Novel squid quality degree grading judgment method based on physicochemical indexes of freshness

Abstract

The invention discloses a novel squid quality degree grading judgment method based on physical and chemical indexes of freshness, which comprises the following steps: selecting different squids as samples, respectively carrying out volatile basic nitrogen content detection and total texture analysis on the squids, measuring the freshness and the physical and chemical indexes of the squids, carrying out Pearson correlation on the two, and determining the quality degree of the squids. Through principal component analysis and combination of TVB-N content on a freshness limit value of an animal aquatic product, texture physicochemical indexes having the greatest influence on squid quality grading are found. According to the method, the quality of the squids can be quickly judged by performing texture analysis on the squids, combining with the limit value of the content of volatile basic nitrogen in animal aquatic products in national standard and measuring the elasticity index of the sample, so that quick quality grading judgment can be performed on the squids on the basis, and scientific guidance is provided for further quick quality identification of marine foods.

Description

Technical Field

[0001] This invention relates to the field of marine food testing technology, specifically to the volatile basic nitrogen and texture indicators of squid, and to the correlation analysis and regression equation establishment of each indicator. In particular, it relates to a novel method for grading the quality of squid based on physicochemical indicators of freshness. Background Technology

[0002] Squid, a type of cuttlefish, is rich in nutrients and is one of the world's underutilized marine fishery resources with significant potential. As a nutritious and delicious seafood, the market demand for squid is expected to continue to grow. However, due to the high content of endogenous enzymes and water in squid products, various quality issues and freshness requirements arise during processing, transportation, and storage. Currently, the two commonly used physicochemical indicators for determining squid freshness are volatile basic nitrogen and histamine.

[0003] Volatile basic nitrogen (TVB-N) refers to the alkaline nitrogenous substances such as ammonia and amines produced during the spoilage of animal-based foods due to the decomposition of proteins by enzymes and bacteria. These substances are volatile; a higher TVB-N content indicates greater destruction of amino acids, particularly methionine and tyrosine, thus significantly impacting nutritional value. It is a key indicator of the freshness of raw fish and meat. TVB-N is also an important indicator of the freshness of aquatic animal products. Spoilage of aquatic products is caused by the growth and reproduction of bacteria and the action of enzymes, leading to the decomposition of proteins and the production of ammonia and amines. A higher TVB-N value indicates lower freshness of the aquatic product and may pose certain health risks. However, current methods are relatively cumbersome and time-consuming. Furthermore, during sample distillation, the alkali causes the sample solution in the reaction chamber to boil violently and foam, and the foam containing magnesium oxide particles often contaminates the top of the reaction chamber, making it difficult to clean.

[0004] Total Texture Analysis (TPA) simulates the chewing motion of human teeth by compressing solid and semi-solid samples twice to obtain textural parameters such as hardness, elasticity, cohesion, chewiness, and resilience. Compared to traditional sensory evaluation methods, it avoids subjective errors introduced by evaluators and has the advantages of objectivity, scientific rigor, speed, and convenience. TPA is suitable for rapid detection of large numbers of samples, requiring no sample pretreatment or chemical solvents, resulting in no hygiene pollution and saving time.

[0005] Linear regression is a statistical analysis method used to establish a linear relationship between independent variables (input features) and dependent variables (target variables). Its basic form is: In this equation, Y is the dependent variable, X is the independent variable, a is the regression coefficient, and b is the error term. Linear regression is widely used in prediction and modeling, aiming to find a best-fitting straight line to describe the relationship between the independent and dependent variables. Summary of the Invention

[0006] This invention addresses the current lack of a rapid method for evaluating and determining squid quality by providing a novel squid quality grading method based on freshness physicochemical indicators. This method utilizes objective and rapid physicochemical indicator measurement to evaluate squid quality, thereby avoiding the inconvenience and instability of other physicochemical indicators and subjective sensory evaluations.

[0007] This invention provides a method for evaluating and grading the quality of squid based on physicochemical indicators of freshness, comprising the following steps:

[0008] Step (1): Select squid from different manufacturers as samples, and determine the volatile basic nitrogen content of all squid samples using an automatic Kjeldahl nitrogen analyzer;

[0009] Step (2): Simultaneously perform a full texture analysis on the squid from step (1);

[0010] Step (3): Perform principal component analysis on the results obtained in steps (1) and (2) and verify the relationship between volatile basic nitrogen and texture through loading diagram;

[0011] Step (4): Perform Pearson correlation analysis on the results obtained in steps (1) and (2), establish a correlation heatmap, screen the texture physicochemical index with the highest positive correlation with volatile basic nitrogen, and use the data quality judgment method of squid under this index to evaluate the quality of pre-prepared squid.

