CN-120427918-B - Amino acid biomarker for distinguishing wild and cultured silver carp, method and application

CN120427918BCN 120427918 BCN120427918 BCN 120427918BCN-120427918-B

Abstract

The application relates to the technical field of silver carp judgment or confirmation, in particular to an amino acid biomarker for judging wild and cultured silver carps, a method and application thereof. The biomarker comprises at least one of aspartic acid, alanine, phenylalanine, histidine and arginine. The biomarker can accurately judge wild and cultivated silver carp, provides scientific basis and technical support for relevant law enforcement departments, strengthens supervision and hit strength of illegal fishing behaviors in Yangtze river basin, reduces excessive fishing of wild resources, and further provides a technical foundation for better management and protection of silver carp resources.

Inventors

YU YALI
Qin Qiuying
ZHANG HUI
ZHANG LANG
XU JING
YE LITING
ZHOU QIONG
ZHOU JIANGUANG
LI TONG
LI ZHEN

Assignees

中国水产科学研究院长江水产研究所

Dates

Publication Date: 20260512
Application Date: 20250303

Claims (4)

1. The method for distinguishing the wild silver carps and the cultivated silver carps in the Yangtze river basin comprises the following steps: (1) Collecting upper back muscles of silver carps to be detected as detection samples, freezing the samples by liquid nitrogen, transferring the samples to a refrigerator at-80 ℃ for storage, detecting the absolute contents of aspartic acid, alanine, phenylalanine, histidine and arginine in the samples by a method of GB 5009.124-2016 (determination of amino acids in food safety national standard food), and calculating the relative content percentage of each amino acid, wherein the relative content percentage is the proportion of the absolute content of single amino acid to the total absolute content of 16 detection amino acids; (2) Substituting the relative content percentages of the aspartic acid, the alanine, the phenylalanine, the histidine and the arginine into a first logistic regression model, and calculating to obtain a first wild probability, wherein the first logistic regression model is as follows: X=-2.707×A+2.776×B-1.281×C-2.114×D+5.323×E-29.477; Wherein A, B, C, D, E is the relative content percentage of aspartic acid, alanine, phenylalanine, histidine and arginine respectively in turn, and the first wild probability=1/(1+e －X ); (3) Comparing the first wild probability to a first threshold; if the first wild probability is greater than the first threshold, the tested sample is a wild silver carp; if the first wild probability is smaller than the first threshold value, the tested sample is cultured silver carp; wherein the first threshold is 0.1928.
2. The method for distinguishing the wild silver carps and the cultivated silver carps in the Yangtze river basin comprises the following steps: (1) Collecting upper back muscles of silver carps to be detected as detection samples, freezing the samples by liquid nitrogen, transferring the samples to a refrigerator at-80 ℃ for storage, detecting the absolute contents of aspartic acid, alanine, histidine and arginine in the samples by a method of GB 5009.124-2016 (determination of amino acids in food safety national standard food), and calculating the relative content percentage of each amino acid, wherein the relative content percentage is the proportion of the absolute content of single amino acid to the total absolute content of 16 detection amino acids; (2) Substituting the relative content percentages of the aspartic acid, the alanine, the histidine and the arginine into a second logistic regression model, and calculating to obtain a second wild probability, wherein the second logistic regression model is as follows: X=-2.769×A+2.938×B-2.378×C+5.239×D-34.888; wherein A, B, C, D is the relative content percentage of aspartic acid, alanine, histidine and arginine respectively in turn, and the second wild probability=1/(1+e －X ); (3) Comparing the second wild probability to a second threshold; If the second wild probability is greater than the second threshold, the tested sample is a wild silver carp; if the second wild probability is smaller than the second threshold value, the tested sample is cultured silver carp; Wherein the second threshold is 0.1795.
3. The method for distinguishing the wild silver carps and the cultivated silver carps in the Yangtze river basin comprises the following steps: (1) Collecting upper back muscles of silver carps to be detected as detection samples, freezing the samples by liquid nitrogen, transferring the samples to a refrigerator at-80 ℃ for storage, detecting the absolute content of alanine, histidine and arginine in the samples by a method of GB 5009.124-2016 (determination of amino acids in food safety national standard food), and calculating the relative content percentage of each amino acid, wherein the relative content percentage is the proportion of the absolute content of single amino acid to the total absolute content of 16 detection amino acids; (2) Substituting the relative content percentages of alanine, histidine and arginine into a third logistic regression model, and calculating to obtain a third wild probability, wherein the third logistic regression model is as follows: X=2.494×A-2.157×B+7 .298×C-74.351; Wherein A, B, C is the relative content percentage of alanine, histidine, arginine respectively in turn, the third wild probability=1/(1+e －X ); (3) Comparing the third wild probability to a third threshold; if the third wild probability is greater than the third threshold, the tested sample is a wild silver carp; if the third wild probability is smaller than the third threshold value, the tested sample is cultured silver carp; Wherein the third threshold is 0.2855.
4. The method for distinguishing the wild silver carps and the cultivated silver carps in the Yangtze river basin comprises the following steps: (1) Collecting upper back muscles of silver carps to be detected as detection samples, freezing the samples by liquid nitrogen, transferring the samples to a refrigerator at-80 ℃ for storage, detecting the absolute content of alanine and arginine in the samples by a method of GB 5009.124-2016 (determination of amino acids in food safety national standard food), and calculating the relative content percentage of each amino acid, wherein the relative content percentage is the proportion of the absolute content of single amino acid to the total absolute content of 16 detection amino acids; (2) Substituting the relative content percentages of alanine and arginine into a fourth logistic regression model, and calculating to obtain a vast expanse of country th biological probability, wherein the fourth logistic regression model is as follows: X=4.334×A+7.664×B-97.780; Wherein A, B, C is the relative content percentage of alanine and arginine respectively in turn, and the a vast expanse of country th probability=1/(1+e －X ); (3) Comparing the a vast expanse of country th probability with a fourth threshold; If the a vast expanse of country th birth probability is larger than the fourth threshold, the tested sample is wild silver carp, and if the a vast expanse of country th birth probability is smaller than the fourth threshold, the tested sample is cultured silver carp; Wherein the fourth threshold is 0.3020.

