Freshwater fish processing suitability evaluation index and evaluation method

Abstract

The invention belongs to the field of food processing, and particularly relates to a freshwater fish processing suitability evaluation index and an evaluation method. After back meat, belly meat, belly meat and tail meat of the freshwater fish are respectively steamed and fried, values of the following indexes are obtained: basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content and volatile odor substance content. According to the method, a variable coefficient method, a principal component analysis method and a clustering analysis method are combined to determine freshwater fish processing suitability key evaluation indexes, an evaluation model is established, freshwater fish steaming and frying evaluation scores are calculated, and steaming and frying suitability is analyzed and evaluated in combination with K-means clustering. According to the invention, scientific and objective freshwater fish processing suitability evaluation indexes and evaluation methods are established, so that freshwater fish processing suitability evaluation has controllability, quantification and visibility, and application of freshwater fish processing suitability evaluation in the freshwater fish processing industry is promoted.

Description

Technical Field

[0001] The invention belongs to the field of food processing, and in particular relates to a freshwater fish processing suitability evaluation index and an evaluation method. Background Art

[0002] As an important source of protein in the dietary structure of Chinese residents, freshwater fish plays an increasingly important role in people's lives. The "four major carps" are the most widely consumed freshwater fish. Among them, silver carp is second only to grass carp in production among major freshwater fish. It is usually sold fresh and has the advantages of tender meat, delicious taste, rich nutrition and low cost. However, due to its own shortcomings such as many bones, strong earthy smell and easy corruption and deterioration after slaughter, the development of the silver carp industry is seriously restricted.

[0003] Due to the lack of deep processing technology for freshwater fish in my country, the economic value of the silver carp industry has not been fully developed and utilized. Therefore, it is of great significance to actively carry out the fine segmentation and processing of freshwater fish, especially low-value freshwater fish. Studies have shown that the nutritional components, physical and chemical properties, and structural characteristics of fine segmentation products of silver carp vary greatly. Affected by processing methods, temperature, time, etc., the nutritional components, digestive characteristics, and flavor quality of fine segmentation products of silver carp have certain differences, which will eventually affect the edible quality of fish meat. Therefore, choosing different appropriate processing methods for fine segmentation products of silver carp can maximize the flavor of fish meat and increase the added value of high-yield, low-value freshwater fish.

[0004] In view of the above problems, it is necessary to finely divide silver carp, select appropriate processing methods, determine the key quality indicators of finely divided silver carp products under different processing methods, systematically evaluate the processing suitability of finely divided freshwater fish products, promote the development of the silver carp deep processing industry, improve the competitiveness of my country's low-value freshwater fish, develop diversified freshwater fish products, and provide scientific guidance for manufacturers and consumers. Summary of the invention

[0005] The present invention uses silver carp as the research object, scientifically screens out key quality indicators for evaluating the processing methods of finely cut freshwater fish products, and establishes a processing method suitability evaluation model, filling the gap in the research on the suitability of freshwater fish processing in China. The big data statistical method is used to objectively and scientifically quantify the processing quality of freshwater fish, realize the controllability, quantification, and visibility of the suitability evaluation of freshwater fish processing methods, overcome the defects of subjectivity and non-visibility, reduce influencing factors, and eliminate the interference of non-critical factors, so that the evaluation results are more objective and scientific, and the screening of processing methods is easier to achieve.

[0006] The technical scheme of the present invention is a freshwater fish processing suitability evaluation index, comprising a steaming processing evaluation index and a frying processing evaluation index, wherein the steaming processing evaluation index comprises an L* value, a protein content, an inosinic acid IMP content, a (E)-2-nonenal ((E)-2-Nonenal) content, and a lysine Lys content, and the frying processing evaluation index comprises an inosinic acid IMP content, a moisture content, a 2-acetyl-1-pyrroline (2-Acetyl-1-Pyrroline) content, and a proline Pro content.

[0007] The freshwater fish processing suitability evaluation index of the present invention adopts a coefficient of variation method to perform positive and standardized processing on the values ​​of various indexes such as basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content, volatile odor substance content, etc., and determines the coefficient of variation and weight of various indexes by a principal component analysis method; adopts a cluster analysis method to cluster various indexes such as basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content, volatile odor substance content, etc. to obtain similar groups; and selects and retains indexes with large coefficient of variation and high weight in various similar groups as evaluation indexes.

[0008] A method for screening freshwater fish processing suitability evaluation indicators, comprising the steps of:

[0009] (1) Obtaining the values ​​of the following indicators of the back meat, belly meat, belly meat and tail meat of freshwater fish after steaming and frying respectively: basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content, and volatile odor substance content;

[0010] (2) using the coefficient of variation method to perform positive and normalization processing on the values ​​of each index after steaming and frying in step (1), and then determining the principal component items by the principal component analysis method, and calculating the coefficient of variation and weight of each index in the principal component items;

[0011] Cluster analysis was performed on each index after steaming and frying in step (1) to obtain similar groups;

[0012] Among the similar groups obtained by cluster analysis of steaming and frying, the indicators with large coefficient of variation and high weight were selected and retained as key evaluation indicators.

[0013] In step (1), the freshwater fish is silver carp.

[0014] In step (1), the back meat and belly meat are divided according to the spine, the belly meat and flank meat are divided according to the presence or absence of ribs, and the tail meat is divided based on the anus.

