Method and program product for predicting fruit hardness and shelf life of prune during storage period

Abstract

The invention relates to the technical field of storage after picking of fruit trees, in particular to a method and program product for predicting fruit hardness and shelf life of prunes during storage. According to the method, the initial hardness of the prune fruits is measured, the temperature and time of the fruits in the cold chain transportation and cold storage storage process are recorded, and the hardness change of the prune fruits under different transportation conditions is predicted in combination with a hardness attenuation model. By comparing the actually measured fruit hardness with the predicted value, the residual shelf life of the prune fruit can be accurately judged. The method comprises the following specific steps: harvesting standardized prune fruits, measuring initial hardness, recording transportation and storage conditions, establishing a regression model of hardness and transportation and storage temperature, and predicting the shelf life. The method effectively reduces production waste, improves stability and accuracy in the storage and transportation process of the prune fruits, has the advantages of being easy and convenient to operate, high in accuracy and good in repeatability, and is suitable for cold-chain transportation and market supply management of the prunes.

Description

Technical Field

[0001] The invention relates to the technical field of post-harvest storage of fruit trees, and in particular to a method and a program product for predicting the hardness and shelf life of prunes during storage. Background Art

[0002] Xinjiang is the main production area of ​​prunes in my country. Its main variety, French prunes, is of excellent quality and has obvious competitive advantages in the market. However, the consumption areas are concentrated in the eastern region, and the transportation distance after harvest is long. Fresh prunes need to be transported over long distances to reach the consumption areas. Prunes are respiratory climacteric fruits. During transportation, fluctuations in transportation temperature are the main reason for the rapid decline in the shelf life of prunes. Excessive transportation temperature will accelerate the respiratory climacteric of prunes, accelerate fruit softening, shorten the shelf life, and affect storage life. Domestic prunes are concentrated on the market in August and September. It is difficult to accurately predict the shelf life, which can easily lead to unstable market supply and unnecessary waste. Periodic market gaps and oversupply are the main challenges facing the current fresh prune industry.

[0003] At present, the method for judging the shelf life of prunes mostly adopts manual sensory evaluation, which is easily affected by the subjective factors of the appraiser. In addition, the cold chain transportation temperature and time of prune fruits between different transport vehicles and transport batches are different, resulting in different remaining shelf life. Full coverage inspection in cold storage consumes a lot of manpower and material resources.

[0004] In addition, the above-mentioned determination method can only determine the current quality of prunes through real-time testing, and cannot determine the remaining life of the storage period based on the appearance characteristics of the product at a specific period, which is not conducive to the research and judgment of market sales forms. Summary of the invention

[0005] In order to solve the above-mentioned technical problems, the first purpose of the present invention is a method for predicting the hardness of prunes during storage. The method predicts the hardness attenuation value of prune fruits during storage under different transportation temperature conditions, and then predicts the hardness of prune fruits, and then predicts the remaining shelf life of prunes, effectively reducing production waste.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0007] A method for predicting the hardness of prunes during storage, the method using a predicted hardness model to calculate the predicted hardness of prunes, the predicted hardness model is as follows:

[0008] ;

[0009] Among them, F 0 is the initial hardness of prunes, T 1 is the temperature during cold chain transportation, t 1is the time in the cold chain transportation process, T 2 is the storage temperature, t 2 For storage time.

[0010] As a preferred embodiment, the initial hardness of the prune is F 0 The range is 0.01-5.00N.

[0011] As a preferred embodiment, the cold chain transportation temperature of the prunes is T 1 Range is 0-10℃, transportation time t 1 The range is 0-7 days.

[0012] Preferably, the storage temperature T 2 At 5°C, storage time t 2 The range is 0-90 days.

