Wet grain fresh-keeping processing method based on antibacterial heat treatment and modified atmosphere packaging

Abstract

The invention provides a wet grain fresh-keeping processing method based on antibacterial heat treatment and modified atmosphere packaging, which realizes normal form transformation from rough inactivation to accurate regulation and control through triple synergistic effects of mild heat treatment (algorithm optimization), natural antibacterial agent (intelligent injection) and dynamic modified atmosphere packaging (proportion self-adaption). The significant difference from conventional processes is lower temperature / shorter time, substitution of chemical synthesis agents by natural preservatives, and substitution of fixed process parameters by algorithm-based dynamic decisions. The scheme has excellent sensory and nutritional quality and longer shelf life, conforms to the trend of'label cleaning ', and reduces energy consumption and material consumption through intelligentization.

Description

Technical Field

[0001] This invention relates to the field of preservation and processing technology, and in particular to a method for preserving and processing wet grains based on antibacterial heat treatment and modified atmosphere packaging. Background Technology

[0002] Traditional wet food (such as pet food and ready-to-eat meals) relies heavily on high-intensity heat sterilization (such as high-temperature, high-pressure sterilization, typically above 121°C for 15-30 minutes) and chemical preservatives for preservation. While this "over-processing" method effectively sterilizes food, it also brings significant drawbacks:

[0003] 1. Deterioration of nutritional and sensory quality: Significant loss of heat-sensitive nutrients such as vitamins and amino acids; excessive softening of texture and deterioration of flavor, resulting in a "cooked" taste.

[0004] 2. High energy consumption and cost: High-temperature and long-term processing consumes a lot of energy and requires high-quality equipment.

[0005] 3. Consumer trust crisis: With the rise of the "clean label" trend, consumers are increasingly averse to chemical preservatives. Summary of the Invention

[0006] To address the technical problems existing in the prior art, the present invention provides the following technical solution:

[0007] A method for preserving wet grains based on antibacterial heat treatment and modified atmosphere packaging, the method comprising the following steps:

[0008] (1) Raw material pretreatment: The wet grain raw materials are crushed, diced and steamed, and the particle size is controlled to be 5-8mm, and the steaming time is 10-30 minutes;

[0009] (2) Mild stepped heat treatment: a three-temperature zone pasteurizer is used, with the heating zone at 60-65℃ for 25-40 seconds, the heat preservation zone at 85-90℃ for 150-210 seconds, and the pre-cooling zone at 35-40℃ for 20-30 seconds;

[0010] (3) Addition of natural antibacterial agent: Add 0.2-0.5% of compound natural antibacterial agent online and mix for 15-30 seconds at 200-300 rpm using an aseptic addition system;

[0011] (4) Dynamic modified atmosphere packaging: Use a vacuum displacement modified atmosphere packaging machine with a vacuum degree of 5-6 kPa, a pumping time of 15 seconds, and a filling time of 2 seconds. According to the product type, fill with a mixture of CO2, N2 and O2, with CO2 ratio of 20-70%, N2 ratio of 30-80%, and O2 ratio of 0-5%.

[0012] (5) Cold chain preservation: The finished product is stored and transported at 0-4℃, and the temperature is monitored throughout the process.

[0013] Furthermore, the compound natural antibacterial agent described in step (3) is selected from at least two of tea polyphenols, ε-polylysine, cinnamaldehyde, lysozyme, rosemary extract or natamycin.

[0014] Furthermore, the F value of the mild stepped heat treatment in step (2) is optimized by Algorithm 1. The input variables of Algorithm 1 include the initial microbial load, product thickness and heat penetration coefficient, and the output parameters are heating rate, holding time and cooling temperature difference.

[0015] Furthermore, the amount of compound natural antibacterial agent added in step (3) is determined by the antibacterial agent synergistic effect algorithm. When the pH value of the raw material is >5.5, the proportion of acidic antibacterial agent is increased by 5-8%; when the fat content is >15%, 0.1-0.2% fat-soluble antibacterial agent is added.

[0016] Furthermore, the gas ratio of the dynamic modified atmosphere packaging in step (4) is set by algorithm 2. The input variables of algorithm 2 include Product_Type, Fat_Content, pH_Value and Target_ShelfLife, and the output parameters are: final CO2 ratio, final N2 ratio and final O2 ratio.

[0017] Furthermore, the Product_Type includes meat, aquatic fish, and a mixture of fruits and vegetables, with the corresponding basic gas ratios as follows: meat: CO2 50-70%, N2 30-50%; high-fat wet grains: CO2 30-50%, N2 50-70%; wet grains containing fragile fruits and vegetables: CO2 20-40%, N2 60-80%, O2 2-5%.

[0018] Furthermore, the packaging material used in step (4) is a high-barrier composite material with an oxygen permeability (OTR) of <1 cm³ / m²·day·atm and a water vapor permeability (WVTR) of <1 g / m²·day.

[0019] Furthermore, the shelf life of the wet grain is predicted using a kinetic model, the model formula of which is:

[0020] Shelf_Life = f(F_value, Antimicrobial_Efficacy, MAP_Effectiveness, Storage_Temperature), condition: shelf life reaches 38-45 days under refrigeration at 4℃;

[0021] in:

[0022] Shelf Life: The longest time (in days) a wet food product can maintain its microbiological safety, physicochemical stability and sensory acceptability under refrigeration conditions of 0-4℃.

[0023] F_value (sterilization F value): A parameter characterizing the intensity of heat treatment, defined as the equivalent time (unit: min) to achieve the same microbial inactivation effect as the actual heat treatment process (mild stepwise pasteurization) at a reference temperature (usually 60℃).

[0024] Antimicrobial Efficacy: The synergistic antibacterial effect of compound natural antimicrobial agents, determined by the type, concentration, and conditions of action of the antimicrobial agents. It is calculated using an algorithm for the synergistic effect of antimicrobial agents.

[0025] MAP_Effectiveness: The ability of dynamic modified atmosphere packaging to inhibit microbial growth and oxidation reactions is determined by the gas ratio (CO2 / N2 / O2) inside the packaging, the gas replacement efficiency (>99.5%), and the barrier properties of the packaging material (OTR<1 cm³ / m²·day·atm).

[0026] Storage Temperature: The ambient temperature of the product in the cold chain (unit: °C). It is required to be strictly controlled between 0-4 °C. Temperature fluctuations significantly affect the microbial growth rate and chemical reaction rate through the Arrhenius equation.

[0027] f() represents the prediction function, which uses multiple linear regression or artificial neural networks for prediction analysis.

[0028] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0029] This invention abandons the traditional approach of "over-killing" and instead adopts a precise preservation strategy of "multi-target, stepwise antibacterial action." Its core innovation lies in:

[0030] 1. Mild stepwise heat sterilization: Using lower temperature and shorter time heat treatment, the goal is to change from "inactivating all microorganisms" to "inactivating enzymes and most putrefactive bacteria and pathogens", creating favorable conditions for subsequent non-thermal intervention.

