Organic heterocyclic biomarkers and their applications in wild and farmed four major carp populations

Abstract

The present application relates to the field of determination or confirmation of the four major carps, and specifically to organic heterocyclic biomarkers and applications of wild and cultured four major carps populations. The biomarker includes (1H-imidazole-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazole carboxylic acid, calixin, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazole formic acid, 5,6-dimethyl-1H-benzotriazole and 5-tolyltriazole. At least one of these biomarkers can be accurately used to distinguish wild and cultured 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] This application relates to the technical field of the determination or confirmation of the four major Chinese carps, and specifically relates to organic heterocyclic biomarkers and applications of wild and cultured populations of the four major Chinese carps. Background Art

[0002] Black carp, grass carp, silver carp and bighead carp, these four major Chinese carps are important aquaculture varieties in China and are mainly distributed in the Yangtze River Basin. The Yangtze River Basin has rich fish biodiversity. However, affected by water pollution, overfishing, etc., there are 92 endangered fish species in the Yangtze River Basin. Summary of the Invention

[0003] In view of the problems that the biological characteristics of wild and farmed four major Chinese carps are similar and there is a lack of discrimination technology, for the first time, the ultra-high-throughput liquid chromatography-mass spectrometry technology is used to collect muscle samples of different wild and farmed four major Chinese carp populations, conduct metabolomics analysis, explore the common differences and changes in metabolites of wild and farmed four major Chinese carps, and screen out biomarker organic heterocyclics: (1H-imidazol-4-yl) -acetaldehyde (CAS: 645-14-7), sulfinol (CAS: 137-00-8), N-acetylimidazole (CAS: 2466-76-4), 2-aminobenzimidazole (CAS: 934-32-7), 2-(ethylthio)-1H-benzimidazole (CAS: 14610-11-8), 2-benzimidazolecarboxylic acid (CAS: 2849-93-6), calicin (CAS: 10091-92-6), 6-aminoindole (CAS: 5318-27-4), 2-methylbenzothiazole (CAS: 120-75-2), benzothiazole (CAS: 95-16-9), 1H-benzotriazolecarboxylic acid (CAS: 60932-58-3), 5,6-dimethyl-1H-benzotriazole (CAS: 4184-79-6), 5-tolyltriazole (CAS: 49636-02-4), DIBOA (2,4-dihydroxy-2H-1,4-benzoxazin-3(4H)-one, CAS: 17359-54-5), 3-(methylsulfonylmethyl)-1,4-benzoxanthone (CAS: 331751-12-3), 4-aminophthalhydrazide (CAS: 3682-14-2), 2-methylpyrazine (CAS: 109-08-0), 5-methyl-5(H)-cyclopentylpyrrole (CAS: 65128-99-6), N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide (PubChem SID: 124577890), 6-methoxypurine (CAS: 1074-89-1), 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione (CAS: 893764-14-2), indole-3-acetaldehyde (CAS: 2591-98-2), xylopic acid (CAS: 23141-27-7), bisucaberin (CAS: 71195-57-8), photopigment (CAS: 1086-80-2), etoricoxib (CAS: 202409-33-4), 3,4-diaminopyridine (CAS: 54-96-6), pirbuterol (CAS: 38677-81-5). Using the results detected by liquid chromatography-mass spectrometry, statistical and logistic regression analyses are carried out to obtain a logistic regression analysis model. According to the logistic regression model, the source of the test sample can be judged, and the wild and farmed properties can be accurately discriminated, with good sensitivity and specificity.Moreover, the biomarker and its use provided by this application can provide scientific basis and technical support for relevant law enforcement departments, strengthen the supervision and crackdown on illegal fishing activities in the Yangtze River Basin, reduce overfishing of wild resources, and thus provide a technical foundation for better managing and protecting the resources of the four major Chinese carps.

[0004] For this reason, the embodiments of this application at least disclose the following technical solutions:

[0005] In the first aspect, the embodiment discloses a biomarker, which contains at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, calyxin, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 3-(methylsulfonylmethyl)-1,4-benzoxathiin-6-one, 4-aminophthalhydrazide, 2-methylpyrazine, 5-methyl-5(H)-cyclopentylpyrrole, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, indole-3-acetaldehyde, trichosanthin, bisisatin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol.

[0006] In the embodiment of the first aspect, the biomarker contains at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole.

[0007] In the embodiment of the first aspect, the biomarker contains at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxathiin-6-one, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and trichosanthin.

[0008] In an embodiment of the first aspect, the biomarker comprises at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfitol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicitramide, photopigment, etoricoxib.

[0009] In an embodiment of the first aspect, the biomarker comprises at least one of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicitramide, photopigment, etoricoxib, 3,4-diaminopyridine, pirbuterol.

[0010] In a second aspect, an embodiment discloses a biomarker for discriminating wild black carp from cultured black carp, which contains at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfitol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole.

[0011] In an embodiment of the second aspect, the biomarker comprises (1H-imidazol-4-yl)-acetaldehyde, sulfitol, N-acetylimidazole, 2-aminobenzimidazole and 2-(ethylthio)-1H-benzimidazole.

[0012] In a third aspect, an embodiment discloses a biomarker for discriminating wild grass carp from cultured grass carp, which contains at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfitol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, fructus xanthoceratis glycoside.

[0013] In an embodiment of the third aspect, the biomarker comprises (1H-imidazol-4-yl)-acetaldehyde, sulfitol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and trichosanoside.

[0014] In a fourth aspect, an embodiment discloses a biomarker for discriminating wild silver carp from cultured silver carp, which contains at least one of (1H-imidazol-4-yl)-acetaldehyde, sulfitol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicipidine, photopigment, and etoricoxib.

[0015] In an embodiment of the fourth aspect, the biomarker comprises (1H-imidazol-4-yl)-acetaldehyde, sulfitol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicipidine, photopigment, and etoricoxib.

[0016] In a fifth aspect, an embodiment discloses a biomarker for discriminating wild bighead carp from cultured bighead carp, which contains at least one of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicipidine, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol.

[0017] In an embodiment of the fifth aspect, the biomarker includes (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, pirbuterol.

[0018] In a sixth aspect, an embodiment discloses a reagent composition, which includes a reagent for detecting any one of the biomarkers in the first to fifth aspects.

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

[0020] In a seventh aspect, an embodiment discloses the use of any one of the biomarkers in the first to fifth aspects and / or a reagent for detecting the biomarkers in the first to fifth aspects in differentiating wild and cultured four major Chinese carps.

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

[0022] In an embodiment of the eighth aspect, the method includes a method for testing the content of each biomarker among the biomarkers described in the second aspect in a test sample. In some embodiments, the test sample is an upper back muscle sample of a black carp. In some embodiments, each of the biomarkers includes (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole.

[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 contents of multiple biomarkers 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 cultured black carp according to the magnitude relationship between the first wild probability and the first threshold. In some embodiments, the first logistic regression model is: X = -3.311×A + 3.957×B - 0.104×C - 2.506×D + 20.879×E - 134.052; the first wild probability = 1 / (1 + e -X) where A, B, C, D, and E are the normalized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole, respectively. The first wild probability characterizes the probability that the test sample is a wild black carp. In some embodiments, if the first wild probability is greater than the first threshold, the test sample is a wild black carp. In some embodiments, if the first wild probability is less than the first threshold, the test sample is a farmed black carp.

