Fish gelatin gel performance evaluation method based on multi-scale correlation analysis

Abstract

The invention discloses a fish gelatin jelly gel performance evaluation method based on multi-scale correlation analysis, which comprises the following steps: extracting multi-scale characteristics of molecular characteristics, network structures and macro performance of three fish gelatin jellies, performing data correlation based on three dimensions of molecular characteristics-network structures-macro performance, and combining a stoichiometric analysis method to evaluate the gel performance of the fish gelatin jellies. And a multi-level systematic evaluation system is constructed. According to the method, data of molecular characteristics (secondary structure ratio, particle size, Zeta potential and amide A wave number), network structure (aperture size) and macroscopic performance (imino acid content and gel strength) are integrated to construct the relevance of molecular characteristics-network structure-macroscopic performance, and full-dimensional analysis of the quality of the fish gelatin jelly is realized for the first time. The method breaks through the limitation that the traditional method only depends on single indexes such as protein or collagen content and the like, and clarifies the regulation and control of molecular aggregation behavior, imino acid content and electrostatic interaction on the formation of a gel network.

Description

Technical Field

[0001] This invention belongs to the field of food processing technology, specifically relating to a method for evaluating the performance of fish gelatin gel based on multi-scale correlation analysis. Background Technology

[0002] Fish maw, also known as dried fish bladder, is a dried fish bladder product rich in collagen and widely used in traditional tonics and food processing. With the increasing consumer demand for natural functional foods, the market demand for fish maw continues to rise. However, the diversity of raw material sources leads to inconsistent product quality. Currently, there is a lack of systematic research on the correlation between raw material characteristics and gelling properties, resulting in a lack of scientific basis for the selection of raw materials for high-gelling-performance products. The quality and gelling properties of mainstream fish maws on the market (such as dried croaker maw, white jade maw, and cod maw) vary significantly due to differences in the species of fish from which they are sourced. However, the evaluation of the gelation performance of such raw materials still faces the following technical bottlenecks: Existing studies mostly focus on single physicochemical indicators such as protein content, texture, and sensory evaluation of fish maw, which are difficult to reflect key quality characteristics such as the gelation properties of fish maw jelly; fish maw quality evaluation mainly relies on traditional morphological identification, judging quality solely based on the appearance characteristics of fish maw, which is highly subjective and lacks objective quantitative standards. It cannot analyze the molecular-level driving factors of differences in gelation performance, such as imino acid content and hydrogen bond networks. This superficial evaluation is disconnected from the actual gelation performance, making it impossible to accurately distinguish different fish maw qualities; Existing detection methods are fragmented, with molecular characteristics and macroscopic properties disconnected, failing to construct a correlation chain from molecular structure to network formation to macroscopic properties. This makes it impossible to predict the actual gelation performance during raw material screening, hindering the precise development of high-gelation-performance products. The shortcomings of existing evaluation methods have led to the phenomenon of inferior fish maw being sold as high-quality products in the market and have also hindered the development of high-gelation-performance products. Developing fish maw jelly products with excellent gelation performance can improve the taste, stability, and nutritional value of fish maw jelly products, meeting consumers' health needs.

[0003] Therefore, a systematic multi-scale analysis method is urgently needed to reveal the key influencing factors of fish gelatin gel performance and to provide technical support for the quality control and development of fish gelatin. Summary of the Invention

[0004] [Technical Issues]

[0005] Traditional analytical methods for fish glue are limited to a single dimension and cannot systematically link molecular characteristics, network structure and macroscopic properties. This results in incomplete evaluation methods for fish glue, a lack of scientific basis for raw material screening, difficulty in standardizing product development processes, and ultimately affects the stability of product quality.

[0006] [Technical Solution]

[0007] To address the aforementioned issues, the purpose of this invention is to provide a method for evaluating the gel performance of fish gelatin based on multi-scale correlation analysis. This method correlates data across three dimensions: molecular characteristics, network structure, and macroscopic performance. By extracting multi-scale features of the molecular characteristics, network structure, and macroscopic performance of three types of fish gelatin and combining them with chemometric analysis, a multi-level systematic evaluation system is constructed. This reveals the key influencing factors on the gel performance of fish gelatin, providing a scientific basis for the screening of raw materials for high-gel-performance fish gelatin products, and ultimately providing technical support for the targeted development of high-gel-performance fish gelatin products.

[0008] This invention first provides a method for evaluating the performance of fish gelatin gel based on multi-scale correlation analysis, including the following steps:

[0009] (1) Sample collection: Select three or more different varieties of dried fish glue and rehydrate them;

[0010] (2) Nutritional composition determination: test the moisture, protein, fat, ash, collagen and total sugar content of the rehydrated fish maw obtained in step (1);

[0011] (3) Preparation of fish gelatin jelly: Mix the rehydrated fish gelatin from step (1) with water, heat it, filter out the fish gelatin pieces, and refrigerate the liquid to obtain fish gelatin jelly.

[0012] (4) Performance characterization: The fish gelatin obtained in step (3) was characterized in terms of macroscopic properties, molecular characteristics and network structure;

[0013] (5) Establishment of multi-scale correlation analysis method: Principal component analysis was performed on the macroscopic properties, molecular characteristics and network structure of different varieties of fish gelatin using statistical analysis software to construct the correlation between particle size, Zeta potential, α-helix, random coil, β-sheet, β-turn, imino acid, amide A wavenumber, gel strength and pore size index.