[0013] Furthermore, the present invention also provides a method for establishing a prediction hardness model of the method, the method comprising the following steps:

[0014] 1) Simulate cold chain transportation of prunes: Xinjiang France prunes of the same maturity are picked at the place of origin and immediately airlifted to the laboratory. They are placed in cold storages at 0, 5, and 10°C to simulate cold chain transportation. After 7 days of simulated transportation, the prunes are placed in cold storages at 0, 5, and 10°C for storage;

[0015] 2) Determination of prune fruit hardness: Determine the hardness of prune fruit after peeling in transportation and storage; 0 is the initial hardness, F 1 F is the firmness of prune fruit at the end of cold chain transportation. 2 is the firmness of prune fruit at any time under storage conditions;

[0016] 3) Establish a model of prune fruit hardness and transportation temperature: Origin 2022 software was used to process the experimental data. Based on the zero-order kinetic model, a regression equation of prune fruit hardness F1 and transportation time t1 at different transportation temperatures was established to obtain the reaction rate constant k 1 , thus obtaining the predicted value of fruit hardness under specific transportation temperature and transportation time; 1 The reaction rate constant k 1 Substituting into the Arrhenius equation, we get the prediction model of plum fruit firmness attenuation during cold chain transportation:

[0017] ;

[0018] 4) Establish a model of prune fruit hardness and storage temperature: Based on the first-order kinetic model, establish the prune fruit hardness F under different storage temperature conditions. 2 Storage time t 2The regression equation for the reaction rate constant k is obtained. 2 , thus obtaining the predicted value of fruit firmness under specific storage temperature and storage time; 2 The reaction rate constant k 2 Substituting into the Arrhenius equation, we get the prediction model of plum fruit firmness attenuation during storage:

[0019] .

[0020] Furthermore, the present invention also provides a method for predicting the shelf life of prunes using a prediction hardness model. The method adopts the prediction hardness model of the method, sets the storage hardness F' of prunes when the storage quality of prunes reaches the critical point of the storage period, and determines the shelf life t of prunes using the following formula:

[0021] ;

[0022] Among them, F 0 is the initial hardness of prunes, T 1 is the temperature during cold chain transportation, t 1 is the time in the cold chain transportation process, T 2 is the storage temperature, t 2 is the storage time, and F' is the storage hardness of prune fruit when the storage quality of prune reaches the critical point of storage period.

[0023] Preferably, the storage hardness F' of the prune fruit is set to 0.95-1.20N when the storage quality of the prune reaches the critical point of the storage period.

[0024] Furthermore, the present invention also provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the method.

[0025] Furthermore, the present invention also provides a computer-readable storage medium having a computer program or instruction stored thereon, and the method is implemented when the computer program or instruction is executed by a processor.

[0026] Furthermore, the present invention also provides a computer program product, comprising a computer program or instructions, which implement the method when executed by a processor.

[0027] The method for predicting the shelf life of fresh prunes of the present invention uses the correspondence between the preset shelf life of prunes and the fruit hardness attenuation value to predict the hardness attenuation value of prunes during storage under different transportation temperature conditions, and then predicts the remaining shelf life of prunes, effectively reducing production waste; the shelf life prediction operation is simple, fast, accurate, stable and has good repeatability. According to the length of the shelf life, it is helpful to optimize the selection of the cold chain transportation temperature of prunes, and can realize the rapid classification of prunes, thereby ensuring the quality and production stability of prunes. DETAILED DESCRIPTION

[0028] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with 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.

[0029] Example 1

[0030] The method for establishing the regression equation of the present invention is a conventional method unless otherwise specified, and the raw materials can be obtained from public commercial channels unless otherwise specified; the texture analyzer used is a TA.XT Plus texture analyzer (British SMS), and the test raw materials (prunes, French variety) are harvested from prune planting areas in Jiashi County, Kashgar Prefecture, Xinjiang Uygur Autonomous Region and Yining County, Yili Kazakh Autonomous Prefecture, with uniform size and uniform maturity, and no damaged, diseased or inferior fruits.

[0031] A method for establishing a model for predicting the shelf life of prune fruit, the steps are as follows:

[0032] (1) Simulating cold chain transportation of prunes: Xinjiang France prunes of the same maturity are picked at the place of origin and placed in cold storage at 0, 5, and 10°C to simulate cold chain transportation. After 7 days of simulated transportation, the prunes are placed in cold storage at 0, 5, and 10°C for storage;

[0033] (2) Determination of prune fruit hardness: Determine the hardness of the peeled prune fruit during transportation and storage. 0 is the initial hardness, F 1 F is the pulp hardness at any time under transportation conditions, 2 is the firmness of the pulp at any time under storage conditions;

