Model and method capable of rapidly evaluating shelf life of hotpot condiment

Abstract

The invention relates to the technical field of model prediction, and discloses a model and method capable of rapidly evaluating the shelf life of hotpot condiment, the model comprises a main prediction model and an auxiliary prediction model, the main prediction model takes sensory indexes as measurement indexes, and the auxiliary prediction model takes quality indexes as measurement indexes. The weight ratio of the evaluation result of the main prediction model to the evaluation result of the auxiliary prediction model is (0.7-0.9): (0.1-0.3). The increase of the acid value means that the degree of oxidative rancidity of grease is aggravated, which directly affects the flavor and quality of the hotpot condiment; the sensory score comprehensively considers the spice fragrance, numb feeling, grease fragrance and other visual feeling indexes of consumers, the spice fragrance, grease fragrance and other indexes change obviously, and deterioration of the flavor of the product can be visually reflected. The selection of the two closely surrounds a product deterioration core factor, and lays a foundation for the model to accurately predict the shelf life.

Description

Technical Field

[0001] The present invention relates to the technical field of model prediction, and in particular to a model and method for quickly evaluating the shelf life of hot pot soup base. Background Art

[0002] As a key indicator of food quality and safety, food shelf life is not a simple fixed value; rather, it represents a dynamic range determined by multiple factors, including the food's characteristics, packaging type, and production process. From a food's perspective, intrinsic factors such as the composition of raw materials, initial microbial content, and enzyme activity all have a fundamental impact on shelf life. For example, foods rich in fats and oils are susceptible to shortening their shelf life due to oxidative rancidity, while foods with high moisture content provide a breeding ground for microbial growth. The type of packaging is also crucial. Sealed packaging effectively isolates oxygen, moisture, and microorganisms, extending shelf life; breathable packaging is suitable for some foods that require respiration. Production process is even more crucial. Processes such as high-temperature sterilization and freeze-drying can significantly inhibit microbial growth and chemical reactions, enhancing food stability. These intertwined and mutually influential factors contribute to the vast variations in shelf life between different foods, making it difficult to measure using a single standard. In the food industry, the current national standards system lacks specific laws and regulations regarding shelf life determination methods, nor is there a unified standard for shelf life testing methods. This situation results in food manufacturers lacking an authoritative and unified reference for determining product shelf life, leading to irregularities in food shelf life labeling in the market, affecting consumers' judgment of food quality and posing challenges to food safety regulation. At the technical practice level, current methods for predicting food shelf life mainly rely on reference to destructive tests and data from similar products. Among destructive tests, the accelerated storage life test (ASLT test) is more commonly used. It accelerates the food spoilage process by simulating harsh environments such as high temperature and high humidity, thereby estimating the product shelf life in a short period of time. However, this method has limitations. Different foods have different sensitivities to environmental factors, and there are differences between the simulated environment and the actual storage environment, which may cause the estimated results to deviate from the actual shelf life. Although it is simple to refer to existing shelf life data for similar products, different companies have differences in production processes, raw material quality, etc., and simply copying may not truly reflect the actual shelf life of the product. Focusing on the specialty food of spicy hot pot base, accelerated shelf life assessment presents even greater challenges. Different brands of spicy hot pot base exhibit significant differences in key indicators such as moisture, salt, and pH. These factors directly impact microbial growth and chemical reaction rates, resulting in varying shelf lives. Existing shelf life prediction methods struggle to accurately adapt to the complex ingredient system and production process characteristics of spicy hot pot base. Therefore, there is an urgent need to develop a method that can rapidly assess the shelf life of hot pot base products. This would not only help companies appropriately label shelf life and improve product quality control, but also provide consumers with a reliable basis for purchasing, further ensuring food safety. Summary of the Invention

[0003] The present invention aims to provide a model and method for quickly evaluating the shelf life of hot pot soup base, so as to achieve rapid and accurate prediction of the shelf life of hot pot soup base.

