Fatty acid biomarker for distinguishing wild silver carp and cultured silver carp, method and application

Abstract

The invention relates to the technical field of silver carp judgment or confirmation, in particular to fatty acid biomarkers for judging wild and cultured silver carps, a method and application. The biomarker comprises at least one of tetradecanoic acid, cis, cis, cis-9, 12, 15-octadecatrienoic acid, cis-5, 8, 11, 14-eicosatetraenoic acid, and cis-5, 8, 11, 14, 17-eicosapentaenoic acid. The biomarker can accurately distinguish wild silver carps and cultured silver carps, provides scientific basis and technical support for related law enforcement departments, strengthens supervision and attack of illegal fishing behaviors in the Yangtze River basin, reduces excessive fishing of wild resources, and further provides a technical basis for better management and protection of silver carp resources.

Description

Technical Field

[0001] This application relates to the technical field of silver carp determination or confirmation, and specifically relates to fatty acid biomarkers, methods, and uses for differentiating wild and farmed silver carp. Background Art

[0002] Silver carp is an important aquaculture variety in China, mainly distributed in the Yangtze River Basin. Due to the impacts of water pollution, overfishing, etc., the fish biodiversity in the Yangtze River Basin is continuously decreasing. Therefore, since 2020, China has fully implemented the decision of "a ten-year fishing ban in the Yangtze River", and severely cracked down on illegal fishing in the Yangtze River Basin and the illegal act of selling Yangtze River fishing catches in the market. However, due to the difficulty in distinguishing wild and farmed aquatic products in the current Yangtze River Basin in China, it is difficult to meet the requirements of the "Yangtze River fishing ban" policy. Therefore, developing technologies for determining or confirming wild and farmed silver carp, excavating biomarkers of wild and farmed silver carp, and promoting their application are reliable ways to support the "Yangtze River fishing ban" policy. Summary of the Invention

[0003] In response to the problems that the biological characteristics of wild and farmed silver carp are similar and there is a lack of discrimination technology, this application first uses a gas chromatograph to measure, collects muscle samples of different wild and farmed silver carp populations, conducts fatty acid profile analysis, explores the common differences and changes in fatty acids of wild and farmed silver carp, and screens out fatty acid biomarkers: myristic acid (CAS: 57677-52-8, fatty acid abbreviation: C14:0), cis,cis,cis-9,12,15-octadecatrienoic acid (CAS: 463-40-1, fatty acid abbreviation: C18:3n3), cis-5,8,11,14-eicosatetraenoic acid (CAS: 463-40-1, fatty acid abbreviation: C20:4n6), cis-5,8,11,14,17-eicosapentaenoic acid (CAS: 463-40-1, fatty acid abbreviation: C20:5n3). Using the results detected by the gas chromatograph, 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 is discriminated, and wild and farmed properties can be accurately discriminated, with good sensitivity and specificity. Moreover, the fatty acid biomarkers and uses provided by this application can provide a scientific basis and technical support for relevant law enforcement departments, strengthen the supervision and crackdown on illegal fishing behaviors in the Yangtze River Basin, reduce overfishing of wild resources, and thus provide a technical foundation for better managing and protecting silver carp resources.

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

[0005] In a first aspect, an embodiment discloses a biomarker, which contains at least one of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid.

[0006] In a second aspect, an embodiment discloses a method for discriminating wild silver carp from cultured silver carp. The method includes: obtaining a first logistic regression model and a first threshold; obtaining multiple relative content percentages of multiple biomarkers in a test sample, where the multiple biomarkers are myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid; inputting the multiple relative content percentages into the first logistic regression model to obtain a first wild probability, where the first wild probability represents the probability that the test sample is a wild silver carp; and determining whether the test sample is the wild silver carp or the cultured silver carp according to the magnitude relationship between the first wild probability and the first threshold. Among them, the first logistic regression model is: X = 1.435×A - 2.169×B - 1.198×C + 0.164×D + 17.337; the first wild probability = 1 / (1 + e －X ); where A, B, C, and D are the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in sequence.

[0007] In some embodiments of the second aspect, if the first wild probability is greater than the first threshold, the test sample is a wild silver carp. If the first wild probability is less than the first threshold, the test sample is a cultured silver carp.

[0008] In the embodiments of the second aspect, the step of determining the first threshold includes: using the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid 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 respectively; calculating the sensitivity (true positive rate) and 1 - specificity (false positive rate) of each first 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 first wild probability (Youden index = sensitivity + specificity - 1), and obtaining the maximum Youden index, and the first wild probability corresponding to the maximum Youden index is the first threshold. In some embodiments, the first threshold is 0.4742.

[0009] Thirdly, the embodiment discloses a method for discriminating wild silver carps from cultured silver carps. The method includes obtaining a second logistic regression model and a second threshold; obtaining multiple relative content percentages of multiple biomarkers in a test sample, where the multiple biomarkers are myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid; inputting the multiple relative content percentages into the second logistic regression model to obtain a second wild probability, where the second wild probability represents the probability that the test sample is a wild silver carp; and determining whether the test sample is the wild silver carp or the cultured silver carp according to the magnitude relationship between the second wild probability and the second threshold. Among them, the second logistic regression model is: X = 1.444×A - 2.179×B - 1.071×C + 18.038; the second wild probability = 1 / (1 + e －X ); where A, B, and C are the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid, respectively.

