A method and program product for predicting the fruit firmness and shelf life of plums during storage

Abstract

The present invention relates to the technical field of postharvest storage of pomology, and in particular to a method and program product for predicting the fruit firmness and shelf life of plums during storage. This method measures the initial firmness of plum fruits, records the temperature and time of the fruits during cold chain transportation and cold storage, and combines a firmness decay model to predict the firmness change of plum fruits under different transportation conditions. By comparing the actually measured fruit firmness with the predicted value, the remaining shelf life of plum fruits can be accurately judged. The specific steps include harvesting standardized plum fruits, measuring the initial firmness, recording the transportation and storage conditions, establishing a regression model of firmness with transportation and storage temperature, and predicting the shelf life. This method effectively reduces production waste, improves the stability and accuracy of the storage and transportation process of plum fruits, has the advantages of simple operation, high accuracy, and good repeatability, and is applicable to the cold chain transportation and market supply management of plums.

Description

Technical Field

[0001] The present invention relates to the technical field of postharvest storage of pomology, and particularly to a method and a program product for predicting the fruit hardness and shelf life of plums during storage. Background Art

[0002] Xinjiang is the main producing area of plums in China. Its main cultivated variety, the French plum, has excellent quality and obvious competitive advantages in the market. However, the consumption areas are concentrated in the eastern regions, and the transportation distance after harvesting is long. Fresh plums need to be transported over a long distance to reach the consumption areas. Plums are climacteric fruits. During transportation, the fluctuation of transportation temperature is the main reason for the rapid decay of the shelf life of plum fruits. Too high transportation temperature will accelerate the respiratory climacteric of plum fruits, accelerate fruit softening, shorten the shelf life, and affect the storage life. Domestic plums are concentrated on the market from August to September. It is difficult to accurately predict the shelf life, which easily leads to unstable market supply and unnecessary waste. Periodic market gaps and oversupply are the main challenges faced by the current fresh plum industry.

[0003] At present, for the judgment method of the shelf life of plums, artificial sensory evaluation is mostly used. Sensory evaluation is easily affected by the subjective factors of appraisers. Moreover, the cold chain transportation temperature and time of plum fruits vary between different transport vehicles and transportation batches, resulting in different remaining shelf lives. Conducting a full-coverage inspection in the cold storage consumes a large amount of manpower and material resources.

[0004] In addition, the above judgment method can only determine the current quality of plums through real-time detection, and cannot determine the remaining life of the storage period according to the appearance characteristics of the product at a specific period, which is not conducive to the study and judgment of the market sales situation. Summary of the Invention

[0005] In order to solve the above technical problems, the first object of the present invention is a method for predicting the fruit hardness of plums during storage. This method predicts the hardness decay value of plum fruits during storage under different transport temperature conditions, and then predicts the fruit hardness of plums, and further can predict the remaining shelf life of plums, effectively reducing production waste.

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

[0007] A method for predicting the fruit hardness of plums during storage, which uses a predicted hardness model to calculate the predicted hardness of plum fruits. The predicted hardness model is as follows:

[0008] ;

[0009] wherein, F0 is the initial hardness of the plum, 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.

[0010] Preferably, the initial hardness F0 of the prunes ranges from 0.01 to 5.00 N.

[0011] Preferably, the temperature T1 for the cold-chain transportation of the prunes ranges from 0 to 10 °C, and the transportation time t1 ranges from 0 to 7 days.

[0012] Preferably, the storage temperature T2 is 5 °C, and the storage time t2 ranges from 0 to 90 days.

