Fresh meat product preservation evaluation method

Abstract

The invention relates to the technical field of fresh meat preservation, in particular to a preservation evaluation method for fresh meat products, which can more comprehensively and accurately evaluate the quality change of the fresh meat products in the preservation process. The method comprises the following steps: on the basis of a preset physicochemical detection frequency, performing physicochemical index detection on the fresh meat product to obtain a plurality of physicochemical detection results; performing microbiological detection on the fresh meat product based on a preset microbiological detection frequency to obtain a plurality of microbiological detection results; based on a data acquisition time sequence, performing data feature alignment and arrangement on the plurality of physicochemical detection results and the plurality of microbiological detection results respectively to obtain a raw fresh meat physicochemical feature time sequence matrix and a raw fresh meat microbiological feature time sequence matrix respectively; carrying out preservation effect evaluation on the raw fresh meat physicochemical characteristic time sequence matrix by utilizing a pre-trained physicochemical characteristic preservation evaluation model to obtain a raw fresh meat preservation physicochemical evaluation value; and preserving the evaluation model by using pre-trained microbial characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh meat preservation, and particularly to a method for evaluating the preservation of fresh meat products. Background Art

[0002] In the production, processing, transportation and sales processes of fresh meat products, maintaining their quality and safety is of utmost importance. The quality and safety of fresh meat products are affected by various factors, including but not limited to temperature control, packaging materials, storage time, and microbial activities, etc. Changes in these factors will cause changes in the physical and chemical properties and microbial communities of fresh meat products, thereby affecting their edible quality and safety.

[0003] Existing methods for evaluating the preservation of fresh meat products often rely on manual detection and sampling analysis. This method is not only time-consuming and laborious, but also may not be able to comprehensively and accurately reflect the quality changes of fresh meat products during the entire preservation process due to the limitation of detection frequency. In addition, existing methods usually only focus on the detection results at a certain time point or a certain stage, while ignoring the continuous characteristics of the quality change of fresh meat products over time. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for evaluating the preservation of fresh meat products that can more comprehensively and accurately evaluate the quality changes of fresh meat products during the preservation process.

[0005] In a first aspect, the present invention provides a method for evaluating the preservation of fresh meat products, the method comprising:

[0006] Performing physical and chemical index detection on the fresh meat product based on a preset physical and chemical detection frequency to obtain a plurality of physical and chemical detection results;

[0007] Performing microbial detection on the fresh meat product based on a preset microbial detection frequency to obtain a plurality of microbial detection results;

[0008] Aligning and arranging the data characteristics of the plurality of physical and chemical detection results and the plurality of microbial detection results respectively according to the time sequence of data acquisition to obtain a fresh meat physical and chemical characteristic time series matrix and a fresh meat microbial characteristic time series matrix;

[0009] Using a pre-trained physical and chemical characteristic preservation evaluation model to evaluate the preservation effect of the fresh meat physical and chemical characteristic time series matrix to obtain a fresh meat preservation physical and chemical evaluation value;

[0010] Using a pre-trained microbial characteristic preservation evaluation model to evaluate the preservation effect of the fresh meat microbial characteristic time series matrix to obtain a fresh meat preservation microbial evaluation value;

[0011] Based on the preset weight coefficients, perform weighted calculations on the physicochemical evaluation value and the microbial evaluation value of the fresh meat preservation to obtain the comprehensive score of the fresh meat preservation;

[0012] Based on the preset fresh meat preservation scoring standard, conduct a pass judgment on the comprehensive score of the fresh meat preservation.

[0013] Further, the physicochemical test results include pH value, moisture content, and volatile basic nitrogen.

[0014] Further, the microbial test results include total number of bacteria, number of Escherichia coli, number of molds and yeasts.

[0015] Further, the method for setting the physicochemical test frequency includes:

[0016] Obtain the fresh meat variety information and the preservation environment information;

[0017] Based on the fresh meat variety information, extract the set of preservation environment parameters corresponding to the fresh meat variety information from the preservation environment information;

[0018] Input the set of preservation environment parameters into the pre-constructed preservation environment impact analysis model to obtain the preservation environment sensitivity;

[0019] In the pre-set physicochemical test frequency comparison table, extract the physicochemical test frequency corresponding to the preservation environment sensitivity; the physicochemical test frequency comparison table includes multiple physicochemical test frequencies, and each physicochemical test frequency corresponds to a preservation environment sensitivity range.

[0020] Further, the method for setting the microbial test frequency includes:

[0021] Obtain the fresh meat variety information and the preservation environment information of the fresh meat to be evaluated;

[0022] Based on the fresh meat variety information, extract the set of preservation environment parameters corresponding to the fresh meat variety information from the preservation environment information;

[0023] Input the set of preservation environment parameters into the pre-constructed preservation environment impact analysis model to obtain the preservation environment sensitivity;

[0024] In the pre-set microbial test frequency comparison table, extract the microbial test frequency corresponding to the preservation environment sensitivity; the microbial test frequency comparison table includes multiple microbial test frequencies, and each microbial test frequency corresponds to a preservation environment sensitivity range.