[0034] (3) Establish a model of prune fruit hardness and transportation temperature: Origin 2022 software was used to process the experimental data, and based on the zero-order kinetic model, a model of prune fruit hardness F under different transportation temperature conditions was established. 1 With the transportation time t 1 The regression equation for the reaction rate constant k is obtained. 1, thus obtaining the predicted value of fruit firmness attenuation under specific transportation temperature and transportation time. 1 The reaction rate constant k 1 Substituting into the Arrhenius equation, we get the prediction model of plum fruit firmness attenuation during cold chain transportation:

[0035] (1);

[0036] (4) Establish a model of prune fruit hardness and storage temperature: Based on the first-order kinetic model, establish a model of prune fruit hardness F under different storage temperature conditions. 2 Storage time t 2 The regression equation for the reaction rate constant k is obtained. 2 , thus obtaining the predicted value of fruit firmness attenuation under specific storage temperature and storage time. 2 The reaction rate constant k 2 Substituting into the Arrhenius equation, we get the prediction model of plum fruit firmness attenuation during storage:

[0037] (2);

[0038] (5) Establishing a shelf life prediction model: The critical point of the shelf life is set when the hardness of prune fruit decays to 1.00N. Combining the fruit hardness and transportation temperature model and the fruit hardness and storage temperature model, the shelf life prediction model of prune fruit is obtained:

[0039] (3);

[0040] (6) Verification steps for the shelf life of prune fruit: record the temperature and time experienced by the prune fruit during storage and transportation, and enter them into the model to obtain the predicted hardness; measure the actual hardness of the prune fruit using a texture analyzer, and compare it with the fruit hardness predicted by the model to calculate the relative error.

[0041] In the present invention, formula (1) predicts the attenuation model of the hardness of French plums during cold chain transportation, and the specific operation is as follows:

[0042] 1) Determination of prune fruit firmness

[0043] Plum fruits that mature in mid-August were selected as the experimental raw materials, and the test samples were collected from randomly selected prune fruits in a simulated cold chain transportation process.

[0044] 2) Data analysis

[0045] The test data was processed using Origin 2022 software.

[0046] This experiment used fresh Xinjiang French plums that were airlifted to the laboratory after harvest as raw materials, and simulated cold chain transportation. The fruit hardness was measured after peeling every day. The results are shown in Table 1 below:

[0047] Table 1: Plum fruit hardness values ​​during transportation at three simulated transportation temperatures

[0048]

[0049] The hardness value of prune fruit is F. 1 With the transportation time t 1 Establish the regression equation:

[0050] Based on the zero-order kinetic model, the hardness F of prune fruit under different transportation temperatures was established. 1 With the transportation time t 1 The reaction rate constant k at different temperatures was obtained by using the regression equation 1 : The results of regression analysis are shown in Table 2:

[0051] Table 2: Results of regression analysis of plum fruit hardness during transportation at three simulated transportation temperatures

[0052]

[0053] Different transport temperatures T 1 The reaction rate constant k 1 Substituting the Arrhenius equation, we obtained the prediction model of plum fruit firmness attenuation during cold chain transportation. The fitting regression analysis results are shown in Table 3:

[0054] Table 3: Results of Arrhenius equation fitting regression analysis of prune fruit during transportation at three simulated transportation temperatures

[0055]

[0056] Regression results analysis:

[0057] As can be seen from Table 2, the reaction rate k value of the hardness decay of prune fruit during transportation is affected by the transportation temperature and increases with the temperature. The decay rate of prune fruit is the fastest at 10°C. The F values ​​for verifying the significance of the regression equation at 0°C, 5°C, and 10°C are 208151.398, 60270.102, and 335.458, respectively, and the sig. is 0.000<0.01, which is "extremely significant", indicating that the constituent regression equations are statistically extremely significant.

[0058] It can be seen from Table 3 that in the Arrhenius equation, the reaction rate k values ​​under 0℃, 5℃, and 10℃ conditions are fitted according to the equation to obtain a model of transport temperature and reaction rate, and its regression coefficient P value (sig.) is less than 0.01, which is extremely significant.

