Amino acid biomarker for distinguishing wild and cultured hypophthalmichthys nobilis, method and application thereof
By using an automated amino acid analyzer and logistic regression analysis, amino acid biomarkers were screened, solving the problem of distinguishing between wild and farmed silver carp and enabling efficient identification and management of wild silver carp resources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YANGTZE RIVER FISHERIES RES INST CHINESE ACAD OF FISHERY SCI
- Filing Date
- 2025-03-03
- Publication Date
- 2026-05-12
AI Technical Summary
Current technology lacks effective methods to distinguish between wild and farmed silver carp, leading to overfishing of wild resources and making scientific management and protection difficult.
An automated amino acid analyzer was used to measure muscle samples from wild and farmed silver carp. Amino acid biomarkers, such as aspartic acid, alanine, phenylalanine, histidine, and arginine, were screened by amino acid profile analysis, and their origins were determined using a logistic regression analysis model.
It provides a highly sensitive and specific discrimination method that can accurately distinguish between wild and farmed silver carp, providing scientific evidence to support law enforcement agencies in combating illegal fishing and reducing damage to wildlife resources.
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Figure CN120427918B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of silver carp identification or confirmation technology, specifically to amino acid biomarkers, methods, and applications for distinguishing between wild and farmed silver carp. Background Technology
[0002] Silver carp is an important aquaculture species in my country, mainly distributed in the Yangtze River basin. Developing or confirming technologies for wild and farmed silver carp, identifying biomarkers for these species, and promoting their application can reduce overfishing of wild resources. Summary of the Invention
[0003] This application addresses the problem of similar biological traits and lack of differentiation technology between wild and farmed silver carp. For the first time, an automated amino acid analyzer was used to determine amino acid profiles. Muscle samples from different wild and farmed silver carp populations were collected, and the commonalities, differences, and variations in amino acids between wild and farmed silver carp were explored. Biomarkers for amino acids were screened: 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). Statistical and logistic regression analyses were performed using the results from the automated amino acid analyzer, resulting in a logistic regression model. This model accurately distinguishes between wild and farmed silver carp, demonstrating good sensitivity and specificity. Furthermore, the amino acid biomarkers and their uses provided in this application can provide scientific evidence 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 silver carp resources.
[0004] Therefore, the embodiments of this application disclose at least the following technical solutions:
[0005] In one aspect, the embodiments disclose biomarkers containing at least one of aspartic acid, alanine, phenylalanine, histidine, and arginine.
[0006] Secondly, the embodiments disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a first logistic regression model and a first threshold; obtaining multiple relative percentages of multiple biomarkers in a test sample, wherein the multiple biomarkers are aspartic acid, alanine, phenylalanine, histidine, and arginine; inputting the multiple relative percentages into the first logistic regression model to obtain a first wild probability, the first wild probability representing the probability that the test sample is a wild silver carp; and determining whether the test sample is a wild silver carp or a farmed silver carp based on the magnitude of the first wild probability and the first threshold. The first logistic regression model is: X = -2.707 × A + 2.776 × B - 1.281 × C - 2.114 × D + 5.323 × E - 29.477; the first wild probability = 1 / (1 + e^(-2.707 × A + 2.776 × B - 1.281 × C - 2.114 × D + 5.323 × E - 29.477); the first wild probability = 1 / (1 + e^(-2.707 × A + 2.776 × B - 1.281 × C - 2.114 × D + 5.323 × E - 29.477); －X ); where A, B, C, D, and E are the relative percentage contents of aspartic acid, alanine, phenylalanine, histidine, and arginine, respectively.
[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 farmed silver carp.
[0008] In a second aspect embodiment, the step of determining the first threshold includes: using the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine in the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain multiple first wild-type probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) for each first wild-type probability value, and plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each first wild-type probability, and obtaining the maximum Youden index, wherein the first wild-type probability corresponding to the maximum Youden index is the first threshold. In some embodiments, the first threshold is 0.1928.
[0009] Thirdly, the embodiments disclose a method for distinguishing between wild and farmed silver carp. The method includes obtaining a second logistic regression model and a second threshold; obtaining multiple relative percentages of multiple biomarkers in the test sample, wherein the multiple biomarkers are aspartic acid, alanine, histidine, and arginine; inputting the multiple relative percentages into the second logistic regression model to obtain a second wild probability, the second wild probability representing the probability that the test sample is a wild silver carp; and determining whether the test sample is a wild silver carp or a farmed silver carp based on the magnitude of the second wild probability and the second threshold. The second logistic regression model is: X = -2.769×A + 2.938×B - 2.378×C + 5.239×D - 34.888; the second wild probability = 1 / (1+e －X ); where A, B, C, and D are the relative percentage contents of aspartic acid, alanine, histidine, and arginine, 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 farmed silver carp.
[0011] In an embodiment of the third aspect, the step of determining the second threshold includes: using the relative percentages of aspartic acid, alanine, histidine, and arginine in the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain multiple second wild-type probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) for each second wild-type probability value, and plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each second wild-type probability, and obtaining the maximum Youden index, wherein the second wild-type probability corresponding to the maximum Youden index is the second threshold. In some embodiments, the second threshold is 0.1795.
[0012] Fourthly, the embodiments disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a third logistic regression model and a third threshold; obtaining multiple relative percentages of the content of multiple biomarkers in the test sample, wherein the multiple biomarkers are alanine, histidine, and arginine; inputting the multiple relative 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 a wild silver carp; and determining whether the test sample is a wild silver carp or a farmed silver carp based on the magnitude of the third wild probability and the third threshold. Wherein, the third logistic regression model is: X = 2.494 × A - 2.157 × B + 7.298 × C - 74.351; the third wild probability = 1 / (1 + e^(-1 / 2)). －X); where A, B, and C are the relative percentages of alanine, histidine, and arginine, 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 farmed silver carp.
