Quality evaluation method for cultured takifugu obscurus and application

Abstract

The invention discloses a method for evaluating the quality of cultured takifugu obscurus and application, a key technical support is provided for quality grading and grading of the variety through a standardized process, and the method for evaluating the quality of high-quality cultured takifugu obscurus, which can be directly applied to practice, is constructed by focusing on deep combination of evaluation method construction and grading application in specific steps. As a core tool for realizing quality grading and grading of takifugu obscurus, products are graded and graded according to comprehensive scores. According to the method, the blank that a unified quality grading standard is lacked in the industry is filled, high-quality takifugu obscurus is preferably selected for consumers and dealers, and the consumer experience and the product credibility are improved. Meanwhile, the market benefits of high-quality takifugu obscurus can be improved for farmers, a good market form with high quality and high price is realized, and the added value and market competitiveness of products are further improved.

Description

Technical Field

[0001] This invention relates to the technical field of quality evaluation and grading system for farmed dark-spotted pufferfish, and particularly to the quality evaluation method and application of farmed dark-spotted pufferfish. Background Technology

[0002] The dark-spotted pufferfish, also known as the river pufferfish, is a species of fish belonging to the genus *Tetraodon* in the family Tetraodontidae of the order Tetraodontiformes. In China, it is mainly distributed in the East China Sea and the Yellow Sea. The dark-spotted pufferfish has tender and succulent flesh, rich in protein and fat, making it popular with consumers. Furthermore, it can be farmed in freshwater, brackish water, or seawater, resulting in high economic benefits. Through several generations of artificial breeding, it has achieved low-toxicity or even non-toxicity levels.

[0003] Currently, the farmed pufferfish market faces the dual challenges of quality gaps and lack of standards, which severely restricts the release of industry value and the establishment of consumer trust. Scientific quality evaluation methods and a clear grading system are the core keys to breaking this deadlock.

[0004] From the consumer's perspective, the quality of commercially available farmed pufferfish varies significantly. Some products have a dull, lackluster surface, loose and inelastic muscles, and lack flavor or even have an off-putting odor after steaming or boiling. In contrast, high-quality products have clear dark stripes, firm flesh, and a rich, umami flavor. However, due to the lack of a unified quality assessment standard, consumers can only rely on subjective experience to make purchases, making it difficult to accurately identify high-quality products. This not only leads to significant fluctuations in the eating experience but also gradually erodes trust in the pufferfish category, ultimately inhibiting further release of consumer demand.

[0005] From an aquaculture perspective, the quality of farmed dark-striped pufferfish is influenced by multiple parameters: these include sensory indicators such as appearance (surface luster, eye condition, clarity of dark stripes) and muscle tissue (firmness, elasticity); nutritional indicators such as protein, fat, amino acids (e.g., umami amino acids Glu, Asp), and fatty acids (e.g., DHA, EPA); and flavor indicators such as free amino acids, nucleotides (e.g., flavor-enhancing IMPs), and fishy substances (e.g., MIB, GSM). The numerous and complex parameters leave farmers without a clear "quality-oriented farming philosophy"—they cannot accurately grasp the key control directions for improving product quality, leading to most products falling into "homogenized low-price competition," making it difficult to achieve premium pricing through quality upgrades. Consequently, the economic benefits of farming remain at a low level, dampening farmers' enthusiasm for farming.

[0006] From an industry perspective, the lack of quality evaluation methods and the absence of a grading system have directly led to the "disorderly development" of the pufferfish industry: the market lacks a unified standard for quality measurement, high-quality products cannot achieve value recognition through clear grading, while inferior products disrupt market order with low prices, creating a vicious cycle of "bad money driving out good." At the same time, without standardized quality evaluation support, the industry struggles to establish a full-chain quality control system from farming to distribution, failing to form a virtuous market structure of "high quality, high price," ultimately hindering the transformation of the pufferfish industry from "scale expansion" to "quality upgrading" and impeding the healthy and sustainable development of the industry.