[0024] In an embodiment of the eighth aspect, the step of determining the first threshold includes: using the normalized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole in the collected muscle samples as a training set; training a binary logistic regression model with this training set to obtain multiple first wild probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each first wild probability value, and plotting an 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. In some embodiments, the first threshold is 0.50.

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

[0026] In an embodiment of the ninth aspect, the method includes a method for testing the content of each biomarker in the biomarkers 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, the biomarkers include (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopic 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 contents of multiple biomarkers in a 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 relationship between the second wild probability and the second threshold. In some embodiments, the second logistic regression model is: X = -8.305×A + 1.952×B - 2.288×C - 3.550×D + 8.016×E - 2.809×F + 12.453×G - 0.052×H - 1.753×I - 1.993×J + 20.845×K - 204.906; the second wild probability = 1 / (1 + e -X ); where A, B, C, D, E, F, G, H, I, J, and K are the standardized values of (1H-imidazol-4-yl)acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxathiin-6-one, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopic 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, the test sample is a wild grass carp. In some embodiments, if the second wild probability is less than the second threshold, the test sample is a farmed grass carp.

[0028] In an embodiment of the ninth aspect, the step of determining the second threshold includes: using the normalized values of (1H-imidazol-4-yl)-acetaldehyde, sulfitol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and trichosanoside in the collected muscle samples as the training set; training a binary logistic regression model with this training set to obtain multiple second wild probability values respectively; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each second wild probability value, and plotting an 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, and the second wild probability corresponding to the maximum Youden index is the second threshold. In some embodiments, the second threshold is 0.50.

[0029] In the tenth aspect, an embodiment discloses a method for discriminating wild silver carp from cultured silver carp.

[0030] In an embodiment of the tenth aspect, the method includes a method for testing the content of each biomarker among the biomarkers 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, the various biomarkers include (1H-imidazol-4-yl)-acetaldehyde, sulfitol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, and etoricoxib.

[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 contents of multiple biomarkers in a 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 relationship between the third wild probability and the third threshold. In some embodiments, the third logistic regression model is: X = 3.027×A + 5.743×B - 5.007×C + 12.617×D - 5.317×E - 24.232×F + 0.827×G - 2.494×H - 3.803×I - 4.047×J - 9.330×K - 0.849×L + 3.107×M - 0.607×N + 0.180×O + 259.309; the third wild probability = 1 / (1 + e -X ); where A, B, C, D, E, F, G, H, I, J, K, L, M, N, O are the standardized values of (1H-imidazol-4-yl)acetaldehyde, sulfonol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, and etoricoxib respectively, and the third wild probability represents the probability that the test sample is a wild silver carp. In some embodiments, if the third wild probability is greater than the third threshold, the test sample is a wild silver carp. In some embodiments, if the third wild probability is less than the third threshold, the test sample is a farmed silver carp.

[0032] In an embodiment of the tenth aspect, the steps for determining the third threshold include: using the standardized values of (1H-imidazol-4-yl)acetaldehyde, sulfonol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, and etoricoxib in the collected muscle samples as a training set; training a binary logistic regression model with this training set to obtain multiple third wild probability values respectively; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each third wild probability value respectively, and plotting 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, and the third wild probability corresponding to the maximum Youden index is the third threshold. In some embodiments, the third threshold is 0.50.

[0033] In the eleventh aspect, the embodiment discloses a method for discriminating wild bighead carp from cultured bighead carp.

[0034] In the embodiment of the eleventh aspect, the method includes a method for testing the content of each biomarker among the biomarkers described in the fifth aspect in a test sample. In some embodiments, the test sample is an upper back muscle sample of bighead carp. In some embodiments, each of the biomarkers includes (1H-imidazol-4-yl)acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib, 3,4-diaminopyridine, pirbuterol.

[0035] In the 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 biomarkers 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 cultured bighead carp according to the magnitude relationship between the fourth wild probability and the fourth threshold. In some embodiments, the fourth logistic regression model is: X = -11.334×A + 0.688×B - 16.103×C + 3.445×D + 7.697×E + 7.952×F - 42.764×G - 2.901×H - 3.361×I - 4.023×J - 0.480×K + 1.858×L - 1.962×M + 4.825×N + 11.671×O + 3.131×P - 0.710×Q + 0.398×R + 10.332×S + 13.628×T + 136.168; the fourth wild probability = 1 / (1 + e -X); where A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, and T are respectively, in sequence, (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and the standardized values of pirbuterol. The fourth wild probability characterizes 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, the test sample is a wild bighead carp. In some embodiments, if the fourth wild probability is less than the fourth threshold, the test sample is a cultured bighead carp.

[0036] In an embodiment of the eleventh aspect, the steps for determining the fourth threshold include: using the standardized values of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol in the collected muscle samples as the training set; training a binary logistic regression model with this training set to obtain multiple fourth wild probability values respectively; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each fourth wild probability value, and plotting 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. In some embodiments, the fourth threshold is 0.50.

[0037] In an embodiment of the eighth to eleventh aspects, the standardized value is a value obtained by normalizing the chromatographic peak areas detected for each biomarker and performing a base-10 logarithmic transformation.

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

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

[0040] In the embodiments of any one of the eighth to eleventh aspects, in the detection step of the mass spectrometry, electrospray ionization (ESI) was used for positive ion (+) and negative ion (-) mode detections. After separation by UPLC, mass spectrometry analysis was performed using a QE Plus mass spectrometer (Thermo Scientific). Ionization was carried out using an HESI source, and the ionization conditions were as follows: spray voltage was 3.8 kv (+) and 3.2 kv (-); capillary temperature was 320 °C; probe heater temperature was 350 °C; sheath gas flow was 30 arbitrary units; auxiliary gas flow was 5 arbitrary units; S-Lens RF level was 50 arbitrary units.

[0041] In the embodiments of any one of the eighth to eleventh aspects, in the detection step of the mass spectrometry, the mass spectrometry acquisition time was 15 min. The parent ion scan range was 75 - 1050 m / z; the first-stage mass spectrometry resolution was 70,000 @ m / z 200; the number of target ions was 3e6; the maximum injection time for the first stage was 100 ms. The second-stage mass spectrometry analysis was acquired according to the following method: After each full scan, the second-stage mass spectrometry spectra of 10 of the highest-intensity parent ions were triggered for acquisition. The second-stage mass spectrometry resolution was 17,500 @ m / z 200; the number of target ions was 1e5, and the maximum injection time for the second stage was 50 ms.

[0042] In the embodiments of any one of the eighth to eleventh aspects, quality control was performed on the results obtained from the liquid chromatography - mass spectrometry. This quality control step included: preparing a quality control (QC) sample by mixing aliquots of the supernatants 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 residues 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 the embodiments of the eighth to eleventh aspects, the original 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 Confusion matrix test results of the metabolic marker composition ((1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole) provided for the examples for discriminating wild black carp from cultured black carp.

[0045] Figure 2 Confusion matrix test results of the metabolic marker composition ((1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, xylopic acid) provided for the examples for discriminating wild grass carp from cultured grass carp.

[0046] Figure 3 Confusion matrix test results of the metabolic marker composition ((1H-imidazol-4-yl)-acetaldehyde, sulfonol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib) provided for the examples for discriminating wild silver carp from cultured silver carp.

