Construction method and application of salmon germplasm identification and origin traceability discrimination model

Abstract

The invention belongs to the technical field of food detection, and particularly relates to a construction method and application of a salmon germplasm identification and origin traceability discrimination model. According to the application, 54 salmon samples from Norway Atlantic salmon, Chile Atlantic salmon and Chinese Qinghai rainbow trout are taken as research objects, acid hydrolysis extraction-gas chromatography is applied to collect fatty acid data, and chemometrics are combined to take an Atlantic salmon discrimination rate and a salmon origin traceability discrimination rate as indexes; a salmon germplasm identification and origin traceability discrimination model is established, and a theoretical basis is provided for rapidly discriminating the authenticity of the Atlantic salmon and the origin of the salmon by using fatty acid fingerprint spectrum analysis.

Description

Technical Field

[0001] This application belongs to the technical field of food detection, and specifically relates to a method for constructing a discriminant model for salmon germplasm identification and origin traceability and its application. Background Art

[0002] Salmon has high economic value, is rich in high-quality protein and Ω-3 series polyunsaturated fatty acids, and can effectively prevent cardiovascular and cerebrovascular diseases. In addition, salmon is rich in DHA and EPA, which has a good promoting effect on the development of children's brain nerve cells and visual development. Therefore, salmon is deeply loved by consumers. Biologically, salmon originally referred to the Atlantic salmon (Salmo salar) of the genus Salmo in the Salmonidae family, such as the well-known Norwegian salmon and Chilean salmon that consumers are familiar with. With the increasing demand for salmon consumption by consumers, in recent years, the high-quality rainbow trout (Oncorhynchus mykiss) in the rainbow trout farming bases in Qinghai, China has also seen rapid development. Although the Salmonidae family also includes rainbow trout (genus Oncorhynchus), there is currently a large gap in the selling price between rainbow trout and Atlantic salmon. At the same time, due to different origins, there are also flavor differences among Norwegian salmon, Chilean salmon, and Qinghai rainbow trout. Since salmon is mostly displayed in the form of meat blocks in the market and is served to consumers in the form of sashimi, it is very difficult for ordinary consumers to distinguish the origin or species of salmon just from the appearance of the meat. Consumers are particularly concerned about whether their right to know and choose products can be guaranteed.

[0003] Currently, the detection technologies for salmon species identification mainly include molecular biology methods and spectroscopy methods. Among them, molecular biology methods include real-time fluorescence PCR method, isothermal PCR method, and ordinary PCR method that rely on sequencing or biosensors. Xu et al. established a method of double real-time fluorescence PCR melting curve and successfully identified Atlantic salmon and rainbow trout. Wu Ting et al. explored the problem of the impersonation of Norwegian salmon by chum salmon from Heilongjiang, freshwater rainbow trout, and Chilean Pacific salmon through infrared spectroscopy combined with partial least squares discriminant analysis (PLS-DA). In the current real-time fluorescence PCR method and isothermal PCR method, the number of species detected each time is only one; the ordinary PCR method has a long detection cycle, is time-consuming and laborious; the spectroscopy method has high requirements for the matrix, and the accuracy is easily affected by matrix interference and is relatively low. These problems make the supervision work of salmon aquatic products face many difficulties.

[0004] Therefore, a simple and rapid detection model for salmon germplasm identification and origin traceability is urgently needed to be developed. Summary of the Invention

[0005] Based on this, an embodiment of this application provides a method for constructing a discriminant model for salmon germplasm identification and origin traceability and its application.

[0006] On the one hand, the present application provides a method for constructing a discriminant model for salmon germplasm identification and origin traceability, including:

[0007] S1. Respectively collect fish meat samples of salmon from different origins and germplasms;

[0008] S2. Extract fatty acids from the collected fish meat samples to obtain the content data of fatty acids;

[0009] S3. Conduct variance analysis, principal component analysis, cluster analysis and discriminant analysis on the content data of the fatty acids, and screen out the fatty acid components for establishing the discriminant model;

[0010] S4. Use the screened fatty acid components to construct a discriminant model for salmon germplasm identification and origin traceability;

[0011] The species sources of the fish meat samples of the salmon include Norwegian Atlantic salmon, Chilean Atlantic salmon and Chinese Qinghai rainbow trout.

[0012] In one embodiment, the salmon samples include the middle dorsal muscles of the Norwegian Atlantic salmon, the Chilean Atlantic salmon and the Chinese Qinghai rainbow trout;

[0013] In one embodiment, the variance analysis includes eliminating the fatty acid components with insignificant differences shown in the results;

[0014] In one embodiment, the principal component analysis and cluster analysis include conducting an equality test of group means before discriminant analysis and eliminating the fatty acid components that fail the test; and,

[0015] In one embodiment, the discriminant analysis includes conducting a variable failure tolerance test before discriminant analysis and eliminating the fatty acids that fail the test.

[0016] In one embodiment, the software used for variance analysis, principal component analysis, cluster analysis and / or discriminant analysis includes SPSS.