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

[0014] 1) Simulating the cold-chain transportation of prunes: Immediately after picking the Xinjiang French prunes with consistent maturity at the production area, they are air-transported to the laboratory and placed in cold storages at 0, 5, and 10 °C respectively to simulate the 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) Measuring the fruit hardness of prunes: Measuring the fruit hardness of the peeled prunes in the transportation state and the storage state; among them, F0 is the initial hardness, F1 is the fruit hardness of the prunes at the end of the cold-chain transportation, and F2 is the fruit hardness of the prunes at any time under the storage conditions;

[0016] 3) Establishing a model of the fruit hardness of prunes and the transportation temperature: Using Origin 2022 software to process the experimental data, based on the zero-order kinetic model, establishing a regression equation between the fruit hardness F1 of prunes and the transportation time t1 at different transportation temperatures, obtaining the reaction rate constant k1, and thus obtaining the predicted value of the fruit hardness at a specific transportation temperature and transportation time; Substituting the reaction rate constant k1 at different transportation temperatures T1 into the Arrhenius equation, obtaining a prediction model for the attenuation of the fruit hardness of prunes during the cold-chain transportation:

[0017] ;

[0018] 4) Establishing a model of the fruit hardness of prunes and the storage temperature: Based on the first-order kinetic model, establishing a regression equation between the fruit hardness F2 of prunes and the storage time t2 under different storage temperature conditions, obtaining the reaction rate constant k2, and thus obtaining the predicted value of the fruit hardness at a specific storage temperature and storage time; Substituting the reaction rate constant k2 at different storage temperatures T2 into the Arrhenius equation, obtaining a prediction model for the attenuation of the fruit hardness of prunes during storage:

[0019] 。

[0020] Further, the present invention also provides a method for predicting the shelf life of prunes using a predicted hardness model. The method uses the predicted hardness model of the above method 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. The method determines the shelf life t of the prune using the following formula:

[0021] ;

[0022] where F0 is the initial hardness of the prune, 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' is the storage hardness of the prune fruit when the storage quality of the prune reaches the critical point of the storage period.

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

[0024] Further, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the above method.

[0025] Further, the present invention also provides a computer-readable storage medium, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the above method is implemented.

[0026] Further, the present invention also provides a computer program product, including a computer program or instruction. When the computer program or instruction is executed by a processor, the above method is implemented.

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

[0028] The following combines the embodiments of the present invention and clearly and completely describes the technical solutions in the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0029] Example 1

[0030] The method for establishing the regression equation described in 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 type texture analyzer (SMS, UK). The test raw materials (Prunes, variety: French) were harvested from the Prune planting areas in Jiashi County, Kashgar Prefecture, Xinjiang Uygur Autonomous Region and Yining County, Ili Kazakh Autonomous Prefecture. They are of uniform size, consistent maturity, and free from defective, diseased, or substandard fruits.

[0031] A method for establishing a shelf life prediction model for Prune fruits comprises the following steps:

[0032] (1) Simulate the cold chain transportation of Prunes: Pick mature Prunes of French variety in Xinjiang with consistent maturity at the origin, and place them in cold storages at 0, 5, and 10 °C respectively to simulate cold chain transportation. After 7 days of simulated transportation, place the Prunes in cold storages at 0, 5, and 10 °C for storage;

[0033] (2) Measure the firmness of Prune fruits: Measure the firmness of the flesh of Prune fruits after peeling under the transportation state and the storage state. Among them, F0 is the initial firmness, F1 is the firmness of the flesh at any time under the transportation state, and F2 is the firmness of the flesh at any time under the storage condition;

[0034] (3) Establish a model of Prune fruit firmness and transportation temperature: Use Origin 2022 software to process the test data. Based on the zero-order kinetic model, establish a regression equation between the Prune fruit firmness F1 and the transportation time t1 under different transportation temperature conditions, obtain the reaction rate constant k1, and thus obtain the predicted value of the firmness decay of the fruit at a specific transportation temperature and transportation time. Substitute the reaction rate constant k1 in different transportation temperatures T1 into the Arrhenius equation to obtain the predicted model for the firmness decay of Prune fruits during cold chain transportation:

[0035] (1);