[0004] To achieve the above-mentioned purpose, the present invention adopts the following technical solution: a model for quickly evaluating the shelf life of hot pot base, comprising a main prediction model and an auxiliary prediction model, wherein the main prediction model uses sensory indicators as measurement indicators, and the auxiliary prediction model uses quality indicators as measurement indicators, and the weight ratio of the evaluation results of the main prediction model to the evaluation results of the auxiliary prediction model is 0.7~0.9:0.1~0.3. Preferably, as an improvement, the sensory indicators include at least two of the following: numbness, spiciness, freshness, spice aroma and oil aroma.

[0005] Preferably, as an improvement, the factor set of the sensory evaluation index is spice aroma, numbness, and oil aroma, and the weight ratio of spice aroma, numbness, and oil aroma is 0.5:0.2:0.3.

[0006] Preferably, as an improvement, the quality indicator is at least one of acid value, peroxide value, and pH.

[0007] Preferably, as an improvement, the quality indicator is acid value.

[0008] Preferably, as an improvement, the main prediction model is t = (A-A0) / (k2×e （-Ea / RT） ), where: t is the product storage time / day, A is the final sensory score, A0 is the initial sensory score, k2 is the pre-exponential factor 2.61*10 16 , Ea is the activation energy of the reaction / (kJ / mol), T is the absolute temperature of storage / K, and R is the gas constant 8.314×10 －3 kJ / (mol·k).

[0009] Preferably, as an improvement, the auxiliary prediction model t = (A-A0) / (k2×e （-Ea / RT）), where: A is the ending acid value, A0 is the initial acid value, and k2 is the pre-exponential factor 6.26*10 11 , Ea is the activation energy of the reaction / (kJ / mol), T is the absolute temperature of storage / K, and R is the gas constant 8.314×10 －3 kJ / (mol·k).

[0010] Preferably, as an improvement, the ending acid value A is 3.5 mg / g.

[0011] Preferably, as an improvement, a method for quickly evaluating the shelf life of hot pot base is provided. The above-mentioned model for quickly evaluating the shelf life of hot pot base is used to calculate the shelf life of the sample to be tested using the main prediction model and the auxiliary prediction model respectively, and then the evaluation results of the main prediction model and the evaluation results of the auxiliary prediction model are calculated according to the weights of 0.7~0.9:0.1~0.3 to obtain the shelf life prediction value.

[0012] Preferably, as an improvement, the moisture content of the sample is 5-15%, and the salt content is 7-11%.

[0013] The principles and advantages of this solution are: In this technical solution, spicy clear oil hot pot base is taken as the research object, and a shelf life prediction model is constructed based on the principle of food deterioration kinetics and the relationship between chemical reaction rate and temperature. Spicy clear oil hot pot base is a semi-solid compound seasoning with high fat content. During shelf storage, oil oxidation causes an increase in acid value and peroxide value, and the rising trends of acid value and peroxide value are different at different stages. Oil oxidation can easily bring unpleasant taste and is not good for health. Acid value and peroxide value are generally used as the main evaluation indicators for the quality of high-fat products. During the shelf life of the clear oil hot pot base, when the acid value and peroxide value do not exceed the limit of the product implementation standard, the sensory characteristics of the product have shown obvious differences. In the process of model construction, this solution comprehensively evaluates the shelf life of spicy clear oil hot pot base by combining sensory and acid value shelf life models. Acid value and sensory evaluation were selected as key indicators. Pearson correlation analysis revealed a significant correlation between sensory scores and acid value at storage temperatures of 26°C, 36°C, and 46°C. Acid value had the strongest correlation with sensory evaluation among all physical and chemical indicators, with correlation coefficients reaching -0.996, -0.949, and -0.97 at different temperatures, respectively. An increase in acid value indicates increased oxidative rancidity of the oil, which directly impacts the flavor and quality of hot pot base. Sensory evaluation comprehensively considers consumer perceptions of spice aroma, numbness, and oil aroma. Significant changes in spice aroma and oil aroma can directly reflect flavor degradation. The selection of these two indicators closely addresses the core factors of product deterioration, laying the foundation for the model to accurately predict shelf life.

