Preservation method for breaking the growing period of meat products based on liquid nitrogen segmented quick freezing

Abstract

The present application relates to the field of food processing and preservation technology, and more particularly to a preservation method for breaking the growth phase of meat products based on liquid nitrogen segmented quick freezing, comprising: real-time monitoring of the three-dimensional temperature field in the quick freezing cabinet, identifying the local area with abnormal maximum ice crystal belt time, starting targeted biological monitoring for the abnormal area, obtaining colony distribution and characteristic volatile organic compound concentration data, and diagnosing the microbial risk state, and finally executing a hierarchical control strategy according to the diagnosis result, including maintaining the process, starting directional low-temperature plasma antibacterial or strengthening liquid nitrogen spraying; the present application realizes real-time and accurate identification and dynamic intervention of local microbial risk in large-scale quick freezing process, fundamentally improving the reliability of breaking the growth phase, and the present method takes into account the temperature uniformity and quality safety in the equipment, realizing high-quality, standardized intelligent preservation production.

Description

Technical Field

[0001] This invention relates to the field of food processing and preservation technology, and in particular to a preservation method for meat products based on liquid nitrogen segmented quick-freezing to block the bacterial growth period. Background Technology

[0002] Liquid nitrogen flash freezing technology, due to its ability to rapidly pass through the zone of maximum ice crystal formation and effectively inhibit the formation of large ice crystals, has become an important industrial method for maintaining the texture, color, and nutritional value of meat products. However, in actual large-scale production, especially in the processing of large-scale meat products weighing over 1000 kg, this technology still faces a series of bottlenecks: At the equipment level, large cabinet-type quick-freezing machines are prone to uneven temperature and flow fields, causing meat products from different locations in the same batch to undergo different freezing processes: some areas take too long to pass through the maximum ice crystal zone due to insufficient cooling, resulting in cell damage and loss of thawed juices; while other areas may crack on the product surface due to overcooling. This poor consistency in the process is the primary challenge restricting high-quality standardized production.

[0003] Regarding the core preservation objective, traditional freezing processes focus on the control of physical temperature and generally lack the ability to directly monitor and intervene in the growth activity of microorganisms during the basal phase, i.e. the pre-freezing and process. Existing technologies mostly rely on fixed temperature-time curves and cannot respond to the microbial risks caused by differences in the initial colonies of different batches of raw materials or changes in the local microenvironment within the equipment. Therefore, developing an intelligent liquid nitrogen quick-freezing preservation method that can balance process uniformity within large-scale equipment, accurately identify and dynamically inhibit microbial growth activity in real time, and is economically feasible has become a key technical problem that the industry urgently needs to overcome.

[0004] Application No. 202411634263.8 discloses a method and application of three-stage variable-temperature liquid nitrogen quick-freezing for preserving beef, including: S1: pre-treatment of beef such as trimming, removing fascia, and pre-cooling; S2: using three-stage variable-temperature liquid nitrogen quick-freezing to achieve a core temperature of -18℃; S3: after 28 days of frozen storage, quality testing and analysis are performed. The three-stage variable-temperature liquid nitrogen quick-freezing method proposed in this invention can effectively reduce freezing time and cooking losses in beef, delay protein and lipid spoilage, maintain better muscle tissue morphology, and preserve the fresh appearance of beef. Therefore, the existing technology has the following problems: Existing liquid nitrogen quick-freezing technology cannot detect, accurately diagnose, and dynamically intervene in local microbial risks caused by uneven temperature in large-scale production, resulting in technical defects such as unstable preservation effect and poor product quality uniformity. Summary of the Invention

[0005] Therefore, the present invention provides a preservation method for meat products based on liquid nitrogen segmented quick-freezing to block the bacterial growth period, thereby overcoming the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period, comprising: Step S1: Obtain real-time temperature data of each monitoring point inside the blast freezer and generate a three-dimensional temperature field; Step S2: Determine the actual time required for the corresponding position to pass through the maximum ice crystal formation zone based on the temperature change curves at each location in the three-dimensional temperature field; Step S3: Determine several abnormal temperature zones based on the deviation between the actual time taken and the preset time taken; Step S4: In response to the generation of the abnormal zone, the targeted biological monitoring unit corresponding to each abnormal temperature zone is activated; Step S5: Determine the colony distribution heatmap and volatile organic compound concentration change curve for the corresponding abnormal temperature zone based on the biological data collected by the targeted biological monitoring unit. Step S6: Calculate the colony distribution uniformity and the concentration change rate of a specific volatile organic compound based on the colony distribution heatmap and the concentration change curve, respectively. The specific volatile organic compounds mentioned include ammonia, hydrogen sulfide, and trimethylamine; Step S7: Diagnose the microbial risk status of the corresponding abnormal temperature zone based on the colony distribution uniformity and the concentration change rate; Step S8: In response to the diagnostic results of the microbial risk status, execute the corresponding control strategy.

[0007] As a preferred technical solution for the preservation method of blocking the bacterial growth period of meat products by segmented quick-freezing with liquid nitrogen, step S2 includes: Step S21: Obtain the temperature change curve of each monitoring point, and record the moment when the temperature of the curve first drops to the first preset temperature as the entry time; Step S21: Record the moment when the temperature of the temperature change curve first drops to the second preset temperature as the departure time; Step S21: Determine the actual time required for the monitoring point to pass through the maximum ice crystal formation zone based on the time difference between the departure time and the entry time.