[0059] In the present invention, formula (1) predicts the attenuation model of the hardness of French prunes during storage. Prune fruits after cold chain transportation are selected as experimental materials, and the test samples are collected from randomly selected prune fruits in simulated low-temperature storage. The hardness of the fruit is measured after peeling every 7 days in the early stage of storage, and once every 10 days in the later stage. The obtained fruit hardness results are shown in Table 4 below:

[0060] Table 4: Results of regression analysis of plum fruit hardness during storage at three simulated transportation temperatures

[0061]

[0062] The hardness value of prune fruit is F. 2 With the transportation time t 2 Establish the regression equation:

[0063] Based on the first-order kinetic model, the hardness F of plum fruit at different storage temperatures was established. 2 With the transportation time t 2 The reaction rate constant k at different temperatures was obtained by using the regression equation 2 : The results of regression analysis are shown in Table 5:

[0064] Table 5: Results of regression analysis of plum fruit hardness during storage at three simulated transportation temperatures

[0065]

[0066] Different transport temperatures T 2 The reaction rate constant k 2 Substituting the Arrhenius equation, we get the prediction model of plum fruit firmness attenuation during cold chain transportation. The fitting results are shown in Table 6:

[0067] Table 6: Results of Arrhenius equation fitting regression analysis of prune fruit during storage at three simulated transportation temperatures

[0068]

[0069] Regression results analysis:

[0070] As can be seen from Table 5, the reaction rate k value of the firmness decay of prune fruit during storage is affected by the storage temperature and increases with the temperature. The decay rate of prune fruit is the fastest at 10°C. The F values ​​for verifying the significance of the regression equation at 0°C, 5°C, and 10°C are 520.947, 1404.665, and 201.777, respectively, and the sig. is 0.000<0.01, which is "extremely significant", indicating that the constituent regression equations are statistically extremely significant.

[0071] It can be seen from Table 6 that in the Arrhenius equation, the reaction rate k values ​​under 0℃, 5℃, and 10℃ conditions were fitted according to Arrhenius to obtain a model of storage temperature and reaction rate, and its regression coefficient P value (sig.) was less than 0.01, which was extremely significant.

[0072] Example 2

[0073] A method for predicting the shelf life of prunes, the steps are as follows:

[0074] (1) Samples to be tested: Harvest French prunes of uniform size, uniform maturity, and free of damaged, diseased, or inferior fruits at the prune producing area;

[0075] (2) Determination of initial hardness of prunes: After precooling and prestorage of prunes, the hardness of the peeled prunes was measured and recorded as initial hardness (F 0 );

[0076] (3) Record the temperature and time of cold chain transportation of prunes: Place a temperature recorder in the packaging box of prunes to record the temperature during cold chain transportation (T 1 ) and time (t 1 );

[0077] (4) Prediction of fruit hardness of prune fruit after cold chain transportation: Replace F in step (2) with 0 and the average transport temperature T in step (3) 1 , transportation time t 1 Bring it into the prediction model to obtain the hardness F of the prune fruit after cold chain transportation 1 , wherein the predicted hardness model is:

[0078] ;

[0079] F 0 is the initial hardness of prune fruit, F 1 The hardness of prune fruit after cold chain transportation;

[0080] (5) Comparison of the measured and predicted hardness of prunes after cold chain transportation: Prunes after transportation were selected and the hardness of the fruit after peeling (F 1 ') and the predicted value (F 1 ) for comparison;

[0081] (6) Record the storage temperature and time of prune fruit in cold storage: Record the storage temperature (T 2 ) and storage time (t 2 );

[0082] (7) Prediction of fruit hardness of prunes during storage: The initial hardness F in step (2) is 0, the cold chain transport temperature T in step (3) 1 and time t 1 , the storage temperature T in step (6) 2 and time t 2 Substitute into the prediction model to obtain the predicted hardness of the prune fruit, wherein the predicted hardness model is:

[0083] ;

[0084] (8) Determining the shelf life of prunes by fruit hardness: When the storage hardness of prunes is ＇ When it reaches 1.00N, the storage quality of prunes reaches the critical point of storage period, thus obtaining the shelf life of prunes under different transportation conditions, the formula is:

[0085] ;

[0086] (9) Comparison of measured and predicted values ​​of prune hardness during storage: Prune fruits were selected at any time during the storage period and their peeled fruit hardness (F 2 ') and the predicted value (F 2 ) to compare and calculate the difference;

[0087] Step (3) and step (6) transport temperature T 1 and storage temperature T 2 Take the average temperature.

