ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each fourth wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the fourth wild probability corresponding to the maximum Youden index is the fourth threshold value. In some embodiments, the fourth threshold value is 0.585.

[0037] In an embodiment of any one of the eighth to eleventh aspects, the standardized value is a value obtained by normalizing the chromatographic peak area detected by each marker and performing a logarithmic transformation with a base of 10.

[0038] In an embodiment of any one of the eighth to eleventh aspects, the method further comprises a liquid chromatography-mass spectrometry method.

[0039] In an embodiment of any one of the eighth to eleventh aspects, in the liquid chromatography detection step, a SHIMADZU-LC30 ultra high performance liquid chromatography system (UHPLC) is used for detection, and an ACQUITY HSS T3 (2.1×100 mm, 1.8 μm) (Waters, Milford, MA, USA) chromatographic column, column temperature 40°C, chromatographic mobile phase A is 0.1% formic acid aqueous solution, chromatographic mobile phase B is 100% acetonitrile solution, elution mode is gradient elution, elution time is 15 minutes, flow rate is 0.3 mL / min, specific injection volume is 4 μL. The specific procedure of chromatographic gradient elution is as follows: 0→2 min, 0% B; 2-6 min, B changes linearly from 0% to 48%; 6→10 min, B changes linearly from 48% to 100%; 10→12 min, B is maintained at 100%; 12→12.1 min, B changes linearly from 100% to 0%, 12.1→15 min, B is maintained at 0%.

[0040] In an embodiment of any one of the eighth to eleventh aspects, in the mass spectrometry detection step, electrospray ionization (ESI) is used for positive ion (+) and negative ion (-) mode detection. After UPLC separation, mass spectrometry analysis is performed using a QE Plus mass spectrometer (ThermoScientific), and ionization is performed using a HESI source, and the ionization conditions are as follows: spray voltage is 3.8 kV (+) and 3.2 kV (-); capillary temperature is 320°C; probe heater temperature is 350°C; sheath gas is 30 arbitrary units; auxiliary gas flow is 5 arbitrary units; S-Lens radio frequency level is 50 arbitrary units.

[0041] In an embodiment of any one of the eighth to eleventh aspects, in the mass spectrometry detection step, the mass spectrometry acquisition time is 15 minutes. The parent ion scanning range is 75-1050 m / z; the primary mass spectrometry resolution is 70,000 @ m / z 200; the number of target ions is 3e6; the maximum injection time of the primary mass spectrometry is 100 ms. The secondary mass spectrometry analysis is collected according to the following method: after each full scan, the secondary mass spectrometry of the 10 highest intensity parent ions is triggered, and the secondary mass spectrometry resolution is 17,500 @ m / z 200; the number of target ions is 1e5, and the maximum injection time of the secondary mass spectrometry is 50 ms.

[0042] In an embodiment of any one of the eighth to eleventh aspects, the results obtained by the liquid chromatography-mass spectrometry are subjected to quality control. The quality control step comprises: preparing a quality control (QC) sample by mixing aliquots of the supernatant from all samples; evaluating the repeatability of the data and the stability of the instrument based on the overlapping base peak ion chromatogram (BPC) of the QC sample; determining the residue using the BPC of the blank sample; and evaluating the reliability and stability of the instrument analysis using principal component analysis (PCA) of all samples.

[0043] In an embodiment of any one of the eighth to eleventh aspects, the raw data obtained by liquid chromatography-mass spectrometry is subjected to peak alignment, retention time correction and peak area extraction using MSDIAL software. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The metabolic marker composition (1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-ureidoisobutyric acid) provided in the example is used to distinguish the confusion matrix test results of wild black carp and farmed black carp.

[0045] Figure 2 The metabolic marker composition (4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, morpholin-4-acetic acid) provided in the example is used to distinguish the confusion matrix test results of wild grass carp and farmed grass carp.

[0046] Figure 3 The metabolic marker composition (L-prolyl-L-phenylalanine, ornithine, palmitoylethanolamide, morpholin-4-acetic acid and imidazole-4-carboxamide) provided in the examples is used to distinguish the confusion matrix test results of wild silver carp and farmed silver carp.

[0047] Figure 4 The metabolic marker composition provided in the examples (D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline) is used to distinguish the confusion matrix test results of wild bighead carp and farmed bighead carp. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. The reagents not described separately in detail in the present application are all conventional reagents and can be obtained from commercial channels; the methods not described in detail are all conventional experimental methods and can be obtained from the prior art.

[0049] Mining of organic acid metabolic markers for distinguishing wild and farmed four major carps

[0050] 1. Muscle sample collection of the four major carps: grass carp, silver carp, and bighead carp

[0051] The wild black carp populations involved in the embodiments come from the Minjiang River, Dongting Lake and Poyang Lake in the Yangtze River Basin. The wild grass carp populations involved in the embodiments come from the Minjiang River, Jialing River, Hukou River section and Poyang Lake in the Yangtze River Basin. The wild silver carp populations involved in the embodiments come from the Minjiang River, Jialing River, Three Gorges Reservoir, Gong'an River section, Hukou River section, Dongting Lake and Poyang Lake in the Yangtze River Basin. The wild bighead carp populations involved in the embodiments come from the Gong'an River section, Hukou River section, Dongting Lake and Poyang Lake in the Yangtze River Basin.

[0052] The farmed silver carp, grass carp, bighead carp and bighead carp populations involved in the examples were from farms in Hubei, Hunan and Jiangsu provinces, respectively. The samples were taken from the upper back muscles of the four major carps and immediately frozen in liquid nitrogen after sampling, and then transferred to a -80°C refrigerator for storage until the subsequent metabolite extraction.