[0047] Figure 4 Confusion matrix test results of the metabolic marker composition ((1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib, 3,4-diaminopyridine, pirbuterol) provided for the examples for discriminating wild bighead carp from cultured bighead carp. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. Reagents not described in detail and individually in the present application are all conventional reagents and can be obtained from commercial channels; methods not described in detail and specifically are all conventional experimental methods and can be learned from the prior art.

[0049] Discovery of Organic Heterocyclic Biomarkers for Distinguishing Wild and Cultured Four Major Chinese Carps

[0050] 1. Collection of muscle samples of the four major Chinese carps: black carp, grass carp, silver carp, and bighead carp

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

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

[0053] 2. Extraction of sample metabolites

[0054] The muscle sample was made into minced meat with a meat grinder. 2 g of the sample was weighed, 2 medium-sized steel beads were added, and 200 μL of pre-cooled methanol-aqueous solution (1:1, v / v) was added. After homogenizing in a tissue disruptor for 5 min, it was centrifuged at 2000 r / min for 5 min to remove the liquid; 0.05 mL of 10 M sodium hydroxide solution and 0.5 mL of 1% 2,6-di-tert-butyl-p-cresol solution were added, then 10 mL of acetonitrile was added, vortexed for 1 min, then 5 mL of ethyl acetate was added, vortex-mixed, sonicated for 5 min, centrifuged at 6000 r / min for 5 min, and the supernatant was filtered through anhydrous sodium sulfate and rotary evaporated to near dryness; 3 mL of acetonitrile-0.1 M hydrochloric acid solution (1:1) was added to dissolve the residue, vortex-mixed for 1 min, sonicated for 5 min, transferred to a 15 mL centrifuge tube, then 5 mL of n-hexane was added to the centrifuge tube, vortex-mixed for 1 min, centrifuged at 6000 r / min for 5 min, the upper layer of n-hexane was discarded, then 3 mL of 0.1 M hydrochloric acid solution was added and mixed evenly as the stock solution. The MCX column (XH1220CGOOET, Comma Biotech) was activated with 3 mL of methanol and 3 mL of 0.1 M hydrochloric acid solution in sequence. All the stock solution was passed through the column, and the column was rinsed with 3 mL of 0.1 M hydrochloric acid solution and 3 mL of methanol respectively, dried by suction, eluted with 6 mL of 10% ammonia acetonitrile, and the eluate was dried with nitrogen at 40 °C. The residue was added with 1 mL of acetonitrile-0.1% formic acid solution (1:1), sonicated for 5 min, vortex-mixed thoroughly, and passed through a 0.22 μm filter membrane as the sample for UHPLC-MS / MS detection and analysis.

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

[0056] UHPLC-MS / MS analysis was performed for metabolomics analysis 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). During the whole analysis process, the samples were placed in a 4 °C autosampler. The samples were analyzed using the SHIMADZU-LC30 ultra-high performance liquid chromatography system (UHPLC), using 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, and the specific injection volume is 4 μL. The specific program of chromatographic gradient elution is as follows: 0→2 min, 0% B; 2→6 min, B linearly changes from 0% to 48%; 6→10 min, B linearly changes from 48% to 100%; 10→12 min, B is maintained at 100%; 12→12.1 min, B linearly changes from 100% to 0%, 12.1→15 min, B is maintained at 0%.

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

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

[0059] 4. Quality Control

[0060] Quality control (QC) samples were prepared by mixing aliquots of the supernatants from all samples. The repeatability of the data and the stability of the instrument were evaluated according to the overlapping base peak ion chromatogram (BPC) of the QC samples. The residues were determined using the BPC of the 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 was subjected to peak alignment, retention time correction, and peak area extraction using the MSDIAL software. For metabolite structure identification, exact mass number matching (mass deviation mass tolerance < 10 ppm) and secondary spectrum matching (mass deviation mass tolerance < 0.01 Da) were used to search public databases such as HMDB, MassBank, GNPS, and the self-built Baispectrum metabolite standard library (BP-DB). For the extracted data, ion peaks with more than 50% missing values within the group were deleted and not involved in subsequent statistical analysis; the total peak areas of positive and negative ion data were normalized separately, the positive and negative ion peaks were integrated, and pattern recognition was performed using Python software. After the data was preprocessed by Unit variance scaling (UV), subsequent data analysis was carried out. In this study, the obtained raw data files (.wiff format) were first imported into the Progenesis QI software for preprocessing data procedures. This included steps of retrospective alignment, peak selection, and peak detection, and data normalization was performed simultaneously. Public databases HMDB and LIPID MAPS were used during the process of annotating metabolites. Finally, a data matrix containing m / z, retention time (RT), peak intensity, sample name, and metabolite name was 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 discriminant analysis (OPLS-DA). After the OPLS-DA model was performed, the model was verified through 200 permutation tests and evaluated for overfitting. Differentially altered metabolites (DMs) with a P value < 0.05, Fold change > 1.5 or Fold change < 0.667, and VIP value > 1.0 were obtained based on the OPLS-DA model. Among them, the four groups of wild and cultured differential metabolites of black carp, grass carp, silver carp, and bighead carp were intersected using VEEN to obtain the common significantly different organic heterocyclic metabolites of wild and cultured four major Chinese carps.

[0065] 7. Statistical analysis methods

[0066] GraphPad Prism 8.0 software was used for statistical analysis of biochemical analysis data in the study. Student's t-test was used to calculate the differences in the mean values of data between different groups. SPSS was used for the construction and analysis of the binary logistic regression model. The input data were the standardized values of each metabolite, that is, the logarithm conversion value (log10) with base 10 after peak area normalization, which was used to reduce the skewed distribution of the overall values and make the data approximate a normal distribution. The experimental results were presented in the form of mean ± standard deviation (S.D.), where an asterisk (*) indicated that the difference between the experimental group and the control group was statistically significant. Specifically, *P < 0.05 indicated a significant difference, and **P < 0.01 indicated a highly significant difference.

[0067] 8. Results

[0068] Through the constructed OPLS-DA model, two groups of Culter alburnus samples from wild and cultured populations were compared. Metabolites with OPLS-DA VIP values greater than 1.0 obtained by analysis of variance were considered potential biomarkers contributing to class discrimination. The combination of fold change (FC) ≥ 1.5 or ≤ 0.667 and P < 0.05 was used to screen for significantly different metabolites based on P-values and VIP values. The study of significantly different metabolites in a single fish species was prone to being affected by the inherent characteristics and errors of that specific species, while the analysis of significantly different metabolites common to different fish species could avoid the errors of a single fish species and improve the accuracy and reliability of the results.

[0069] Therefore, in the embodiment, by performing VEEN intersection processing on the wild and cultured differential metabolites of four fish species, namely black carp, grass carp, silver carp, and bighead carp, the significant differential metabolites common to these four fish species under wild and cultured conditions were obtained. Subsequently, cluster analysis was performed on these common significant differential metabolites, and it was found that they included 28 organic heterocyclic substances, which were successively: (1H-imidazol-4-yl)acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, calyxin, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 3-(methylsulfonylmethyl)-1,4-benzoxazepin-2-one, 4-aminophthalhydrazide, 2-methylpyrazine, 5-methyl-5(H)-cyclopentylpyrrole, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, indole-3-acetaldehyde, xylopinine, bisucaberin, photopigment, etoricoxib, 3,4-diaminopyridine, pirbuterol. The reference mass-to-charge ratio, retention time (min), ionization mode, and subcategory of the above organic heterocyclic substances are shown in Table 1, and the wild and cultured differential multiples of each common significant differential metabolite in black carp, grass carp, silver carp, and bighead carp are shown in Table 2.