[0017] In one embodiment, it further includes:

[0018] The step of collecting the fatty acid content data of known salmon verification samples, inputting them into the discriminant model for salmon germplasm identification and origin traceability, and outputting the categories of salmon to verify the accuracy of the established model.

[0019] In one embodiment, the fatty acids screened in step S4 include lauric acid (C12:0), myristic acid (C14:0), myristoleic acid (C14:1), pentadecanoic acid (C15:0), palmitic acid (C16:0), margaric acid (C17:0), stearic acid (C18:0), trans-oleic acid (C18:1n9t), oleic acid (C18:1n9c), trans-linoleic acid (C18:2n6t), linoleic acid (C18:2n6c), arachidic acid (C20:0), γ-linolenic acid (C18:3n6), eicosenoic acid (C20:1), α-linolenic acid (C18:3n3), eicosadienoic acid (C20:2), eicosatrienoic acid (C20:3n6), erucic acid (C22:1n9), eicosatrienoic acid (C20:3n3), tricosanoic acid (C23:0), arachidonic acid (C20:4n6), docosadienoic acid (C22:2), tetracosanoic acid (C24:0), eicosapentaenoic acid (C20:5n3), and nervonic acid (C24:1).

[0020] On the other hand, the present application provides a method for identifying salmon germplasm and tracing the origin of production areas, including:

[0021] Collecting fatty acid content data of salmon verification samples, inputting the data into the salmon germplasm identification and origin tracing discrimination model constructed by the above construction method, and performing discrimination using the Fisher function according to the fatty acid content data.

[0022] In one embodiment, the discrimination process includes: according to the coefficients in the table, substituting the 25 fatty acid contents of the salmon to be tested into the following three formulas, calculating the scores respectively, and selecting the origin and germplasm with the highest score as the predicted origin and germplasm;

[0023] S Qinghai, China = ∑(fatty acid content × corresponding coefficient for Qinghai) - 236054.153;

[0024] S Chile = ∑(fatty acid content × corresponding coefficient for Chile) - 23555834;

[0025] S Norway = ∑(fatty acid content × corresponding coefficient for Norway) - 23424732;

[0026]

[0027] On the other hand, the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0028] On the other hand, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0029] On the other hand, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0030] Details of one or more embodiments of the present application are presented in the following description, and other features, objectives, and advantages of the present application will become apparent from the specification and its claims. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application and more fully understand the present application and its beneficial effects, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative efforts.

[0032] Figure 1 Steps for constructing a discrimination model for salmon germplasm identification and origin traceability of the present application;

[0033] Figure 2 Principal component score charts of salmon from different origins;

[0034] Figure 3 Dendrogram using average linkage. Detailed Embodiments

[0035] The present application will be further described in detail below in combination with the embodiments and examples. It should be understood that these embodiments and examples are only used to illustrate the present application and not to limit the scope of the present application. The purpose of providing these embodiments and examples is to make the understanding of the disclosed content of the present application more thorough and comprehensive. It should also be understood that the present application can be implemented in many different forms and is not limited to the embodiments and examples described herein. Those skilled in the art can make various changes or modifications without departing from the connotation of the present application, and the equivalent forms obtained also fall within the protection scope of the present application. In addition, in the following description, a large number of specific details are given to provide a more thorough understanding of the present application. It should be understood that the present application can be implemented without one or more of these details.

[0036] Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as commonly understood by those skilled in the technical field of the present application.

[0037] Term

[0038] Unless otherwise specified or there is a contradiction, the terms or phrases used in this document have the following meanings:

[0039] As used herein, the alternative ranges of the terms "and / or", "or / and", and "and / or" include any one of two or more related listed items, and also include any and all combinations of the related listed items. The said any and all combinations include combinations of any two related listed items, any more related listed items, or all related listed items. It should be noted that when at least three items are connected by at least two conjunctions selected from "and / or", "or / and", and "and / or", it should be understood that in this application, this technical solution undoubtedly includes the technical solution connected by "logical AND", and also undoubtedly includes the technical solution connected by "logical OR". For example, "A and / or B" includes three parallel solutions: A, B, and A + B. Another example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (that is, the technical solution connected by "logical OR"), and also includes any and all combinations of A, B, C, and D, that is, it includes combinations of any two or any three of A, B, C, and D, and also includes the four-item combination of A, B, C, and D (that is, the technical solution connected by "logical AND").

[0040] In this application, the terms "multiple", "diverse", "multiple times", "multiple elements", etc., unless otherwise specified, refer to a quantity greater than or equal to 2. For example, "one or more" means one or greater than or equal to two.

[0041] As used herein, "its combination", "any combination thereof", "any combination mode thereof", etc. include all suitable combination modes of any two or more of the listed items.

[0042] In this document, the "suitable" in "suitable combination mode", "suitable mode", "any suitable mode", etc. is subject to being able to implement the technical solution of this application, solve the technical problems of this application, and achieve the expected technical effects of this application.