[0053] 2. Sample metabolite extraction

[0054] Thaw the sample at 4°C, weigh 50 mg of the sample, add 2 medium-sized steel balls, add 200 μL of pre-cooled methanol-water solution (4:1, v / v), homogenize and break in a tissue disruptor, then add 800 μL of pre-cooled 80% methanol-water solution, vortex mix, ultrasonicate in an ice bath for 20 minutes, stand overnight at -20°C, centrifuge at 16000g at 4°C for 20 minutes, take the supernatant, and evaporate the supernatant in a high-speed vacuum concentrator centrifuge. For mass spectrometry detection, add 100 μL of methanol-water solution (1:1, v / v) to re-dissolve, centrifuge at 20000g at 4°C for 15 minutes, and take the supernatant for UHPLC-MS / MS detection and analysis.

[0055] 3. UHPLC-MS / MS Detection and Analysis

[0056] UHPLC-MS / MS analysis Metabolomics analysis was performed using a UPLC-ESI-Q-Orbitrap-MS system (UHPLC, Shimadzu Nexera X2LC-30AD, Shimadzu Corporation, Japan) combined with Q-Exactive Plus (Thermo Scientific, San Jose, USA). The samples were placed in an autosampler at 4 °C throughout the analysis. The samples were analyzed using a SHIMADZU-LC30 ultra-high performance liquid chromatography system (UHPLC) and ACQUITY HSS T3 (2.1×100 mm, 1.8 μm) (Waters, Milford, MA, USA) chromatographic column, column temperature 40°C, chromatographic mobile phase A is 0.1% formic acid aqueous solution, chromatographic mobile phase B is 100% acetonitrile solution, elution mode is gradient elution, elution time is 15 minutes, flow rate is 0.3 mL / min, specific injection volume is 4 μL. The specific procedure of chromatographic gradient elution is as follows: 0→2 min, 0% B; 2→6 min, B changes linearly from 0% to 48%; 6→10 min, B changes linearly from 48% to 100%; 10→12 min, B is maintained at 100%; 12→12.1 min, B changes linearly from 100% to 0%, 12.1→15 min, B is maintained at 0%.

[0057] Each sample was detected by electrospray ionization (ESI) in positive (+) and negative (-) modes. After UPLC separation, the samples were analyzed by mass spectrometry using a QE Plus mass spectrometer (Thermo Scientific), using a HESI source for ionization. The ionization conditions were as follows: spray voltage of 3.8 kV (+) and 3.2 kV (-); capillary temperature of 320 °C; probe heater temperature of 350 °C; sheath gas of 30 arbitrary units; auxiliary gas flow of 5 arbitrary units; S-Lens radio frequency level of 50 arbitrary units.

[0058] The mass spectrometry acquisition settings were as follows: the mass spectrometry acquisition time was 15 min. The parent ion scan range was 75-1050 m / z; the primary mass spectrometry resolution was 70,000 @ m / z 200; the number of target ions was 3e6; and the maximum primary injection time was 100 ms. The secondary mass spectrometry analysis was acquired according to the following method: the secondary mass spectra of the 10 highest intensity parent ions were triggered after each full scan, the secondary mass spectrometry resolution was 17,500 @ m / z 200; the number of target ions was 1e5, and the maximum secondary injection time was 50 ms.

[0059] 4. Quality Control

[0060] Quality control (QC) samples were prepared by pooling aliquots of supernatant from all samples. Data repeatability and instrument stability were evaluated based on the overlapping base peak ion chromatograms (BPCs) of the QC samples. Residues were determined using the BPCs of blank samples. The reliability and stability of the instrument analysis were evaluated using principal component analysis (PCA) of all samples.

[0061] 5. Processing and analysis of metabolomics data

[0062] The raw data were aligned, retained and corrected using MSDIAL software, and the peak areas were extracted. The metabolite structures were identified by accurate mass matching (mass deviation mass tolerance <10ppm) and secondary spectrum matching (mass deviation mass tolerance <0.01Da), and public databases such as HMDB, MassBank, GNPS, and the self-built Baipu metabolite standard library (BP-DB) were searched. For the extracted data, the ion peaks with missing values ​​>50% within the group were deleted and did not participate in the subsequent statistical analysis; the total peak area of ​​the positive and negative ion data was normalized, the positive and negative ion peaks were integrated, and Python software was used for pattern recognition. The data was preprocessed by Unit variance scaling (UV) before subsequent data analysis. In this study, the raw data files (.wiff format) were first imported into Progenesis QI software for data preprocessing procedures. This included reviewing the steps of alignment, peak selection, and peak detection, and performing data normalization at the same time. The HMDB and LIPID MAPS public databases were used in the annotation of metabolites. Finally, a data matrix containing m / z, retention time (RT), peak intensity, sample name, and metabolite name is generated.

[0063] 6. Screening of significantly different metabolites

[0064] The data matrix was imported into SIMCA 14.1 software for multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection latent variable structure discriminant analysis (OPLS-DA). After the OPLS-DA model was constructed, 200 permutation tests were performed to verify the model and evaluate whether it was overfitting. Based on the differentially changed metabolites (DMs) obtained from the OPLS-DA model with P value < 0.05, Fold change > 1.5 or Fold change < 0.667, and VIP value > 1.0, the four groups of wild and farmed differential metabolites of black carp, grass carp, silver carp, and bighead carp were intersected using VEEN to obtain the organic acid metabolites with significant differences between the wild and farmed four major carps.

[0065] 7. Statistical analysis methods

[0066] GraphPad Prism 8.0 software was used to perform statistical analysis on the biochemical analysis data. Student's t-test was used to calculate the differences in data means between different groups. SPSS was used to construct and analyze the binary logistic regression model, and the input data were the standardized values ​​of each metabolite, that is, the logarithmic transformation value (log10) with a base of 10 after peak area normalization, which was used to reduce the skewed distribution of the overall values ​​and make the data close to a normal distribution. The experimental results are presented in the form of mean plus or minus standard deviation (SD), where the asterisk (*) indicates that the difference between the experimental group and the control group is statistically significant. Specifically, *P<0.05 indicates a significant difference, and **P<0.01 indicates an extremely significant difference.

