71 results about "Meat sample" patented technology

The invention discloses a rapid marinating soup quality parameter acquisition method and a marinating time optimization method in a sauced product cooling process, and belongs to the technical field of sauced product production processes, and the method comprises the following steps: S1, acquiring near infrared spectrum data of a meat sample by adopting a near infrared spectrum technology; performing two-dimensional correlation analysis on the near infrared spectrum data, converting the one-dimensional spectrum data into two-dimensional correlation spectrum data, and improving the resolution of the one-dimensional spectrum data and the separation degree of overlapping peaks to obtain a two-dimensional correlation analysis result; s2, processing the two-dimensional correlation spectrum data by using a moving window technology, observing a time period in which a functional group changes along with external interference, and converting static detection of a substance into dynamic analysis to obtain a moving window technology processing result; and S3, acquiring nuclear magnetic resonance data of the meat sample by adopting a nuclear magnetic resonance technology, and reflecting the motion state of hydrogen protons. According to the invention, the meat moisture migration and fat oxidation degree can be accurately identified.

The invention belongs to the technical field of food flavor analysis and quality control, and particularly relates to a dynamic monitoring and quality evaluation method for flavor substances in the processing process of braised meat. According to the method disclosed by the invention, various detection means such as an electronic tongue, HS-SPME-GC-MS and HS-GC-IMS are adopted to detect and identify flavor components in different braising stages of the braised pork, so that the comprehensiveness and accuracy of flavor detection in the braised pork processing process are remarkably improved; besides, by improving the detection key conditions of the HS-GC-IMS, especially by adopting the treatment of balancing for 30 minutes at the temperature of 80 DEG C in the headspace equilibrium stage, the low-boiling-point volatile matters in the meat sample can be fully released, the desorption efficiency of small molecules is improved, the volatilization loss or thermal decomposition cannot be caused, and the detection sensitivity in the early stage is further improved.

The application provides a method for detecting the content of sodium hypochlorite in chicken, and belongs to the technical field of quantitative detection. Terahertz spectrum testing is performed on a dried chicken sample to obtain the peak height of a sodium hypochlorite characteristic peak; then, according to a standard curve and the peak height of the sodium hypochlorite characteristic peak, the content of sodium hypochlorite in the chicken is obtained; the frequency of the sodium hypochlorite characteristic peak is 1.10 THz or 1.25 THz; and the standard curve is a fitting curve of the mass content of sodium hypochlorite in the chicken and the peak height of the sodium hypochlorite characteristic peak. Different molecules exhibit different absorption peaks in terahertz absorption spectrum, and the application detects the content of sodium hypochlorite in chicken by using a terahertz spectrum method, and obtains the content of sodium hypochlorite in chicken by using the fitting relationship between the peak height of the sodium hypochlorite absorption peak and the content of sodium hypochlorite. The method provided by the application is simple, and can quickly and accurately obtain the content of sodium hypochlorite in chicken.

The invention relates to the technical field of analysis and detection, in particular to a method for detecting zeranol in a meat sample. The detection method comprises the following steps: pretreating a meat sample by adopting a magnetic dispersive solid-phase extraction method, and then detecting zeranol by adopting an immunochromatography technology, the magnetic dispersion solid-phase extraction method adopts an HLB magnetic nano material. The detection method provided by the invention has good accuracy, sensitivity and specificity, and can meet the requirement of rapid detection of zeranol in meat samples. The method is simple and convenient to operate, does not need professional detection equipment, can be operated by common law enforcement officers or food supervisors through simple training, and has a wide application prospect.

The invention provides a method for extracting and detecting free amino acids in meat. The method comprises the following steps: cutting a meat sample into blocks, freeze-drying, crushing and sieving to prepare meat powder; weighing meat powder, adding distilled water, uniformly mixing by vortex, performing water-bath constant-temperature oscillation extraction, and centrifuging to obtain a crude extract; adding sulfosalicylic acid precipitated protein into the crude extracting solution, centrifuging again, taking supernate, and adjusting the pH value with diluted hydrochloric acid to obtain an extracting solution; and filtering the extracting solution by a filter membrane, and detecting by a full-automatic amino acid analyzer. The method takes distilled water as an extraction solvent, efficiently extracts the free amino acid through water bath oscillation, is simple and convenient to operate, high in extraction efficiency and good in stability, is suitable for free amino acid detection of various meat samples such as chicken, beef and pork, and is high in practicability.