[0070] Table 1 Information on Biomarkers with Significant Differences between Wild and Cultured Conditions Common to Black Carp, Grass Carp, Silver Carp, and Bighead Carp

[0071]

[0072] Table 2 Differential Multiples of Biomarkers Common to Black Carp, Grass Carp, Silver Carp, and Bighead Carp between Wild and Cultured Conditions

[0073]

[0074]

[0075] Biomarkers for Distinguishing Wild and Cultured Black Carps and Their Uses

[0076] The main body of the current data discrimination model is comprehensively determined by constructing a model with multiple indicators. Among them, logistic regression, as a simple and intuitive model, has many advantages. First, it is easy to understand and interpret, does not require complex mathematical derivations or calculations, and has high computational efficiency. Second, logistic regression can provide highly interpretable result outputs, can be classified by setting thresholds, and the coefficients and intercepts of the model can provide the contribution degree and direction of variables to the target variable.

[0077] 1. Selection of Biomarkers

[0078] The example is based on liquid-phase detection of the metabolite contents in muscle samples of wild black carp and cultured black carp, and training of a binary logistic regression model is carried out according to the detection results. Compounds with high statistical significance, small measurement errors, and high data integrity are selected as biomarkers for discriminating wild black carp and cultured black carp. The biomarkers for discriminating wild black carp and cultured black carp provided by the example include (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole.

[0079] 2. Method for discriminating wild black carp and cultured black carp

[0080] The example also discloses a method for discriminating wild black carp and cultured black carp. The method includes: obtaining a first logistic regression model and a first threshold; obtaining multiple standardized values of the contents of multiple biomarkers 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 cultured black carp according to the magnitude relationship between the first wild probability and the first threshold. Among them, the multiple biomarkers include (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole.

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

[0082] 1) Obtaining a training sample

[0083] In the example, 12 muscle samples of wild black carp and 12 muscle samples of cultured black carp are subjected to the same liquid-phase detection as above to obtain the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole in each sample. These chromatographic peak areas are standardized to obtain standardized values. The steps of this standardization include: after normalizing the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole respectively, performing a base-10 logarithmic transformation to obtain the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole respectively. The standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole in these muscle samples are used as the training sample.

[0084] 2) Training

[0085] Using the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole as independent variables, and the probability that the sample is wild as the dependent variable, a binary logistic regression model is trained. In some embodiments, the training process of the binary logistic regression model includes: performing a composition analysis on the dependent variable and the independent variables to determine whether they meet the prerequisite conditions of logistic regression; performing a significance test on the independent variables, including degrees of freedom, significance, and scoring; performing a Hosmer-Lemeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; and training the binary logistic regression model to obtain a first logistic regression model based on the influence degree of the independent variables on the dependent variable.

[0086] In some steps, the embodiments perform a single-degree-of-freedom test on the muscle sample contents of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole in wild and farmed black carp, including giving degrees of freedom, significance, and scoring tests. The results are shown in Table 3. The overall statistical scores of the contents of these four biomarkers as independent variables are relatively high, and the significance is less than 0.05, indicating that they will have a significant impact on the corresponding variables overall.

[0087] Table 3 Significance test and scoring of independent variables of the logistic regression model of biomarkers in wild and farmed black carp

[0088]

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

[0090] Table 4 Hosmer-Lemeshow test of the logistic regression model of biomarkers in wild and farmed black carp

[0091] Steps Chi-square Degree of freedom Significance 1 <0.01 8 1.000

[0092] 3) Obtain the first logistic regression model

[0093] In some steps, the embodiments perform a binary logistic regression analysis on the logistic regression model of biomarkers in wild and farmed black carp. Table 5 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0094] Table 5 Binary logistic regression model analysis in the biomarker model of wild and farmed black carp

[0095]

[0096]

[0097] The first logistic regression model is obtained according to the standardized values of the muscle samples of the above 12 wild black carp and the muscle samples of 12 farmed black carp as follows:

[0098] X = -3.311×A + 3.957×B - 0.104×C - 2.506×D + 20.879×E - 134.052;

[0099] The first wild probability = 1 / (1 + e -X );

[0100] where A, B, C, D, and E are the standardized values of (1H-imidazol-4-yl)acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole in sequence, and the first wild probability characterizes the probability that the test sample is a wild black carp.

[0101] The steps provided in the examples calculate the first threshold through the ROC curve. The steps provided in some examples include: using the standardized values of (1H-imidazol-4-yl)acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole in the muscle samples of 12 wild black carp and 12 farmed black carp as the training set; training a binary logistic regression model with this 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, and plotting 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.

[0102] In some embodiments, if the first wild probability is greater than the first threshold, the test sample is determined to be a wild black carp. If the first wild probability is less than the first threshold, the test sample is determined to be a farmed black carp.

[0103] In some embodiments, using the above test set and calculating through the ROC curve, the obtained first threshold is 0.50. That is, when the first wild probability is greater than 0.50, the test sample is determined to be a wild black carp; when the first wild probability is less than 0.50, the test sample is determined to be a farmed black carp.

[0104] 3. Discrimination Application and Testing

[0105] In some test cases, (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole of 12 muscle samples (including 6 wild and 6 farmed) were selected as biomarkers, and their standardized values were input into the above first logistic regression model to obtain the first wild probability; according to the magnitude of the first wild probability and the first threshold (0.50), it was determined whether the test sample was a wild black carp or a farmed black carp.

[0106] Separate binary logistic regression models were constructed for the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole in the above training set respectively, and a first logistic regression model was constructed by combining the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole. The 12 samples were respectively detected, and the discrimination results were tested by the ROC curve. The results are shown in Table 6. It can be seen from Table 6 that by combining the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole to construct the first logistic regression model, it has higher AUC value, sensitivity and specificity.

[0107] Table 6 ROC test results of each biomarker and its combination for discriminating wild and farmed black carp

[0108] Biomarker AUC Sensitivity Specificity (1H-Imidazol-4-yl)-acetaldehyde 0.917 0.833 1.000 Sulfolene 0.889 0.833 1.000 N-Acetylimidazole 0.842 0.833 0.833 2-Aminobenzimidazole 0.813 0.833 0.667 2-(Ethylthio)-1H-benzimidazole 0.856 0.667 1.000 Composition 1.000 1.000 1.000

[0109] The discrimination results were input into the SPSS 20.0 analysis software for confusion matrix testing. The confusion matrix, also known as the error matrix, is a standard format for expressing accuracy evaluation and is represented in the form of an n×n matrix. The 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 the predicted category, and the total number of each column represents the number of data predicted as this category; each row represents the true attribution category of the data, and the total number of data in each row represents the number of data instances of this category. The results are as Figure 1 shown. The determination of 12 black carp muscle samples was all correct, and the overall correct rate of cross-validation was as high as 100.0%. This shows that taking (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole as biomarkers can be used to identify wild black carp and farmed black carp in the Yangtze River Basin. Its excellent ROC curve results, good correct diagnosis rate and good complementarity of biomarkers all indicate that the four organic acids and derivatives have the potential for the determination of wild black carp and farmed black carp.