[0014] In some embodiments of the fourth aspect, the step of determining the third threshold includes: using the relative percentages of alanine, histidine, and arginine in the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain multiple third wild-type probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) for each third wild-type probability value, and plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each third wild-type probability, and obtaining the maximum Youden index, wherein the third wild-type probability corresponding to the maximum Youden index is the third threshold. In some embodiments, the third threshold is 0.2855.
[0015] Fifthly, the embodiments disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple relative percentages of the content of multiple biomarkers in the test sample, wherein the multiple biomarkers are alanine and arginine; inputting the multiple relative 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; and determining whether the test sample is a wild silver carp or a farmed silver carp based on the magnitude of the fourth wild probability and the fourth threshold. Wherein, the fourth logistic regression model is: X = 4.334 × A + 7.664 × B - 97.780; the fourth wild probability = 1 / (1 + e^(-A / B) - 97.780); －X ); where A and B are the relative percentage contents of alanine and arginine, respectively.
[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 farmed silver carp.
[0017] In some embodiments of the fifth aspect, the step of determining the fourth threshold includes: using the relative percentages of alanine and arginine in the collected muscle samples as a training set; training a binary logistic regression model with the training set to obtain multiple fourth wild-type probability values; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) for each fourth wild-type probability value, and plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each fourth wild-type probability, and obtaining the maximum Youden index, wherein the fourth wild-type probability corresponding to the maximum Youden index is the fourth threshold. In some embodiments, the fourth threshold is 0.3020.
[0018] In a sixth aspect, the embodiments disclose the use of the biomarkers described in the first aspect and / or reagents for detecting the biomarkers described in the first aspect in distinguishing between wild and farmed silver carp. Attached Figure Description
[0019] Figure 1 The confusion matrix test results of the amino acid biomarker composition (aspartic acid, alanine, phenylalanine, histidine, arginine) provided in the examples for distinguishing between wild and farmed silver carp.
[0020] Figure 2 The confusion matrix test results of the amino acid biomarker composition (aspartic acid, alanine, histidine, arginine) provided in the examples for distinguishing between wild and farmed silver carp.
[0021] Figure 3 The confusion matrix test results of the amino acid biomarker composition (alanine, histidine, arginine) provided in the examples for distinguishing between wild and farmed silver carp.
[0022] Figure 4 The confusion matrix test results of the amino acid biomarker composition (alanine, arginine) provided in the examples for distinguishing between wild and farmed silver carp. Detailed Implementation
[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Reagents not specifically described in detail herein are all conventional reagents and are commercially available; methods not specifically described in detail are all conventional experimental methods and can be learned from the prior art.
[0024] Discovery of amino acid biomarkers for distinguishing between wild and farmed silver carp
[0025] 1. Collection of muscle samples from silver carp
[0026] The wild silver carp populations involved in the examples came from the Minjiang River, Jialing River, Three Gorges Reservoir area, Dongting Lake, and Poyang Lake in the Yangtze River. The farmed silver carp populations involved in the examples came from farms in Hubei Province, Hunan Province, and Jiangsu Province. All samples were taken from the upper back muscles of the silver carp and immediately frozen in liquid nitrogen after sampling, then transferred to a -80°C freezer for storage, pending subsequent amino acid extraction.
[0027] 2. Determination and analysis of amino acid content in samples
[0028] The determination of amino acids was performed according to GB 5009.124-2016 "National Food Safety Standard - Determination of Amino Acids in Food". This method can determine the content of 16 amino acids. Approximately 0.02 g of dried muscle sample was weighed and placed in a hydrolysis tube. 10 mL of 6 mol / L hydrochloric acid was added, the tube was sealed under vacuum, and hydrolyzed at 110℃ for 22 h. After cooling, the volume was adjusted to 25 mL with pure water, then 5 mL was taken to dilute to 10 mL. 5 mL of this solution was then transferred to a small weighing bottle and heated to dryness in an 80℃ water bath. 1 mL of deionized water was added, and the mixture was heated to dryness in a water bath. This process was repeated three times. The residue in the weighing bottle was dissolved in 5 mL of 0.02 mol / L hydrochloric acid, transferred to a centrifuge tube, and mixed thoroughly. 1 mL of the solution was filtered through a 0.22 μm aqueous phase filter membrane and transferred to a sample vial. The mixed amino acid standard working solution and the sample determination solution were injected into the amino acid analyzer in equal volumes. The concentration of amino acids in the sample determination solution was calculated using the peak area method via the external standard method.
[0029] The absolute content of each amino acid in the sample solution is determined according to formula. Calculate, where c i The content of amino acid i in the sample solution is measured in nanomoles per milliliter (nmol / mL); A i A represents the peak area of amino acid i in the sample assay solution; s c represents the peak area of amino acid S in the amino acid standard working solution; s The concentration of amino acid S in the amino acid standard working solution is expressed in nanomoles per milliliter (nmol / mL). The absolute concentration of each amino acid in the sample is calculated according to the formula... The calculation is as follows: Xi is the content of amino acid i in the sample, in grams per 100 grams (g / 100g); ci is the content of amino acid i in the sample assay solution, in nanomoles per milliliter (nmol / mL); F is the dilution factor; V is the volume of the sample hydrolysate transferred and diluted, in milliliters (mL); M is the relative molecular mass of amino acid i, in grams per mole (g / mol), the names and relative molecular masses of each amino acid are shown in Table 1; m is the sample weight, in grams (g); 10 9 The coefficient is used to convert the sample content from nanograms (ng) to grams (g); 100 is the conversion factor. The relative percentage content of each amino acid in the sample is calculated according to the formula. In the formula Y iX represents the relative percentage content of amino acid i in the sample; i The absolute content of amino acid i in the sample.