[0007] Therefore, it is evident that constructing a scientific and unified quality evaluation method for farmed pufferfish and establishing clear grading standards is not only the foundation for protecting consumer rights and rebuilding consumer trust, but also the key to guiding farmers to accurately improve quality and increase profits. Furthermore, it is the core support for promoting the standardized and high-value development of the entire pufferfish industry. Summary of the Invention

[0008] To address the aforementioned problems, this invention aims to provide a method and application for evaluating the quality of farmed pufferfish (Takifugu obscurus). This includes systematically collecting farmed pufferfish samples from representative farming areas and the market; accurately measuring the physicochemical and sensory evaluation indicators of farmed pufferfish from different sources; screening core influencing factors through data difference analysis; establishing a structured quality evaluation system for farmed pufferfish through indicator parameter optimization and integration, clarifying the core dimensions of quality assessment; and deeply exploring and analyzing the characteristic patterns of flavor substances in farmed pufferfish, integrating them deeply into the quality evaluation system. Through this evaluation method and its grading system, the quality of commercially available farmed pufferfish can be evaluated and graded, thereby enabling consumers and distributors to select high-quality farmed pufferfish, enhancing consumer experience and product trust.

[0009] To achieve the above objectives, the technical solution adopted by this invention is as follows: A method for evaluating the quality of cultured dark-spotted pufferfish, comprising the following steps: 1) Collect pufferfish of the dark stripe from representative aquaculture areas and market sales; 2) Determine the physicochemical properties and sensory evaluation of pufferfish from different sources; 3) Select index parameters to establish a quality evaluation method for cultured dark-spotted pufferfish; 4) The samples were comprehensively scored according to the quality evaluation method for cultured dark-spotted pufferfish; 5) Determine the grade threshold based on the comprehensive score and establish a grading system for farmed dark-spotted pufferfish products.

[0010] Preferably, step 2) involves the determination of physicochemical indicators, which includes a data difference analysis of multiple indicator parameters in the muscle of pufferfish from different sources.

[0011] Preferably, the data difference analysis is a significant difference analysis of each indicator obtained by SPSS through one-way ANOVA.

[0012] Preferably, the plurality of the index parameters include crude protein, crude fat, GMP, IMP, ATP, Asp, Glu, Ser, Gly, Thr, Ala, Pro, Lys, Trp, Val, Met, Phe, Ile, Leu, Arg, Tyr, THR, SER, GLY, HIS, ARG, C16:0, C18:1n9c, C21:0, C22:1n9, EPA, DHA, Na, K, Ca, Mg, Fr, Cu, Zn, Mn, MIB, and GSM, wherein the index parameters have significant differences (P<0.05).

[0013] Preferably, before establishing a quality evaluation method for high-quality cultured dark-spotted pufferfish, the index parameters are simplified through hierarchical cluster analysis to form a similar hierarchical diagram.

[0014] Preferably, the hierarchical clustering analysis uses correlation as the distance type, group average as the clustering method, and distance sum as the distance method. Multiple index parameters of farmed pufferfish are clustered into 9 categories when the distance is 0.5, namely crude protein, crude fat, DHA, EPA, C22:1n9, Pro, SER, Na, and Fe.

[0015] Preferably, factor analysis is used to perform multivariate statistics on the nine groups, including principal component analysis and factor extraction.

[0016] Preferably, the coefficient corresponding to each indicator parameter is the ratio of the principal component coefficient of each indicator in the component matrix to the square root of the corresponding eigenvalue. The score expressions of the four principal components are constructed as follows: F 1= -0.433 x 1-0.418 x 2+0.413 x 3+0.412 x 4+0.340 x 5+0.332 x 6+0.015 x 7+0.130 x 8-0.094 x 9+0.008 x 10 +0.045 x 11 -0.115x 12 -0.172 x 13 F2 = -0.201 x 1-0.124 x 2+0.111 x 3-0.266 x 4+0.101 x 5-0.259 x 6+0.502 x 7+0.479 x 8+0.430 x 9-0.237 x 10 -0.006 x 11 +0.091 x 12 +0.231 x 13 F3=0.099 x 1+0.033 x 2-0.181 x 3+0.013 x 4+0.312 x 5+0.115 x 6+0.058 x 7+0.059 x 8+0.144 x 9+0.583 x 10 +0.560 x 11 -0.170 x 12 +0.366 x 13 F4 = -0.140 x 1-0.289 x 2+0.241 x 3+0.031 x 4 ± 0.210 x 5-0.236 x 6-0.185 x 7-0.209 x 8-0.152 x 9+0.044 x 10 +0.264 x 11 +0.644 x 12+0.389 x 13 ; The function for evaluating the overall quality of cultured dark-spotted pufferfish is as follows: F = 0.383F1+0.312F2+0.161F3+0.144F4.