The invention discloses a meat freshness rapid detection and prediction method based on laser-induced graphene. The method comprises the following steps: firstly, preparing a laser-induced graphene impedance sensor with a porous structure on a polyimide film through laser processing; then acquiring impedance spectrum data of a meat sample in a wide frequency range by using the sensor, extracting a phase angle and an impedance amplitude as characteristic data, and constructing a training set by combining a volatile basic nitrogen measured value; a neural network model is further constructed and trained, and accurate detection from impedance features to TVB-N values is achieved; meanwhile, a dynamic prediction model with environmental specificity is established, prediction of the TVB-N change trend and freshness grade judgment in the future are supported, the laser-induced graphene impedance sensor is combined with the electrochemical impedance spectroscopy technology, and detection and prediction of the TVB-N value of the meat sample can be completed within a few minutes.

The invention discloses a portable meat doping identification chip and a detection method, and the method comprises the following steps: placing a meat sample slice on a sample bearing layer of the chip, starting the chip to collect a terahertz time-domain spectral signal, and calculating optical parameters of a meat sample in a frequency band of 0.2-2.0 THz; performing principal component analysis on the high-dimensional data of the optical parameters in the whole frequency band to obtain feature vectors; and inputting the feature vector into a pre-trained artificial intelligence model, respectively performing qualitative identification of meat species and quantitative prediction of the doping content, and outputting an analysis report containing a species identification result and a doping ratio.

This invention provides a colorimetric detection method and related apparatus for meat freshness based on a dynamic reference library. The method includes: acquiring a test strip image and meat type information of the meat sample to be tested; determining the region of interest (ROI) of the test strip image, extracting features from the ROI to obtain a feature vector, wherein the feature vector is a feature vector composed of RGB feature values; calculating a predicted concentration value and a confidence index based on the feature vector and a user-defined dynamic reference library using a K-nearest neighbor regression algorithm, wherein the dynamic reference library is constructed based on the mapping relationship between concentration gradient values ​​and RGB feature values ​​of a standard color chart image set and freshness threshold ranges for different meats; and comparing the predicted concentration value with the corresponding freshness threshold range for the meat to determine the freshness level information of the corresponding meat sample. Based on this, this invention can achieve high-precision detection of the freshness of different meats.

The invention relates to a rapid detection method for food-borne pathogenic bacteria based on the action of meat-derived symbiotic microorganisms, belongs to the technical field of rapid detection and spectral analysis of fresh meat quality, and aims to solve the problems of tedious detection operation and high dependency on biochemical reagents caused by interference of a complex substrate and symbiotic microorganisms of fresh meat in the prior art. The method comprises the following steps: firstly, carrying out elution separation-homogeneous centrifugation-coating culture pretreatment on a fresh meat sample to eliminate the interference of a fresh meat matrix, and then preferably selecting a high-discrimination characteristic wave band from high-dimensional spectral data by applying a combined characteristic screening algorithm; and finally, detecting the food-borne pathogenic bacteria of the fresh meat by utilizing a qualitative and quantitative model established on the basis of a culture sample under the interference of the meat-borne symbiotic microorganisms in the earlier stage. The detection accuracy and sensitivity of the food-borne pathogenic bacteria in the fresh meat can be improved, meanwhile, the method has the advantages of being rapid, easy and convenient to operate and economical in cost, and technical support can be provided for detection of the food-borne pathogenic bacteria in the fresh meat.

The present disclosure relates to a kind of deep learning-based multi-source spectral fusion chicken quality detection method and sorting equipment, wherein the method comprises: obtaining the near-infrared hyperspectral image and visible-near-infrared hyperspectral image of the chicken to be detected;The near-infrared hyperspectral image and visible-near-infrared hyperspectral image are input into the chicken quality detection model, and the volatile base nitrogen content and total number of colonies of the chicken to be detected are obtained;According to the volatile base nitrogen content and total number of colonies, the quality of the chicken to be detected is determined.The purpose of the present disclosure is to provide a kind of deep learning-based multi-source spectral fusion chicken quality detection method and sorting equipment, which can realize the detection of chicken quality without damaging the integrity of the chicken to be detected.In addition, the detection process does not require human intervention, which not only improves the detection efficiency of chicken quality, but also can be applied to fully automated unmanned actual detection, which is conducive to the detection of large-scale chicken samples.

This utility model discloses a bacterial detection device for meat food safety, relating to the field of food testing technology. It includes a detection machine with a rotating motor installed at its detection port. The output end of the rotating motor is connected to a sealing shell that is in sealing contact with the detection machine. Detection trays are slidably connected to both sides of the sealing shell. This utility model involves placing a meat sample onto the detection tray inside the protective shell, then feeding the meat sample into the sealing shell. The sealing shell rotates, feeding the meat sample into the detection machine, thus completing the meat sample detection. The partition and sealing shell ensure that the device is in a closed state during meat sample detection, preventing external interference. After detection, a reset limiting plate feeds a new set of meat samples into the sealing shell. The sealing shell rotates, feeding the new set of meat samples into the detection machine for a new round of detection. The detected meat sample is then rotated out, thus completing the automatic detection of the meat sample.