[0025] Further, the method for obtaining the fresh meat physicochemical feature time series matrix and the fresh meat microbial feature time series matrix includes:

[0026] Obtain all physical and chemical test results and microbiological test results, and identify the time stamps of each test result;

[0027] Align the physical and chemical test results and the microbiological test results in chronological order;

[0028] Arrange all the physical and chemical test results in chronological order to form a physical and chemical characteristic time series matrix, where the rows represent time points and the columns represent different physical and chemical indexes;

[0029] Arrange all the microbiological test results in chronological order to form a microbiological characteristic time series matrix, where the rows represent time points and the columns represent different microbiological indexes.

[0030] Furthermore, the calculation formula for the comprehensive preservation score of fresh meat is:

[0031] S 综合 =W 理化 ×S 理化 +W 微生物 ×S 微生物 ;

[0032] Among them, S 综合 represents the comprehensive preservation score of fresh meat, W 理化 and +W 微生物 are the weight coefficients of the physical and chemical preservation evaluation value and the microbiological preservation evaluation value of fresh meat respectively; S 理化 represents the physical and chemical preservation evaluation value of fresh meat; S 微生物 represents the microbiological preservation evaluation value of fresh meat; the comprehensive preservation score of fresh meat is used to reflect the quality change of fresh meat products during the whole preservation process.

[0033] On the other hand, the present application also provides a fresh meat product preservation evaluation system, and the system includes:

[0034] A physical and chemical test module, which is used to test the physical and chemical indexes of fresh meat products based on a preset physical and chemical test frequency; the physical and chemical test module can obtain a plurality of physical and chemical test results, including pH value, moisture content and volatile basic nitrogen;

[0035] A microbiological test module, which is used to test the microbiological indexes of fresh meat products based on a preset microbiological test frequency; the microbiological test module can obtain a plurality of microbiological test results, including total number of bacteria, number of Escherichia coli, number of molds and yeasts;

[0036] A data processing module, which is used to align and arrange the data characteristics of a plurality of physical and chemical test results and a plurality of microbiological test results based on the data collection time sequence, and respectively construct a physical and chemical characteristic time series matrix of fresh meat and a microbiological characteristic time series matrix of fresh meat;

[0037] The physical and chemical characteristics preservation evaluation module stores a pre-trained physical and chemical characteristics preservation evaluation model, which is used to evaluate the preservation effect of the time series matrix of the physical and chemical characteristics of fresh meat and output the physical and chemical evaluation value of fresh meat preservation.

[0038] The microbial characteristics preservation evaluation module stores a pre-trained microbial characteristics preservation evaluation model, which is used to evaluate the preservation effect of the time series matrix of the physical and chemical characteristics of fresh meat and output the physical and chemical evaluation value of fresh meat preservation.

[0039] The comprehensive score calculation module is used to calculate the weights of the physical and chemical evaluation value of fresh meat preservation and the microbial evaluation value of fresh meat preservation based on the preset weight coefficients, and output the comprehensive score of fresh meat preservation.

[0040] The qualified judgment module is used to judge whether the fresh meat product meets the preservation requirements by making a qualified judgment on the comprehensive score of fresh meat preservation based on the preset fresh meat preservation scoring standard and outputting the judgment result.

[0041] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. When the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the above methods are implemented.

[0043] The beneficial effects of the present invention compared with the prior art are as follows: By setting predetermined physical and chemical detection frequencies and microbial detection frequencies, the present invention can continuously and systematically collect data on multiple key indicators during the preservation of fresh meat products, including changes in physical and chemical properties and microbial communities, overcoming the disadvantages of low detection frequency and incomplete data in traditional methods, and providing more complete and continuous quality change information. By aligning and arranging multiple physical and chemical detection results and microbial detection results in chronological order to form a time series matrix, the dynamic characteristics of the quality of fresh meat products changing over time can be captured, which helps to more accurately evaluate the preservation effect. Using pre-trained physical and chemical characteristic preservation evaluation models and microbial characteristic preservation evaluation models, an objective and quantitative evaluation of the preservation effect of fresh meat products can be achieved, avoiding the subjectivity and errors of manual detection, and improving the accuracy and reliability of the evaluation. By combining the physical and chemical evaluation values and microbial evaluation values and calculating the weights according to the pre-set weight coefficients, a comprehensive score can be obtained to comprehensively reflect the preservation effect of fresh meat products. At the same time, based on the pre-set scoring criteria, a pass / fail determination can be made for the comprehensive score, providing a clear basis for the quality control of fresh meat products. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flowchart of the method for evaluating the preservation of fresh meat products in the embodiment;

[0045] Figure 2 is a structural diagram of the system for evaluating the preservation of fresh meat products in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, a device, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, which contains computer program code.

[0047] The above computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, device, or component.

[0048] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0049] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0050] It should be understood that each block of the flowchart and / or block diagram, as well as the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices, resulting in a device that implements the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0051] These computer-readable program instructions can also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes the instructions for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0052] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing device, or other device, such that a series of operation steps are executed on the computer, other programmable data processing device, or other device, resulting in a computer-implemented process. Thus, the instructions executed on the computer or other programmable data processing device can provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0053] The present application will be described below in conjunction with the accompanying drawings in the present application.

[0054] Example 1: As Figure 1 shown, a method for evaluating the preservation of fresh meat products of the present invention specifically includes the following steps:

[0055] Step S1: Based on a preset physical and chemical detection frequency, conduct physical and chemical index detections on fresh meat products to obtain multiple physical and chemical detection results. The physical and chemical detection results include pH value, moisture content, and volatile basic nitrogen.

