Freshness composite indicator label for fresh prepared meat products, method of preparation and use

Abstract

This invention relates to the field of meat quality monitoring and preservation technology, specifically to a composite indicator label for the freshness of prepared meat products, its preparation method, and its application. The preparation method includes preparing a preservation layer, preparing an indicator layer, and preparing a composite film using a layer-by-layer casting method. The prepared solution is poured onto a model lined with the indicator film, and the composite indicator label is formed using supercritical drying. Based on the changes in the RGB values ​​of the composite indicator label's color, fuzzy normalization is performed using fuzzy mathematics to make a fuzzy comprehensive evaluation, effectively distinguishing prepared meat products of different freshness levels. The composite indicator label prepared by this invention can effectively distinguish prepared meat products of different freshness levels.

Description

Technical Field

[0001] This invention relates to the field of meat quality monitoring and preservation technology, specifically to a composite indicator label for the freshness of fresh prepared meat products, its preparation method, and its application. Background Technology

[0002] Prepared meat products have developed rapidly in recent years along with the rapid growth of the national economy. Their convenience, nutrition, and speed of consumption meet market demands, making them one of the fastest-growing food industries. Fresh prepared meat products are a type of prepared meat product, referring to non-ready-to-eat meat products made primarily from livestock and poultry meat. This meat is ground or chopped, then seasoned and processed. A rapid cooling process is used to lower the core temperature of the product to above the freezing point but below 7°C, and it is stored, transported, and sold at 0-4°C. Fresh prepared meat products are characterized by high edible quality, rich nutritional value, and convenience. However, because they do not undergo high-temperature sterilization, fresh meat products are prone to deterioration, spoilage, and loss, leading to a shortened shelf life.

[0003] To prevent resource waste caused by discarding products due to uncertain spoilage and to protect consumer health from product deterioration during storage, transportation, and sales, real-time quality evaluation is necessary. Freshness is a crucial indicator for evaluating the quality of fresh processed meat products, significantly impacting product quality and the processing suitability of raw materials. Currently, consumers primarily rely on the shelf-life date indicated on product packaging to judge food freshness; however, due to various factors affecting food spoilage, the shelf-life date is not always reliable. Therefore, food freshness testing has become a primary means of assessing food quality and plays a vital guiding role in consumer behavior. Methods for evaluating the freshness of processed meat products mainly include conventional sensory evaluation methods, microbiological detection methods, high-performance liquid chromatography (HPLC), and, in recent years, methods such as electronic noses, electronic tongues, and hyperspectral imaging. However, sensory evaluation relies heavily on consumer subjective judgment and lacks objectivity and scientific rigor. Professional instrument testing generally suffers from drawbacks such as complex sample pretreatment processes, cumbersome operations, and high costs, and is mostly used in universities and testing institutions, failing to meet the requirements of rapid on-site testing.

[0004] Developing quick and easy-to-use freshness indicator labels is beneficial for regulators, distributors, and consumers to control product safety. Freshness indicator labels can reflect the freshness of the food inside the packaging through intuitive color changes. This small-sized, low-cost, real-time, non-destructive monitoring technology has good application potential in monitoring the freshness of meat products.

[0005] In recent years, smart label technology solutions for monitoring food freshness have emerged in large numbers.

[0006] Patent CN102660629A discloses a method for rapidly identifying dominant spoilage bacteria in poultry meat based on olfactory visualization technology. This method involves fixing an acid-base indicator, porphyrin compounds, and solvent-based color-changing dye onto a silicone plate to obtain a visualized gas sensor array. However, the dye used in this technology poses a potential food safety risk. Patent CN103776828A discloses a color-changing label for monitoring the freshness of food and its preparation method. This method uses the synthetic pigment bromocresol green as a color-changing indicator dye, and employs binders, solvents, and additives to create an indicator ink, which is then printed onto a food packaging film. However, the use of synthetic pigments limits its widespread application. Patent CN110506785A discloses an active packaging film for chilled meat preservation, characterized by strong preservation capabilities. However, the film formed by the casting and drying method involved in this technology has low mechanical properties and cannot achieve real-time monitoring of food freshness.