[0014] (6) Quality evaluation: Calculate the comprehensive score of the three types of fish jelly to quantify the quality of different fish jelly.

[0015] The macroscopic properties described in steps (4) and (5) include imino acid content and gel strength, the molecular properties include secondary structure ratio, particle size, Zeta potential and amide A wavenumber, and the network structure includes pore size.

[0016] In one embodiment of the present invention, the fish glue in step (1) includes at least three of the following: Zorro glue, Golden Dragon glue, Icelandic cod glue, red fish glue, white jade glue, and croaker glue, preferably cod glue, white jade glue, and croaker glue.

[0017] In one embodiment of the present invention, the specific steps of the rehydration treatment in step (1) are as follows: fish glue and deionized water are soaked at 3-5°C for 54-56 hours at a mass ratio of 1:19 to 1:21.

[0018] In one embodiment of the present invention, the specific steps for making fish gelatin jelly in step (3) are as follows: the rehydrated fish gelatin and water are heated at 80-100°C for 1.5-2.5 hours at a mass ratio of 1:7 to 1:9. Then, the fish gelatin blocks and juice are filtered out with 80-mesh gauze, cooled to room temperature, and the juice is refrigerated in a refrigerator at 3-5°C for 11-13 hours.

[0019] In one embodiment of the present invention, the secondary structure described in step (5) includes α-helix, random coil, β-fold, and β-turn.

[0020] In one embodiment of the present invention, the principal component analysis is performed using XLSTAT 2020 software to analyze the macroscopic properties, molecular characteristics, and network structure of at least three types of fish gelatin. The top k principal components with eigenvalues ​​>1 and cumulative variance contribution rates greater than 85% are extracted, namely principal component 1, principal component 2, ..., principal component k. The weight value corresponding to the kth principal component is calculated based on the rotated component matrix, and the score of the kth principal component is calculated according to the following formula:

[0021] F k =a k X1+b k X2+c k X3+d k X4+e k X5+f k X6+g k X7+h k X8+i k X9+j k X 10

[0022] Among them, X1~X 10 These are, respectively, particle size, Zeta potential, α-helix, random coil, β-sheet, β-turn, imino acid content, amide A wavenumber, gel strength, and pore size. k -j k is the weight value corresponding to the k-th principal component. k is the index of the principal component (k = 1, 2, 3, ...).

[0023] The weights corresponding to the eigenvalues ​​of the principal components are calculated based on their eigenvalues, and the resulting index correlation model is as follows:

[0024]

[0025] Among them, Fk Let x be the evaluation score of the k-th principal component. k The weights corresponding to the eigenvalues ​​of the principal components. F n The overall score is calculated based on the total score.

[0026] In one embodiment of the present invention, k is set to 2, that is, the principal components used for model construction include principal component 1 and principal component 2. Among them, the particle size, Zeta potential, β-sheet, β-turn, imino acid content and gel strength have the largest weight in principal component 1, and principal component 2 is composed of α-helix, random coil, amide A wavenumber and pore size.

[0027] In one embodiment of the present invention, PCA two-dimensional diagrams of three test samples are established using the principal component 1 and the principal component 2. The croaker gelatin is distributed in the fourth quadrant, the white jade gelatin is distributed in the first quadrant, and the cod gelatin is distributed in the third quadrant.

[0028] This invention also discloses the application of the above-mentioned evaluation method in evaluating the performance of fish gelatin gel.

[0029] Beneficial effects:

[0030] (1) This invention integrates data on molecular characteristics (proportion of secondary structures, particle size, Zeta potential, and amide A wavenumber), network structure (pore size), and macroscopic properties (imino acid content, gel strength) to construct a correlation between "molecular characteristics-network structure-macroscopic properties," achieving for the first time a comprehensive analysis of fish gelatin quality. This method overcomes the limitations of traditional methods that rely solely on single indicators such as protein or collagen content, elucidating the regulation of gel network formation by molecular aggregation behavior, imino acid content, and electrostatic interactions.

[0031] (2) This invention prepares fish gelatin by selecting three types of fish gelatin with very obvious differences in gel properties, and performs principal component correlation analysis based on the corresponding test data. By constructing a comprehensive scoring model (croaker gelatin 1.95 > white jade gelatin 0.66 > cod gelatin -2.61), the quality of fish gelatin is quantitatively evaluated. This solves the evaluation contradiction that cannot be explained by traditional methods, such as cod gelatin having high protein content but no gel properties, and white jade gelatin having high collagen content but moderate gel properties.

[0032] (3) The combination of characterization data selected in this invention can effectively predict and evaluate the gelation properties of different fish gels, breaking through the limitations of traditional evaluation. It proves that multi-scale correlation analysis can more comprehensively and accurately reflect the true quality of fish gels, providing a scientific basis and technical support for the raw material screening, process optimization and quality control of high gelation performance fish gel products. Attached Figure Description

[0033] Figure 1The appearance of the three types of fish gelatin obtained in Example 1 is shown.

[0034] Figure 2 The gel strength and imino acid content of the fish gelatin obtained in Example 1 are shown.

[0035] Figure 3 The microstructure and pore size of the fish gelatin obtained in Example 1 are shown.

[0036] Figure 4 The particle size and zeta potential of the fish gelatin obtained in Example 1 are shown.