[0014] During the technology development phase, in the process of establishing the shelf life acceleration factor, the inventors tried to use the ASLT test to find the shelf life acceleration factor Q 10 , (Q 10 =θ S (T1) / θ S (T2),θ S (T1) represents the shelf life of the sample at 36°C in days, θ S (T2) represents the shelf life of the sample at 46℃ in days) Initially, the accelerated test was conducted at 36℃ and 46℃. It was found that the acid value and sensory changes of the samples at 36℃ and 46℃ were slightly different. The acid value Q was obtained according to the ASLT test. 10 =1.28, sensory Q 10 =1.5; the shelf life at room temperature of 25°C is 140 days, which is much lower than the actual shelf life of 660 days at 25°C. This makes it difficult to accurately predict and evaluate the shelf life of the product. (θ S (T) = θ S (T1) × Q 10 （△T / 10） ), θ S (T1) represents the shelf life in days at 36°C during the accelerated test, and ΔT represents an 11°C temperature difference. This indicates that using the ASLT test with only accelerated tests at 36°C and 46°C is insufficient to generate an accurate shelf life model. Therefore, the inventors added shelf life tracking experiments at 26°C to search for a model that could accurately measure shelf life.

[0015] In summary, the beneficial effects of this technical solution are: 1. Fast and accurate prediction: This model changes the traditional approach of relying on destructive testing or referencing shelf life data of similar products. It can quickly predict the shelf life of spicy hot pot base. At room temperature (25°C), the relative errors of the model based on sensory scores and acid value are -5.45% and -1.8%, respectively, demonstrating high accuracy.

[0016] 2. Innovation in evaluation methods: In terms of sensory evaluation, the fuzzy mathematics comprehensive evaluation method is used to determine the weight set of sensory indicators, and quantitatively evaluate the spice aroma, numbness, and oil aroma. The subjective sensory evaluation is converted into objective data, which improves the accuracy and reliability of the evaluation. It is an improvement and innovation of traditional sensory evaluation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 36°C during storage.

[0018] Figure 2 1 is a comparison chart of changes in different sensory indicators during storage at 46° C. in the examples of the present invention.

[0019] Figure 3 2 is a comparison chart of changes in different sensory indicators during storage at 26°C in the examples of the present invention.

[0020] Figure 4 This is a comparison chart of the changes in quality indicators during the constant temperature period of 36°C in the embodiments of the present invention.

[0021] Figure 5 A comparison chart of the changes in quality indicators during the 46°C constant temperature period in the embodiment of the present invention Figure 6 This is a comparison chart of the changes in quality indicators during a constant temperature of 26°C in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following is further described in detail through specific embodiments, but the embodiments of the present invention are not limited thereto. Unless otherwise specified, the technical means used in the following embodiments are conventional means well known to those skilled in the art; the experimental methods used are all conventional methods; and the materials, reagents, etc. used are all commercially available.

[0023] Program Overview: Target object of detection: The representative spicy hot pot base (water content: 5-15%, salt content: 7-11%) was selected as the test sample.

[0024] A method for quickly evaluating the shelf life of hot pot soup base comprises the following steps: Step 1: Establishment of an accelerated shelf life model: At storage temperatures of 26°C, 36°C, and 46°C, the key indicators affecting the shelf life of the samples were fitted linearly and nonlinearly using zero-order (Formula I) and first-order (Formula II) kinetic equations, respectively. B = B0 ± kt (Formula I) B=B0e ±kt (Formula II) Where: t is the storage time of the product / day; B is the index value at storage time t; B0 is the initial value of the index; k is the reaction rate constant of the index; The Arrhenius equation (Formula III) was used to reflect the relationship between reaction rate and temperature and to predict the shelf life of samples at different temperatures. k=k0×e （-Ea / RT） (Formula III) Where: k is the reaction rate constant; Ea is the activation energy of the reaction / (kJ / mol); T is the absolute temperature of the storage / K; k0 is the pre-exponential factor; R is the gas constant (8.314×10 －3 kJ / (mol·k)); Combining Equations I–III, we derived Equations (IV) and (V). Substituting Ea and k0 from the Arrhenius equations for the key indicators into Equation (IV) or (V) yielded a shelf-life prediction model based on these key indicators. Substituting the storage endpoint and initial values ​​of the key food indicators into the prediction model, the sample's shelf life was calculated, resulting in the predicted shelf life value. The predicted value was compared with the measured value, and the relative error ((predicted value - measured value) / measured value × 100%) was calculated to evaluate the reliability of the shelf-life prediction model.