[0007] Currently, the main problems facing the development and application of food freshness indicator labels are: high purity of synthetic pigments, which are too sensitive to color change and pose safety risks when used for indication; insufficient stability of natural pigments, which are prone to volatility and dissolution of the carrier; and poor air permeability of indicator labels prepared by ordinary drying methods, which affects sensitivity. Summary of the Invention

[0008] The present invention provides a composite indicator label for the freshness of fresh prepared meat products, a preparation method thereof, and its application, which can better distinguish fresh prepared meat products with different levels of freshness.

[0009] The method for preparing a composite indicator label for the freshness of prepared meat products according to the present invention includes the following steps:

[0010] Step 1, Preparation of the preservation layer:

[0011] Prepare a 10-20 g / L zein solution by dissolving zein powder in 80%-90% ethanol; prepare a 5-10% gelatin solution by dissolving gelatin in distilled water, and mix the zein solution with the gelatin solution. Maintain the solution temperature at 50-80℃, and magnetically stir at 500-1000 rpm for 10-30 min. Add 0.05-2% thyme essential oil and 0.05-1 g dihydromyricetin, homogenize at high speed for 3 min, and then ultrasonically remove bubbles before use.

[0012] Step 2, Preparation of the indicator layer:

[0013] 2.1 To screen natural indicators that exhibit color change response and have the same response range for volatile gases containing spoilage characteristics in fresh and processed meat products;

[0014] 2.2 Add the natural indicator to the high amylose corn starch / glucomannan / carrageenan / chitosan / glycerol matrix, keep the solution temperature at 50-80℃, and use magnetic stirring to mix and dissolve it thoroughly to obtain the indicator solution. Use citric acid / sodium dihydrogen phosphate standard solution to adjust the pH of the indicator solution to 6.0-7.0, and then use ultrasonic defoaming to obtain the indicator solution.

[0015] 2.3. Measure a certain volume of indicator solution and inject it into a syringe that can be raised and lowered freely. Place the syringe in an electrospinning machine and use a dual-nozzle device for spinning. Finally, place the resulting spun film in a vacuum drying oven and dry it to constant weight to obtain the label indicator layer.

[0016] Step 3: Prepare a composite film using the layer-by-layer casting method. Pour the solution prepared in Step 1 onto a model with an indicator film, and form a composite indicator label by supercritical drying.

[0017] Preferably, in step 2.1, the characteristic gas of putrefaction is one or more of nitrogen compounds, hydrogen sulfide, trimethylamine, and thiols.

[0018] Preferably, in step 2.1, the natural indicator is one or more of anthocyanins, curcumin, alizarin, and betaine, with a mass fraction of 0.05%-0.5%.

[0019] Preferably, in step 2.2, the mass fraction of high amylose corn starch is 0.5-3%, the mass fraction of glucomannan is 0.1-2%, the mass fraction of carrageenan is 0.5-2%, the mass fraction of chitosan is 0.5-2%, and the mass fraction of glycerol is 0.5-3%.

[0020] Preferably, in step 2.3, the spinning parameters are set as follows: voltage -5-20kV, relative air humidity 20%, solution feed rate 1-10mL / h, drum speed 100-150r / min, fiber accumulation distance 15-20cm, and accumulation time 30min.

[0021] Preferably, in step 3, the supercritical drying method uses a drying temperature of 30-40℃ and a pressure of 5-10MPa under supercritical conditions.

[0022] This invention provides a composite indicator label for the freshness of prepared meat products, which adopts the above-mentioned preparation method for the composite indicator label for the freshness of prepared meat products.

[0023] This invention provides an application of a composite indicator label for the freshness of prepared meat products. It uses the aforementioned composite indicator label for the freshness of prepared meat products and includes: performing fuzzy normalization processing based on the changes in the RGB values ​​of the composite indicator label color using fuzzy mathematics, and then making a fuzzy comprehensive evaluation to effectively distinguish fresh prepared meat products with different freshness levels.