[0067] 8. Results

[0068] The constructed OPLS-DA model was used to compare the two groups of C. alba samples from wild and farmed populations. Metabolites with OPLS-DA VIP values ​​greater than 1.0 obtained by variance analysis were considered to be potential metabolic markers that contribute to class discrimination. The combination of difference fold (FC) ≥1.5 or ≤0.667 and P<0.05 was used to screen significantly differential metabolites based on P value and VIP value. The study of significantly differential metabolites in a single fish species is easily affected by the inherent characteristics and errors of the specific species, while the analysis of significantly differential metabolites shared by different fish species can avoid the errors of a single fish species and improve the accuracy and reliability of the results.

[0069] Therefore, the embodiment is carried out VEEN intersection processing on the wild and cultured differential metabolites of four fish species, black carp, grass carp, silver carp and bighead carp, and obtains the significant differential metabolites shared by these four fish species under wild and cultured conditions. Subsequently, cluster analysis was carried out for these shared significant differential metabolites, and it was found that 12 kinds of organic acids and derivatives were included therein, namely: 1-methyl-L-histidine, D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, 6-methylpiperidine-2-carboxylic acid, N-benzyloxycarbonyl-D-proline, morpholine-4-acetic acid, imidazole-4-formamide, 1,3-dicyclohexylurea, B-ureidoisobutyric acid. The reference mass-to-charge ratio, retention time (min), ionization mode, and subclass of the above-mentioned organic acids and derivatives are shown in Table 1, and the wild and cultured difference multiples of each shared significant differential metabolite in black carp, grass carp, silver carp and bighead carp are shown in Table 2.

[0070] Table 1 Metabolic markers with significant differences between wild and farmed silver carp and bighead carp

[0071]

[0072]

[0073] Table 2 Differences in the metabolic markers common to wild and cultured silver carp and bighead carp

[0074]

[0075] Metabolic markers for distinguishing wild and farmed black carp and their uses

[0076] The main body of the current data discrimination model is to build a model through multiple indicators for comprehensive discrimination. Among them, logistic regression, as a simple and intuitive model, has many advantages. First, it is easy to understand and explain, does not require complex mathematical derivation or calculation, and has high computational efficiency. Secondly, logistic regression can provide highly interpretable result output, can be classified by setting thresholds, and the coefficients and intercepts of the model can provide the degree and direction of the contribution of the variable to the target variable.

[0077] 1. Selection of metabolic markers

[0078] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild black carp and farmed black carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as metabolic markers for distinguishing wild black carp from farmed black carp. The metabolic markers for distinguishing wild black carp from farmed black carp provided in the embodiment include 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and B-ureidoisobutyric acid.

[0079] 2. How to distinguish wild black carp from farmed black carp

[0080] The embodiment also discloses a method for distinguishing wild black carp from farmed black carp. The method comprises: obtaining a first logistic regression model and a first threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in a test sample; inputting the multiple standardized values ​​into the first logistic regression model to obtain a first wild probability; and determining whether the test sample is the wild black carp or the farmed black carp according to the magnitude of the first wild probability and the first threshold. The multiple metabolite markers include 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-ureidoisobutyric acid.

[0081] In some embodiments, the step of obtaining a first logistic regression model includes:

[0082] 1) Obtain training samples

[0083] Embodiment The muscle samples of 12 wild black carps and the muscle samples of 12 farmed black carps were tested by the same liquid phase as above to obtain the chromatographic peak areas of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-urea isobutyric acid in each sample. These chromatographic peak areas were standardized to obtain standardized values. The step of the standardized treatment includes: respectively normalizing the chromatographic peak areas of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-urea isobutyric acid, and performing a logarithmic conversion with a base of 10 to obtain the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-urea isobutyric acid. The standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and B-urea isobutyric acid of these muscle samples are used as training samples.

[0084] 2) Training

[0085] The standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and β-ureidoisobutyric acid are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train a binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training of the binary logistic regression model, and obtaining the first logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0086] In some steps, the embodiment performs a single degree of freedom test on the content of the independent variables 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and β-ureidoisobutyric acid in the muscle samples of wild black carp and farmed black carp, including giving degrees of freedom, significance and score detection. The results are shown in Table 3. The overall statistical score of the content of these four metabolite markers as independent variables is high, and the significance is less than 0.05, indicating that it will have a significant impact on the corresponding variables in general.

[0087] Table 3 Significance test and score of independent variables in logistic regression model of metabolic markers of wild and farmed black carp

[0088]

[0089] In some steps, the embodiment performs a Hosmer-Lemmeshaw test on the logistic regression model of metabolic markers of wild black carp and farmed black carp. Under the condition of multiple degrees of freedom, the significance of the binary logistic regression model is P=1.000>0.05, that is, the zero hypothesis is accepted. The results are shown in Table 4. The model fits the real data well and can truly and reliably reflect the reliable relationship between the original variables.

[0090] Table 4 Hosmer-Lemmeshow test for logistic regression model of metabolic markers in wild and farmed black carp

[0091] step Chi-square Degrees of Freedom Significance 1 0.026 8 1.000

[0092] 3) Obtain the first logistic regression model

[0093] In some steps, the embodiment performs binary logistic regression analysis on the logistic regression model of the metabolic markers of wild black carp and farmed black carp. Table 5 shows the effect size Exp(B) of the regression model and the significance of each metabolic marker.

[0094] Table 5 Analysis of binary logistic regression model in the metabolic marker model of wild and farmed black carp

[0095]

[0096] Embodiment According to the standardized values ​​of the above 12 wild black carp muscle samples and 12 farmed black carp muscle samples, the first logistic regression model is as follows:

[0097] X=1.384×A-1.253×B+7.954×C-9.283×D+20.067;

[0098] First wild probability = 1 / (1+e －X );

[0099] Among them, A, B, C, and D are the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and β-ureidoisobutyric acid, respectively, and the first wild probability represents the probability that the test sample is wild black carp.