[0030] 3. Statistical Analysis Methods
[0031] The t-test was used to calculate the significant differences in amino acid data between different wild and farmed silver carp populations. The relative percentage content of each amino acid was entered into SPSS for the construction and analysis of a binary logistic regression model. The relative percentage content of each amino acid is presented as mean minus standard deviation (SD), where an asterisk (*) indicates a statistically significant difference between the experimental and control groups. Specifically, *P < 0.05 indicates a significant difference, and **P < 0.01 indicates a highly significant difference.
[0032] 4. Results
[0033] Comparing wild and farmed silver carp samples, a p-value < 0.05 was used to screen for significantly different amino acids. Five significantly different amino acids were identified: aspartic acid, alanine, phenylalanine, histidine, and arginine. The relative percentages and p-values of these amino acids are shown in Table 1.
[0034] Table 1. Comparison of amino acid biomarkers between wild and farmed silver carp.
[0035]
[0036] Amino acid biomarker compositions for distinguishing wild and farmed silver carp and their applications
[0037] Current data discrimination models primarily rely on multi-indicator model construction for comprehensive judgment. Logistic regression, as a simple and intuitive model, is easy to understand and interpret, requiring no complex mathematical derivation or calculation, and is computationally efficient. Furthermore, logistic regression provides highly interpretable results; classification can be performed by setting thresholds, and the model's coefficients and intercepts can indicate the degree and direction of a variable's contribution to the target variable.
[0038] 1. Selection of amino acid biomarker compositions
[0039] This embodiment is based on the detection of amino acid content in muscle samples from wild and farmed silver carp, and the results are used to train a binary logistic regression model. Amino acids with high statistical significance, small measurement error, and high data integrity are selected as biomarkers to distinguish between wild and farmed silver carp. The amino acid biomarkers provided in this embodiment for distinguishing between wild and farmed silver carp include aspartic acid, alanine, phenylalanine, histidine, and arginine.
[0040] 2. Methods for distinguishing between wild and farmed silver carp
[0041] The embodiments also disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a first logistic regression model and a first threshold; obtaining multiple relative percentages of the content of multiple biomarkers in the test sample; inputting the multiple relative percentages into the first logistic regression model to obtain a first wild probability; and determining whether the test sample is a wild or farmed silver carp based on the magnitude of the first wild probability and the first threshold. The multiple biomarkers include aspartic acid, alanine, phenylalanine, histidine, and arginine.
[0042] In some embodiments, the step of obtaining the first logistic regression model includes:
[0043] 1) Obtain training samples
[0044] In this example, muscle samples from 30 wild-caught and 30 farmed silver carp were analyzed using the same amino acid content determination method described above. The peak areas of aspartic acid, alanine, phenylalanine, histidine, and arginine in each sample were obtained. The relative percentage content of the target amino acids was calculated using the respective formulas. These relative percentage contents of aspartic acid, alanine, phenylalanine, histidine, and arginine from the muscle samples were used as training samples.
[0045] 2) Training
[0046] A binary logistic regression model was trained using the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine as independent variables and the probability of the sample being wild-type as the dependent variable. In some embodiments, the training process of the binary logistic regression model includes: performing compositional analysis on the dependent and independent variables to determine whether they meet the preconditions for logistic regression; conducting significance tests on the independent variables, including performing degrees of freedom, significance, and scoring; performing the Hosmer-Lemersho test to verify the applicability and rationality of the model and ensure its accuracy; and training the binary logistic regression model to obtain a first logistic regression model based on the degree of influence of the independent variables on the dependent variable.
[0047] In some steps, the examples performed single-degree-of-freedom tests on the content of independent variables aspartic acid, alanine, phenylalanine, histidine, and arginine in muscle samples from wild and farmed silver carp. The results are shown in Table 2. The overall statistical scores of these four biomarkers as independent variables were high, with a significance level of less than 0.05, indicating that they would have a significant impact on the corresponding variables overall.
[0048] Table 2. Significance tests and scores of independent variables in the logistic regression models for biomarkers of wild and farmed silver carp.
[0049]
[0050] In some steps, the embodiments performed the Hosmer-Lemersho test on the logistic regression models of biomarkers for wild and farmed silver carp. Under multi-degree-of-freedom conditions, the significance of the binary logistic regression model was P = 0.552 > 0.05, i.e., the null hypothesis was accepted. The results are shown in Table 3. The model fits the real data well and can reliably reflect the reliable relationship between the original variables.
[0051] Table 3. Hossmer-Lempers test for logistic regression models of biomarkers in wild and farmed silver carp.
[0052] step Card side Degrees of freedom Significance 1 6.862 8 0.552
[0053] 3) Obtain the first logistic regression model
[0054] In some steps, the examples performed binary logistic regression analysis on logistic regression models of biomarkers for wild and farmed silver carp. Table 4 shows the effect size Exp(B) of the regression models and the significance of each biomarker.
[0055] Table 4. Analysis of binary logistic regression models in biomarker models of wild and farmed silver carp.
[0056]
[0057] Based on the relative percentage content of muscle samples from 30 wild silver carp and 30 farmed silver carp, the first logistic regression model obtained in the example is as follows:
[0058] X=-2.707×A+2.776×B-1.281×C-2.114×D+5.323×E-29.477;
[0059] The probability of first wild animal = 1 / (1+e) －X );
[0060] Where A, B, C, D, and E represent the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine, respectively, and the first wild probability represents the probability that the test sample is a wild silver carp.