[0024] The fuzzy mathematics method includes the following steps:

[0025] a. Establishment of factor set, comment set, and weight set

[0026] The factor set consists of evaluation indicators. U1, U2, and U3 constitute the factor set U = {U1, U2, U3}; U1, U2, and U3 represent the sets of R, G, and B values, respectively.

[0027] The comment set consists of feedback information from evaluation indicators, including evaluation level information. Fresh V1, less fresh V2, and rotten V3 constitute the comment set V = {V1, V2, V3}.

[0028] The weight set is represented by the proportion of each evaluation index in the overall evaluation. The weights form the weight domain, which is called the fuzzy vector. By using a binary comparison method with 10-20 evaluators, the factors in the factor set U are compared one-to-one. Important factors get 1 point, and minor factors get 0 points. The weight set is determined based on the score results and is represented by X.

[0029] b. Matrix Establishment and Transformation

[0030] The fuzzy relation matrix R is obtained by dividing the number of votes for different indicators by the number of evaluators. The fuzzy relation evaluation set is composed of the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. Then, the evaluation result of the i-th sample can be expressed as Y. i =X×R i i is 1 to 10; the evaluation level set K is identified, and the total fuzzy comprehensive evaluation score T is obtained by calculation. The calculation formula is T = Y × K; the higher the comprehensive score, the better the product freshness.

[0031] The purpose of this invention is to provide a composite indicator label for the freshness of prepared meat products and its preparation method. This label integrates natural preservation technology and freshness monitoring, aiming to proactively maintain, improve, control, record, and indicate the quality of packaged products. By preparing a composite preservation indicator label, rapid, non-destructive, and real-time monitoring of freshness is achieved, solving the problems of cumbersome, time-consuming, and untimely physicochemical testing. Simultaneously, it achieves antibacterial preservation effects, extending shelf life. The preparation of the upper and lower preservation layers on the outer side of the composite label maximizes the effective preservation area, improves the mechanical properties of the indicator label, inhibits the dissolution of indicators in the inner indicator layer, reduces moisture evaporation from the indicator layer, and improves the stability of the freshness indicator label. The preparation of the indicator layer using electrospinning technology increases its specific surface area, increasing the reaction area between spoilage odor factors and indicators. The preparation of the composite label using supercritical drying improves the label's air permeability and enhances its sensitivity. Based on the changes in the RGB values ​​of the label color, fuzzy mathematical comprehensive evaluation can systematically, scientifically, and accurately distinguish fresh prepared meat products with different freshness levels.

[0032] The beneficial effects of this invention are as follows:

[0033] 1. This invention uses zein, gelatin, thyme essential oil, and dihydromyricetin to make a preservation layer, which has good stability and antioxidant and antibacterial activity. The preparation of upper and lower preservation layers increases the effective preservation area and inhibits the dissolution of indicators in the label's internal indicator layer, reducing moisture evaporation and improving the stability of the freshness indicator label. All raw materials for the preservation layer are green, environmentally friendly, and non-toxic natural materials that are easily degraded after disposal, posing no food safety risks.

[0034] 2. By combining one or more natural indicators such as anthocyanins, curcumin, alizarin, and betaine with a high-amylose corn starch / glucomannan (KGM) / carrageenan (CAR) / chitosan / glycerol matrix, a label indicator layer is formed, exhibiting excellent antioxidant, antibacterial, oxygen-barrier, oil-barrier, antifreeze, hydrophobic, and mechanical properties. This layer is then fabricated using electrospinning technology to increase its specific surface area and improve sensitivity. Furthermore, the raw materials for the indicator layer are derived from nature, are non-toxic and harmless, and are suitable for edible products such as meat.

[0035] 3. Use supercritical drying to prepare composite indicator labels, improve the air permeability of the labels, and enhance their sensitivity.