[0015] In step (1), the basic nutrient content includes but is not limited to moisture content, crude protein content, crude fat content, ash content, color difference value includes but is not limited to brightness value, red-green value, yellow-blue value and whiteness value calculated according to brightness value, red-green value and yellow-blue value, free amino acid content includes but is not limited to arginine Arg content, threonine Thr content, proline Pro content, lysine Lys content, alanine Ala content, glycine Gly content, histidine His content, cysteine ​​Cys content, flavor nucleotide content includes but is not limited to The content of inosinic acid IMP and inosinic acid HxR, the content of volatile odor substances include but are not limited to (E)-2-nonenal ((E)-2-Nonenal) content, 1-octen-3-one (1-Octen-3-one) content, 2-acetyl-1-pyrroline content, 2-methyl-3-methylthiofuran (2-Methyl-3-(methylthio)furan) content, and hexanal (Hexanal) content.

[0016] In step (2), the values ​​of the various indicators after steaming and frying in step (1) are first preliminarily screened to remove the detection indicators with little change, and then the values ​​of the various indicators after steaming and frying in step (1) are positively and standardized using the coefficient of variation method.

[0017] In step (2), three principal components are determined by principal component analysis.

[0018] In step (2), cluster analysis is performed on the indicators after steaming in step (1) to obtain 5 similarity groups, which are a similarity group consisting of brightness value L* and whiteness value W, a similarity group consisting of inosine nucleotide HxR and 1-octen-3-one, a similarity group consisting of yellow-blue value b* and 2-acetyl-1-pyrroline, a similarity group consisting of crude protein, arginine Arg, glycine Gly, alanine Ala, histidine His, inosinic acid IMP, proline Pro, (E)-2-nonenal and threonine Thr, a similarity group consisting of lysine Lys, cysteine ​​Cys and hexanal, and a similarity group consisting of crude fat, 2-methyl-3-methylthiofuran and protein digestibility.

[0019] In step (2), cluster analysis is performed on the various indicators after frying in step (1) to obtain 6 similarity groups, which are the similarity group composed of brightness value L*, arginine Arg, hexanal (Hexanal), whiteness value W, moisture, threonine Thr, and inosinic acid IMP, the similarity group composed of glycine Gly, (E)-2-nonenal ((E)-2-Nonenal), and 2-acetyl-1-pyrroline (2-Acetyl-1-Pyrroline), the similarity group composed of proline Pro, the similarity group composed of yellow-blue value b*, histidine His, and 1-octen-3-one (1-Octen-3-one), the similarity group composed of inosine nucleotide HxR, and the similarity group composed of crude protein, crude fat, protein digestibility, alanine Ala, lysine Lys, cysteine ​​Cys and 2-methyl-3-methylthiofuran (2-Methyl-3-(methylthio)furan).

[0020] In step (2), the coefficient of variation is not less than 10% and the weight is not less than 0.1.

[0021] In step (2), the index with the largest coefficient of variation and the highest weight is selected and retained as the key evaluation index from each similarity group obtained by cluster analysis of steaming and frying.

[0022] In step (2), the key evaluation indicators of steaming processing include L* value, protein content, inosinic acid IMP content, (E)-2-nonenal ((E)-2-Nonenal) content, and lysine Lys content; the key evaluation indicators of frying processing include inosinic acid IMP content, moisture content, 2-acetyl-1-pyrroline (2-Acetyl-1-Pyrroline) content, and proline Pro content.

[0023] A freshwater fish processing suitability evaluation model includes a steaming evaluation model and a frying evaluation model. L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content and lysine Lys content are selected as key evaluation indicators. The weights of the key evaluation indicators are analyzed to construct a steaming evaluation model to obtain an evaluation score Y. The steaming evaluation model is:

[0024] Y=0.12051X1+0.1880X2+0.2162X3+0.2083×X4+0.1824X5

[0025] X1-X5 represent L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content and lysine Lys content respectively;

[0026] Inosinic acid IMP content, moisture content, 2-acetyl-1-pyrroline content and proline Pro content were selected as key evaluation indicators, and the weights of the key evaluation indicators were analyzed to construct an evaluation model for frying, and the evaluation score Y was obtained. The evaluation model for frying is:

[0027] Y=0.1900X1+0.2720X2+0.3030X3+0.2350X4

[0028] X1-X4 represent the inosinic acid IMP content, water content, 2-acetyl-1-pyrroline content and proline Pro content respectively.

[0029] A method for evaluating the processing suitability of freshwater fish comprises the following steps:

[0030] (1) The L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content, lysine Lys content, water content, 2-acetyl-1-pyrroline content and proline Pro content of back meat, belly meat, belly meat and tail meat of freshwater fish were obtained respectively, and the results were positively converted and standardized, and then substituted into the evaluation model;

[0031] (2) According to the freshwater fish processing suitability evaluation model, the evaluation score Y is obtained:

[0032] The evaluation model for steaming is:

[0033] Y=0.12051X1+0.1880X2+0.2162X3+0.2083×X4+0.1824X5

[0034] X1-X5 represent L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content and lysine Lys content respectively;

[0035] The evaluation model for frying is:

[0036] Y=0.1900X1+0.2720X2+0.3030X3+0.2350X4

[0037] X1-X4 represent the inosinic acid IMP content, water content, 2-acetyl-1-pyrroline content and proline Pro content respectively;

[0038] (3) K-means clustering was performed on the key evaluation indicators of steaming and frying the back meat, belly meat, belly meat and tail meat after positive and normalization processing to obtain the K-means clustering analysis results; the evaluation scores Y of steaming and frying the back meat, belly meat, belly meat and tail meat were sorted in order from large to small, and the sorted evaluation scores Y were classified according to the sameness or similarity of their corresponding K-means clustering analysis results. According to the order of the evaluation scores Y corresponding to the classification, the processing suitability of steaming and frying was evaluated from suitable to unsuitable.