[0100] The steps provided in the embodiments are to obtain the first threshold value by ROC curve calculation. The steps provided in some embodiments include: using the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and β-ureidoisobutyric acid of 12 muscle samples of wild black carp and 12 muscle samples of farmed black carp as training sets; training a binary logistic regression model with the training set to obtain multiple first wild probability values ​​as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each first wild probability value respectively, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the first predicted wild probability corresponding to the maximum Youden index is the first threshold value.

[0101] In some embodiments, if the first wild probability is greater than the first threshold, the test sample is determined to be wild black carp. If the first wild probability is less than the first threshold, the test sample is determined to be farmed black carp.

[0102] In some embodiments, the first threshold value obtained by using the above test set and calculating the ROC curve is 0.4996. That is, when the first wild probability is greater than 0.4996, the test sample is determined to be wild black carp; when the first wild probability is less than 0.4996, the test sample is determined to be farmed black carp.

[0103] 3. Identification application and testing

[0104] In some test cases, 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and β-ureidoisobutyric acid of 12 muscle samples (including 6 wild and 6 farmed) were selected as metabolic markers, and their standardized values ​​were input into the above-mentioned first logistic regression model to obtain the first wild probability; based on the size of the first wild probability and the first threshold (0.4996), it was determined whether the test sample was wild black carp or farmed black carp.

[0105] The standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and β-ureidoisobutyric acid in the above training set were used to construct binary logistic regression models separately, and the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and β-ureidoisobutyric acid were combined to construct the first logistic regression model. The 12 samples were tested respectively, and the discrimination results were subjected to ROC curve test. The results are shown in Table 6. As can be seen from Table 6, the first logistic regression model constructed by combining the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and β-ureidoisobutyric acid has a higher AUC value, sensitivity, and specificity.

[0106] Table 6 ROC test results of each metabolite marker and its combination for distinguishing wild and farmed black carp

[0107] Metabolic markers AUC Sensitivity Specificity 1-Methyl-L-histidine 0.904 0.833 0.833 6-Methylpiperidine-2-carboxylic acid 0.863 1.000 0.667 1,3-Dicyclohexylurea 0.914 0.833 0.833 β-Urediisobutyric acid 0.828 0.667 0.833 Composition 0.947 1.000 0.833

[0108] The discrimination results were input into the SPSS20.0 analysis software for confusion matrix test. The confusion matrix, also known as the error matrix, is a standard format for expressing accuracy evaluation, expressed in the form of a matrix with n rows and n columns. Specific evaluation indicators include overall accuracy, mapping accuracy, user accuracy, etc. These accuracy indicators reflect the accuracy of image classification from different aspects. Each column of the confusion matrix represents a predicted category, and the total number of each column represents the number of data predicted to be in that category; each row represents the true category of the data, and the total number of data in each row represents the number of data instances in that category. The results are as follows: Figure 1 As shown in the figure, only one sample of the 12 black carp muscle samples was misclassified, and the overall correct rate of cross-validation was as high as 91.67%. This shows that 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and β-ureidoisobutyric acid can be used as metabolic markers to distinguish wild black carp from farmed black carp in the Yangtze River Basin. The excellent ROC curve results, good correct diagnosis rate and good complementarity of metabolic markers all indicate that the four organic acids and derivatives have the potential to distinguish wild black carp from farmed black carp.

[0109] Metabolic markers for distinguishing wild and farmed grass carp and their uses

[0110] 1. Selection of metabolic markers

[0111] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild grass carp and farmed grass carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as metabolic markers for distinguishing wild grass carp from farmed grass carp. The metabolic markers for distinguishing wild grass carp from farmed grass carp provided in the embodiment include 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and morpholin-4-acetic acid.

[0112] The embodiment also provides a kit for detecting these metabolite markers, which includes reagents for detecting these metabolite markers, such as reagents required for liquid chromatography-mass spectrometry.

[0113] 2. How to distinguish wild grass carp from farmed grass carp

[0114] The embodiment also discloses a method for distinguishing wild grass carp from farmed grass carp. The method comprises: obtaining a second logistic regression model and a second threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the second logistic regression model to obtain a second wild probability; and determining whether the test sample is the wild grass carp or the farmed grass carp according to the magnitude of the second wild probability and the second threshold. The multiple metabolite markers include 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid.

[0115] In some embodiments, the step of obtaining a second logistic regression model includes:

[0116] 1) Obtain training samples

[0117] Embodiment The muscle samples of 24 wild grass carps and the muscle samples of 24 cultivated grass carps are detected by the same liquid phase as above to obtain the chromatographic peak areas of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamine and morpholine-4-acetic acid in each sample. These chromatographic peak areas are standardized to obtain standardized values. The step of the standardized treatment comprises: after the chromatographic peak areas of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamine and morpholine-4-acetic acid are normalized respectively, the logarithmic conversion with the base number of 10 is performed, and the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamine and morpholine-4-acetic acid can be obtained respectively. The standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamine and morpholine-4-acetic acid of these muscle samples are used as training samples.

[0118] 2) Training

[0119] The standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train a binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training the binary logistic regression model to obtain a second logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0120] In some steps, the embodiment performs a single degree of freedom test on the muscle sample content of the independent variables 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid, including giving degrees of freedom, significance and score detection. The results are shown in Table 7. The overall statistical score of the content of these four metabolic markers as independent variables is high, and the significance is less than 0.001, indicating that the independent variables will have a very significant impact on the corresponding variables in general.

[0121] Table 7 Significance test and score of independent variables in the logistic regression model of metabolic markers of wild grass carp and farmed grass carp

[0122]

[0123] In some steps, the embodiment performs a Hosmer-Lemmeshaw test on the logistic regression model of metabolic markers of wild grass carp and farmed grass carp. Under the condition of multiple degrees of freedom, the significance of the binary logistic regression model is P=1.000>0.05, that is, the 0 hypothesis is accepted. The results are shown in Table 8. The constructed binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0124] Table 8 Hosmer-Lemmeshaw test for logistic regression model of wild and farmed grass carp

[0125] step Chi-square Degrees of Freedom Significance 1 0.136 8 1.000

[0126] 3) Obtain the second logistic regression model

[0127] In some steps, the embodiment performs binary logistic regression analysis on the metabolite markers for distinguishing wild grass carp from farmed grass carp. Table 9 shows the effect size Exp(B) of the regression model and the significance of each metabolite marker.