[0056] The main purpose of step S1 is to conduct regular physical and chemical index detections on fresh meat products to obtain data on the changes in their physical and chemical properties, including but not limited to pH value, moisture content, and volatile basic nitrogen. Specifically:

[0057] pH value detection: The pH value is an important indicator for measuring the acidity and alkalinity of meat products and is of great significance for evaluating the freshness and spoilage degree of meat products. The pH value can be measured by a laboratory pH meter, which can accurately measure the acidity and alkalinity of the solution.

[0058] Moisture content detection: The moisture content is one of the key factors affecting the shelf life of meat products. The moisture content can be determined by various methods, including oven drying method, vacuum drying method, distillation method, microwave method, and infrared absorption spectroscopy method, etc. Each method has its specific application scenarios, advantages, and disadvantages. For example, the oven drying method is suitable for the moisture determination of general foods, while the vacuum drying method is suitable for foods that are easily deteriorated at high temperatures. The distillation method evaporates the moisture in the sample together with an organic solvent and then condenses and separates the moisture to obtain the moisture content of the sample.

[0059] Volatile basic nitrogen (TVB-N) detection: TVB-N is an important chemical indicator for measuring the spoilage degree of meat products. The higher its content, the more amino acids are destroyed, especially methionine and tyrosine, so the nutritional value is greatly affected. The determination of TVB-N can be carried out by methods such as distillation method, direct colorimetry, gas chromatography, electrochemical sensor method, and enzyme-linked immunosorbent assay (ELISA), etc. The distillation method is a classic TVB-N detection method. The volatile basic nitrogen in the sample is distilled and released under alkaline conditions, then absorbed by acidification, and finally determined by titration or colorimetry.

[0060] More specifically, determine the physical and chemical detection frequency by comprehensively considering the specific variety of fresh meat products and the storage environment. The setting method of the physical and chemical detection frequency includes:

[0061] Step a1: Obtain the information of fresh meat variety and preservation environment information; Ensure that the setting of the detection frequency is based on specific product characteristics and actual storage conditions, so as to improve the pertinence and effectiveness of the evaluation; Specifically, collect the specific types of fresh meat products (such as beef, pork, chicken, etc.), cutting methods (whole piece, sliced, minced, etc.) and processing conditions (such as pickling, smoking, etc.) as the fresh meat variety information, which will directly affect the change rate of the meat product during preservation; Record and analyze the key parameters of the preservation environment, including but not limited to temperature, humidity, packaging material type (such as vacuum packaging, modified atmosphere packaging MAP), gas composition (such as oxygen, carbon dioxide concentration), etc. These environmental factors jointly determine the preservation conditions of the meat product;

[0062] Step a2: Based on the fresh meat variety information, extract the set of preservation environment parameters corresponding to the fresh meat variety information from the preservation environment information; Identify those preservation environment parameters that have the greatest impact on a specific fresh meat variety to more accurately set the detection frequency; According to existing research data or historical experience, determine which preservation environment parameters are the most critical for a specific type of fresh meat product; For example, some meat products may be particularly sensitive to temperature, while others may be more susceptible to humidity or the gas composition inside the package; Screen out the parameters closely related to the fresh meat variety from all the preservation environment information obtained in step a1 to form a "set of preservation environment parameters that affect";

[0063] Step a3: Input the set of preservation environment parameters that affect into a pre-constructed preservation environment impact analysis model to obtain the preservation environment sensitivity; Through the quantitative analysis of the preservation environment impact analysis model, calculate the sensitivity of fresh meat products under different preservation environment conditions, providing a basis for the subsequent selection of the detection frequency; Use a pre-developed mathematical model or algorithm to evaluate the impact degree of each parameter on the quality of fresh meat; The preservation environment impact analysis model is based on a large amount of experimental data and statistical analysis results, and can accurately reflect the interaction between parameters and their impact on the physical and chemical properties of meat products; Take the set of preservation environment parameters that affect determined in step a2 as input variables and send them into the preservation environment impact analysis model. After calculation, a value representing the overall preservation environment sensitivity is obtained, reflecting the change speed and risk level of fresh meat products under the current preservation environment;

[0064] Step a4: Extract the physical and chemical detection frequency corresponding to the preservation environment sensitivity in the pre-set physical and chemical detection frequency comparison table; the physical and chemical detection frequency comparison table includes multiple physical and chemical detection frequencies, and each physical and chemical detection frequency corresponds to a preservation environment sensitivity range; according to the calculated sensitivity value, select the most appropriate physical and chemical detection frequency from the comparison table to ensure effective monitoring of quality changes without causing waste of resources; establish a comparison table containing various physical and chemical detection frequencies and their corresponding sensitivity ranges; the comparison table covers various sensitivity levels from low to high and clearly indicates the recommended detection frequencies at each level; for example, products with high sensitivity may require daily or even hourly detection, while products with low sensitivity can be relaxed to once a week; according to the sensitivity value obtained in step a3, find the matching interval in the comparison table to determine the final physical and chemical detection frequency; if the sensitivity value is between two intervals, a more conservative (i.e., higher frequency) option can be selected according to the actual situation to ensure safety and accuracy.