[0043] In this application, terms such as "further", "even further", "especially", etc. are used for descriptive purposes, indicating differences in content, but should not be construed as limiting the protection scope of this application.

[0044] In this application, "optionally", "optional", "optional" mean that it can be either present or absent, that is, it refers to any one of the two parallel options of "present" or "absent". If "optional" appears multiple times in a technical solution, unless otherwise specified and there is no contradiction or mutual restriction relationship, each "optional" is independent of each other.

[0045] In this application, among the technical features described in an open-ended manner, it includes a closed technical solution composed of the listed features, and also includes an open technical solution containing the listed features.

[0046] In this application, when it comes to a numerical interval (i.e., a numerical range), unless otherwise specified, the optional numerical values are considered continuous within the above numerical interval, and include the two numerical endpoints (i.e., the minimum value and the maximum value) of this numerical range, as well as each numerical value between these two numerical endpoints. Unless otherwise specified, when the numerical interval only refers to the integers within this numerical interval, it includes the two endpoint integers of this numerical range, as well as each integer between the two endpoints. In this article, it is equivalent to directly listing each integer. For example, when t is an integer selected from 1 to 10, it means that t is any integer selected from the integer group consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. In addition, when providing multiple range descriptions for features or characteristics, these ranges can be combined. In other words, unless otherwise specified, the ranges disclosed in this article should be understood to include any and all sub-ranges subsumed therein.

[0047] The temperature parameter in this application, unless otherwise specified, allows for isothermal treatment and also allows for variations within a certain temperature range. It should be understood that the so-called isothermal treatment allows the temperature to fluctuate within the accuracy range controlled by the instrument. It is allowed to fluctuate within a range such as ±5°C, ±4°C, ±3°C, ±2°C, ±1°C.

[0048] In this application, %(w / w) and wt% both represent weight percentages, %(v / v) refers to volume percentages, and %(w / v) refers to mass-volume percentages.

[0049] All documents mentioned in this application are cited as references in this application, just as if each document is cited separately as a reference. Unless it conflicts with the invention purpose and / or technical solution of this application, otherwise, the cited documents involved in this application are cited for all contents and all purposes. When this application involves cited documents, the definitions of relevant technical features, terms, nouns, phrases, etc. in the cited documents are also cited together. When this application involves cited documents, the examples and preferred methods of the relevant technical features cited can also be incorporated as references into this application, but only to the extent that this application can be implemented. It should be understood that when the cited content conflicts with the description in this application, this application shall prevail or be amended adaptively according to the description in this application.

[0050] The term "origin and germplasm" is a composite concept. Atlantic salmon and rainbow trout are two different varieties. Chinese Qinghai rainbow trout is a germplasm, and Chilean Atlantic salmon and Norwegian Atlantic salmon are another germplasm. It can be understood that there are differences in the market values among Chinese Qinghai rainbow trout, Chilean Atlantic salmon, and Norwegian Atlantic salmon, and both the origin and the germplasm have great significance for identification.

[0051] The term "fatty acid" Fatty acids are a class of compounds composed of carbon, hydrogen, and oxygen, and are the main components of neutral fats, phospholipids, and glycolipids. Fatty acid metabolism Fatty acids can be further classified according to the length of the carbon chain: short-chain fatty acids, with less than 6 carbon atoms in the carbon chain, also known as volatile fatty acids; medium-chain fatty acids, referring to fatty acids with 6-12 carbon atoms in the carbon chain, mainly composed of caprylic acid (C8) and capric acid (C10); long-chain fatty acids, with more than 12 carbon atoms in the carbon chain. Most of the fatty acids contained in general foods are long-chain fatty acids. Fatty acids can be divided into 3 categories according to the saturation and unsaturation of the carbon-hydrogen chain, namely: saturated fatty acids, without unsaturated bonds in the carbon-hydrogen; monounsaturated fatty acids, with one unsaturated bond in the carbon-hydrogen chain; polyunsaturated fatty acids, with two or more unsaturated bonds in the carbon-hydrogen chain.

[0052] Fatty acid fingerprint analysis is a common method for authenticity identification and origin tracing. By collecting the fatty acid information of a certain number of samples from the target origin, the most representative component combination is screened out, and a discriminant model is established through chemometrics for the origin tracing of such samples.

[0053] On the one hand, the present application provides a method for constructing a discriminant model for salmon germplasm identification and origin tracing, including:

[0054] S1. Respectively collect the fish meat samples of salmon from different origins and germplasms;

[0055] S2. Extract the fatty acids from the collected fish meat samples to obtain the content data of fatty acids;

[0056] S3. Perform variance analysis, principal component analysis, cluster analysis, and discriminant analysis on the fatty acid content data, and screen out the fatty acid components for establishing the discriminant model according to the preset conditions;

[0057] S4. Use the screened fatty acid components to construct a discriminant model for salmon germplasm identification and origin tracing;

[0058] Among them, the salmon samples include Norwegian Atlantic salmon, Chilean Atlantic salmon, and Chinese Qinghai rainbow trout.