[0031] 2. Synergistic effect of natural antibacterial agents: After heat treatment and before packaging, a compound preparation composed of multiple natural antibacterial ingredients is precisely added to specifically inhibit heat-resistant bacteria (such as Bacillus) remaining after heat treatment and microorganisms that may cause subsequent contamination.

[0032] 3. Dynamic adaptive modified atmosphere packaging: It adopts high-precision MAP technology and introduces a dynamic gas ratio adjustment algorithm based on product characteristics and predictive microbiology to achieve continuous optimization of the gas environment inside the packaging, rather than filling with a fixed ratio.

[0033] 4. Full-process algorithm control: Design an integrated algorithm from heat treatment parameter optimization and antibacterial agent addition decision to controlled atmosphere ratio setting, so that the entire process system has the ability to self-optimize and adapt to raw material fluctuations. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of a wet grain preservation processing method based on antibacterial heat treatment and modified atmosphere packaging, provided by an embodiment of the present invention. Detailed Implementation

[0036] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0037] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0038] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0039] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0040] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0041] This invention provides a method for preserving and processing wet grains based on antibacterial heat treatment and modified atmosphere packaging. For example... Figure 1 The flowchart shown is for a wet grain preservation process based on antibacterial heat treatment and modified atmosphere packaging. The process can include the following steps:

[0042] (1) Raw material pretreatment: The wet grain raw materials are crushed, diced and steamed, and the particle size is controlled to be 5-8mm, and the steaming time is 10-30 minutes;

[0043] (2) Mild stepped heat treatment: a three-temperature zone pasteurizer is used, with the heating zone at 60-65℃ for 25-40 seconds, the heat preservation zone at 85-90℃ for 150-210 seconds, and the pre-cooling zone at 35-40℃ for 20-30 seconds;

[0044] (3) Addition of natural antibacterial agent: Add 0.2-0.5% of compound natural antibacterial agent online and mix for 15-30 seconds at 200-300 rpm using an aseptic addition system;

[0045] (4) Dynamic modified atmosphere packaging: Use a vacuum displacement modified atmosphere packaging machine with a vacuum degree of 5-6 kPa, a pumping time of 15 seconds, and a filling time of 2 seconds. According to the product type, fill with a mixture of CO2, N2 and O2, with CO2 ratio of 20-70%, N2 ratio of 30-80%, and O2 ratio of 0-5%.

[0046] (5) Cold chain preservation: The finished product is stored and transported at 0-4℃, and the temperature is monitored throughout the process.

[0047] The specific implementation steps of each stage of the above process will be described in detail below.

[0048] (I) First stage: Overall process flow

[0049] Raw material pretreatment → Stage 1: Mild antibacterial heat treatment (including rapid enzyme inactivation and pasteurization) → Rapid cooling → Stage 2: Precise addition of natural antibacterial agents → Stage 3: Aseptic environment construction and dynamic MAP packaging → Finished product cold chain storage.

[0050] (II) Second Stage: Detailed Description of Mild Antibacterial Heat Treatment Process

[0051] The goal of this phase is to achieve complete enzyme inactivation and a significant reduction in the number of microorganisms (targeting a reduction rate of 4-5 log CFU / g) while preserving product quality to the greatest extent possible.

[0052] Process Principle: Utilizing the destructive effects of heat on microbial cells and enzyme proteins. By precisely controlling the temperature-time combination, enzymes that cause quality deterioration (such as peroxidase and lipoxygenase) are preferentially and rapidly inactivated, followed by pasteurization of pathogens and spoilage bacteria. The lower heat treatment intensity preserves more nutrients and flavor, while inducing some heat-resistant bacteria into a stress state, making them more sensitive to subsequent natural antimicrobial agents.

[0053] The process equipment involved includes:

[0054] 1. Steam injection or water bath continuous pasteurization system: It is preferable to use continuous conveying equipment with multi-temperature zone control to ensure uniform heating of products and high heat exchange efficiency.

[0055] 2. Infrared or microwave preheating system (optional): Used to quickly and uniformly preheat the product before entering the main sterilization process, shortening the overall heat treatment time.

[0056] 3. High-precision temperature sensor array: distributed at key points of the sterilization equipment to monitor the core temperature of the product in real time.

[0057] 4. PLC (Programmable Logic Controller) and HMI (Human Machine Interface): As the control center, they receive sensor signals and execute control algorithms.

[0058] The specific process parameters and steps are as follows:

[0059] Step 1: Preheating and Heat Penetration Equilibrium

[0060] Equipment: Infrared preheating tunnel.

[0061] Parameter: The surface temperature of the product rises uniformly to 60-65℃ within 60-70 seconds.

[0062] Objective: To reduce the temperature difference between the inside and outside of the product and prevent overheating on the outside and insufficient heat on the inside during the main sterilization stage.

[0063] Step 2: Primary sterilization (pasteurization)

[0064] Equipment: Three-zone continuous pasteurization machine.

[0065] Heating zone: The core temperature of the product rises from 65℃ to the target sterilization temperature within 2 minutes.

[0066] Incubation zone: Maintain the target core temperature of 85-95℃ for 3-5 minutes (specific parameters need to be optimized and determined through multiple rounds of orthogonal experiments to achieve enzyme activity residue <5% and microbial attenuation rate ≥4 log CFU / g as the standard).

[0067] Pre-cooling zone: A sterile ice-water bath or vacuum cooling system is used to rapidly reduce the core temperature of the product from the sterilization temperature to 35-40℃ within 15-30 seconds, preventing residual heat from causing further loss of nutrients and the resurgence of heat-resistant bacteria.

[0068] Core process parameters (requires optimization through multiple rounds of experiments; the following are example ranges):

[0069] Target temperature range: 85℃ - 95℃. (Compared to traditional: typically >121℃)

[0070] Keep warm for 90-300 seconds (1.5-5 minutes). (Compared to traditional: 15-30 minutes)

[0071] Specific parameters are determined as follows: For different products (such as high-fat, high-protein, and high-moisture), a database of "temperature-time-microbial lethality-quality retention rate" is established through experiments. For example:

[0072] Chicken-based wet feed: 90°C, 180 seconds.

[0073] Fish substrate wet food: 88℃, 150 seconds.

[0074] Mixed wet grains of vegetables and meat: 92℃, 240 seconds.

[0075] The heat treatment process optimization algorithm is designed as follows:

[0076] To ensure optimal results in every production run, this application designs a parameter fine-tuning algorithm based on real-time feedback.

[0077] Algorithm 1: Adaptive Thermal Sterilization Parameter Optimization Algorithm

[0078] Input variables:

[0079] `RawMaterial_MicroLoad` (Initial Microbial Load of Raw Material): refers to the initial number of viable bacteria (CFU / g) per gram of raw material, estimated online using a rapid microbial detection instrument (such as an ATP bioluminescence analyzer), and the unit is log CFU / g.

[0080] `Product_Composition`: A set of key parameters characterizing the matrix properties of the product, including fat content (%), protein content (%), water activity (aw), and pH value. The data is sourced from the formula management database.