[0010] In some embodiments of the third aspect, if the second wild probability is greater than the second threshold, the test sample is a wild silver carp. If the second wild probability is less than the second threshold, the test sample is a cultured silver carp.

[0011] In the embodiments of the third aspect, the steps for determining the second threshold include: using the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in the collected muscle samples as a 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 respectively, 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 0.4677.

[0012] Fourthly, the embodiments disclose 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 relative content percentages of the contents of multiple biomarkers in a test sample, the multiple biomarkers being cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid; inputting the multiple relative content percentages into the third logistic regression model to obtain a third wild probability, where the third wild probability represents the probability that the test sample is a wild silver carp; 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 third logistic regression model is: X = -1.781×A - 1.505×B + 21.011; the third wild probability = 1 / (1 + e －X ); where A and B are the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid in sequence respectively.

[0013] In some embodiments of the fourth aspect, if the third wild probability is greater than the third threshold, the test sample is a wild silver carp. If the third wild probability is less than the third threshold, the test sample is a cultured silver carp.

[0014] In some embodiments of the fourth aspect, the steps for determining the third threshold include: using the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid 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 0.7032.

[0015] Fifth aspect, the embodiment discloses a method for discriminating wild silver carp from cultured silver carp. The method includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple relative content percentages of the contents of multiple biomarkers in a test sample, the multiple biomarkers being myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid; inputting the multiple relative content percentages into the fourth logistic regression model to obtain a fourth wild probability, the fourth wild probability representing the probability that the test sample is a wild silver carp; determining whether the test sample is the wild silver carp or the cultured silver carp according to the magnitude relationship between the fourth wild probability and the fourth threshold. Among them, the fourth logistic regression model is: X = 2.724×A - 2.675×B + 11.124; the fourth wild probability = 1 / (1 + e －X ); where A and B are the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in sequence.

[0016] In some embodiments of the fifth aspect, if the fourth wild probability is greater than the fourth threshold, the test sample is a wild silver carp. If the fourth wild probability is less than the fourth threshold, the test sample is a cultured silver carp.

[0017] In some embodiments of the fifth aspect, the step of determining the fourth threshold includes: using the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in the collected muscle samples as a 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 respectively, 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, and the fourth wild probability corresponding to the maximum Youden index is the fourth threshold. In some embodiments, the fourth threshold is 0.4672.

[0018] Sixth aspect, the embodiment discloses the use of the biomarker described in the first aspect and / or the reagent for detecting the biomarker described in the first aspect in discriminating wild silver carp from cultured silver carp. Description of the Drawings

[0019] Figure 1 Confusion matrix test results of the fatty acid biomarker composition (myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, cis-5,8,11,14,17-eicosapentaenoic acid) provided by the embodiment for discriminating wild silver carp from cultured silver carp.

[0020] Figure 2Confusion matrix test results of the fatty acid biomarker composition (tetradecanoic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid) provided for the examples for discriminating wild silver carp from farmed silver carp.

[0021] Figure 3 Confusion matrix test results of the fatty acid biomarker composition (cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid) provided for the examples for discriminating wild silver carp from farmed silver carp.

[0022] Figure 4 Confusion matrix test results of the fatty acid biomarker composition (tetradecanoic acid, cis,cis,cis-9,12,15-octadecatrienoic acid) provided for the examples for discriminating wild silver carp from farmed silver carp. Detailed implementation manners

[0023] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the examples. It should be understood that the specific examples described herein are only used to explain the present application and are not used to limit the present application. The reagents not described in detail and separately in the present application are all conventional reagents and can be obtained from commercial channels; the methods not described in detail and specifically are all conventional experimental methods and can be learned from the prior art.

[0024] Discovery of Fatty Acid Biomarkers for Discriminating Wild and Cultured Silver Carp

[0025] 1. Collection of muscle samples of silver carp

[0026] The wild silver carp populations involved in the examples are from the Minjiang River, Jialing River, Three Gorges Reservoir Area, Dongting Lake and Poyang Lake of the Yangtze River respectively. The farmed silver carp populations involved in the examples are from farms in Hubei Province, Hunan Province and Jiangsu Province respectively. The samples are all taken from the upper back muscles of silver carp, immediately frozen and stored in liquid nitrogen after sampling, and then transferred to a refrigerator at -80 °C for storage until subsequent fatty acid extraction.

[0027] 2. Determination of fatty acid content in samples

[0028] The determination of fatty acid content was carried out by the internal standard method of GB 5009.168-2016 "Determination of Fatty Acids in Foods", and this method can determine the content of 37 kinds of fatty acids. The specific process is as follows: Accurately weigh about 1.00 g of muscle dry sample into a hydrolysis tube, add 1 mL of 10000 mg / L tristearin internal standard, hydrolyze with 10 mL of hydrochloric acid, and then extract with 25 mL of an equal-volume mixture of ether and petroleum ether. Take the supernatant into a pear-shaped flask. Repeat this operation three times. Rotate and evaporate the extract to dryness, add 8 mL of 2% sodium hydroxide methanol solution for saponification, then add 7 mL of 14% boron trifluoride methanol solution for methylation, and finally add 10 mL of n-hexane, vortex and mix well. After standing and separating, take 1 mL of the supernatant into an injection vial and use an Agilent 7890A gas chromatograph for determination.