[0061] The steps provided in the embodiments are used to calculate the first threshold using ROC curves. Some embodiments include the following steps: using the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine in muscle samples from 30 wild silver carp and 30 farmed silver carp as a 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) for each first wild probability value, plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each predicted wild probability, and obtaining the maximum Youden index, where the first predicted wild probability corresponding to the maximum Youden index is the first threshold.
[0062] In some embodiments, if the first wild probability is greater than a first threshold, the test sample is identified as a wild silver carp. If the first wild probability is less than the first threshold, the test sample is identified as a farmed silver carp.
[0063] In some embodiments, the first threshold obtained by calculating the ROC curve using the above test set is 0.1928. That is, when the first wild probability is greater than 0.1928, the test sample is determined to be a wild silver carp; when the first wild probability is less than 0.1928, the test sample is determined to be a farmed silver carp.
[0064] 3. Identify applications and conduct tests
[0065] In some test cases, aspartic acid, alanine, phenylalanine, histidine, and arginine from 58 muscle samples (including 28 wild and 30 farmed) were selected as biomarkers. Their relative percentage content was input into the first logistic regression model mentioned above to obtain the first wild probability. Based on the magnitude of the first wild probability and the first threshold (0.1928), the test sample was determined to be either wild or farmed silver carp.
[0066] The relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine in the training set were used to construct separate binary logistic regression models, and a first logistic regression model was constructed by combining the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine. These models were then used to analyze 58 muscle samples, and the ROC curves were tested. The results are shown in Table 5. Table 5 shows that the first logistic regression model constructed using the combination of the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine has higher AUC values, sensitivity, and specificity.
[0067] Table 5. ROC test results of various biomarkers and their combinations in distinguishing wild and farmed silver carp.
[0068] biomarkers AUC Sensitivity Specificity Aspartic acid 0.849 0.714 0.900 alanine 0.850 1.000 0.667 Phenylalanine 0.857 0.857 0.800 Histidine 0.826 0.821 0.700 Arginine 0.920 0.893 0.800 Composition 0.964 1.000 0.833
[0069] 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, expressed as an n x n matrix. Specific evaluation indicators include overall accuracy, mapping accuracy, and user accuracy, which reflect the accuracy of image classification from different perspectives. Each column of the confusion matrix represents the predicted class, and the total number in each column represents the number of data points predicted as belonging to that class; each row represents the true class of the data, and the total number of data points in each row represents the number of data instances belonging to that class. The results are as follows: Figure 1 As shown, only 5 out of 58 silver carp samples were incorrectly identified, resulting in an overall accuracy rate of 91.38% for cross-validation.
[0070] This demonstrates that aspartic acid, alanine, phenylalanine, histidine, and arginine can be used as biomarkers to distinguish between wild and farmed silver carp in the Yangtze River basin. The excellent ROC curve results, good diagnostic accuracy, and strong complementarity of the biomarkers all indicate that these five amino acids have the potential to differentiate between wild and farmed silver carp.
[0071] Amino acid biomarker compositions for distinguishing wild and farmed silver carp and their applications
[0072] 1. Selection of amino acid biomarker compositions
[0073] This embodiment is based on the detection of amino acid content in muscle samples from wild and farmed silver carp, and the results are used to train a binary logistic regression model. Amino acids with high statistical significance, small measurement error, and high data integrity are selected as biomarkers to distinguish between wild and farmed silver carp. The amino acid biomarkers provided in this embodiment for distinguishing between wild and farmed silver carp include aspartic acid, alanine, histidine, and arginine.
[0074] The embodiments also provide kits for detecting these biomarkers, which include reagents for detecting these biomarkers, such as reagents required for automated amino acid analyzer testing.
[0075] 2. Methods for distinguishing between wild and farmed silver carp
[0076] The embodiments also disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a second logistic regression model and a second threshold; obtaining multiple relative percentages of the content of multiple biomarkers in the test sample; inputting the multiple relative percentages into the second logistic regression model to obtain a second wild probability; and determining whether the test sample is a wild or farmed silver carp based on the magnitude of the second wild probability and the second threshold. The multiple biomarkers include aspartic acid, alanine, histidine, and arginine.
[0077] In some embodiments, the step of obtaining the second logistic regression model includes:
[0078] 1) Obtain training samples
[0079] In this example, muscle samples from 30 wild-caught and 30 farmed silver carp were analyzed using the same automated amino acid analyzer described above. The peak areas of aspartic acid, alanine, histidine, and arginine in each sample were obtained. The relative percentage content of the target amino acids was calculated using the respective formulas. These relative percentage contents of aspartic acid, alanine, histidine, and arginine from the muscle samples were used as training samples.
[0080] 2) Training
[0081] Using the relative percentages of aspartic acid, alanine, histidine, and arginine as independent variables and the probability of a sample being wild-type as the dependent variable, a binary logistic regression model is trained. In some embodiments, the training process of this binary logistic regression model includes: performing compositional analysis on the dependent and independent variables to determine whether they meet the preconditions for logistic regression; conducting significance tests on the independent variables, including performing degrees of freedom, significance, and scoring; performing the Hosmer-Lemersho test to verify the applicability and rationality of the model and ensure its accuracy; and training the binary logistic regression model to obtain a second logistic regression model based on the degree of influence of the independent variables on the dependent variable.