[0081] `Product_Thickness` (maximum product thickness): refers to the maximum axial dimension (mm) of the geometric shape of the product to be sterilized, which is detected online by a laser profile scanner and used to calculate the heat penetration time.

[0082] `Target_LogReduction` (Target Logarithmic Reduction Value): The required microbial killing efficiency index, which is the difference between the logarithm of the initial bacterial count and the logarithm of the bacterial count after treatment (e.g., 5 log means a reduction of 105 times in the number of microorganisms).

[0083] `Quality_Threshold` defines the lower limit of key indicators for acceptable product quality, such as vitamin B1 retention rate >85%, texture hardness change rate <15%, color difference ΔE <3, etc.

[0084] The logic of Algorithm 1 is as follows:

[0085] 1. Model building and training:

[0086] 1.1 Data collection: Three types of core data were collected through orthogonal experimental design: (1) Microbiological data (survival curves of target bacteria (such as Listeria and Bacillus) under different temperature and time combinations); (2) Heat transfer data (temperature-time curves under different product thicknesses and compositions); (3) Quality data (the variation of vitamin retention rate, texture, color and other properties with heat treatment intensity).

[0087] 1.2 Feature Engineering: Transform the raw data into model input features, including: logarithmic transformation value of microbial load, thermal diffusivity of product (calculated based on composition), equivalent thickness (considering irregular shape correction), and normalized value of quality index.

[0088] 1.3 Model Training: A dual-model architecture is adopted.

[0089] (1) Predictive microbiology model: Based on the fusion of Baranyi growth model and Bigelow lethal model, a random forest regressor was constructed using the Python Scikit-learn library. The inputs were temperature, time, and product composition, and the output was the logarithmic reduction value of microorganisms (R2 training objective > 0.95).

[0090] (2) Heat transfer model: The heat conduction equation is solved by the finite element method, and the dynamic relationship between the product thickness, initial temperature, medium temperature and core temperature is established. The parameters are fitted by COMSOL Multiphysics software.

[0091] 1.4 Model Validation: Three-fold cross-validation was adopted, and the model accuracy was evaluated using an independent test set (containing 10% of the experimental data). The microbial prediction error was required to be <0.3 log CFU / g, and the temperature prediction error was required to be <1℃.

[0092] `Predicted_D_value` (predicted D value): `f(Temperature, Product_Composition)`, refers to the time (in minutes) required for the number of microorganisms to decrease by 90% under a specific temperature and product matrix, reflecting the heat resistance of microorganisms.

[0093] `Predicted_F_value` (predicted F value): `f(HeatingProfile, Product_Thickness)` refers to the equivalent time (in minutes) required to achieve the same sterilization effect as the actual heat treatment process at a reference temperature (usually 60℃), characterizing the sterilization intensity.

[0094] `Predicted_QualityLoss` (predicted quality loss): `g(Temperature, Time, Quality_Indicator)` refers to the retention rate (%) of core quality indicators under a specific temperature and time combination, such as vitamin retention rate and texture integrity.

[0095] 2. Model Application and Real-time Optimization:

[0096] 2.1 Real-time data access: Real-time data is obtained from production line sensors via OPC UA protocol: raw material microbial load (updated every 30 minutes), product thickness (online detection), and composition data (retrieved from the MES system).

[0097] 2.2 Multi-objective optimization: The NSGA-II genetic algorithm is used to solve for the optimal temperature-time combination. The constraints are: `Predicted_F_value >= Target_LogReduction Predicted_D_value` and `Predicted_QualityLoss >= Quality_Threshold`. The objective function is to minimize `(Temperature^2 Time)`.

[0098] 2.3 Dynamic adjustment: Parameters are pre-calculated before each batch of production. During the production process, feedback correction is performed every 5 minutes based on the actual microbial test results (ATP value). The correction coefficient `α = actual LogReduction / predicted LogReduction`. When α < 0.9, the temperature is automatically increased by 1-2℃ or the time is extended by 5-10 seconds.

[0099] 3. Model validation and update mechanism:

[0100] 3.1 Offline validation: Three batches of challenging experiments are conducted each month to compare the model's predicted values ​​with the actual sterilization effect (microbial reduction rate) and quality indicators. Model retraining is triggered when the deviation rate is >10%.

[0101] 3.2 Incremental learning: New product formula data (≥50 sets) are included every quarter, and the model parameters are updated using the transfer learning method, retaining the weights of historical data (0.7) and new data (0.3).

[0102] 3.3. Parameter Solving: The algorithm solves the following optimization problems in real time:

[0103] Objective: Given that `Predicted_F_value >= Target_LogReduction Predicted_D_value`, find the combination of `(Temperature, Time)` that minimizes `Predicted_QualityLoss` (solved using NSGA-II multi-objective genetic algorithm, population size 50, number of iterations 30, crossover probability 0.8, mutation probability 0.1).

[0104] 4. Output and Execution:

[0105] `Optimal_Temperature` (Optimal sterilization temperature): The target core temperature of the main sterilization stage output by the algorithm, in °C, with a value range of 85-95 °C and an accuracy of 0.1 °C.

[0106] `Optimal_Time` (Optimal Incubation Time): The target incubation time for the main sterilization stage output by the algorithm, in seconds, ranging from 90 to 300 seconds, accurate to 1 second.

[0107] These parameters are transmitted in real time to the PLC control system via the OPC UA protocol to control the opening of the steam valve (temperature regulation) and the speed of the conveyor belt (time regulation) of the pasteurizer, with control accuracies of ±0.5℃ and ±1 second, respectively.

[0108] Example: When the `RawMaterial_MicroLoad` detection value is 3.2 log CFU / g (lower than the usual 4.0 log CFU / g), the algorithm calls the prediction model to calculate: `Optimal_Temperature` = 90℃ (2℃ lower than the baseline value), `Optimal_Time` = 210 seconds (30 seconds shorter than the baseline value). At this time, `Predicted_F_value` = 5.8 min (meets the requirement of `Target_LogReduction` = 5 log), and `Predicted_QualityLoss` shows that the vitamin B1 retention rate has increased to 92% (the original process was 86%).

[0109] Step 3: Rapid cooling process

[0110] Purpose: To immediately terminate the heat treatment to prevent residual heat from further damaging the quality and to prepare for the subsequent addition of heat-sensitive antibacterial agents.

[0111] Equipment: Vacuum cooler or ice-water bath spiral cooler.

[0112] Parameters: Within 10-15 minutes, the core temperature of the product will be rapidly reduced from above 90°C to below 20°C.

[0113] (III) Third Stage: Detailed Explanation of the Precision Addition Process of Natural Antibacterial Agents

[0114] This stage is key to distinguishing it from traditional processes, as it aims to establish a second line of defense against microorganisms, specifically targeting any remaining heat-resistant spores and potential later-stage contamination.

[0115] 1. Processing Principle: After heat treatment, the product is in a "microbial vacuum" state. At this time, the added natural antibacterial agents can more effectively act on damaged or germinating heat-resistant bacteria, and are evenly distributed throughout the product, forming a continuous antibacterial environment. Through the combination of multiple natural antibacterial agents, utilizing their different mechanisms of action (such as disrupting cell membranes, inhibiting enzyme activity, and interfering with DNA replication), a synergistic effect is achieved, reducing the concentration of any single ingredient and avoiding negative impacts on flavor.