[0065] The above method for setting the physical and chemical detection frequency customizes the detection frequency based on the specific product characteristics and actual storage conditions to ensure the pertinence and effectiveness of the evaluation and avoid a one-size-fits-all management method; secondly, by identifying the preservation environment parameters that have the greatest impact on specific fresh meat varieties, the detection accuracy is improved, unnecessary detection times are reduced, and resources are saved; furthermore, quantitative analysis is carried out using the pre-constructed preservation environment impact analysis model, making the sensitivity calculation more scientific and reasonable and enhancing the reliability of decision-making; the physical and chemical detection frequency comparison table is adopted to flexibly adjust the detection frequency according to the sensitivity value, ensuring the effectiveness of product quality monitoring while taking into account the flexibility and economy of operation.

[0066] Step S2: Based on the pre-set microbial detection frequency, conduct microbial detection on fresh meat products to obtain multiple microbial detection results.

[0067] Step S2 is to obtain sufficient data points to reflect the microbial change trend of the meat products during the entire preservation process; among them, the method for setting the microbial detection frequency is the same as that for the physical and chemical detection frequency, which will not be elaborated here; the specific operation of step S2 is as follows:

[0068] Step S21: Determine the microbial indicators, including but not limited to total number of bacteria, number of Escherichia coli, number of molds and yeasts.

[0069] Total number of bacteria: Through the plate counting method or membrane filtration method, the total number of bacteria in meat products can be quantitatively analyzed, which involves culturing meat product samples on a specific culture medium and then counting the formed colonies to evaluate the total number of bacteria.

[0070] Escherichia coli: It is a common pathogenic bacterium in food, and its presence may indicate fecal contamination of meat products. The detection of Escherichia coli usually uses selective media and differential media, combined with biochemical tests for confirmation; for example, specific serotypes such as Escherichia coli O157:H7 in meat products can be synchronously detected by multiplex real-time PCR method;

[0071] Molds and yeasts: They are another type of microorganisms that affect the shelf life of meat products. Their detection usually involves culturing on specific media and observing and counting the formed colonies; the growth of these microorganisms in meat products may lead to spoilage and corruption of meat products;

[0072] Step S22: Follow the principle of random sampling to ensure that the samples are representative; the sampling locations should cover different parts and take into account the possible differences within the package; sufficient samples should be taken for each batch or each shipment to ensure the validity of the results; the collected samples should be immediately subjected to necessary pre-treatments such as weighing, homogenization, etc. to ensure the accuracy of the test results; for some test items (such as rapid detection), on-site immediate processing should be carried out to avoid result deviation caused by time delay;

[0073] Step S23: All instruments and tools used for microbial detection must be calibrated regularly to ensure that their precision meets the requirements; for example, the incubator needs to be regularly calibrated for temperature to ensure the consistency of the culture conditions; a detailed SOP should be developed for each test item to guide the operators to correctly perform various tasks and reduce human errors.

[0074] Through the operations in Step S2, multiple microbial detection results are obtained, including key indicators such as total bacterial count, Escherichia coli count, mold and yeast count, etc.; these data provide basic data support for the subsequent construction of the time series matrix and the evaluation of preservation effects; by detecting multiple key microbial indicators, the microbial contamination situation of fresh meat products can be comprehensively reflected; based on the preset detection frequency, the microbial contamination situation of fresh meat products can be monitored in real time; the systematic detection and recording process facilitate the accumulation and analysis of data, providing support for subsequent optimization and improvement.

[0075] Step S3: Based on the time sequence of data collection, align and arrange the multiple physicochemical detection results and multiple microbial detection results respectively to obtain the time series matrix of fresh meat physicochemical characteristics and the time series matrix of fresh meat microbial characteristics;

[0076] Step S3 sorts and arranges the physicochemical detection results and microbial detection results in chronological order to form the time series matrix of fresh meat physicochemical characteristics and the time series matrix of fresh meat microbial characteristics; the time series matrix of fresh meat physicochemical characteristics and the time series matrix of fresh meat microbial characteristics can reflect the continuous change characteristics of the physicochemical properties and microbial communities of fresh meat products during the preservation process; the specific implementation is as follows:

[0077] Step S31: All the physical and chemical test results and microbiological test results obtained from Step S1 and Step S2; ensure that each test result has a clear time stamp for chronological arrangement.

[0078] Step S32: Align the physical and chemical test results and microbiological test results in chronological order; for cases where the time points are not exactly the same, interpolation or other data processing methods can be used for supplementation and adjustment to ensure data continuity and integrity.

[0079] Step S33: Arrange all the physical and chemical test results (such as pH value, moisture content, volatile basic nitrogen) in chronological order to form a physical and chemical characteristic time series matrix, where the rows represent time points and the columns represent different physical and chemical indicators.

[0080] Step S34: Arrange all the microbiological test results (such as total number of bacteria, number of Escherichia coli, number of molds and yeasts) in chronological order to form a microbiological characteristic time series matrix, where the rows represent time points and the columns represent different microbiological indicators.