[0088] Example 3

[0089] French prunes of uniform size, uniform maturity, and free of damaged, diseased, or inferior fruits were harvested in Yining County, Ili Kazakh Autonomous Prefecture, Xinjiang Uygur Autonomous Region. Twelve prunes were taken and the hardness of the prunes was measured after peeling. The average value was taken as the initial hardness. The cold chain transportation temperature and time were monitored. After storage, the prunes were grouped and stored according to the transportation temperature. Twelve prunes were selected from each group every 7 days to measure the hardness of the peeled prunes. The average value was the actual measured value of the fruit hardness at a specific time during the storage period (F 2 '). Other steps are the same as in Example 2, the measurement environment is 25°C, the ambient humidity is ≤85%, and the initial hardness, transportation and storage stage temperature and time are brought into the prediction model to obtain the predicted hardness (F). Set the predicted fruit hardness (F ＇ ) is 1.00N, the prune reaches its shelf life, and the shelf life of the prune fruit (t 2 ).

[0090] Example 4

[0091] French prunes of uniform size, uniform maturity, and free of damaged, diseased, or inferior fruits were harvested in Jiashi County, Kashgar Prefecture, Xinjiang Uygur Autonomous Region. Twelve prunes were taken and the hardness of the prunes was measured after peeling. The average value was taken as the initial hardness. The cold chain transportation temperature and time were monitored. After storage, the prunes were grouped and stored according to the transportation temperature. Twelve prunes were selected from each group every 7 days to measure the hardness of the peeled prunes. The average value was the actual measured value of the fruit hardness at a specific time during the storage period (F 2 '). Other steps are the same as in Example 2, the measurement environment is 25°C, the ambient humidity is ≤85%, and the initial hardness, transportation and storage stage temperature and time are brought into the prediction model to obtain the predicted hardness (F). Set the fruit hardness (F ＇ ) is 1.00N, the shelf life of prune fruit (t 2 ).

[0092] Example 5

[0093] In order to demonstrate the accuracy and stability of the shelf life prediction of the present invention, prune fruits with two different random transportation routes and different transportation times were verified, wherein the predicted hardness value was determined according to the prediction model of the present invention, and the actual predicted value was determined by a texture analyzer.

[0094] A batch of fresh French prunes were picked in Jiashi County, Kashgar, Xinjiang. The initial hardness was about 5.00N. After pre-cooling and packaging, a temperature monitor was placed in the packaging box to record the temperature experienced by the prunes during transportation and storage. After 5 days of cold chain transportation, the prunes with different transportation temperatures were stored in groups. After 55 days of storage in a 0-5℃ cold storage, the hardness of the prunes was measured using a texture analyzer to obtain the actual hardness value F. 2 '. The temperature and time experienced by the prune fruit during storage and transportation are put into the formula to obtain the predicted value F of the prune fruit hardness. 2 , compared with the measured values, the results are shown in Table 7 below:

[0095] Table 7: List of predicted and measured values ​​of fruit firmness F0 of plums during storage in Jiashi County

[0096]

[0097] A batch of fresh French prunes were picked in Yining County, Yili, Xinjiang. The initial hardness was about 4.00N. After pre-cooling and packaging, a temperature monitor was placed in the packaging box to record the temperature experienced by the prunes during transportation and storage. After 5.08 days of cold chain transportation, the prunes with different transportation temperatures were grouped and stored in a 0-5℃ cold storage. The hardness of the prunes was measured every 7 days using a texture analyzer to obtain the actual hardness value F. 2 '. The temperature and time experienced by the prune fruit during storage and transportation are put into the formula to obtain the predicted value F of the prune fruit hardness. 2, compared with the measured values, the results are shown in Table 8 below:

[0098] Table 8: List of predicted and measured values ​​of fruit hardness F0 of prunes during storage in Yining County

[0099]

[0100] During the storage and transportation of Kashgar prunes, after 5 days of transportation and 55 days of storage, the absolute value of the difference between the predicted value and the measured value of the prune fruit was 0.21 N at most and 0.01 N at most. During the storage and transportation of Yili prunes, after 5.08 days of transportation, the absolute value of the difference between the predicted value and the measured value was 0.70 N at most and 0.09 N at most. It can be seen that the predicted value detection result of the present invention is close to the measured value, and the prediction result has high accuracy, and can be applied to predict the fruit hardness of prune fruit after the storage and transportation process, and the longer the storage time, the closer the predicted value of the fruit hardness is to the actual predicted value.