[0029] Chromatographic column: CP-Sil 88 capillary column (100 m × 0.25 mm, 0.20 μm); Temperature programming: Initial temperature 100 °C, hold for 13 min, increase to 180 °C at a rate of 10 °C / min, hold for 6 min; then increase to 200 °C at a rate of 1 °C / min, hold for 20 min; finally increase to 230 °C at a rate of 4 °C / min, hold for 15 min. The injection port and detection temperatures are set at 270 °C and 280 °C respectively.

[0030] After the gas chromatography analysis is completed, each peak in the obtained chromatogram represents a specific fatty acid methyl ester. Through the chromatographic data processing software, the area of each peak can be calculated. By comparing the peak area of the target fatty acid methyl ester with the peak area of the internal standard, the absolute content of the fatty acid can be calculated. After obtaining the absolute content of each fatty acid, the relative content percentage in the total fatty acids can be calculated by the following formula, which is the percentage of the content of a specific fatty acid in the total content of all fatty acids.

[0031] 3. Statistical analysis method

[0032] The t-test was used to calculate the significant differences in fatty acid data between different wild silver carp populations and cultured silver carp populations. The relative content percentage of each fatty acid was input into SPSS for the construction and analysis of a binary logistic regression model. Among them, the relative content percentage of each fatty acid was presented in the form of mean plus or minus standard deviation (S.D.), and the asterisk (*) indicates that the difference between the experimental group and the control group is statistically significant. Specifically, *P < 0.05 indicates a significant difference, and **P < 0.01 indicates a highly significant difference.

[0033] 4. Results

[0034] Two groups of silver carp samples, namely wild silver carp population and cultured silver carp population, were compared. Combinations with a difference multiple ≥ 1.5 or ≤ 0.667 and P < 0.05 were used to screen significantly different fatty acids. Through analysis, 4 significantly different fatty acids were found, which were, in sequence: tetradecanoic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid. The relative content percentages, difference multiples, and P values of the above fatty acid substances are shown in Table 1. In Table 1, the difference multiple is the ratio of the relative content percentage of wild silver carp to that of cultured silver carp.

[0035] Table 1 Difference multiples of fatty acid biomarkers between wild and cultured silver carp

[0036]

[0037] Fatty Acid Biomarker Composition for Discriminating Wild and Cultured Silver Carp and Its Use

[0038] The main body of the current data discrimination model is to make a comprehensive judgment through constructing a model with multiple indicators. Among them, logistic regression, as a simple and intuitive model, has many advantages. First of all, it is easy to understand and interpret, does not require complex mathematical derivations or calculations, and has high computational efficiency. Secondly, logistic regression can provide strongly 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.

[0039] 1. Selection of fatty acid biomarker composition

[0040] The examples were based on detecting the contents of fatty acids in the muscle samples of wild silver carp and cultured silver carp, and training a binary logistic regression model according to the detection results. Fatty acids with high statistical significance, small measurement errors, and high data integrity were selected as biomarkers for discriminating wild silver carp and cultured silver carp. The fatty acid biomarkers for discriminating wild silver carp and cultured silver carp provided by the examples include tetradecanoic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid.

[0041] 2. Method for discriminating wild silver carp and cultured silver carp

[0042] The embodiment also discloses a method for discriminating wild silver carp from cultured silver carp. The method includes: obtaining a first logistic regression model and a first threshold; obtaining multiple relative content percentages of the contents of multiple biomarkers in a test sample; inputting the multiple relative content percentages into the first logistic regression model to obtain a first 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 first wild probability and the first threshold. Among them, the multiple biomarkers include myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid.

[0043] In some embodiments, the step of obtaining the first logistic regression model includes:

[0044] 1) Obtaining a training sample

[0045] In the embodiment, the muscle samples of 30 wild silver carp and 30 cultured silver carp are detected by the same gas chromatography as above to obtain the peak areas of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in each sample. By comparing the peak area of the target fatty acid methyl ester with the peak area of the internal standard, the absolute contents of the target fatty acid and the total fatty acid are calculated, and according to the formula: (content of specific fatty acid / total content of all fatty acids) × 100, the relative content percentage of the target fatty acid is obtained. The relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in these muscle samples are used as the training sample.

[0046] 2) Training

[0047] Using the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid as independent variables and the probability that the sample is wild as the dependent variable to train a binary logistic regression model. 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 the first logistic regression model according to the influence degree of the independent variables on the dependent variable.

[0048] In some steps, the examples perform a single-degree-of-freedom test on the muscle sample contents of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid as independent variables in wild silver carp and cultured silver carp, including giving the degrees of freedom, significance, and scoring tests. The results are shown in Table 2. 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 dependent variable overall.

[0049] Table 2 Significance test and scoring of independent variables of the logistic regression model of biomarkers in wild silver carp and cultured silver carp

[0050]

[0051] In some steps, the examples perform the Hosmer-Lemeshow test on the logistic regression model of biomarkers in wild silver carp and cultured silver 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 3. The model fits the real data well and can truly and reliably reflect the reliable relationship between the original variables.