[0036] 4. Based on the changes in the RGB values ​​of the indicator label colors, fuzzy mathematics is used to quantitatively and mathematically analyze the freshness of processed meat products as reflected by the indicator labels, reducing the interference of human factors and improving the accuracy of freshness evaluation. Attached Figure Description

[0037] Figure 1This is a flowchart of a method for preparing a composite indicator label for the freshness of prepared meat products, as described in Example 1. Detailed Implementation

[0038] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0039] Example 1

[0040] like Figure 1 As shown in the figure, this embodiment provides a method for preparing a composite indicator label for the freshness of fresh prepared meat products, which includes the following steps:

[0041] Step 1, Preparation of the preservation layer:

[0042] Prepare a 10-20 g / L zein solution by dissolving zein powder in 80%-90% ethanol; prepare a 5-10% gelatin solution by dissolving gelatin in distilled water, and mix the zein solution with the gelatin solution. Maintain the solution temperature at 50-80℃, and magnetically stir at 500-1000 rpm for 10-30 min. Add 0.05-2% thyme essential oil and 0.05-1 g dihydromyricetin, homogenize at high speed for 3 min, and then ultrasonically remove bubbles before use.

[0043] Step 2, Preparation of the indicator layer:

[0044] 2.1 To screen natural indicators that exhibit color change response and have the same response range for volatile gases containing spoilage characteristics in fresh and processed meat products;

[0045] In step 2.1, the characteristic gases of putrefaction are one or more of nitrogen compounds, hydrogen sulfide, trimethylamine, and thiols. The natural indicators are one or more of anthocyanins, curcumin, alizarin, and betaine, with a mass fraction of 0.05%-0.5%.

[0046] 2.2 Add the natural indicator to the high amylose corn starch / glucomannan / carrageenan / chitosan / glycerol matrix, keep the solution temperature at 50-80℃, and use magnetic stirring to mix and dissolve it thoroughly to obtain the indicator solution. Use citric acid / sodium dihydrogen phosphate standard solution to adjust the pH of the indicator solution to 6.0-7.0, and then use ultrasonic defoaming to obtain the indicator solution.

[0047] 2.3. Measure a certain volume of indicator solution and inject it into a syringe that can be raised and lowered freely. Place the syringe in an electrospinning machine and use a dual-nozzle device for spinning. Finally, place the resulting spun film in a vacuum drying oven and dry it to constant weight to obtain the label indicator layer.

[0048] Step 3: Prepare the composite film using a layer-by-layer casting method. Pour the solution prepared in Step 1 onto a model with an indicator film, and form a composite indicator label using supercritical drying. The drying temperature under supercritical conditions is 30-40℃, and the pressure is 5-10MPa.

[0049] Zein, used in step 1, possesses excellent properties such as biodegradability, non-toxicity, and plasticity, making it an ideal candidate material among many biopolymers. Furthermore, zein exhibits hydrophobic, heat-resistant, oxygen-barrier, UV-resistant, aroma-preserving, oil-blocking, and antistatic properties, making it a very attractive biopolymer for food packaging. The concentration of the prepared zein solution is 10-20 g / L.

[0050] The gelatin in step 1 is a macromolecular hydrophilic colloid that is widely used in the pharmaceutical and food packaging fields due to its good gelling, thickening and film-forming properties.

[0051] Thyme essential oil (TEO) in step 1, as an antibacterial agent derived from thyme, can disrupt cell membrane permeability, inhibit normal cell function, and cause leakage of intracellular components. Due to the high hydrophobicity and volatility of thyme essential oil, protein emulsion-based encapsulation can improve its stability and antibacterial activity. Moreover, emulsions prepared through ultrasonic treatment exhibit even better stability and antibacterial activity due to the improved active surface area.

[0052] Dihydromyricetin (DMY) in step 1 is a natural flavonoid compound with various biological activities such as antioxidation, antibacterial, anti-inflammatory, regulation of lipid and glucose metabolism, and neuroprotection.

[0053] In step 2.2, high-amylose corn starch and fiber have similar functions. Amylose is a linear polymer composed of α-(1,4)-linked glucose units, possessing good oxygen barrier properties, oil barrier properties, transparency, flexibility, tensile strength, and hydrophobicity. It can also achieve a stable gel structure, making it suitable for electrospinning. Its mass fraction is 0.5-3%.

[0054] In step 2.2, glucomannan (KGM), as a type of polysaccharide gum, possesses excellent film-forming ability and can synergistically interact with starch. It can inhibit the recrystallization of high-amylose corn starch molecules, improving the flexibility and water resistance of the composite film. Its mass fraction is 0.1-2%.