[0012] Step (5) Develop a linear regression equation for volatile basic nitrogen and elasticity index based on the results obtained in steps (3) and (4), establish the relationship between elasticity and freshness, and quickly determine the freshness quality of squid through elasticity values ​​and regression equation.

[0013] The identification method of this invention uses instrumental analysis to quickly and accurately determine the quality of pre-prepared squid food products. The determination results are accurate and stable, and the work efficiency is high.

[0014] Preferably, in step (1), the selected squid samples are no less than 20, for example, 24 different types of squid processed in different ways. The large sample size ensures that the obtained data is comprehensive and representative.

[0015] In the specific implementation, in step (1), the method for detecting the volatile basic nitrogen content of squid samples is based on GB5009.228-2016 National Food Safety Standard, where the determination of volatile basic nitrogen in food is performed using an automatic Kjeldahl nitrogen analyzer.

[0016] More specifically, the method for determining the volatile basic nitrogen content is as follows: For each measurement, 10g of sample is cut and added to 100mL of distilled water. After homogenization using a homogenizer, 10mL of the supernatant is accurately measured and placed in a digestion tube. Simultaneously, 0.1g of light magnesium oxide is added to assist distillation. The titration is then completed, and the measured value is recorded. The titrating acid used in the experiment is 0.01mol / L hydrochloric acid. Two blank controls are prepared to eliminate the influence of TVB-N content in the solvent on the experimental results.

[0017] The formula for calculating volatile basic nitrogen is:

[0018]

[0019] In the above formula: X is the TVB-N content in the sample, mg / 100g; V1 is the volume of standard titration acid consumed in the sample determination, mL; V2 is the volume of hydrochloric acid consumed in the blank, mL; c is the actual concentration of the hydrochloric acid standard solution, mol / L; m is the mass of the sample measured, g.

[0020] Specifically, in step (2), the parameters for the full texture analysis include: hardness, elasticity, cohesiveness, chewiness, and adhesiveness.

[0021] More specifically, the specific method for performing full texture analysis in step (2) is as follows: the squid sample is stored in a -20℃ freezer, and then thawed at 4℃. The thawed squid sample is then cut into 2×1×1cm pieces. 3 The samples were cube-shaped. The hardness, cohesiveness, elasticity, adhesiveness and chewiness of the samples were determined by TPA analysis. Each sample was measured in parallel 10 times. After screening out data with large differences, the average value was taken. The texture measurement probe was a TMS 38.1mm, the force sensor was 250N, the detection speed was 60mm / min, the deformation percentage was 50%, and the interval was 1s.

[0022] More specifically, after collecting the data obtained in steps (1) and (2), the following two steps are performed, including:

[0023] First, all raw data were standardized. Second, principal component analysis was performed on the standardized data. Third, Pearson correlation heatmap analysis was performed on the standardized data.

[0024] Indicators representing squid quality were selected through Pearson correlation heatmap analysis.

[0025] In a specific implementation, principal component analysis is performed on the results obtained in steps (1) and (2) to determine volatile basic nitrogen (TVB-N), hardness, springiness, cohesiveness, chewiness, and guminess as the original variables. The relationships and differences between the variables are determined based on the proportion of the principal components in the total variance and the correlation between the original variables.

[0026] Preferably, based on the establishment method in step (4), the index with the highest positive correlation with volatile basic nitrogen is selected as elasticity. A linear regression equation between volatile basic nitrogen and the elasticity index is constructed based on the results obtained in steps (3) and (4), with elasticity as the independent variable X and volatile basic nitrogen as the dependent variable Y. The formula for the linear regression equation is:

[0027] ,

[0028] In the above formula: Y is the dependent variable TVB-N content, mg / 100g; X is the independent variable elasticity, mm; a is the regression coefficient; b is the error term; and n is the number of independent variables. This equation quantifies the predictive power of elasticity on TVB-N using statistical methods. If the regression coefficient a is significant (e.g., p < 0.05) and the coefficient of determination (R²) is high, it indicates that elasticity can effectively explain the variation in TVB-N. Therefore, the linear regression equation in this study can effectively explain the correlation between TVB-N and elasticity, because elasticity is the most representative indicator selected through statistical methods. Although clustering algorithms were not used directly, Pearson correlation analysis was used to select elasticity, the texture indicator with the highest positive correlation to TVB-N. This process is also based on data distribution characteristics (linear correlation) to select the key variable that is most explanatory to freshness grading. Both methods extract core variables from multi-dimensional indicators using data-driven methods, and are logically consistent.

