CN110083959B - Quality grade control method for low-temperature meat paste product - Google Patents
Quality grade control method for low-temperature meat paste product Download PDFInfo
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Abstract
The invention provides a method for regulating and controlling quality grade of a low-temperature meat paste product, which comprises the following steps: (1) investigating the influence of the raw and auxiliary materials with different addition amounts on the texture characteristics to obtain basic data among different raw and auxiliary materials and the texture characteristics; (2) MATLAB is utilized to carry out correlation analysis between the types and the texture characteristics of the raw and auxiliary materials, and the correlation relationship between the raw and auxiliary materials and the texture characteristics is determined; (3) adopting MATLAB to carry out function screening and fitting, and constructing models among different auxiliary materials and qualitative and constitutive property indexes; (4) performing model back calculation according to enterprise standards to obtain theoretical addition amounts of different raw and auxiliary materials in different grades of products; (5) the proper addition amount of the raw and auxiliary materials in products with different quality grades is determined by utilizing the national standard and combining with the actual production. The invention constructs a model between the raw and auxiliary materials and the texture characteristics, establishes the relation between the raw and auxiliary materials and the quality grade by means of enterprise texture grading standards, and realizes the method for regulating and controlling the quality grade of the meat paste product based on the raw and auxiliary materials.
Description
Technical Field
The invention relates to the field of low-temperature meat paste product quality evaluation, in particular to a method for regulating and controlling quality grade of a low-temperature meat paste product.
Background
China is a world with large meat product production and consumption, and is the first place in the world for years. The consumption amount of pork products accounts for about 65% of the total consumption amount of meat products. Wherein, the low-temperature meat paste product is very popular with consumers due to the characteristics of nutrition, sanitation, convenience, unique texture, mouthfeel and the like. But has the problems of low water retention rate, low oil retention rate, low yield and the like, thus causing the products to have rough texture and poor mouthfeel.
In the process of processing low-temperature meat products, different enterprises utilize different raw and auxiliary materials, particularly starch, colloid, protein and the like to improve the quality characteristics of the meat products, so that different enterprises, different products and different types, different addition amounts and different quality grades of the added auxiliary materials are caused. At present, quality grade regulating and controlling technologies based on raw materials and auxiliary materials are lacked among similar products of different meat enterprises, new products are developed blindly, and the product quality is uncontrollable and unpredictable. Therefore, it is necessary to establish a correspondence relationship between the raw and auxiliary materials and the quality grade.
The texture characteristics are one of the core problems influencing the quality of meat products, and are important bases for product development and quality evaluation of enterprises. The traditional texture evaluation method is mainly carried out by using sensory evaluation and has the defects of time consumption, large error and the like, and the texture characteristic is measured by using a texture analyzer, so that the method is convenient and rapid, and the data is objective and quantifiable. Quality grade discrimination technology based on the texture characteristics is constructed by some enterprises, and the quality grade of products is judged by using the texture characteristics. Therefore, if the corresponding relation between the raw and auxiliary materials and the texture characteristics can be established, the relation between the raw and auxiliary materials and the quality grades is established by means of enterprise texture grading standards, and the adding range of the raw and auxiliary materials corresponding to products with different quality grades is determined, the product quality grade technology can be regulated and controlled by the raw and auxiliary materials, the product development and quality control of enterprises can be guided, the new product development feasibility can be improved, and the development of meat enterprises can be promoted.
Disclosure of Invention
The invention provides a quality grade regulating method for low-temperature meat emulsion products, which establishes a relation model between raw and auxiliary materials and texture characteristics, utilizes a low-temperature meat emulsion preparation quality structure grading method used by enterprises to determine the adding range of different types of raw and auxiliary materials in products with different quality grades, forms a product quality grade regulating method, guides the product development and quality control of the enterprises, improves the development feasibility of new products, and promotes the development of meat enterprises.