[0017] Preferably, a comprehensive score is given to all cultured pufferfish samples based on the established comprehensive quality evaluation method function for cultured pufferfish; all scores are sorted from high to low, grade thresholds are determined, and a grading system for cultured pufferfish products is formed.

[0018] This invention also provides an application of the above-mentioned evaluation method in the quality evaluation of cultured pufferfish.

[0019] The beneficial effects of this invention are as follows: The establishment of this evaluation model provides a basis for the quality evaluation and grading of the dark-spotted pufferfish. This evaluation model provides consumers and distributors with quantifiable criteria for quality judgment, helping to select high-quality dark-spotted pufferfish and improve consumer experience and product trust. Simultaneously, it clarifies key directions for quality improvement for fish farmers, assisting them in optimizing their farming practices, escaping homogeneous low-price competition, increasing the market benefits of high-quality products, achieving a market structure of "high quality and high price," and ultimately enhancing product added value and market competitiveness.

[0020] On the other hand, this technology can also promote the transformation of the aquaculture industry of pufferfish from "scale expansion" to "quality upgrade", help build a full-chain quality control system from breeding to distribution, and even combine regional ecological advantages to create high-quality products as local specialties, drive the coordinated development of related industries, optimize the regional industrial structure, and make pufferfish farming a new highlight of local economic development. Attached Figure Description

[0021] Figure 1-1 These are 1-14 quality difference factors for the dark-spotted pufferfish from different origins, as described in this invention.

[0022] Figure 1-2 These are 15-28 quality difference factors for the dark-spotted pufferfish from different origins, as described in this invention.

[0023] Figure 1-3 These are 29-42 quality difference factors for the dark-spotted pufferfish from different origins, as described in this invention.

[0024] Figure 2 This is a hierarchical clustering analysis diagram of 42 indicators of the dark-striped pufferfish of this invention.

[0025] Figure 3 This is the relationship between the number of factors and eigenvalues ​​in this invention (crush plot). Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0027] See attached document Figure 1-1 , 1-2 The quality evaluation methods for cultured dark-spotted pufferfish shown in 1-3 and 2-3 include the following steps: 1) Collect samples of farmed pufferfish from representative aquaculture areas and markets nationwide. Three pufferfish were selected from each sampling point: two for physicochemical testing and one for sensory evaluation. The whole fish should be frozen immediately after sampling and thawed according to requirements upon return to the laboratory. Sensory evaluation of the pufferfish should be conducted within 24 hours. Sample preparation should be carried out according to "GB / T 30891-2014 Sampling Specifications for Aquatic Products". The whole fish and liver were weighed separately. The muscle tissue from two pufferfish was removed and homogenized together to form one sample.

[0028] 2) The physicochemical indicators and sensory evaluation of farmed pufferfish from different sources were determined. Sensory quality is the most direct manifestation of aquatic product quality and the most direct basis for actual production and consumption. Food flavor is a multi-dimensional and complex system, encompassing multiple levels such as texture, aroma, and taste, which are perceived through the combined action of various human senses. Sensory evaluation, by simulating the actual eating experience of consumers, can systematically describe and analyze various aspects of food flavor. Sensory evaluation uses a series of standardized and quantitative methods to objectively and accurately assess the flavor characteristics of food. Evaluators, after professional training, can use their sensory abilities to make detailed observations and descriptions of food flavor. Through smelling, tasting, and experiencing texture, they capture subtle differences in food, thereby revealing the uniqueness and complexity of food flavor.

[0029] To assess the characteristics of the flesh of the dark-spotted pufferfish, this application presents a sensory evaluation form for it. Sensory evaluators conducted sensory evaluations on 89 batches of dark-spotted pufferfish samples based on the sensory evaluation form.

[0030] Table 1 Sensory Evaluation Table of Dark-spotted Pufferfish The determination of physicochemical indicators was carried out according to national standards or self-developed methods. To more intuitively understand the differences in physicochemical indicators of pufferfish from different origins, this application conducted a data difference analysis on more than 80 indicators and parameters, including general nutritional components, amino acids, free amino acids, fatty acids, nucleotides, elements, and fishy substances, in the muscle of pufferfish from different origins. Specific evaluation indicators and detection methods are shown in Table 2 below.