[0081] Through the operations in Step S3, a physical and chemical characteristic time series matrix of fresh meat is obtained, which is used to reflect the changes in the physical and chemical properties of fresh meat products during storage; the microbiological characteristic time series matrix of fresh meat is used to reflect the changes in the microbial community of fresh meat products during storage; by constructing the time series matrix, the continuous change characteristics of the physical and chemical properties and microbial community of fresh meat products during storage can be comprehensively reflected; the systematic sorting and arrangement method ensures data integrity and consistency, facilitating subsequent analysis and evaluation; the time series matrix can provide real-time data support to help detect and handle potential problems in a timely manner; the data processing method based on chronological order ensures the accuracy and reliability of the evaluation results; in summary, Step S3 ensures that the quality changes of fresh meat products during the entire storage process can be comprehensively and accurately reflected by constructing the physical and chemical characteristic time series matrix and the microbiological characteristic time series matrix, providing a reliable data basis for subsequent evaluation and management.

[0082] Step S4: Use the pre-trained physical and chemical characteristic storage evaluation model to evaluate the storage effect of the physical and chemical characteristic time series matrix of fresh meat, and obtain the physical and chemical evaluation value of fresh meat storage.

[0083] Step S4 uses the pre-trained physical and chemical characteristic storage evaluation model to evaluate the storage effect of the physical and chemical characteristic time series matrix of fresh meat, thereby obtaining the physical and chemical evaluation value of fresh meat storage; among them, the physical and chemical characteristic storage evaluation model adopts a convolutional neural network (CNN) structure, which can perform convolutional calculations on the physical and chemical characteristic time series matrix of fresh meat to calculate the physical and chemical evaluation value of fresh meat storage. The following is the specific structure of this model:

[0084] Input layer: Receives and preprocesses the time series matrix of the physical and chemical characteristics of fresh meat constructed in step S3.

[0085] Convolutional layer: Designs multiple convolutional kernels of different sizes and shapes to capture local features in the time series data, including periodic changes and trend changes in physical and chemical properties, etc.; slides the convolutional kernel on the time series matrix, calculates the dot product between the convolutional kernel and the local area of the matrix, thereby extracting features; uses non-linear activation functions such as ReLU (Rectified Linear Unit) to increase the non-linear expression ability of the model.

[0086] Pooling layer: Performs downsampling on the feature map output by the convolutional layer to reduce the dimensionality and computational amount of the data; common pooling operations include max pooling and average pooling; helps to extract the most representative features and reduce the overfitting risk of the model.

[0087] Fully connected layer: Maps the features extracted by the convolutional layer and the pooling layer to a higher-level abstract representation, which is ultimately used for the prediction task; the fully connected layer consists of multiple neurons, each neuron is connected to all neurons in the previous layer, performs a linear transformation through a weight matrix, and performs a non-linear transformation through an activation function (such as ReLU).

[0088] Output layer: The physical and chemical evaluation value of fresh meat preservation; the physical and chemical evaluation value of fresh meat preservation adopts any one of a score, a grade, and a probability, specifically depending on the output design and evaluation criteria of the model.

[0089] Through the specific structural design of the above physical and chemical characteristics preservation evaluation model, complex time series features can be automatically extracted without manual feature design; it can process time series data of different lengths, with good flexibility; it can capture non-linear relationships in the data, improve the prediction accuracy; it can effectively capture the change patterns of the physical and chemical properties of fresh meat products during preservation, thereby quantifying the preservation effect and obtaining a reliable physical and chemical evaluation value of fresh meat preservation.

[0090] Step S5: Utilize the pre-trained microbial characteristics preservation evaluation model to evaluate the preservation effect of the time series matrix of fresh meat microbial characteristics, and obtain the microbial evaluation value of fresh meat preservation.

[0091] Since the specific structure of the physical and chemical characteristics preservation evaluation model in step S4 has been described in detail, and the microbial characteristics preservation evaluation model in step S5 adopts a similar convolutional neural network (CNN) architecture, the differences between the two and the adjustments in specific applications will be highlighted here; the microbial characteristics preservation evaluation model also uses a convolutional neural network (CNN), and its structure is similar to that of the physical and chemical characteristics preservation evaluation model, including an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer; however, due to the uniqueness of microbial characteristics and temporal change patterns, the model has been specifically optimized in some aspects. Specifically:

[0092] Input data: The microbial characteristics time series matrix, which contains the detection results of the total number of bacteria, the number of Escherichia coli, the number of molds and yeasts, etc. that change over time;

[0093] Convolutional layer: Extract local features through convolution operations to identify the key microbial patterns that affect the quality change of fresh meat; if the importance of different microbial indicators is different, each indicator can be regarded as an independent channel to form a multi-channel input; use multiple convolutional kernels (filters) to perform sliding calculations on the input matrix, and each convolutional kernel is responsible for capturing different local features; considering the characteristics of the microbial growth curve, a convolutional kernel of a specific size can be selected to capture short-term or long-term change trends;

[0094] Model training: Use historical microbial detection data and the corresponding preservation effect labels to train the model, and optimize the network weights to minimize the prediction error;

[0095] Output interpretation: The scoring value output by the model reflects the preservation effect of the current fresh meat product in terms of microorganisms. The higher the score, the lower the risk of microbial contamination and the better the preservation effect.

[0096] Through the specific structure design of the above microbial characteristics preservation evaluation model, the change patterns of the microbial community in fresh meat products during the preservation process can be effectively captured, thereby quantifying the preservation effect and obtaining a reliable microbial evaluation value for fresh meat preservation; the deep learning-based method not only improves the accuracy of the evaluation, but also enhances the robustness and generalization ability of the model, providing a solid scientific basis for subsequent comprehensive scoring and passing judgment; although the model structure is similar to that of the physical and chemical characteristics evaluation model, necessary adjustments and optimizations have been made for microbial characteristics to better adapt to the time series characteristics and change patterns of microbial data, ensuring that the model can play an important role in evaluating the microbial safety of fresh meat products.