The technical scheme for realizing the invention is as follows:
a quality grade control method for low-temperature meat emulsion products comprises the following steps:
(1) investigating the influence of different types and different addition amounts of raw and auxiliary materials on the texture characteristics to obtain basic data among different raw and auxiliary materials and the texture characteristics;
(2) MATLAB is utilized to carry out correlation analysis between the types and the texture characteristics of raw and auxiliary materials;
(3) adopting a cftool box in MATLAB to carry out function screening and fitting, constructing a relation model between different raw and auxiliary materials and a texture characteristic index, and carrying out accuracy verification;
(4) performing model back calculation according to enterprise standards to obtain theoretical addition amounts of different raw and auxiliary materials in products with different quality grades;
(5) the addition amounts of different raw and auxiliary materials in products with different quality grades are determined by referring to national standards and combining the actual production conditions of enterprises.
The texture characteristics in the step (1) comprise hardness, brittleness, adhesiveness, elasticity, cohesiveness and chewiness.
And (3) abandoning the construction of a model between the raw and auxiliary materials and the texture characteristics without significant or extremely significant correlation in the step (2).
The auxiliary materials in the step (3) are subjected to graphic drawing by adopting an MATLAB drawing tool, the approximate graphic trend between the addition amount of different types of raw and auxiliary materials and the texture characteristic data is observed, and the function type is determined according to the graphic trend; and then, performing function fitting by using MATLAB codes and a cftool box to construct a model between the raw and auxiliary materials and the qualitative and constitutive property indexes.
The model verification between the raw and auxiliary materials and the qualitative and constitutive characteristic indexes in the step (3) adopts a model fitting coefficient R2Error sum of squares SSE, root mean square error RMSE, accuracy factor AfDeviation factor BfReflecting the accuracy of the model.
Discarding the fitting coefficient R in the step (3)2Below 0.6 and a less accurate model.
And (4) determining the theoretical addition amount in the step (4), calculating by using a solve function, an exponential function and a back calculation code of a trigonometric function in an MATLAB tool according to the model between the raw and auxiliary materials and the qualitative characteristic indexes constructed in the step (3) and the qualitative and constitutive standard of the low-temperature meat-like products of enterprises, and determining the theoretical addition range of different types of raw and auxiliary materials for different quality grades of products.
And (5) determining the proper addition amount of the raw and auxiliary materials in the products with different quality grades, and performing the determination by referring to national standards and combining the actual production conditions of enterprises on the basis of the theoretical addition ranges of different types of raw and auxiliary materials of the products with different quality grades.
The invention has the beneficial effects that: the invention utilizes MATLAB, according to enterprise standards, combines national standard regulations and practical application conditions, determines the adding range of different types of raw and auxiliary materials in products with different quality grades, and forms a method for regulating and controlling the quality grade of meat paste products based on the raw and auxiliary materials.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Content of research
Study on influence of starch and colloid on texture characteristics of meat emulsion product
The research of the part investigates the influence of different raw and auxiliary materials and different addition levels on the texture characteristics of the product, and obtains basic data of model construction between the raw and auxiliary materials and the texture characteristics. Investigating that the starch mainly comprises corn starch, corn acetylated distarch adipate, cassava native starch, cassava acetylated distarch adipate, cassava hydroxypropyl starch, cassava hydroxypropyl distarch phosphate, cassava phosphate distarch, cassava acetate starch, potato native starch, potato acetylated distarch phosphate, potato acetate; the investigation colloid mainly comprises seaweed gel, microbial gel and vegetable gel; the texture characteristics mainly include hardness, brittleness, elasticity, adhesiveness, chewiness, cohesiveness and the like.
1.1 instruments and reagents
Sausage filler (sincerity & machinery limited); meat mincer (model C12, new tongli food machinery limited, shaoguan); chopper mixer (K15E, Talsabell s.a.); a smoke oven (BYXX-50, China Aibo Co.); TA-XT2i physical Property Analyzer (UK SMS Co.); corn starch, tapioca starch, potato starch, etc., Tianjin acromatic starch development Limited.