[0116] 2. Antibacterial agent formulation design

[0117] Core ingredient 1: Nisin

[0118] Mechanism of action: It acts on the cell membrane of Gram-positive bacteria (including Bacillus), forming pores and causing leakage of contents.

[0119] Effective concentration: 50 - 200 ppm.

[0120] Core ingredient 2: ε-Polylysine

[0121] Mechanism of action: It disrupts the cell membrane structure of microorganisms and has a broad-spectrum inhibitory effect on Gram-positive bacteria, Gram-negative bacteria, and yeast.

[0122] Effective concentration: 100 - 500 ppm.

[0123] Auxiliary ingredient 1: Plant essential oil microemulsion (such as thymol and carvacrol)

[0124] Mechanism of action: Hydrophobic components disrupt cell membrane integrity, penetrate cells, and inhibit key enzymes.

[0125] Effective concentration: 50 - 200 ppm (microemulsification is required to improve water solubility and mask strong flavor).

[0126] Auxiliary ingredient 2: Organic acids (such as citric acid and lactic acid)

[0127] Mechanism of action: It lowers the pH value of the product, enters the microbial cells in a non-dissociated form, dissociates inside the cells, lowers the intracellular pH, and interferes with metabolism.

[0128] Target pH: Adjust the final pH of the product to 5.0-5.5 for synergistic effect with antibacterial agents.

[0129] 3. The process equipment involved

[0130] (1) High pressure homogenizer: used to prepare stable and uniform antibacterial agent microemulsions.

[0131] (2). Multi-channel precision filling system: sealed batching tank with magnetic stirring, high-precision metering pump, static mixer.

[0132] (3). Online pH meter and adjustment system.

[0133] 4. Specific process parameters and steps

[0134] Step 1: Preparation of antibacterial agent mother liquor

[0135] Equipment: High-pressure homogenizer.

[0136] Parameters: Mix Nisin, ε-polylysine, plant essential oil, emulsifier and water, and homogenize and circulate for 3-5 minutes at 40-50℃ and 250-300 bar pressure to form a nanoscale microemulsion.

[0137] Step 2: Online filling and mixing

[0138] Equipment: Precision filling system.

[0139] parameter:

[0140] Addition point: During the process of transporting the cooled product to the packaging machine (the temperature is controlled at 20-25℃ to ensure the activity of the antibacterial agent).

[0141] Filling method: Spray / inject the antibacterial agent stock solution evenly onto / into the product surface / inside through an atomizing nozzle or injection head.

[0142] Dosage: Based on the product weight, the dosage is precisely controlled by a metering pump to ensure that each antibacterial agent component reaches the target final concentration (e.g., the total dosage is 0.5%-1.5% of the product weight).

[0143] Mixing: Then transfer to a low-speed twin-helix mixer and gently mix for 60-90 seconds to ensure even distribution and avoid damaging the product structure.

[0144] (IV) Fourth Stage: Construction of Aseptic Environment and Detailed Description of Dynamic MAP Packaging Process

[0145] This stage is the final guarantee of preservation. By creating an anaerobic, high-carbon dioxide packaging environment, in conjunction with antibacterial agents inside and outside, the growth of aerobic bacteria and mold is completely inhibited, and oxidative deterioration is delayed.

[0146] 1. Process Principle: In a sterile or near-sterile environment, the air (mainly oxygen) inside the packaging container is removed, and a pre-selected mixture of gases (usually high CO2 and high N2) is introduced. CO2 is soluble in the aqueous and lipid phases of the product, lowers the pH, and penetrates the cell membranes of microorganisms, interfering with their physiological activities. N2, as an inert filling gas, prevents the packaging from collapsing and further isolates oxygen.

[0147] 2. Dynamic controlled atmosphere concept

[0148] Traditional modified atmosphere packaging (MAP) uses a fixed gas ratio. This solution proposes "dynamic modified atmosphere packaging," which fine-tunes the initial gas ratio based on the product's specific characteristics (such as fat content and pH) and shelf-life targets. Theoretically, it also allows for future development of "smart packaging" capable of sensing changes in the gas content within the packaging and providing feedback adjustments.

[0149] 3. The process equipment involved

[0150] (1) Sterile packaging chamber: Equipped with high efficiency air filter (HEPA) to maintain ISO 5 (Class 100) cleanliness.

[0151] (2). Automatic weighing and tray arrangement system.

[0152] (3) Fully automatic vacuum replacement modified atmosphere packaging machine: equipped with multi-component gas mixer, high-precision gas flow meter and residual oxygen analyzer.

[0153] (4) Packaging materials: High-barrier composite materials are used, such as PET / AL / CPP (polyester / aluminum foil / cast polypropylene) or high-barrier nylon co-extruded film. Oxygen transmission rate (OTR) < 1 cm³ / m²·day·atm, water vapor transmission rate (WVTR) < 1 g / m²·day.

[0154] 4. Specific process parameters and steps

[0155] Step 1: Maintaining a sterile environment

[0156] Equipment: Aseptic packaging chamber.

[0157] Parameters: Total environmental bacterial count < 0.1 CFU / plate·hour (sedimentation method). All equipment surfaces and packaging film rolls entering the packaging area are sterilized online using hydrogen peroxide (H2O2) atomization or ultraviolet lamps.

[0158] Step 2: Vacuuming and Inflating

[0159] Equipment: Modified atmosphere packaging machine.

[0160] parameter:

[0161] Vacuum level: Initial vacuuming to an absolute pressure of < 5 kPa (residual oxygen content approximately 1%).

[0162] Gas flushing: A two-stage replacement method of "vacuuming-gas filling-re-vacuuming-re-gas filling" is adopted to ensure a gas replacement efficiency of > 99.5%;

[0163] Target initial residual oxygen concentration: < 0.5% (compared to traditional MAP: typically < 2%).

[0164] Dynamic MAP Gas Ratio Setting Algorithm Design - Algorithm 2: Initial Gas Ratio Optimization Algorithm Based on Product Characteristics

[0165] Input variables:

[0166] `Product_Type` (Product Type): The core category is divided according to the characteristics of the raw material matrix, including livestock and poultry meat (such as chicken and beef), aquatic fish (such as salmon and cod), and mixed fruits and vegetables, etc. It determines the selection of the base gas ratio. The data comes from the product formula management system.

[0167] `Fat_Content` (fat content): The percentage of crude fat in the product by mass (%), determined by Soxhlet extraction or near-infrared spectroscopy. It affects CO2 solubility and oxidation risk assessment, with a threshold set at 15% (the high-fat / low-fat dividing point).

[0168] `pH_Value` (Product pH): The acidity or alkalinity of the product's aqueous phase, measured using a precision pH meter (accuracy ±0.01), which affects the CO2 antibacterial effect (enhanced by an acidic environment). The key threshold is set at 5.5 (the high / low pH dividing point).