[0031] Table 2 Sensory evaluation of the quality of the dark-spotted pufferfish samples Note 1: Aspartic acid (ASP), glutamic acid (GLU), serine (SER), histidine (HIS)#, glycine (GLY), threonine (THR)*, arginine (ARG)#, alanine (ALA), tyrosine (TYR), valine (VAL)*, methionine (MET)*, phenylalanine (PHE)*, isoleucine (ILE)*, leucine (LEU)*, lysine (LYS)*, proline (PRO), (* represents 8 essential amino acids EAA, # represents 2 semi-essential amino acids HEAA, and the rest are 8 non-essential amino acids NEAA).

[0032] Note 2: C14:0, C15:0, C15:1, C16:0, C16:1, C17:0, C18:0, C18:1n9c, C18:2n6t, C18:2n6c, C18:3n6, C20:1, C18:3n3, C21:0, C20:2, C20:3n6, C22:1n9, C20:3n3, C20:4n6, C20:5n3, C24:1, C22:6n3.

[0033] Note 3: Aspartic acid (Asp), glutamic acid (Glu), serine (Ser), histidine (His), glycine (Gly), threonine (Thr), arginine (Arg), alanine (Ala), tyrosine (Tyr), valine (Val), methionine (Met), phenylalanine (Phe), isoleucine (Ile), leucine (Leu), lysine (Lys), proline (Pro), tryptophan (Trp).

[0034] Note 4: Adenosine monophosphate (AMP), cytidine monophosphate (CMP), guanylic acid (GMP), inosine monophosphate (inosine monophosphate, IMP), uridine monophosphate (UMP), adenosine triphosphate (ATP), adenosine diphosphate (ADP), inosine (HX), and inosine (HXR).

[0035] Note 5: trans-1,10-dimethyl-trans-9-naphthyl alcohol (GSM, geosmin), 2-methylisosinol (MIB).

[0036] 3) Select key indicator parameters to establish a quality evaluation method for cultured dark-spotted pufferfish. One-way ANOVA using SPSS was used to analyze the statistical significance of each indicator. 42 parameters were identified as having significant differences (P < 0.05), namely: crude protein, crude fat, GMP, IMP, ATP, Asp, Glu, Ser, Gly, Thr, Ala, Pro, Lys, Trp, Val, Met, Phe, Ile, Leu, Arg, Tyr, THR, SER, GLY, HIS, ARG, C16:0, C18:1n9c, C21:0, C22:1n9, EPA, DHA, Na, K, Ca, Mg, Fr, Cu, Zn, Mn, MIB, and GSM. (Among them, Asp, Glu, Ser, Gly, Thr, Ala, Pro, Lys, Trp, Val, Met, Phe, Ile, Leu, Arg, and Tyr are free amino acids, while THR, SER, GLY, HIS, and ARG are bound amino acids). Differences between groups are shown in […]. Figures 1-1 to 1-3 (* indicates a significant difference between groups).

[0037] Due to the diversity and correlation among the indicators for farmed pufferfish, direct comprehensive evaluation can lead to information overlap. Therefore, these indicators need to be simplified before evaluation. Hierarchical clustering analysis can create a hierarchical graph of similarity; the shorter the distance between data points, the higher the similarity. Starting with the most similar objects and gradually clustering them into categories, it is easier to intuitively determine the division between categories. This application uses correlation as the distance type, group average as the clustering method, and distance sum as the distance method. Based on correlation analysis, the most representative indicators were automatically selected from 42 indicator parameters. Figure 2 In the diagram, different colors represent different categories. The 42 quality indicators of the dark-spotted pufferfish were clustered into 9 categories at a distance of 0.5. The most representative indicators of each group were crude protein, crude fat, DHA, EPA, C22:1n9, Pro, SER, Na, and Fe, indicating that these 9 quality indicators are the core indicators of the quality of the dark-spotted pufferfish.

[0038] Factor analysis is a multivariate statistical analysis method that starts by studying the dependencies within the correlation matrix of research indicators, reducing variables with overlapping information and complex relationships to a few uncorrelated composite factors. It aims to find latent factors hidden in multivariate data that cannot be directly observed but influence or dominate measurable variables, and to estimate the degree of influence of latent factors on measurable variables and the correlation between latent factors. Its basic idea is to group variables according to the strength of their correlation, ensuring high correlation among variables within the same group, while variables in different groups are uncorrelated or have low correlation. Each group of variables represents a basic structure—a common factor. Factor analysis typically involves three steps: first, determining whether factor analysis is suitable; second, determining the correspondence between factors and items; and third, naming the factors.