1.2 test methods
1.2.1 Process flow
Raw meat selection → pretreatment → rubbing and pickling → chop and mix → filling → baking → boiling → cooling → refrigeration
1.2.2 base recipe
Calculated by 1kg of meat (lean meat: fat meat is 4: 1), 0.15g of composite nitrate, 25g of salt, 1.8g of ginger powder, 2g of white pepper, 10g of cane sugar, 0.5g of sodium erythorbate, 1 g of fennel powder, 5g of garlic powder, 5g of monosodium glutamate, 250g of ice water (the proportion of the ice water is adjusted according to seasons), and 3g of composite phosphate.
1.2.3 Experimental design
The addition amounts of the various starches were 0, 2%, 4%, 6%, 8%, 10%, and the colloid addition amounts are shown in table 1.
TABLE 1 type of edible gum and addition level
1.2.4 determination of the texture Properties
Measured using a TA-XT2i texture analyzer at ambient temperature of 22 ℃. Texture Profile Analysis (TPA) measurement conditions: a P50 probe; the speed before, after and after the test is respectively 2.0, 0.8 and 0.8 mm/s; measuring the interval time of 5 s; the compression ratio is 75%. TPA results were analyzed using TPA-macro.
1.2.5 data analysis
Data were obtained using One-way ANOVA in Excel2003 and SPSS 10.0.
1.3 results and analysis
1.3.1 Effect of tapioca starch on texture characteristics of smoked sausages
Starch and its modified starches have a significant effect on TPA of smoked sausages and the results are shown in table 2.
As can be seen from the table 2, as the addition amount of the cassava starch is increased, the brittleness and the adhesiveness of the product are obviously increased (p is less than 0.05); the hardness is remarkably reduced within the range of 6 percent; the elasticity is obviously higher than that of the blank in the range of 4-10%; at the 6% level cohesiveness, chewiness were significantly higher than at other levels. The cassava hydroxypropyl starch with the level of 4% enables the hardness, brittleness and chewiness of the smoked and cooked sausage to be obviously higher than other levels, and the influence on the elasticity and the adhesiveness is not obvious. The cassava hydroxypropyl distarch phosphate has no obvious influence on hardness and adhesiveness; the brittleness is obviously improved within the range of 4-10 percent; within the 4% range, there is no significant effect on elasticity, but cohesiveness and chewiness are significantly reduced. The cassava acetylated distarch adipate obviously increases the adhesion and has no obvious influence on the elasticity; the addition range of 6-10% obviously increases the hardness of the product, and 8-10% obviously increases the brittleness and the chewiness; 6% significantly improved the cohesion of the product. The addition of the cassava acetate starch remarkably increases the elasticity of the smoked and cooked sausage, and has no remarkable influence on the adhesiveness, cohesiveness and chewiness; 2-4% hardness has no significant effect, and 6-8% reduces hardness significantly; 6-10% increase brittleness significantly. The cassava phosphate double starch has a remarkable increase effect on the hardness, cohesiveness, chewiness and adhesiveness of the smoked and cooked sausage along with the increase of the addition amount, and at the 6% level, the chewiness, cohesiveness and hardness reach the maximum value; brittleness and elasticity have no obvious influence.
TABLE 2 Effect of tapioca starch on the smoked sausage TPA
Note: the different letters in the same column of each starch indicate that the difference is obvious, the obvious level p is less than 0.05, and no comparison exists among different starches.