[0059] The present application takes 54 salmon samples from Norwegian Atlantic salmon, Chilean Atlantic salmon, and Chinese Qinghai rainbow trout as the research object, applies acid hydrolysis extraction-gas chromatography to collect fatty acid data, and combines chemometrics. Taking the discriminant rate of Atlantic salmon and the discriminant rate of salmon origin tracing as indicators, a discriminant model for salmon germplasm identification and origin tracing is established, providing a theoretical basis for using fatty acid fingerprint analysis to quickly discriminate the authenticity of Atlantic salmon and the origin of salmon.

[0060] In some of these embodiments, the salmon samples include the mid-back meat of Norwegian Atlantic salmon, Chilean Atlantic salmon, and Chinese Qinghai rainbow trout.

[0061] In some of these embodiments, specifically including:

[0062] S1. Collect 37 samples of the mid-back fish meat of Norwegian Atlantic salmon, Chilean Atlantic salmon, and Chinese Qinghai rainbow trout respectively;

[0063] S2. Extract fatty acids from the collected fish meat samples to obtain the content data of 37 fatty acids;

[0064] S3. Subject the content data of the 37 fatty acids collected in S2 to analysis of variance, principal component analysis, cluster analysis, and discriminant analysis, screen out the fatty acid components suitable for establishing a discriminant model, and construct a discriminant model for salmon germplasm identification and origin traceability;

[0065] S4. Collect the fatty acid content data of the salmon verification samples, input them into the salmon germplasm identification and origin traceability discriminant model, output the category of the salmon, and verify the accuracy of the established model.

[0066] In the above S1, 500 g of the mid-back fish meat of salmon needs to be stirred and mashed, and then 5.00 g is weighed as the representative sample for detection.

[0067] In the above S301, the content data of 37 fatty acids are subjected to standard normal variable transformation and dimensionality reduction processing.

[0068] In the above S302, 8 out of the 37 fatty acids are not detected, 2 show non-significant differences through analysis of variance, and 27 fatty acids enter the principal component analysis and cluster analysis steps.

[0069] In the above S303, an equality test of group means is performed on the 27 fatty acids, and 1 fatty acid that fails the test is excluded; a variable failure tolerance test is performed on the remaining 26 fatty acids, and 1 fatty acid that fails the test is excluded. 25 fatty acids enter the discriminant analysis step.

[0070] In the above S4, a Fisher linear discriminant function is established using the 25 fatty acid characteristic indexes.

[0071] In some of these embodiments, the software used for analysis of variance, principal component analysis, cluster analysis, and discriminant analysis includes SPSS.

[0072] In some of these embodiments, the above method further includes the step of collecting the fatty acid content data of the salmon verification samples, inputting them into the salmon germplasm identification and origin traceability discriminant model, outputting the category of the salmon, and verifying the accuracy of the established model.

[0073] The present application establishes a method for identifying the germplasm of salmon and tracing its origin based on fatty acid fingerprint analysis, providing technical support for the supervision of salmon and a method for consumers to safeguard their right to know and right to choose. In addition, for the detection method established in the present application, through cross-validation of the training set and testing of the test set, the correct discrimination rate of the authenticity of Atlantic salmon reaches 100%, and the correct discrimination rate of origin tracing meets the methodological requirements.

[0074] In some of these embodiments, the fatty acids screened in step S4 include lauric acid (C12:0), myristic acid (C14:0), myristoleic acid (C14:1), pentadecanoic acid (C15:0), palmitic acid (C16:0), margaric acid (C17:0), stearic acid (C18:0), trans-oleic acid (C18:1n9t), oleic acid (C18:1n9c), trans-linoleic acid (C18:2n6t), linoleic acid (C18:2n6c), arachidic acid (C20:0), γ-linolenic acid (C18:3n6), eicosenoic acid (C20:1), α-linolenic acid (C18:3n3), eicosadienoic acid (C20:2), eicosatrienoic acid (C20:3n6), erucic acid (C22:1n9), eicosatrienoic acid (C20:3n3), tricosanoic acid (C23:0), arachidonic acid (C20:4n6), docosadienoic acid (C22:2), tetracosanoic acid (C24:0), eicosapentaenoic acid (C20:5n3), and nervonic acid (C24:1).

[0075] On the other hand, the present application provides a method for identifying the germplasm of salmon and tracing its origin, including:

[0076] Collecting the fatty acid content data of the salmon verification sample, inputting the data into the salmon germplasm identification and origin tracing discrimination model constructed by the construction method, and performing discrimination using the Fisher function based on the fatty acid content data.

[0077] In some of these embodiments, the discrimination process includes: substituting the 25 fatty acid contents of the salmon to be tested into the following three formulas according to the coefficients in the table, calculating the scores respectively, and selecting the origin and germplasm with the highest score as the prediction result;

[0078] S Qinghai, China = ∑(fatty acid content × corresponding coefficient for Qinghai) - 236054.153;

[0079] S Chile = ∑(fatty acid content × corresponding coefficient for Chile) - 235559.834;

[0080] S Norway = ∑(fatty acid content × corresponding coefficient for Norway) - 234246.732;

[0081]

[0082]

[0083] On the other hand, the present application provides a computer device, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor is caused to execute the steps of the above method for constructing a discriminant model for salmon germplasm identification and origin traceability.