[0110] Biomarkers for Distinguishing Wild and Cultured Grass Carps and Their Uses

[0111] 1. Selection of Biomarkers

[0112] In the examples, liquid-phase detection was performed on the metabolite contents in muscle samples of wild grass carp and cultured grass carp, and a binary logistic regression model was trained based on the detection results. Compounds with high statistical significance, small measurement errors, and high data integrity were selected as biomarkers for differentiating wild grass carp and cultured grass carp. The biomarkers for differentiating wild grass carp and cultured grass carp provided in the examples include (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopic acid.

[0113] The examples also provide a kit for detecting these biomarkers, and the kit includes reagents for detecting these biomarkers, such as reagents required for liquid chromatography-mass spectrometry.

[0114] 2. Method for Differentiating Wild Grass Carp and Cultured Grass Carp

[0115] The examples also disclose a method for differentiating wild grass carp and cultured grass carp. The method includes: obtaining a second logistic regression model and a second threshold; obtaining multiple standardized values of the contents of multiple biomarkers in a 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 cultured grass carp according to the magnitude relationship between the second wild probability and the second threshold. Among them, the multiple biomarkers include (1H-imidazol-4-yl)-acetaldehyde, sulfonol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopic acid.

[0116] In some examples, the step of obtaining the second logistic regression model includes:

[0117] 1) Obtaining a training sample

[0118] Examples: The muscle samples of 24 wild grass carps and 24 farmed grass carps were subjected to the same liquid-phase detection as above, and the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicin in each sample were obtained. These chromatographic peak areas were normalized to obtain normalized values. The steps of this normalization process include: after normalizing the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicin respectively, performing a base-10 logarithmic transformation to obtain the normalized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicin respectively. The normalized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicin in these muscle samples were used as training samples.

[0119] 2) Training

[0120] Taking the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicoside as independent variables, and the probability that the sample is wild as the dependent variable, a binary logistic regression model is trained. In some embodiments, the training process of the binary logistic regression model includes: performing a component analysis on the dependent variable and the independent variables to determine whether they meet the prerequisite conditions of logistic regression; performing a significance test on the independent variables, including degrees of freedom, significance, and scoring; performing a Hosmer-Lemeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; and training the binary logistic regression model to obtain a second logistic regression model based on the influence degree of the independent variables on the dependent variable.

[0121] In some steps, the embodiments perform a single-degree-of-freedom test on the muscle sample contents of the independent variables (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicoside, including giving degrees of freedom, significance, and scoring tests. The results are shown in Table 7. The overall statistical scores of the contents of these four biomarkers as independent variables are relatively high, and the significance is less than 0.001, indicating that the independent variables will have a highly significant impact on the dependent variable overall.

[0122] Table 7 Significance test and scoring of independent variables in the logistic regression model of biomarkers of wild and cultured grass carp

[0123]

[0124] In some steps, the embodiments perform a Hosmer-Lemeshow test on the logistic regression model of biomarkers of wild and cultured grass carp. Under the condition of multiple degrees of freedom, the significance P of the binary logistic regression model is 1.000 > 0.05, that is: the 0 hypothesis is accepted. The results are shown in Table 8. The established binary logistic regression model fits well with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0125] Table 8 Hosmer-Lemeshow test of the logistic regression model of wild and cultured grass carp

[0126] Steps Chi-square Degree of freedom Significance 1 <0.01 8 1.000

[0127] 3) Obtain the second logistic regression model

[0128] In some steps, the embodiments perform binary logistic regression analysis on the biomarkers for discriminating wild grass carp and cultured grass carp. Table 9 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0129] Table 9 Binary Logistic Regression Analysis of Wild Grass Carp and Cultured Grass Carp

[0130]

[0131]

[0132] The embodiments obtain the first logistic regression model according to the standardized values of the muscle samples of the above 24 wild grass carp and the muscle samples of 24 cultured grass carp as follows:

[0133] X = -8.305×A + 1.952×B - 2.288×C - 3.550×D + 8.016×E - 2.809×F + 12.453×G - 0.052×H - 1.753×I - 1.993×J + 20.845×K - 204.906;

[0134] The second wild probability = 1 / (1 + e -X )

[0135] where A, B, C, D, E, F, G, H, I, J, K are the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylomolloside respectively, and the second wild probability characterizes the probability that the test sample is wild grass carp.

[0136] The steps provided by the embodiments calculate the second threshold through the ROC curve. The steps provided by some embodiments include: using the normalized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopinine in 24 muscle samples of wild grass carp and 24 muscle samples of cultured grass carp as the training set; training a binary logistic regression model with this training set to obtain multiple second wild probabilities as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each second wild probability, and plotting 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.

[0137] In some embodiments, if the second wild probability is greater than the second threshold, the test sample is determined to be a wild grass carp. If the second wild probability is less than the second threshold, the test sample is determined to be a cultured grass carp.

[0138] In some embodiments, through calculation using the ROC curve with the above test set, the obtained second threshold is 0.50. That is, when the second wild probability is greater than 0.50, the test sample is determined to be a wild grass carp; when the second wild probability is less than 0.50, the test sample is determined to be a cultured grass carp.

[0139] 3. Discrimination Application and Testing

[0140] In some test cases, 24 muscle samples (including 12 wild and 12 cultured) of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calicin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopinine are selected as biomarkers, and their normalized values are input into the above second logistic regression model to obtain the second wild probability; based on the magnitude relationship between the second wild probability and the second threshold (0.50), it is determined whether the test sample is a wild grass carp or a cultured grass carp.

[0141] The standardized values of the above training set (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylomannan were used to separately construct binary logistic regression models. Additionally, the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylomannan were combined to construct a second logistic regression model. These models were used to detect the 24 samples respectively, and the discrimination results were tested using the ROC curve. The results are shown in Table 10. As can be seen from Table 10, when using the combined standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthione, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylomannan to construct the second logistic regression model for discriminating test samples, it has a higher AUC value, sensitivity, and specificity.

[0142] Table 10 ROC test results of various biomarkers and their combinations for discriminating wild and farmed grass carp

[0143] Biomarker AUC Sensitivity Specificity (1H-Imidazol-4-yl)-acetaldehyde 0.889 0.917 0.917 Sulfolene 0.875 0.833 0.750 N-Acetylimidazole 0.736 0.583 1.000 2-Aminobenzimidazole 0.879 0.833 0.917 2-(Ethylthio)-1H-benzimidazole 0.748 0.917 0.667 Kalihin 0.882 0.917 0.833 3-(Methylsulfonylmethyl)-1,4-benzoxanthone 0.694 0.750 0.583 N-[4-(Methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide 0.868 0.917 0.833 6-Methoxypurine 0.734 0.750 0.750 8-[(2-Hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione 0.769 0.667 0.833 Xylofolin 0.843 0.833 0.750 Composition 0.986 1.000 0.917

[0144] The discrimination results were input into the SPSS 20.0 analysis software for confusion matrix testing. The results are as Figure 2 shown. Only 1 out of the 24 grass carp samples was misjudged, and the overall correct rate of cross-validation was as high as 95.83%.

[0145] This indicates that using (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calyxin, 3-(methylsulfonylmethyl)-1,4-benzoxanthone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xylopicin as biomarkers can be used to distinguish wild grass carp and cultured grass carp from the Yangtze River Basin. The excellent ROC curve results, good correct diagnosis rate, and good complementarity of the biomarkers all indicate that the four organic acids and derivatives have the potential to be used for the determination of wild grass carp and cultured grass carp.