[0052] Table 3 Hosmer-Lemeshow test of the logistic regression model of biomarkers in wild silver carp and cultured silver carp

[0053] Step Chi-square Degree of freedom Significance 1 0.277 7 1.000

[0054] 3) Obtain the first logistic regression model

[0055] In some steps, the examples perform binary logistic regression analysis on the logistic regression model of biomarkers in wild silver carp and cultured silver carp. Table 4 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0056] Table 4 Binary logistic regression model analysis in the biomarker model of wild silver carp and cultured silver carp

[0057]

[0058] The examples obtain the first logistic regression model as follows according to the relative content percentages of the muscle samples of 30 wild silver carp and 30 cultured silver carp:

[0059] X = 1.435×A - 2.169×B - 1.198×C + 0.164×D + 17.337;

[0060] The first wild probability = 1 / (1 + e －X );

[0061] Wherein A, B, C, and D are the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in sequence, and the first wild probability characterizes the probability that the tested sample is a wild silver carp.

[0062] The steps provided in the examples obtain the first threshold through the ROC curve. The steps provided in some examples include: using the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in the muscle samples of 30 wild silver carps and 30 cultured silver carps 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 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. The first predicted wild probability corresponding to the maximum Youden index is the first threshold.

[0063] In some examples, if the first wild probability is greater than the first threshold, the tested sample is determined to be a wild silver carp. If the first wild probability is less than the first threshold, the tested sample is determined to be a cultured silver carp.

[0064] In some examples, through the calculation of the ROC curve using the above test set, the obtained first threshold is 0.4742. That is, when the first wild probability is greater than 0.4742, the tested sample is determined to be a wild silver carp; when the first wild probability is less than 0.4742, the tested sample is determined to be a cultured silver carp.

[0065] 3. Discrimination Application and Testing

[0066] In some test cases, the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in 55 muscle samples (including 29 wild and 26 cultured) are selected as biomarkers, and their relative content percentages are input into the above first logistic regression model to obtain the first wild probability; according to the magnitude relationship between the first wild probability and the first threshold (0.4742), it is determined whether the tested sample is a wild silver carp or a cultured silver carp.

[0067] The relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid in the above training set were used to separately construct binary logistic regression models. Additionally, the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid were combined to construct a first logistic regression model. These models were used to detect 55 muscle samples respectively, and the discrimination results were tested using the ROC curve. The results are shown in Table 5. As can be seen from Table 5, the first logistic regression model constructed using the combined relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid has higher AUC values, sensitivity, and specificity.

[0068] Table 5 ROC test results of each biomarker and its combination for discriminating wild and cultured silver carp

[0069] Biomarker AUC Sensitivity Specificity Myristic acid 0.667 1.000 0.308 cis,cis,cis-9,12,15-Octadecatrienoic acid 0.942 0.931 0.846 cis-5,8,11,14-Eicosatetraenoic acid 0.886 0.793 0.846 cis-5,8,11,14,17-Eicosapentaenoic acid 0.772 0.690 0.962 Composition 0.998 0.966 1.000

[0070] The discrimination results were input into SPSS 20.0 analysis software for confusion matrix testing. The confusion matrix, also known as the error matrix, is a standard format for representing accuracy evaluation and is presented in the form of an n×n matrix. 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 in the confusion matrix represents the predicted category, and the total number in each column indicates the number of data predicted for that category; each row represents the true belonging category of the data, and the total number of data in each row indicates the number of data instances in that category. The results are as Figure 1 shown. Only 1 out of 55 silver carp samples was misjudged, and the overall correct rate of cross-validation was as high as 98.18%.

[0071] This indicates that using myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid as biomarkers can be used to distinguish wild and cultured silver carp in the Yangtze River Basin. Its excellent ROC curve results, good correct diagnosis rate, and good complementarity of the biomarkers all indicate that these four fatty acids have the potential for the determination of wild and cultured silver carp. Fatty Acid Biomarker for Discriminating Wild and Cultured Silver Carp Composition and Use

[0072] 1. Selection of fatty acid biomarker combinations

[0073] The embodiments are based on detecting the fatty acid content in muscle samples of wild silver carp and cultured silver carp, and training a binary logistic regression model according to the detection results. Fatty acids with high statistical significance, small measurement errors, and high data integrity are selected as biomarkers for differentiating wild silver carp and cultured silver carp. The fatty acid biomarkers for differentiating wild silver carp and cultured silver carp provided by the embodiments include myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid.

[0074] The embodiments also provide a kit for detecting these biomarkers, and the kit includes reagents for detecting these biomarkers, such as reagents required for gas chromatography inspection.

[0075] 2. Method for differentiating wild silver carp and cultured silver carp

[0076] The embodiments also disclose a method for differentiating wild silver carp and cultured silver carp. The method includes: obtaining a second logistic regression model and a second threshold; obtaining multiple relative content percentages of the contents of multiple biomarkers in a test sample; inputting the multiple relative content percentages into the second logistic regression model to obtain a second 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 second wild probability and the second threshold. Among them, the multiple biomarkers include myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid.

[0077] In some embodiments, the step of obtaining the second logistic regression model includes:

[0078] 1) Obtaining a training sample

[0079] In the embodiments, 30 muscle samples of wild silver carp and 30 muscle samples of cultured silver carp are subjected to the same gas chromatography detection as above to obtain the peak areas of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in each sample. By comparing the peak area of the target fatty acid methyl ester with the peak area of the internal standard, the absolute contents of the target fatty acid and the total fatty acid are calculated, and according to the formula: (content of specific fatty acid / total content of all fatty acids) × 100, the relative content percentage of the target fatty acid is obtained. The relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in these muscle samples are used as the training sample.