[0029] Preferably, the establishment of a squid quality evaluation and grading system is used to determine the quality evaluation and grading of squid. Squid are graded according to the volatile basic nitrogen content and the limits for fresh and frozen aquatic animal products in the GB2733-2015 National Food Safety Standard. The grading is carried out by combining the range of correlation and elasticity indicators and using elasticity indicator data and total volatile component content.

[0030] Establishment of Squid Quality Evaluation and Grading: Squid are graded based on their volatile basic nitrogen content and the limits set in GB2733-2015 National Food Safety Standard for Fresh and Frozen Aquatic Animal Products. The grading is further refined using correlation and elasticity index data, as follows:

[0031] The grading results of volatile basic nitrogen content (mg / 100g) are based on the degree of data aggregation, referring to GB2733-2015 National Food Safety Standard, Limits for Fresh and Frozen Aquatic Animal Products.

[0032] More specifically, elasticity showed the strongest positive correlation with sensory evaluation results and volatile basic nitrogen content, thus separating squid when using elasticity for grading. Elasticity (mm) grading results were based on data clustering, combined with volatile basic nitrogen content, classifying squid into two grades. Corresponding values ​​were assigned to each prepared dish, categorized as excellent and good according to standards.

[0033] Among them, elasticity ≥1.5 is excellent, and elasticity <1.5 is good;

[0034] An overall preference score of ≥4 is considered excellent, while a preference score <4 is considered good.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] Currently, the quality evaluation of aquatic products in China is mostly based on sensory evaluation. For animal-based aquatic products, the freshness is mainly determined by volatile basic nitrogen content. This method lacks objective and effective data support and is time-consuming and labor-intensive. Furthermore, the quality of sensory evaluation is affected by many objective and subjective factors, making the evaluation unstable. This invention combines textural analysis of squid with national standards for the volatile basic nitrogen content limits of animal-based aquatic products, linking these three factors together. By measuring the elasticity of the sample, the quality of squid can be quickly determined using a single index. Based on this, rapid quality grading of squid can be performed, providing scientific guidance for further rapid quality identification of marine foods. Attached Figure Description

[0037] Figure 1 This describes the process of establishing a physicochemical index determination model for squid quality assessment according to the present invention.

[0038] Figure 2 The principal component analysis results are based on the standardized values ​​of volatile basic nitrogen in Table 1 and data in Table 2, which are the basis for this invention.

[0039] Figure 3 The results are Pearson correlations established by this invention based on the standardized values ​​of volatile basic nitrogen in Table 1 and data in Table 2.

[0040] Figure 4 This invention establishes a linear regression equation model based on the volatile basic nitrogen data in Table 1 and the data in Table 2. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the embodiments described below are intended to facilitate the understanding of the present invention and do not limit it in any way.

[0042] See appendix Figure 1-4 .

[0043] Example 1

[0044] A novel method for grading the quality of squid based on physicochemical indicators of freshness includes the following steps:

[0045] 1. Determination of volatile basic nitrogen content in squid

[0046] (1) Selection of squid samples

[0047] We selected 24 types of squid from different manufacturers; all of these samples came from Rongcheng, Shandong.

[0048] (2) The volatile basic nitrogen of the squid samples was determined using an automatic Kjeldahl nitrogen analyzer.

[0049] The method was followed according to GB 5009.228—2016, "National Food Safety Standard - Determination of Volatile Basic Nitrogen in Food". Specifically, the method for determining the volatile basic nitrogen content was as follows: For each measurement, 10g of sample was cut and added to 100mL of distilled water. After homogenization using a homogenizer, 10mL of the supernatant was accurately measured and placed in a digestion tube. Simultaneously, 0.1g of light magnesium oxide was added to assist distillation. The titration was then completed, and the measured value was recorded. The titrating acid used in the experiment was 0.01mol / L hydrochloric acid. Two blank controls were prepared to eliminate the influence of TVB-N content in the solvent on the experimental results.

[0050] The formula for calculating volatile basic nitrogen is:

[0051]

[0052] In the above formula: X is the TVB-N content in the sample, mg / 100g; V1 is the volume of standard titration acid consumed in the sample determination, mL; V2 is the volume of hydrochloric acid consumed in the blank, mL; c is the actual concentration of the hydrochloric acid standard solution, mol / L; m is the mass of the sample measured, g.