[0008] As a preferred technical solution for the preservation method of blocking the bacterial growth period of meat products by segmented quick-freezing with liquid nitrogen, step S3 includes: Step S31: Calculate the time deviation based on the actual time and the preset time; Step S32: Record the monitoring points where the time deviation is greater than the preset deviation as abnormal monitoring points; Step S33: Perform spatial clustering analysis based on the coordinate positions of each abnormal monitoring point within the three-dimensional space of the blast freezer, wherein: Step S331: Group the abnormal monitoring points whose spatial distance is less than the preset clustering distance into an abnormal set; Step S332: Identify the unmerged anomaly monitoring points as independent anomaly monitoring points; Step S34: Determine the three-dimensional spatial envelope range formed by all the abnormal monitoring points of each abnormal set as the abnormal temperature zone corresponding to that abnormal set. Step S35: Determine the smallest independent physical compartment to which each independent anomaly monitoring point belongs as the abnormal temperature zone.

[0009] As a preferred technical solution for the preservation method of blocking the bacterial growth period of meat products by segmented quick-freezing with liquid nitrogen, in step S4, the targeted biological monitoring unit includes a slide rail set on the inner side wall of the quick-freezing cabinet, a number of sliders slidably connected to the slide rail, and a hyperspectral imaging probe and electronic nose sensor array set on the sliders. The slider is configured to move to the physical monitoring compartment to which the center of the abnormal temperature zone belongs and perform targeted biological monitoring in response to the generation of the abnormal zone; The number of sliders is positively correlated with the size of the blast freezer.

[0010] As a preferred technical solution for the preservation method of blocking the bacterial growth period of meat products by segmented quick-freezing with liquid nitrogen, step S5 includes: Step S51: Obtain the hyperspectral image acquired by the hyperspectral imaging probe in the abnormal temperature zone and the original gas concentration acquired by the electronic nose sensor array in the abnormal temperature zone, respectively. Step S52: Based on the preset colony spectral quantitative analysis model, perform pixel-level analysis and classification on the hyperspectral image to generate a colony distribution heatmap reflecting the spatial density distribution of colonies. Step S53: Extract the sensor response sequence corresponding to at least one specific volatile organic compound from the original gas concentration, and convert the response sequence into a concentration change curve over time.

[0011] As a preferred technical solution for the preservation method of blocking the bacterial growth period of meat products based on liquid nitrogen segmented quick-freezing, the process of calculating the uniformity of colony distribution in step S6 includes: Step S611: Divide the planar area covered by the colony distribution heatmap into a regular grid of M×N. Where M and N are both positive integers not less than 2; Step S612: Calculate the average colony density of all pixels in each grid and record it as the representative density value of that grid. Step S613: Calculate the standard deviation of the density represented by each grid. Step S614: The reciprocal of the standard deviation is normalized and recorded as the colony distribution uniformity in the abnormal temperature zone.

[0012] As a preferred technical solution for the preservation method of blocking the bacterial growth period of meat products based on liquid nitrogen segmented quick-freezing, the process of calculating the concentration change rate of specific volatile organic compounds in step S6 includes: Step S621: Select a continuous time window of a preset duration on the concentration change curve; Step S622: Perform linear fitting on the concentration data points within the continuous time window and determine the slope of the fitted line; Step S623: The slope of the fitted straight line is recorded as the average concentration change rate of a specific volatile organic compound within the corresponding continuous time window; Step S624: Record the absolute value of the average concentration change rate as the concentration change rate value.

[0013] As a preferred technical solution for a preservation method based on liquid nitrogen segmented quick-freezing to block the bacterial growth period of meat products, in step S7, the microbial risk status of the corresponding abnormal temperature zone is diagnosed according to the uniformity of colony distribution and the rate of concentration change, including: If the colony distribution uniformity is less than or equal to a preset uniformity and the concentration change rate is less than or equal to a preset concentration change rate, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in a safe period. If the colony distribution uniformity is greater than a preset uniformity or the concentration change rate is greater than a preset concentration change rate, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in a risk period. If the uniformity of colony distribution is greater than a preset uniformity and the rate of concentration change is greater than a preset rate of concentration change, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in the growth phase.