[0055] In step 2.2, carrageenan (CAR) is a hydrophilic colloid that dissolves in water at approximately 80°C, forming a viscous, transparent, or slightly milky-white, easily flowing solution. It is more readily dispersed in water if pre-wetted with ethanol, glycerol, or a saturated sucrose aqueous solution. A solution boiled in 30 times its volume of water for 10 minutes, upon cooling, forms a colloid. Its viscosity increases when bound to water, and it reacts with proteins to emulsify and stabilize the emulsion. Its mass fraction is 0.5-2%.

[0056] In step 2.2, chitosan is a positively charged, basic, high-molecular-weight polysaccharide obtained by deacetylation of chitin. It is widely used in membranes due to its non-toxicity, biocompatibility, biodegradability, excellent film-forming properties, gas barrier properties (CO2 and O2), antioxidant activity, and antibacterial activity. Its mass fraction is 0.5-2%.

[0057] In step 2.2, glycerol is colorless, odorless, and sweet-tasting. It is an organic compound that can trap hydrogen sulfide from the air and possesses moisturizing, hydrating, highly active, antioxidant, and antifreeze properties. Its mass fraction is 0.5-3%.

[0058] In step 2.3, the spinning parameters are set as follows: voltage -5-20kV, relative humidity 20%, solution feed rate 1-10mL / h, drum speed 100-150r / min, fiber accumulation distance 15-20cm, and accumulation time 30min.

[0059] This embodiment provides a composite indicator label for the freshness of prepared meat products, which adopts the above-mentioned preparation method for the composite indicator label for the freshness of prepared meat products.

[0060] This embodiment provides an application (method) of a composite indicator label for the freshness of prepared meat products. It uses the aforementioned composite indicator label and includes: performing fuzzy normalization processing based on the changes in the RGB values ​​of the composite indicator label's colors using fuzzy mathematics, and then making a fuzzy comprehensive evaluation to effectively distinguish prepared meat products of different freshness levels. Fuzzy mathematics is a theory and method for studying how to describe some fuzzy phenomena using precise mathematical methods. It can quantify some factors that are difficult to quantify. Fuzzy mathematical analysis can be used to quantify and mathematically analyze concepts or things like color.

[0061] Fuzzy mathematics methods include the following steps:

[0062] a. Establishment of factor set, comment set, and weight set

[0063] The factor set consists of evaluation indicators. U1, U2, and U3 constitute the factor set U = {U1, U2, U3}; U1, U2, and U3 represent the sets of R, G, and B values, respectively.

[0064] The comment set consists of feedback information from evaluation indicators, including evaluation level information. Fresh V1, less fresh V2, and rotten V3 constitute the comment set V = {V1, V2, V3}.

[0065] The weight set is represented by the proportion of each evaluation index in the overall evaluation. The weights form the weight domain, which is called the fuzzy vector. By using a binary comparison method with 10-20 evaluators, the factors in the factor set U are compared one-to-one. Important factors get 1 point, and minor factors get 0 points. The weight set is determined based on the score results and is represented by X.

[0066] b. Matrix Establishment and Transformation

[0067] The fuzzy relation matrix R is obtained by dividing the number of votes for different indicators by the number of evaluators. The fuzzy relation evaluation set is composed of the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. Then, the evaluation result of the i-th sample can be expressed as Y. i =X×R i i is 1 to 10; the evaluation level set K is identified, and the total fuzzy comprehensive evaluation score T is obtained by calculation. The calculation formula is T = Y × K; the higher the comprehensive score, the better the product freshness.

[0068] Example 2

[0069] This embodiment provides a method for preparing a composite indicator label for the freshness of fresh prepared meat products, which includes the following steps:

[0070] Step 1, Preparation of the preservation layer:

[0071] Prepare a 20 g / L zein solution by dissolving zein powder in 90% ethanol; prepare a 10% gelatin solution by dissolving gelatin in distilled water, and mix the zein solution with the gelatin solution. Keep the solution temperature at 50℃ and stir magnetically at 1000 rpm for 30 min. Add 2% thyme essential oil and 1 g dihydromyricetin, homogenize at high speed for 3 min, and then sonicate the solution to remove bubbles before use.