1.3.2 Effect of alginate jelly on texture characteristics of smoked and cooked sausages
Table 3 shows the effect of three algins on the texture properties of the smoked pork sausage. As can be seen from the table, the brittleness, elasticity and chewiness of the product are reduced remarkably (p <0.05) with the increase of the addition amount of sodium alginate, and when the addition amount is less than 0.8%, the hardness, adhesiveness and cohesiveness of the product are not significantly different from those of the blank (p > 0.05). The addition range of the carrageenan is 0-1.2%, the hardness, brittleness and chewiness of the sausage tend to increase along with the increase of the addition amount, and the carrageenan is consistent with the research results of the predecessors, probably because the carrageenan is colloid with strong anionic groups and can react with polar groups of protein (amino acid), so that the characteristics of mixed gel are changed, the texture of meat products is improved, the adhesiveness tends to increase firstly and decrease secondly, the carrageenan reaches the maximum when the addition amount is 0.9%, but the carrageenan has little influence on the elasticity and cohesion of the sausage. The addition of agar has little influence on the texture parameters of the sausage, probably because the temperature of the sausage in the baking and boiling processes does not reach the melting temperature of the agar (>85 ℃), agar molecules are not fully spread, and a better gel cannot be formed with muscle protein, so that the influence on the texture of the product is not obvious. When the agar is added in an amount of 1.2%, the sausage has the greatest hardness and chewiness.
TABLE 3 Effect of three alginate gel additions on smoked and cooked sausage TPA
Note: different alphabets in the same column of each glue show obvious difference, the obvious level p is less than 0.05, and no comparison exists among different kinds of glue.
1.4 conclusion
The starch substance and the colloid substance have obvious influence on the texture characteristics of the smoked and cooked sausage.
2 construction of model between raw and auxiliary materials and texture characteristic indexes of smoked and cooked sausage
In the research of the part, firstly, the correlation analysis between the raw and auxiliary materials and the quality and structure characteristics is carried out, and the correlation relationship between the raw and auxiliary materials and the quality and structure characteristics of the product is inspected; then, for the raw and auxiliary materials and the product texture characteristics which have obvious or extremely obvious correlation, an MATLAB drawing tool is adopted to carry out graphic drawing, the approximate graphic trends between different auxiliary material addition amounts and texture data are observed, and the function type is roughly determined according to the graphic trends. And then, performing function fitting by using MATLAB codes and a cftool box according to the estimated function type, and constructing a model between the raw and auxiliary materials and the qualitative and constitutive property indexes.
2.1 method
2.1.1 correlation analysis
Correlation analysis was performed using MATLAB 12 a.
2.1.2 model construction and validation
Dividing data into a training set and a verification set, selecting appropriate codes and a cftool kit to perform function fitting according to the data characteristics of the training set by using MATLAB 12a, and verifying the model. Determining a coefficient R using a model2Error sum of squares SSE, root mean square error RMSE, accuracy factor AfDeviation factor BfEtc. reflect the accuracy of the model.
2.2 results and analysis
2.2.1 starch and texture Property model construction
2.2.1.1 analysis of correlation between starch material and texture Property index
Table 4 shows the results of the correlation analysis between the starch and the new index of texture characteristics. From the results, it can be seen that besides the cassava acetate, all starch substances and the hardness of the smoked and cooked sausage have a significant or extremely significant positive correlation; besides corn acetylated distarch adipate, all starch substances and crispness of the smoked and cooked sausage have obvious or extremely obvious positive correlation; besides the cassava native starch, the cassava hydroxypropyl distarch phosphate and the potato native starch, all starch substances and the adhesiveness of the smoked and cooked sausage have obvious or extremely obvious negative correlation; corn starch, cassava phosphate distarch, cassava acetate and product elasticity have obvious or extremely obvious positive correlation; significant or extremely significant correlation exists among corn starch, cassava hydroxypropyl distarch phosphate, potato acetylated distarch phosphate and product cohesiveness; besides tapioca hydroxypropyl starch, tapioca acetate and potato acetate starch, all starch substances have a significant or very significant negative correlation with the chewiness of the smoked and cooked sausage.
TABLE 4 correlation coefficient between starch and steam-cooked sausage texture characteristic indexes
Note: significant correlation at 0.01 level (double-sided), significant correlation at 0.05 level (double-sided).