[0014] As a preferred technical solution for the preservation method of blocking the microbial growth period of meat products based on liquid nitrogen segmented quick-freezing, in step S8, a corresponding control strategy is executed in response to the diagnostic result of the microbial risk status, including: In response to the microbial risk status being in a safe period, the current liquid nitrogen segmented quick-freezing process parameters in the abnormal temperature zone are maintained. In response to the microbial risk status being in the risk period, the low-temperature plasma generator integrated in the air duct of the quick-freezing cabinet is activated to perform targeted antibacterial treatment on the abnormal temperature zone and maintain the current segment temperature; In response to the microbial risk state being in the proliferation phase, the opening of the liquid nitrogen spray valve above the abnormal temperature zone is dynamically increased, and the speed of the circulating fan and the angle of the guide plate corresponding to the abnormal temperature zone are adjusted in conjunction to execute an enhanced cooling program.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention overcomes the defects of traditional liquid nitrogen quick-freezing processes and achieves high-quality, intelligent preservation. First, through real-time three-dimensional temperature field monitoring, abnormal temperature zones that have been in the maximum ice crystal formation zone for too long are accurately identified, macroscopically locating weak points and potential risk areas in the process. Second, using temperature anomalies as trigger signals, expensive hyperspectral and electronic nose technologies are activated only for risk areas to perform targeted biological monitoring, economically and efficiently acquiring microbial activity data that directly reflect the state of the microbial growth period, including colony distribution and changes in the concentration of characteristic volatile organic compounds (ammonia, hydrogen sulfide, and trimethylamine). Finally, by fusing analysis of colony spatial uniformity and metabolite change rate, the risk is quantitatively diagnosed as a safe period, a risk warning period, or an active proliferation period, and differentiated control strategies such as maintenance processes, low-temperature plasma sterilization, or enhanced liquid nitrogen spraying and airflow are triggered accordingly. This method transforms the traditional passive freezing of fixed processes into dynamic sensing and active intervention, ensuring temperature uniformity in large-scale production while achieving early identification and precise blocking of microbial risks, significantly improving the consistency, safety, and shelf life of frozen meat products. In particular, step S2 defines the qualitative requirements of rapid freezing by calculating the start and end temperatures of the maximum ice crystal formation zone and the actual time taken, thus transforming them into accurately measurable process parameters and ensuring a scientific benchmark for quality control. Step S3, through the determination of time deviation thresholds and spatial cluster analysis, intelligently filters and transforms a large amount of temperature data into abnormal temperature zones with clear spatial boundaries. This not only achieves a precise mapping from discrete points to problem areas, providing a unique coordinate for subsequent targeted monitoring, but also allows intervention strategies to flexibly adapt to different types of abnormal patterns by distinguishing between sets and independent points. In particular, by mapping the quantitative indicators of colony distribution and metabolic activity into three risk levels—safe period, risk warning period, and active proliferation period—the nature and urgency of the risk were distinguished, providing a basis for accurate intervention. Based on this diagnosis, a graded matching and cost-optimized control strategy was set: non-thermal low-temperature plasma was used for precise chemical attack during the risk warning period, while enhanced liquid nitrogen spraying and airflow were activated for rapid physical suppression during the active proliferation period. This dynamic response maximized the efficiency and reliability of blocking microbial risks while minimizing energy consumption and interference with the overall process, ultimately realizing the industrialization of high-quality intelligent preservation. Attached Figure Description

[0016] Figure 1This is a flowchart illustrating the steps of a preservation method for meat products based on liquid nitrogen segmented quick-freezing to block the bacterial growth period, as described in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Please see Figure 1 The diagram shows the steps of a preservation method for meat products based on liquid nitrogen segmented quick-freezing to block the bacterial growth period, as described in an embodiment of the present invention.

[0022] This invention provides a method for preserving meat products by using liquid nitrogen segmented quick-freezing to interrupt the bacterial growth period, comprising: Step S1: Obtain real-time temperature data of each monitoring point inside the blast freezer and generate a three-dimensional temperature field. It should be understood that, based on the actual internal structure of the blast freezer (such as shelf height, support column position, and air supply / return vent layout), the internal load-bearing space is virtually divided into several regular hexahedral grid units along its length, width, and height. Each grid unit is defined as a physical monitoring compartment. This division is only for the logical structure of spatial positioning and data management and does not change the physical structure of the cabinet.

[0023] In implementation, a low-temperature resistant wireless digital temperature sensor is deployed at the geometric center point of each physical monitoring compartment and at the center point of each side. The temperature sensor is powered by a built-in battery or by drawing power from inside the cabinet and integrates a wireless transmission module. All temperature sensors synchronously measure the temperature value at their location according to a unified time reference and at a sampling frequency of no less than once per second. Each measurement data packet contains a unique sensor ID, temperature value, and timestamp. The data is transmitted to the central processing unit in real time through a wireless sensor network (such as ZigBee or LoRa network).

[0024] It is understandable that after the central processing unit receives the data: (1) it associates the sensor ID with the preset physical monitoring compartment and the specific three-dimensional coordinates (X,Y,Z); (2) it uses the coordinates and real-time temperature values ​​of all monitoring points as known data and employs a spatial interpolation algorithm to calculate the estimated temperature values ​​of all spatial locations in the freezer; (3) it constructs the interpolation calculation results into a continuous three-dimensional temperature field model, which can be visualized in the form of a three-dimensional temperature cloud map and allows querying the real-time temperature and historical change curves of any location.