[0037] Figure 5 The relative contents of FT-IR and secondary structures of the fish gelatin obtained in Example 1 are shown.

[0038] Figure 6 This is a correlation heatmap of the fish gelatin obtained in Example 1.

[0039] Figure 7 This is a principal component analysis diagram of the fish gelatin obtained in Example 1.

[0040] Figure 8 This is a principal component analysis diagram of the fish gelatin obtained in Comparative Example 1.

[0041] Figure 9 This is a principal component analysis diagram of the fish gelatin obtained in Comparative Example 2.

[0042] Figure 10 The results show the molecular weight of the fish gelatin tested in Comparative Example 3, where (a) is an SDS-PAGE image and (b) is the relative content of α1 chain, α2 chain and β chain.

[0043] Figure 11 This is a principal component analysis diagram of the fish gelatin in Comparative Example 3. Detailed Implementation

[0044] The following embodiments, in conjunction with the accompanying drawings and tables, further illustrate the present invention.

[0045] Test methods

[0046] Nutritional composition analysis of fish maw:

[0047] Moisture content was determined by direct drying method according to "National Food Safety Standard - Determination of Moisture in Food: GB 5009.3—2016"; ash content was determined by high-temperature ignition method according to "National Food Safety Standard - Determination of Ash in Food: GB 5009.4—2016"; protein content was determined by Kjeldahl nitrogen determination method according to "National Food Safety Standard - Determination of Protein in Food: GB 5009.5—2016"; fat content was determined by Soxhlet extraction method according to "National Food Safety Standard - Determination of Fat in Food: GB 5009.6—2016"; and total sugar content was determined by spectrophotometry according to "National Food Safety Standard - Determination of Total Sugar Content in Meat Products: GB / T 9695.31—2008".

[0048] Collagen content determination: Weigh approximately 0.2g of sample into a glass tube, and cut the tissue into small pieces for digestion. Add 2mL of 6M hydrochloric acid to the hydrolysis tube and incubate at 110℃ for 6 hours until no large clumps are visible. After cooling, adjust the pH to 7 with NaOH solution, and then bring the volume to 100mL with distilled water. Perform the determination according to the instructions of the hydroxyproline reagent kit (Beijing Solarbio Science & Technology Co., Ltd.), and then multiply by a factor of 11.1 to obtain the collagen content.

[0049] The imino acid content of fish gelatin:

[0050] Accurately weigh 5 mg of lyophilized powder (lyophilized fish gelatin), add it to a hydrolysis tube, add 3 mL of 6M hydrochloric acid, seal with an alcohol burner, and place in an oven at 110℃ for 22 h for hydrolysis. After hydrolysis, allow to cool to room temperature and dilute to a 25 mL volumetric flask. Pipette 1 mL of the diluted sample, dry with liquid nitrogen, add 1 mL of 0.02M hydrochloric acid to fully dissolve, filter through a 0.22 μm filter membrane, and determine the proline content using a Biocom amino acid analyzer. Weigh approximately 0.02 g of lyophilized powder into a glass tube, add 2 mL of 6M hydrochloric acid to a hydrolysis tube, and digest in an oven at 110℃ for 6 h until no large visible clumps remain. After cooling, adjust the pH to 7 with NaOH solution, and then dilute to 25 mL with distilled water. Determine the hydroxyproline content according to the instructions of the hydroxyproline kit (Beijing Solarbio Science & Technology Co., Ltd.). The imino acid content is the sum of the hydroxyproline and proline contents.

[0051] Determination of gel strength of fish gelatin:

[0052] The sample was placed in a small beaker with a diameter of 30 mm and a height of 20 mm, and stored at 4 °C for 12 h before the gel strength was measured. Texture determination was performed using a physical property analyzer with a P 0.5 cylindrical probe. The initial probe speed was 1.5 mm / s, and the speeds during and after the probe were 1 mm / s. The maximum force applied when the probe penetrated 4 mm into the sample was taken as the gel strength.

[0053] Microstructure determination of fish gelatin:

[0054] The freeze-dried samples were fixed on a dedicated SEM sample kit and subjected to surface gold plating under vacuum conditions. After the gold plating process was completed, the samples were observed under a scanning electron microscope at a working voltage of 5kV and a magnification of 100x. The aperture size was calculated using ImageJ software.

[0055] Particle size determination of fish gelatin:

[0056] The sample solution was filtered through a 0.22 μm filter membrane using a 5 mL syringe, and the particle size distribution characteristics of the sample at 25 °C were measured using a Malvern nanoparticle size analyzer.

[0057] Zeta potential determination of fish gelatin:

[0058] The zeta potential of the sample was measured at room temperature using a zeta potential analyzer. The sample solution was injected into the sample cell using a 5 mL syringe for detection. Before the measurement, the pH of the sample was adjusted to 7.0 using 1 M HCl or NaOH.

[0059] Fourier transform infrared spectroscopy determination of fish gelatin:

[0060] Take 1 mg of sample and grind it together with potassium bromide at a ratio of 1:100. Compress the mixture into tablets using a tablet press at 4000-4000 cm⁻¹. -1 Interval scanning, with a resolution of 4cm -1 The scanning frequency was 16 times. Omnic software was used to scan 1700-1600 cm⁻¹. -1 The spectrum within the range was fitted to analyze the relative content of secondary structures.