[0146] Biomarkers for Distinguishing Wild and Cultured Silver Carps and Their Uses

[0147] 1. Selection of Biomarkers

[0148] The example is based on liquid-phase detection of the metabolite content in muscle samples of wild silver carp and cultured silver carp, and the training of a binary logistic regression model is carried out according to the detection results. Compounds with high statistical significance, small measurement errors, and high data integrity are selected as biomarkers for distinguishing wild silver carp and cultured silver carp. The biomarkers for distinguishing wild silver carp and cultured silver carp provided in the example include (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, and etoricoxib.

[0149] The example also provides a kit for detecting these biomarkers, which includes reagents for detecting these biomarkers, such as reagents required for liquid chromatography-mass spectrometry.

[0150] 2. Method for Distinguishing Wild Silver Carp and Cultured Silver Carp

[0151] The embodiment also discloses a method for discriminating wild silver carp from cultured silver carp. The method includes: obtaining a third logistic regression model and a third threshold; obtaining multiple standardized values of the contents of multiple biomarkers in a 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 cultured silver carp according to the magnitude relationship between the third wild probability and the third threshold. Among them, the multiple biomarkers include (1H-imidazol-4-yl)acetaldehyde, sulfonol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib.

[0152] In some embodiments, the step of obtaining the third logistic regression model includes:

[0153] 1) Obtaining a training sample

[0154] Examples 60 muscle samples of wild silver carps and 48 muscle samples of farmed silver carps were subjected to the same liquid-phase detection as above, and the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicitramide, photopigment, and etoricoxib in each sample were obtained. These chromatographic peak areas were standardized to obtain standardized values. The steps of this standardization process include: respectively normalizing the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicitramide, photopigment, and etoricoxib, and then performing a base-10 logarithmic transformation to respectively obtain the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicitramide, photopigment, and etoricoxib. The standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicitramide, photopigment, and etoricoxib in these muscle samples were used as training samples.

[0155] 2) Training

[0156] Using the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifalidine, photopigment, and etoricoxib as independent variables, and the probability that the sample is wild as the dependent variable, a binary logistic regression model is trained. In some embodiments, the training process of the binary logistic regression model includes: performing a component analysis on the dependent variable and the independent variables to determine whether they meet the prerequisite conditions of logistic regression; performing a significance test on the independent variables, including degrees of freedom, significance, and scoring; performing a Hosmer-Lemeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; and training the binary logistic regression model to obtain a third logistic regression model based on the influence degree of the independent variables on the dependent variable.

[0157] In some steps, in the embodiments, degrees of freedom, significance tests, and scoring are performed on the muscle sample contents of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifalidine, photopigment, and etoricoxib. The results are shown in Table 11. The overall statistical score of the independent variables is relatively high, and the significance is less than 0.0001, indicating that the independent variables will have a highly significant impact on the corresponding variable overall.

[0158] Table 11 Significance test and scoring of independent variables in the logistic regression model of biomarkers of wild silver carp and cultured silver carp

[0159]

[0160]

[0161] To further analyze the influence of constants and independent variables on the dependent variable in terms of multiple degrees of freedom, the embodiments also perform a Hosmer-Lemeshow test on the logistic regression model. Under the condition of multiple degrees of freedom, the significance P of the binary logistic regression model is 1.000 > 0.05, that is: the null hypothesis is accepted. The results are shown in Table 12. The established binary logistic regression model fits well with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0162] Table 13 Hosmer-Lemeshow test of the logistic regression model of wild silver carp and cultured silver carp

[0163] Steps Chi-square Degree of freedom Significance 1 0.001 8 1.000

[0164] 3) Obtain the third logistic regression model

[0165] In some steps, the embodiment conducts binary logistic regression analysis on the logistic regression models of biomarkers 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 biomarker.

[0166] Table 13 Binary Logistic Regression Analysis of Wild Silver Carp and Cultured Silver Carp

[0167]

[0168] The embodiment obtains the third logistic regression model according to the standardized values of 60 muscle samples of wild silver carp and 48 muscle samples of cultured silver carp as follows:

[0169] X = 3.027×A + 5.743×B - 5.007×C + 12.617×D - 5.317×E - 24.232×F + 0.827×G - 2.494×H - 3.803×I - 4.047×J - 9.330×K - 0.849×L + 3.107×M - 0.607×N + 0.180×O + 259.309;

[0170] The third wild probability = 1 / (1 + e -X )

[0171] where A, B, C, D, E, F, G, H, I, J, K, L, M, N, O are the standardized values of (1H-imidazol-4-yl)acetaldehyde, sulfonated alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifalconidine, photopigment, etoricoxib respectively, and the third wild probability represents the probability that the test sample is wild silver carp.

[0172] The steps provided by the embodiments calculate the third threshold through the ROC curve. The steps provided by some embodiments include: using the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib in 60 muscle samples of wild silver carp and 48 muscle samples of cultured silver carp as the training set; training a binary logistic regression model with this training set to obtain multiple third wild probabilities as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each third wild probability respectively, and plotting 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, and the third predicted wild probability corresponding to the maximum Youden index is the third threshold.

[0173] 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 cultured silver carp.

[0174] In some embodiments, through calculation using the above test set via the ROC curve, the obtained third threshold is 0.50. That is, when the third wild probability is greater than 0.50, the test sample is determined to be wild silver carp; when the third wild probability is less than 0.50, the test sample is determined to be cultured silver carp.

[0175] 3. Discrimination Application and Testing

[0176] In some test cases, select the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib in 54 muscle samples (including 30 wild and 24 cultured) as biomarkers, input their standardized values into the above third logistic regression model to obtain the third wild probability; determine whether the test sample is wild silver carp or cultured silver carp according to the magnitude relationship between the third wild probability and the third threshold (0.50).

[0177] The standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifaline, photopigment, and etoricoxib in the above training set were used to separately construct binary logistic regression models. Additionally, a third logistic regression model was constructed by combining the standardized values of (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifaline, photopigment, and etoricoxib. These 12 samples were separately tested, and the discrimination results were subjected to ROC curve testing. 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 (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bisifaline, photopigment, and etoricoxib has higher AUC values, sensitivity, and specificity.

[0178] Table 14 ROC test results of various biomarkers and their combinations for discriminating wild and farmed silver carp

[0179] Biomarker AUC Sensitivity Specificity (1H-Imidazol-4-yl)-acetaldehyde 0.940 0.833 0.917 Sulfolene 0.878 0.733 0.833 2-Aminobenzimidazole 0.842 0.867 0.833 2-(Ethylthio)-1H-benzimidazole 0.860 0.867 0.792 6-Aminoindole 0.688 0.867 0.500 Benzothiazole 0.790 0.967 0.542 1H-Benzotriazolecarboxylic acid 0.765 0.767 0.625 5,6-Dimethyl-1H-benzotriazole 0.926 0.833 0.917 5-Tolyltriazole 0.846 0.667 0.917 DIBOA 0.807 0.633 1.000 5-Methyl-5(H)-cyclopentylpyrrole 0.810 1.000 0.542 Indole-3-acetaldehyde 0.846 0.667 0.917 Bisifadin 0.903 0.900 0.750 Photopigment 0.860 0.867 0.792 Etoricoxib 0.807 0.633 1.000 Composition 0.983 0.967 0.958

[0180] The discrimination results were input into SPSS 20.0 analysis software for confusion matrix testing. The results are as Figure 3 shown. Only 2 out of 54 silver carp samples were misjudged, and the overall correct rate of cross-validation was as high as 96.30%.