[0128] Table 9 Binary logistic regression analysis of wild and farmed grass carp

[0129]

[0130] Embodiment According to the standardized values ​​of the above 24 wild grass carp muscle samples and 24 farmed grass carp muscle samples, the first logistic regression model is as follows:

[0131] X=-4.442×A-34.516×B-1.251×C-32.472×D+595.011;

[0132] Second wild probability = 1 / (1+e －X );

[0133] Among them, A, B, C, and D are the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamine, and morpholin-4-acetic acid, respectively, and the second wild probability represents the probability that the test sample is a wild grass carp.

[0134] The steps provided in the embodiment are to obtain the second threshold value by ROC curve calculation. The steps provided in some embodiments include: using the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and morpholine-4-acetic acid of 24 muscle samples of wild grass carp and 24 muscle samples of farmed grass carp as training sets; training a binary logistic regression model with the training set to obtain multiple second wild probability values ​​as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each second wild probability value respectively, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the first predicted wild probability corresponding to the maximum Youden index is the second threshold value.

[0135] In some embodiments, if the second wild probability is greater than the second threshold, the test sample is determined to be wild grass carp. If the second wild probability is less than the second threshold, the test sample is determined to be farmed grass carp.

[0136] In some embodiments, the second threshold value obtained by using the above test set and calculating the ROC curve is 0.5038. That is, when the second wild probability is greater than 0.5038, the test sample is judged to be wild grass carp; when the second wild probability is less than 0.5038, the test sample is judged to be farmed grass carp.

[0137] 3. Identification application and testing

[0138] In some test cases, 24 muscle samples (including 12 wild and 12 farmed) of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholine-4-acetic acid were selected as metabolic markers, and their standardized values ​​were input into the above-mentioned second logistic regression model to obtain the second wild probability; based on the size of the second wild probability and the second threshold (0.5038), it was determined whether the test sample was wild grass carp or farmed grass carp.

[0139] The standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid in the above training set were used to construct binary logistic regression models separately, and the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid were combined to construct a second logistic regression model, and the 24 samples were tested respectively, and the discrimination results were subjected to ROC curve test, and the results are shown in Table 2. As can be seen from Table 2, the second logistic regression model constructed by combining the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and morpholin-4-acetic acid for discrimination of the test samples has a higher AUC value, sensitivity and specificity.

[0140] Table 10 ROC test results of each metabolite marker and its combination for distinguishing wild and farmed grass carp

[0141] Metabolic markers AUC Sensitivity Specificity 4-Methyl-2-pyrrolidinecarboxylic acid 0.903 0.917 0.833 Ornithine 0.785 1.000 0.500 Palmitoylethanolamide 0.653 0.917 0.667 Morpholin-4-ylacetic acid 0.840 0.667 0.833 Composition 0.984 0.917 1.000

[0142] The discrimination results were input into SPSS20.0 analysis software for confusion matrix test. The results are as follows: Figure 2 As shown in the figure, only one of the 24 grass carp samples was misclassified, and the overall accuracy of the interactive validation was as high as 95.83%.

[0143] This shows that 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecanoylethanolamine and morpholin-4-acetic acid can be used as metabolic markers to distinguish wild grass carp from farmed grass carp in the Yangtze River Basin. The excellent ROC curve results, good correct diagnosis rate and good complementarity of metabolic markers all indicate that the four organic acids and derivatives have the potential to distinguish wild grass carp from farmed grass carp.

[0144] Metabolic markers for distinguishing wild and farmed silver carp and their uses

[0145] 1. Selection of metabolic markers

[0146] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild silver carp and farmed silver carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as metabolic markers for distinguishing wild silver carp from farmed silver carp. The metabolic markers for distinguishing wild silver carp from farmed silver carp provided in the embodiment include L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholin-4-acetic acid, and imidazole-4-carboxamide.

[0147] The embodiment also provides a kit for detecting these metabolite markers, which includes reagents for detecting these metabolite markers, such as reagents required for liquid chromatography-mass spectrometry.

[0148] 2. How to distinguish wild silver carp from farmed silver carp

[0149] The embodiment also discloses a method for distinguishing wild silver carp from farmed silver carp. The method comprises: obtaining a third logistic regression model and a third threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the third logistic regression model to obtain a third wild probability; and determining whether the test sample is the wild silver carp or the farmed silver carp according to the magnitude of the third wild probability and the third threshold. The multiple metabolite markers include L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholin-4-acetic acid, and imidazole-4-carboxamide.

[0150] In some embodiments, the step of obtaining the third logistic regression model includes:

[0151] 1) Obtain training samples

[0152] Embodiment The muscle samples of 60 wild silver carps and the muscle samples of 48 cultured silver carps were detected by the same liquid phase as above to obtain the chromatographic peak areas of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-yl acetic acid and imidazole-4-carboxamide in each sample. These chromatographic peak areas were standardized to obtain standardized values. The step of the standardized treatment comprises: after the chromatographic peak areas of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-yl acetic acid and imidazole-4-carboxamide were normalized respectively, the logarithmic conversion with the base number of 10 was performed, and the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-yl acetic acid and imidazole-4-carboxamide were obtained respectively. The standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-yl acetic acid and imidazole-4-carboxamide of these muscle samples were used as training samples.

[0153] 2) Training

[0154] The standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-acetic acid and imidazole-4-carboxamide are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train the binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training the binary logistic regression model to obtain a third logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0155] In some steps, the embodiment uses L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamine, morpholine-4-acetic acid and imidazole-4-carboxamide in the muscle sample content of wild silver carp and farmed silver carp to perform a single degree of freedom test, including giving degrees of freedom, significance and score detection. As shown in Table 11, the overall statistical score of the independent variable is high, and the significance is less than 0.0001, indicating that the independent variable will have a very significant effect on the corresponding variable in general.