[0082] In some steps, the examples performed single-degree-of-freedom tests on the muscle sample contents of the independent variables aspartic acid, alanine, histidine, and arginine, including providing the degrees of freedom, significance, and scoring. The results are shown in Table 6. The total statistical scores of these four biomarkers as independent variables were high, with a significance level of less than 0.001, indicating that the independent variables have a highly significant impact on the corresponding variables overall.
[0083] Table 6. Significance tests and scores of independent variables in the logistic regression models of biomarkers for wild and farmed silver carp.
[0084]
[0085] In some steps, the embodiments performed the Hosmer-Lemersho test on the logistic regression models of biomarkers for wild and farmed silver carp. Under multi-degree-of-freedom conditions, the significance of the binary logistic regression model was P = 0.611 > 0.05, i.e., the null hypothesis was accepted. The results are shown in Table 7. The established binary logistic regression model fits the real data well and can reliably reflect the reliable relationship between the original variables.
[0086] Table 7. Hossmer-Lempers test for logistic regression models of wild and farmed silver carp.
[0087] step Card Degrees of freedom Significance 1 6.326 8 0.611
[0088] 3) Obtain the second logistic regression model
[0089] In some steps, the examples used binary logistic regression analysis to distinguish between wild and farmed 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 and farmed silver carp.
[0091]
[0092] Based on the relative percentage content of muscle samples from 30 wild silver carp and 30 farmed silver carp, the first logistic regression model obtained in the example is as follows:
[0093] X=-2.769×A+2.938×B-2.378×C+5.239×D-34.888;
[0094] The probability of a second wild animal is 1 / (1+e) －X );
[0095] Where A, B, C, and D are the relative percentage contents of aspartic acid, alanine, histidine, and arginine, respectively, and the second wild probability represents the probability that the test sample is a wild silver carp.
[0096] The steps provided in the embodiments are used to calculate the second threshold using ROC curves. Some embodiments include the following steps: using the relative percentages of aspartic acid, alanine, histidine, and arginine in muscle samples from 30 wild silver carp and 30 farmed silver carp as a training set; training a binary logistic regression model with this training set to obtain multiple second wild probability values as described above; calculating the sensitivity (true positive rate) and 1-specificity (false positive rate) for each second wild probability value, plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each predicted wild probability, and obtaining the maximum Youden index, where 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 a second threshold, the test sample is identified as a wild silver carp. If the second wild probability is less than the second threshold, the test sample is identified as a farmed silver carp.
[0098] In some embodiments, the second threshold obtained by calculating the ROC curve using the above test set is 0.1795. That is, when the second wild probability is greater than 0.1795, the test sample is determined to be a wild silver carp; when the second wild probability is less than 0.1795, the test sample is determined to be a farmed silver carp.
[0099] 3. Identify applications and conduct tests
[0100] In some test cases, aspartic acid, alanine, histidine, and arginine from 58 muscle samples (including 28 wild and 30 farmed) were selected as biomarkers. Their relative percentage content was input into the second logistic regression model mentioned above to obtain the second wild probability. Based on the magnitude of the second wild probability and the second threshold (0.1795), the test sample was determined to be either wild or farmed silver carp.
[0101] The relative percentages of aspartic acid, alanine, histidine, and arginine in the training set were used to construct separate binary logistic regression models, and a second logistic regression model was constructed by combining the relative percentages of aspartic acid, alanine, histidine, and arginine. These models were then used to detect 58 muscle samples, and the ROC curves were analyzed. The results are shown in Table 9. Table 9 shows that the second logistic regression model, constructed by combining the relative percentages of aspartic acid, alanine, histidine, and arginine, exhibits higher AUC values, sensitivity, and specificity in the discrimination of the test samples.
[0102] Table 9. ROC test results of various biomarkers and their combinations in distinguishing wild and farmed silver carp.
[0103] biomarkers AUC Sensitivity Specificity Aspartic acid 0.849 0.714 0.900 alanine 0.850 1.000 0.667 Histidine 0.826 0.821 0.700 Arginine 0.920 0.893 0.800 Composition 0.967 1.000 0.833
[0104] The discrimination results were input into SPSS 20.0 analysis software for confusion matrix testing, and the results are as follows: Figure 2 As shown, only 5 out of 58 silver carp samples were incorrectly identified, resulting in an overall accuracy rate of 91.38% for cross-validation.
[0105] This demonstrates that aspartic acid, alanine, histidine, and arginine can be used as biomarkers to distinguish between wild and farmed silver carp in the Yangtze River basin. The excellent ROC curve results, good diagnostic accuracy, and strong complementarity of the biomarkers all indicate that these four amino acids have the potential to differentiate between wild and farmed silver carp.
[0106] Amino acid biomarker compositions for distinguishing wild and farmed silver carp and their applications
[0107] 1. Selection of amino acid biomarker compositions
[0108] The examples are based on the detection of amino acid content in muscle samples from wild and farmed silver carp, and the results are used to train a binary logistic regression model. Amino acids with high statistical significance, small measurement error, and high data integrity are selected as biomarkers to distinguish between wild and farmed silver carp. The amino acid biomarkers provided in the examples for distinguishing between wild and farmed silver carp include alanine, histidine, and arginine.
[0109] The embodiments also provide kits for detecting these biomarkers, which include reagents for detecting these biomarkers, such as reagents required for automated amino acid analyzer testing.
[0110] 2. Methods for distinguishing between wild and farmed silver carp
[0111] The embodiments also disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a third logistic regression model and a third threshold; obtaining multiple relative percentages of the content of multiple biomarkers in the test sample; inputting the multiple relative percentages into the third logistic regression model to obtain a third wild probability; and determining whether the test sample is a wild or farmed silver carp based on the magnitude of the third wild probability and the third threshold. The multiple biomarkers include alanine, histidine, and arginine.