[0097] Step S6: Based on the pre-set weight coefficients, calculate the weights of the physical and chemical evaluation value of fresh meat preservation and the microbial evaluation value of fresh meat preservation to obtain the comprehensive score of fresh meat preservation;

[0098] To ensure that the weight coefficients can accurately reflect the characteristics of different fresh meat products during storage, the setting of weight coefficients needs to comprehensively consider the information of fresh meat varieties; the following are the specific determination methods:

[0099] Step S61: Obtain specific information about fresh meat varieties, including but not limited to meat types (such as beef, pork, chicken, etc.) and cutting methods (whole piece, sliced, minced, etc.), which will directly affect the change rate of meat products during storage;

[0100] Step S62: According to the fresh meat variety information, evaluate the importance of physical and chemical properties and microbial activities for this variety; by inviting experts in the fields of food science and microbiology for consultation, and combining practical experience to judge the influence of physical and chemical properties and microbial activities; or by analyzing the storage data of past similar meat products, and counting the problem frequencies caused by physical and chemical properties or microbial activities, so as to quantify the importance of the two;

[0101] Step S63: Establish a weight coefficient determination model for automatically determining weight coefficients according to fresh meat variety information; select key features affecting weight coefficients, such as meat type, cutting method, etc.; if a certain variety of fresh meat is particularly sensitive to microbial contamination, the weight coefficient of the microbial index can be increased; for example, some high-grade meats or specific parts of meat products; for varieties with higher natural antibacterial components or longer shelf lives, the weight of the microbial index can be appropriately reduced, while increasing the attention to physical and chemical indexes;

[0102] Step S64: Use machine learning algorithms (such as decision trees, random forests, support vector machines, etc.) to train the model, with the above features as the input and the weight coefficients corresponding to the physical and chemical evaluation values and microbial evaluation values of fresh meat storage as the output; through cross-validation and hyperparameter tuning, ensure the prediction accuracy and generalization ability of the model.

[0103] The above process of determining weight coefficients, by obtaining detailed fresh meat variety information and combining expert consultation and historical data analysis, can accurately quantify the importance of physical and chemical properties and microbial activities, and improve the rationality of weight setting; establishing a weight coefficient determination model and selecting key features, such as meat type and cutting method, enables the model to be flexibly adjusted according to specific situations, enhancing flexibility and pertinence; using machine learning algorithms for model training, and through cross-validation and hyperparameter tuning, ensures the high prediction accuracy and good generalization ability of the model, thereby improving the reliability of the comprehensive score.

[0104] Through the above process, the weight coefficients corresponding to the physical and chemical characteristics and the microbial characteristics are determined. The physical and chemical evaluation value for the preservation of fresh meat is obtained from step S4, and the microbial evaluation value for the preservation of fresh meat is obtained from step S5. The determined weight coefficients are used to perform weighted summation on the two evaluation values to calculate the comprehensive score for the preservation of fresh meat. The formula is as follows:

[0105] S 综合 =W 理化 ×S 理化 +W 微生物 ×S 微生物 ;

[0106] Wherein, S 综合 represents the comprehensive score for the preservation of fresh meat, W 理化 and +W 微生物 are the weight coefficients of the physical and chemical evaluation value for the preservation of fresh meat and the microbial evaluation value for the preservation of fresh meat, respectively; S 理化 represents the physical and chemical evaluation value for the preservation of fresh meat; S 微生物 represents the microbial evaluation value for the preservation of fresh meat; the comprehensive score for the preservation of fresh meat is used to reflect the quality change of fresh meat products during the entire preservation process; it should be noted that before performing weighted summation, it is necessary to ensure that the sum of the two evaluation values is within the same scoring range, or they have been made comparable through appropriate conversion.

[0107] Through step S6, the evaluation results in terms of physical and chemical aspects and microbial aspects can be integrated into a comprehensive score, providing a comprehensive quantitative index for the preservation effect of fresh meat products, which helps to more accurately evaluate and monitor the preservation status of meat products and ensure their quality and safety throughout the supply chain.

[0108] Step S7: Based on the preset scoring standard for the preservation of fresh meat, determine whether the comprehensive score for the preservation of fresh meat is qualified;

[0109] In step S7, the purpose of the qualification determination is to judge whether the fresh meat products meet the predetermined preservation quality and safety standards according to the comprehensive score for the preservation of fresh meat. Therefore, a scientific and reasonable scoring standard for the preservation of fresh meat needs to be formulated; this standard should be determined according to industry norms, food safety regulations, and the enterprise's own quality control requirements; the scoring standard usually includes the following aspects:

[0110] Range of physical and chemical indicators: Specify the qualified range of physical and chemical indicators such as pH value, moisture content, and volatile basic nitrogen;

[0111] Limit values of microbial indicators: Define the maximum allowable values of microbial indicators such as total bacterial count, Escherichia coli count, and mold and yeast count;

[0112] Comprehensive scoring threshold: Set multiple comprehensive scoring thresholds to distinguish different levels of preservation effects; set a qualified range, for example, a product with a comprehensive score above 80 is regarded as a qualified product; set a warning range, such as between 60 - 80 points, indicating that there are certain quality problems with the product, but they can be improved by adjusting preservation conditions or other measures; products with a score below 60 are regarded as unqualified and corrective measures need to be taken immediately, such as recall, destruction or reprocessing.