[0169] `Target_ShelfLife` (Target Refrigerated Shelf Life): The number of days (in days) that a product is expected to maintain acceptable quality at 4°C. This is set according to market demand (e.g., 30 days / 45 days / 60 days) and serves as the basis for adjusting the aggressiveness of the gas ratio.

[0170] Algorithm logic:

[0171] (1). Data input and preprocessing: Convert `Fat_Content`, `pH_Value`, and `Target_ShelfLife` into standardized parameters (0-1 range) that the algorithm can recognize, and establish a mapping relationship with the gas ratio.

[0172] Livestock and poultry meat wet feed: CO2: 50-70% (benchmark value 60%), N2: 30-50% (benchmark value 40%) (high CO2 is suitable for the antibacterial requirements of high protein matrix)

[0173] High-fat wet food: CO2: 30-50% (benchmark 40%), N2: 50-70% (benchmark 60%) (reduce CO2 to avoid rancidity of fatty acids and packaging collapse)

[0174] Contains fragile fruits, vegetables, and wet grains: CO2: 20-40% (benchmark value 30%), N2: 60-80% (benchmark value 65%), O2: 2-5% (benchmark value 5%) (low CO2 prevents fruits and vegetables from producing acid through anaerobic respiration, and adequate O2 maintains cell activity).

[0175] (2). Parameter fine-tuning (multi-factor coordinated adjustment): The basic ratio is adjusted in a stepwise manner based on the product characteristic parameters.

[0176] pH-driven adjustment: IF `pH_Value` > 5.5 (weakly acidic / neutral matrix) THEN CO2 ratio +5% (enhanced antibacterial effect); IF `pH_Value` ≤ 5.5 (acidic matrix) THEN CO2 ratio -3% (avoid excessive acidification affecting flavor).

[0177] Fat content adjustment: IF `Fat_Content` > 15% (high-fat matrix) THEN CO2 ratio -5% (to reduce the risk of rancidity), N2 ratio +5%; IF `Fat_Content` ≤ 15% (low-fat matrix) THEN maintain the basic ratio.

[0178] Shelf life driven adjustment: IF `Target_ShelfLife` > 60 days THEN, CO2 + 8% (aggressive antimicrobial activity) on the baseline ratio; IF 45 days ≤ `Target_ShelfLife` ≤ 60 days THEN, CO2 + 3%; IF `Target_ShelfLife` < 45 days THEN, maintain the baseline ratio.

[0179] (3). Parameter boundary verification and output execution: Ensure that the gas ratio is within a safe and effective range.

[0180] `Final_CO2_Percentage` (Final CO2 percentage): The volume percentage (%) of carbon dioxide optimized by the algorithm, accurate to 0.1%, with a range of 20-75%.

[0181] `Final_N2_Percentage`: The percentage of nitrogen by volume (%) after algorithm optimization, accurate to 0.1%, used as a balance gas to ensure a total percentage of 100%.

[0182] `Final_O2_Percentage` (Final O2 Ratio): The percentage of oxygen by volume (%) after algorithm optimization, accurate to 0.1%. For fruits and vegetables, it is usually 2-5%, and for other types of products, it is ≤0.5%.

[0183] These parameters are transmitted in real time to the PLC control system of the gas mixer via industrial Ethernet (Profinet protocol), with a control accuracy of ±0.3%, and the gas ratio is calibrated using a gas chromatograph before each batch of production (error ≤0.5%).

[0184] Step 3: Heat sealing and testing

[0185] Parameters: Heat sealing temperature, pressure, and time are optimized according to the packaging material (e.g., for CPP layer sealing, temperature 160-180℃, pressure 0.3-0.5 MPa, time 1-1.5 seconds).

[0186] Quality control: 100% online residual oxygen detection; any package with residual oxygen >0.8% is automatically rejected.

[0187] (V) Fifth Stage: Preservation, Verification and Overall Algorithm Integration

[0188] 1. Finished product preservation and cold chain

[0189] Conditions: Finished products must be stored and transported in a cold chain environment of 0-4℃.

[0190] Monitoring: It is recommended to place a temperature recorder inside the packaging box to ensure that the temperature is controllable throughout the process.

[0191] 2. Shelf life prediction and process validation

[0192] Establish a shelf-life prediction system based on a kinetic model:

[0193] Model: `Shelf_Life = f( F_value, Antimicrobial_Efficacy, MAP_Effectiveness, Storage_Temperature )`

[0194] Definitions of each term in the model formula:

[0195] Shelf Life: refers to the longest time (in days) that wet grain products can maintain their microbial safety, physicochemical stability and sensory acceptability under refrigeration conditions of 0-4℃. It is the core output indicator of this process.

[0196] F_value (sterilization F-value): A parameter characterizing the intensity of heat treatment, defined as the equivalent time (in minutes) required to achieve the same microbial inactivation effect as an actual heat treatment process (mild stepwise pasteurization) at a reference temperature (typically 60°C). It is obtained through optimization using Algorithm 1 and is positively correlated with heat treatment temperature, time, product thickness, and initial microbial load.

[0197] Antimicrobial Efficacy: The synergistic antimicrobial effect of compound natural antimicrobial agents is determined by the type, concentration, and conditions of action of the antimicrobial agents. It is calculated using an antimicrobial synergistic effect algorithm. For example, when the pH of the raw material is >5.5, the proportion of acidic antimicrobial agents is increased by 5-8%, and finally quantified as the microbial growth inhibition rate (%).

[0198] MAP_Effectiveness: The ability of dynamic modified atmosphere packaging to inhibit microbial growth and oxidation reactions is determined by the gas ratio (CO2 / N2 / O2) inside the packaging, the gas replacement efficiency (>99.5%), and the barrier properties of the packaging material (OTR<1 cm³ / m²·day·atm).

[0199] Storage Temperature: The ambient temperature (unit: °C) of the product in the cold chain. It is required to be strictly controlled between 0-4 °C. Temperature fluctuations significantly affect the microbial growth rate and chemical reaction rate through the Arrhenius equation.

[0200] Model operation mechanism:

[0201] This shelf-life prediction model is based on a dynamic equation of the synergistic effect of multiple factors, and its operating mechanism is as follows:

[0202] 1) Parameter input and data acquisition: F_value is calculated in real time through Algorithm 1, Antimicrobial_Efficacy is output by combining the antimicrobial synergistic effect algorithm, MAP_Effectiveness is determined by Algorithm 2, and Storage_Temperature is collected by the cold chain temperature recorder.

[0203] 2) Core Algorithm and Model Fitting: The Baranyi model is used to describe the microbial growth curve. The F_value, Antimicrobial_Efficacy, MAP_Effectiveness, and Storage_Temperature are correlated with the shelf life through multiple linear regression or artificial neural networks. The model parameters are obtained by fitting orthogonal experimental data.

[0204] Core Algorithm and Model Fitting:

[0205] Microbial growth kinetics model: The Baranyi model is used to describe the growth curves of microorganisms (such as Bacillus and yeast) under combined antibacterial conditions.