2.2.1.2 construction and verification of model between starch substance and texture characteristic indexes
On the basis of correlation analysis, a drawing tool in MATLAB is utilized to observe the approximate graphic trend between different starch addition amounts and texture data, and the function type is roughly determined according to the graphic trend. And finally, approximately drawing the trend of a graph between the addition amount of 11 kinds of starch substances and texture data, and determining function types, wherein 25 are unitary first-order function relations, 27 are unitary second-order function relations, and 14 are trigonometric function relations. 66 models among starch and new indexes of each texture are constructed by using a cftool, and the models are verified by using two modes of an accuracy factor and a deviation factor, and the results are shown in table 5.
As can be seen from table 5, the model fitting coefficients of 92.4% in these models are higher than 0.6, except that the model fitting coefficients between potato acetylated distarch phosphate and elasticity, tapioca acetate starch and hardness, potato acetate starch and cohesiveness, and potato acetate starch and chewiness are lower than 0.6; 66.2 percentIs higher than 0.8; the model fitting coefficient of 37.8% is higher than 0.9. Wherein, the model fitting coefficient between the acetylated potato distarch phosphate and elasticity, the cassava acetate starch and hardness, the potato acetate starch and cohesiveness, and the potato acetate starch and chewiness is lower than 0.6, but A from the modelfAnd BfThe values are all close to 1. Therefore, in view of model fitting accuracy, it is suggested that these models can be considered to be dropped later in the model application process.
TABLE 5 model between starch and textural Property indices
2.2.1.3 conclusion
(1) There is a very significant correlation between starch and the texture properties of smoked and cooked sausages.
(2) Functional relations between 11 types of starch substance addition amounts and texture data are determined, wherein 25 types of starch substance addition amounts are unitary first-order functional relations, 27 types of starch substance addition amounts are unitary second-order functional relations, and 14 types of starch substance addition amounts are trigonometric functional relations.
(3) 66 models are constructed, 92.4% of model fitting coefficients are all higher than 0.6, and 37.8% of model fitting coefficients are higher than 0.9.
(4) By verification, models with lower precision (potato acetylated distarch phosphate and elasticity, tapioca acetate starch and hardness, potato acetate starch and cohesiveness, potato acetate starch and chewiness) are abandoned, and other models can be used.
2.2.2 model construction between colloid and texture Property indices
2.2.2.1 correlation between colloid and Mass Property indices
Table 6 shows the correlation analysis results between the new indicators of colloid and texture properties. The results show that except that the carrageenan has no obvious correlation with the elasticity and the cohesion of the smoked and cooked sausage, the konjac gum has no obvious correlation with the hardness, the elasticity, the cohesion and the chewiness, agar and all texture characteristic indexes have no obvious correlation, and other colloids have obvious or extremely obvious correlation with the texture characteristic indexes of the sausage. Therefore, the discard agar was modeled in the later stage, and modeling was performed between other colloidal and textural properties.
TABLE 6 correlation coefficient between colloid and steam-cooked sausage texture characteristic index
Note: significant correlation at 0.01 level (double-sided), significant correlation at 0.05 level (double-sided).
2.2.2.2 construction and verification of model between colloid and texture Property indices
On the basis of correlation analysis, a drawing tool in the METLAB is utilized to observe the approximate graphic trend between different colloid addition amounts and texture data, and the function type is roughly determined according to the graphic trend. And finally, drawing a rough graphic trend between 7 colloid addition amounts and texture data, and determining function types, wherein 1 is a unitary first-order function relationship, 33 is a unitary second-order function relationship, 2 is an exponential function relationship, and 6 is a trigonometric function relationship. 42 models among colloid and each texture specific new index are constructed by using a cftool, and the models are verified by using two modes of an accuracy factor and a deviation factor, and the results are shown in table 7.