[0080] 2) Training

[0081] Taking the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid 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 second logistic regression model based on the influence degree of the independent variables on the dependent variable.

[0082] In some steps, the embodiments perform a single-degree-of-freedom test on the muscle sample contents of the independent variables myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid, including giving degrees of freedom, significance, and scoring detections. The results are shown in Table 7. The overall statistical scores of the contents of these three 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 corresponding variables overall.

[0083] Table 6 Significance test and scoring of independent variables in the logistic regression model of biomarkers of wild silver carp and cultured silver carp

[0084]

[0085] In some steps, the embodiments perform the Hosmer-Lemeshow test on the logistic regression model of biomarkers of wild silver carp and cultured silver 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 7. The established binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0086] Table 7 Hosmer-Lemeshow test of the logistic regression model of wild silver carp and cultured silver carp

[0087] Step Chi-square Degree of freedom Significance 1 0.286 7 1.000

[0088] 3) Obtain a second logistic regression model

[0089] In some steps, the embodiments perform a binary logistic regression analysis on the biomarkers for discriminating wild silver carp and cultured silver carp. Table 8 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0090] Table 8 Binary logistic regression analysis of wild silver carp and cultured silver carp

[0091]

[0092] The first logistic regression model was obtained according to the relative content percentages of the muscle samples of the above-mentioned 30 wild silver carps and 30 farmed silver carps as follows:

[0093] X = 1.444×A - 2.179×B - 1.071×C + 18.038;

[0094] The second wild probability = 1 / (1 + e －X );

[0095] where A, B, and C are the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in sequence, and the second wild probability represents the probability that the test sample is a wild silver carp.

[0096] The second threshold was calculated through the ROC curve in the steps provided by the embodiment. The steps provided by some embodiments include: using the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in the muscle samples of 30 wild silver carps and 30 farmed silver carps as the training set; training a binary logistic regression model with this training set to obtain multiple second wild probability values as described above respectively; calculating the sensitivity (true positive rate) and 1 - specificity (false positive rate) of each second 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 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.

[0097] In some embodiments, if the second wild probability is greater than the second threshold, the test sample is determined to be a wild silver carp. If the second wild probability is less than the second threshold, the test sample is determined to be a farmed silver carp.

[0098] In some embodiments, through calculation using the above test set via the ROC curve, the obtained second threshold is 0.4677. That is, when the second wild probability is greater than 0.4677, the test sample is determined to be a wild silver carp; when the second wild probability is less than 0.4677, the test sample is determined to be a farmed silver carp.

[0099] 3. Discrimination Application and Testing

[0100] In some test cases, myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in 55 muscle samples (including 29 wild and 26 farmed) were selected as biomarkers, and their relative content percentages were input into the above second logistic regression model to obtain the second wild probability; according to the magnitude of the second wild probability and the second threshold (0.4677), it was determined whether the test sample was a wild silver carp or a farmed silver carp.

[0101] Separate binary logistic regression models were constructed for the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid in the above training set, and a second logistic regression model was constructed by combining the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid. These 55 muscle samples were separately detected, and the discrimination results were tested by the ROC curve. The results are shown in Table 9. It can be seen from Table 9 that when using the combination of the relative content percentages of myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid to construct the second logistic regression model for the discrimination of test samples, it has a higher AUC value, sensitivity, and specificity.

[0102] Table 9 ROC test results of each biomarker and its combination for discriminating wild and farmed silver carp

[0103] Biomarker AUC Sensitivity Specificity Myristic acid 0.667 1.000 0.308 cis,cis,cis-9,12,15-Octadecatrienoic acid 0.942 0.931 0.846 cis-5,8,11,14-Eicosatetraenoic acid 0.886 0.793 0.846 Composition 0.998 0.966 1.000

[0104] 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 sample out of 55 silver carp samples was misjudged, and the overall correct rate of cross-validation was as high as 98.18%.

[0105] This shows that using myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid as biomarkers can be used to identify wild and farmed silver carp in the Yangtze River Basin. Its excellent ROC curve results, good correct diagnosis rate, and good complementarity of biomarkers all indicate that these three fatty acids have the potential for the determination of wild and farmed silver carp.

[0106] Fatty Acid Biomarker Composition for Discriminating Wild and Cultured Silver Carp and Its Use

[0107] 1. Selection of fatty acid biomarker combinations

[0108] The embodiments are based on detecting the contents of fatty acids in muscle samples of wild silver carp and cultured silver carp, and training a binary logistic regression model according to the detection results. Fatty acids with high statistical significance, small measurement errors, and high data integrity are selected as biomarkers for discriminating wild silver carp and cultured silver carp. The fatty acid biomarkers for discriminating wild silver carp and cultured silver carp provided by the embodiments include cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid.

[0109] The embodiments also provide a kit for detecting these biomarkers, and the kit includes reagents for detecting these biomarkers, such as reagents required for gas chromatography inspection.

[0110] 2. Method for discriminating wild silver carp and cultured silver carp

[0111] The embodiments also disclose a method for discriminating wild silver carp and cultured silver carp. The method includes: obtaining a third logistic regression model and a third threshold; obtaining multiple relative content percentages of the contents of multiple biomarkers in a test sample; inputting the multiple relative content percentages 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 cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid.