[0039] According to the above evaluation method, the freshwater fish processing suitability evaluation standard established by the present invention is:

[0040] During steaming: Level Ⅰ, suitable: Y ≥ 0.77; Level Ⅱ, relatively suitable: 0.05 < Y < 0.77; Level Ⅲ, unsuitable: Y ≤ 0.05;

[0041] When frying: Level Ⅰ, suitable: Y ≥ 0.57; Level Ⅱ, relatively suitable: 0.04 < Y < 0.57; Level Ⅲ, unsuitable: Y ≤ 0.04.

[0042] A method for constructing a freshwater fish processing suitability evaluation model, comprising the steps of:

[0043] (1) Obtain the key evaluation indicators and their weights for steaming and frying freshwater fish;

[0044] (2) The key evaluation indicators of steaming and frying were weighted normalized respectively, and the evaluation models of steaming and frying were established.

[0045] In step (1), the method for obtaining key evaluation indicators and their weights for steaming and frying freshwater fish comprises:

[0046] a. Obtain the values ​​of the following indicators of the back meat, belly meat, belly meat and tail meat of freshwater fish after steaming and frying: basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content, volatile odor substance content;

[0047] b. The coefficient of variation method was used to normalize and standardize the values ​​of each index after steaming and frying in step a, and then the principal component items were determined by principal component analysis, and the coefficient of variation and weight of each index in the principal component items were calculated;

[0048] Cluster analysis was used to analyze the indicators after steaming and frying in step a to obtain similar groups;

[0049] Among the similar groups obtained by cluster analysis of steaming and frying, the indicators with large coefficient of variation and high weight were selected and retained as key evaluation indicators.

[0050] In step a, the freshwater fish is silver carp.

[0051] In step a, the back meat and belly meat are divided according to the spine, the belly meat and flank meat are divided according to the presence or absence of ribs, and the tail meat is divided based on the anus.

[0052] In step a, the basic nutrient content includes but is not limited to moisture content, crude protein content, crude fat content, ash content, color difference value includes but is not limited to brightness value, red-green value, yellow-blue value and whiteness value calculated according to brightness value, red-green value and yellow-blue value, free amino acid content includes but is not limited to arginine Arg content, threonine Thr content, proline Pro content, lysine Lys content, alanine Ala content, glycine Gly content, histidine His content, cysteine ​​Cys content, flavor nucleotide content includes but is not limited to The inosinic acid IMP content, the hypoxanthine nucleotide HxR content, the volatile odor substance content includes but is not limited to the (E)-2-nonenal ((E)-2-Nonenal) content, the 1-octen-3-one (1-Octen-3-one) content, the 2-acetyl-1-pyrroline (2-Acetyl-1-Pyrroline) content, the 2-methyl-3-methylthiofuran (2-Methyl-3-(methylthio)furan) content, and the hexanal (Hexanal) content.

[0053] In step b, the values ​​of the various indicators after steaming and frying in step (1) are firstly screened to remove the detection indicators with little change, and then the values ​​of the various indicators after steaming and frying in step (1) are positively and standardized using the coefficient of variation method.

[0054] In step b, three principal components are determined by principal component analysis.

[0055] In step b, cluster analysis is performed on the indicators after steaming in step a to obtain 5 similarity groups, which are the similarity group composed of brightness value L* and whiteness value W, the similarity group composed of inosine nucleotide HxR and 1-octen-3-one, the similarity group composed of yellow-blue value b* and 2-acetyl-1-pyrroline, the similarity group composed of crude protein, arginine Arg, glycine Gly, alanine Ala, histidine His, inosinic acid IMP, proline Pro, (E)-2-nonenal and threonine Thr, the similarity group composed of lysine Lys, cysteine ​​Cys and hexanal, and the similarity group composed of crude fat, 2-methyl-3-methylthiofuran and protein digestibility.

[0056] In step b, cluster analysis is performed on the indicators after frying in step a, and 6 similarity groups are obtained, which are the similarity group composed of brightness value L*, arginine Arg, hexanal (Hexanal), whiteness value W, moisture, threonine Thr, and inosinic acid IMP, the similarity group composed of glycine Gly, (E)-2-nonenal ((E)-2-Nonenal), and 2-acetyl-1-pyrroline (2-Acetyl-1-Pyrroline), the similarity group composed of proline Pro, the similarity group composed of yellow-blue value b*, histidine His, and 1-octen-3-one (1-Octen-3-one), the similarity group composed of inosine nucleotide HxR, and the similarity group composed of crude protein, crude fat, protein digestibility, alanine Ala, lysine Lys, cysteine ​​Cys and 2-methyl-3-methylthiofuran (2-Methyl-3-(methylthio)furan).

