Method for predicting water migration during vacuum cooling of high-temperature baked goods

Abstract

The application discloses a kind of high-temperature baked food vacuum cooling process in the prediction method of moisture migration, method is: to high-temperature baked food sample Establishing porous medium model, the temperature, moisture content and pressure data of sample are predicted;Sample is placed in the vacuum cavity of vacuum cooling device, set vacuum cooling working condition parameter, start and begin vacuum cooling, and obtain pressure, temperature and moisture content data by collection;In the process of sample vacuum cooling, the transverse relaxation time T2 spectrum of sample in the process of vacuum cooling is collected using nuclear magnetic resonance moisture meter;According to the relationship between moisture content and T2 relaxation spectrum in the process of vacuum cooling Peak area, the function model of moisture content change in the process of vacuum cooling is obtained by fitting function;Verify porous medium model.The application can predict the moisture migration of high-temperature baked food in the process of vacuum cooling by establishing porous medium model, and is verified by vacuum cooling device, to avoid the subjectivity and tediousness of experiment.

Description

Technical Field

[0001] This invention belongs to the technical field of vacuum cooling, specifically relating to a method for predicting moisture migration during the vacuum cooling process of high-temperature baked foods. Background Technology

[0002] Pre-cooling technology, as the first link in the cold chain, effectively reduces the respiration rate of post-harvest food, inhibits microbial growth, and reduces enzyme activity, which is crucial for maintaining food quality and extending shelf life. After high-temperature baking, if the cooling rate is slow or the cooling time is too long, bacteria will proliferate rapidly in the temperature range of 4℃ to 60℃, causing food quality deterioration. Therefore, high-temperature baked goods require a rapid cooling technology to quickly pass through the "danger temperature zone." Unlike conventional conduction or convection heat transfer methods, vacuum cooling utilizes the rapid boiling phase change of water under low pressure to transfer heat, thereby lowering the temperature of high-temperature food to a preset temperature. It has advantages such as fast cooling rate, environmental friendliness, and energy saving, and is especially suitable for cooling high-temperature porous foods. Vacuum cooling is a food cooling technology with great development potential; however, due to problems such as high water loss rate and temperature uniformity, the improvement and optimization of vacuum cooling equipment has always been a key focus for researchers both domestically and internationally.

[0003] Currently, obtaining temperature changes and moisture migration during vacuum cooling processes primarily relies on experimental measurements. Numerical simulation, however, is a computer simulation method that effectively describes the heat and mass transfer mechanisms of vacuum cooling processes. Compared to experimental methods, computer simulation offers advantages such as lower cost, ease of operation, and comprehensive data acquisition. Therefore, numerical simulation has significant advantages for equipment design, process optimization, and product control in the food industry. Currently, researchers have established various numerical models for vacuum-cooled cooked meat products and leafy vegetables, which can predict temperature and moisture content changes in food during vacuum cooling relatively well; however, issues such as model complexity and the structural diversity of food products still need to be addressed. Furthermore, experimental verification is the most crucial step in demonstrating the accuracy of numerical models, but existing methods for predicting vacuum cooling processes still have some shortcomings and limitations.

[0004] (1) The numerical models for different foods in the vacuum cooling process have different focuses and are not universal; (2) Foods are diverse and their physical properties and transfer parameters in the processing process are different; (3) The model verification device generally uses thermocouples to measure the temperature of local points, which is not representative; (4) The model verification device uses a weight sensor to measure weight loss and takes it as the amount of water loss.

[0005] Therefore, establishing a set of accurate and comprehensive data verification methods is essential for the improvement of numerical models. Summary of the Invention

[0006] The main objective of this invention is to overcome the shortcomings and deficiencies of the prior art and provide a method for predicting moisture migration during the vacuum cooling process of high-temperature baked foods. By establishing a porous medium model for high-temperature baked food samples, the temperature, moisture content and pressure data of the samples are predicted. Then, a vacuum cooling device is used to perform dual data verification, and a functional model of moisture migration during the vacuum cooling process is obtained by fitting.

[0007] To achieve the above objectives, this invention employs a method for predicting moisture migration during the vacuum cooling process of high-temperature baked foods, comprising the following steps:

[0008] S1. Use the computer simulation software COMSOL Multiphysics to establish a porous media model for high-temperature baked food samples and predict the temperature, moisture content and pressure data of the samples.

[0009] S2. Place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters, start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor; collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer; measure the weight loss during the vacuum cooling process through a weight sensor to obtain the moisture content data.

[0010] S3. During the vacuum cooling process of the sample, the transverse relaxation time T2 spectrum was collected using a nuclear magnetic resonance moisture analyzer.

[0011] S4. Based on the relationship between moisture content and peak area in the transverse relaxation time T2 spectrum during vacuum cooling, a functional model of moisture content change during vacuum cooling is obtained by fitting a function.

[0012] S5. Compare the temperature, moisture content and pressure data of the sample predicted by the porous media model with the fitted function model to verify the porous media model.

[0013] As a preferred technical solution, the establishment of the porous medium model specifically includes:

[0014] The external dimensions of high-temperature baked food samples were obtained using measuring tools, and a matching geometric model was built in the computer simulation software COMSOL Multiphysics.

[0015] The geometric model and physical field structure parameters are set, including: setting boundary conditions and initial conditions according to the working conditions required for vacuum cooling; setting physical property parameters according to the characteristics of food materials; using adaptive mesh generation to form a mesh for the geometric model; setting a transient solver for solving the problem; setting the physical field interface as a fluid heat transfer interface, Darcy's law interface, and rare substance transfer interface to calculate the temperature T, pressure P, and moisture content C of the geometric model during the vacuum cooling process.

[0016] A porous medium model under vacuum cooling process is established based on thermodynamic principles to analyze its heat and mass transfer characteristics.