[0101] When the fruit hardness is determined to be 1.00N, the commerciality of the prune fruit reaches a critical value, which is the critical point of the shelf life. It can be seen from this that the accuracy and stability of the shelf life prediction in the present invention can be applied to the determination of the shelf storage period of bulk commodities in actual operation, predict the remaining shelf life of prune fruits, and reduce production waste. Determining the length of the shelf life helps to select the temperature and time for cold chain transportation of prune fruits, provides guidance for loading and unloading, stacking, and grading during cold chain transportation of prune fruits, and ensures the stability of storage, transportation, and sales of prune fruit products.

[0102] The above is a description of the embodiments of the present invention. Through the above description of the disclosed embodiments, professionals and technicians in the field can implement or use the present invention. Various modifications to these embodiments will be apparent to professionals and technicians in the field. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will conform to the widest range consistent with the principles and novelties disclosed herein.

Claims

1. A method for predicting the hardness of prunes during storage, characterized in that: The method uses a prediction hardness model to calculate the predicted hardness F of the prune fruit, and the prediction hardness model is as follows: ； Among them, F0 is the initial hardness of prunes, T1 is the temperature during cold chain transportation, t1 is the time during cold chain transportation, T2 is the storage temperature, and t2 is the storage time.

2. The method according to claim 1, characterized in that: The initial hardness F0 of prunes ranges from 0.01 to 5.00N.

3. The method according to claim 1, characterized in that The cold chain transportation temperature T1 of the prunes ranges from 0 to 10°C, and the transportation time t1 ranges from 0 to 7 days.

4. The method according to claim 1, characterized in that: The storage temperature T2 is 5°C and the storage time t2 ranges from 0 to 90 days.

5. The method for establishing the prediction hardness model according to any one of claims 1 to 4, characterized in that: The method comprises the following steps: 1) Simulate cold chain transportation of prunes: Xinjiang France prunes of the same maturity are picked at the place of origin and immediately airlifted to the laboratory. They are placed in cold storages at 0, 5, and 10°C to simulate cold chain transportation. After 7 days of simulated transportation, the prunes are placed in cold storages at 0, 5, and 10°C for storage; 2) Determination of prune fruit hardness: Determine the hardness of prune fruit after peeling in transportation and storage conditions; where F0 is the initial hardness, F1 is the hardness of prune fruit at the end of cold chain transportation, and F2 is the hardness of prune fruit at any time under storage conditions; 3) Establish a model of prune fruit hardness and transportation temperature: Origin 2022 software was used to process the experimental data. Based on the zero-order kinetic model, a regression equation of prune fruit hardness F1 and transportation time t1 at different transportation temperatures was established to obtain the reaction rate constant k1, thereby obtaining the predicted value of fruit hardness at specific transportation temperature and transportation time; the reaction rate constant k1 at different transportation temperatures T1 was substituted into the Arrhenius equation to obtain the prediction model of prune fruit hardness attenuation during cold chain transportation: ； 4) Establish a model of prune fruit hardness and storage temperature: Based on the first-order kinetic model, a regression equation of prune fruit hardness F2 and storage time t2 under different storage temperatures was established to obtain the reaction rate constant k2, thereby obtaining the predicted value of fruit hardness under specific storage temperature and storage time; the reaction rate constant k2 at different storage temperatures T2 was substituted into the Arrhenius equation to obtain the prediction model of prune fruit hardness attenuation during storage: 。 6. A method for predicting the shelf life of prunes using a hardness prediction model, characterized in that: The method adopts the prediction hardness model of any one of claims 1 to 4, and sets the storage hardness F of the prune fruit when the storage quality of the prune reaches the critical point of the storage period. ＇ , this method uses the following formula to determine the shelf life t of prunes: ； Among them, F0 is the initial hardness of prunes, T1 is the temperature during cold chain transportation, t1 is the time during cold chain transportation, T2 is the storage temperature, t2 is the storage time, and F ＇ It refers to the storage hardness of prune fruit when the storage quality of prune reaches the critical point of storage period.

7. The method according to claim 6, characterized in that Set the storage hardness F of prune fruit when the storage quality of prune reaches the critical point of storage period ＇ 0.95-1.20N.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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