[0112] In some embodiments, the step of obtaining the third logistic regression model includes:
[0113] 1) Obtain training samples
[0114] In this example, muscle samples from 30 wild-caught and 30 farmed silver carp were analyzed using the same automated amino acid analyzer described above. The peak areas of alanine, histidine, and arginine in each sample were obtained. The relative percentage content of the target amino acid was calculated using the respective formulas. These relative percentage contents of alanine, histidine, and arginine from the muscle samples were used as training samples.
[0115] 2) Training
[0116] A binary logistic regression model was trained using the relative percentages of alanine, histidine, and arginine as independent variables and the probability of a sample being wild-type as the dependent variable. In some embodiments, the training process of the binary logistic regression model includes: performing compositional analysis on the dependent and independent variables to determine whether they meet the preconditions for logistic regression; conducting significance tests on the independent variables, including performing degrees of freedom, significance, and scoring; performing the Hosmer-Lemersho test to verify the applicability and rationality of the model and ensure its accuracy; training the binary logistic regression model to obtain a third logistic regression model based on the degree of influence of the independent variables on the dependent variable.
[0117] In some steps, the examples used muscle samples containing the independent variables alanine, histidine, and arginine for single-degree-of-freedom tests, including providing the degrees of freedom, significance, and scoring. As shown in Table 10, the overall statistical scores of the independent variables were high, with a significance level less than 0.001, indicating that the independent variables would have a highly significant impact on the corresponding variables overall.
[0118] Table 10. Significance tests and scores of independent variables in the logistic regression models of biomarkers for wild and farmed silver carp.
[0119]
[0120] To further analyze the impact of constants and independent variables on the dependent variable in terms of multiple degrees of freedom, the example also conducted the Hosmer-Lemersho test on the logistic regression model. Under multiple degrees of freedom conditions, the significance of the binary logistic regression model was P = 0.338 > 0.05, meaning the null hypothesis was accepted. The results, shown in Table 11, indicate that the established binary logistic regression model fits the actual data well and can reliably reflect the reliable relationship between the original variables.
[0121] Table 11. Hossmer-Lempers test for logistic regression models of wild and farmed silver carp.
[0122] step Card Degrees of freedom Significance 1 9.057 8 0.338
[0123] 3) Obtain the third logistic regression model
[0124] In some steps, the embodiments performed binary logistic regression analysis on the logistic regression models of biomarkers for wild and farmed 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 and Farmed Silver Carp
[0126]
[0127]
[0128] Based on the relative percentage content of muscle samples from 30 wild silver carp and 30 farmed silver carp, the third logistic regression model obtained in the example is as follows:
[0129] X=2.494×A-2.157×B+7.298×C-74.351;
[0130] The probability of a third wild animal is 1 / (1+e) －X );
[0131] Where A, B, and C represent the relative percentages of alanine, histidine, and arginine, respectively, and the third wild probability represents the probability that the test sample is a wild silver carp.
[0132] The steps provided in the embodiments are used to calculate the third threshold using ROC curves. Some embodiments include the following steps: using the relative percentages of alanine, histidine, and arginine content in muscle samples from 30 wild silver carp and 30 farmed silver carp as a 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) for each third wild probability value, plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each predicted wild probability, and obtaining the maximum Youden index, where the third predicted wild probability corresponding to the maximum Youden index is the third threshold.
[0133] In some embodiments, if the third wild probability is greater than a third threshold, the test sample is identified as a wild silver carp. If the third wild probability is less than the third threshold, the test sample is identified as a farmed silver carp.
[0134] In some embodiments, the third threshold obtained by calculating the ROC curve using the above test set is 0.2855. That is, when the third wild probability is greater than 0.2855, the test sample is determined to be a wild silver carp; when the third wild probability is less than 0.2855, the test sample is determined to be a farmed silver carp.
[0135] 3. Identify applications and conduct tests
[0136] In some test cases, alanine, histidine, and arginine from 58 muscle samples (including 28 wild and 30 farmed) were selected as biomarkers. Their relative percentage content was input into the third logistic regression model mentioned above to obtain the third wild probability. Based on the magnitude of the third wild probability and the third threshold (0.2855), the test sample was determined to be either wild or farmed silver carp.
[0137] A binary logistic regression model was constructed using the relative percentages of alanine, histidine, and arginine in the training set, and a third logistic regression model was constructed by combining the relative percentages of alanine, histidine, and arginine. These models were then used to analyze 58 muscle samples, and the ROC curves were tested. The results are shown in Table 14. Table 14 shows that the third logistic regression model constructed using the combination of the relative percentages of alanine, histidine, and arginine exhibits higher AUC values, sensitivity, and specificity.
[0138] Table 14. ROC test results of various biomarkers and their combinations in distinguishing wild and farmed silver carp.
[0139] biomarkers AUC Sensitivity Specificity alanine 0.850 1.000 0.667 Histidine 0.826 0.821 0.700 Arginine 0.920 0.893 0.800 Composition 0.954 1.000 0.800
[0140] The discrimination results were input into SPSS 20.0 analysis software for confusion matrix testing, and the results are as follows: Figure 3 As shown, only 6 out of 58 silver carp samples were incorrectly identified, resulting in an overall accuracy rate of 89.66% for cross-validation.
[0141] This demonstrates that a logistic regression model constructed using alanine, histidine, and arginine as biomarkers, based on their relative percentage content, can be used to distinguish between wild and farmed silver carp in the Yangtze River Basin. The excellent ROC curve results, good correct diagnostic rate, and strong complementarity of the biomarkers indicate that these three amino acids have the potential to differentiate between wild and farmed silver carp.