[0017] As a preferred technical solution, the governing equations of the porous media model are:

[0018] 1) Mass transfer behavior of liquid water:

[0019]

[0020] In the formula, ρ w The density of liquid water in the sample is expressed in kg / m³. 3 S w t represents the liquid water saturation level; t represents the vacuum cooling time in seconds; u w D is the flow rate of liquid water in the sample, expressed in m / s; w,cap It is the capillary diffusivity of liquid water in the pores of the sample, expressed in meters. 2 / s; It is the evaporation rate of free water in the sample, expressed in kg / m³. 3 ·s, The porosity of the sample;

[0021] 2) Mass transfer behavior of water vapor:

[0022]

[0023] In the formula, ρ v The density of water vapor in the sample is expressed in kg / m³. 3 S v The water vapor saturation in the sample; u v D is the flow rate of water vapor in the sample, expressed in m / s; v It is the diffusion coefficient of water vapor in the sample, with units of m. 2 / s;

[0024] 3) Heat transfer behavior:

[0025]

[0026] In the formula, the effective parameter ρ eff C P,eff 、(ρCP u) fluid k eff Defined as:

[0027]

[0028]

[0029]

[0030]

[0031] Where, ρ eff Effective density, unit: kg / m³ 3 ;ρ s The density of the solid matrix in the sample;

[0032] C P,eff The effective specific heat capacity is expressed in J / kg·K; m s This represents the total mass fraction of the solid phase in the sample. m is the specific heat capacity of the solid matrix in the sample. w This represents the total mass fraction of the liquid aqueous phase in the sample. m is the specific heat capacity of liquid water in the sample. v This represents the total mass fraction of the water vapor phase in the sample. The specific heat capacity of water vapor in the sample;

[0033] k eff The effective thermal conductivity is expressed in J / m·K·s; k v k is the thermal conductivity of water vapor in the sample. w k is the thermal conductivity of the liquid water in the sample. s The thermal conductivity of the solid matrix in the sample;

[0034] (ρC P u) fluid For fluid flow terms; ρ is density, in kg / m³. 3 C P is specific heat capacity, measured in J / kg·K; u is fluid velocity, measured in m / s; For pressure gradient;

[0035] T is the sample temperature in K; λ is the latent heat of vaporization of water vapor in the sample in J / kg.

[0036] 4) Stress item:

[0037]

[0038]

[0039]

[0040] Where i is the liquid water phase or water vapor phase; u i K represents the flow velocity of fluid i, in m / s. i This is the penetration rate of i, in meters. 2 μ i K is the viscosity of fluid i, measured in Pa·s; evap M is the evaporation coefficient; v ρ represents the relative mole fraction of water vapor, in kg / mol; R is the gas constant, in J / (mol*K); p v,eq The equilibrium vapor pressure is expressed in Pa; p v This refers to water vapor pressure, measured in Pa.

[0041] 5) Specific heat capacity formula

[0042]

[0043] 6) Thermal conductivity formula

[0044] K = 0.006 w +0.120(12)

[0045] 7) Formulas for heat transfer coefficient and mass transfer coefficient

[0046]

[0047]

[0048] Among them, M w C represents moisture content. p ρ is the specific heat capacity of the sample, in J / kg·K; K is the thermal conductivity of the sample's solid matrix, in J / m·K·s; Nu is the Nusselt number; h t The convective heat transfer coefficient is expressed in J / m³. 2 ·K·s; L is the characteristic length in meters; Re is the Reynolds number; Pr is the Prandtl number; Sh is the Sherwood number; h m Sc is the mass transfer coefficient, in m / s; Sc is the Schmidt number.

[0049] The Reynolds number and Schmidt number are calculated based on fluid properties, using the following formula:

[0050]

[0051]

[0052] Where, ρ aair density; μ a ρ represents air viscosity; v represents air velocity in m / s; D represents the fluid diffusion coefficient in m³ / s. 2 / s.

[0053] As a preferred technical solution, the vacuum cooling device includes a vacuum chamber, a pressure sensor, a temperature sensor, an infrared thermometer, a weight sensor, a vacuum pump, a venting valve, a gas storage tank, a regulating valve, and a processor.

[0054] The pressure sensor is connected to the vacuum chamber to measure the pressure inside;

[0055] The temperature sensor and infrared thermometer are used to measure the temperature of the sample during vacuum cooling.

[0056] The weight sensor is placed below the sample and is used to measure the weight loss of the sample during vacuum cooling.

[0057] The vacuum pump is connected to the vacuum chamber via a gas storage tank;

[0058] The regulating valve is located on the connecting pipe between the vacuum pump and the gas storage tank;

[0059] The vent valve is located between the gas storage tank and the vacuum chamber, and is used to connect the vacuum chamber to the external environment.

[0060] As a preferred technical solution, step S2 specifically includes:

[0061] Place the sample on the weight sensor in the vacuum chamber; place the temperature sensors on the surface and center of the sample respectively; close the cover of the vacuum chamber to make the vacuum chamber a sealed state.

[0062] Set the depressurization rate and final temperature required for vacuum cooling of the sample, turn on the temperature sensor, infrared thermometer, weight sensor and pressure sensor, and monitor and collect temperature, weight and pressure data during the vacuum cooling process;

[0063] Turn on the vacuum pump to start vacuum cooling. Stop when the sample temperature drops to the set temperature. Open the vent valve to restore the pressure inside the chamber to atmospheric pressure.

[0064] The processor processes the temperature, weight, and pressure data collected during the vacuum cooling process to obtain temperature, moisture content, and pressure data during the vacuum cooling process.

[0065] As a preferred technical solution, in step S3, the transverse relaxation time T2 spectrum of the sample before vacuum cooling is acquired using the CPMG pulse sequence parameters of the nuclear magnetic resonance moisture analyzer.

[0066] During vacuum cooling, the transverse relaxation time T2 spectrum of the sample was continuously acquired every 50s using the same CPMG pulse sequence parameters until the vacuum cooling was completed.

[0067] As a preferred technical solution, the CPMG pulse sequence parameters include: sampling frequency: 200kHz; main frequency: 40MHz; RF delay: 0.08~1ms; analog gain: -3~40dB; digital gain: 0~7; waiting time: 1000ms; number of accumulations: 4, 8; echo time: 0.10~1ms; number of echoes: 1000~18000.