[0025] Step S2: Determine the actual time required for the corresponding position to pass through the maximum ice crystal formation zone based on the temperature change curves at each location in the three-dimensional temperature field; Specifically, step S2 includes: Step S21: Obtain the temperature change curve of each monitoring point, and record the moment when the temperature of the curve first drops to the first preset temperature as the entry time. The first preset temperature is the upper limit temperature of the maximum ice crystal formation zone. Step S21: The moment when the temperature of the temperature change curve first drops to the second preset temperature is recorded as the departure time. The second preset temperature is the lower limit temperature of the maximum ice crystal formation zone and is lower than the first preset temperature. Step S23: Determine the time difference between the departure time and the entry time as the actual time required for the monitoring point to pass through the maximum ice crystal formation zone; Understandably, step S2 quantifies the freezing rate of meat and assesses the potential damage risk of ice crystals to its cell structure by monitoring the time it takes for the meat to pass through a specific critical temperature range. During the freezing process of meat products, most of the water does not freeze instantly at 0°C, but forms a large number of ice crystals in a temperature range below 0°C (usually -1°C to -5°C). This range is called the maximum ice crystal formation zone. The size and distribution of ice crystals directly determine the degree of damage to muscle cells: ① When passing slowly, ice crystals have sufficient time to grow, forming large and sharp-edged ice crystals. These ice crystals can pierce cell membranes, leading to severe loss of juices, nutrients, and flavor substances during thawing, thus resulting in a deterioration in meat quality. ② When passing quickly, water freezes rapidly, forming small and evenly distributed ice crystals. These ice crystals cause minimal damage to cell structure and can maintain the original quality of the meat to the greatest extent. Therefore, monitoring and controlling the time it takes for the core temperature of the meat to pass through this range (i.e., the actual time) is the most direct and critical quantitative indicator for evaluating and optimizing quick-freezing processes and achieving high-quality preservation.

[0026] In practice, the first preset temperature is -1℃ and the second preset temperature is -5℃. For most meat products (including pork, beef, and mutton), the freezing point of free water in their muscle fibers is usually between -0.8℃ and -1.5℃. Setting the first preset temperature to -1℃ can relatively accurately capture the starting point when free water inside the meat begins to crystallize in large quantities, marking the beginning of the dangerous temperature range where ice crystals form rapidly. When the temperature of the meat drops to -5℃, about 80% or more of the free water has frozen, and the intense ice crystal formation activity has basically ended. Setting this point as the departure time can fully cover the core temperature range where ice crystal growth is most active and has the greatest impact on quality. Therefore, monitoring the time spent between -1℃ and -5℃ can most effectively characterize the level of protection of cell structure by the freezing process.

[0027] Step S3: Determine several abnormal temperature zones based on the deviation between the actual time taken and the preset time taken; Specifically, step S3 includes: Step S31: Calculate the time deviation based on the actual time and the preset time. In practice, the preset time is usually 2 to 5 minutes. It is understood that in high-quality liquid nitrogen quick-freezing, the shorter the time spent in the maximum ice crystal formation zone (-1°C to -5°C), the smaller the ice crystals formed, and the better the quality retention. For large-scale meat products weighing 1000 kg, the lower limit of the time deviation represents the ideal rapid freezing target that can be achieved under constraints of equipment capacity, energy consumption, and cost; shorter times are usually difficult to achieve stably. The upper limit of the time deviation is a quality and safety red line determined based on extensive experiments and industry experience. The risk of damage to cell structure increases significantly when the thawing time exceeds 5 minutes, which may lead to indicators such as thawing loss rate and color deterioration exceeding the quality product standard. Therefore, the preset time is usually set within this range according to the best practices of specific products (such as steak, meat chunks, whole carcasses). Preferably, the preset time for whole carcasses / half carcasses (thickest part > 200mm) is 4 minutes, the preset time for large-sized cuts of meat (including hind leg meat, shoulder, etc., with a thickness between 100mm and 200mm) is 3 minutes, and the preset time for cut chops / slices of meat (thickness between 20mm and 50mm) is 1.5 minutes.