[0036] (4) Establish a model of Prune fruit firmness and storage temperature: Based on the first-order kinetic model, establish a regression equation between the Prune fruit firmness F2 and the storage time t2 under different storage temperature conditions, obtain the reaction rate constant k2, and thus obtain the predicted value of the firmness decay of the fruit at a specific storage temperature and storage time. Substitute the reaction rate constant k2 in different storage temperatures T2 into the Arrhenius equation to obtain the predicted model for the firmness decay of Prune fruits during storage:

[0037] (2);

[0038] (5) Establish a shelf life prediction model: Set the critical point of the shelf life when the firmness of Prune fruits decays to 1.00 N. Combine the fruit firmness and transportation temperature model and the fruit firmness and storage temperature model to obtain the Prune fruit shelf life prediction model:

[0039] (3);

[0040] (6) Validation steps for the shelf life of Prune fruits: Record the temperature and time experienced during the storage and transportation of Prune fruits, and substitute them into the model to obtain the predicted hardness; Measure the actual hardness of Prune fruits through a texture analyzer, and compare it with the fruit hardness predicted by the model to calculate its relative error.

[0041] In the present invention, the decay model for predicting the hardness of French Prune during cold chain transportation by formula (1) is specifically operated as follows:

[0042] 1) Measurement of the hardness of Prune fruits

[0043] Select Prune fruits ripe in mid-August as the test raw materials, and the test samples are randomly selected Prune fruits during the simulated cold chain transportation process.

[0044] 2) Data analysis

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

[0046] In this test, fresh Xinjiang French Prune transported by air to the laboratory after harvest is used as the raw material for simulated cold chain transportation. The hardness of the fruit is measured daily after peeling, and the results are shown in Table 1 below:

[0047] Table 1: Hardness values of Prune fruits during transportation at 3 groups of simulated transportation temperatures

[0048]

[0049] Establish a regression equation with the hardness value F1 of Prune fruits and the transportation time t1:

[0050] Based on the zero-order kinetic model, establish a regression equation between the hardness F1 of Prune fruits and the transportation time t1 under different transportation temperature conditions to obtain the reaction rate constant k1 at different temperatures: The regression analysis results are shown in Table 2:

[0051] Table 2: Fitting regression analysis results of the hardness of Prune fruits during transportation at 3 groups of simulated transportation temperatures

[0052]

[0053] Substitute the reaction rate constant k1 at different transportation temperatures T1 into the Arrhenius equation to obtain the prediction model for the hardness decay of Prune fruits during cold chain transportation. The fitting regression analysis results are shown in Table 3:

[0054] Table 3: Fitting regression analysis results of the Arrhenius equation for Prune fruits during transportation at 3 groups of simulated transportation temperatures

[0055]

[0056] Regression result analysis:

[0057] As can be seen from Table 2, the reaction rate constant k value of the hardness decay of Prunus domestica fruits during transportation is affected by the transportation temperature and increases with the increase of temperature. The decay rate of Prunus domestica fruits 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. values are all 0.000 < 0.01, which is "extremely significant", indicating that the components of the regression equation are all extremely significant statistically.

[0058] It can be obtained from Table 3 that in the Arrhenius equation, the reaction rate constant k values under the conditions of 0°C, 5°C, and 10°C are used to fit the equation to obtain the model of transportation temperature and reaction rate. The P value (sig.) of its regression coefficient is less than 0.01, showing extremely significant.