[0068] As a preferred technical solution, step S4 specifically includes:

[0069] Based on the transverse relaxation time T2 spectrum of the sample during vacuum cooling and the data recorded by the weight sensor during vacuum cooling, the peak area and sample mass change data in the T2 relaxation spectrum were extracted, and a linear fitting relationship was established.

[0070] Based on the established linear fitting relationship, a functional model is established between the peak area y and the moisture content x in the transverse relaxation time T2 spectrum. That is, the change in moisture content during the vacuum cooling process is verified by the change in the peak area in the transverse relaxation time T2 spectrum.

[0071] After obtaining the function model, a regression analysis was performed with the moisture content data predicted by the porous media model to calculate the coefficient of determination R. 2 The accuracy of the porous media model was evaluated. At the same time, the temperature data predicted by the porous media model was verified by the temperature data collected by the temperature sensor and the infrared thermometer during vacuum cooling. The pressure data predicted by the porous media model was verified by the pressure data collected by the pressure sensor during vacuum cooling.

[0072] On the other hand, the present invention also provides a prediction system for moisture migration during vacuum cooling of high-temperature baked foods, which is applied to the above-mentioned prediction method for moisture migration during vacuum cooling of high-temperature baked foods. The system includes a model prediction module, a data acquisition module, a T2 spectrum acquisition module, a relationship fitting module, and a model verification module.

[0073] The model prediction module is used to establish a porous media model for high-temperature baked food samples using the computer simulation software COMSOL Multiphysics, and to predict the temperature, moisture content and pressure data of the samples.

[0074] The data acquisition module is used to place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters, start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor, collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer, and measure the weight loss during the vacuum cooling process through a weight sensor to obtain moisture content data.

[0075] The T2 spectrum acquisition module is used to acquire the transverse relaxation time T2 spectrum of the sample during the vacuum cooling process using a nuclear magnetic resonance moisture analyzer.

[0076] The relationship fitting module is used to obtain a functional model of the change in moisture content during vacuum cooling by fitting a function based on the relationship between moisture content and peak area in the transverse relaxation time T2 spectrum during vacuum cooling.

[0077] The model validation module is used to compare the temperature, moisture content and pressure data of the sample predicted by the porous media model with the fitted function model to validate the porous media model.

[0078] In another aspect, the present invention provides a computer-readable storage medium storing a program in a memory, wherein when the program is executed by a processor, it implements the above-described method for predicting moisture migration during vacuum cooling of high-temperature baked foods.

[0079] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0080] 1. This invention uses the computer simulation software COMSOL Multiphysics to establish a porous media model to predict temperature changes and moisture migration in high-temperature baked foods during vacuum cooling, and has universal applicability.

[0081] 2. By constructing empirical formulas related to moisture content (specific heat capacity, thermal conductivity, transfer coefficient, and mass transfer coefficient), the physical properties of high-temperature baked foods can be predicted more accurately, avoiding the subjectivity and cumbersomeness of experiments.

[0082] 3. By using a temperature sensor (infrared thermometer) and a weight sensor (nuclear magnetic resonance moisture analyzer) for dual data verification, the temperature transfer and moisture migration during the vacuum cooling process of food can be predicted in real time and accurately, providing guidance for further development of efficient vacuum cooling technology and equipment upgrades. Attached Figure Description

[0083] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0084] Figure 1 This is a flowchart illustrating a method for predicting moisture migration during vacuum cooling of high-temperature baked goods, as described in an embodiment of the present invention.

[0085] Figure 2 This is a schematic diagram of the vacuum cooling device in an embodiment of the present invention.

[0086] Figure 3 This is a pressure change curve inside the vacuum cavity in an embodiment of the present invention.

[0087] Figure 4 This is a graph showing the change between the predicted temperature and the measured temperature of the sample in an embodiment of the present invention.

[0088] Figure 5 This is a graph showing the changes in predicted and measured moisture content of samples in an embodiment of the present invention.

[0089] Figure 6 The T2 relaxation spectrum of the sample in this embodiment of the invention was obtained after vacuum cooling in a nuclear magnetic resonance moisture analyzer.

[0090] Figure 7 The images shown are infrared thermal images of the sample before and after vacuum cooling in a vacuum cooling device, as described in this embodiment of the invention.

[0091] Figure 8 This is a graph showing the fitting relationship between the moisture content of the sample and the peak area of ​​the T2 relaxation spectrum in an embodiment of the present invention.

[0092] Figure 9 This is a structural diagram of the prediction system for moisture migration during the vacuum cooling process of high-temperature baked food in an embodiment of the present invention.

[0093] Figure 10 This is a structural diagram of a computer-readable storage medium according to an embodiment of the present invention.

[0094] Explanation of reference numerals in the attached diagram: 1. Vacuum pump, 2. Regulating valve, 3. Gas storage tank, 4. Venting valve, 5. Pressure sensor, 6. Vacuum chamber, 7. Infrared thermometer, 8. Weight sensor, 9. High-temperature baked food sample, 10. Temperature sensor, 11. Processor. Detailed Implementation

[0095] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0096] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0097] like Figure 1 As shown in the figure, this embodiment discloses a method for predicting moisture migration during vacuum cooling of high-temperature baked foods, including the following steps:

[0098] S1. Use the computer simulation software COMSOL Multiphysics to establish a porous media model for high-temperature baked food samples and predict the temperature, moisture content and pressure data of the samples.

[0099] The microstructure of high-temperature baked foods is naturally formed during fermentation and baking, consisting of tiny pores trapped in the dough. These pores are filled with water and gas, forming a perfect porous medium. Currently, many studies have used food as a porous medium to establish heat transfer models, achieving good predictive results. However, due to differing understandings of the heat transfer mechanism in vacuum cooling processes among researchers, heat transfer models for food vacuum cooling processes vary. This invention considers the most important convective heat transfer, conduction, and phase change heat transfer processes in vacuum cooling, as well as the mass transfer processes of diffusion, fluid flow, and capillary diffusion, to establish a universal porous medium model applicable to high-temperature baked foods. The specific steps for establishing the porous medium model are as follows:

[0100] The external dimensions of high-temperature baked food samples were obtained using measuring tools, and a matching geometric model was built in the computer simulation software COMSOL Multiphysics.