[0057] In step b, the coefficient of variation is not less than 10% and the weight is not less than 0.1.

[0058] In step b, the indicators with the largest coefficient of variation and the highest weight are selected and retained as key evaluation indicators from the similarity groups obtained by cluster analysis of steaming and frying.

[0059] In step b, the key evaluation indicators of steaming processing include L* value, protein content, inosinic acid IMP content, (E)-2-nonenal ((E)-2-Nonenal) content, and lysine Lys content; the key evaluation indicators of frying processing include inosinic acid IMP content, moisture content, 2-acetyl-1-pyrroline (2-Acetyl-1-Pyrroline) content, and proline Pro content.

[0060] The process of evaluating the processing suitability of freshwater fish using the evaluation model includes: obtaining the values ​​of key evaluation indicators of steaming and frying the back meat, belly meat, belly meat and tail meat of freshwater fish respectively and performing positive and normalization processing, and obtaining the evaluation score Y using the evaluation model; performing K-means clustering on the key evaluation indicators of steaming and frying the back meat, belly meat, belly meat and tail meat respectively to obtain the K-means clustering analysis results; sorting the evaluation scores Y of steaming and frying the back meat, belly meat, belly meat and tail meat respectively in order from large to small, classifying the sorted evaluation scores Y according to the sameness or similarity of the corresponding K-means clustering analysis results, and evaluating the processing suitability of steaming and frying from suitable to unsuitable levels according to the front and back order of the evaluation scores Y corresponding to the classification.

[0061] According to the above-mentioned method for constructing the freshwater fish processing suitability evaluation model and the freshwater fish processing suitability evaluation process, the freshwater fish processing suitability evaluation model and suitability evaluation criteria constructed by the present invention include:

[0062] The evaluation model for steaming is:

[0063] Y=0.12051X1+0.1880X2+0.2162X3+0.2083×X4+0.1824X5

[0064] X1-X5 represent L* value, protein content, inosinic acid IMP content, (E)-2-nonenal ((E)-2-Nonenal) content and lysine Lys content respectively;

[0065] During steaming: Level Ⅰ, suitable: Y ≥ 0.77; Level Ⅱ, relatively suitable: 0.05 < Y < 0.77; Level Ⅲ, unsuitable: Y ≤ 0.05;

[0066] The evaluation model for frying is:

[0067] Y=0.1900X1+0.2720X2+0.3030X3+0.2350X4

[0068] X1-X4 represent the inosinic acid IMP content, water content, 2-acetyl-1-pyrroline content and proline Pro content respectively. When fried: Level I, suitable: Y ≥ 0.57; Level II, more suitable: 0.04 < Y < 0.57; Level III, unsuitable: Y ≤ 0.04.

[0069] The freshwater fish is silver carp.

[0070] The method for constructing a freshwater fish processing suitability evaluation model of the present invention also includes: taking the evaluation scores of the evaluation model established after steaming and frying the back meat, belly meat, belly meat and tail meat respectively as independent variables, taking the sensory evaluation value as the dependent variable, establishing a linear regression model, the determination coefficient is greater than 0.9, the regression model is good, and the accuracy of the evaluation model is verified.

[0071] The linear regression model established by steaming is: Y = 3.205X-1.785 (R 2 =0.956), the linear regression model established by frying is: Y = 3.091X + 6.736 (R 2 =0.919).

[0072] The sensory evaluation value uses tissue state, texture, smell and taste as evaluation indicators, and establishes an evaluation model: overall acceptability = tissue state score × 0.2 + texture score × 0.25 + smell score × 0.25 + taste score × 0.3.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] (1) The silver carp processing suitability evaluation method of the present invention fills the gap in the research of freshwater fish processing suitability evaluation methods. By measuring the key quality parameters of silver carp and substituting them into the quality evaluation model formula, the appropriate cooking method results can be obtained. The method has the advantages of simple operation, intuitive results, easy standardization and strong repeatability.

[0075] (2) The present invention models the processing suitability of flavor indicators such as free amino acids, flavor nucleotides, and volatile odor substances, and verifies them in combination with sensory data. Compared with sensory indicator determination, this method has the advantages of being objective, quantitative, and having fewer influencing factors.

[0076] (3) The silver carp processing suitability evaluation method of the present invention has a wide range of applications in silver carp processing quality grading standards, and provides a scientific basis for the standardization and normalization of freshwater fish processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 Cluster analysis of key quality indicators of steamed silver carp fine cuts.

[0078] Figure 2 To conduct correlation analysis on key quality indicators of steamed silver carp fine cuts.

[0079] Figure 3 Cluster analysis of key quality indicators of fried silver carp fine cuts.

[0080] Figure 4 To conduct correlation analysis on key quality indices of fried silver carp fine cuts. DETAILED DESCRIPTION

[0081] The technical solution of the present invention is described below in conjunction with specific embodiments.

[0082] Example 1: Evaluation of the processing suitability of steamed silver carp fine cuts

[0083] The back and belly meat of silver carp were divided according to the spine, the belly meat and belly meat were divided according to the presence or absence of ribs, and the tail meat was divided according to the anus. The back meat and belly meat were cut into 7cm×3cm×2cm fish pieces, the belly meat was cut into 7cm×2cm×0.8cm fish pieces, and the tail meat was cut into 6cm×2cm×1cm. The fish pieces were marinated in 1.5% salt water at 4℃ for 30min, and the surface moisture was wiped off after marinating. After the water in the steamer boiled, the fish pieces were put into the steamer at 100℃ for 5min, and the surface moisture was sucked off after taking out, and then waited for subsequent measurement.