[0025] t = (B-B0) / (k0×e （-Ea / RT） ) Formula (IV) t = (ln B - ln B0) / (k0 × e （-Ea / RT） ) Formula (V) Where: t is the shelf life / day.

[0026] Experimental Example 1: Determination of key indicators for measuring quality changes Samples that meet these conditions are placed in a thermostat at 26°C, 36°C, and 46°C for a gradient experiment, and the sample quality indicators are tested periodically. The quality indicators include acid value, peroxide value, pH, and sensory properties. The testing methods for each indicator are as follows: (1) Acid value test method: refer to GB 5009.229-2016 “Food Safety Standard - Determination of Acid Value in Foods”; (2) Peroxide value detection method: refer to GB 5009.227-2016 "Food Safety Standard - Determination of Peroxide Value in Food"; (3) pH value detection method: Weigh 10g of the mixed sample, add 90g of pure water, stir evenly and place at room temperature for 2 hours, then filter with absorbent cotton and detect the filtrate with a pH meter.

[0027] The test results show that during the acceleration period, the acid value and peroxide value of the samples continued to rise, while the pH and sensory properties continued to decline. Sensory properties were the most obvious indicator of change, followed by acid value.

[0028] Since the sensory quality of hot pot base is mainly evaluated from the four aspects of numbness, spiciness, freshness and fragrance, the sensory evaluation of the samples was conducted based on the numbness, spiciness, freshness, spice aroma and oil aroma. The specific sensory changes are shown below. Figure 1-3 As shown in the figure, during storage at different temperatures, the sensory radar chart shows that spiciness and freshness change the slowest compared to other indicators. In particular, spiciness has the smallest variability throughout the storage period, which is consistent with the changes in capsaicin content detected by HPLC.

[0029] By comparing and analyzing the changes in different sensory indicators, we selected the most variable sensory indicators—spice aroma (U1), numbness (U2), and oily aroma (U3)—as sensory evaluation indicators. The sensory intensity of each indicator was categorized as strong, average, weak, or unacceptable. The resulting fuzzy sensory analysis factor set for different samples at different time periods was U = {U1, U2, U3}. The weighted set of sensory indicators was determined to be R = {R1, R2, R3} = {0.5, 0.2, 0.3}, where spice aroma accounted for 0.5, numbness for 0.2, and oily aroma for 0.3. The fuzzy mathematical comprehensive evaluation was calculated as Y = Ry, where R is the weighted set and y is the fuzzy mathematical matrix. y is calculated by dividing the number of people who commented on each factor by the total number of evaluators. The total number of evaluators for this sensory evaluation was 10, all of whom had received professional sensory evaluation training. Based on the order of comparison for the fuzzy vector unification, the four grades (dark, normal, light, and unacceptable) were assigned scores of 90, 80, 60, and 40, respectively. Each quantity in the comprehensive evaluation result was multiplied by the corresponding score and summed to obtain the final score for each sample group.

[0030] During storage at different temperatures, the acid value, peroxide value, sensory score and pH changes of the samples were tracked and tested, as follows: Figure 4-6 As shown in the figure, throughout the entire experiment, acid value and peroxide value continued to increase, while pH and sensory scores continued to decrease. Sensory changes were the most obvious, followed by acid value. Using sensory scores as a metric for the accelerated experiment, a model for shelf life changes was established.

[0031] Experimental Example 2: Establishment of the kinetic model 1. Establish a dynamic model using sensory branches as measurement indicators Since sensory index is the most direct and simplest indicator to measure product deterioration, the sensory score during storage is used as the main indicator of quality deterioration kinetics. The samples stored at different temperatures (26℃, 36℃ and 46℃) are subjected to linear regression fitting of the kinetic equation sensory score y and storage time x to obtain the corresponding reaction rate constant k and determination coefficient R. 2 . Coefficient of determination R 2 The larger the value is, the better the fitting degree of the kinetic model is. The zero-order and first-order kinetic models at different temperatures are shown in Table 1 below. The zero-order kinetic coefficients at 36℃ and 46℃ are both higher than those of the first-order kinetic model R 2 It is larger, indicating that the changes of sensory indices of samples during accelerated storage are more consistent with the zero-order kinetic model.