[0072] Step 2, Preparation of the indicator layer:

[0073] 2.1 For volatile nitrogen-containing compounds in the product, anthocyanins and beetroot blues that have color change responses and the same response range are screened as natural indicators;

[0074] 2.2 Add anthocyanins and betaine to the high-amylose corn starch / glucomannan (KGM) / chitosan / glycerol matrix, keep the solution temperature at 80℃, and use magnetic stirring to mix and dissolve them thoroughly to obtain an indicator solution. Use citric acid / sodium dihydrogen phosphate standard solution to adjust the pH of the indicator solution to 7.0, and then use ultrasonic defoaming to obtain the indicator solution.

[0075] 2.3. Measure a certain volume of indicator solution and inject it into a syringe that can be raised and lowered freely. Place the syringe in an electrospinning machine and use a dual-nozzle device for spinning. Finally, place the resulting spun film in a vacuum drying oven and dry it to constant weight to obtain the label indicator layer.

[0076] Step 3: Prepare a composite film using a layer-by-layer casting method. Pour the preservation solution prepared in Step 1 onto a model with an indicator film, and form a composite indicator label using supercritical drying. The drying temperature under supercritical conditions is 31℃, and the pressure is 7MPa.

[0077] This embodiment provides an application (method) of a composite indicator label for the freshness of prepared meat products, which uses the aforementioned composite indicator label for the freshness of prepared meat products and includes:

[0078] Based on the changes in the RGB values ​​of the indicator label colors, fuzzy normalization is performed using fuzzy mathematics to make a fuzzy comprehensive evaluation, which effectively distinguishes fresh and prepared pork of different freshness levels.

[0079] a. Establishment of factor set, comment set, and weighted set

[0080] The factor set consists of evaluation indicators. U1, U2, and U3 constitute the factor set U = {U1, U2, U3}; U1, U2, and U3 represent the sets of R, G, and B values, respectively.

[0081] The evaluation set consists of feedback information from evaluation indicators, including evaluation level information. Fresh (V1), not very fresh (V2), and spoiled (V3) constitute the evaluation set V = {V1, V2, V3}.

[0082] The weight set is represented by the proportion of each evaluation index in the overall evaluation. The weights form a weight domain called a fuzzy vector. Ten evaluators use a binary comparison method to compare the factors in the evaluation factor set U one-to-one. Important factors get 1 point, and less important factors get 0 points. The weight set is determined based on the score results and is represented by X.

[0083] b. Matrix Establishment and Transformation

[0084] Dividing the number of votes obtained from different indicators by 10 yields the fuzzy relation matrix R. The fuzzy relation evaluation set is composed of the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. The evaluation result of the i-th sample can then be represented as Y. i =X×R i (i is 1 to 10). The evaluation level set K is identified, and the total fuzzy comprehensive evaluation score T is calculated using the formula T = Y × K. A higher comprehensive score indicates better product freshness.

[0085] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for preparing a composite indicator label for the freshness of prepared meat products, characterized in that: Includes the following steps: Step 1, Preparation of the preservation layer: Prepare a 10-20 g / L zein solution by dissolving zein powder in 80%-90% ethanol; prepare a 5-10% gelatin solution by dissolving gelatin in distilled water, and mix the zein solution with the gelatin solution. Maintain the solution temperature at 50-80℃, and magnetically stir at 500-1000 rpm for 10-30 min. Add 0.05-2% thyme essential oil and 0.05-1 g dihydromyricetin, homogenize at high speed for 3 min, and then ultrasonically remove bubbles before use. Step 2, Preparation of the indicator layer: 2.1 To screen natural indicators that exhibit color change response and have the same response range for volatile gases containing spoilage characteristics in fresh and processed meat products; 2.2 Add the natural indicator to the high amylose corn starch / glucomannan / carrageenan / chitosan / glycerol matrix, keep the solution temperature at 50-80℃, and use magnetic stirring to mix and dissolve it thoroughly to obtain the indicator solution. Use citric acid / sodium dihydrogen phosphate standard solution to adjust the pH of the indicator solution to 6.0-7.0, and then use ultrasonic defoaming to obtain the indicator solution. 2.