[0156] Table 11 Significance test and score of independent variables in the logistic regression model of metabolic markers of wild and farmed silver carp

[0157]

[0158]

[0159] In order to further analyze the influence of constant and independent variable on dependent variable in multiple degrees of freedom, the embodiment also performs Hosmer-Lemmeshow test on the logistic regression model. Under multiple degrees of freedom, the significance of binary logistic regression model is P=0.923>0.05, that is, the 0 hypothesis is accepted. As shown in Table 12, the constructed binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0160] Table 12 Hosmer-Lemmeshaw test for logistic regression model of wild and farmed silver carp

[0161] step Chi-square Degrees of Freedom Significance 1 3.172 8 0.923

[0162] 3) Obtain the third logistic regression model

[0163] In some steps, the embodiment performs binary logistic regression analysis on the logistic regression model of the metabolite markers of wild silver carp and cultured silver carp. Table 13 shows the effect size Exp(B) of the regression model and the significance of each metabolite marker.

[0164] Table 13 Binary logistic regression analysis of wild and farmed silver carp

[0165]

[0166] Embodiment According to the standardized values ​​of the above 60 muscle samples of wild silver carp and 48 muscle samples of farmed silver carp, the third logistic regression model is as follows:

[0167] X=3.645×A-4.261×B+0.349×C-13.196×D-22.73×E+293.016;

[0168] The third wild probability = 1 / (1+e －X );

[0169] A, B, C, D, and E are the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholin-4-acetic acid, and imidazole-4-carboxamide, respectively, and the third wild probability represents the probability that the test sample is wild silver carp.

[0170] The steps provided in the embodiment are calculated by ROC curve to obtain the third threshold value. The steps provided in some embodiments include: using the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, palmitoylethanolamide, morpholine-4-acetic acid, and imidazole-4-carboxamide of 60 muscle samples of wild silver carp and 48 muscle samples of farmed silver carp as training sets; training a binary logistic regression model with the training set to obtain multiple third wild probability values ​​as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each third wild probability value respectively, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the third predicted wild probability corresponding to the maximum Youden index is the third threshold value.

[0171] In some embodiments, if the third wild probability is greater than the third threshold, the test sample is determined to be wild silver carp. If the third wild probability is less than the third threshold, the test sample is determined to be farmed silver carp.

[0172] In some embodiments, the third threshold value obtained by using the above test set and calculating the ROC curve is 0.3817. That is, when the third wild probability is greater than 0.3817, the test sample is judged to be wild silver carp; when the third wild probability is less than 0.3817, the test sample is judged to be farmed silver carp.

[0173] 3. Identification application and testing

[0174] In some test cases, L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamine, morpholine-4-acetic acid, and imidazole-4-carboxamide of 54 muscle samples (including 30 wild and 24 farmed) were selected as metabolic markers, and their standardized values ​​were input into the third logistic regression model to obtain the third wild probability; based on the size of the third wild probability and the third threshold value (0.3817), it was determined whether the test sample was wild silver carp or farmed silver carp.

[0175] The standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-acetic acid, and imidazole-4-carboxamide in the above training set were used to construct a binary logistic regression model separately, and the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-acetic acid, and imidazole-4-carboxamide were combined to construct a third logistic regression model, and the 54 samples were tested respectively, and the discrimination results were subjected to ROC curve test, and the results are shown in Table 14. As can be seen from Table 14, the third logistic regression model constructed by combining the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholine-4-acetic acid, and imidazole-4-carboxamide has a higher AUC value, sensitivity, and specificity.

[0176] Table 14 ROC test results of various metabolite markers and their combinations for distinguishing wild and farmed silver carp

[0177] Metabolic markers AUC Sensitivity Specificity L-Prolyl-L-phenylalanine 0.818 0.633 0.958 Ornithine 0.864 0.900 0.708 Palmitoylethanolamide 0.794 0.800 0.750 Morpholin-4-ylacetic acid 0.864 0.767 0.833 Imidazole-4-carboxamide 0.771 0.533 0.958 Logo composition 0.983 0.967 0.917

[0178] The discrimination results were input into SPSS20.0 analysis software for confusion matrix test. The results are as follows: Figure 3 As shown in the figure, only 3 samples among the 54 silver carp samples were misclassified, and the overall accuracy of the interactive validation was as high as 94.44%.

[0179] This shows that the logistic regression model constructed with L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamine, morpholin-4-acetic acid, and imidazole-4-carboxamide as metabolic markers and their standardized values ​​can be used to distinguish wild and farmed silver carp in the Yangtze River Basin. The excellent ROC curve results and good correct diagnosis rate as well as the good complementarity of metabolic markers indicate that the five organic acids and derivatives have the potential to distinguish wild and farmed silver carp.

[0180] Metabolic markers for distinguishing wild and farmed bighead carp and their uses

[0181] 1. Selection of metabolic markers

[0182] The embodiment is based on liquid phase detection of the content of metabolites in muscle samples of wild bighead carp and farmed bighead carp, and training of a binary logistic regression model is performed according to the detection results. Compounds with high statistical significance, small measurement error, and high data integrity are selected as metabolic markers for distinguishing wild bighead carp from farmed bighead carp. The metabolic markers for distinguishing wild bighead carp from farmed bighead carp provided in the embodiment include D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline.

[0183] The embodiment also provides a kit for detecting these metabolite markers, which includes reagents for detecting these metabolite markers, such as reagents required for liquid chromatography-mass spectrometry.

[0184] 2. How to distinguish wild bighead carp from farmed bighead carp

[0185] The embodiment also discloses a method for distinguishing wild bighead carp from farmed bighead carp. The method comprises: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple standardized values ​​of the content of multiple metabolite markers in the test sample; inputting the multiple standardized values ​​into the fourth logistic regression model to obtain a fourth wild probability; and determining whether the test sample is the wild bighead carp or the farmed bighead carp according to the size of the fourth wild probability and the fourth threshold. Among them, the multiple metabolite markers include D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline.