[0032] Table 1 Kinetic model between storage time and sensory scores at different temperatures

[0033] At different accelerated storage temperatures (26°C, 36°C, and 46°C), the reaction rate constant k and storage temperature T in the zero-order kinetic model of the sample were substituted into the Arrhenius equation for fitting, and the Arrhenius curve of the sensory score was established: ln k = -12217.6 (1 / T) + 37.8; the corresponding activation energy EA was 101.577 kJ / mol. Combined with the zero-order quality deterioration kinetic model, a shelf life prediction model using sensory scores as quality indicators at different storage temperatures was obtained: model t = (A-A0) / (k2×e （-Ea / RT） )(A is the ending sensory score, A0 is the initial sensory score, k2 is the pre-exponential factor 2.61*10 16 ) The samples were placed at room temperature (25°C) and the predicted values ​​obtained by the shelf life prediction model were compared with the measured values. The predicted shelf life of the samples at 25°C was 696 days, and the relative error between this and the actual shelf life experimental verification days of 660 days was -5.45%, indicating that this model has certain application value in predicting the shelf life of such samples.

[0034] 2. Establish a kinetic model using acid value as a measurement indicator 2.1 Correlation Analysis By establishing Pearson correlations between quality indicators and sensory scores, the quality indicator with the strongest correlation to sensory scores was selected as an auxiliary factor for measuring quality change. Table 2 shows that at storage temperatures of 26°C, 36°C, and 46°C, the sensory scores of the samples showed significant correlations with acid value, peroxide value, and pH. Increased acid value and peroxide value were the primary cause of sample quality deterioration during storage. Among all physicochemical indicators, acid value had the strongest correlation with sensory scores, reaching an extremely significant level (26°C: r = -0.996; 36°C: r = -0.949; 46°C: r = -0.97). Therefore, acid value can be used as an auxiliary indicator in the sample kinetic equation and shelf life prediction model.

[0035] Table 2 Correlation analysis between sensory indices and other quality indices at storage temperatures of 26℃, 36℃ and 46℃

[0036] 2.2 Changes in acid value during accelerated shelf life Using acid value as an auxiliary indicator of quality deterioration kinetics, the samples stored at different temperatures (26℃, 36℃ and 46℃) were subjected to linear regression fitting of the kinetic equation of acid value y and storage time x, and the corresponding reaction rate constant k and determination coefficient R were obtained. 2 . Coefficient of determination R 2The larger the value is, the better the fitting degree of the kinetic model is. The zero-order and first-order kinetic models at different temperatures are shown in Table 3 below. The zero-order kinetic coefficients at 36℃ and 46℃ are both higher than those of the first-order kinetic model R 2 It is large, indicating that the change of AV of the sample during accelerated storage is more consistent with the zero-order kinetic model.

[0037] Table 3 Kinetic model between storage time and acid value at different temperatures

[0038] At different accelerated storage temperatures (26°C, 36°C, and 46°C), the reaction rate constant k and storage temperature T in the zero-order kinetic model of the sample were substituted into the Arrhenius equation for fitting, and the sensory Arrhenius curve was established: lnk=27.163-9802.82(1 / T); the corresponding activation energy EA was 81.5kJ / mol. Combined with the zero-order quality deterioration kinetic model, a shelf life prediction model using sensory quality indicators at different storage temperatures was obtained: model t=(A-A0) / (k2×e （-Ea / RT） ) (A is the ending acid value, A0 is the initial acid value, k2 is the pre-exponential factor 6.26*1011), combined with the sensory scores, an acid value of 3.5 mg / g was selected as the ending acid value.

[0039] The samples were placed at room temperature (25°C) and the predicted values ​​obtained by the shelf life prediction model were compared with the measured values. The predicted shelf life of the samples at 25°C was 672 days, and the relative error between this and the actual shelf life experimental verification days of 660 days was -1.8%, indicating that this model can be used to predict the shelf life of such samples.