[0206] ,

[0207] Where N(t) is the number of microorganisms at time t (log CFU / g), N0 is the initial number of bacteria, μmax is the maximum specific growth rate (affected by F_value, Antimicrobial_Efficacy, MAP_Effectiveness and Storage_Temperature), tlag is the lag period (h), and A is the model parameter.

[0208] Multi-factor coupling equation: F_value (X1), Antimicrobial_Efficacy (X2), MAP_Effectiveness (X3), Storage_Temperature (X4) are associated with shelf life (Y) through multiple linear regression or artificial neural networks. The equation is in the following form:

[0209] Y = k_0 + k_1 X_1 + k_2 X_2 + k_3 X_3 + k_4 X_4,

[0210] Wherein, k_0 is a constant term, and k_1 to k_4 are the main effect coefficients of each factor. The model parameters were obtained by fitting orthogonal experimental data (such as the 45-day shelf life corresponding to F=3.2 min, antimicrobial agent 0.5%, CO2 60%, and storage at 4℃ in the example).

[0211] 3). Output and Validation

[0212] Shelf life prediction: The model outputs the time when the product’s microbial count reaches the warning value (6 log CFU / g) or the sensory quality drops to the threshold (e.g., total score <70 points) at the target storage temperature (0-4℃), which is the shelf life (e.g., 45 days in Example 1).

[0213] Dynamic correction: During each batch of production, key parameters such as sterilization F value, antibacterial agent addition amount, and residual oxygen content are collected in real time through the central decision support system (DSS) to correct the model online and ensure that the prediction error is less than 10%.

[0214] 3. Application Scenarios

[0215] Process optimization: The effects of different F values, antimicrobial agent concentrations or gas ratios on shelf life are simulated by modeling to quickly screen the optimal process parameters (such as the combination of 90℃ / 180 seconds heat treatment + 0.5% antimicrobial agent + 60% CO2 in Example 1).

[0216] Cold chain monitoring: By combining temperature recorder data, when the storage temperature deviates from 0-4℃, the model updates the remaining shelf life in real time to guide logistics scheduling (such as prioritizing the delivery of products nearing the warning period).

[0217] This model, through the cross-disciplinary integration of food microbiology, heat transfer, and materials science, has achieved an upgrade from "experience-based judgment" to "data-driven" shelf-life prediction, providing core algorithmic support for the precise control of wet grain preservation processes.

[0218] 4. Output and Validation: The model outputs the time it takes for the product to reach the microbial warning value or sensory threshold at the target storage temperature, i.e., the shelf life. For each batch, key parameters are collected in real-time through a central decision support system to correct the model online, ensuring that the prediction error is <10%.

[0219] Validation experiments: Periodically sample finished products and conduct accelerated destructive experiments at 4°C and abuse temperatures (e.g., 8°C) to monitor microbiological indicators (total bacterial count, coliforms, yeast and mold, specific pathogens), physicochemical indicators (pH, TVBN, peroxide value), and sensory quality to verify whether the actual shelf life meets the target (e.g., 45-60 days).

[0220] 5. Integrate the above three algorithms into a central decision support system (DSS).

[0221] Central DSS Workflow:

[0222] (1) The system obtains batch information (raw material micro-load, product formula).

[0223] (2). Algorithm 1 is triggered to calculate and set the optimal heat sterilization parameters.

[0224] (3) Rapid microbial detection after heat treatment.

[0225] (4) Antibacterial agent addition.

[0226] (5) When the product enters the packaging area, Algorithm 2 calculates the optimal gas ratio based on the product characteristics and shelf life target.

[0227] (6) The key process parameters of the entire batch (sterilization F value, amount of antibacterial agent added, residual oxygen content) are recorded and bound to the unique code of the batch to achieve full-chain traceability.

[0228] (VI) Phase 6: Examples and Experimental Verification

[0229] Example Design: The practical application effect of this process was verified by comparing three examples with one traditional process. All examples used the same raw material formula (60% chicken breast, 15% carrot, 10% peas, and 15% brown rice), with differences only in process parameters.

[0230] Example 1: Standard Process Group (Wet Grains for Livestock and Poultry Meat)

[0231] Process steps and parameters:

[0232] Step 1: Raw material pretreatment - Chicken breast is minced into 5mm particles using a meat grinder (model JR-120), carrots are diced (8mm×8mm), peas are steamed at 100℃ for 10 minutes and then cooled to room temperature, and brown rice is soaked for 30 minutes before steaming.

[0233] Step 2: Mixing and stirring – Use a twin-shaft paddle mixer (model HJJ-50) at 300 rpm for 5 minutes to ensure uniform material mixing;

[0234] Step 3: Filling and shaping - Aseptic filling machine (model GF-800) fills 100g / bag, packaging film is PET / AL / CPP composite film, heat sealing temperature is 180℃, heat sealing time is 2 seconds;

[0235] Step 4: Stepped heat treatment – ​​Three-zone pasteurizer (model BSJ-3), heating zone 65℃ / 30 seconds, holding zone 90℃ / 180 seconds (F value = 3.2 min), pre-cooling zone 40℃ / 20 seconds (sterile ice water bath, temperature difference ≤ 5℃).

[0236] Step 5: Antibacterial agent addition – Add 0.3% tea polyphenols (purity ≥98%) + 0.2% ε-polylysine (molecular weight 3000 Da) online using the aseptic addition system (model TJA-50), stirring speed 200 rpm, mixing time 15 seconds;

[0237] Step 6: Dynamic modified atmosphere packaging – Vacuum displacement modified atmosphere packaging machine (model MAP-600), vacuum degree 5kPa, evacuation time 15 seconds, filling time 2 seconds, gas ratio CO2 60%, N2 40% (residual oxygen concentration 0.3%, detected by residual oxygen analyzer model OXY-100).

[0238] Antibacterial agent addition: 0.3% tea polyphenols + 0.2% ε-polylysine (total addition 0.5%);

[0239] MAP gas ratio: CO2 60%, N2 40% (residual oxygen concentration 0.3%).

[0240] Example 2: High-fat process group (fat content 20%)

[0241] Process steps and parameters:

[0242] Step 1: Raw material pretreatment - replace chicken breast with chicken thigh meat containing 20% ​​fat, mince to 8mm particles, and process other raw materials in the same way as in Example 1;

[0243] Step 2: Mixing and stirring – Twin-shaft paddle mixer (250 rpm, 8 minutes), add 5% soybean oil (adjust fat content to 20%).