Description

Amino acid biomarker for distinguishing wild and cultured silver carp, method and application Technical Field The application relates to the technical field of silver carp judgment or confirmation, in particular to an amino acid biomarker for judging wild and cultured silver carps, a method and application thereof. Background Silver carp is an important cultivated variety in China and is mainly distributed in the Yangtze river basin. A judging or confirming technology of the wild and cultivated silver carps is developed, biomarkers of the wild and cultivated silver carps are mined, and the wild and cultivated silver carps are popularized and applied, so that excessive fishing of wild resources can be reduced. Disclosure of Invention Aiming at the problems of the approach of the biological characters of wild and cultivated silver carp and the lack of discrimination technology, the application adopts an automatic amino acid analyzer to measure for the first time, collects muscle samples of different wild and cultivated silver carp groups, analyzes the amino acid spectrum, discusses the common difference and change of the wild and cultivated silver carp amino acid, and screens out the biomarkers of amino acids, including aspartic acid (CAS: 56-84-8), alanine (CAS: 56-41-7), phenylalanine (CAS: 63-91-2), histidine (CAS: 71-00-1) and arginine (CAS: 74-79-3). And the results detected by the amino acid automatic analyzer are utilized to carry out statistics and logistic regression analysis to obtain a logistic regression analysis model, and the sources of the tested samples are judged according to the logistic regression model, so that the wild and the breedability can be accurately judged, and the sensitivity and the specificity are better. In addition, the amino acid biomarker and the application thereof provided by the application can provide scientific basis and technical support for relevant law enforcement departments, strengthen supervision and striking force of illegal fishing behaviors of Yangtze river basin, reduce excessive fishing of wild resources, and further provide a technical foundation for better management and protection of silver carp resources. Therefore, the embodiment of the application at least discloses the following technical scheme: In a first aspect, embodiments disclose biomarkers comprising at least one of aspartic acid, alanine, phenylalanine, histidine, arginine. In a second aspect, embodiments disclose methods of distinguishing wild silver carps from farmed silver carps. The method comprises the steps of obtaining a first logistic regression model and a first threshold value, obtaining a plurality of relative content percentages of a plurality of biomarkers in a tested sample, wherein the biomarkers are aspartic acid, alanine, phenylalanine, histidine and arginine, inputting the relative content percentages into the first logistic regression model to obtain a first wild probability, wherein the first wild probability represents the probability that the tested sample is wild silver carp, and determining that the tested sample is the wild silver carp or the cultured silver carp according to the first wild probability and the first threshold value. The first logistic regression model is that X= -2.707 XA+ 2.776 XB-1.281 XC-2.114 XD+ 5.323 XE-29.477, and the first wild probability=1/(1+e －X), wherein A, B, C, D, E is the relative content percentage of aspartic acid, alanine, phenylalanine, histidine and arginine respectively. In some embodiments of the second aspect, the test sample is a wild silver carp if the first wild probability is greater than the first threshold. And if the first wild probability is smaller than the first threshold value, the tested sample is cultured silver carp. In an embodiment of the second aspect, the determining of the first threshold includes the steps of taking the relative content percentages of aspartic acid, alanine, phenylalanine, histidine and arginine of the collected muscle samples as a training set, training a binary logistic regression model by the training set to obtain a plurality of first wild probability values respectively, calculating sensitivity (true case rate) and 1-specificity (false case rate) of each of the first wild probability values respectively, drawing a ROC curve by taking the 1-specificity as an abscissa and the sensitivity as an ordinate, calculating about a log index (about log index=sensitivity+specificity-1) corresponding to each of the first wild probabilities, and obtaining a maximum about log index, wherein the first wild probability corresponding to the maximum about log index is the first threshold. In some embodiments, the first threshold is 0.1928. In a third aspect, the embodiments disclose a method for distinguishing wild silver carps from cultivated silver carps. The method comprises the steps of obtaining a second logistic regression model and a second threshold value, obtaining a plurality of relative content