The utility model discloses a sample treatment device for food detection, and relates to the technical field of food detection. According to the technical key points, the device comprises a sample treatment table, a horizontally-placed mounting frame is arranged on the upper side of the sample treatment table, a grating cutter is embedded in the mounting frame, and blades in the grating cutter are arranged in a criss-cross mode; a vertical guide rod is arranged on each of opposite sides of the sample treatment table, a sliding block is arranged on each guide rod in a sleeving manner, and opposite sides of the two sliding blocks are fixedly connected with the mounting frame; and the lower ends of the two guide rods are fixedly connected with the sample treatment table through extension rods. When the equipment is used, an experimenter presses down the mounting frame with two hands, a meat sample can be quickly cut into a plurality of small blocks by utilizing the grating cutter in the mounting frame, and compared with the prior art, the processing efficiency of the meat sample is greatly improved, and the risk that the hands of the experimenter are injured in the cutting process can be reduced.

The application discloses a microbial nano-enzyme probe, a method for detecting salbutamol and application, and comprises a salbutamol monoclonal antibody and a signal carrier, wherein the signal carrier is a yeast cell loaded with iridium oxide nanoparticles. The application firstly constructs a probe by using a yeast cell loaded with iridium oxide nanoparticles in the detection of an immunochromatographic test strip, uses [3, 3', 5, 5' tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2)] as a substrate to trigger a color developing reaction, can realize high-sensitivity target detection, and the detection limit is as low as 0.012 ng / mL, which is 55 times higher than the sensitivity of a traditional gold-labeled test strip. In addition, the application has a good application prospect, can provide a universal platform for sensitive, accurate and rapid detection of food-borne stimulants, and can be successfully applied to the detection of salbutamol in pig liver and beef samples.

The invention discloses a preparation method of a beef frozen wet powder matrix standard substance, and solves the technical problems of poor authenticity, low fidelity, poor uniformity and high energy consumption when a beef ractopamine residual matrix standard substance is prepared in the prior art. Obtaining a ractopamine positive beef sample; performing acid discharge treatment on the beef sample; pretreating the beef subjected to acid discharge, and cutting the beef into diced meat with the particle size of 5-10mm; preparing a frozen wet powder matrix standard substance by adopting a multi-stage freezing and crushing and pulse screening process; and uniformly mixing the final product at a low temperature, sub-packaging, and storing in an environment of-40 DEG C or-80 DEG C in a sealing manner. The traditional freeze-dried powder process is abandoned, the frozen wet powder is used as a carrier form, and the original physicochemical characteristics of fresh tissues are perfectly reserved by the form.

The application provides a SNP molecular marker and a breeding application method which are significantly related to the content of chicken meat flavor substance (E, E)-2,4-decadienal. The SNP molecular marker is rs316165567, contains a nucleotide sequence with a polymorphism of C / A at 2,519,798 bp of chicken chromosome 14, and when the genotype of the site is AA, the content of (E, E)-2,4-decadienal of the chicken meat sample is higher. The established molecular assisted breeding technology can early select the high-quality chicken flavor quality traits by determining the genotype of the SNP site of the measured chicken, can improve the content of (E, E)-2,4-decadienal of the chicken breast, and has great industrial application value and economic and scientific research value.

The application belongs to the technical field of detecting meat components based on nucleic acid technology, and particularly relates to a primer pair, a kit and a detection method for detecting horse meat components in food. The sequences of the primer pair are shown in SEQ ID NO. 1 and SEQ ID NO. 2. The application further discloses a kit for detecting horse meat components in food and a method for detecting horse meat components. The detection method comprises a pair of primers labeled with 6-FAM fluorescent groups and biotin, can realize high-sensitivity target detection, and can detect as low as 0.1% horse meat in adulterated meat samples. Compared with recombinant enzyme polymerase isothermal amplification real-time fluorescence detection, there is no need to design and screen RPA fluorescent probes. In addition, the application has good application prospect and can be successfully used for detecting horse meat components in donkey meat products, beef products, mutton products and venison products.