[0186] In some embodiments, the step of obtaining the fourth logistic regression model includes:

[0187] 1) Obtain training samples

[0188] Embodiment The muscle samples of 36 wild bighead carps and the muscle samples of 36 cultured bighead carps were tested by the same liquid phase as above to obtain the chromatographic peak areas of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, hexadecanoylethanolamide and N-benzyloxycarbonyl-D-proline in each sample. These chromatographic peak areas were standardized to obtain standardized values. The step of the standardized treatment comprises: respectively normalizing the chromatographic peak areas of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, hexadecanoylethanolamide and N-benzyloxycarbonyl-D-proline, and performing a logarithmic conversion with a base of 10 to obtain the standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, hexadecanoylethanolamide and N-benzyloxycarbonyl-D-proline. The standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, hexadecanoylethanolamide and N-benzyloxycarbonyl-D-proline of these muscle samples are used as training samples.

[0189] 2) Training

[0190] The standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline are used as independent variables, and the probability of the sample being wild is used as the dependent variable to train the binary logistic regression model. In some embodiments, the training process of the binary logistic regression model includes: composition analysis of the dependent variable and the independent variable to determine whether they meet the prerequisites of the logistic regression; significance test of the independent variable, including degrees of freedom, significance and score; Hosmer-Lemmeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; training the binary logistic regression model to obtain a fourth logistic regression model according to the degree of influence of the independent variable on the dependent variable.

[0191] In some steps, the embodiment performs a single degree of freedom test on the muscle sample content of the independent variables D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline, including providing degrees of freedom, significance and score detection. The results are shown in Table 15, and the overall statistical scores of these independent variables are high, and the significance is less than 0.0001, indicating that the independent variables will have a very significant impact on the corresponding variables in general.

[0192] Table 15 Significance test and score of independent variables in the joint metabolic marker model of wild bighead carp and farmed bighead carp

[0193]

[0194] In some steps, the embodiment performs a Hosmer-Lemmeshaw test on the logistic regression model of metabolic markers of wild bighead carp and farmed bighead carp. Under the condition of multiple degrees of freedom, the significance of the binary logistic regression model is P=0.976>0.05, that is, the 0 hypothesis is accepted. As shown in Table 16, the constructed binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0195] Table 16 Hosmer-Lemmeshaw test for binary logistic regression model of wild and farmed bighead carp

[0196] step Chi-square Degrees of Freedom Significance 1 1.667 7 0.976

[0197] 3) Obtain the fourth logistic regression model

[0198] In some steps, the embodiment performs binary logistic regression analysis on the metabolic markers for distinguishing wild bighead carp from farmed bighead carp. Table 17 shows the effect size Exp(B) of the regression model and the significance of each metabolic marker.

[0199] Table 17 Binary logistic regression analysis of wild and farmed bighead carp

[0200]

[0201] Embodiment According to the standardized values ​​of the above 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples, the fourth logistic regression model is as follows:

[0202] X=-17.948×A-18.644×B-13.235×C+0.897×D+6.003×E+330.941;

[0203] Fourth wild probability = 1 / (1+e －X );

[0204] Among them, A, B, C, D, and E are the standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline, respectively, and the fourth wild probability represents the probability that the test sample is wild bighead carp.

[0205] The steps provided in the embodiment are to obtain the fourth threshold value by ROC curve calculation. The steps provided in some embodiments include: using the standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline of 36 wild bighead carp muscle samples and 36 farmed bighead carp muscle samples as training sets; training a binary logistic regression model with the training set to obtain multiple fourth wild probability values ​​as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each fourth wild probability value respectively, and drawing the ROC curve with 1-specificity as the abscissa and sensitivity as the ordinate; calculating the Youden index corresponding to each predicted wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, the fourth predicted wild probability corresponding to the maximum Youden index is the fourth threshold value.

[0206] In some embodiments, if the fourth wild probability is greater than the fourth threshold, the test sample is determined to be wild bighead carp. If the fourth wild probability is less than the fourth threshold, the test sample is determined to be farmed bighead carp.

[0207] In some embodiments, the fourth threshold value obtained by using the above test set and calculating the ROC curve is 0.585. That is, when the fourth wild probability is greater than 0.585, the test sample is determined to be wild bighead carp; when the fourth wild probability is less than 0.585, the test sample is determined to be farmed bighead carp.

[0208] 3. Identification application and testing

[0209] In some test cases, 36 muscle samples (including 18 wild and 18 farmed) of D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline were selected as metabolic markers, and their standardized values ​​were input into the fourth logistic regression model to obtain the fourth wild probability; according to the size of the fourth wild probability and the fourth threshold (0.585), it was determined whether the test sample was wild bighead carp or farmed bighead carp.

[0210] The standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline in the above training set were used to construct binary logistic regression models separately, and the standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline were combined to construct a fourth logistic regression model, and the 36 samples were tested respectively, and the discrimination results were subjected to ROC curve test, and the results are shown in Table 18. As can be seen from Table 18, the fourth logistic regression model constructed by combining the standardized values ​​of D-aminoglucosic acid, 4-methyl-2-pyrrolidine carboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline for discrimination of test samples has higher AUC value, sensitivity and specificity.

[0211] Table 18 ROC test results of various metabolite markers and their combinations for distinguishing wild and farmed bighead carp

[0212] Metabolic markers AUC Sensitivity Specificity D-Glucosamine 0.929 0.833 0.833 4-Methyl-2-pyrrolidinecarboxylic acid 0.852 0.889 0.722 Ornithine 0.781 0.500 1.000 Palmitoylethanolamide 0.889 0.722 0.944 N-Benzyloxycarbonyl-D-proline 0.861 0.722 0.833 Logo composition 0.988 0.944 0.944

[0213] The discrimination results were input into SPSS20.0 analysis software for confusion matrix test, such as Figure 4 As shown in the figure, only 2 samples among the 36 bighead carp muscle samples were misclassified, and the overall accuracy of the interactive validation was as high as 94.44%.