[0061] Establishment of a multi-scale correlation analysis method for fish gelatin:

[0062] The macroscopic properties (imino acid content, gel strength), molecular characteristics (secondary structure ratio, particle size, zeta potential and amide A wavenumber), and network structure (pore size) data of fish gel were imported into Origin 2025 software, and a correlation heatmap was constructed using the Pearson correlation method.

[0063] Quality evaluation of fish gelatin: Principal component analysis (PCA) was performed on the above multi-scale data using XLSTAT 2020 software to extract key influencing factors. The comprehensive score of the fish gelatin was calculated to quantify its quality.

[0064] Fish gelatin molecular weight test

[0065] The sample was mixed with 5× loading buffer at a 4:1 volume ratio to prepare a protein solution with a concentration of 2 mg / mL. 10 μL was loaded into each well. Electrophoresis was performed using a BeyoGel SDS-PAGE precast gel (Tris-Gly, 4-20%, 12 wells), initially at 80 V for 20 min, then adjusted to 120 V for 60 min until the bromophenol blue band migrated to the bottom of the gel. After removing the precast gel, it was stained with Coomassie Brilliant Blue for 2 h, then destained repeatedly with destaining solution until the bands were clear. Finally, the electrophoretic pattern was acquired using a gel imaging system. The gray values ​​of the gel electrophoresis bands were analyzed using ImageJ software to calculate the relative content (%).

[0066] Example 1

[0067] A method for evaluating the performance of fish gelatin gel based on multi-scale correlation analysis includes the following steps:

[0068] S1: The dried croaker glue, dried white jade glue, and dried cod glue were subjected to standardized rehydration treatment. Specifically, the dried fish glue and water were soaked at 4℃ for 55 hours in a mass ratio of 1:20, and then the water was absorbed by filter paper.

[0069] S2: Determine the nutritional components of the fish maw after rehydration in step S1.

[0070] S3: Next, cut the fish maw into 2cm×2cm pieces. Take the soaked fish maw and water and heat them at 90℃ for 2 hours in a mass ratio of 1:8. Then filter out the fish maw pieces and juice through 80-mesh gauze. Cool to room temperature and refrigerate the juice in a 4℃ refrigerator for 12 hours to form fish maw jelly.

[0071] S4: Characterize the macroscopic properties (imino acid content, gel strength), network structure (pore size), and molecular characteristics (secondary structure ratio, particle size, Zeta potential, and amide A wavenumber) of fish gelatin.

[0072] S5: Correlation analysis and principal component analysis were performed using statistical analysis software to integrate macroscopic performance data (imino acid content, gel strength), network structure (pore size), and molecular characteristics (secondary structure ratio, particle size, zeta potential, and amide A wavenumber) of fish gelatin, and to construct the correlation between the indicators.

[0073] S6: Quality evaluation of fish jelly. The quality of fish jelly is quantified by calculating the comprehensive score of the fish jelly.

[0074] Table 1 shows the basic nutritional components of fish maw. Compared with the cod and croaker maw groups, the white jade maw group had the highest moisture content. There were no significant differences in protein and total sugar content among the three groups of fish maw. The main nutritional component of fish maw is protein, reaching over 90%. In the dry matter, collagen accounts for approximately 51.90%, 64.91%, and 39.37% of the protein content in croaker maw, white jade maw, and cod maw, respectively. Based on the protein content (croaker maw 94.62g / 100g, white jade maw 91.84g / 100g, cod maw 95.13g / 100g) and collagen content (croaker maw 49.13g / 100g, white jade maw 59.59g / 100g, cod maw 37.45g / 100g) of fish maw, the following data is presented:

[0075] Table 1. Composition and nutritional components of three types of fish maw

[0076]

[0077] Figure 1 The appearance morphology of three types of fish gelatin. From Figure 1 It can be seen that the fish gelatin prepared from croaker gelatin and white jade gelatin both maintained a complete solidified state and showed no fluidity. However, the sample prepared from cod gelatin remained liquid and fluid after cooling, without any solidification. During high-temperature heating, collagen gradually denatured, its triple helix structure was destroyed and released into gelatin molecules. The concentration of these molecules increased with heating time, and then, through hydrophobic interactions, hydrogen bonds, and electrostatic interactions, the molecular chains aggregated to form a three-dimensional network structure, ultimately achieving gelation.

[0078] Figure 2 The results show the gel strength and imino acid content of three types of fish gelatin. From... Figure 2 It was found that the gel strength of croaker gelatin was significantly higher than that of white jade gelatin, mainly due to the high imino acid content in croaker gelatin. Gel formation and the stability of the triple helix structure are highly dependent on the number of imino acids (hydroxyproline and proline). The hydrogen bonds formed between the pyrrolidine ring of imino acids and the amino acid residues are crucial for gel stability. Croaker gelatin had the highest imino acid content (20.59 g / 100 g), significantly higher than white jade gelatin (19.07 g / 100 g) and cod gelatin (15.82 g / 100 g), further supporting its superior gel performance. This finding breaks through the traditional understanding of evaluating fish glue quality solely based on protein or collagen content.