[0181] This shows that the logistic regression model constructed with (1H-imidazole-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicifadine, photochrome, and etoricoxib as biomarkers and their standardized values ​​can be used to identify wild and farmed silver carp in the Yangtze River Basin. Organic acids and derivatives have the potential to determine wild and farmed silver carp. The excellent ROC curve results and good correct diagnosis rate as well as good complementarity of biomarkers indicate that the five have

[0182] Biomarkers for Distinguishing Wild and Cultured Bighead Carps and Their Uses

[0183] 1. Selection of biomarkers

[0184] The embodiment is based on liquid phase detection of metabolite contents 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 determination error and high data integrity are selected as biomarkers for distinguishing wild bighead carp from farmed bighead carp. The biomarkers for distinguishing wild bighead carp from farmed bighead carp provided in the embodiment include (1H-imidazole-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazole carboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicifadine, photochrome, etoricoxib, 3,4-diaminopyridine and pirbuterol.

[0185] The embodiment also provides a kit for detecting these biomarkers, which includes reagents for detecting these biomarkers, such as reagents required for liquid chromatography-mass spectrometry.

[0186] 2. How to distinguish wild bighead carp from farmed bighead carp

[0187] The embodiment also discloses a method for discriminating wild bighead carp from cultured bighead carp. The method includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple standardized values of the contents of multiple biomarkers in a 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 cultured bighead carp according to the magnitude relationship between the fourth wild probability and the fourth threshold. Among them, the multiple biomarkers include (1H-imidazol-4-yl)acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol.

[0188] In some embodiments, the step of obtaining the fourth logistic regression model includes:

[0189] 1) Obtaining a training sample

[0190] Examples The muscle samples of 36 wild bighead carps and 36 farmed bighead carps were subjected to the same liquid-phase detection as above, and the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicipidine, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol in each sample were obtained. These chromatographic peak areas were standardized to obtain standardized values. The steps of this standardization process include: after normalizing the chromatographic peak areas of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicipidine, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol respectively, performing a base-10 logarithmic transformation, and then the standardized values of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicipidine, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol can be obtained respectively. The standardized values of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicipidine, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol in these muscle samples were used as training samples.

[0191] 2) Training

[0192] Taking the standardized values of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol as independent variables, and the probability that the sample is wild as the dependent variable, a binary logistic regression model is trained. In some embodiments, the training process of the binary logistic regression model includes: performing a component analysis on the dependent variable and the independent variables to determine whether they meet the prerequisite conditions of logistic regression; performing a significance test on the independent variables, including degrees of freedom, significance, and scoring; performing the Hosmer-Lemeshow test to verify the applicability and rationality of the model and ensure the accuracy of the model; and training the binary logistic regression model to obtain a fourth logistic regression model based on the influence degree of the independent variables on the dependent variable.

[0193] In some steps, the embodiments perform a single-degree-of-freedom test on the muscle sample content of the independent variables (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol, including giving degrees of freedom, significance, and scoring tests. The results are shown in Table 16. The overall statistical scores of these independent variables are relatively high, and the significance is less than 0.0001, indicating that the independent variables will have a highly significant impact on the dependent variable overall.

[0194] Table 16 Significance test and scoring of independent variables in the combined biomarker model of wild and cultured bighead carps

[0195]

[0196] In some steps, the embodiments perform the Hosmer-Lemeshow test on the logistic regression model of the biomarkers of wild and cultured bighead carps. Under the condition of multiple degrees of freedom, the significance P of the binary logistic regression model is 1.00 > 0.05, that is: the null hypothesis is accepted. As shown in Table 17, the established binary logistic regression model fits well with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0197] Table 17 Hosmer-Lemeshow test of the binary logistic regression model of wild and cultured bighead carps

[0198]

[0199]

[0200] 3) Obtain the fourth logistic regression model

[0201] In some steps, the embodiments perform binary logistic regression analysis on the biomarkers for discriminating wild bighead carp and cultured bighead carp. Table 18 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0202] Table 18 Binary Logistic Regression Analysis of Wild Bighead Carp and Cultured Bighead Carp

[0203]

[0204] The embodiments obtain the following fourth logistic regression model according to the standardized values of the muscle samples of 36 wild bighead carp and 36 cultured bighead carp:

[0205] X = -11.334×A + 0.688×B - 16.103×C + 3.445×D + 7.697×E + 7.952×F - 42.764×G - 2.901×H - 3.361×I - 4.023×J - 0.480×K + 1.858×L - 1.962×M + 4.825×N + 11.671×O + 3.131×P - 0.710×Q + 0.398×R + 10.332×S + 13.628×T + 136.168;

[0206] Fourth wild probability = 1 / (1 + e -X );

[0207] where A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T are the standardized values of (1H-imidazol-4-yl)acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol respectively, and the fourth wild probability represents the probability that the test sample is wild bighead carp.

[0208] The steps provided by the embodiments calculate the fourth threshold through the ROC curve. The steps provided by some embodiments include: using the standardized values of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol in 36 muscle samples of wild bighead carp and 36 muscle samples of cultured bighead carp as the training set; training a binary logistic regression model with this training set to obtain multiple fourth wild probabilities as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) of each fourth wild probability, and plotting 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.

[0209] In some embodiments, if the fourth wild probability is greater than the fourth threshold, the test sample is determined to be a wild bighead carp. If the fourth wild probability is less than the fourth threshold, the test sample is determined to be a cultured bighead carp.

[0210] In some embodiments, through calculation using the above test set via the ROC curve, the obtained fourth threshold is 0.50. That is, when the fourth wild probability is greater than 0.50, the test sample is determined to be a wild bighead carp; when the fourth wild probability is less than 0.50, the test sample is determined to be a cultured bighead carp.

[0211] 3. Discrimination Application and Testing

[0212] In some test cases, 36 muscle samples (including 18 wild and 18 cultured) of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol are selected as biomarkers, and their standardized values are input into the above fourth logistic regression model to obtain the fourth wild probability; based on the magnitude relationship between the fourth wild probability and the fourth threshold (0.50), it is determined whether the test sample is a wild bighead carp or a cultured bighead carp.

[0213] The standardized values of the above training set (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol were used to separately construct binary logistic regression models. Additionally, the standardized values of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol were combined to construct a fourth logistic regression model. These models were used to detect 36 samples respectively, and the discrimination results were tested by the ROC curve. The results are shown in Table 19. As can be seen from Table 19, when using the standardized value combination of (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifalcon, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol to construct the fourth logistic regression model for discrimination of test samples, it has higher AUC value, sensitivity, and specificity.

[0214] Table 19 ROC test results of each biomarker and its composition for discriminating wild and cultured bighead carp

[0215]

[0216]

[0217] The discrimination results were input into the SPSS 20.0 analysis software for confusion matrix testing, as Figure 4 shown. Among the 36 bighead carp muscle samples, only 1 sample was misjudged, and the overall correct rate of cross-validation was as high as 97.22%.

[0218] This shows that using (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bisifadin, photopigment, etoricoxib, 3,4-diaminopyridine, and pirbuterol as biomarkers and the regression model has good stability for discriminating wild bighead carp from cultured bighead carp. The excellent ROC curve results, good correct diagnosis rate, and good complementarity of the biomarkers indicate that the five organic acids and derivatives have the potential for discriminating wild bighead carp from cultured bighead carp.