[0039] Principal component analysis (PCA) is a multivariate statistical method that uses dimensionality reduction to transform multiple parameters into a few composite parameters for evaluation with minimal information loss. The resulting composite parameters are typically called principal components. Each principal component is a linear combination of the original variables, and these principal components are uncorrelated with each other. This gives principal components certain superior properties compared to the original variables. When studying complex problems, only a few principal components need to be considered without losing too much information, making it easier to grasp the main contradictions, reveal the patterns between variables within a phenomenon, simplify the problem, and improve analytical efficiency. This method achieves this by creating new, uncorrelated variables (i.e., principal components), which are linear combinations of the original variables.

[0040] Principal component analysis was performed on 13 indicators, including nine representative indicators derived from cluster analysis, MIB and GSM (essential fishy-smelling substances in aquatic products), and Glu and IMP (the main umami substances in pufferfish). First, the KMO test and Bartlett's test of sphericity were conducted. The KMO statistic is used to compare simple correlation coefficients and partial correlation coefficients between variables. It is mainly applied to factor analysis in multivariate statistics. The KMO statistic ranges from 0 to 1. Kaiser provides commonly used KMO metrics: above 0.9 indicates very good; 0.8 indicates good; 0.7 indicates moderate; 0.6 indicates poor; and below 0.5 indicates very poor. When the sum of squares of simple correlation coefficients among all variables is much greater than the sum of squares of partial correlation coefficients, the KMO value approaches 1. A KMO value closer to 1 indicates a stronger correlation between variables, making the original variables more suitable for factor analysis. When the sum of squared simple correlation coefficients among all variables is close to 0, the KMO value is close to 0. A KMO value closer to 0 indicates a weaker correlation between variables, making the original variables less suitable for factor analysis. Bartlett's test of sphericity is used to test the correlation between variables in a correlation matrix, specifically whether the matrix is ​​an identity matrix, i.e., whether each variable is independent. If the variables are independent, common factors cannot be extracted, and factor analysis cannot be applied. Bartlett's test of sphericity determines that if the correlation matrix is ​​an identity matrix, the variables are independent, and factor analysis is invalid. The SPSS test results show a KMO value of 0.678 (>0.5) and Sig. <0.05 (i.e., p-value <0.05) (Table 3), indicating a correlation between the variables and valid factor analysis.

[0041] Table 3 KMO test and Barlertt's test Principal component analysis was used to extract factors from 13 indicators. The relationship between the number of factors and eigenvalues ​​(scratch plot) was plotted by analyzing the correlation matrix, combined with a total variance explanation table. Generally, a cumulative variance percentage greater than 80% means that the selected principal components can explain more than 80% of the variation in the original dataset. Eigenvalues ​​are a measure of the degree to which principal components explain the variation in the original data. Principal components with eigenvalues ​​greater than 1 are generally considered important. This application extracted principal components based on the principle that eigenvalues ​​> 1. The results show that when four principal components were extracted, all eigenvalues ​​were > 1. Figure 3 The results indicate that at least four feature factors can be extracted. The cumulative variance explained by factor 4 can reach 85.653%, indicating that the four factors can express more than 85% of the variables. The component with the higher coefficient among these four factors can be selected for further analysis.

[0042] Table 4 Explanation of Total Variance Analysis showed that Principal Component 1 contributed 32.827% of the variance (Table 4), with representative indicators being GSM, C22:1n9, SER, MIB, Na, and Fe. This indicates that the variation trends of these indicator parameters in the dataset are highly correlated, and this trend is the most significant among all core indicators. Principal Component 2 contributed 26.701% of the variance, with representative indicators being Pro, Glu, and IMP. Principal Component 3 contributed 13.768% of the variance, with representative indicators being Crudelipid and CrudeProtein. Principal Component 4 contributed 12.357% of the variance, with representative indicator being EPA. In PCA, component coefficients represent the strength and direction of the linear relationship between each original variable and each principal component. By comparing component coefficients, this application can understand the relationship between each principal component and the original variables. In summary, the PCA analysis successfully simplified 13 key indicators into 4 principal components (Table 5), each principal component representing a different variation pattern in the original dataset. This helps this application to better understand the relationship between these indicators and how they collectively affect the overall quality of the dark-spotted pufferfish.