As can be seen from Table 7, these models are based on the combination of konjac gum and elasticity, cohesion, curdlan and cohesionBesides the fitting coefficient of the model is lower than 0.8, the fitting coefficients of other 95 percent of the models are higher than 0.8, and the fitting coefficient of 70 percent of the models is higher than 0.9. But from A of the modelfAnd BfIn addition to agar and adhesiveness, konjac gum and cohesiveness, elasticity, adhesiveness, flaxseed gum and model AfOther models A than the larger valuefAnd BfThe values are all close to 1. Therefore, in view of the model fitting accuracy, it is recommended that a model with a model fitting coefficient lower than 0.6 (konjac gum and cohesiveness) can be considered in the model application process at a later stage, a model with lower accuracy (linseed gum and chewiness, brittleness, konjac gum and elasticity) is abandoned, and other models can be used.
TABLE 7 model between colloid and texture Property indices
2.2.2.3 conclusion
(1) Besides agar, the colloid has a very obvious correlation with the texture characteristics of the smoked sausage.
(2) And determining the functional relationship between the addition amount of 7 colloid substances and the texture data, wherein 1 is a unitary first-order functional relationship, 33 is a unitary second-order functional relationship, 2 is an exponential functional relationship, and 6 is a trigonometric functional relationship.
(3) 42 models are constructed, 95% of model fitting coefficients are all higher than 0.8, and 70% of model fitting coefficients are higher than 0.9.
(4) By verification, the model with the model fitting coefficient lower than 0.6 (konjac gum and cohesiveness) can be used by discarding other models with lower precision (linseed gum and chewiness, brittleness, konjac gum and elasticity).
3 determination of the addition of raw and auxiliary materials based on texture standards
3.1 method
And calculating by using a solve function, an exponential function and a back calculation code of a trigonometric function in an MATLAB tool.
3.2 results and analysis
3.2.1 reverse calculation of starch and colloid addition range of different grades of products
According to the model between starch and texture characteristics constructed in the early stage and according to the low-temperature meat quality and structure standard (HZS 001-. The texture range of the different grades is shown in table 8, the starch addition range of the different grades is shown in table 9, and the colloid addition range of the different grades is shown in table 10.
TABLE 8 texture characteristic ranges of smoked and cooked sausages of different grades
TABLE 9 theoretical addition range of starch for smoked and cooked sausages of different grades
TABLE 10 colloid theory addition ranges for smoked and cooked sausages of different grades
3.2.2 starch application addition Range determination for different grades of products
The amount of acetate starch, phosphate distarch, hydroxypropyl starch, hydroxypropyl distarch phosphate, acetylated distarch adipate, phosphorylated distarch phosphate and the like added to the food may be appropriately added according to the actual requirements, as specified in the national standard GB 2760-2014.
Corn starch, corn acetylated distarch adipate, tapioca raw starch, tapioca acetylated distarch adipate, tapioca hydroxypropyl starch, tapioca hydroxypropyl distarch phosphate, tapioca phosphate distarch, tapioca acetate starch, potato raw starch, potato acetylated distarch phosphate and potato acetate starch which are investigated by the research institute can be properly added according to actual requirements.
In the low-temperature meat product processing, natural starch and modified starch in enterprises and products have no clear adding range, and starch and product quality have no clear relation. However, a great deal of research has shown that both native and modified starches can be used to improve the quality properties of the gel, such as elasticity, toughness, mouthfeel, etc., in different types of products.
Therefore, the adding range of different starch substances in different grades of products is determined according to the practical application and the application condition of the texture indexes in the meat products by combining the national standard regulations, and the specific range is shown in table 11.
TABLE 11 starch addition ranges for smoked and cooked sausages of different grades
3.2.3 determination of addition Range for colloid applications for different grades of products
The addition amount of sodium alginate, guar gum, xanthan gum, carrageenan, curdlan, konjac glucomannan, linseed gum and other colloids in the meat product can be properly added according to actual requirements specified in the national standard GB 2760-2014.