[0244] Step 3: Filling and shaping – Filling amount 120g / bag, heat sealing temperature 185℃, heat sealing time 2.5 seconds;

[0245] Step 4: Stepped heat treatment – ​​Three-zone pasteurizer, heating zone 62℃ / 40 seconds, holding zone 88℃ / 210 seconds (F value = 3.0 min), pre-cooling zone 38℃ / 25 seconds;

[0246] Step 5: Antibacterial agent addition—0.2% cinnamaldehyde (oil-soluble, 99% purity) + 0.3% lysozyme (enzyme activity 2000U / mg), using an emulsification addition system (model RH-30), emulsification speed 15000rpm;

[0247] Step 6: Dynamic modified atmosphere packaging – vacuum degree 6 kPa, gas ratio CO2 40%, N2 60% (residual oxygen concentration 0.4%)

[0248] Antibacterial agent added: 0.2% cinnamaldehyde + 0.3% lysozyme (total addition 0.5%)

[0249] MAP gas composition: CO2 40%, N2 60% (residual oxygen concentration 0.4%)

[0250] Example 3: Fruit and vegetable combination (including perishable fruits and vegetables)

[0251] Process steps and parameters:

[0252] Step 1: Raw material pretreatment - Add 10% of fragile fruits and vegetables (spinach, blueberries). Blanch the spinach for 30 seconds and then cool it in ice water. Use whole blueberries (5-8mm in diameter).

[0253] Step 2: Mix and stir – 200 rpm for 3 minutes (to prevent damage to fruits and vegetables).

[0254] Step 3: Filling and shaping – Filling amount 90g / bag, heat sealing temperature 175℃, heat sealing time 1.5 seconds;

[0255] Step 4: Stepped heat treatment – ​​Three-zone pasteurizer, heating zone 60℃ / 25 seconds, holding zone 85℃ / 150 seconds (F value = 2.8 min), pre-cooling zone 35℃ / 30 seconds;

[0256] Step 5: Antibacterial agent addition – 0.2% rosemary extract (lipid-soluble) + 0.2% natamycin (concentration 5000ppm), addition temperature ≤40℃;

[0257] Step 6: Dynamic modified atmosphere packaging – vacuum degree 4kPa, gas ratio CO2 30%, N2 65%, O2 5% (residual oxygen concentration 0.5%)

[0258] Antibacterial agent added: 0.2% rosemary extract + 0.2% natamycin (total addition 0.4%)

[0259] MAP gas composition: CO2 30%, N2 65%, O2 5% (residual oxygen concentration 0.5%)

[0260] Comparative Example 1: Traditional High-Temperature Sterilization Group

[0261] Process parameters: 121℃ / 20 minutes (commercial sterile), no antimicrobial agents, standard vacuum packaging.

[0262] Comparative Example 2: Traditional MAP group (without antibacterial agent)

[0263] Process parameters: 90℃ / 180 seconds heat treatment (same as Example 1), no antibacterial agent added, conventional MAP packaging (CO2 50%, N2 50%, residual oxygen concentration 2.0%).

[0264] Comparative Example 3: Antimicrobial Agent Only (No Modified Atmosphere Packaging)

[0265] Process parameters: 90℃ / 180 seconds heat treatment (same as Example 1), addition of 0.5% antibacterial agent (same as Example 1), ordinary vacuum packaging (no gas replacement)

[0266] (VII) Seventh Stage: Experimental Design

[0267] 1. Microbial indicator detection

[0268] Testing items and equipment: Total bacterial count (GB 4789.2, HH.B11.600 constant temperature incubator, 36±1℃ for 48 hours); Heat-resistant Bacillus (GB 4789.14, MLS-3750 autoclave, 100℃ heat treatment for 10 minutes followed by 36℃ incubation for 72 hours); Coliform bacteria (GB 4789.3, VRBA plate count, 36℃ for 24 hours); Molds and yeasts (GB4789.15, Bengal red agar, 28℃ for 5 days).

[0269] Sampling and testing procedures: Randomly select 10 bags of samples from each batch, and aseptically homogenize 25g of sample with 225mL of sterile physiological saline (using a STOMACHER 400 homogenizer for 2 minutes), then serially dilute to 10⁻ 6 Three suitable dilutions were selected for plating, with each dilution performed twice. The average value of the results was taken.

[0270] 2. Nutritional and physicochemical index testing

[0271] Nutrient retention assays: Vitamin B1 (Agilent 1260 high-performance liquid chromatograph, C18 column 4.6×250mm, mobile phase methanol-0.05mol / L potassium dihydrogen phosphate = 20:80, flow rate 1mL / min, detection wavelength 254nm); Vitamin C (2,6-dichlorophenolindophenol titration, 50mL grade A burette, titration endpoint was a pink solution that remained unchanged for 30 seconds); Crude protein (Kjeldahl nitrogen analyzer K9840, digestion temperature 420℃, digestion time 60 minutes, distillation time 5 minutes).

[0272] Oxidation index detection: Peroxide value (GB 5009.227, titration method, using automatic potentiometric titrator model ZDJ-5, titrant 0.01mol / L sodium thiosulfate); TVB-N (GB 5009.228, semi-micro nitrogen determination method, distillation apparatus model SZF-06A, absorption solution is 2% boric acid solution)

[0273] 3. Sensory evaluation

[0274] Scoring system: color (10 points), aroma (20 points), texture (30 points), flavor (40 points), total score 100 points.

[0275] Evaluation team: 10 trained sensory evaluators (ISO 8586 standard)

[0276] 4. Experimental Results and Analysis

[0277] 4.1 Comparison of Microbial Control Effects (Unit: log CFU / g)

[0278] Group initial value 15 days 30 days 45 days 60 days Example 1 2.3 3.1 4.5 5.8 7.2# Example 2 2.5 3.3 4.8 6.1 7.5# Example 3 2.7 3.5 5.0 6.5 7.8# Comparative Example 2 2.1 4.2 6.3 7.5# - Comparative Example 3 2.2 3.8 5.9 7.3# - Comparative Example 1 0.0 0.0 0.0 0.0 1.2

[0279] Note: # indicates that the microbial warning value has been reached (6 log CFU / g), and # indicates that the shelf life has exceeded the end point (7 log CFU / g).

[0280] 4.2 Comparison of nutrient retention rates (unit: %)

[0281] Group Vitamin B1 Vitamin C crude protein Example 1 89.2 78.5 98.3 Example 2 87.6 76.3 97.8 Example 3 85.3 82.1 98.1 Comparative Example 2 86.5 75.2 97.6 Comparative Example 3 84.8 74.3 97.9 Comparative Example 1 45.7 12.3 96.5

[0282] 4.3 Sensory evaluation results (after 60 days of storage, unit: points)

[0283] Group Color odor texture Flavor Total Score Example 1 8.5 17.2 26.8 35.6 88.1 Example 2 8.3 16.5 25.4 34.2 84.4 Example 3 8.7 16.8 24.6 33.5 83.6 Comparative Example 2 7.2 14.5 22.3 30.1 74.1 Comparative Example 3 7.5 15.2 23.1 31.4 77.2 Comparative Example 1 5.2 10.3 18.5 22.7 56.7

[0284] 4.4 Comparison of Shelf Life and Oxidation Indicators

[0285] Group Shelf life (days) Peroxide value (meq / kg) TVB-N (mg / 100g) Example 1 45 3.2 18.5 Example 2 42 4.1 19.2 Example 3 38 2.8 17.8 Comparative Example 2 30 5.3 22.6 Comparative Example 3 35 4.8 21.4 Comparative Example 1 180+ 6.8 25.7

[0286] Note: Comparative Example 1 is the traditional high-temperature sterilization group, Comparative Example 2 is the modified atmosphere only group without antimicrobial agent, and Comparative Example 3 is the antimicrobial agent only group without modified atmosphere; "-" indicates that the shelf life has expired and no testing was performed.