The utility model belongs to the technical field of meat product quality detection, and particularly relates to a sample hanging bag for juice loss determination based on meat product quality detection, which comprises a fixing plate, a bag body fixedly connected to the bottom of the fixing plate, a limiting rod fixedly connected to the bottom of the fixing plate and located in the bag body, and a hook slidably connected to the outside of the limiting rod. The two sides of the interior of the fixing plate are rotationally connected with rotating shafts. The utility model provides a sample hanging bag for juice loss determination based on meat product quality detection, and solves the problems that in the prior art, during juice loss determination of a meat sample, the meat sample is mostly manually hung on a hook and then hung in a packaging bag in a suspended manner, so that the manual operation is relatively troublesome, the labor intensity is low, and the like. And the problems that the meat sample is easy to shake due to manual operation, the bag body is easy to be collided by an external object after being hung, the meat sample is in contact with the bag body, the outflow of juice is hindered, or the juice on the bag body is absorbed by the meat sample to flow back, and the accuracy of final determination is influenced are solved.

The application provides a kind of meat sample drip loss continuous determination device and method, it is related to meat sample determination technical field, including refrigerated cabinet is equipped with multiple refrigeration cavities, refrigeration cavity is sealingly connected with closed door, the rear end of closed door is connected with bearing frame, bearing frame is slidingly connected in refrigeration cavity;Two weight monitoring mechanisms are arranged in the upper portion of bearing frame, the front end of closed door is connected with shell, digital display controller is arranged in shell;Weight monitoring mechanism is connected with meat sample positioning mechanism;Suction mechanism is arranged below bearing frame.The water film formed in suction mechanism is contacted with the surface of breast muscle sample or leg muscle sample, to avoid friction and extrusion on breast muscle sample or leg muscle sample, digital display controller cooperates with weight monitoring mechanism and suction mechanism, continuously and synchronously obtains the drip loss data of breast muscle sample, leg muscle sample multiple time points, realizes in the same closed space, continuously determines breast muscle sample and leg muscle sample.

This invention discloses a method for detecting the freshness of giant salamander meat based on color constancy and integrated intelligent vision, belonging to the fields of food safety and computer vision technology. The method includes: acquiring RAW images of the meat sample and an indicator film and resolving them into linear radiosity tensors; constructing a color correction model using a latent diffusion local redraw network and a conditional control network; synthesizing a structure-constrained virtual reference color card in a masked region; extracting ambient light source features and performing adaptive white balance compensation to output a standard color image; inputting this image into a deep network optimized by ArcFace to extract a high-dimensional feature vector; and finally inputting this vector into an XGBoost model to fit a nonlinear decision boundary and outputting a freshness rating. This invention effectively reduces color shift interference from complex light sources, improves the recognition accuracy of samples in the critical period of color change, and provides a highly robust solution for non-destructive monitoring of giant salamander meat freshness in actual market environments.

The invention provides a correlation analysis method for flavor deterioration of chicken with different quality grades, and relates to the technical field of food science and meat quality analys.The correlation analysis method comprises the steps that chicken samples with different quality grades are grouped to obtain first samples for volatile flavor substance and sensory analysis; and a second sample for non-volatile metabolite analysis; carrying out detection and qualitative and quantitative analysis on the volatile organic compounds in the first sample by adopting a gas chromatography-ion mobility spectrometry technology; carrying out non-targeted detection and qualitative and quantitative analysis on the non-volatile metabolites in the second sample by adopting a liquid chromatography-tandem mass spectrometry technology; carrying out sensory evaluation on the color, texture, appearance and smell of the chicken samples with different quality grades; by combining a multivariate statistical analysis method, volatile organic compounds, non-volatile metabolites and sensory evaluation data are comprehensively analyzed, and flavor characteristics, metabolite changes and correlation of chicken with different lignification degrees are analyzed.

The present application relates to the technical field of meat detection, and particularly relates to a data-driven meat quality rapid detection method and system, which collects the physical and chemical data such as spectrum, image, pH value, temperature and humidity, volatile gas and the like of a meat sample through a multi-modal sensor, constructs a meat quality characteristic map, analyzes the map by using a machine learning algorithm, identifies key influencing factors and extracts quality characteristic data such as spectrum, texture and chemistry, analyzes the influence law of different factors on quality based on the characteristic data, obtains meat quality influence data, determines the judgment parameters of each quality grade in combination with the influence data, constructs and trains a deep learning detection model, inputs the real-time data of the meat to be detected into the model, realizes rapid identification and evaluation of quality, makes grade judgment according to the evaluation result, and automatically executes classification processing, improves detection accuracy and stability, and realizes non-destructive, intelligent and efficient meat quality detection.