The addition ranges of edible colloids in the low-temperature meat product processing are different, the addition ranges of xanthan gum are 0.1-0.5%, the addition ranges of carrageenan are 0.2-1.5%, the addition ranges of flaxseed gum are 0.4-0.6%, the addition ranges of curdlan, guar gum, sodium alginate, konjac gum and the like are not clear, and the edible colloids can be used for improving the texture characteristics of gel, such as elasticity, toughness, mouthfeel and the like of products in different types of products. Therefore, the adding ranges of different colloids in different grades of products are determined according to the practical application and the application condition of the texture indexes in the meat products by combining the national standard regulations, and the specific ranges are shown in table 12.
TABLE 12 colloid addition range for smoked and cooked sausage of different grades
3.3 conclusion
3.3.1 the theoretical adding ranges of 11 starch substances and 7 colloid substances in products with different quality grades are calculated by using MATLAB.
3.3.2 the proper adding range of 11 kinds of starch substances and 7 kinds of colloid substances in products with different quality grades is determined according to the practical application and the application condition of texture indexes in meat products by combining the national standard.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A method for regulating and controlling quality grade of low-temperature meat paste products is characterized by comprising the following steps:
(1) investigating the influence of different types and different addition amounts of raw and auxiliary materials on the texture characteristics to obtain basic data among different raw and auxiliary materials and the texture characteristics;
(2) MATLAB is utilized to carry out correlation analysis between the types and the texture characteristics of raw and auxiliary materials;
(3) adopting a cftool box in MATLAB to carry out function screening and fitting, constructing a relation model between different raw and auxiliary materials and a texture characteristic index, and carrying out accuracy verification;
the auxiliary materials are subjected to graphic drawing by adopting an MATLAB drawing tool, the general graphic trend between the addition amount of different types of raw and auxiliary materials and the texture characteristic data is observed, and the function type is determined according to the graphic trend; then, using MATLAB codes and a cftool box to perform function fitting, and constructing a model between the raw and auxiliary materials and the qualitative and constitutive property indexes;
model verification between raw and auxiliary materials and qualitative and constitutive characteristic indexes by adopting model fitting coefficientR 2 Error sum of squaresSSERoot mean square errorRMSEAccuracy factorA f Deviation factorB f Reflecting the accuracy of the model;
(4) performing model back calculation according to enterprise standards to obtain theoretical addition amounts of different raw and auxiliary materials in products with different quality grades;
determining the theoretical addition amount in the step (4), calculating by using a solve function, an exponential function and a back calculation code of a trigonometric function in an MATLAB tool according to the model between the raw and auxiliary materials and the qualitative characteristic indexes constructed in the step (3) and the qualitative and constitutive standard of the low-temperature meat-like products of enterprises, and determining the theoretical addition ranges of different types of raw and auxiliary materials for different quality grades of products;
(5) the addition amounts of different raw and auxiliary materials in products with different quality grades are determined by referring to national standards and combining the actual production conditions of enterprises.
2. The method of claim 1 for regulating the quality level of a low temperature meat emulsion product, wherein: the texture characteristics in the step (1) comprise hardness, brittleness, adhesiveness, elasticity, cohesiveness and chewiness.
3. The method of claim 1 for regulating the quality level of a low temperature meat emulsion product, wherein: and (3) abandoning the construction of a model between the raw and auxiliary materials and the texture characteristics without significant or extremely significant correlation in the step (2).
4. The method of claim 1 for regulating the quality level of a low temperature meat emulsion product, wherein: discarding the fitting coefficient R in the step (3)2Below 0.6 and a less accurate model.
5. The quality grade control method for low-temperature meat emulsion products according to claim 1, characterized in that the determination of the proper addition amount of the raw and auxiliary materials in the products with different quality grades in the step (5) is carried out by referring to national standards and combining the actual production conditions of enterprises on the basis of the theoretical addition ranges of the raw and auxiliary materials of different types in the products with different quality grades.
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