[0113] Through the operation of step S7 above, a qualified judgment result of the comprehensive score of fresh meat preservation can be obtained, which not only reflects the overall quality change of fresh meat products during the preservation process, but also provides an important decision-making basis for subsequent quality control; the qualified judgment method ensures the objectivity and reliability of the evaluation results, helps to detect and solve potential quality problems in a timely manner, and guarantees the safety and edible quality of fresh meat products.

[0114] Example two: As Figure 2 shown, a fresh meat product preservation evaluation system of the present invention specifically includes the following modules:

[0115] A physical and chemical detection module, used to detect the physical and chemical indicators of fresh meat products based on a preset physical and chemical detection frequency; the physical and chemical detection module can obtain multiple physical and chemical detection results, including but not limited to key indicators such as pH value, moisture content, and volatile basic nitrogen; these physical and chemical detection results provide basic data for subsequent construction of a time series matrix and preservation effect evaluation;

[0116] A microorganism detection module, used to detect the microorganisms of fresh meat products based on a preset microorganism detection frequency; the microorganism detection module can obtain multiple microorganism detection results, including microorganism indicators such as total number of bacteria, number of Escherichia coli, number of molds and yeasts; these microorganism detection results also provide key data for subsequent construction of a time series matrix and preservation effect evaluation;

[0117] A data processing module, used to align and arrange the data features of multiple physical and chemical detection results and multiple microorganism detection results based on the data acquisition time sequence, and respectively construct a fresh meat physical and chemical feature time series matrix and a fresh meat microorganism feature time series matrix to reflect the continuous change characteristics of the physical and chemical properties and microorganism communities of fresh meat products during the preservation process;

[0118] A physical and chemical feature preservation evaluation module, storing a pre-trained physical and chemical feature preservation evaluation model, used to evaluate the preservation effect of the fresh meat physical and chemical feature time series matrix, and output a fresh meat preservation physical and chemical evaluation value;

[0119] A microorganism feature preservation evaluation module, storing a pre-trained microorganism feature preservation evaluation model, used to evaluate the preservation effect of the fresh meat physical and chemical feature time series matrix, and output a fresh meat preservation physical and chemical evaluation value;

[0120] The comprehensive scoring calculation module is used to perform weighted calculations on the physical and chemical evaluation values and microbial evaluation values of fresh meat preservation based on preset weight coefficients, and output the comprehensive score of fresh meat preservation, which is used to reflect the overall quality change of fresh meat products during the preservation process;

[0121] The qualified judgment module is used to perform a qualified judgment on the comprehensive score of fresh meat preservation based on the preset fresh meat preservation scoring standard, and output the judgment result, to determine whether the fresh meat products meet the preservation requirements, providing a clear basis for quality control.

[0122] In this embodiment, the system can automatically perform detections at a preset frequency through the physical and chemical detection module and the microbial detection module, avoiding the time-consuming and laborious manual detection and sampling analysis; the data processing module automatically performs data feature alignment and time series matrix construction, reducing manual intervention and improving work efficiency; the system can comprehensively reflect the quality change of fresh meat products during the preservation process by continuously detecting and collecting multiple physical and chemical indicators and microbial indicators; the construction of the time series matrix enables the system to capture the continuous characteristics of quality change over time; the pre-trained physical and chemical feature preservation evaluation model and microbial feature preservation evaluation model can learn based on a large amount of data, improving the accuracy of evaluation; the use of the model avoids the subjectivity and uncertainty of manual evaluation, making the evaluation results more objective and reliable; the comprehensive scoring calculation module can obtain a comprehensive score by combining the physical and chemical evaluation values and microbial evaluation values and performing calculations based on the weight coefficients, comprehensively reflecting the overall quality change of fresh meat products during the preservation process; the qualified judgment module determines the comprehensive score according to the preset scoring standard, and can clearly judge whether the fresh meat products meet the preservation requirements, providing a clear basis for quality control.

[0123] The various change methods and specific embodiments of the fresh meat product preservation evaluation method in the foregoing Embodiment 1 are equally applicable to the fresh meat product preservation evaluation system of this embodiment. Through the foregoing detailed description of the fresh meat product preservation evaluation method, those skilled in the art can clearly know the implementation method of the fresh meat product preservation evaluation system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0124] In addition, this application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it implements each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0125] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the preservation of fresh meat products, characterized in that: The method comprises: Based on the pre-set physical and chemical testing frequency, the physical and chemical indexes of fresh meat products are tested to obtain multiple physical and chemical testing results; Based on the pre-set microbial testing frequency, the fresh meat products are tested for microorganisms to obtain multiple microbial testing results; Based on the time sequence of data collection, multiple physical and chemical test results and multiple microbial test results are aligned and arranged respectively to obtain the time series matrix of physical and chemical characteristics of fresh meat and the time series matrix of microbial characteristics of fresh meat respectively; Using the pre-trained physicochemical characteristics preservation evaluation model, the preservation effect of the physicochemical characteristics time series matrix of fresh meat is evaluated to obtain the physicochemical evaluation value of fresh meat preservation; Using the pre-trained microbial feature preservation evaluation model, the preservation effect of the fresh meat microbial feature time series matrix is ​​evaluated to obtain the fresh meat preservation microbial evaluation value; Based on a preset weight coefficient, weight calculation is performed on the fresh meat preservation physicochemical evaluation value and the fresh meat preservation microbiological evaluation value to obtain a comprehensive score for fresh meat preservation; Based on the preset fresh meat preservation scoring standards, the comprehensive score of fresh meat preservation is judged as qualified.