[0079] Figure 3 Microstructure and pore size diagrams of three types of fish gelatin. Figure 3 It can be seen that all samples exhibit a honeycomb-like network structure, while the croaker gelatin shows a more uniform microstructure with an ordered network arrangement. This is mainly due to the hydrogen bonds and cross-linking between gelatin molecules, which is related to its higher gel strength. Figure 2 The results are consistent with those of cod gelatin. Compared to croaker gelatin, the pore size of the gel network in white jade gelatin is larger and the pore distribution is uneven. This may be due to the decrease in triple helix content after heating, leading to an increase in the disordered aggregation of gelatin chains. In contrast, the gel network structure in cod gelatin is the most porous, with irregular honeycomb-like pores, which helps to form a more hydrophilic protein surface structure, thereby improving its dispersibility in aqueous solution. The average network pore size of croaker gelatin (55.72±4.77μm) is significantly smaller than that of cod gelatin (136.89±10.26μm). The smaller pore size indicates a denser and more uniform gel network, which helps to improve gel strength.

[0080] Figure 4 Particle size distribution and zeta potential diagrams for three types of fish gelatin. Figure 4 It was found that the average particle size of the croaker gelatin was the largest (104.75±1.59 nm), significantly higher than that of the white jade gelatin (85.22±2.00 nm) and cod gelatin (57.05±0.71 nm). The particle size of the cod gelatin was 45.50% smaller than that of the croaker gelatin, indicating a lower degree of intermolecular aggregation. The increased particle size in the croaker gelatin system indicated the formation of larger molecular aggregates. Further Zeta potential analysis revealed that the charge of the croaker gelatin was significantly lower than that of the cod gelatin, and the weakened electrostatic repulsion may have promoted hydrogen bond-dominated aggregation behavior, thus forming aggregates with larger particle sizes.

[0081] Figure 5 Fourier transform infrared spectra of three types of fish gelatin, by Figure 5 It can be seen that the amide A band wavenumber of the croaker gelatin is the lowest (3403 cm⁻¹). -1 ), while cod jelly had the highest (3411cm). -1 Since the wavenumber of amide A is negatively correlated with hydrogen bond strength, an increase in wavenumber indicates a decrease in the density of the hydrogen bond network. The high imino acid content in cod gelatin may stabilize the molecular conformation and suppress wavenumber shifts through hydrogen bond formation. When the imino acid content increased from 15.82 g / 100 g (cod gelatin) to 20.59 g / 100 g (cod gelatin), the wavenumber of amide A correspondingly increased from 3411 cm⁻¹. -1 Reduced to 3403cm -1 The offset range reached 8cm -1 This confirmed that the formation of hydrogen bond networks caused a regular shift in the characteristic peak of amide A. Furthermore, the intensity of the amide III band was closely related to the integrity of the collagen triple helix structure. The amide III band intensity was highest in cod jelly, further confirming that its triple helix structure was more intact. Secondary structure fitting analysis revealed that the proportion of random coils in cod jelly was 13.94%, significantly higher than that in cod jelly (10.55%) and white jade jelly (11.69%).

[0082] Therefore, cod gelatin, with its high protein content, has the highest random coil content (13.94%) and loose network structure, resulting in the worst actual gel performance. Conversely, white jade gelatin, with its high collagen content, has a higher proportion of random coils than croaker gelatin, lower molecular order, insufficient imino acid content (19.07 g / 100 g), and lower hydrogen bond network density than croaker gelatin, resulting in moderate gel performance. Furthermore, the high hydroxyproline content (20.59 g / 100 g) in croaker gelatin promotes the shift of the characteristic peak of amide A to a higher frequency (3403 cm⁻¹). -1 This enhances the density of the intermolecular hydrogen bond network, thereby driving the densification of the gel network and improving the gel strength (41.05g), providing a molecular-level scientific basis for raw material screening.

[0083] Figure 6 Correlation heatmaps for three types of fish gelatin. (Source: [Insert source here]) Figure 6 It was found that particle size was significantly positively correlated with Zeta potential (r = 0.99), gel strength was significantly positively correlated with particle size (r = 1), Zeta potential (r = 0.98), and imino acid content (r = 0.98). Amide A wavenumber was significantly negatively correlated with gel strength (r = -0.96), and random coiling was significantly negatively correlated with gel strength (r = -0.85), suggesting that disordered structures weaken network stability.

[0084] Data Modeling and Quality Evaluation

[0085] Multiscale data (particle size, zeta potential, α-helix, random coil, β-sheet, β-turn, imino acid content, amide A wavenumber, gel strength, and pore size) of three types of fish gelatin were standardized. The standardized data were then imported into XLSTAT 2020 software for principal component analysis (PCA) of the three fish gelatin samples. XLSTAT outputs the eigenvalues ​​and variance contribution rates of the principal components, as well as the rotated component matrix.

[0086] The correlation model of indicators constructed based on the principal component analysis results is as follows:

[0087]

[0088] Among them, F k =a k X1+b k X2+c k X3+d k X4+e k X5+f k X6+g k X7+h k X8+i k X9+j k X 10 X1~X 10These are, respectively, particle size, Zeta potential, α-helix, random coil, β-sheet, β-turn, imino acid content, amide A wavenumber, gel strength, and pore size. k -j k F represents the weight value corresponding to the k-th principal component. k is the index of the principal component (k = 1, 2, 3, ...). k Let x be the evaluation score of the k-th principal component. k The weights corresponding to the eigenvalues ​​of the principal components. F n The overall score is calculated based on the total score.

[0089] The value of k is determined based on the sum of the cumulative variance contribution rates of the principal components. When the sum of the cumulative variance contribution rates of the k principal components is greater than 85%, the first k principal components are selected for subsequent analysis.