[0084] On the other hand, the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor is caused to execute the steps of the above method for constructing a discriminant model for salmon germplasm identification and origin traceability.

[0085] On the other hand, the present application provides a computer program product including a computer program. When the computer program is executed by a processor, the processor is caused to execute the steps of the above method for constructing a discriminant model for salmon germplasm identification and origin traceability.

[0086] Next, the implementation scheme of the present application will be described in detail in conjunction with the embodiments. It should be understood that these embodiments are only used to illustrate the present application and not to limit the scope of the present application. For the experimental methods without specific conditions noted in the following embodiments, the guidance given in the present application is preferably referred to, and it can also be carried out according to the experimental manuals or conventional conditions in the art, or according to the conditions recommended by the manufacturer, or referring to the experimental methods known in the art.

[0087] In the following specific embodiments, for the measurement parameters of the raw material components, if there is no special instruction, there may be slight deviations within the weighing accuracy range. For the temperature and time parameters, acceptable deviations caused by the instrument test accuracy or operation accuracy are allowed.

[0088] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0089] Example 1

[0090] The present application provides a method for constructing a discriminant model for salmon germplasm identification and origin traceability, including the following steps:

[0091] Step 1: Respectively collect the data of the content of 37 fatty acids in the middle part of the back of the fish meat of Norwegian salmon, Chilean salmon, and Chinese Qinghai rainbow trout.

[0092] Step 2: Analyze the data of the 37 fatty acid contents collected in Step 1 through variance analysis, principal component analysis, cluster analysis, and discriminant analysis, screen out the fatty acid components suitable for establishing a discriminant model, and construct a discriminant model for salmon germplasm identification and origin traceability.

[0093] Step 3: Collect the fatty acid content data of salmon verification samples, input them into the discriminant model for salmon germplasm identification and origin traceability, output the category of salmon, and verify the accuracy of the established model.

[0094] Specifically, it includes the following steps:

[0095] 1. Materials and Methods

[0096] 1.1 Materials and Reagents

[0097] 18 samples each of rainbow trout from Qinghai, China, Atlantic salmon from Chile, and Atlantic salmon from Norway. 15 samples randomly selected from the salmon of each of the 3 origins are used for model training research, and the remaining 3 samples from each origin are used for the test and verification of the discriminant model.

[0098] 37 - component fatty acid methyl ester mixed standard solution, Sigma Corporation; hydrochloric acid, ether, petroleum ether, ethanol, sodium hydroxide (all analytical grade), Guangzhou Chemical Reagent Factory; methanol, isooctane (both chromatographic grade), Merck & Co., Inc.

[0099] 1.2 Instruments and Equipment

[0100] Liquid chromatograph (Agilent 7890B type, equipped with a flame ionization detector): Agilent Technologies, USA; vortex oscillator (M3 digital display type): IKA - Werke GmbH & Co. KG, Germany; one - percent analytical balance: Mettler - Toledo International Inc., Switzerland; ultrapure water machine: Chongqing Aikepu Technology Co., Ltd., China; thermostatic shaking water bath: Beijing Labtech Instruments Co., Ltd.

[0101] 1.3 Methods

[0102] 1.3.1 Sample Preparation

[0103] Take 500 g of the middle part of the back of the salmon, mash it in a blender, and place it in a sample bag for standby.

[0104] 1.3.2 Fatty Acid Determination Method

[0105] Accurately weigh 5.00 g (accurate to 0.01 g) of the mashed middle part of the back of the above - mentioned salmon sample, and determine the 37 fatty acids in the salmon according to the acid hydrolysis - extraction method in GB5009.168 - 2016 "National Food Safety Standard - Determination of Fatty Acids in Foods" (the second method, external standard method). The determination result is expressed as the ratio of a single fatty acid to the total fatty acid content.

[0106] 1.3.3. Data Analysis Method

[0107] The data was subjected to variance analysis, principal component analysis, cluster analysis, and discriminant analysis using SPSS 26 software.

[0108] 2. Results and Analysis

[0109] 2.1. Analysis of the Differences in Fatty Acid Contents in Salmon from Different Origins and Germplasms

[0110] Among the 37 fatty acids, the contents of 8 fatty acids (namely: butyric acid (C4:0), caproic acid (C6:0), caprylic acid (C8:0), capric acid (C10:0), undecanoic acid (C11:0), pentadecenoic acid (C15:1), heptadecenoic acid (C17:1), heneicosanoic acid (C21:0)) were not detected, and the contents of 29 fatty acids were detected.