[0084] Example 2: Evaluation of Processing Suitability of Deep-Fried Silver Carp Finely Cut Products

[0085] The back and belly meat of silver carp were divided according to the spine, the belly meat and belly meat were divided according to the presence or absence of ribs, and the tail meat was divided according to the anus. The back meat and belly meat were cut into 7cm×3cm×2cm fish pieces, the belly meat was cut into 7cm×2cm×0.8cm fish pieces, and the tail meat was cut into 6cm×2cm×1cm. The fish pieces were marinated in 1.5% salt water at 4°C for 30 minutes, and the surface moisture was wiped off after marinating. The silver carp pieces were put into 180°C hot oil and fried for 4 minutes, and naturally drained to remove surface oil drops.

[0086] This experimental example measured the quality and evaluated the processing suitability of the cooked fish meat fine cuts in Example 1 and Example 2, mainly including:

[0087] 1. Determination of color difference of finely divided silver carp

[0088] The colorimeter was used to measure the brightness value (L*), red-green value (a*), and yellow-blue value (b*) of the finely divided silver carp. During the experiment, the colorimeter was placed vertically and closely against the sample surface to ensure that there was no light leakage from the lens port, and the background and light of the sample were consistent for each experiment, and the whiteness value (Whiteness, W) was calculated according to the formula.

[0089]

[0090] 2. Determination of basic nutritional composition of finely divided silver carp

[0091] The method for determining moisture content is in accordance with the direct drying method of GB5009.3-2016 National Food Safety Standard Determination of Water in Food. The method for determining crude protein content is in accordance with the Kjeldahl method of GB5009.5-2016 National Food Safety Standard Determination of Protein in Food. The method for determining crude fat content is in accordance with the Soxhlet extraction method of GB5009.6-2016 National Food Safety Standard Determination of Fat in Food. The method for determining ash content is in accordance with the high temperature burning method of GB5009.4-2016 National Food Safety Standard Determination of Ash in Food.

[0092] 3. Determination of protein digestibility of fine cuts of silver carp

[0093] Accurately weigh 1g of meat into a centrifuge tube, add 6mL of water and homogenize, then adjust the pH to 3.0±0.2, add 6mL of gastric digestion fluid, shake at a constant temperature of 37℃ for 2h, adjust the pH to 7.0±0.2 to terminate the reaction, add 12mL of intestinal digestion fluid, shake at a constant temperature of 37℃ for 2h, add 7.5mL of 10% TCA and let it stand at 4℃ for 12h, then centrifuge (10000g, 4℃, 20min), remove the supernatant, and leave the precipitate. After digesting the precipitate, use an automatic Kjeldahl nitrogen analyzer to determine the protein content.

[0094] 4. GC-IMS determination of volatile odor substances in finely divided silver carp

[0095] 2.0g of meat sample was accurately weighed and placed in a 20mL headspace bottle with a magnetic screw-sealed cap, waiting for detection. All samples were measured in parallel 3 times. The GC-IMS injection conditions were: incubation at 60℃ for 20min; injection method was headspace injection, injection temperature was 60℃; shaking heating, incubation shaking speed was 500r / min; injection volume was 500μL; needle temperature was 85℃. The sample was passed through a MXT-WAX column (30m×0.53mm, 1μm) with carrier gas (N2) at a flow rate of: 0~2min, 2μL / min; 2~10min, 2~10μL / min; 10~20min, 10~100μL / min; 20~30min, 100~150μL / min.

[0096] 5. Determination of free amino acids in finely divided silver carp

[0097] Take 0.5g of meat sample, add 15mL of 5% TCA reagent, homogenize for 30s, repeat 3 times, ultrasonicate for 15min in ice bath, stand in 4℃ refrigerator for 2h, centrifuge (10000g, 4℃, 10min), take 5mL of supernatant in a new centrifuge tube, adjust pH to 2±0.02 and make up to 10mL, pass through 0.22μm aqueous filter membrane and use automatic amino acid analyzer for determination. Amino acid automatic analyzer conditions: chromatographic column (4.6mm×150mm, 7μm); column temperature 50℃; flow rate of 1 channel: 0.4mL / min, flow rate of 2 channels: 0.35mL / min. Mobile phase: mixed buffer of sodium citrate and citric acid (pH value is 3.2, 3.3, 4.0, 4.9) and ninhydrin buffer (4%).