[0101] The geometric model and physical field structure parameters are set, including: setting boundary conditions and initial conditions according to the working conditions required for vacuum cooling; setting physical property parameters according to the characteristics of food materials; using adaptive mesh generation to form a mesh for the geometric model; setting a transient solver for solution calculation; the physical field interface adopts the fluid heat transfer interface, Darcy's law interface and rare matter transfer interface built into the computer simulation software COMSOL Multiphysics, which are used to calculate the temperature T, pressure P and moisture content C of the geometric model during the vacuum cooling process;

[0102] The porous media model established by computer simulation software is based on the thermodynamic principles of the vacuum cooling process. The thermodynamic principle of the vacuum cooling process is as follows: after the vacuum pump is started, the pressure inside the vacuum chamber drops rapidly. When the chamber pressure drops to the equilibrium vapor pressure corresponding to the sample moisture temperature, the moisture begins to evaporate violently, and the sample temperature drops rapidly. Therefore, the heat transfer behavior in the vacuum cooling process is defined as convective heat transfer from the airflow inside the chamber, and heat conduction and phase change processes due to the temperature difference between the inside and outside of the sample. The mass transfer behavior mainly consists of the overall flow caused by the pressure gradient inside the sample and the diffusion process caused by the concentration gradient. Based on the above thermodynamic principles, a porous media model under vacuum cooling is established, and its heat and mass transfer model is analyzed. The governing equations of the porous media model are:

[0103] 1) Mass transfer behavior of liquid water:

[0104]

[0105] In the formula, ρ w The density of liquid water in the sample is expressed in kg / m³. 3 S w t represents the liquid water saturation level; t represents the vacuum cooling time in seconds; u w D is the flow rate of liquid water in the sample, expressed in m / s; w,cap It is the capillary diffusivity of liquid water in the pores of the sample, expressed in meters. 2 / s; It is the evaporation rate of free water in the sample, expressed in kg / m³. 3 ·s, The porosity of the sample;

[0106] 2) Mass transfer behavior of water vapor:

[0107]

[0108] In the formula, ρ v The density of water vapor in the sample is expressed in kg / m³. 3 S v The water vapor saturation in the sample; u v D is the flow rate of water vapor in the sample, expressed in m / s;v It is the diffusion coefficient of water vapor in the sample, with units of m. 2 / s;

[0109] 3) Heat transfer behavior:

[0110]

[0111] In the formula, the effective parameter ρ eff C P,eff 、(ρC P u) fluid k eff Defined as:

[0112]

[0113]

[0114]

[0115]

[0116] Where, ρ eff Effective density, unit: kg / m³ 3 ;ρ s The density of the solid matrix in the sample;

[0117] C P,eff The effective specific heat capacity is expressed in J / kg·K; m s This represents the total mass fraction of the solid phase in the sample. m is the specific heat capacity of the solid matrix in the sample. w This represents the total mass fraction of the liquid aqueous phase in the sample. m is the specific heat capacity of liquid water in the sample. v This represents the total mass fraction of the water vapor phase in the sample. The specific heat capacity of water vapor in the sample;

[0118] k eff The effective thermal conductivity is expressed in J / m·K·s; k v k is the thermal conductivity of water vapor in the sample. w k is the thermal conductivity of the liquid water in the sample. s The thermal conductivity of the solid matrix in the sample;

[0119] (ρC P u) fluid For fluid flow terms; ρ is density, in kg / m³. 3 C P is specific heat capacity, measured in J / kg·K; u is fluid velocity, measured in m / s; For pressure gradient;

[0120] T is the sample temperature in K; λ is the latent heat of vaporization of water vapor in the sample in J / kg.

[0121] 4) Stress item:

[0122]

[0123]

[0124]

[0125] Where i is the liquid water phase or water vapor phase; u i K represents the flow velocity of fluid i, in m / s. i This is the penetration rate of i, in meters. 2 μ i K is the viscosity of fluid i, measured in Pa·s; evap M is the evaporation coefficient; v ρ represents the relative mole fraction of water vapor, in kg / mol; R is the gas constant, in J / (mol*K); p v,eq The equilibrium vapor pressure is expressed in Pa; p v This refers to water vapor pressure, measured in Pa.

[0126] Due to the diversity of food types, their physical properties vary considerably. Different physical properties (such as specific heat capacity and thermal conductivity) have a significant impact on heat and mass transfer processes. However, current experimental measurement procedures for these parameters are lengthy and complex. Furthermore, the heat and mass transfer parameters of food vary significantly under different operating conditions. These parameters are often used as boundary conditions in numerical models, significantly affecting the calculation results. Heat and mass transfer parameters are typically calculated through experimental fitting. Therefore, this invention provides a method for measuring heat and mass transfer parameters related to moisture content (M... w The relevant empirical formulas can accurately predict the physical properties of high-temperature baked foods, avoiding the subjectivity and tediousness of experiments. The details are as follows:

[0127] 5) Specific heat capacity formula

[0128]

[0129] 6) Thermal conductivity formula

[0130] K = 0.006 w +0.120(12)

[0131] 7) Formulas for heat transfer coefficient and mass transfer coefficient

[0132]

[0133]

[0134] Among them, M w C represents moisture content. p ρ is the specific heat capacity of the sample, in J / kg·K; K is the thermal conductivity of the sample's solid matrix, in J / m·K·s; Nu is the Nusselt number; h t The convective heat transfer coefficient is expressed in J / m³. 2 ·K·s; L is the characteristic length in meters; Re is the Reynolds number; Pr is the Prandtl number; Sh is the Sherwood number; h m Sc is the mass transfer coefficient, in m / s; Sc is the Schmidt number.

[0135] The Reynolds number and Schmidt number are calculated based on fluid properties, using the following formula:

[0136]

[0137]

[0138] Where, ρ a air density; μ a ρ represents air viscosity; v represents air velocity in m / s; D represents the fluid diffusion coefficient in m³ / s. 2 / s.

