Shelf life prediction method and system, electronic equipment and storage medium

Abstract

The invention discloses a shelf life prediction method and system, electronic equipment and a storage medium, and relates to the technical field of shelf life determination. The method comprises the following steps: acquiring basic parameters of a target product; the basic parameters comprise storage initial stage index data and storage final stage index data; constructing a shelf life dynamic prediction model; the shelf life dynamic prediction model is constructed according to a transformation relation between pH and [H +] and an Arrhenius equation; and inputting the basic parameters into the shelf life dynamic prediction model, and performing prediction operation through the shelf life dynamic prediction model to obtain the shelf life of the target product. The shelf life prediction accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shelf life determination, and in particular to a shelf life prediction method, system, electronic equipment and storage medium. Background Art

[0002] The shelf life of a product may be affected by its specific formulation, processing technology and storage conditions. In addition, the physical, chemical and sensory characteristics of the product will gradually change with the extension of storage time until it is inedible to humans. Therefore, limited production resources and storage conditions will lead to a shorter shelf life of fermented products.

[0003] Taking yogurt, which is greatly affected by pH, as an example, as the storage time increases, the microbial risk and the possibility of physical and chemical changes will increase, so it is crucial to establish a digital model to predict the quality changes and shelf life of shelf products. pH is an important parameter affecting the quality of yogurt. Many existing studies have established shelf life prediction models based on pH at different storage temperatures. However, the results show that at higher temperatures, the product indicators change rapidly, resulting in a large relative error between the predicted value and the actual value of the shelf life prediction model based on pH. Summary of the invention

[0004] The purpose of the present invention is to provide a shelf life prediction method, system, electronic device and storage medium, which can improve the accuracy of shelf life prediction.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A shelf life prediction method, comprising:

[0007] Obtaining basic parameters of the target product; the basic parameters include storage initial index data and storage final index data;

[0008] Construct a shelf life kinetic prediction model; the shelf life kinetic prediction model is based on pH and [H + ] and the Arrhenius equation;

[0009] The basic parameters are input into the shelf life kinetic prediction model, and the shelf life kinetic prediction model is used to perform prediction operations to obtain the shelf life of the target product.

[0010] Optionally, the construction process of the shelf life kinetic prediction model is:

[0011] Construct zero-order reaction equations, first-order reaction equations, and Arrhenius equations;

[0012] Taking the logarithm of both sides of the Arrhenius equation, a linear relationship between the product quality change rate and 1000 / T is obtained;

[0013] Based on the variation law at different temperatures represented by the linear relationship, according to the Arrhenius equation and the relationship between pH and [H + ] are used to model the product quality change rate and obtain the shelf life kinetic prediction model.

[0014] Optionally, the formula of the zero-order reaction equation is:

[0015] A=A 0 -K a t

[0016] Among them, A 0 is the product characteristic under initial conditions; K a is the product quality change rate; t is the storage time.

[0017] Optionally, the formula of the first-order reaction equation is:

[0018]

[0019] Among them, A 0 is the product characteristic under initial conditions; K a is the product quality change rate; t is the storage time.

[0020] Optionally, the expression for the conversion relationship between pH and [H+] is:

[0021] [H + ]=10 -pH

[0022] Among them, [H + ] is the hydrogen ion concentration; pH is the acidity and alkalinity.

[0023] The present invention also provides a shelf life prediction system, comprising:

[0024] A data acquisition module, used to obtain basic parameters of the target product; the basic parameters include storage initial index data and storage final index data;

[0025] The model building module is used to build a shelf life kinetic prediction model; the shelf life kinetic prediction model is based on pH and [H + ] and the Arrhenius equation;

[0026] The model prediction module is used to input the basic parameters into the shelf life kinetic prediction model, perform prediction calculations through the shelf life kinetic prediction model, and obtain the shelf life of the target product.

[0027] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned shelf life prediction method.

[0028] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above-mentioned shelf life prediction method when executed by a processor.