[0219] As described above, the above are only the preferred specific embodiments 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 those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application.

Claims

1. Use of biomarkers and / or reagents for detecting the biomarkers in distinguishing wild from farmed black carp, wherein the distinction comprises: Testing the content of biomarkers in the test sample, wherein the biomarkers include (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole; obtaining a first logistic regression model and a first threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers in the test samples; Inputting the plurality of the standardized values ​​into the first logistic regression model to obtain a first wild probability; Determine whether the test sample is the wild black carp or the farmed black carp according to the first wild probability and the first threshold; If the first wild probability is greater than the first threshold, the test sample is a wild black carp; as well as If the first wild probability is less than the first threshold, the test sample is farmed black carp; Among them, the first logistic regression model is: X = -3.311 × A + 3.957 × B - 0.104 × C - 2.506 × D + 20.879 × E - 134.052; the first wild probability = 1 / (1 + e －X ), wherein A, B, C, D, and E are the standardized values ​​of (1H-imidazole-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, and 2-(ethylthio)-1H-benzimidazole, respectively, and the first wild probability represents the probability that the test sample is wild black carp; Among them, the calculation method of the first threshold is: respectively calculate the true positive rate and the false positive rate of each of the first wild probability values, and draw the ROC curve with the false positive rate as the horizontal coordinate and the true positive rate as the vertical coordinate; calculate the Youden index corresponding to each predicted wild probability, and obtain the maximum Youden index, the first predicted wild probability corresponding to the maximum Youden index is the first threshold.

2. Use of biomarkers and / or reagents for detecting the biomarkers in distinguishing wild from farmed grass carp, the distinction comprising: The step of testing the content of biomarkers in the test sample, wherein each biomarker includes (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calixin, 3-(methylsulfonylmethyl)-1,4-benzoxabenzoin ketone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xifoside; Obtain a second logistic regression model and a second threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers 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; If the second wild probability is greater than the second threshold, the test sample is a wild grass carp; as well as If the second wild probability is less than the second threshold, the test sample is farmed grass carp; Among them, the second logistic regression model is: X = -8.305 × A + 1.952 × B - 2.288 × C - 3.550 × D + 8.016 × E - 2.809 × F + 12.453 × G - 0.052 × H - 1.753 × I - 1.993 × J + 20.845 × K - 204.906; the second wild probability = 1 / (1 + e -X ); wherein A, B, C, D, E, F, G, H, I, J, and K are respectively the standardized values ​​of (1H-imidazole-4-yl)-acetaldehyde, sulfonyl alcohol, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, calixine, 3-(methylsulfonylmethyl)-1,4-benzoxabenzoin ketone, N-[4-(methylethyl)phenyl]-2-(9-methyl-6-oxopyrimidin-8-ylthio)acetamide, 6-methoxypurine, 8-[(2-hydroxyethyl)methylamino]-1,3,9-trimethyl-1,3-dihydropyrimidine-2,6-dione, and xiguobin, and the second wild probability represents the probability that the test sample is wild grass carp; Among them, the calculation method of the second threshold is: respectively calculate the true positive rate and false positive rate of each second wild probability value, and draw the ROC curve with the false positive rate as the horizontal coordinate and the true positive rate as the vertical coordinate; calculate the Youden index corresponding to each predicted wild probability, and obtain the maximum Youden index, and the second predicted wild probability corresponding to the maximum Youden index is the second threshold.

3. Use of biomarkers and / or reagents for detecting the biomarkers in distinguishing wild from farmed silver carp, the distinction comprising: Testing the content of biomarkers in the test sample, wherein each biomarker includes (1H-imidazol-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicifadine, photochrome, etoricoxib; Obtaining a third logistic regression model and a third threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers 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; If the third wild probability is greater than the third threshold, the test sample is a wild silver carp; and If the third wild probability is less than the third threshold, the tested sample is farmed silver carp; Wherein, the third logistic regression model is: X=3.027×A+5.743×B-5.007×C+12.617×D-5.317×E-24.232×F+0.827×G-2.494×H-3.803×I-4.047×J-9.330×K-0.849×L+3.107×M-0.607×N+0.180×O+259.309; the third wild probability=1 / (1+e －X ); wherein A, B, C, D, E, F, G, H, I, J, K, L, M, N, and O are respectively (1H-imidazole-4-yl)-acetaldehyde, sulfonyl alcohol, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 6-aminoindole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 5-methyl-5(H)-cyclopentylpyrrole, indole-3-acetaldehyde, bicifadine, photochrome, and the standardized values ​​of etoricoxib, and the third wild probability represents the probability that the test sample is wild silver carp; Among them, the calculation method of the third threshold is: respectively calculate the true positive rate and false positive rate of each of the third wild probability values, and draw the ROC curve with the false positive rate as the horizontal axis and the true positive rate as the vertical axis; calculate the Youden index corresponding to each predicted wild probability, and obtain the maximum Youden index, the third predicted wild probability corresponding to the maximum Youden index is the third threshold.

4. Use of biomarkers and / or reagents for detecting said biomarkers in distinguishing wild from farmed bighead carp, said distinction comprising: Testing the content of biomarkers in the test sample, wherein each biomarker includes (1H-imidazol-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicifadine, photochrome, etoricoxib, 3,4-diaminopyridine, and pirbuterol; obtaining a fourth logistic regression model and a fourth threshold; Obtaining multiple standardized values ​​of the contents of multiple biomarkers 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; If the fourth wild probability is greater than the fourth threshold, the test sample is a wild bighead carp; as well as If the fourth wild probability is less than the fourth threshold value, the test sample is farmed bighead carp; Wherein, the fourth logistic regression model is: X = -11.334 × A + 0.688 × B - 16.103 × C + 3.445 × D + 7.697 × E + 7.952 × F - 42.764 × G - 2.901 × H - 3.361 × I - 4.023 × J - 0.480 × K + 1.858 × L - 1.962 × M + 4.825 × N + 11.671 × O + 3.131 × P - 0.710 × Q + 0.398 × R + 10.332 × S + 13.628 × T + 136.168, the fourth wild probability = 1 / (1 + e －X ), wherein A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, and T are respectively (1H-imidazole-4-yl)-acetaldehyde, N-acetylimidazole, 2-aminobenzimidazole, 2-(ethylthio)-1H-benzimidazole, 2-benzimidazolecarboxylic acid, 6-aminoindole, 2-methylbenzothiazole, benzothiazole, 1H-benzotriazolecarboxylic acid, 5,6-dimethyl-1H-benzotriazole, 5-tolyltriazole, DIBOA, 4-aminophthalhydrazide, 2-methylpyrazine, indole-3-acetaldehyde, bicifadine, photochrome, etoricoxib, 3,4-diaminopyridine, and the standardized values ​​of pirbuterol, and the fourth wild probability represents the probability that the test sample is wild bighead carp; Among them, the calculation method of the fourth threshold is: respectively calculate the true positive rate and false positive rate of each of the fourth wild probability values, and draw the ROC curve with the false positive rate as the horizontal coordinate and the true positive rate as the vertical coordinate; calculate the Youden index corresponding to each predicted wild probability, and obtain the maximum Youden index, the fourth predicted wild probability corresponding to the maximum Youden index is the fourth threshold.

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