[0139] S2. Place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters (such as final cooling temperature, vacuum pump speed, vacuum regulating valve mode, etc.), start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor; collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer; measure the weight loss during the vacuum cooling process through a weight sensor to obtain moisture content data.

[0140] Furthermore, such as Figure 2 As shown, the vacuum cooling device includes a vacuum chamber 6, a pressure sensor 5, a temperature sensor 10, an infrared thermometer 7, a weight sensor 8, a vacuum pump 1, a venting valve 4, a gas storage tank 3, a regulating valve 2, and a processor 11. The pressure sensor is connected to the vacuum chamber to measure the pressure within it. The temperature sensor and infrared thermometer are used to measure the temperature of the sample during vacuum cooling. The weight sensor is placed below the sample to measure the weight loss of the sample during vacuum cooling. The vacuum pump is connected to the vacuum chamber through the gas storage tank. The regulating valve is located on the connecting pipe between the vacuum pump and the gas storage tank. The venting valve is located between the gas storage tank and the vacuum chamber, connecting the vacuum chamber to the external environment.

[0141] Therefore, the specific steps for using the vacuum cooling device in step S2 are as follows:

[0142] First, place the sample on the weight sensor in the vacuum chamber; place the temperature sensors on the surface and center of the sample respectively, and close the cover of the vacuum chamber to ensure its airtightness; then set the depressurization rate and final temperature required for vacuum cooling of the sample, and turn on the temperature sensor, infrared thermometer, weight sensor, and pressure sensor to monitor data changes during the vacuum cooling process and collect temperature, weight, and pressure data; turn on the vacuum pump to start vacuum cooling, and stop when the sample temperature drops to the set temperature, then open the vent valve to restore the pressure in the chamber to atmospheric pressure; the processor processes the temperature, weight, and pressure data collected during the vacuum cooling process to obtain temperature, moisture content, and pressure data during the vacuum cooling process.

[0143] In this embodiment, the processor uses a gravimetric moisture determination method to process the collected weight data and obtain moisture content data.

[0144] In this embodiment, the final temperature is set to 10℃, the vacuum pump speed is set to 0.02m / s, and the vacuum regulating valve is set to fully open mode.

[0145] S3. During the vacuum cooling process of the sample, the transverse relaxation time T2 spectrum was collected using a nuclear magnetic resonance moisture analyzer.

[0146] During the vacuum cooling process of the sample, low-field nuclear magnetic resonance (NMR) technology is used to detect the moisture content in the sample. Low-field NMR is a rapid, non-destructive, and pollution-free technique for detecting moisture migration. When water and substrate are tightly bound, the T2 (water vapor deposition time) decreases; free water, with its high mobility, exhibits a larger T2. The peak area corresponding to the relaxation time in the T2 relaxation spectrum can be divided into three categories: bound water peak area with a T2 relaxation time of 0–10 ms, bound water peak area with a T2 relaxation time of 10–100 ms, and free water peak area with a T2 relaxation time of 100–1000 ms. A larger peak area indicates a higher water content. Therefore, by utilizing the different transverse relaxation times and peak areas of water in different states, the state and content of water can be distinguished. Traditional measurements often use gravimetric sensors, taking weight changes as the amount of water loss, resulting in relatively coarse measurement results. Low-field NMR technology can accurately determine the state and content of water. Therefore, this invention uses both gravimetric and low-field NMR methods to verify the change in moisture content of the sample during the vacuum cooling process, providing greater accuracy.

[0147] In step S3, the transverse relaxation time T2 spectrum of the sample before vacuum cooling is first acquired using the CPMG pulse sequence parameters of a low-field nuclear magnetic resonance moisture analyzer. During vacuum cooling, the same CPMG pulse sequence parameters are used to continuously acquire the transverse relaxation time T2 spectrum of the sample every 50 seconds until vacuum cooling is completed. The CPMG pulse sequence parameters include: sampling frequency: 200kHz; main frequency: 40MHz; RF delay: 0.08~1ms; analog gain: -3~40dB; digital gain: 0~7; waiting time: 1000ms; number of accumulations: 4, 8; echo time: 0.10~1ms; number of echoes: 1000~18000.

[0148] In this embodiment, the CPMG pulse sequence parameters are set as follows: sampling frequency: 200kHz; main frequency: 40MHz; RF delay: 0.08ms; analog gain: 20.0dB; digital gain: 2; waiting time: 1000ms; number of accumulations: 4; echo time: 0.10ms; number of echoes: 12000.

[0149] S4. Based on the relationship between moisture content and peak area in the T2 relaxation spectrum during vacuum cooling, a functional model of moisture content change during vacuum cooling is obtained by fitting a function.

[0150] S5. Compare the temperature, moisture content and pressure data of the sample predicted by the porous media model with the fitted function model to verify the porous media model.

[0151] Furthermore, based on the transverse relaxation time T2 spectrum of the sample during the vacuum cooling process and the data recorded by the weight sensor during the vacuum cooling process, the peak area and sample mass change data in the T2 relaxation spectrum are extracted, and a linear fitting relationship is established. Then, based on the established linear fitting relationship, a function model between the peak area (y) and moisture content (x) in the T2 relaxation spectrum is established, and the change in moisture content during the vacuum cooling process can be verified by the change in peak area.

[0152] After obtaining the function model, a regression analysis was performed with the moisture content data predicted by the porous media model to calculate the coefficient of determination R. 2 The accuracy of the porous media model was evaluated. At the same time, the temperature data predicted by the porous media model was verified by the temperature data collected by the temperature sensor and the infrared thermometer during vacuum cooling. The pressure data predicted by the porous media model was verified by the pressure data collected by the pressure sensor during vacuum cooling.