[0111] Variance analysis was respectively performed on the contents of the 29 detected fatty acids in salmon from the three main producing regions of China's Qinghai, Norway, and Chile. The analysis results are shown in Table 1. The results show that for salmon from different origins, the contents of 25 fatty acids (namely: lauric acid (C12:0), myristic acid (C14:0), myristoleic acid (C14:1), palmitic acid (C16:0), margaric acid (C17:0), stearic acid (C18:0), trans-oleic acid (C18:1n9t), oleic acid (C18:1n9c), trans-linoleic acid (C18:2n6t), linoleic acid (C18:2n6c), arachidic acid (C20:0), γ-linolenic acid (C18:3n6), eicosenoic acid (C20:1), α-linolenic acid (C18:3n3), eicosadienoic acid (C20:2), eicosatrienoic acid (C20:3n6), erucic acid (C22:1n9), eicosatrienoic acid (C20:3n3), tricosanoic acid (C23:0), arachidonic acid (C20:4n6), docosadienoic acid (C22:2), tetracosanoic acid (C24:0), eicosapentaenoic acid (C20:5n3), nervonic acid (C24:1), docosahexaenoic acid (C22:6n3)) have extremely significant differences, the contents of 2 fatty acids (namely: pentadecanoic acid (C15:0), palmitoleic acid (C16:1)) have significant differences, while the contents of 2 fatty acids (namely: tridecanoic acid (C13:0), docosanoic acid (C22:0)) have no significant differences.

[0112] Table 1 Analysis of the Differences in Fatty Acid Contents in Salmon from Different Origins and Germplasms

[0113]

[0114]

[0115] Note: Different lowercase letters in the table indicate significant differences (P < 0.05).

[0116] 2.2 Principal component analysis of fatty acid contents in salmon from different origins and germplasms

[0117] Principal component analysis was performed on 27 fatty acids with significant and highly significant differences in salmon from the three main salmon-producing regions of Qinghai, China, Chile, and Norway. The eigenvectors and cumulative variance contribution rates in the obtained principal components are shown in Table 2. It can be seen that the total variance contribution rate of the first three principal components reached 86.184%.

[0118] Table 2 Eigenvectors and cumulative variance contribution rates of each variable in the first three principal components

[0119]

[0120]

[0121] The principal component loadings are shown in Table 3. By analyzing the principal component loading table, it is known that the contents of myristic acid, oleic acid, arachidic acid, eicosenoic acid, α-linolenic acid, erucic acid, eicosatrienoic acid, docosadienoic acid, eicosapentaenoic acid, and nervonic acid have relatively large loadings on the first principal component, indicating a relatively high degree of correlation between the contents of these fatty acids and the first principal component. While the contents of palmitic acid, stearic acid, trans-linoleic acid, linoleic acid, γ-linolenic acid, eicosadienoic acid, eicosatrienoic acid, arachidonic acid, and docosahexaenoic acid have relatively large absolute values of loadings on the first principal component, indicating a relatively high degree of negative correlation between the contents of these fatty acids and the first principal component; the contents of lauric acid, pentadecanoic acid, margaric acid, trans-oleic acid, and tricosanoic acid have relatively large loadings on the second principal component, indicating a relatively high degree of correlation between the contents of these fatty acids and the second principal component; the contents of myristoleic acid and palmitoleic acid have relatively large loadings on the third principal component, indicating a relatively high degree of correlation between the contents of these fatty acids and the third principal component. While the content of lignoceric acid has a relatively large absolute value of loading on the third principal component, indicating a relatively high degree of negative correlation between the content of this fatty acid and the third principal component. Therefore, the composition of the principal components is as follows: the first principal component: myristic acid, palmitic acid, stearic acid, oleic acid, trans-linoleic acid, linoleic acid, arachidic acid, γ-linolenic acid, eicosenoic acid, α-linolenic acid, eicosadienoic acid, eicosatrienoic acid, erucic acid, eicosatrienoic acid, arachidonic acid, docosadienoic acid, eicosapentaenoic acid, nervonic acid, docosahexaenoic acid; the second principal component: lauric acid, pentadecanoic acid, margaric acid, trans-oleic acid, tricosanoic acid; the third principal component: myristoleic acid, palmitoleic acid, lignoceric acid.

[0122] Table 3 Principal component loading table

[0123]

[0124]

[0125] The standardized scores of the first, second, and third principal components were plotted, as shown in Figure 2 . From Figure 2 , it can be seen that in the three-dimensional graph, the samples from the three producing areas are basically non-overlapping visually and are well distinguished. The first, second, and third principal components mainly synthesize the information on the contents of 27 fatty acids in the salmon samples. It can be seen that principal component analysis can more intuitively present the information on various fatty acids in the samples through a comprehensive method.