2. The method for evaluating the preservation of fresh meat products according to claim 1, characterized in that: The physical and chemical test results include pH value, moisture content and volatile basic nitrogen.

3. The method for evaluating the preservation of fresh meat products according to claim 2, characterized in that: The microbial detection results include total bacteria count, E. coli count, mold and yeast count.

4. The method for evaluating the preservation of fresh meat products according to claim 3, characterized in that: The method for setting the physical and chemical detection frequency includes: Obtain information on fresh meat varieties and storage environment; Based on the fresh meat variety information, extracting a set of parameters affecting the storage environment corresponding to the fresh meat variety information from the storage environment information; Inputting the storage environment-affecting parameter set into a pre-built storage environment impact analysis model to obtain storage environment sensitivity; In a preset physical and chemical detection frequency comparison table, the physical and chemical detection frequency corresponding to the storage environment sensitivity is extracted; the physical and chemical detection frequency comparison table includes multiple physical and chemical detection frequencies, wherein each physical and chemical detection frequency corresponds to a storage environment sensitivity range.

5. The method for evaluating the preservation of fresh meat products according to claim 4, characterized in that: The method for setting the frequency of microbial detection comprises: Obtain information on the fresh meat variety and storage environment of the fresh meat to be evaluated; Based on the fresh meat variety information, extracting a set of parameters affecting the storage environment corresponding to the fresh meat variety information from the storage environment information; Inputting the storage environment-affecting parameter set into a pre-built storage environment impact analysis model to obtain storage environment sensitivity; In a pre-set microbial detection frequency comparison table, the microbial detection frequency corresponding to the storage environment sensitivity is extracted; the microbial detection frequency comparison table includes multiple microbial detection frequencies, each of which corresponds to a storage environment sensitivity range.

6. The method for evaluating the preservation of fresh meat products according to claim 5, characterized in that: The method for obtaining a time series matrix of physical and chemical characteristics of fresh meat and a time series matrix of microbiological characteristics of fresh meat comprises: Obtain all physical, chemical and microbiological test results and identify the time stamp of each test result; Align the physical and chemical test results with the microbiological test results in chronological order; All physical and chemical test results were arranged in chronological order to form a physical and chemical characteristic time series matrix, where rows represent time points and columns represent different physical and chemical indicators; All microbial test results were arranged in chronological order to form a microbial feature time series matrix, in which rows represented time points and columns represented different microbial indicators.

7. The method for evaluating the preservation of fresh meat products according to claim 6, characterized in that: The calculation formula for the comprehensive score of fresh meat preservation is: S 综合 =W 理化 ×S 理化 +W 微生物 ×S 微生物 ; Among them, S 综合 represents the comprehensive score of fresh meat preservation, W 理化 and +W 微生物 are weight coefficients of the fresh meat preservation physicochemical evaluation value and the fresh meat preservation microbiological evaluation value respectively; S 理化 represents the physical and chemical evaluation value of the fresh meat preservation; S 微生物 It represents the fresh meat preservation microbial evaluation value; the fresh meat preservation comprehensive score is used to reflect the quality changes of fresh meat products during the entire preservation process.

8. A fresh meat product preservation evaluation system, characterized in that: The system comprises: A physical and chemical testing module is used to test the physical and chemical indicators of fresh meat products based on a pre-set physical and chemical testing frequency; the physical and chemical testing module can obtain multiple physical and chemical testing results, including pH value, moisture content and volatile basic nitrogen; A microbial detection module is used to perform microbial detection on fresh meat products based on a pre-set microbial detection frequency; the microbial detection module can obtain multiple microbial detection results, including total bacteria count, E. coli count, mold count and yeast count; A data processing module is used to align and arrange data features of multiple physical and chemical test results and multiple microbial test results based on the time sequence of data collection, and to construct a time series matrix of physical and chemical features of fresh meat and a time series matrix of microbial features of fresh meat respectively; A physicochemical characteristic preservation evaluation module stores a pre-trained physicochemical characteristic preservation evaluation model, which is used to evaluate the preservation effect of the physicochemical characteristic time series matrix of fresh meat and output the physicochemical evaluation value of fresh meat preservation; A microbial characteristic preservation evaluation module stores a pre-trained microbial characteristic preservation evaluation model, which is used to evaluate the preservation effect of the physical and chemical characteristic time series matrix of fresh meat and output the physical and chemical evaluation value of fresh meat preservation; A comprehensive score calculation module is used to perform weight calculation on the fresh meat preservation physical and chemical evaluation value and the fresh meat preservation microbiological evaluation value based on a preset weight coefficient, and output a comprehensive score for fresh meat preservation; The qualified judgment module is used to make qualified judgment on the comprehensive score of fresh meat preservation based on the preset fresh meat preservation scoring standard, and output the judgment result to determine whether the fresh meat product meets the preservation requirements.

9. An electronic device for preserving and evaluating fresh meat products, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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

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