[0028] Step S32: Record the monitoring points where the time deviation is greater than the preset deviation as abnormal monitoring points. In practice, the preset deviation is usually 20% to 30% of the preset time. In actual production, due to slight differences in the initial temperature of the material and instantaneous fluctuations in airflow, the time taken at each point cannot be completely consistent. Therefore, a 20% deviation is allowed to provide the system with a reasonable fault tolerance margin to prevent false alarms from becoming rampant. When the deviation exceeds 30%, it means that the freezing rate at that point has significantly lagged behind the process requirements. Preferably, the preset deviation is 28% of the preset time. Understandably, the actual performance (i.e., actual time) of each monitoring point through the maximum ice crystal formation zone is compared with the ideal process benchmark (preset time). The freezing effect is quantified by calculating the time deviation. Comparing it with the preset deviation avoids the system from overreacting to small normal fluctuations and ensures that the selected monitoring points are true abnormal monitoring points that significantly deviate from the process standard. Step S33: Perform spatial clustering analysis based on the coordinates of each abnormal monitoring point in the three-dimensional space of the blast freezer. In this implementation, the spatial clustering analysis is implemented by a density-based clustering algorithm (DBSCAN algorithm): (1) Input the three-dimensional coordinate set of all abnormal monitoring points, and preset two key parameters (i.e., preset clustering distance and minimum number of points). Preferably, the preset clustering distance (Eps) is set to 0.3m to 0.5m according to the physical structure of the equipment, and the minimum number of points (MinPts) is usually set to 2 (i.e., at least two neighboring points are needed to form a cluster); (2) The algorithm traverses each For each point, its coordinates are used as the center and Eps is used as the radius to define the neighborhood. If the number of points contained in the neighborhood of a point is not less than MinPts, it is marked as a core point. Then, all points in the neighborhood of the core point (including other core points and boundary points) are recursively assigned to the same cluster (i.e., an anomaly set); (3) Points that cannot be included in the neighborhood of any core point, i.e., isolated points whose distance from any point is greater than Eps, are marked as noise points and recorded as independent anomaly monitoring points; (4) Output several anomaly sets (each set contains several spatially adjacent anomaly monitoring points) and a list of independent anomaly monitoring points; Step S331: Abnormal monitoring points whose spatial distance is less than the preset clustering distance are grouped into an abnormal set. In practice, the preset clustering distance is usually 0.3m to 0.5m. In a typical 1000kg cabinet-type quick-freezing equipment, the shelf height, the gap between goods stacking, and the width of key airflow channels (such as the distance between the air duct and the goods) are usually designed within the range of 0.3m to 0.5m. Setting the preset clustering distance within this range means that the system only determines that two abnormal monitoring points belong to the same problem area when the distance between them is less than or close to the size of a typical physical structural unit. This ensures that the clustering results are consistent with the actual equipment. Matching the actual physical layout avoids forcibly merging isolated anomalies that do not belong to the same functional area in space (such as different shelf layers or different air duct coverage areas), thus achieving accurate positioning of the problem area. If the distance is set too large, it may lead to multiple anomalies caused by different reasons being misjudged as the same large area, masking the true distribution of the problem and rendering subsequent targeted intervention meaningless. If the distance is set too small, it is impossible to aggregate adjacent anomalies in the same cold air dead corner or the same densely packed area into a complete area, causing the system to identify a continuous problem area as multiple scattered points, interfering with accurate control. Step S332: Identify the unmerged anomaly monitoring points as independent anomaly monitoring points; Understandably, in actual production, cold air short circuits and excessively dense local stacking usually affect an area rather than isolated points. By using spatial clustering analysis (including the DBSCAN algorithm), grouping spatially adjacent abnormal monitoring points into anomaly sets can reveal the distribution patterns of abnormal monitoring points and connect discrete monitoring signals into a holistic outline of the problem area, providing a direct basis for subsequent regional adjustments. In addition, the processing of independent abnormal monitoring points also ensures the integrity of monitoring. Step S34: The three-dimensional spatial envelope range formed by all abnormal monitoring points of each abnormal set is determined as the abnormal temperature zone corresponding to that abnormal set. It can be understood that the three-dimensional spatial envelope range is a continuous and minimized three-dimensional spatial region determined by the spatial coordinates of all abnormal monitoring points in a certain abnormal set. This region completely contains all points in the set and constitutes the geometric definition of the problem area represented by these discrete points in the equipment space. In implementation, it is automatically determined by a spatial algorithm (this is existing technology and will not be elaborated here), including convex hull, axial bounding box and specific shape envelope. This invention preferentially selects the convex hull (i.e., the smallest convex polyhedron that can contain all points in the set) as the three-dimensional spatial envelope range. Step S35: Determine the smallest independent physical compartment to which each independent anomaly monitoring point belongs as the abnormal temperature zone.

[0029] Step S4: In response to the generation of the abnormal zone, the targeted biological monitoring unit corresponding to each abnormal temperature zone is activated; Specifically, the targeted biological monitoring unit includes a slide rail disposed on the inner wall of the quick-freezing cabinet, several sliders slidably connected to the slide rail, and a hyperspectral imaging probe and electronic nose sensor array disposed on the sliders; The slider is configured to move to the physical monitoring compartment to which the center of the abnormal temperature zone belongs and perform targeted biological monitoring in response to the generation of the abnormal zone; The number of sliders is positively correlated with the size of the blast freezer. For a 1000 kg blast freezer with an effective length between 8 and 12 meters, the preferred number of sliders is 1 or 2: when configured with 1 slider, the cost is the lowest; when configured with 2 sliders, the two modules can be controlled independently and can perform targeted monitoring of abnormal temperature zones in different locations in parallel, thereby achieving faster system response and higher reliability.

[0030] In implementation, a pair of high-precision linear guides are installed parallel to each other on the inner sidewalls of both sides of the quick-freezing cabinet along its length (X-axis) to form a horizontal main slide rail. Multiple sliders are set up to form a sliding connection with the main slide rail through slider seats. Each slider integrates a vertical lifting mechanism (including electric push rod, lead screw slide, or any mechanism that can enable the slider to move in the vertical direction). At the end of the lifting mechanism, a horizontally extendable mechanical arm (Y-axis) is installed, and its end is a sensor mounting platform. Each sensor mounting platform integrates a miniature hyperspectral imaging probe and a multi-channel electronic nose sensor array to form a targeted biological monitoring component. This component is connected to the central controller and data analysis system outside the cabinet via a traveling cable or sliding contact line to achieve power supply and high-speed data transmission. The central controller has a built-in three-dimensional digital map of the blast freezer, which is perfectly aligned with the coordinates of the physical monitoring compartment defined in step S1. When an abnormal temperature zone is determined to be generated, the controller sends the spatial coordinates of the zone (usually the physical monitoring compartment number to which its center point belongs) to the target biological monitoring unit. The drive system (including the X-axis main slide rail motor, Z-axis lifting motor, and Y-axis telescopic motor) receives the instruction and controls the slider to carry the monitoring components, move and position them along the optimal path, so that the sensor installation platform can accurately reach the preset monitoring point of the target compartment.