[0098] 6. Determination of flavor nucleotides in finely divided silver carp

[0099] The method for determining the flavor nucleotide in the silver carp processing suitability evaluation method is as follows: taking 3 g of meat sample in a centrifuge tube, adding 10 mL of 10% perchloric acid solution, and homogenizing, ultrasonicating for 5 min under ice bath conditions, and then centrifuging (10000 g, 4° C., 15 min), collecting the supernatant and storing it in a 4° C. refrigerator, adding 5 mL of 5% perchloric acid solution to the precipitate, repeating the above operation twice, combining the supernatants three times, adjusting the pH value to 5.75±0.02 with KOH solution, standing in a 4° C. refrigerator for 30 min, taking the supernatant to make up to 50 mL, shaking it thoroughly, passing it through a 0.22 μm aqueous filter membrane into a sample injection bottle, and determining it with a high performance liquid chromatograph. HPLC parameters: chromatographic column GL-SCIENCE ODS3 C18 (4.6 mm × 250 mm, 5 μm); mobile phase A was pure methanol solution, mobile phase B was a mixed solution of 20 mmol / L dipotassium hydrogen phosphate and 20 mmol / L potassium dihydrogen phosphate (1:1, V / V), and the pH value was adjusted to 5.75 with phosphoric acid; column temperature 25 °C; injection volume 10 μL; detection wavelength 254 nm.

[0100] 7. Determination of sensory evaluation of fine cuts of silver carp

[0101] The sensory evaluation method in the silver carp processing suitability evaluation method is: select an evaluation team composed of 10 trained and selected professional sensory evaluators. Prepare mineral water, sensory trays and other items before the test, inform the sensory evaluators of the precautions, keep quiet during the entire sensory evaluation process, and score the steamed and fried fish tissue state, texture, smell and taste respectively, as shown in Table 1, and calculate the overall acceptability according to the formula, overall acceptability = tissue state × 0.2 + texture × 0.25 + smell × 0.25 + taste × 0.3.

[0102] Table 1 Sensory scoring table for silver carp

[0103]

[0104] 8. Model establishment and verification of silver carp processing suitability

[0105] In order to finally determine the key quality evaluation indicators for the suitability of silver carp processing by steaming and frying, according to the above analysis results, firstly, the indicators with little change were preliminarily screened out according to the above analysis. After preliminary screening, there were 21 quality indicators in steaming and 22 quality indicators in frying, including physical and chemical properties, nutritional quality, digestion quality, odor quality and taste quality, in order to finally determine the key quality indicators for the suitability of silver carp processing. First, the coefficient of variation and principal component analysis were calculated to simplify the data set, and the results are shown in Table 2. According to the results of principal component analysis, the processing quality was summarized into 3 principal components, and the cumulative variance contribution rates of steaming and frying were 95.42% and 92.35%, respectively, indicating that most of the processing quality information can be effectively represented. Based on the results of principal component analysis, the indicator weights of finely segmented silver carp products under each processing method were further calculated, and then cluster analysis was performed. The analysis results are as follows Figure 1-4 shown.

[0106] The 21 indicators of steamed silver carp were clustered and finally classified into 5 categories. In the first category of indicators, the coefficient of variation of L* value and W value were both greater than 7%, but the weight of W value was small, so this indicator was deleted. Therefore, L* value was selected as the first category indicator as the key physical and chemical indicator of steamed silver carp. In the second category of indicators, the weight of 1-Octen-3-one content was small, only -0.0095, and HXR content was selected to represent the second category of indicators. In the third category of indicators, the weights of b* value and 2-Acetyl-1-Pyrroline content were both low, so the third category of indicators were not selected. In the fourth category of indicators, the weights of Thr, Ala, and Gly contents were low, 0.0484, 0.0932, and 0.0768, so they were deleted. Since IMP content was significantly correlated with Arg, Gly, Ala, Pro, and Lys content (P < 0.05), IMP content was selected as a representative, and since IMP and HXR were significantly correlated (P < 0.05), IMP was selected to replace HXR to represent the taste quality of steamed silver carp. Protein content, IMP content, and (E)-2-Nonenal content were comprehensively selected as the fourth category of key indicators, representing nutrition, taste, and odor quality, respectively. Among the fifth category of indicators, the weights of Cys and Hexanal contents were relatively low, at 0.0633 and 0.0267, so they were deleted, so Lys content represented the fifth category of indicators. Among the sixth category of indicators, the weights of fat content and 2-Methyl-3-(methylthio)furan content were relatively low, so they were deleted, and the coefficient of variation of protein digestibility was relatively low, at 4.12%, so it was deleted, so the sixth category of indicators was deleted.

[0107] The 22 indices of fried silver carp were clustered and finally classified into 6 categories. In the frying process: in the first category of indicators, the coefficient of variation of L* value, Arg content, W value, and Thr content was small, which was 7.65%, 8.46%, 8.28%, and 9.66%, so they were deleted. The weight of IMP content was small, which was 0.1298, so it was deleted. The weights of Hexanal and IMP content were similar, and IMP and Hexanal content were significantly correlated (P < 0.05). Therefore, IMP content and moisture content represent the first category of indicators, representing the nutrition and taste quality indicators of fried silver carp. Among the second category of indicators, the weight of Gly content is small, which is 0.1276, so it is deleted. The weights of (E)-2-Nonenal and 2-Acetyl-1-Pyrroline are similar, while the coefficient of variation of 2-Acetyl-1-Pyrroline content is large, and 2-Acetyl-1-Pyrroline is significantly correlated with (E)-2-Nonenal (P < 0.001), so 2-Acetyl-1-Pyrroline content is selected as the second category of indicators, representing the smell quality index of fried silver carp. For the third category of indicators, Pro content is selected as the third category of indicators, which is the key indicator of taste quality. Among the fourth category of indicators, the coefficient of variation of His content is small, which is 9.11%, so it is deleted, and the weights of b* and 1-Octen-3-one content are small, which are -0.0918 and -0.1298 respectively, so the fourth category of indicators is deleted. Among the fifth category of indicators, the weight of HXR content is relatively small, which is -0.0524, so the fifth category of indicators is deleted. Among the sixth category of indicators, the coefficient of variation of protein digestibility is relatively small, which is 3.64%, so it is deleted. The weights of crude protein and crude fat content are relatively small, which are -0.1693 and -0.1894, respectively, so they are deleted. Ala content is significantly correlated with crude protein, crude fat, Lys, Cys, and 2-Methyl-3-(methylthio)furan content (P < 0.01), so Ala content is selected, and moisture content is significantly correlated with Ala content, so moisture content is used instead of Ala content.