[0142] Amino acid biomarker compositions for distinguishing wild and farmed silver carp and their applications
[0143] 1. Selection of amino acid biomarker compositions
[0144] The examples are based on the detection of amino acid content in muscle samples from wild and farmed silver carp, and the results are used to train a binary logistic regression model. Amino acids with high statistical significance, small measurement error, and high data integrity are selected as biomarkers to distinguish between wild and farmed silver carp. The amino acid biomarkers provided in the examples for distinguishing between wild and farmed silver carp include alanine and arginine.
[0145] The embodiments also provide kits for detecting these biomarkers, which include reagents for detecting these biomarkers, such as reagents required for automated amino acid analyzer testing.
[0146] 2. Methods for distinguishing between wild and farmed silver carp
[0147] The embodiments also disclose a method for distinguishing between wild and farmed silver carp. The method includes: obtaining a fourth logistic regression model and a fourth threshold; obtaining multiple relative percentages of the content of multiple biomarkers in the test sample; inputting the multiple relative percentages into the fourth logistic regression model to obtain a fourth wild probability; and determining whether the test sample is a wild or farmed silver carp based on the magnitude of the fourth wild probability and the fourth threshold. The multiple biomarkers include alanine and arginine.
[0148] In some embodiments, the step of obtaining the fourth logistic regression model includes:
[0149] 1) Obtain training samples
[0150] In this example, muscle samples from 30 wild silver carp and 30 farmed silver carp were analyzed using the same automated amino acid analyzer described above to obtain the peak areas of alanine and arginine in each sample. The relative percentage content of the target amino acid was calculated using various formulas. These relative percentage contents of alanine and arginine from the muscle samples were used as training samples.
[0151] 2) Training
[0152] A binary logistic regression model was trained using the relative percentages of alanine and arginine as independent variables and the probability of the sample being wild-type as the dependent variable. In some embodiments, the training process of the binary logistic regression model includes: performing compositional analysis on the dependent and independent variables to determine whether they meet the preconditions for logistic regression; conducting significance tests on the independent variables, including performing degrees of freedom, significance, and scoring; performing the Hosmer-Lemersho test to verify the applicability and rationality of the model and ensure its accuracy; and training the binary logistic regression model to obtain a fourth logistic regression model based on the degree of influence of the independent variables on the dependent variable.
[0153] In some steps, the examples performed single-degree-of-freedom tests on the muscle sample content of the independent variables alanine and arginine, including providing the degrees of freedom, significance, and scoring. The results are shown in Table 15. The overall statistical scores of these independent variables were high, with a significance level of less than 0.001, indicating that the independent variables have a highly significant impact on the corresponding variables overall.
[0154] Table 15. Significance tests and scores of independent variables in the combined biomarker model of wild and farmed silver carp.
[0155]
[0156] In some steps, the embodiments performed the Hosmer-Lemersho test on the logistic regression models of biomarkers for wild and farmed silver carp. Under multi-degree-of-freedom conditions, the significance of the binary logistic regression model was P = 0.633 > 0.05, i.e., the null hypothesis was accepted. As shown in Table 16, the established binary logistic regression model fits the real data well and can reliably reflect the reliable relationship between the original variables.
[0157] Table 16. Hossmer-Lempers test for the binary logistic regression model of wild and farmed silver carp.
[0158] step Card side Degrees of freedom Significance 1 6.130 8 0.633
[0159] 3) Obtain the fourth logistic regression model
[0160] In some steps, the examples performed binary logistic regression analysis on biomarkers for distinguishing between wild and farmed silver carp. Table 17 shows the effect size Exp(B) of the regression model and the significance of each biomarker.
[0161] Table 17 Binary Logistic Regression Analysis of Wild and Farmed Silver Carp
[0162]
[0163] Based on the relative percentage content of muscle samples from 30 wild silver carp and 30 farmed silver carp, the fourth logistic regression model was obtained as follows:
[0164] X=4.334×A+7.664×B-97.780;
[0165] The probability of the fourth wild animal is 1 / (1+e) －X );
[0166] Where A and B are the relative percentage contents of alanine and arginine, respectively, and the fourth wild probability represents the probability that the test sample is a wild silver carp.
[0167] The steps provided in the embodiments are used to calculate the fourth threshold using ROC curves. Some embodiments include the following steps: using the relative percentages of alanine and arginine content in muscle samples from 30 wild silver carp and 30 farmed silver carp as a 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) for each fourth wild probability value, plotting an ROC curve with 1-specificity as the x-axis and sensitivity as the y-axis; calculating the Youden index (Youden index = sensitivity + specificity - 1) corresponding to each predicted wild probability, and obtaining the maximum Youden index, where the fourth predicted wild probability corresponding to the maximum Youden index is the fourth threshold.
[0168] In some embodiments, if the fourth wild probability is greater than the fourth threshold, the test sample is identified as a wild silver carp. If the fourth wild probability is less than the fourth threshold, the test sample is identified as a farmed silver carp.
[0169] In some embodiments, the fourth threshold obtained by calculating the ROC curve using the above test set is 0.3020. That is, when the fourth wild probability is greater than 0.3020, the test sample is determined to be a wild silver carp; when the fourth wild probability is less than 0.3020, the test sample is determined to be a farmed silver carp.
[0170] 3. Identify applications and conduct tests
[0171] In some test cases, alanine and arginine from 58 muscle samples (including 28 wild and 30 farmed) were selected as biomarkers. Their relative content percentages were input into the fourth logistic regression model mentioned above to obtain the fourth wild probability. Based on the magnitude of the fourth wild probability and the fourth threshold (0.3020), the test sample was determined to be either wild or farmed silver carp.