[0090] Figure 7 Principal component analysis diagrams for three types of fish gelatin. Figure 7 It can be seen that principal component 1 and principal component 2 contribute 71.81% and 24.17% of the variance respectively, with a cumulative contribution rate greater than 85%, indicating that these two principal components can effectively explain the main variation information in the data. Figure 7 It was found that the three types of fish gelatin exhibited differences in molecular characteristics (α-helix, random coil, β-sheet, β-turn, particle size, Zeta potential, and amide A wavenumber), network structure (pore size), and macroscopic properties (imino acid content, gel strength). The clustering effect among each type of fish gelatin was good, and no outliers were observed. Figure 7 It can be seen that the croaker gelatin is distributed in the fourth quadrant, and it has a high imino acid content (20.59g / 100g) and a dense hydrogen bond network (amide A wavenumber 3403cm). -1 The white jade jelly, with its superior gel strength (41.05 g), significantly distinguished itself from the other two types. The white jade jelly was distributed in the second quadrant, and its imine content (19.07 g / 100 g) differed from that of the croaker jelly. The cod jelly was concentrated in the third quadrant, and its low imine content (15.82 g / 100 g) and high proportion of irregular curls (13.94%) significantly differentiated it from the other two types of samples.

[0091] Table 2. Eigenvalues ​​and variance contribution rates of principal components.

[0092]

[0093] Note: Eigenvalues ​​represent the variance explained by each principal component. Cumulative variance contribution rate represents the proportion of variance explained by the first few principal components.

[0094] From Table 2, two principal components with eigenvalues ​​> 1 can be extracted, namely principal component 1 and principal component 2. According to Table 3, in principal component 1, particle size, Zeta potential, β-sheet, β-turn, imino acid content, and gel strength have the largest weights. Principal component 2 is composed of α-helices, random coils, amide A wavenumber, and pore size. Combined with... Figure 6 The correlation analysis results showed that gel strength was significantly positively correlated with particle size (r=1), imino acid content (r=0.98), and zeta potential (r=0.98). This indicates that molecular aggregation behavior (particle size), imino acid content, and electrostatic interaction (zeta potential) are the key driving factors for gel network formation, laying the foundation for establishing a complete predictive model for fish gel performance.

[0095] Based on the data in Table 2, the weights corresponding to the principal component eigenvalues ​​are calculated using the following formula (1), resulting in weights of 0.748 and 0.252 for the two eigenvalues. According to the correlation model formula, the scores corresponding to each principal component are summed to obtain the comprehensive scores for the three different types of fish jelly.

[0096] Weight = Eigenvalue / Sum of Eigenvalues ​​(1)

[0097] Based on the data in Table 3, the scoring formulas for principal component 1 and principal component 2 are as follows:

[0098] F1=0.372X1+0.366X2-0.001X3-0.332X4+0.034X5+0.282X6+0.366X7-0.360X8+0.371X9-0.367X 10

[0099] F2=0.028X1+0.097X2+0.642X3-0.096X4-0.630X5-0.397X6+0.096X7-0.061X8-0.002X9-0.001X 10

[0100] The formula for calculating the overall score is: F (Overall Score) = 0.748F1 + 0.252F2

[0101] Table 3. Rotated component matrix

[0102]

[0103] Note: The corresponding data are the loadings (weights) of the respective variables on each principal component.

[0104] The scores for the three types of fish gelatin are shown in Table 4. Table 4 shows that the scores for the three types of fish gelatin are: croaker gelatin > white jade gelatin > cod gelatin, indicating that croaker gelatin has a higher overall quality. This is consistent with the results obtained from the previous analysis of fish gelatin performance. This demonstrates that this method can quantify the differences in fish gelatin quality and guide the selection of raw materials for products with high gelling performance.

[0105] Table 4. Overall scores of the three types of fish gelatin

[0106]

[0107] However, if we rely solely on the nutritional data in Table 1 to judge quality, it can be seen that cod gelatin has the highest protein content, while white jade gelatin has the highest collagen content. This method of judgment cannot explain why white jade gelatin has weaker gelling properties than croaker gelatin, and why cod gelatin cannot form a gel.

[0108] Comparative Example 1

[0109] The difference between Comparative Example 1 and Example 1 is that, in step S5, when performing principal component analysis, the data selected are molecular characteristics (α-helix, random coil, β-sheet, β-turn, particle size, Zeta potential, and amide A wavenumber).

[0110] The constructed indicator correlation model is as follows:

[0111]

[0112] Among them, F k =a k X1+b k X2+c k X3+d k X4+e k X5+f k X6+g k X7, X1 to X7 represent particle size, Zeta potential, α-helix, random coil, β-sheet, β-turn, and amide A wavenumber, respectively. k -j k F represents the weight value corresponding to the k-th principal component. k is the index of the principal component (k = 1, 2, 3, ...). k Let x be the evaluation score of the k-th principal component. k The weights corresponding to the eigenvalues ​​of the principal components. F n The overall score is calculated based on the total score.

[0113] The value of k is determined based on the sum of the cumulative variance contribution rates of the principal components. When the sum of the cumulative variance contribution rates of the k principal components is greater than 85%, the first k principal components are selected for subsequent analysis.