[0214] This shows that the stability of using D-aminoglucosamine, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline as metabolic markers and regression models for distinguishing wild bighead carp from farmed bighead carp is good. The excellent ROC curve results and good correct diagnosis rate as well as the good complementarity of metabolic markers indicate that the five organic acids and derivatives have the potential to distinguish wild bighead carp from farmed bighead carp.

[0215] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. Metabolic markers used to distinguish wild and farmed black carp include 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea and β-ureidoisobutyric acid.

2. Metabolic markers used to distinguish wild grass carp from farmed grass carp include 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, hexadecylethanolamide and morpholin-4-acetic acid.

3. Metabolic markers used to distinguish wild silver carp from farmed silver carp include L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholin-4-acetic acid and imidazole-4-carboxamide.

4. Metabolic markers used to distinguish wild bighead carp from farmed bighead carp include D-aminoglucosic acid, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide and N-benzyloxycarbonyl-D-proline.

5. Use of the metabolite marker according to claim 1 and / or a reagent for detecting the metabolite marker according to claim 1 in distinguishing wild black carp from farmed black carp, wherein the distinguishing comprises the step of testing the content of the metabolite marker according to claim 1 in a test sample; The determination also includes: obtaining a first logistic regression model and a first threshold; Obtain multiple standardized values ​​of the contents of multiple metabolite markers in the test samples; Inputting the plurality of the standardized values ​​into the first logistic regression model to obtain a first wild probability; Determining whether the test sample is the wild black carp or the farmed black carp according to the first wild probability and the first threshold; The first logistic regression model is X=1.384×A-1.253×B+7.954×C-9.283×D+20.067, and the first wild probability=1 / (1+e －X ); wherein A, B, C, and D are the standardized values ​​of 1-methyl-L-histidine, 6-methylpiperidine-2-carboxylic acid, 1,3-dicyclohexylurea, and B-ureidoisobutyric acid, respectively, and the first wild probability represents the probability that the test sample is a wild black carp; If the first wild probability is greater than the first threshold, the test sample is a wild black carp; If the first wild probability is less than the first threshold, the test sample is farmed black carp.

6. Use of the metabolite marker according to claim 2 and / or a reagent for detecting the metabolite marker according to claim 2 in distinguishing wild grass carp from farmed grass carp, wherein the distinguishing comprises the step of testing the content of the metabolite marker according to claim 2 in a test sample; The determination also includes: Obtain a second logistic regression model and a second threshold; Obtain multiple standardized values ​​of the contents of multiple metabolite markers in the test samples; Inputting the plurality of the standardized values ​​into the second logistic regression model to obtain a second wild probability; Determine whether the test sample is the wild grass carp or the farmed grass carp according to the second wild probability and the second threshold; The second logistic regression model is X=-4.442×A-34.516×B-1.251×C-32.472×D+595.011, and the second wild probability=1 / (1+e －X ), wherein A, B, C, and D are the standardized values ​​of 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and morpholin-4-acetic acid, respectively, and the second wild probability represents the probability that the test sample is a wild grass carp; If the second wild probability is greater than the second threshold, the test sample is a wild grass carp; If the second wild probability is less than the second threshold, the test sample is farmed grass carp.

7. Use of the metabolite marker according to claim 3 and / or a reagent for detecting the metabolite marker according to claim 3 in distinguishing wild silver carp from farmed silver carp, wherein the distinguishing comprises the step of testing the content of the metabolite marker according to claim 3 in a test sample; The determination also includes: Obtaining a third logistic regression model and a third threshold; Obtain multiple standardized values ​​of the contents of multiple metabolite markers in the test samples; Inputting the plurality of standardized values ​​into the third logistic regression model to obtain a third wild probability; Determine whether the test sample is the wild silver carp or the farmed silver carp according to the third wild probability and the third threshold; The third logistic regression model is X=3.645×A-4.261×B+0.349×C-13.196×D-22.73×E+293.016, and the third wild probability=1 / (1+e －X ); wherein A, B, C, D, and E are the standardized values ​​of L-prolyl-L-phenylalanine, ornithine, hexadecanoylethanolamide, morpholin-4-acetic acid, and imidazole-4-carboxamide, respectively, and the third wild probability represents the probability that the test sample is a wild silver carp; If the third wild probability is greater than the third threshold, the test sample is a wild silver carp; If the third wild probability is less than the third threshold, the test sample is farmed silver carp.

8. Use of the metabolite marker according to claim 4 and / or a reagent for detecting the metabolite marker according to claim 4 in distinguishing wild bighead carp from farmed bighead carp, wherein the distinguishing comprises the step of testing the content of the metabolite marker according to claim 4 in a test sample; The determination also includes: obtaining a fourth logistic regression model and a fourth threshold; Obtain multiple standardized values ​​of the contents of multiple metabolite markers in the test samples; Inputting the plurality of standardized values ​​into the fourth logistic regression model to obtain a fourth wild probability; Determine whether the test sample is the wild bighead carp or the farmed bighead carp according to the fourth wild probability and the fourth threshold value; The fourth logistic regression model is X=-17.948×A-18.644×B-13.235×C+0.897×D+6.003×E+330.941, and the fourth wild probability=1 / (1+e －X ); wherein A, B, C, D, and E are the standardized values ​​of D-aminoglucosamine, 4-methyl-2-pyrrolidinecarboxylic acid, ornithine, palmitoylethanolamide, and N-benzyloxycarbonyl-D-proline, respectively, and the fourth wild probability characterizes the probability that the test sample is a wild bighead carp; If the fourth wild probability is greater than the fourth threshold, the test sample is a wild bighead carp; If the fourth wild probability is less than the fourth threshold value, the test sample is farmed bighead carp.

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