[0031] In practice, the electronic nose sensor array is configured to detect characteristic volatile organic compounds (VOCs) generated during the spoilage of meat products. It includes a metal oxide semiconductor sensor for detecting ammonia, an electrochemical sensor for specifically detecting hydrogen sulfide, and a conductive polymer sensor for detecting trimethylamine. The array's response signal is processed by a pre-trained pattern recognition model to analyze and output the concentration change curve of the specific VOC.

[0032] Step S5: Determine the colony distribution heatmap and volatile organic compound concentration change curve for the corresponding abnormal temperature zone based on the biological data collected by the targeted biological monitoring unit. Specifically, step S5 includes: Step S51: Acquire the hyperspectral image collected by the hyperspectral imaging probe in the abnormal temperature zone and the original gas concentration collected by the electronic nose sensor array in the abnormal temperature zone, respectively. In practice, the hyperspectral imaging probe performs a rapid scan of the located abnormal temperature zone and acquires a set of hyperspectral image cube data containing hundreds of continuous narrow band reflectance information. The electronic nose sensor array is activated (continuously acquiring gas in the region at a frequency of not less than 1Hz) and outputs the original voltage or current response values ​​of each gas sensor channel, forming the original gas concentration data stream. Step S52: Based on the preset colony spectral quantitative analysis model, perform pixel-level analysis and classification on the hyperspectral image to generate a colony distribution heatmap reflecting the spatial density distribution of colonies. In implementation, the central controller is pre-loaded with a colony spectral quantitative analysis model (this model is obtained by training a large amount of hyperspectral data of meat samples with known colony concentrations through machine learning, including support vector machines and convolutional neural networks, and can identify colony metabolites, including cell secretions, biofilms, and characteristic absorption and scattering patterns of light in specific wavelength bands). After inputting the hyperspectral image of the abnormal temperature zone into the model, the model performs spectral analysis on each pixel in the image and outputs the estimated colony density level of the pixel. Then, all pixels are rendered in pseudo-color according to their density level with different colors (e.g., dark blue for low density and red for high density) to generate a colony distribution heat map that intuitively shows the spatial aggregation and dispersion of colonies. Step S53: Extract the sensor response sequence corresponding to at least one specific volatile organic compound from the original gas concentration, and convert the response sequence into a concentration change curve over time; In practice, the specific sensor channel for the target volatile organic compounds (VOCs) is locked from the raw gas concentration data stream of the electronic nose, and the response sequence of the channel changing over time is extracted. According to the pre-calibration curve of the sensor (i.e., the correspondence between the known concentration of gas and the sensor output value), the voltage response sequence is converted into the concentration value sequence of the target VOCs. This concentration value sequence is then connected in chronological order to generate a concentration change curve that reflects the dynamic fluctuation of the concentration of the VOCs during the monitoring period.

[0033] Step S6: Calculate the colony distribution uniformity and the concentration change rate of a specific volatile organic compound based on the colony distribution heatmap and the concentration change curve, respectively. The specific volatile organic compounds mentioned include ammonia, hydrogen sulfide, and trimethylamine; Specifically, the process of calculating the uniformity of colony distribution includes: Step S611: Divide the planar area covered by the colony distribution heatmap into a regular grid of M×N. Where M and N are both positive integers not less than 2; in one implementation, M=N=3; Step S612: Calculate the average colony density of all pixels in each grid and record it as the representative density value of that grid. Step S613: Calculate the standard deviation of the density represented by each grid. It can be understood that the standard deviation is the core indicator in statistics for measuring the dispersion of a set of data. The larger the standard deviation of the density represented by each grid, the greater the difference in average density between grids and the more uneven the distribution of colonies (i.e., some areas are densely distributed and some areas are sparsely distributed). Step S614: Normalize the reciprocal of the standard deviation and record it as a value between 0 and 1, and use this value to represent the uniformity of colony distribution in the abnormal temperature zone. It can be understood that the reciprocal of the standard deviation makes the value positively correlated with the uniformity, that is, the more uniform the distribution, the smaller the standard deviation and the larger the reciprocal. Therefore, the closer the value is to 1, the more uniform the distribution, and the closer it is to 0, the more uneven the distribution.