[0059] In the present invention, the decay model of the hardness of Prunus domestica during storage is predicted by formula (1). The Prunus domestica fruits after the end of cold chain transportation are selected as experimental materials, and the samples for detection are randomly selected Prunus domestica fruits in simulated low-temperature storage. The hardness of the fruits is measured every 7 days in the early stage of storage after peeling, and once every 10 days in the later stage. The results of the fruit hardness are shown in Table 4 below:

[0060] Table 4: Fitting regression analysis results of the hardness of Prunus domestica fruits during storage under 3 groups of simulated transportation temperatures

[0061]

[0062] A regression equation is established with the hardness value F2 of Prunus domestica fruits and the transportation time t2:

[0063] Based on the first-order kinetic model, the regression equations of the hardness F2 of Prunus domestica fruits and the transportation time t2 at different storage temperatures are established to obtain the reaction rate constants k2 at different temperatures: The regression analysis results are shown in Table 5:

[0064] Table 5: Fitting regression analysis results of the hardness of Prunus domestica fruits during storage under 3 groups of simulated transportation temperatures

[0065]

[0066] The reaction rate constants k2 at different transportation temperatures T2 are substituted into the Arrhenius equation to obtain the prediction model of the hardness decay of Prunus domestica fruits during cold chain transportation. The fitting results are shown in Table 6:

[0067] Table 6: Fitting regression analysis results of the Arrhenius equation of Prunus domestica fruits during storage under 3 groups of simulated transportation temperatures

[0068]

[0069] Regression result analysis:

[0070] As can be seen from Table 5, the reaction rate constant k value of the firmness decay of Prunus domestica fruits during storage is affected by the storage temperature and increases with the increase of temperature. The decay rate of Prunus domestica fruits 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. values are all 0.000 < 0.01, which is "extremely significant", indicating that the regression equations are all extremely significant statistically.

[0071] It can be obtained from Table 6 that in the Arrhenius equation, the reaction rate constant k values under the conditions of 0°C, 5°C, and 10°C are used to fit the model of storage temperature and reaction rate according to Arrhenius. The regression coefficient P value (sig.) of it is less than 0.01, showing extremely significant.

[0072] Example 2

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

[0074] (1) Harvest the samples to be inspected: Harvest the French Prunus domestica fruits with uniform size, consistent maturity, and no defective, diseased, or inferior fruits at the Prunus domestica production area;

[0075] (2) Measure the initial firmness of Prunus domestica: After pre-cooling and pre-storage treatment of Prunus domestica fruits, measure the firmness after peeling the fruits, and record it as the initial firmness (F0);

[0076] (3) Record the cold chain transportation temperature and time of Prunus domestica fruits: Place a temperature recorder in the transportation packaging box of Prunus domestica, and record the temperature (T1) and time (t1) during cold chain transportation;

[0077] (4) Prediction of the firmness of Prunus domestica fruits after cold chain transportation: Substitute F0 in step (2), the average transportation temperature T1 in step (3), and the transportation time t1 into the prediction model to obtain the firmness F1 of Prunus domestica fruits after cold chain transportation, where the prediction firmness model is:

[0078] ;

[0079] F0 is the initial firmness of Prunus domestica fruits, and F1 is the firmness of Prunus domestica fruits after cold chain transportation;

[0080] (5) Comparison between the measured value and the predicted value of the firmness of Prunus domestica after cold chain transportation: Select Prunus domestica after transportation, measure the firmness after peeling the fruits (F1') and compare it with the predicted value (F1);

[0081] (6) Record the cold storage temperature and time of prune fruits: Record the storage temperature (T2) and storage time (t2) of prune fruits during cold storage.

[0082] (7) Prediction of fruit firmness during prune storage: Substitute the initial firmness F0 in step (2), the cold chain transportation temperature T1 and time t1 in step (3), and the storage temperature T2 and time t2 in step (6) into the prediction model to obtain the predicted firmness of prune fruits, where the prediction firmness model is:

[0083] ;

[0084] (8) Determine the shelf life of prunes based on fruit firmness: When the storage firmness F ＇ of prune fruits reaches 1.00 N, the storage quality of prunes reaches the critical point of the storage period, thereby obtaining the shelf life of prune fruits under different transportation conditions. The formula is:

[0085] ;

[0086] (9) Comparison of the measured value and predicted value of fruit firmness during prune storage: Select prune fruits at any time during the storage period, measure the firmness of the peeled fruits (F2') and compare it with the predicted value (F2), and calculate the difference.