3. Measure a certain volume of indicator solution and inject it into a syringe that can be raised and lowered freely. Place the syringe in an electrospinning machine and use a dual-nozzle device for spinning. Finally, place the resulting spun film in a vacuum drying oven and dry it to constant weight to obtain the label indicator layer. Step 3: Prepare a composite film using the layer-by-layer casting method. Pour the solution prepared in Step 1 onto a model with an indicator film, and form a composite indicator label by supercritical drying.

2. The method for preparing the composite indicator label for the freshness of prepared meat products according to claim 1, characterized in that: In step 2.1, the characteristic gases of putrefaction are one or more of nitrogen compounds, hydrogen sulfide, trimethylamine, and thiols.

3. The method for preparing the composite indicator label for the freshness of prepared meat products according to claim 1, characterized in that: In step 2.1, the natural indicator is one or more of anthocyanins, curcumin, alizarin, and betaine, with a mass fraction of 0.05%-0.5%.

4. The method for preparing the composite indicator label for the freshness of prepared meat products according to claim 1, characterized in that: In step 2.2, the mass fraction of high amylose corn starch is 0.5-3%, the mass fraction of glucomannan is 0.1-2%, the mass fraction of carrageenan is 0.5-2%, the mass fraction of chitosan is 0.5-2%, and the mass fraction of glycerol is 0.5-3%.

5. The method for preparing the composite indicator label for the freshness of prepared meat products according to claim 1, characterized in that: In step 2.3, the spinning parameters are set as follows: voltage -5-20kV, relative air humidity 20%, solution feed rate 1-10mL / h, drum speed 100-150r / min, fiber accumulation distance 15-20cm, and accumulation time 30min.

6. The method for preparing the composite indicator label for freshness of prepared meat products according to claim 1, characterized in that: In step 3, the supercritical drying method involves drying at a temperature of 30-40℃ and a pressure of 5-10MPa under supercritical conditions.

7. A composite label indicating the freshness of prepared meat products, characterized in that: It employs the method for preparing a composite indicator label for the freshness of prepared meat products as described in any one of claims 1-6.

8. The application of composite indicator labels for the freshness of prepared meat products, characterized in that: It uses the freshness composite indicator label for fresh prepared meat products as described in claim 7, and includes: performing fuzzy normalization processing based on the changes in the RGB values ​​of the composite indicator label color using fuzzy mathematics, and making a fuzzy comprehensive evaluation to effectively distinguish fresh prepared meat products with different freshness levels.

9. The application of the composite indicator label for freshness of prepared meat products according to claim 8, characterized in that: Fuzzy mathematics methods include the following steps: a. Establishment of factor set, comment set, and weight set The factor set consists of evaluation indicators. U1, U2, and U3 constitute the factor set U = {U1, U2, U3}; U1, U2, and U3 represent the sets of R, G, and B values, respectively. The comment set consists of feedback information from evaluation indicators, including evaluation level information. Fresh V1, less fresh V2, and rotten V3 constitute the comment set V = {V1, V2, V3}. The weight set is represented by the proportion of each evaluation index in the overall evaluation. The weights form the weight domain, which is called the fuzzy vector. By using a binary comparison method with 10-20 evaluators, the factors in the factor set U are compared one-to-one. Important factors get 1 point, and minor factors get 0 points. The weight set is determined based on the score results and is represented by X. b. Matrix Establishment and Transformation The fuzzy relation matrix R is obtained by dividing the number of votes for different indicators by the number of evaluators. The fuzzy relation evaluation set is composed of the weight set X and the fuzzy relation matrix R, denoted as Y = X × R. Then, the evaluation result of the i-th sample can be expressed as Y. i =X×R i i is 1 to 10; the evaluation level set K is identified, and the total fuzzy comprehensive evaluation score T is obtained by calculation. The calculation formula is T = Y × K; the higher the comprehensive score, the better the product freshness.

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