[0287] in conclusion:

[0288] 1. This process, through the synergistic effect of "mild heat treatment + natural antibacterial agent + dynamic modified atmosphere", extends the shelf life by 15-20% and reduces the peroxide value by more than 30% compared with a single antibacterial method (comparative examples 2-3), proving that the triple synergistic antibacterial effect is significantly better than a single technology;

[0289] 2. Example 1 (standard process group) showed the best overall performance, with the best balance between microbial control effect (5.8 log CFU / g in 45 days) and nutrient retention rate (89.2% of vitamin B1). It improved the sensory score by 19% compared with Comparative Example 2 (modified atmosphere only) and extended the shelf life by 29% compared with Comparative Example 3 (antimicrobial agent only).

[0290] 3. Compared with traditional high-temperature sterilization processes (Comparative Example 1), this solution has five major technical advantages: ① Significantly improved nutrient retention (vitamin B1 increased by more than 40%, vitamin C increased by 520%); ② Comprehensive optimization of sensory quality (total score increased by 55.4% and texture score increased by 44.9% after 60 days of storage); ③ Energy consumption reduced by 62% (90℃ / 180 seconds vs. 121℃ / 20 minutes); ④ Natural antibacterial agents replace chemical preservatives, conforming to the "clean label" trend; ⑤ Dynamic modified atmosphere packaging controls oxygen permeability to below 1 cm³ / m²·day·atm, reducing oxidation rate by 59% compared to traditional vacuum packaging. Although the shelf life is 25% of the traditional process, it fully meets the 30-45 day cold chain distribution requirements.

[0291] 4. The shelf life of the high-fat formula (Example 2) is slightly shortened, and further optimization is needed by adjusting the ratio of antimicrobial agents;

[0292] 5. This process achieves precise preservation of different product types through algorithm-driven adaptive parameter adjustment (such as heat treatment optimization algorithm and gas ratio dynamic algorithm). Compared with the traditional fixed process parameter mode, the process adaptability is improved by 40% and the raw material utilization rate is increased by 12%.

[0293] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0294] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0295] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0296] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for preserving and processing wet grains based on antibacterial heat treatment and modified atmosphere packaging, characterized in that, The method includes the following steps: (1) Raw material pretreatment: The wet grain raw materials are crushed, diced and steamed, and the particle size is controlled to be 5-8mm, and the steaming time is 10-30 minutes; (2) Mild stepped heat treatment: a three-temperature zone pasteurizer is used, with the heating zone at 60-65℃ for 25-40 seconds, the heat preservation zone at 85-90℃ for 150-210 seconds, and the pre-cooling zone at 35-40℃ for 20-30 seconds; (3) Addition of natural antibacterial agent: Add 0.2-0.5% of compound natural antibacterial agent online and mix for 15-30 seconds at 200-300 rpm using an aseptic addition system; (4) Dynamic modified atmosphere packaging: Use a vacuum displacement modified atmosphere packaging machine with a vacuum degree of 5-6 kPa, a pumping time of 15 seconds, and a filling time of 2 seconds. According to the product type, fill with a mixture of CO2, N2 and O2, with CO2 ratio of 20-70%, N2 ratio of 30-80%, and O2 ratio of 0-5%. (5) Cold chain preservation: The finished product is stored and transported at 0-4℃, and the temperature is monitored throughout the process.

2. The method according to claim 1, characterized in that, The compound natural antibacterial agent mentioned in step (3) is selected from at least two of tea polyphenols, ε-polylysine, cinnamaldehyde, lysozyme, rosemary extract or natamycin.

3. The method according to claim 1, characterized in that, The F value of the mild stepped heat treatment in step (2) is optimized by Algorithm 1. The input variables of Algorithm 1 include the initial microbial load, product thickness and heat penetration coefficient, and the output parameters are heating rate, holding time and cooling temperature difference.

4. The method according to claim 1, characterized in that, The amount of compound natural antibacterial agent added in step (3) is determined by the antibacterial agent synergistic effect algorithm. When the pH value of the raw material is >5.5, the proportion of acidic antibacterial agent is increased by 5-8%; when the fat content is >15%, 0.1-0.2% fat-soluble antibacterial agent is added.

5. The method according to claim 1, characterized in that, The gas ratio of the dynamic modified atmosphere packaging in step (4) is set by algorithm 2. The input variables of algorithm 2 include Product_Type, Fat_Content, pH_Value and Target_ShelfLife. The output parameters are: final CO2 ratio, final N2 ratio and final O2 ratio.

6. The method according to claim 5, characterized in that, The Product_Type includes livestock and poultry meat, aquatic fish, and mixed fruits and vegetables, with corresponding basic gas ratios of: livestock and poultry meat CO2 50-70%, N2 30-50%; high-fat wet grains CO2 30-50%, N2 50-70%. Contains fragile fruits, vegetables, and wet grains with CO2 20-40%, N2 60-80%, and O2 2-5%.

7. The method according to claim 1, characterized in that, The packaging material used in step (4) is a high-barrier composite material with an oxygen permeability (OTR) of <1 cm. 3 / m 2 • day·atm, water vapor transmission rate (WVTR) < 1 g / m 2 ·day.

8. The method according to claim 1, characterized in that, The shelf life of the wet grains was predicted using a kinetic model, the formula of which is: Shelf_Life = f(F_value, Antimicrobial_Efficacy, MAP_Effectiveness, Storage_Temperature), condition: shelf life reaches 38-45 days under refrigeration at 4℃; in: Shelf Life: The longest time (in days) a wet food product can maintain its microbiological safety, physicochemical stability and sensory acceptability under refrigeration conditions of 0-4℃. F_value (sterilization F value): A parameter characterizing the intensity of heat treatment, defined as the equivalent time (unit: min) to achieve the same microbial inactivation effect as the actual heat treatment process (mild stepwise pasteurization) at a reference temperature (usually 60℃). Antimicrobial Efficacy: The synergistic antibacterial effect of compound natural antimicrobial agents, determined by the type, concentration, and conditions of action of the antimicrobial agents. It is calculated using an algorithm for the synergistic effect of antimicrobial agents. MAP_Effectiveness: The ability of dynamically modified atmosphere packaging to inhibit microbial growth and oxidation reactions, determined by the gas ratio (CO2 / N2 / O2) within the packaging, gas replacement efficiency (>99.5%), and the barrier properties of the packaging material (OTR <1cm). 3 / m 2 (day / atm) jointly decided; Storage Temperature: The ambient temperature (unit: °C) of the product in the cold chain. It is required to be strictly controlled between 0-4 °C. Temperature fluctuations significantly affect the microbial growth rate and chemical reaction rate through the Arrhenius equation. f() represents the prediction function, which uses multiple linear regression or artificial neural networks for prediction analysis.

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