[0029] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0030] The present invention discloses a shelf life prediction method, system, electronic device and storage medium. The method comprises obtaining basic parameters of a target product; the basic parameters comprise initial storage index data and final storage index data; constructing a shelf life kinetic prediction model; the shelf life kinetic prediction model is based on pH and [H + ] and the Arrhenius equation; the basic parameters are input into the shelf life kinetic prediction model, and the shelf life of the target product is obtained by performing a prediction operation through the shelf life kinetic prediction model. The present invention can improve the accuracy of shelf life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 Schematic diagram of the process of the shelf life prediction method of the present invention;

[0033] Figure 2 Schematic diagram of the change of yogurt pH at different storage temperatures in this embodiment; wherein, part (a) is a schematic diagram of the change of pH of sea buckthorn and coix seed yogurt; part (b) is a schematic diagram of the change of pH of potato and blueberry yogurt; and part (c) is a schematic diagram of the change of pH of corn yogurt. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The purpose of the present invention is to provide a shelf life prediction method, system, electronic device and storage medium, which can improve the accuracy of shelf life prediction.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, the present invention provides a shelf life prediction method, comprising:

[0038] Step 100: Obtain basic parameters of the target product; the basic parameters include storage initial index data and storage final index data;

[0039] Step 200: Construct a shelf life kinetic prediction model; the shelf life kinetic prediction model is based on pH and [H + ] and the Arrhenius equation;

[0040] Step 300: Input the basic parameters into the shelf life kinetic prediction model, perform prediction calculations using the shelf life kinetic prediction model, and obtain the shelf life of the target product.

[0041] Based on the above technical solution, the following embodiments are provided.

[0042] First prepare the product sample:

[0043] Rosa ciliata and coix seed yogurt: The method for preparing the yogurt sample is as follows: skimmed milk powder (17%), sucrose (4%) and water (41%) are mixed evenly, homogenized (6000r / min, 5min), heated at 90°C for 10min, cooled to room temperature, mixed with the previously fermented Rosa ciliata and coix seed rice fermentation liquid (36%), added with 2% activated lactic acid bacteria seed liquid (LB12:BLH1:Q-1=2:1:1), fermented at 39°C until the pH reaches between 4.6±0.1, fermentation is terminated, and post-ripening is carried out at 4°C for 24h.

[0044] Potato blueberry yogurt: The yogurt ingredients were mixed evenly, including potato saccharification liquid (33%), sucrose (3.7%), skimmed milk powder (14%), blueberry juice (22%), water (45%) and PO (1%). The mixture was homogenized at 20 MPa and heated at 90 ° C for 10 min. After cooling to room temperature, 4% yogurt culture (Thermophilic Streptococcus Q-1: Lactobacillus plantarum LB12: Bifidobacterium animalis BZ11 = 2:1:1) was inoculated. The inoculated mixture was fermented in a 38 ° C incubator for 12.5 h until the pH value reached 4.4. Finally, the yogurt was placed in a 4 ° C refrigerator for 24 h for further maturation.

[0045] Corn yogurt: Sweet corn extract was added to red sweet potato extract (15%), mung bean extract (10%), sugar (15%) and skimmed milk powder (10%). The next stage was pasteurization at 70°C for 15 min and then placed at 40-43°C. The next step was to add yogurt culture and incubate at 37°C for 8h.

[0046] The pH values ​​of the yogurt samples were measured using a digital pH meter calibrated with standard buffers of pH 4 and 7.

[0047] Shelf life prediction: Accelerated Shelf Life Testing (ASLT) is a method used to predict how long a food will maintain its quality and safety under different storage conditions. This method accelerates the aging process of food by simulating more extreme storage conditions in a shorter period of time, thereby quickly assessing the shelf life of food.

[0048] During food processing and storage, changes related to food quality mostly follow zero-order (1) or first-order (2) reaction models.

[0049] A=A 0 -K a t (1)

[0050]

[0051] Where A is the actual quality of the product characteristics during storage; A 0 is the product characteristic under initial conditions; K a is the product quality change rate; t is the storage time (d).

[0052] The Arrhenius equation is shown in (3).

[0053]

[0054] In the formula, K a is the product quality change rate; K a0 is the pre-exponential constant or absolute rate (independent of temperature); E ais the activation energy of the mass characteristic change reaction (kJ / mol); R is the ideal gas constant (8.314 J / mol·K); and T is the absolute temperature (K).