[0112] In some embodiments, the step of obtaining the third logistic regression model includes:

[0113] 1) Obtaining a training sample

[0114] In the embodiments, 30 muscle samples of wild silver carp and 30 muscle samples of cultured silver carp are subjected to the same gas chromatography detection as above to obtain the peak areas of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid in each sample. By comparing the peak area of the target fatty acid methyl ester with the peak area of the internal standard, the absolute contents of the target fatty acid and the total fatty acids are calculated, and according to the formula: (content of specific fatty acid / total content of all fatty acids) × 100%, the relative content percentage of the target fatty acid is obtained. The relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid in these muscle samples are used as the training sample.

[0115] 2) Training

[0116] Taking the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid as independent variables and the probability of the sample being 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 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 third logistic regression model based on the influence degree of the independent variables on the dependent variable.

[0117] In some steps, the embodiments perform a single degree of freedom test on muscle samples of the independent variables cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid, including giving degrees of freedom, significance, and scoring tests. As shown in Table 10, the overall statistical score of the independent variables is relatively high and the significance is less than 0.001, indicating that the independent variables will have a highly significant impact on the corresponding variables overall.

[0118] Table 10 Significance test and scoring of independent variables in the logistic regression model of biomarkers of wild silver carp and cultured silver carp

[0119]

[0120] 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 the 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. As shown in Table 11, the established binary logistic regression model has a good fit with the real data and can truly and reliably reflect the reliable relationship between the original variables.

[0121] Table 11 Hosmer-Lemeshow test of the logistic regression model of wild silver carp and cultured silver carp

[0122] Step Chi-square Degree of freedom Significance 1 0.409 7 1.000

[0123] 3) Obtain a third logistic regression model

[0124] In some steps, the embodiments perform binary logistic regression analysis on the logistic regression model of biomarkers of wild silver carp and cultured silver carp. Table 12 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0125] Table 12 Binary logistic regression analysis of wild silver carp and cultured silver carp

[0126]

[0127] The third logistic regression model is obtained according to the relative content percentages of the muscle samples of the above 30 wild silver carps and the muscle samples of 30 cultured silver carps as follows:

[0128] X = -1.781×A - 1.505×B + 21.011;

[0129] The third wild probability = 1 / (1 + e －X );

[0130] where A and B are the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid in sequence, and the third wild probability characterizes the probability that the test sample is a wild silver carp.

[0131] The steps provided in the embodiment are used to calculate the third threshold through the ROC curve. The steps provided in some embodiments include: using the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid in the muscle samples of 30 wild silver carps and 30 cultured silver carps as the training set; training a binary logistic regression model with this training set to obtain multiple third wild probability values as described above; calculating the sensitivity (true positive rate) and 1 - specificity (false positive rate) of each third wild probability value respectively, and 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 third predicted wild probability corresponding to the maximum Youden index is the third threshold.

[0132] In some embodiments, if the third wild probability is greater than the third threshold, the test sample is determined to be a wild silver carp. If the third wild probability is less than the third threshold, the test sample is determined to be a cultured silver carp.

[0133] In some embodiments, through calculation using the above test set by the ROC curve, the obtained third threshold is 0.7032. That is, when the third wild probability is greater than 0.7032, the test sample is determined to be a wild silver carp; when the third wild probability is less than 0.7032, the test sample is determined to be a cultured silver carp.

[0134] 3. Discrimination Application and Testing

[0135] In some test cases, 55 muscle samples (including 29 wild and 26 farmed) were selected, and cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid were used as biomarkers. Their relative content percentages were input into the above third logistic regression model to obtain the third wild probability. According to the comparison between the third wild probability and the third threshold (0.7032), it was determined whether the tested sample was a wild silver carp or a farmed silver carp.

[0136] Separate binary logistic regression models were constructed for the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid in the above training set, and a third logistic regression model was constructed by combining the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid. These 55 muscle samples were respectively detected, and the discrimination results were tested by the ROC curve. The results are shown in Table 13. It can be seen from Table 13 that the third logistic regression model constructed by combining the relative content percentages of cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid has higher AUC value, sensitivity and specificity.

[0137] Table 13 ROC test results of each biomarker and its combination for discriminating wild and farmed silver carp

[0138] Biomarker AUC Sensitivity Specificity cis,cis,cis-9,12,15-Octadecatrienoic acid 0.942 0.931 0.846 cis-5,8,11,14-Eicosatetraenoic acid 0.886 0.793 0.846 Composition 0.973 0.931 0.962

[0139] The discrimination results were input into the SPSS 20.0 analysis software for confusion matrix testing. The results are as Figure 3 shown. Only 3 out of 55 silver carp samples had incorrect judgments, and the overall correct rate of cross-validation was as high as 94.54%.

[0140] This shows that taking cis,cis,cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid as biomarkers and constructing a logistic regression model with their relative content percentages can be used to identify wild silver carp and farmed silver carp in the Yangtze River Basin. The excellent ROC curve results, good correct diagnosis rate and good complementarity of the biomarkers indicate that these two fatty acids have the potential for judging wild silver carp and farmed silver carp.