[0126] 2.3 Cluster analysis of fatty acid contents in salmon from different producing areas and germplasms

[0127] Using the hierarchical clustering method, hierarchical clustering analysis was performed on the contents of 27 fatty acids in 45 salmon samples from Qinghai, China, Chile, and Norway. The results are as shown in Figure 3 . From Figure 3 , it can be seen that by cutting the dendrogram at the middle of the clustering distance of 8 - 9, the samples were divided into three categories: the first category is the samples from Qinghai, China; the second category is the samples from Chile, and one Norwegian sample (serial number 41) was misclassified; the third category is the Norwegian samples. Only 1 sample out of 45 samples was misclassified, and the correct classification rate of the salmon producing areas reached 97.8%. In addition, if the dendrogram is cut at the middle of the clustering distance of 9 - 25, the samples are divided into two categories: the first category is the rainbow trout samples from Qinghai, China, and the second category is the sum of the samples of Atlantic salmon from Chile and Norway, which also implies that this clustering model can be well applied to the germplasm identification of rainbow trout and Atlantic salmon.

[0128] 2.4 Discriminant analysis of fatty acid contents in salmon from different producing areas and germplasms

[0129] Taking the contents of 27 fatty acids that showed significant and extremely significant differences in the differential analysis as characteristic indicators, an equality test of group means was carried out, and 1 fatty acid content index (palmitoleic acid) with a significance of P > 0.01, that is, the test failed, was excluded. Subsequently, taking the remaining 26 fatty acid content indices as characteristic indicators, a variable failure tolerance test was carried out, and 1 fatty acid content index (docosahexaenoic acid) that failed the test was excluded. Taking the remaining 25 fatty acid content indices that passed the test as characteristic indicators, the Fisher function and cross-validation were used to conduct research on the origin tracing and germplasm identification of 45 salmon samples from 3 producing areas and germplasms. The classification function coefficients are shown in Table 4.

[0130] Table 4 Classification function coefficients

[0131]

[0132]

[0133] The Fisher linear discriminant function can be obtained from Table 4. According to the Fisher linear discriminant function, the classification of discrimination is obtained. As can be seen from Table 5, 100% of the salmon samples of the origin and germplasm of rainbow trout from Qinghai, China, Atlantic salmon from Chile, and Atlantic salmon from Norway in the initial grouped cases have been correctly classified; 93.3% of the salmon samples of the origin and germplasm in the cross-validation grouped cases have been correctly classified. Among them, 100% of the samples of rainbow trout from Qinghai, China have been correctly identified, 93.3% of the samples of Atlantic salmon from Chile have been correctly identified, and 86.7% of the samples of Atlantic salmon from Norway have been correctly identified. That is, the correct identification rate between rainbow trout from Qinghai, China and Atlantic salmon is 100%, and there are a small number of misidentifications between Atlantic salmon from Chile and Atlantic salmon from Norway. The misjudgment rate of cross-validation is 6.7% < 10%, meeting the requirement of the misjudgment rate of the discrimination effect.

[0134] According to the coefficients in the table, substitute the 25 fatty acid contents of the salmon to be tested into the following three formulas, calculate the scores respectively, and select the origin and germplasm with the highest score as the prediction result;

[0135] S_Qinghai_China = ∑(fatty acid content × corresponding coefficient of Qinghai) - 236054.153;

[0136] S_Chile = ∑(fatty acid content × corresponding coefficient of Chile) - 235559.834;

[0137] S_Norway = ∑(fatty acid content × corresponding coefficient of Norway) - 234246.732;

[0138] Substitute the above coefficients and the measured fatty acid contents into the formula:

[0139] S Qinghai = (C12.0 content × -9248.622) + (C14.0 content × 6359.54) + (C14:1 content × 127229.163) + (C15.0 content × 135583.239) + (C16.0 content × 3010.892) + (C17.0 content × -13575.018) + (C18.0 content × 8037.858) + (C18:1n9t content × -27205.212) + (C18:1n9c content × 4805.793) + (C18:2n6t content × -17884.961) + (C18:2n6c content × 4675.981) + (C20:0 content × 33726.136) + (C18:3n6 content × 17411.104) + (C20:1 content × 2890.611) + (C18:3n3 content × 2785.851) + (C20:2 content × 13610.693) + (C20:3n6 content × -252.798) + (C22:1n9 content × 16117.191) + (C20:3n3 content × 7973.152) + (C23:0 content × -36243.972) + (C20:4n6 content × 32945.003) + (C22:2 content × 2686.929) + (C24:0 content × 71327.938) + (C20:5n3 content × 6484.661) + (C24:1 content × 5626.57) - 236054.153 (constant term).

[0140] S Chile = (C12.0 content × 2146.07) + (C14.0 content × 6823.078) + (C14:1 content × 87798.324) + (C15.0 content × 133240.063) + (C16.0 content × 3070.723) + … (all fatty acid terms follow this pattern) + (C24:1 content × 2968.198) - 235559.834.

[0141] S Norway = (C12.0 content × 904.393) + (C14.0 content × 6803.237) + (C14:1 content × 91156.789) + (C15.0 content × 131600.175) + (C16.0 content × 3095.443) + … (all fatty acid terms follow this pattern) + (C24:1 content × 3125.586) - 234246.732.