[0040] Experimental Example 3: Model Combination The sensory evaluation model of this technical solution is direct and simple, and can quickly measure changes in sample quality during the shelf life, but it is greatly affected by human factors; the acid value evaluation model is accurate and scientific, but the acid value limit in the sample execution standard cannot be used as a direct indicator for quality measurement, and it needs to be combined with sensory factors.

[0041] This approach uses sensory evaluation as the primary prediction model for direct evaluation, and acid value as a secondary prediction model for auxiliary evaluation. The two are combined to comprehensively evaluate the shelf life of samples. The evaluation results of the primary and secondary prediction models are weighted in a ratio of 0.7-0.9:0.1-0.3. For spicy hot pot bases containing oil-based ingredients, this weighted ratio yields the closest prediction results to the product's actual shelf life. Weighted ratios outside this range result in a certain degree of prediction error. In practical applications, the appropriate weighting ratio for comprehensive evaluation can be selected based on the product characteristics of different spicy hot pot bases. The above is only an embodiment of the present invention, and the common knowledge such as the specific technical solutions and / or characteristics in the solution are not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A model for quickly evaluating the shelf life of hot pot soup base, characterized by: It includes a main prediction model and an auxiliary prediction model. The main prediction model uses sensory indicators as measurement indicators, and the auxiliary prediction model uses quality indicators as measurement indicators. The weight ratio of the evaluation results of the main prediction model to the evaluation results of the auxiliary prediction model is 0.7~0.9:0.1~0.

3.

2. A model capable of quickly evaluating the shelf life of hot pot soup base according to claim 1, characterized in that: The sensory indicators include at least two of the following: numbness, spiciness, freshness, spice aroma and oil aroma.

3. A model capable of quickly evaluating the shelf life of hot pot soup base according to claim 2, characterized in that: The factor set of the sensory evaluation index is spice aroma, numbness, and oil aroma, and the weight ratio of spice aroma, numbness, and oil aroma is 0.5:0.2:0.

3.

4. A model capable of quickly evaluating the shelf life of hot pot soup base according to claim 3, characterized in that: The quality indicator is at least one of acid value, peroxide value, and pH.

5. A model capable of quickly evaluating the shelf life of hot pot soup base according to claim 4, characterized in that: The quality indicator is acid value.

6. A model for quickly evaluating the shelf life of hot pot soup base according to claim 5, characterized in that: The main prediction model is t = (A-A0) / (k2×e （-Ea / RT） ), where: t is the product storage time / day, A is the final sensory score, A0 is the initial sensory score, k2 is the pre-exponential factor 2.61*10 16 , Ea is the activation energy of the reaction / (kJ / mol), T is the absolute temperature of storage / K, and R is the gas constant 8.314×10 －3 kJ / (mol·k).

7. A model for quickly evaluating the shelf life of hot pot soup base according to claim 6, characterized in that: The auxiliary prediction model t = (A-A0) / (k2×e （-Ea / RT） ), where: A is the ending acid value, A0 is the initial acid value, and k2 is the pre-exponential factor 6.26*10 11 , Ea is the activation energy of the reaction / (kJ / mol), T is the absolute temperature of storage / K, and R is the gas constant 8.314×10 －3 kJ / (mol·k).

8. A model capable of quickly evaluating the shelf life of hot pot soup base according to claim 7, characterized in that: The final acid value A is 3.5 mg / g.

9. A method for quickly evaluating the shelf life of hot pot soup base, characterized by: Using the model for quickly evaluating the shelf life of hot pot base as described in any one of claims 1 to 8, the shelf life of the sample to be tested is calculated using the main prediction model and the auxiliary prediction model respectively, and then the evaluation results of the main prediction model and the evaluation results of the auxiliary prediction model are calculated according to the weights of 0.7~0.9:0.1~0.3 to obtain the shelf life prediction value.

10. A method for quickly evaluating the shelf life of hot pot soup base according to claim 9, characterized in that: The sample has a moisture content of 5-15% and a salt content of 7-11%.

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