[0153] To further illustrate the effectiveness and effects of this invention, this embodiment uses cheesecake as an example to predict its moisture migration during vacuum cooling. A porous media model is established to calculate its temperature, moisture content, and pressure. The accuracy is verified and compared using a vacuum cooling device and a nuclear magnetic resonance moisture meter. Specifically:

[0154] First, based on the dimensions of the cheesecake, a two-dimensional axisymmetric geometric model was established in COMSOL Multiphysics. Next, the geometric model and physical field parameters were set within the software, including: the physical field interfaces were set to fluid heat transfer, Darcy's law, and rarefied matter transfer interfaces, used to calculate the temperature T, pressure P, and moisture content C of the sample during vacuum cooling; boundary and initial conditions were set according to the required operating parameters for vacuum cooling; physical property parameters were set according to the characteristics of the food materials; an adaptive mesh was used to generate a mesh for the geometric model; a transient solver was set to perform the calculation, with the model solving up to 400 seconds and a time step of 5 seconds; the predicted temperature, moisture content, and pressure data were then compared with the verification results described later.

[0155] Vacuum cooling devices employ, for example Figure 2 The small device shown has a vacuum pump with a pumping speed of 0.2 m / s. The vacuum chamber is made of quartz glass with an inner diameter of 20 mm and a height of 10 mm. The vacuum regulating valve is set to the fully open position, and the final sample temperature is 10℃. It is equipped with a temperature sensor, an infrared thermometer, a weight sensor, and a nuclear magnetic resonance analyzer for the analysis of heat and mass transfer.

[0156] The above-mentioned verification device was used to detect the temperature, moisture content, and pressure data of the cheesecake during the vacuum cooling process. The specific operation is as follows:

[0157] (1) Place the freshly baked cheesecake into the vacuum chamber; set the operating parameters and configure the nuclear magnetic resonance moisture analyzer parameters;

[0158] (2) Turn on the temperature sensor, infrared thermometer, pressure sensor, weight sensor and nuclear magnetic resonance moisture meter to collect data;

[0159] (3) Turn on the vacuum pump. The pressure inside the vacuum chamber drops rapidly until the temperature reaches the set temperature of 10°C. Then the vacuum pump stops working. Open the vent valve to restore the pressure inside the vacuum chamber to atmospheric pressure.

[0160] (4) To avoid the randomness of the results, data were collected three times using samples of the same mass.

[0161] (5) Based on the collected data, establish the relationship between the moisture content and the peak area in the transverse relaxation time T2 spectrum during the vacuum cooling process. Obtain the functional model of the moisture content change during the vacuum cooling process by fitting the function, and compare and analyze it with the calculation results of the porous media model.

[0162] Combined with pressure curves (e.g.) Figure 3 As shown in the figure, it can be observed that the cavity pressure decreases rapidly before 50 seconds, and gradually levels off and remains constant after approaching the set pressure. Through correlation coefficient calculation, the predicted pressure (Simulation) and the measured pressure (Experiment) have a correlation of 0.9998, exhibiting excellent correlation. The temperature versus time curves during the vacuum cooling process are observed (e.g.,...). Figure 4 As shown in the figure, after 60 seconds of slow cooling, the temperature of the sample center (core simulation) and surface (surface simulation) decreased rapidly. After 300 seconds, the sample temperature basically reached the set temperature of 10℃. This is mainly because the pressure in the cavity decreased below the equilibrium vapor pressure of water, causing water to evaporate rapidly and the temperature to drop rapidly. In addition, the temperature distribution of the surface (surface experiment) and the center (core experiment) measured by the infrared thermometer showed similar results to the prediction model and temperature sensor data. Correlation analysis showed that the correlation coefficients of the surface and the center were 0.9948 and 0.9855, respectively, indicating that the prediction model (i.e., the porous media model) has good consistency with the actual results.

[0163] Comparison of moisture content calculated using a porous media model (Simulation) and moisture loss measured by gravimetric method (Experiment) (e.g.) Figure 5 As shown in the figure, water loss occurs rapidly after 60 seconds of vacuum cooling, mainly due to mass loss caused by phase transition, with a correlation coefficient of 0.9845. Furthermore, simultaneous nuclear magnetic resonance analysis of the water state and content during the vacuum cooling process (e.g., Figure 6 As shown), cheesecake mainly contains free water and bound water, and the vacuum cooling process is mainly the transfer of free water; analysis of the peak area of ​​the T2 relaxation spectrum shows (as shown) Figure 7 As shown in the figure, the free water content during the vacuum cooling process continuously decreases as vacuum cooling progresses, and its trend is consistent with the predicted model and actual weight measurements. Analysis of quantitative data on peak area and moisture content reveals a strong linear relationship between moisture content and peak area (e.g., ...). Figure 8 As shown), its correlation is 0.9367, and the peak area (y) and moisture content (x) have the following functional model:

[0164] y = -12345 + 258078 (15)

[0165] The linear fit between moisture content and peak area shows that all data points are within the 95% confidence interval, indicating that there is a good predictive relationship between moisture content and peak area.

[0166] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously.

[0167] Based on the same idea as the method for predicting moisture migration during vacuum cooling of high-temperature baked foods in the above embodiments, this invention also provides a system for predicting moisture migration during vacuum cooling of high-temperature baked foods, such as... Figure 9 As shown, the system includes a model prediction module, a data acquisition module, a T2 spectrum acquisition module, a relationship fitting module, and a model validation module.

[0168] The model prediction module is used to establish a porous media model for high-temperature baked food samples using the computer simulation software COMSOL Multiphysics, and to predict the temperature, moisture content and pressure data of the samples.

[0169] The data acquisition module is used to place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters, start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor, collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer, and measure the weight loss during the vacuum cooling process through a weight sensor to obtain moisture content data.

[0170] The T2 spectrum acquisition module is used to acquire the transverse relaxation time T2 spectrum of the sample during the vacuum cooling process using a nuclear magnetic resonance moisture analyzer.

[0171] The relationship fitting module is used to obtain a functional model of the change in moisture content during vacuum cooling by fitting a function based on the relationship between moisture content and peak area in the transverse relaxation time T2 spectrum during vacuum cooling.

[0172] The model validation module is used to compare the temperature, moisture content, and pressure data of the sample predicted by the porous media model with the fitted function model to validate the porous media model.