[0141] Fatty Acid Biomarker Composition for Discriminating Wild and Cultured Silver Carp and Its Use

[0142] 1. Selection of fatty acid biomarker combinations

[0143] The example is based on detecting the content of fatty acids in muscle samples of wild silver carp and cultured silver carp, and training a binary logistic regression model according to the detection results. Fatty acids with high statistical significance, small measurement errors, and high data integrity are selected as biomarkers for discriminating wild silver carp and cultured silver carp. The fatty acid biomarkers for discriminating wild silver carp and cultured silver carp provided by the example include myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid.

[0144] The example also provides a kit for detecting these biomarkers, and the kit includes reagents for detecting these biomarkers, such as reagents required for gas chromatography inspection.

[0145] 2. Method for discriminating wild silver carp and cultured silver carp

[0146] The example also discloses a method for discriminating wild silver carp and cultured silver carp. The method includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple relative content percentages of the contents of multiple biomarkers in a test sample; inputting the multiple relative content percentages into the fourth logistic regression model to obtain a fourth 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 fourth wild probability and the fourth threshold. Among them, the multiple biomarkers include myristic acid, cis,cis,cis-9,12,15-octadecatrienoic acid.

[0147] In some examples, the step of obtaining the fourth logistic regression model includes:

[0148] 1) Obtaining a training sample

[0149] In the example, 30 muscle samples of wild silver carp and 30 muscle samples of cultured silver carp are detected by the same gas chromatography as above to obtain the peak areas of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in each sample. By comparing the peak area of the target fatty acid methyl ester with the peak area of the internal standard, the absolute content of the target fatty acid and the total fatty acid is calculated, and according to the formula: (content of specific fatty acid / total content of all fatty acids) × 100, the relative content percentage of the target fatty acid is obtained. The relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in these muscle samples are used as the training sample.

[0150] 2) Training

[0151] Taking the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid 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 compositional 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.

[0152] In some steps, the embodiments perform a single-degree-of-freedom test on the muscle sample contents of the independent variables myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid, including giving the degrees of freedom, significance, and scoring tests. The results are shown in Table 14. The overall statistical scores of these 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 corresponding variables overall.

[0153] Table 14 Significance test and scoring of independent variables in the combined biomarker model of wild and farmed silver carp

[0154]

[0155] In some steps, the embodiments perform the Hosmer-Lemeshow test on the logistic regression model of the biomarkers of wild and farmed silver 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. As shown in Table 15, 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.

[0156] Table 15 Hosmer-Lemeshow test of the binary logistic regression model of wild and farmed silver carp

[0157] Step Chi-square Degree of freedom Significance 1 0.424 7 1.000

[0158] 3) Obtain the fourth logistic regression model

[0159] In some steps, the embodiments perform a binary logistic regression analysis on the biomarkers for discriminating wild and farmed silver carp. Table 16 shows the effect size Exp(B) of the regression model and the significance of each biomarker.

[0160] Table 16 Binary logistic regression analysis of wild and farmed silver carp

[0161]

[0162] The fourth logistic regression model is obtained according to the relative content percentages of the muscle samples of the above-mentioned 30 wild silver carps and 30 cultured silver carps as follows:

[0163] X = 2.724×A - 2.675×B + 11.124;

[0164] The fourth wild probability = 1 / (1 + e －X );

[0165] where A and B are the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid respectively, and the fourth wild probability represents the probability that the test sample is a wild silver carp.

[0166] The steps provided in the embodiments obtain the fourth threshold through the ROC curve. The steps provided in some embodiments include: using the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in the muscle samples of 30 wild silver carps and 30 cultured silver carps as the training set; training a binary logistic regression model with this training set to obtain multiple fourth wild probability values as described above; calculating the sensitivity (true positive rate) and 1 - specificity (false positive rate) of each fourth wild probability value respectively, and 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 fourth predicted wild probability corresponding to the maximum Youden index is the fourth threshold.

[0167] In some embodiments, if the fourth wild probability is greater than the fourth threshold, the test sample is determined to be a wild silver carp. If the fourth wild probability is less than the fourth threshold, the test sample is determined to be a cultured silver carp.

[0168] In some embodiments, through the calculation of the ROC curve using the above test set, the obtained fourth threshold is 0.4672. That is, when the fourth wild probability is greater than 0.4672, the test sample is determined to be a wild silver carp; when the fourth wild probability is less than 0.4672, the test sample is determined to be a cultured silver carp.

[0169] 3. Discrimination application and testing

[0170] In some test cases, the myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in 55 muscle samples (including 29 wild and 26 cultured) are selected as biomarkers, and their relative content percentages are input into the above fourth logistic regression model to obtain the fourth wild probability; according to the magnitude relationship between the fourth wild probability and the fourth threshold (0.4672), it is determined whether the test sample is a wild silver carp or a cultured silver carp.

[0171] Separate binary logistic regression models were constructed using the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid in the above training set respectively, and a fourth logistic regression model was constructed by combining the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid. These 55 muscle samples were detected respectively, and the discrimination results were tested by ROC curve. The results are shown in Table 17. It can be seen from Table 17 that when using the relative content percentages of myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid to construct the fourth logistic regression model for the discrimination of test samples, it has higher AUC value, sensitivity and specificity.