[0172] The relative percentages of alanine and arginine in the training set were used to construct separate binary logistic regression models, and a fourth logistic regression model was constructed by combining the relative percentages of alanine and arginine. These models were then used to test 58 muscle samples, and the ROC curves were analyzed. The results are shown in Table 18. Table 18 shows that the fourth logistic regression model constructed by combining the relative percentages of alanine and arginine exhibits higher AUC values, sensitivity, and specificity in the discrimination of the test samples.
[0173] Table 18. ROC test results of various biomarkers and their combinations in distinguishing wild and farmed silver carp.
[0174] biomarkers AUC Sensitivity Specificity alanine 0.850 1.000 0.667 Arginine 0.920 0.893 0.800 Composition 0.951 1.000 0.800
[0175] The discrimination results are then input into SPSS 20.0 analysis software for confusion matrix testing, such as... Figure 4 As shown, only 6 out of 58 silver carp samples were incorrectly identified, and the overall accuracy of the cross-validation was as high as 89.66%.
[0176] This demonstrates the good stability of using alanine and arginine as biomarkers and regression models to distinguish between wild and farmed silver carp. The excellent ROC curve results, high correct diagnostic rate, and good complementarity of the biomarkers indicate that these two amino acids have the potential to differentiate between wild and farmed silver carp.
[0177] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. A method for distinguishing between wild and farmed silver carp in the Yangtze River basin, including the following steps: (1) Collect the upper back muscle of the silver carp to be tested as a test sample. After freezing the sample with liquid nitrogen, transfer it to a -80 ℃ freezer for storage. Use the method of GB 5009.124-2016 "National Food Safety Standard Determination of Amino Acids in Food" to detect the absolute content of aspartic acid, alanine, phenylalanine, histidine and arginine in the sample. Calculate the relative content percentage of each amino acid. The relative content percentage is the proportion of the absolute content of a single amino acid to the total absolute content of the 16 tested amino acids. (2) Substitute the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine into the first logistic regression model to calculate the first wild probability. 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; Where A, B, C, D, and E represent the relative percentages of aspartic acid, alanine, phenylalanine, histidine, and arginine, respectively, and the first wild-type probability = 1 / (1+e) －X ); (3) Compare the first wild probability with the first threshold; 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 farmed silver carp. The first threshold is 0.1928.
2. The method for distinguishing between wild and farmed silver carp in the Yangtze River Basin includes the following steps: (1) Collect the upper back muscle of the silver carp to be tested as a test sample. After freezing the sample with liquid nitrogen, transfer it to a -80 ℃ refrigerator for storage. Use the method of GB 5009.124-2016 "National Food Safety Standard Determination of Amino Acids in Food" to detect the absolute content of aspartic acid, alanine, histidine and arginine in the sample. Calculate the relative content percentage of each amino acid. The relative content percentage is the proportion of the absolute content of a single amino acid to the total absolute content of 16 tested amino acids. (2) Substitute the relative percentages of aspartic acid, alanine, histidine, and arginine into the second logistic regression model to calculate the second wild probability. The second logistic regression model is as follows: X=-2.769×A+2.938×B-2.378×C+5.239×D-34.888; Where A, B, C, and D are the relative percentage contents of aspartic acid, alanine, histidine, and arginine, respectively, and the second wild-type probability = 1 / (1+e －X ); (3) Compare the second wild probability with the second threshold; 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 farmed silver carp. The second threshold is 0.1795.
3. The method for distinguishing between wild and farmed silver carp in the Yangtze River Basin includes the following steps: (1) Collect the upper back muscle of the silver carp to be tested as a test sample. After freezing the sample with liquid nitrogen, transfer it to a -80 ℃ refrigerator for storage. The absolute content of alanine, histidine and arginine in the sample is detected by the method of GB 5009.124-2016 "National Food Safety Standard Determination of Amino Acids in Food". The relative content percentage of each amino acid is calculated. The relative content percentage is the proportion of the absolute content of a single amino acid to the total absolute content of 16 tested amino acids. (2) Substitute the relative percentages of alanine, histidine, and arginine into the third logistic regression model to calculate the third wild probability. The third logistic regression model is as follows: X=2.494×A-2.157×B+7 .298×C-74.351; Where A, B, and C represent the relative percentages of alanine, histidine, and arginine, respectively, and the third wild probability is 1 / (1+e^(-1 / ... －X ); (3) Compare the third wild probability with the third threshold; 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 farmed silver carp. The third threshold is 0.2855.
4. The method for distinguishing between wild and farmed silver carp in the Yangtze River Basin includes the following steps: (1) Collect the upper back muscle of the silver carp to be tested as a test sample. After freezing the sample with liquid nitrogen, transfer it to a -80 ℃ refrigerator for storage. Use GB 5009.124-2016 "National Food Safety Standard for Determination of Amino Acids in Food" to detect the absolute content of alanine and arginine in the sample. Calculate the relative content percentage of each amino acid. The relative content percentage is the proportion of the absolute content of a single amino acid to the total absolute content of 16 tested amino acids. (2) Substitute the relative percentages of alanine and arginine into the fourth logistic regression model to calculate the fourth wild probability. The fourth logistic regression model is as follows: X=4.334×A+7.664×B-97.780; Where A, B, and C represent the relative percentages of alanine and arginine, respectively, and the fourth wild probability is 1 / (1+e). －X ); (3) Compare the fourth wild probability with the fourth threshold; 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 farmed silver carp. The fourth threshold is 0.3020.