[0043] Table 5 Component Matrix The coefficient corresponding to each indicator parameter is the ratio of the principal component coefficient of each indicator in the component matrix to the square root of the corresponding eigenvalue. The score expressions of the four principal components are constructed as follows: F 1= -0.433 x 1-0.418 x 2+0.413 x 3+0.412 x 4+0.340 x 5+0.332 x 6+0.015 x 7+0.130 x 8-0.094 x 9+0.008 x 10 +0.045 x 11 -0.115 x 12 -0.172 x 13 ; F2=-0.201 x 1-0.124 x 2+0.111 x 3-0.266x 4+0.101 x 5-0.259 x 6+0.502 x 7+0.479 x 8+0.430 x 9-0.237 x 10 -0.006 x 11 +0.091 x 12 +0.231 x 13 ; F3=0.099 x 1+0.033 x 2-0.181 x 3+0.013 x 4+0.312 x 5+0.115 x 6+0.058 x 7+0.059 x 8+0.144 x 9+0.583 x 10 +0.560 x 11 -0.170 x 12 +0.366 x 13 ; F4=-0.140 x 1-0.289 x 2+0.241 x 3+0.031 x 4+-0.210 x 5-0.236 x 6-0.185 x 7-0.209 x 8-0.152 x 9+0.044 x 10 +0.264 x 11 +0.644 x 12 +0.389 x 13 ; Considering that each principal component may have a different variance contribution, the ratio of the variance contribution of each principal component to the cumulative variance contribution is used as a weight to establish a comprehensive evaluation method. Therefore, the comprehensive quality evaluation method for cultured pufferfish is established as follows: F = 0.383F1+0.312F2+0.161F3+0.144F4.

[0044] 4) Based on the established comprehensive evaluation method function, 89 cultured pufferfish samples were scored and sorted from high to low as shown in Table 6 below.

[0045] Table 6. Overall Scores of Cultured Pufferfish (Tetraodon nigra) Samples 5) Determine the grade threshold based on the comprehensive score and establish a grading system for farmed pufferfish products. Based on the loglogistic distribution function curve, and using the 25th and 75th percentiles as cutoff values, these comprehensive quality scores were divided into three quality levels (excellent, good, and poor). These accounted for 24.7%, 49.4%, and 25.8% of the total sample, respectively (see Table 7).

[0046] Table 7 Grading System for Cultured Pufferfish with Dark Stripes The evaluation method provided by this invention can be applied to the quality evaluation of cultured pufferfish, enabling the production of high-quality cultured pufferfish.

[0047] The principle of this invention is as follows: The method and application for evaluating the quality of farmed pufferfish (Takifugu obscurus) includes systematically collecting farmed pufferfish samples from representative farming areas and markets across the country to ensure the breadth and representativeness of the sample coverage, laying the foundation for the universality of subsequent evaluation results; then, for samples from different sources, precise determination of physicochemical indicators and sensory evaluation is carried out, and a standardized scoring table is used to achieve quantitative assessment and comprehensively capture the differences in sample quality; subsequently, through hierarchical cluster analysis, using correlation as the distance type and group average as the clustering method, key indicators are screened, and after factor analysis, principal component analysis is used to construct a comprehensive quality evaluation function to achieve quantitative scoring of sample quality; finally, all sample scores are sorted from high to low, grade thresholds are determined, and a three-level grading system is finally established.

[0048] The establishment of this quality evaluation method and grading system fills the gap in the industry where there is a lack of unified quality grading standards for farmed pufferfish. From the consumer perspective, quantifiable quality evaluation indicators and clear grading standards break the limitations of consumers' previous reliance on subjective experience in purchasing, helping them accurately identify high-quality products, significantly improving their eating experience and trust in the products, thereby stimulating the continuous release of consumer demand. From the farming perspective, it provides farmers with precise "quality farming guidance," helping them to optimize their farming plans, escape the predicament of homogeneous low-price competition, achieve market premiums by improving product quality, effectively increase the market benefits of high-quality pufferfish, and ensure their enthusiasm for farming. From the industry perspective, a unified quality evaluation standard and grading system can effectively regulate market order, avoid the vicious cycle of "bad money driving out good," and promote the industry's transformation from "scale expansion" to "quality upgrading." At the same time, it provides key technical support for building a full-chain quality control system from farming to distribution. It can even combine regional ecological advantages to develop high-quality products into local characteristic industries, drive the coordinated development of related industrial chains, optimize the regional industrial structure, and make the farmed pufferfish industry a new growth point for local economic development, playing an irreplaceable role in promoting the healthy and sustainable development of the entire industry.