[0087] In steps (3) and (6), the transportation temperature T1 and storage temperature T2 are both taken as the average temperature.

[0088] Example 3

[0089] Harvest French prune fruits with uniform size, consistent maturity, and no damaged, diseased, or inferior fruits in Yining County, Ili Kazakh Autonomous Prefecture, Xinjiang Uygur Autonomous Region. Take 12 prune fruits, measure the firmness of the peeled prune fruits after peeling, and take the average value as the initial firmness. Monitor the cold chain transportation temperature and time. After storing in the warehouse, group and store according to the transportation temperature. Select 12 prune fruits every 7 days for each group of prunes to measure the firmness of the peeled fruits, and its average value is the measured value of the fruit firmness (F2') at a specific time during the storage period. Other steps are the same as in Example 2. The measurement environment is 25 °C, and the environmental humidity ≤ 85%. Substitute the initial firmness, the temperature and time in the transportation and storage stages into the prediction model to obtain the predicted firmness (F ＇ ). When the predicted fruit firmness (F

[0090] Example 4

[0091] In Jiashi County, Kashgar Prefecture, Xinjiang Uygur Autonomous Region, pick French plum fruits that are uniform in size, consistent in maturity, and free from damaged, diseased, or inferior fruits. Take 12 plum fruits, measure the hardness of the plum fruits after peeling, and take the average value as the initial hardness. Monitor the cold chain transportation temperature and time. After storage, group and store according to the transportation temperature. For each group of plums, select 12 plums every 7 days to measure the hardness of the fruits after peeling, and the average value is the measured value of the fruit hardness at a specific time during the storage period (F2'). Other steps are the same as in Example 2. The measurement environment is 25°C, and the environmental humidity is ≤85%. Substitute the initial hardness, the temperature and time during transportation and storage into the prediction model to obtain the predicted hardness (F). Set the fruit hardness (F ＇ ) to 1.00 N, and the shelf life (t2) of the plum fruits can be calculated.

[0092] Example 5

[0093] To demonstrate the accuracy and stability of the shelf life prediction of the present invention, verify with plum fruits from 2 randomly selected different transportation routes and different transportation times. Among them, the predicted hardness value is determined according to the prediction model in the present invention, and the actual predicted value is determined by a texture analyzer.

[0094] Pick a batch of fresh French plum fruits in Jiashi County, Kashgar, Xinjiang. The initial hardness is about 5.00 N. After pre-cooling and packaging, place a temperature monitor in the packaging box to record the temperature experienced by the plum fruits during transportation and storage. After 5 days of cold chain transportation, group and store the plums with different transportation temperatures, and store them in a 0-5°C cold storage for 55 days. Then use a texture analyzer to measure the hardness of the plum fruits to obtain the measured value of the fruit hardness F2'. Substitute the temperature and time experienced by the plum fruits during storage and transportation into the formula to obtain the predicted value F2 of the fruit hardness of the plum fruits, and compare it with the measured value. The results are shown in Table 7 below:

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

[0096]

[0097] Pick a batch of fresh French plum fruits in Yining County, Yili, Xinjiang. The initial hardness is about 4.00 N. After pre-cooling and packaging, place a temperature monitor in the packaging box to record the temperature experienced by the plum fruits during transportation and storage. After 5.08 days of cold chain transportation, group and store the plums with different transportation temperatures, and store them in a 0-5°C cold storage. Use a texture analyzer to measure the hardness of the plum fruits every 7 days to obtain the measured value of the fruit hardness F2'. Substitute the temperature and time experienced by the plum fruits during storage and transportation into the formula to obtain the predicted value F2 of the fruit hardness of the plum fruits, and compare it with the measured value. The results are shown in Table 8 below:

[0098] Table 8: List of comparison results of predicted and measured values of fruit hardness F0 of plums 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; The method for establishing the predicted hardness model 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: 。 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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