[0108] In summary, L* value, protein content, IMP content, (E)-2-Nonenal content, and Lys content were finally selected as the key evaluation indicators for the steaming processing of fine cuts of silver carp, and IMP content, moisture content, 2-Acetyl-1-Pyrroline content, and Pro content were selected as the key indicators for the frying processing of fine cuts of silver carp. They include four aspects, namely, physical and chemical, nutrition, taste and smell, and comprehensively summarize the processing quality characteristics of silver carp, which are typical and representative.

[0109] Table 2 Principal component analysis of silver carp fine cuts under different processing methods

[0110]

[0111]

[0112] After normalizing the weights of principal component analysis, the formula for evaluating the suitability of silver carp processing was determined as follows:

[0113] Steaming process:

[0114] Y = 0.2051 × L* + 0.1880 × crude protein + 0.2162 × IMP + 0.2083 × (E)-2-Nonenal + 0.1824 × Lys.

[0115] Frying Process:

[0116] Y=0.1900×IMP+0.2720×moisture+0.3030×2-Acetyl-1-Pyrroline+0.2350×Pro.

[0117] After the data was processed by positive and normalization, it was inserted into the formula to obtain the processing suitability score Y, and combined with the results of K-means cluster analysis, the scores corresponded to level I (suitable), level II (relatively suitable) and level III (unsuitable), respectively, to evaluate the processing suitability of fine cuts of silver carp, as shown in Table 4. Specifically, in steaming, when Y ≥ 0.77, it is suitable for processing, when 0.05 < Y < 0.77, it is relatively suitable for processing, and when Y ≤ 0.05, it is unsuitable for processing. In frying, when Y ≥ 0.57, it is suitable for processing, when 0.04 < Y < 0.57, it is relatively suitable for processing, and when Y ≤ 0.04, it is unsuitable for processing. That is, the back meat and belly meat of silver carp are suitable for steaming, and the back meat and tail meat are suitable for frying.

[0118] Table 4 Silver carp processing suitability score and K-means cluster analysis results

[0119]

[0120]

[0121] After standardizing the overall acceptability scores of silver carp fine cuts, a linear regression model was established with the processing suitability scores of processed silver carp fine cuts as the independent variable and the overall sensory acceptability as the dependent variable. The results showed that steaming processing: Y = 3.205X-1.785 (R 2 =0.956); frying processing: Y = 3.091X + 6.736 (R 2=0.919), and their determination coefficients were all greater than 0.9, indicating that the models were good. This showed that the processing suitability evaluation model for finely divided silver carp products could accurately predict the overall acceptability of sensory evaluation and better reflect the processing suitability of finely divided silver carp products.

[0122] Although the present invention has been described in detail above with general descriptions and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements may be made thereto based on the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection claimed by the present invention.

Claims

1. A freshwater fish processing suitability evaluation index, characterized in that: It includes steaming processing evaluation indicators and frying processing evaluation indicators. The steaming processing evaluation indicators include L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content, and lysine Lys content. The frying processing evaluation indicators include inosinic acid IMP content, moisture content, 2-acetyl-1-pyrroline content, and proline Pro content.

2. A method for screening freshwater fish processing suitability evaluation indicators, characterized in that the steps include: (1) Obtaining the values ​​of the following indicators of the back meat, belly meat, belly meat and tail meat of freshwater fish after steaming and frying respectively: basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content, and volatile odor substance content; (2) using the coefficient of variation method to perform positive and normalization processing on the values ​​of each index after steaming and frying in step (1), and then determining the principal component items by the principal component analysis method, and calculating the coefficient of variation and weight of each index in the principal component items; Cluster analysis was performed on each index after steaming and frying in step (1) to obtain similar groups; Among the similar groups obtained by cluster analysis of steaming and frying, the indicators with large coefficient of variation and high weight were selected and retained as key evaluation indicators.

3. The method for screening a freshwater fish processing suitability evaluation index according to claim 2, characterized in that: In step (1), the content of basic nutrients includes but is not limited to moisture content, crude protein content, crude fat content, and ash content; the color difference value includes but is not limited to brightness value, red-green value, yellow-blue value, and whiteness value calculated according to the brightness value, red-green value, and yellow-blue value; the free amino acid content includes but is not limited to arginine Arg content, threonine Thr content, proline Pro content, lysine Lys content, alanine Ala content, glycine Gly content, histidine His content, and cysteine ​​Cys content; the flavor nucleotide content includes but is not limited to inosinic acid IMP content and hypoxanthine nucleotide HxR content; the volatile odor substance content includes but is not limited to (E)-2-nonenal content, 1-octen-3-one content, 2-acetyl-1-pyrroline content, 2-methyl-3-methylthiofuran content, and hexanal content.