[0172] Table 17 ROC test results of each biomarker and its combination for discriminating wild and cultured silver carp

[0173] Biomarker AUC Sensitivity Specificity Myristic acid 0.667 1.000 0.308 cis,cis,cis-9,12,15-Octadecatrienoic acid 0.942 0.931 0.846 Composition 0.985 1.000 0.923

[0174] The discrimination results were input into the SPSS 20.0 analysis software for confusion matrix testing, as Figure 4 shown. Among the 55 silver carp samples, only 2 samples were misjudged, and the overall correct rate of cross-validation was as high as 96.36%.

[0175] This shows that using myristic acid and cis,cis,cis-9,12,15-octadecatrienoic acid as biomarkers and the regression model has good stability in discriminating wild silver carp and cultured silver carp. The excellent ROC curve results, good correct diagnosis rate and good complementarity of biomarkers indicate that these two fatty acids have the potential for the discrimination of wild silver carp and cultured silver carp.

[0176] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application.

Claims

1. Biomarkers for distinguishing wild silver carp from farmed silver carp include at least one of tetradecanoid, cis-,cis-,cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid and cis-5,8,11,14,17-eicosapentaenoic acid.

2. Methods for distinguishing wild silver carp from farmed silver carp include: obtaining a first logistic regression model and a first threshold; Obtaining multiple relative content percentages of multiple biomarker contents in the test sample; the multiple biomarkers are tetradecanoic acid, cis, cis, cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid; Inputting the plurality of 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 test sample is a wild silver carp; Determine whether the test sample is the wild silver carp or the farmed silver carp according to the first wild probability and the first threshold; Among them, the first logistic regression model is as follows: X=1.435×A-2.169×B-1.198×C+0.164×D+17.337; First wild probability = 1 / (1+e －X ); Among them, A, B, C, and D are the relative content percentages of tetradecanoic acid, cis-, cis-, cis-9,12,15-octadecatrienoic acid, cis-5,8,11,14-eicosatetraenoic acid, and cis-5,8,11,14,17-eicosapentaenoic acid, respectively.

3. The method according to claim 2, if the first wild probability is greater than the first threshold, the test sample is a wild silver carp; If the first wild probability is less than the first threshold, the test sample is farmed silver carp.

4. Methods for distinguishing wild silver carp from farmed silver carp include: Obtain a second logistic regression model and a second threshold; Obtaining multiple relative content percentages of multiple biomarker contents in the test sample; the multiple biomarkers are tetradecanoic acid, cis, cis, cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid; Inputting a plurality of the relative content percentages into the second logistic regression model to obtain a second wild probability, wherein the second wild probability represents the probability that the test sample is a wild silver carp; Determine whether the test sample is the wild silver carp or the farmed silver carp according to the second wild probability and the second threshold; Among them, the second logistic regression model is as follows: X=1.444×A-2.179×B-1.071×C+18.038; Second wild probability = 1 / (1+e －X ); Among them, A, B, and C are the relative content percentages of tetradecanoic acid, cis-9,12,15-octadecatrienoic acid, and cis-5,8,11,14-eicosatetraenoic acid, respectively.

5. The method according to claim 4, if the second wild probability is greater than the second threshold value, the test sample is a wild silver carp; If the second wild probability is less than the second threshold, the test sample is farmed silver carp.

6. Methods for distinguishing wild silver carp from farmed silver carp include: Obtaining a third logistic regression model and a third threshold; Obtaining multiple relative content percentages of multiple biomarker contents in the test sample, wherein the multiple biomarkers are cis, cis, cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid; Inputting a plurality of the relative content percentages into the third logistic regression model to obtain a third wild probability, wherein the third wild probability represents the probability that the test sample is wild silver carp; 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; Among them, the third logistic regression model is as follows: X=-1.781×A-1.505×B+21.011; The third wild probability = 1 / (1+e －X ); Among them, A and B are the relative content percentages of cis, cis, cis-9,12,15-octadecatrienoic acid and cis-5,8,11,14-eicosatetraenoic acid, respectively.

7. The method according to claim 6, if the third wild probability is greater than the third threshold value, the test sample is a wild silver carp; If the third wild probability is less than the third threshold, the test sample is farmed silver carp.

8. Methods for distinguishing wild silver carp from farmed silver carp include: obtaining a fourth logistic regression model and a fourth threshold; Obtaining multiple relative content percentages of multiple biomarker contents in the test sample, wherein the multiple biomarkers are tetradecanoic acid and cis,cis,cis-9,12,15-octadecatrienoic acid; Inputting a plurality of the relative content percentages into the fourth logistic regression model to obtain a fourth wild probability, wherein the fourth wild probability represents the probability that the test sample is a wild silver carp; Determining whether the test sample is the wild silver carp or the farmed silver carp according to the fourth wild probability and the fourth threshold; Among them, the fourth logistic regression model is as follows: X=2.724×A-2.675×B+11.124; Fourth wild probability = 1 / (1+e －X ); Among them, A and B are the relative content percentages of tetradecanoic acid and cis,cis,cis-9,12,15-octadecatrienoic acid respectively.

9. The method according to claim 8, if the fourth wild probability is greater than the fourth threshold value, the test sample is a wild silver carp; If the fourth wild probability is less than the fourth threshold value, the test sample is farmed silver carp.

10. Use of the biomarker according to claim 1 and / or a reagent for detecting any biomarker according to claim 1 in distinguishing wild and farmed silver carp.

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