[0053] 2. Texture analysis of squid

[0054] The FTC TMS-Touch texture analyzer was used. Total texture analysis (TPA) is a common method for analyzing the texture of food, characterized by subjecting the sample to two deformations and measuring and analyzing the force changes during the process. The squid samples were stored in a -20°C freezer, then thawed at 4°C. The thawed squid samples were then cut into 2×1×1cm pieces. 3The samples were cube-shaped. The hardness, cohesiveness, elasticity, adhesiveness and chewiness of the samples were determined by TPA analysis. Each sample was measured in parallel 10 times. After screening out data with large differences, the average value was taken. The texture measurement probe was a TMS 38.1mm, the force sensor was 250N, the detection speed was 60mm / min, the deformation percentage was 50%, and the interval time was 1s.

[0055] 3. Establishment of a correlation model for physicochemical indicators of squid

[0056] Based on the measured volatile basic nitrogen content and texture data of squid samples, a correlation model was established using statistical methods. For example... Figure 1 As shown, the establishment of the correlation model for the physicochemical indicators of squid includes collecting raw data on volatile basic nitrogen content, texture indicators, and volatile compound content; data standardization preprocessing; principal component analysis; Pearson correlation analysis; determination of representative physicochemical indicators; and establishment of a linear regression equation. The specific steps are detailed below:

[0057] The raw data collected in step (1) is the input data for the correlation model of physicochemical indicators of pre-prepared squid food, including volatile basic nitrogen, total volatile components (as shown in Table 1), and texture indicators (as shown in Table 2).

[0058] Step (2) is a data preprocessing step performed after the raw data collection step, and it standardizes all the raw data.

[0059] The principal component analysis in step (3) is performed using all data after standardization. Figure 2 As shown), Y1-Y8 represent eight different squid species, and the 24 squid samples belong to these eight categories, representing eight different species. Principal component analysis was performed on the results obtained in steps (1) and (2) to identify volatile basic nitrogen (TVB-N), hardness, springiness, cohesiveness, chewiness, and guminess as the original variables. The relationships and differences between the variables were determined based on the proportion of the principal components in the total variance and the correlation between the original variables.

[0060] The Pearson correlation analysis in step (4) was performed using all standardized data. Figure 3 As shown in the figure, the volatile substance index and texture index that are most positively correlated with volatile basic nitrogen were selected by correlation values ​​and used for express delivery identification of squid quality.

[0061] Step (5) Based on the method established in step (4), the index with the highest positive correlation with volatile basic nitrogen is selected as elasticity. A linear regression equation between volatile basic nitrogen and elasticity index is constructed based on the results obtained in steps (3) and (4). Figure 4 As shown), with elasticity as the independent variable X and volatile basic nitrogen as the dependent variable Y, the linear regression equation is as follows:

[0062] ,

[0063] In the above formula: Y is the dependent variable TVB-N content, mg / 100g; X is the independent variable elasticity, mm; a is the regression coefficient; b is the error term; and n is the number of independent variables. This equation quantifies the predictive power of elasticity on TVB-N using statistical methods. If the regression coefficient aa is significant (e.g., p < 0.05) and the coefficient of determination (R²) is high, it indicates that elasticity can effectively explain the variation in TVB-N.

[0064] pass Figure 3 The correlation heatmap revealed that tVB-N had the highest correlation with elasticity. Based on this, a linear regression equation for elasticity was constructed, indicating that a higher X value corresponds to a higher Y value, meaning greater elasticity and a higher TVB-N value, indicating better freshness. Therefore, the linear regression equation in this study effectively explains the correlation between TVB-N and elasticity, as elasticity was selected as the most representative indicator through statistical methods. Although clustering algorithms were not directly used, Pearson correlation analysis was used to identify elasticity as the texture indicator with the highest positive correlation to TVB-N. This process also relied on data distribution characteristics (linear correlation) to select the key variable most explanatory to freshness grading. Both methods extract core variables from multi-dimensional indicators using data-driven approaches, demonstrating logical consistency.