[0114] Figure 8This is a principal component analysis diagram of three types of fish maw. Figure 8 It can be seen that principal component 1 and principal component 2 contribute 61.16% and 34.06% of the variance respectively, with a cumulative contribution rate of more than 85%, indicating that these two principal components can effectively explain the main variation information in the data.

[0115] Table 5. Eigenvalues ​​and Cumulative Variance Contribution Rate of Principal Components

[0116]

[0117] Based on the data in Table 5, the weights of the two eigenvalues ​​are calculated to be 0.642 and 0.358, respectively. The scores are then summed according to the different weights of each principal component in Table 6 to obtain the comprehensive score model function:

[0118] F1=-0.477X1-0.465X+0.038X+0.431X4-0.083X5-0.383X6+0.465X7

[0119] F2=0.076X1+0.145X2+0.645X3-0.142X4-0.627X5-0.363X6-0.109X7

[0120] F (Overall Score) = 0.642F1 + 0.358F2

[0121] Table 6. Rotated composition matrix

[0122]

[0123] Finally, the comprehensive score was calculated based on the principal component data of the fish gelatin, as shown in Table 7. The scores of the three types of fish gelatin were: cod gelatin > white jade gelatin > croaker gelatin, indicating that cod gelatin had a higher overall quality. This result differs from the actual gelling performance, indicating that the evaluation results obtained by reducing the indicators are inaccurate.

[0124] Table 73 Comprehensive Scores of Fish Gelatin

[0125]

[0126] Comparative Example 2

[0127] The difference between Comparative Example 2 and Example 1 is that, in step S5, when performing principal component analysis, the data selected are molecular characteristics (α-helix, random coil, β-sheet, β-turn, particle size, Zeta potential), macroscopic properties (imino acid content), and network structure (pore size) data.

[0128] Figure 9 This is a principal component analysis diagram of three types of fish maw. Figure 9It can be seen that principal component 1 and principal component 2 contribute 65.89% and 30.07% of the variance respectively, with a cumulative contribution rate of more than 85%, indicating that these two principal components can effectively explain the main variation information in the data.

[0129] Based on the data in Table 8, the weights of the two eigenvalues ​​are calculated to be 0.687 and 0.313, respectively. According to the different weights of each principal component in Table 9, the scores are summed to obtain the comprehensive score model function:

[0130] F1=-0.433X1-0.425X+0.014X+0.387X4-0.051X5-0.339X6-0.424X7+0.430X8

[0131] F2=0.047X1+0.116X2+0.644X3-0.113X4-0.630X5-0.382X6+0.115X7-0.020X8

[0132] F (Overall Score) = 0.687F1 + 0.313F2

[0133] Table 8. Eigenvalues ​​and variance contribution rates of principal components.

[0134]

[0135] Table 9. Rotated Component Matrix

[0136]

[0137]

[0138] Finally, the comprehensive score was calculated based on the principal component data of the fish gelatin, as shown in Table 10. The scores of the three types of fish gelatin were: cod gelatin > white jade gelatin > croaker gelatin, indicating that the cod gelatin had a higher overall quality. This result differs from the actual gelling properties, indicating that the evaluation results obtained by reducing the indicators are inaccurate.

[0139] Table 103 Comprehensive Scores of Fish Gelatin

[0140]

[0141] Evaluation method and verification effect of the replacement index in Comparative Example 3

[0142] The difference between Comparative Example 3 and Example 1 is that, in step S5, when performing principal component analysis, the data selected are molecular characteristics (α-helix, random coil, β-sheet, β-turn, α1 chain content, α2 chain content and β chain content), macroscopic properties (imino acid content, gel strength) and network structure (pore size) data.

[0143] The molecular weight distribution and corresponding chain segment content of fish gelatin are as follows: Figure 10 As shown, from Figure 10 As shown in (a), the SDS-PAGE spectra of the three types of fish gelatin mainly show three bands, corresponding to the α1 chain, α2 chain, and β chain of the fish gelatin, respectively. To better describe the changes in these components, Figure 10 (b) Presents the quantitative analysis results of the characteristic protein bands. Higher α-chain content contributes to the formation of an ordered and stable gel structure, resulting in higher gel strength. Croaker gelatin exhibits a relatively high α-chain content and a relatively low β-chain content, while cod gelatin has the lowest relative α-chain content.

[0144] Figure 11 Principal component analysis plots for the three types of fish gelatin are shown in Table 11. As can be seen from Table 11, principal components 1, 2, and 3 contribute 59.62%, 23.83%, and 11.44% of the variance, respectively, with a cumulative contribution rate greater than 85%, indicating that these three principal components can effectively explain the main variation information in the data.

[0145] Based on the data in Table 11, the weights of the three eigenvalues ​​are calculated to be 0.628, 0.251, and 0.121, respectively. According to the different weights of each principal component in Table 12, the scores are summed to obtain the comprehensive score model function:

[0146] F1=0.051X1+0.355X2-0.087X3-0.334X4-0.382X5-0.402X6+0.403X7-0.350X8-0.132X9+0.387X 10

[0147] F2=0.637X1-0.161X2-0.618X3-0.339X4+0.168X5+0.076X6-0.079X7-0.067X8+0.155X9-0.050X 10

[0148] F3=-0.119X1+0.137X2+0.098X3-0.007X4+-0.081X5-0.007X6+0.013X7-0.389X8+0.855X9-0.260X 10

[0149] F (Overall Score) = 0.628F1 + 0.251F2 + 0.121F3

[0150] Table 11 Eigenvalues ​​and variance contribution rates of principal components

[0151]

[0152] Table 12 Rotated composition matrix

[0153]

[0154] Finally, the comprehensive score was calculated based on the principal component data of the fish gelatin, as shown in Table 13. The scores of the three types of fish gelatin were: cod gelatin > white jade gelatin > croaker gelatin, indicating that the overall quality of cod gelatin was higher. This result differs from the actual gel performance, indicating that the evaluation results obtained by replacing particle size, Zeta potential, and amide A wavenumber with molecular weight are inaccurate.