[0173] It should be noted that the prediction system for moisture migration during the vacuum cooling process of high-temperature baked foods of the present invention corresponds one-to-one with the prediction method for moisture migration during the vacuum cooling process of high-temperature baked foods of the present invention. The technical features and beneficial effects described in the embodiments of the prediction method for moisture migration during the vacuum cooling process of high-temperature baked foods are applicable to the embodiments of the prediction system for moisture migration during the vacuum cooling process of high-temperature baked foods. For details, please refer to the description in the embodiments of the method of the present invention, which will not be repeated here.

[0174] Furthermore, in the above embodiments of the prediction system for moisture migration during vacuum cooling of high-temperature baked foods, the logical division of each program module is merely illustrative. In actual applications, the above functions can be assigned to different program modules as needed, for example, for the sake of corresponding hardware configuration requirements or the convenience of software implementation. That is, the internal structure of the prediction system for moisture migration during vacuum cooling of high-temperature baked foods can be divided into different program modules to complete all or part of the functions described above.

[0175] like Figure 10 As shown, in one embodiment, a computer-readable storage medium is provided, storing a program in a memory. When the program is executed by a processor, it implements the method for predicting moisture migration during the vacuum cooling process of high-temperature baked foods, specifically:

[0176] S1. Use the computer simulation software COMSOL Multiphysics to establish a porous media model for high-temperature baked food samples and predict the temperature, moisture content and pressure data of the samples.

[0177] S2. Place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters, start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor; collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer; measure the weight loss during the vacuum cooling process through a weight sensor to obtain the moisture content data.

[0178] S3. During the vacuum cooling process of the sample, the transverse relaxation time T2 spectrum was collected using a nuclear magnetic resonance moisture analyzer.

[0179] S4. Based on the relationship between moisture content and peak area in the transverse relaxation time T2 spectrum during vacuum cooling, a functional model of moisture content change during vacuum cooling is obtained by fitting a function.

[0180] S5. Compare the temperature, moisture content and pressure data of the sample predicted by the porous media model with the fitted function model to verify the porous media model.

[0181] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Furthermore, any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for predicting moisture migration during vacuum cooling of high-temperature baked foods, characterized in that, Includes the following steps: S1. Use the computer simulation software COMSOL Multiphysics to establish a porous media model for high-temperature baked food samples and predict the temperature, moisture content and pressure data of the samples. The establishment of the porous medium model specifically involves: The external dimensions of high-temperature baked food samples were obtained using measuring tools, and a matching geometric model was built in the computer simulation software COMSOL Multiphysics. The geometric model and physical field structure parameters are set, including: setting boundary conditions and initial conditions according to the working conditions required for vacuum cooling; setting physical property parameters according to the characteristics of food materials; using adaptive mesh generation to form a mesh for the geometric model; setting a transient solver for solving the problem; setting the physical field interface as a fluid heat transfer interface, Darcy's law interface, and rare substance transfer interface to calculate the temperature T, pressure P, and moisture content C of the geometric model during the vacuum cooling process. A porous medium model under vacuum cooling process is established based on thermodynamic principles, and its heat and mass transfer laws are analyzed. S2. Place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters, start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor; collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer; measure the weight loss during the vacuum cooling process through a weight sensor to obtain the moisture content data. S3. During the vacuum cooling process of the sample, the transverse relaxation time T2 spectrum was collected using a nuclear magnetic resonance moisture analyzer. S4. Based on the relationship between moisture content and peak area in the transverse relaxation time T2 spectrum during vacuum cooling, a functional model of moisture content change during vacuum cooling is obtained by fitting a function. S5. Compare the temperature, moisture content and pressure data of the sample predicted by the porous media model with the fitted function model to verify the porous media model.

2. The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to claim 1, characterized in that, The governing equations for the porous medium model are: 1) Mass transfer behavior of liquid water: ， In the formula, ρ w The density of liquid water in the sample is expressed in kg / m³. 3 ; S w t represents the liquid water saturation level; t represents the vacuum cooling time in seconds; u w D is the flow rate of liquid water in the sample, expressed in m / s; w,cap It is the capillary diffusivity of liquid water in the pores of the sample, expressed in meters. 2 / s; It is the evaporation rate of free water in the sample, expressed in kg / m³. 3 ·s, where φ is the porosity of the sample; 2) Mass transfer behavior of water vapor: ， In the formula, ρ v The density of water vapor in the sample is expressed in kg / m³. 3 S v The water vapor saturation in the sample; u v D is the flow rate of water vapor in the sample, expressed in m / s; v It is the diffusion coefficient of water vapor in the sample, with units of m. 2 / s; 3) Heat transfer behavior: ， In the formula, the effective parameter ρ eff C P,eff 、(ρC P u) fluid k eff Defined as: ， ， ， ， Where, ρ eff Effective density, unit: kg / m³ 3 ;ρ s The density of the solid matrix in the sample; C P,eff The effective specific heat capacity is expressed in J / kg·K; m s This represents the total mass fraction of the solid phase in the sample. m is the specific heat capacity of the solid matrix in the sample. w This represents the total mass fraction of the liquid aqueous phase in the sample. m is the specific heat capacity of liquid water in the sample. v This represents the total mass fraction of the water vapor phase in the sample. The specific heat capacity of water vapor in the sample; k eff The effective thermal conductivity is expressed in J / m·K·s; k v k is the thermal conductivity of water vapor in the sample. w k is the thermal conductivity of the liquid water in the sample. s The thermal conductivity of the solid matrix in the sample; (ρC P u) fluid For fluid flow terms; ρ is density, in kg / m³. 3 C P is specific heat capacity, measured in J / kg·K; u is fluid velocity, measured in m / s; For pressure gradient; T is the sample temperature in K; λ is the latent heat of vaporization of water vapor in the sample in J / kg. 4) Stress item: ， ， ， Where i is the liquid water phase or water vapor phase; u i K represents the flow velocity of fluid i, in m / s. i This is the penetration rate of i, in meters. 2 μ i K is the viscosity of fluid i, measured in Pa·s; evap M is the evaporation coefficient; v ρ represents the relative mole fraction of water vapor, in kg / mol; R is the gas constant, in J / (mol*K); p v,eq The equilibrium vapor pressure is expressed in Pa; p v This refers to water vapor pressure, measured in Pa. 5) Specific heat capacity formula ， 6) Thermal conductivity formula ， 7) Formulas for heat transfer coefficient and mass transfer coefficient ， ， Among them, M w C represents moisture content. p ρ is the specific heat capacity of the sample, in J / kg·K; K is the thermal conductivity of the sample's solid matrix, in J / m·K·s; Nu is the Nusselt number; h t The convective heat transfer coefficient is expressed in J / m³. 2 ·K·s; L is the characteristic length in meters; Re is the Reynolds number; Pr is the Prandtl number; Sh is the Sherwood number; h m Sc is the mass transfer coefficient, in m / s; Sc is the Schmidt number. The Reynolds number and Schmidt number are calculated based on fluid properties, using the following formula: ， ， Where, ρ a air density; μ a ρ represents air viscosity; v represents air velocity in m / s; D represents the fluid diffusion coefficient in m³ / s. 2 / s.