[0055] Taking the logarithm of both sides of equation (3) yields equation (4), as shown below:

[0056]

[0057] From formula (4), we can see that LnK a It has a good linear correlation with 1000 / T. Based on LnK a The variation law of K at different temperatures was calculated by Arrhenius equation. a Modeling was performed to establish a shelf life kinetic prediction model.

[0058] Where pH and [H + ] is:

[0059] [H + ]=10 -pH (5)

[0060] Results and discussion: Figure 2 As shown in Figure 2, the pH values ​​of samples at different temperatures continued to decrease during storage. Figure 2 Middle (a), Figure 2 Middle (b), Figure 2 (c) shows the pH changes of roxburghii and coix seed yogurt, potato and blueberry yogurt, and corn yogurt at different storage temperatures. The pH at different storage temperatures was converted into [H + ], and then the zero-order and first-order kinetic models were fitted to obtain the quality change kinetic parameters in Table 1.

[0061] Table 1 Kinetic parameters of quality change

[0062]

[0063]

[0064] In Table 1, reference case 1 is roxburghii and coix seed yogurt, reference case 2 is potato and blueberry yogurt, and reference case 3 is corn yogurt packaged in polyethylene terephthalate. As shown in Table 1, the pH and [H + ]R of the zero-order and first-order kinetic models fitted at different temperatures 2 All are different. Comprehensive comparison R 2 It can be seen that the first-order model of pH in the three reference cases is greater than the zero-order model, [H + ] are all larger than the first-order model, so pH and [H +] The first-order and zero-order models are more suitable for reflecting the changes in quality parameters during the storage process of the product.

[0065] Table 2 Parameters of shelf life prediction model at different storage temperatures

[0066]

[0067]

[0068] The quality changes of the products at different storage temperatures were respectively introduced into the corresponding prediction models. After fitting, the change rate constant Ka was obtained. The logarithm of the Ka value was taken to obtain LnKa. The parameters of the shelf life prediction model of the products at different storage temperatures are shown in Table 2. The pH and [H + Substituting the LnKa value of ] into the Arrhenius equation, the linear fitting relationship between LnKa and 1000 / T can be obtained.

[0069] According to the Arrhenius equation (4), the pH and [H + ] Ka 0 and activation energy Ea, as shown in Table 3 below.

[0070] Table 3 Shelf life prediction model parameters

[0071]

[0072] The pH and [H + ] Ka 0 Substituting Ea and Ea into formula (4) we can get:

[0073]

[0074]

[0075] Substituting equations (6), (8), (10) and equations (7), (9), (11) into the first-order kinetic model (2) and the zero-order kinetic model (1), we can obtain:

[0076]

[0077] Formulas (12) and (13) are the pH and [H + ] prediction model, formula (14) and (15) are the pH and [H + ] prediction model, formula (16) and (17) are the pH and [H +] prediction model. According to this model, when the initial and end-point indicators of a product at a certain temperature are known, its shelf life can be calculated. In addition, if the storage time and the indicators at the beginning of storage are known, the pH or [H + ] data indicators. Therefore, the model can determine two of them and predict the last one based on the product storage time, the initial storage indicator data and the indicator data at the storage time point.

[0078] The initial pH values ​​of the yogurts of roxburghii and coix seed rice, potato and blueberry, and corn were 4.47, 4.4, and 4.4, respectively, and the storage end point pH values ​​were all 4.0. + ] The prediction models for sea buckthorn and coix seed rice yogurt are (18) and (19), potato and blueberry yogurt are (20) and (21), and corn yogurt are (22) and (23):

[0079]