[0142] Table 5 Discrimination Results of Salmon Origin and Germplasm Based on Fatty Acid Content Analysis

[0143]

[0144] Note: a Only cross-validation is performed for individual cases in the analysis. In cross-validation, each individual case is classified by functions derived from all other cases except that case.

[0145] In this application, the fatty acid content data of salmon samples from 3 origins and germplasms, with 3 samples for each origin and germplasm, are used as the test set to test and verify the obtained Fisher linear discriminant function. As shown in the results of Table 5, among the 9 test samples, 1 Norwegian Atlantic salmon sample was misjudged as a Chilean Atlantic salmon sample, and the other 8 samples were correctly classified. That is, the correct recognition rate of Chinese Qinghai rainbow trout and Atlantic salmon is 100%, and there are a small number of recognition errors between Chilean Atlantic salmon and Norwegian Atlantic salmon. The correct rate of test set verification reaches 88.9%, and the discrimination effect is good.

[0146] The above-described embodiments merely represent several implementation manners of this application, facilitating the specific and detailed understanding of the technical solution of this application, but should not be construed as limiting the scope of patent protection of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. In addition, it should be understood that after reading the above teachings of this application, those skilled in the art can make various changes or modifications to this application, and the equivalent forms obtained also fall within the protection scope of this application. It should also be understood that the technical solutions obtained by those skilled in the art through logical analysis, reasoning or limited experiments based on the technical solution provided by this application are all within the protection scope of the appended claims of this application. Therefore, the protection scope of this patent application should be subject to the content of the appended claims, and the specification can be used to explain the content of the claims.

Claims

1. A method for constructing a salmon germplasm identification and origin tracing discrimination model, characterized in that: include: S1. Collect salmon meat samples from different origins and species; S2, extracting fatty acids from the collected fish samples to obtain fatty acid content data; S3, performing variance analysis, principal component analysis, cluster analysis and discriminant analysis on the fatty acid content data to screen out fatty acid components for establishing a discriminant model; S4. Use the screened fatty acid components to construct a discrimination model for salmon germplasm identification and origin tracing; The species sources of the salmon meat samples include Norwegian Atlantic salmon, Chilean Atlantic salmon and Chinese Qinghai rainbow trout.

2. The method according to claim 1, characterized in that The salmon samples include the back middle meat of the Norwegian Atlantic salmon, the Chilean Atlantic salmon and the Chinese Qinghai rainbow trout; Optionally, S3 satisfies one or more of the following (1)-(3): (1) ANOVA included elimination of fatty acid components that showed no significant differences; (2) principal component analysis and cluster analysis included a test of the equality of group means before discriminant analysis, and fatty acid components that failed the test were eliminated; as well as, (3) Discriminant analysis includes a variable failure tolerance test before the discriminant analysis, and the fatty acids that fail the test are eliminated.

3. The method according to claim 1, characterized in that The software used for analysis of variance, principal component analysis, cluster analysis and / or discriminant analysis included SPSS.

4. The method according to claim 1, characterized in that Also includes: The steps include collecting fatty acid content data of known salmon verification samples, inputting into the salmon germplasm identification and origin traceability discrimination model, outputting the salmon category, and verifying the accuracy of the established model.

5. The method according to claim 4, characterized in that The fatty acids screened out in step S4 include lauric acid (C12:0), myristic acid (C14:0), myristic oleic acid (C14:1), pentadecanoic acid (C15:0), palmitic acid (C16:0), heptadecanoic acid (C17:0), stearic acid (C18:0), trans-oleic acid (C18:1n9t), oleic acid (C18:1n9c), trans-linoleic acid (C18:2n6t), linoleic acid (C18:2n6c), arachidic acid (C20:0), γ-linolenic acid (C18:3n 6), eicosapentaenoic acid (C20:1), alpha-linolenic acid (C18:3n3), eicosadienoic acid (C20:2), eicosatrienoic acid (C20:3n6), erucic acid (C22:1n9), eicosatrienoic acid (C20:3n3), tricosanoic acid (C23:0), arachidonic acid (C20:4n6), docosadienoic acid (C22:2), tetracosanoic acid (C24:0), eicosapentaenoic acid (C20:5n3), and tetracosenoic acid (C24:1).

6. A method for identifying salmon germplasm and tracing origin, characterized in that: include: Collect fatty acid content data of salmon verification samples, input the data into the salmon germplasm identification and origin tracing discrimination model constructed by the construction method described in any one of claims 1 to 5, and use Fisher function to perform discrimination based on the fatty acid content data.

7. The method according to claim 6, characterized in that The discrimination process includes: according to the coefficients in the table, the 25 fatty acid contents of the salmon to be tested are substituted into the following three formulas, the scores are calculated respectively, and the origin and germplasm with the highest score are selected as the predicted origin and germplasm; S China Qinghai = ∑ (fatty acid content × Qinghai corresponding coefficient) - 236054.153; S Chile = ∑ (fatty acid content × Chile corresponding coefficient) - 235559.834; S Norway = ∑ (fatty acid content × Norway corresponding coefficient) - 234246.732; 8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images