3. The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to claim 1, characterized in that, The vacuum cooling device includes a vacuum chamber, a pressure sensor, a temperature sensor, an infrared thermometer, a weight sensor, a vacuum pump, a venting valve, a gas storage tank, a regulating valve, and a processor. The pressure sensor is connected to the vacuum chamber to measure the pressure inside. The temperature sensor and infrared thermometer are used to measure the temperature of the sample during vacuum cooling. The weight sensor is placed below the sample and is used to measure the weight loss of the sample during vacuum cooling. The vacuum pump is connected to the vacuum chamber via a gas storage tank; The regulating valve is located on the connecting pipe between the vacuum pump and the gas storage tank; The vent valve is located between the gas storage tank and the vacuum chamber, and is used to connect the vacuum chamber to the external environment.

4. The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to claim 3, characterized in that, Step S2 is as follows: Place the sample on the weight sensor in the vacuum chamber; place the temperature sensors on the surface and center of the sample respectively; close the cover of the vacuum chamber to make the vacuum chamber a sealed state. Set the depressurization rate and final temperature required for vacuum cooling of the sample, turn on the temperature sensor, infrared thermometer, weight sensor and pressure sensor, and monitor and collect temperature, weight and pressure data during the vacuum cooling process; Turn on the vacuum pump to start vacuum cooling. Stop when the sample temperature drops to the set temperature. Open the vent valve to restore the pressure inside the chamber to atmospheric pressure. The processor processes the temperature, weight, and pressure data collected during the vacuum cooling process to obtain temperature, moisture content, and pressure data during the vacuum cooling process.

5. The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to claim 1, characterized in that, In step S3, the transverse relaxation time T2 spectrum of the sample before vacuum cooling is acquired using the CPMG pulse sequence parameters of the nuclear magnetic resonance moisture analyzer. During vacuum cooling, the transverse relaxation time T2 spectrum of the sample was continuously acquired every 50s using the same CPMG pulse sequence parameters until the vacuum cooling was completed.

6. The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to claim 5, characterized in that, The CPMG pulse sequence parameters include: sampling frequency: 200kHz; main frequency: 40MHz; RF delay: 0.08~1ms; analog gain: -3~40dB; digital gain: 0~7; waiting time: 1000ms; number of accumulations: 4, 8; echo time: 0.10~1ms; number of echoes: 1000~18000.

7. The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to claim 5, characterized in that, Step S4 is as follows: Based on the transverse relaxation time T2 spectrum of the sample during vacuum cooling and the data recorded by the weight sensor during vacuum cooling, the peak area and sample mass change data in the transverse relaxation time T2 spectrum were extracted, and a linear fitting relationship was established. Based on the established linear fitting relationship, a functional model is established between the peak area y and the moisture content x in the transverse relaxation time T2 spectrum. That is, the change in moisture content during the vacuum cooling process is verified by the change in the peak area in the transverse relaxation time T2 spectrum. After obtaining the function model, a regression analysis was performed with the moisture content data predicted by the porous media model to calculate the coefficient of determination R. 2 The accuracy of the porous media model was evaluated. At the same time, the temperature data predicted by the porous media model was verified by the temperature data collected by the temperature sensor and the infrared thermometer during vacuum cooling. The pressure data predicted by the porous media model was verified by the pressure data collected by the pressure sensor during vacuum cooling.

8. A system for predicting moisture migration during vacuum cooling of high-temperature baked foods, characterized in that, The method for predicting moisture migration during vacuum cooling of high-temperature baked foods according to any one of claims 1-7, the system includes a model prediction module, a data acquisition module, a T2 spectrum acquisition module, a relationship fitting module, and a model verification module; The model prediction module is used to establish a porous media model for high-temperature baked food samples using the computer simulation software COMSOL Multiphysics, and to predict the temperature, moisture content and pressure data of the samples. The data acquisition module is used to place the sample in the vacuum chamber of the vacuum cooling device, set the vacuum cooling operating parameters, start and begin vacuum cooling, collect pressure data during the vacuum cooling process through a pressure sensor, collect temperature data during the vacuum cooling process through a temperature sensor and an infrared thermometer, and measure the weight loss during the vacuum cooling process through a weight sensor to obtain moisture content data. The T2 spectrum acquisition module is used to acquire the transverse relaxation time T2 spectrum of the sample during the vacuum cooling process using a nuclear magnetic resonance moisture analyzer. The relationship fitting module is used to obtain a functional model of the change in moisture content during vacuum cooling by fitting a function based on the relationship between moisture content and peak area in the transverse relaxation time T2 spectrum during vacuum cooling. The model validation module is used to compare the temperature, moisture content and pressure data of the sample predicted by the porous media model with the fitted function model to validate the porous media model.

9. A computer-readable storage medium storing a program in a memory, characterized in that, When the program is executed by the processor, it implements the method for predicting moisture migration during vacuum cooling of high-temperature baked foods as described in any one of claims 1-7.

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