[0155] Table 133 Comprehensive Scores of Fish Gelatin

[0156]

[0157] The embodiments provided above are not intended to limit the scope of the invention, nor are the described steps intended to limit the order of execution. Any obvious modifications made to the invention by those skilled in the art based on existing common knowledge also fall within the scope of protection defined by the claims.

Claims

1. A method for evaluating the performance of fish gelatin gel based on multi-scale correlation analysis, characterized in that, Includes the following steps: (1) Sample collection: Select three or more different varieties of dried fish glue and rehydrate them; (2) Nutritional composition determination: test the moisture, protein, fat, ash, collagen and total sugar content of the rehydrated fish maw obtained in step (1); (3) Preparation of fish gelatin jelly: Mix the rehydrated fish gelatin from step (1) with water, heat it, filter out the fish gelatin pieces, and refrigerate the liquid to obtain fish gelatin jelly. (4) Performance characterization: The fish gelatin obtained in step (3) was characterized in terms of macroscopic properties, molecular characteristics and network structure; (5) Establishment of multi-scale correlation analysis method: Principal component analysis was performed on the macroscopic properties, molecular characteristics and network structure of different varieties of fish gelatin using statistical analysis software to construct the correlation between particle size, Zeta potential, α-helix, random coil, β-sheet, β-turn, imino acid, amide A wavenumber, gel strength and pore size index. (6) Quality evaluation: Calculate the comprehensive score of the three types of fish jelly to quantify the quality of different fish jelly. The macroscopic properties described in steps (4) and (5) include imino acid content and gel strength, the molecular properties include secondary structure ratio, particle size, Zeta potential and amide A wavenumber, and the network structure includes pore size.

2. The evaluation method according to claim 1, characterized in that, The fish maw mentioned in step (1) includes at least three of the following: Zorro maw, Golden Dragon maw, Icelandic cod maw, red fish maw, white jade maw, and croaker maw.

3. The evaluation method according to claim 1, characterized in that, The specific steps of the rehydration treatment in step (1) are as follows: Soak fish glue and deionized water at a mass ratio of 1:19 to 1:21 at 3 to 5°C for 54 to 56 hours.

4. The evaluation method according to claim 1, characterized in that, In step (3), the mass ratio of fish glue to water is 1:7 to 1:9, the heating temperature is 80 to 100℃, the heating time is 1.5 to 2.5 hours, and the fish glue blocks and juice are filtered out using 80-mesh gauze. The juice is refrigerated in a refrigerator at 3 to 5℃ for 11 to 13 hours.

5. The evaluation method according to claim 1, characterized in that, The secondary structures described in step (5) include α-helix, random coil, β-fold, and β-turn.

6. The evaluation method according to claim 1, characterized in that, The principal component analysis was performed using XLSTAT 2020 software to analyze the macroscopic properties, molecular characteristics, and network structure of at least three types of fish gelatin. The top k principal components with eigenvalues ​​>1 and cumulative variance contribution rates greater than 85% were extracted, namely principal component 1, principal component 2, ..., principal component k. The weight value corresponding to the kth principal component was calculated based on the rotated component matrix, and the score of the kth principal component was calculated according to formula (1). F k =a k X1+b k X2+c k X3+d k X4+e k X5+f k X6+g k X7+h k X8+i k X9+j k X 10 (1) Then, the weights corresponding to the principal component eigenvalues ​​are calculated based on the principal component eigenvalues. The index correlation model constructed by combining the principal component scores is as follows: Among them, X1~X 10 These are, respectively, particle size, Zeta potential, α-helix, random coil, β-sheet, β-turn, imino acid content, amide A wavenumber, gel strength, and pore size; a k -j k Here, F represents the weight value corresponding to the k-th principal component; k is the index of the principal component (k = 1, 2, 3, ...); k Let x be the evaluation score of the k-th principal component. k The weights corresponding to the eigenvalues ​​of the principal components; F n The final score is calculated based on the overall score.

7. The evaluation method according to claim 6, characterized in that, Principal component analysis was performed using croaker gelatin, white jade gelatin, and cod gelatin. The principal components used in model construction included principal component 1 and principal component 2. In principal component 1, particle size, zeta potential, β-sheet, β-turn, imino acid content, and gel strength had the largest weights. In principal component 2, α-helix, random coil, amide A wavenumber, and pore size had the largest weights.

8. The evaluation method according to claim 7, characterized in that, In the two-dimensional PCA diagram constructed using principal component 1 and principal component 2, croaker jelly is distributed in the fourth quadrant, white jade jelly is distributed in the first quadrant, and cod jelly is distributed in the third quadrant.

9. The application of the evaluation method according to any one of claims 1 to 8 in evaluating the performance of fish gelatin gel.

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