[0049] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for evaluating the quality of a farmed Takifushi Takifugu obscurus, characterized by, The method comprises the following steps: 1) collecting representative farming areas and market sales of farmed Takifushi; 2) determining the physicochemical indexes and sensory evaluation of the farmed Takifushi samples; 3) selecting index parameters to establish a quality evaluation method for the farmed Takifushi; 4) according to the quality evaluation method of the farmed Takifushi, comprehensively scoring the samples; 5) determining the grade threshold to establish a grading system for the farmed Takifushi.

2. The evaluation method according to claim 1, characterized by: In step 2), the physicochemical index determination comprises data difference analysis of multiple index parameters in the muscle of farmed Takifushi from different producing areas.

3. The evaluation method according to claim 2, characterized by: The data difference analysis is obtained by SPSS through single-factor analysis of variance.

4. The evaluation method according to claim 3, characterized by: The multiple index parameters include crude protein, crude fat, GMP, IMP, ATP, Asp, Glu, Ser, Gly, Thr, Ala, Pro, Lys, Trp, Val, Met, Phe, Ile, Leu, Arg, Tyr, THR, SER, GLY, HIS, ARG, C16:0, C18:1n9c, C21:0, C22:1n9, EPA, DHA, Na, K, Ca, Mg, Fr, Cu, Zn, Mn, MIB and GSM, wherein the index parameters have significant differences P < 0.

05.

5. The evaluation method according to claim 4, characterized by: Before establishing the quality system of high-quality farmed Takifushi, the index parameters are simplified through hierarchical cluster analysis to form a hierarchical chart of similarity.

6. The evaluation method according to claim 5, characterized by: The hierarchical cluster analysis takes correlation as the distance type, group average as the clustering method, distance sum as the distance method, and multiple index parameters of the farmed Takifushi are clustered into 9 categories when the distance is 0.5, which are crude protein, crude fat, DHA, EPA, C22:1n9, Pro, SER, Na and Fe.

7. The evaluation method according to claim 6, characterized by: The 9 groups are subjected to multivariate statistics by factor analysis, including principal component analysis and factor extraction.

8. The evaluation method according to claim 7, characterized by: The coefficient corresponding to each index parameter is the ratio of the principal component coefficient of each index in the component matrix to the square root of the corresponding eigenvalue, and the score expression of the four principal components is constructed as follows: F 1= -0.433 x 1-0.418 x 2+0.413 x 3+0.412 x 4+0.340 x 5+0.332 x 6+0.015 x 7+0.130 x 8-0.094 x 9+0.008 x 10 +0.045 x 11 -0.115 x 12 -0.172 x 13 F2=-0.201 x 1-0.124 x 2+0.111 x 3-0.266 x 4+0.101 x 5-0.259 x 6+0.502 x 7+0.479 x 8+0.430 x 9-0.237 x 10 -0.006 x 11 +0.091 x 12 +0.231 x 13 F3=0.099 x 1+0.033 x 2-0.181 x 3+0.013 x 4+0.312 x 5+0.115 x 6+0.058 x 7+0.059 x 8+0.144 x 9+0.583 x 10 +0.560 x 11 -0.170 x 12 +0.366 x 13 F4=-0.140 x 1-0.289 x 2+0.241 x 3+0.031 x 4+-0.210 x 5-0.236 x 6-0.185 x 7-0.209 x 8-0.152 x 9+0.044 x 10 +0.264 x 11 +0.644 x 12 +0.389 x 13 ； The comprehensive quality evaluation function of the quality of the farmed Takifushi is established as follows: F = 0.383F1+0.312F2+0.161F3+0.144 F4.

9. The evaluation method according to claim 8, characterized by: According to the evaluation function of the farmed Takifushi, all samples are comprehensively scored, and all scores are sorted from high to low to determine the grade threshold and establish a grading system for the farmed Takifushi.

10. An application of the evaluation method of claims 1-9 in the quality evaluation of farmed Takifushi.

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