[0034] Specifically, the process of calculating the rate of change in the concentration of a particular volatile organic compound includes: Step S621: Select a continuous time window of a preset duration (usually 30 seconds or 1 minute) on the concentration change curve; Step S622: Perform linear fitting on the concentration data points within the continuous time window using the least squares method to obtain a straight line that best represents the overall trend of these data changes, and determine the slope of the fitted line. Step S623: The slope of the fitted straight line is recorded as the average concentration change rate of a specific volatile organic compound within the corresponding continuous time window. It can be understood that the average concentration change rate represents the average rate of concentration change over time (unit: ppb / second or μg / m³·min). In practice, a large absolute value of the slope (i.e., a large rate of change) indicates that microbial metabolic activity is vigorous during the observation period, and the rate of production or consumption of the VOCs is rapid, indicating that the microorganisms are in an active proliferation phase. A slope absolute value close to 0 (i.e., a small rate of change) indicates that metabolic activity is slow, and the VOCs concentration is in a dynamic equilibrium or a slow-changing state, indicating that the microorganisms are in an inert or stable phase. Step S624: Record the absolute value of the average concentration change rate as the concentration change rate value; it can be understood that the magnitude of the average concentration change rate directly characterizes the drastic degree of change in organic matter concentration, and is unrelated to the direction of change (concentration increase or concentration decrease), so its absolute value is taken as the concentration change rate value.

[0035] Step S7: Diagnose the microbial risk status of the corresponding abnormal temperature zone based on the colony distribution uniformity and the concentration change rate, wherein: If the uniformity of colony distribution is less than or equal to a preset uniformity and the rate of change of concentration is less than or equal to a preset rate of change of concentration, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in a safe period. It can be understood that this indicates that there are no local outbreak points of colonies on meat products in the quick-freezing cabinet and that the metabolic activity is low, which means that the microorganisms are in a stable or inert state and there is no risk of rapid reproduction or local spoilage. If the uniformity of the colony distribution is greater than the preset uniformity or the rate of change of concentration is greater than the preset rate of change of concentration, the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in a risk period. It can be understood that this indicates that the meat products in the quick-freezing cabinet either have started to accumulate colonies (i.e., there may be local contamination points) or have started to increase metabolic activity (i.e., there are signs of microbial recovery or accelerated reproduction), and early warning and preventive intervention should be carried out. If the uniformity of colony distribution is greater than a preset uniformity and the rate of concentration change is greater than a preset rate of concentration change, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in the proliferation phase. This means that at this time, there is both significant colony aggregation (contamination focus) and high metabolic activity (rapid reproduction). This indicates that a localized, active microbial contamination event is occurring; the strongest physical intervention should be triggered to suppress microbial activity as quickly as possible to prevent its spread and irreversible quality deterioration. In practice, the preset uniformity is usually 0.7. When the index is below 0.7, it indicates that the dispersion of the colony distribution has been significant enough to form local high-density areas (i.e. hot spots). This is an empirical value based on image texture statistical analysis, which marks the spatial distribution from basically uniform to a risky state of obvious non-uniformity. The preset concentration change rate threshold is obtained by calibrating the volatile organic compound release kinetics experiment of normal meat samples and initially rotten meat samples (i.e., monitoring known safe samples and samples in the early stage of rot to determine the distribution of their VOCs concentration change rate), which is usually between 0.5 and 1.

[0036] Step S8: In response to the diagnostic result of the microbial risk status, execute the corresponding control strategy, including: In response to the microbial risk status being in a safe period, the current liquid nitrogen segmented quick-freezing process parameters in the abnormal temperature zone are maintained. In response to the microbial risk status being in the risk period, the low-temperature plasma generator integrated in the air duct of the quick-freezing cabinet is activated to perform targeted antibacterial treatment on the abnormal temperature zone and maintain the current segment temperature. It is understood that low-temperature plasma can generate highly active substances (such as ozone and free radicals) at low temperatures, destroying microbial cell membranes and DNA, achieving efficient non-thermal sterilization, and having minimal impact on the temperature of meat products, thereby eliminating early biological risks without changing the current freezing process. In response to the microbial risk state being in the proliferation phase, the opening of the liquid nitrogen spray valve above the abnormal temperature zone is dynamically increased, and the speed of the circulating fan and the angle of the guide vane corresponding to the abnormal temperature zone are adjusted in conjunction to execute an enhanced cooling program. It can be understood that increasing the liquid nitrogen spray volume can drastically reduce the local temperature, and increasing the fan speed and adjusting the guide vane can improve the heat exchange efficiency of the area, quickly pulling the product away from the temperature range suitable for microbial growth. By creating an extremely low temperature microenvironment, the activity of all microorganisms is fundamentally and rapidly inhibited, which is an emergency response to the problem that has already occurred.

[0037] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period, characterized in that, include: Step S1: Obtain real-time temperature data of each monitoring point inside the blast freezer and generate a three-dimensional temperature field; Step S2: Determine the actual time required for the corresponding position to pass through the maximum ice crystal formation zone based on the temperature change curves at each location in the three-dimensional temperature field; Step S3: Determine several abnormal temperature zones based on the deviation between the actual time taken and the preset time taken; Step S4: In response to the generation of the abnormal zone, the targeted biological monitoring unit corresponding to each abnormal temperature zone is activated; Step S5: Determine the colony distribution heatmap and volatile organic compound concentration change curve for the corresponding abnormal temperature zone based on the biological data collected by the targeted biological monitoring unit. Step S6: Calculate the colony distribution uniformity and the concentration change rate of a specific volatile organic compound based on the colony distribution heatmap and the concentration change curve, respectively. The specific volatile organic compounds mentioned include ammonia, hydrogen sulfide, and trimethylamine; Step S7: Diagnose the microbial risk status of the corresponding abnormal temperature zone based on the colony distribution uniformity and the concentration change rate; Step S8: In response to the diagnostic results of the microbial risk status, execute the corresponding control strategy.

2. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period, as described in claim 1, is characterized in that... Step S2 includes: Step S21: Obtain the temperature change curve of each monitoring point, and record the moment when the temperature of the curve first drops to the first preset temperature as the entry point; Step S21: Record the moment when the temperature of the temperature change curve first drops to the second preset temperature as the departure time; Step S21: Determine the actual time required for the monitoring point to pass through the maximum ice crystal formation zone based on the time difference between the departure time and the entry time.

3. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period, as described in claim 1, is characterized in that... Step S3 includes: Step S31: Calculate the time deviation based on the actual time and the preset time; Step S32: Record the monitoring points where the time deviation is greater than the preset deviation as abnormal monitoring points; Step S33: Perform spatial clustering analysis based on the coordinate positions of each abnormal monitoring point within the three-dimensional space of the blast freezer, wherein: Step S331: Group the abnormal monitoring points whose spatial distance is less than the preset clustering distance into an abnormal set; Step S332: Identify the unmerged anomaly monitoring points as independent anomaly monitoring points; Step S34: Determine the three-dimensional spatial envelope range formed by all the abnormal monitoring points of each abnormal set as the abnormal temperature zone corresponding to that abnormal set. Step S35: Determine the smallest independent physical compartment to which each independent anomaly monitoring point belongs as the abnormal temperature zone.

4. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to block the bacterial growth period according to claim 1, characterized in that, In step S4, the targeted biological monitoring unit includes a slide rail disposed on the inner side wall of the quick-freezing cabinet, a plurality of sliders slidably connected to the slide rail, and a hyperspectral imaging probe and electronic nose sensor array disposed on the sliders. The slider is configured to move to the physical monitoring compartment to which the center of the abnormal temperature zone belongs and perform targeted biological monitoring in response to the generation of the abnormal zone; The number of sliders is positively correlated with the size of the blast freezer.

5. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period according to claim 1, characterized in that, Step S5 includes: Step S51: Obtain the hyperspectral image acquired by the hyperspectral imaging probe in the abnormal temperature zone and the original gas concentration acquired by the electronic nose sensor array in the abnormal temperature zone, respectively. Step S52: Based on the preset colony spectral quantitative analysis model, perform pixel-level analysis and classification on the hyperspectral image to generate a colony distribution heatmap reflecting the spatial density distribution of colonies. Step S53: Extract the sensor response sequence corresponding to at least one specific volatile organic compound from the original gas concentration, and convert the response sequence into a concentration change curve over time.

6. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period according to claim 1, characterized in that, In step S6, the process of calculating the uniformity of colony distribution includes: Step S611: Divide the planar area covered by the colony distribution heatmap into a regular grid of M×N. Where M and N are both positive integers not less than 2; Step S612: Calculate the average colony density of all pixels in each grid and record it as the representative density value of that grid. Step S613: Calculate the standard deviation of the density represented by each grid. Step S614: The reciprocal of the standard deviation is normalized and recorded as the colony distribution uniformity in the abnormal temperature zone.

7. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period according to claim 1, characterized in that, In step S6, the process of calculating the concentration change rate of a specific volatile organic compound includes: Step S621: Select a continuous time window of a preset duration on the concentration change curve; Step S622: Perform linear fitting on the concentration data points within the continuous time window and determine the slope of the fitted line; Step S623: The slope of the fitted straight line is recorded as the average concentration change rate of a specific volatile organic compound within the corresponding continuous time window; Step S624: Record the absolute value of the average concentration change rate as the concentration change rate value.

8. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period according to claim 1, characterized in that, In step S7, diagnosing the microbial risk status of the corresponding abnormal temperature zone based on the colony distribution uniformity and the concentration change rate includes: If the colony distribution uniformity is less than or equal to a preset uniformity and the concentration change rate is less than or equal to a preset concentration change rate, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in a safe period. If the colony distribution uniformity is greater than a preset uniformity or the concentration change rate is greater than a preset concentration change rate, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in a risk period. If the uniformity of colony distribution is greater than a preset uniformity and the rate of concentration change is greater than a preset rate of concentration change, then the microbial risk status of the corresponding abnormal temperature zone is diagnosed as being in the growth phase.

9. The preservation method for meat products based on segmented quick-freezing with liquid nitrogen to interrupt the bacterial growth period according to claim 1, characterized in that, In step S8, a corresponding regulatory strategy is executed in response to the diagnostic result of the microbial risk status, including: In response to the microbial risk status being in a safe period, the current liquid nitrogen segmented quick-freezing process parameters in the abnormal temperature zone are maintained. In response to the microbial risk status being in the risk period, the low-temperature plasma generator integrated in the air duct of the quick-freezing cabinet is activated to perform targeted antibacterial treatment on the abnormal temperature zone and maintain the current segment temperature; In response to the microbial risk state being in the proliferation phase, the opening of the liquid nitrogen spray valve above the abnormal temperature zone is dynamically increased, and the speed of the circulating fan and the angle of the guide plate corresponding to the abnormal temperature zone are adjusted in conjunction to execute an enhanced cooling program.

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