[0080] Table 4 shows the predicted and actual values ​​of the shelf life of the three yogurts. According to the deviation between the predicted and actual values, the accuracy and applicable range of the prediction model can be verified. + ] prediction model, the predicted storage time of sea buckthorn and coix seed rice yogurt at 5℃, 15℃ and 25℃ were 23, 10 and 5 days and 18, 7 and 3 days respectively; the predicted storage time of potato blueberry yogurt at 5℃, 15℃ and 25℃ were 37.6, 13.7 and 5.3 days and 39.8, 10.8 and 3.2 days respectively; the predicted storage time of corn yogurt at 35℃ were 22.7 and 23.2 respectively. The results show that yogurt has a longer storage time under low temperature conditions. The smaller the relative error between the predicted value and the actual value, the higher the model accuracy. At higher temperatures, the relative error of the shelf life prediction model based on pH is larger, which may be because the higher the temperature, the faster the product indicators change, and [H + ]'s shelf life prediction model shortens the relative error between the two and improves the accuracy of the prediction model. + The average relative errors of [H] were reduced by 31.95%, 32.97% and 2.39% compared with pH. + ]'s prediction model can achieve more satisfactory results in predicting the shelf life of yogurt and has higher prediction accuracy and reliability.

[0081] Table 4 Predicted and actual values ​​of shelf life

[0082]

[0083]

[0084] As can be seen from the above, the present invention aims to solve the problem that the relative error between the predicted value and the actual value of the shelf life prediction model based on pH in the high temperature storage stage is large, and converts pH into [H + ] to shorten the relative error between the two, and combine the Arrhenius equation to establish a shelf life prediction model to improve the accuracy of the prediction model. + ] as a quality index to reflect the quality changes of food during storage. The two indicators were fitted with zero-order and first-order kinetic models to obtain the kinetic parameters of quality change, and the regression coefficient R was comprehensively compared. 2 , select a suitable kinetic model to explain the quality changes of food during storage. Fit the fitted Ka value with the Arrhenius equation to obtain the linear fitting relationship between LnKa and 1000 / T. Finally, it was determined that the pH and [H + ] is the shelf life prediction model of the quality standard, and the accuracy of the model prediction results is compared. + ] prediction model, among the three reference cases [H + The average relative errors of ] were reduced by 31.95%, 32.97% and 2.39% compared with pH. + ]'s prediction model can achieve more satisfactory results in predicting the shelf life of yogurt. This method shortens the relative error between the predicted value and the actual value and improves the accuracy of the prediction model.

[0085] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0086] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A shelf life prediction method, characterized in that: include: Obtaining basic parameters of the target product; the basic parameters include storage initial index data and storage final index data; Construct a shelf life kinetic prediction model; the shelf life kinetic prediction model is based on pH and [H + ] and the Arrhenius equation; The basic parameters are input into the shelf life kinetic prediction model, and the shelf life kinetic prediction model is used to perform prediction operations to obtain the shelf life of the target product.

2. The shelf life prediction method according to claim 1, characterized in that: The construction process of the shelf life kinetic prediction model is as follows: Construct zero-order reaction equations, first-order reaction equations, and Arrhenius equations; Taking the logarithm of both sides of the Arrhenius equation, a linear relationship between the product quality change rate and 1000 / T is obtained; Based on the variation law at different temperatures represented by the linear relationship, according to the Arrhenius equation and the relationship between pH and [H + ] are used to model the product quality change rate and obtain the shelf life kinetic prediction model.

3. The shelf life prediction method according to claim 1, characterized in that: The formula of the zero-order reaction equation is: A=A0-K a t Among them, A0 is the product characteristic under the initial condition; K a is the product quality change rate; t is the storage time.

4. The shelf life prediction method according to claim 1, characterized in that: The formula of the first-order reaction equation is: Among them, A0 is the product characteristics under initial conditions; K a is the product quality change rate; t is the storage time.

5. The shelf life prediction method according to claim 1, characterized in that: The pH and [H + The expression of the transformation relationship between ] is: [H + ]＝10 -pH Among them, [H + ] is the hydrogen ion concentration; pH is the acidity and alkalinity.

6. A shelf life prediction system, characterized in that: include: A data acquisition module, used to obtain basic parameters of the target product; the basic parameters include storage initial index data and storage final index data; The model building module is used to build a shelf life kinetic prediction model; the shelf life kinetic prediction model is based on pH and [H + ] and the Arrhenius equation; The model prediction module is used to input the basic parameters into the shelf life kinetic prediction model, perform prediction calculations through the shelf life kinetic prediction model, and obtain the shelf life of the target product.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the shelf life prediction method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the shelf life prediction method according to any one of claims 1 to 5.

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