[0065] Table 1. Content of volatile basic nitrogen components in 24 kinds of squid food products

[0066]

[0067] Table 2. Texture information of 24 different squid species

[0068]

[0069] The embodiments described above provide a detailed explanation of the technical solutions and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A novel method for grading the quality of squid based on physicochemical indicators of freshness, characterized in that, Includes the following steps: Step (1): Select squid from different manufacturers as samples, and determine the volatile basic nitrogen content of all squid samples using an automatic Kjeldahl nitrogen analyzer; Step (2): Simultaneously perform a full texture analysis on the squid from step (1); Step (3): Perform principal component analysis on the results obtained in steps (1) and (2) and verify the relationship between volatile basic nitrogen and texture through loading diagram; Step (4): Perform Pearson correlation analysis on the results obtained in steps (1) and (2), establish a correlation heatmap, screen the texture physicochemical index with the highest positive correlation with volatile basic nitrogen, and use the data quality judgment method of squid under this index to evaluate the quality of pre-prepared squid. Step (5): Construct a linear regression equation between volatile basic nitrogen and elasticity index based on the results obtained in steps (3) and (4), establish the relationship between elasticity and freshness, and quickly determine the freshness quality of squid through elasticity values ​​and regression equation. The identification method uses instrumental analysis to quickly and accurately determine the quality of pre-prepared squid products. The results are accurate and stable, and the work efficiency is high.

2. The grading determination method according to claim 1, characterized in that, In step (1), the number of squid samples selected is no less than 20. Squid in different processing states have a large sample size, and the obtained data are comprehensive and representative.

3. The grading determination method according to claim 1, characterized in that, In step (1), the method for detecting the volatile basic nitrogen content of squid samples is based on GB 5009.228-2016 National Food Safety Standard. The determination of volatile basic nitrogen in food is performed using an automatic Kjeldahl nitrogen analyzer. The specific method for determining the volatile basic nitrogen content is as follows: For each measurement, 10g of sample is cut and 100mL of distilled water is added. After homogenization using a homogenizer, 10mL of the supernatant is accurately measured and placed in a digestion tube. Simultaneously, 0.1g of light magnesium oxide is added to assist distillation. The machine is then allowed to complete the titration operation, and the measured value is recorded. The titrating acid during the experiment is 0.01mol / L hydrochloric acid. Two blank control groups are prepared to eliminate the influence of TVB-N content in the solvent on the experimental results. The formula for calculating volatile basic nitrogen is as follows: In the above formula: X is the TVB-N content in the sample, mg / 100g; V1 is the volume of standard titration acid consumed by the sample, mL; V2 is the volume of hydrochloric acid consumed by the blank, mL; c is the actual concentration of the hydrochloric acid standard solution, mol / L; m is the sample mass of the measured sample, g.

4. The grading determination method according to claim 1, characterized in that, In step (2), the parameters for the full texture analysis include: hardness, elasticity, cohesiveness, chewiness, and adhesiveness; The specific method for performing full texture analysis in step (2) is as follows: Store the squid sample in a -20℃ freezer, thaw it at 4℃ after taking it out, and cut the thawed squid sample into 2×1×1cm pieces. 3 The samples were cube-shaped. The hardness, cohesiveness, elasticity, adhesiveness and chewiness of the samples were determined by TPA analysis. Each sample was measured in parallel 10 times. After screening out data with large differences, the average value was taken. The texture measurement probe was a TMS 38.1mm, the force sensor was 250N, the detection speed was 60mm / min, the deformation percentage was 50%, and the interval time was 1s.

5. The grading determination method according to claim 1, after collecting the data obtained in steps (1) and (2), performs the following three subsequent steps, including: First, all raw data is standardized. Secondly, principal component analysis was performed on the standardized data; thirdly, Pearson correlation heatmap analysis was performed on the standardized data. Indicators representing squid quality were selected through Pearson correlation heatmap analysis.

6. According to the grading method of claim 1, in step (4), the index with the highest positive correlation with volatile basic nitrogen is selected as elasticity. A linear regression equation between volatile basic nitrogen and elasticity is constructed based on the results obtained in steps (3) and (4), with elasticity as the independent variable X and volatile basic nitrogen as the dependent variable Y. The formula for the linear regression equation is: ， In the above formula: Y is the dependent variable TVB-N content, mg / 100g; X is the independent variable elasticity, mm; a is the regression coefficient; b is the error term; and n is the number of independent variables.

7. The grading method according to claim 6, wherein the establishment of squid quality evaluation grading is used for determining squid quality evaluation grading, and squid is graded according to the volatile basic nitrogen content and the limits of fresh and frozen aquatic animal products in GB2733-2015 National Food Safety Standard, and the grading is carried out by combining the correlation and elasticity index range.

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