4. A freshwater fish processing suitability evaluation model, characterized in that: It includes the evaluation model of steaming and the evaluation model of frying. L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content and lysine Lys content are selected as key evaluation indicators. The weights of the key evaluation indicators are analyzed to construct the evaluation model of steaming and obtain the evaluation score Y. The evaluation model of steaming is: Y=0.12051X1+0.1880X2+0.2162X3+0.2083×X4+0.1824X5 X1-X5 represent L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content and lysine Lys content respectively; Inosinic acid IMP content, moisture content, 2-acetyl-1-pyrroline content and proline Pro content were selected as key evaluation indicators, and the weights of the key evaluation indicators were analyzed to construct an evaluation model for frying, and the evaluation score Y was obtained. The evaluation model for frying is: Y=0.1900X1+0.2720X2+0.3030X3+0.2350X4 X1-X4 represent the inosinic acid IMP content, water content, 2-acetyl-1-pyrroline content and proline Pro content respectively.

5. A method for evaluating the suitability of freshwater fish processing, characterized in that the steps include: (1) The L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content, lysine Lys content, water content, 2-acetyl-1-pyrroline content and proline Pro content of back meat, belly meat, belly meat and tail meat of freshwater fish were obtained respectively, and the results were positively converted and standardized, and then substituted into the evaluation model; (2) According to the freshwater fish processing suitability evaluation model, the evaluation score Y is obtained: The evaluation model for steaming is: Y=0.12051X1+0.1880X2+0.2162X3+0.2083×X4+0.1824X5 X1-X5 represent L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content and lysine Lys content respectively; The evaluation model for frying is: Y=0.1900X1+0.2720X2+0.3030X3+0.2350X4 X1-X4 represent the inosinic acid IMP content, water content, 2-acetyl-1-pyrroline content and proline Pro content respectively; (3) K-means clustering was performed on the key evaluation indicators of steaming and frying the back meat, belly meat, belly meat and tail meat after positive and normalization processing to obtain the K-means clustering analysis results; the evaluation scores Y of steaming and frying the back meat, belly meat, belly meat and tail meat were sorted in order from large to small, and the sorted evaluation scores Y were classified according to the sameness or similarity of their corresponding K-means clustering analysis results. According to the order of the evaluation scores Y corresponding to the classification, the processing suitability of steaming and frying was evaluated from suitable to unsuitable.

6. A method for constructing a freshwater fish processing suitability evaluation model, characterized in that the steps include: (1) Obtain the key evaluation indicators and their weights for steaming and frying freshwater fish; (2) The key evaluation indicators of steaming and frying were weighted normalized respectively, and the evaluation models of steaming and frying were established.

7. The method for constructing a freshwater fish processing suitability evaluation model according to claim 6, characterized in that: In step (1), the method for obtaining key evaluation indicators and their weights for steaming and frying freshwater fish comprises: a. Obtain the values ​​of the following indicators of the back meat, belly meat, belly meat and tail meat of freshwater fish after steaming and frying: basic nutrient content, color difference value, protein digestibility, free amino acid content, flavor nucleotide content, volatile odor substance content; b. The coefficient of variation method was used to normalize and standardize the values ​​of each index after steaming and frying in step a, and then the principal component items were determined by principal component analysis, and the coefficient of variation and weight of each index in the principal component items were calculated; Cluster analysis was used to analyze the indicators after steaming and frying in step a to obtain similar groups; Among the similar groups obtained by cluster analysis of steaming and frying, the indicators with large coefficient of variation and high weight were selected and retained as key evaluation indicators.

8. The method for constructing a freshwater fish processing suitability evaluation model according to claim 6, characterized in that: In step a, the content of basic nutrients includes but is not limited to moisture content, crude protein content, crude fat content, and ash content; the color difference value includes but is not limited to brightness value, red-green value, yellow-blue value and whiteness value calculated according to the brightness value, red-green value and yellow-blue value; the free amino acid content includes but is not limited to arginine Arg content, threonine Thr content, proline Pro content, lysine Lys content, alanine Ala content, glycine Gly content, histidine His content, cysteine ​​Cys content; the flavor nucleotide content includes but is not limited to inosinic acid IMP content and hypoxanthine nucleotide HxR content; the volatile odor substance content includes but is not limited to (E)-2-nonenal content, 1-octen-3-one content, 2-acetyl-1-pyrroline content, 2-methyl-3-methylthiofuran content, and hexanal content.

9. The method for constructing a freshwater fish processing suitability evaluation model according to claim 6, characterized in that: In step b, the key evaluation indicators of steaming processing include L* value, protein content, inosinic acid IMP content, (E)-2-nonenal content, and lysine Lys content; the key evaluation indicators of frying processing include inosinic acid IMP content, moisture content, 2-acetyl-1-pyrroline content, and proline Pro content.

10. The method for constructing a freshwater fish processing suitability evaluation model according to claim 6, characterized in that: Also includes: A linear regression model was established with the evaluation scores of the evaluation model established after steaming and frying the back meat, belly meat, belly meat and tail meat respectively as the independent variable and the sensory evaluation value as the dependent variable to verify the accuracy of the evaluation model.

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