The application provides a yak meat intramuscular fat content prediction method and system based on near-infrared spectroscopy, relates to the technical field of data analysis, and comprises the following steps: acquiring the original near-infrared spectroscopy curve of multiple-source yak meat samples and the corresponding intramuscular fat content standard value. Secondly, deep feature extraction is performed on the spectrum through a wavelet scattering network to obtain multi-level scattering feature representation with stable deformation. Then, the pasture and part metadata of the samples are fused to construct a hypergraph structure representing the complex high-order correlation between the samples. Then, a heat diffusion model is established on the hypergraph, and the known fat content is used as a heat source for collaborative diffusion to obtain stable state information values fused with topological information. Finally, a prediction model is constructed by combining the multi-level features and the stable state information, so that the intramuscular fat content of new samples can be quickly and non-destructively predicted. The application improves the accuracy and efficiency of the fat content prediction of multiple-source heterogeneous yak meat samples.

The invention provides an SNP (Single Nucleotide Polymorphism) molecular marker remarkably related to the content of a chicken flavor substance (E, E)-2, 4-decadienal and a breeding application method. The SNP molecular marker is rs316165567 and contains nucleotide sequences with C / A polymorphism at 2nd, 519th and 798bp positions on a chicken chromosome 14, and when the genotype of the site is AA, the content of (E, E)-2, 4-decadienal of a corresponding chicken sample is high. According to the established molecular assisted breeding technology, the flavor quality character of the high-quality broiler chicken is selected in an early stage by determining the genotype of the SNP site of the chicken to be detected, the content of the chicken breast muscle (E, E)-2, 4-decadienal can be increased, and the molecular assisted breeding technology has great industrial application value and economic and scientific research value.

The invention relates to the technical field of image processing, in particular to a meat product grading system and method based on machine vision, and the system comprises an image collection module which is used for collecting a surface image of a meat product to be detected through an image sensor; the image processing module comprises a preprocessing unit which is used for preprocessing the surface image of the meat product to be detected so as to output the visual features of the meat product; the meat product grading module comprises a feature statistical unit used for performing statistical analysis on the meat product visual features to obtain meat product grading reference standards; the filtering adjusting module is used for determining the filtering color standard deviation in the preprocessing process according to the meat grading error rate; the light reflection identification adjusting module is used for determining a highlight discrimination threshold value of a surface image light reflection area mask according to the texture missing rate of the surface image; and the front-end gain adjusting module is used for determining the analog front-end gain multiple in the surface image acquisition process according to the feature preserving rate of the surface image. According to the method, the meat grading accuracy is improved.

The utility model discloses a tray for measuring meat color, which comprises a bottom plate, a bottom cover is arranged on the surface of the bottom plate, a detachable tray body is arranged above the bottom cover, a plurality of placing areas are fixedly mounted in the tray body, a mark number is arranged at the right upper corner of each placing area, and a plurality of grooves are formed in the bottom cover. The top of the bottom plate is fixedly connected with a supporting column, and a light source is fixedly installed at the top of the supporting column. By means of the structure, compared with an existing commonly-used laboratory tray, the tray is provided with the cover with the transparent plane, meat samples to be detected can be prevented from being polluted by dust and sundries, concave-convex parts at the four corners of the tray and the four corners of the cover can be stacked and stored, and refrigeration space is saved; the tray is internally provided with a convex area, scale marks, numbers and the like, so that meat samples are convenient to store and mark; a bottom plate, a supporting column and a light source are externally connected, it is guaranteed that meat color measurement is not affected, and practicability is higher.

The invention provides a yak meat intramuscular fat content prediction method and system based on a near infrared spectrum, and relates to the technical field of data analysis, and the method comprises the steps: obtaining an original near infrared spectrum curve of a multi-source yak meat sample and a corresponding intramuscular fat content standard value; secondly, deep feature extraction is carried out on the spectrum through a wavelet scattering network, and multi-level scattering feature representation with deformation stability is obtained; thirdly, fusing the pasture metadata and the part metadata of the samples, and constructing a hypergraph structure representing complex high-order correlation among the samples; then, a thermal diffusion model is established on the hypergraph, the known fat content is used as a heat source for collaborative diffusion, and a steady-state information value fused with topological information is obtained; and finally, constructing a prediction model in combination with the multi-level features and the steady-state information, and realizing rapid and lossless prediction of the intramuscular fat content of a new sample. According to the method, the accuracy and efficiency of predicting the fat content of the multi-source heterogeneous yak meat sample are improved.

This utility model relates to the field of meat product testing technology, specifically to a heavy metal detection device for fresh meat. It includes a digestion tank, a lid fixedly installed on the top of the digestion tank, a pulverizing component fixedly installed at the front end of the lid, a liquid addition pipe fixedly connected to the top of the lid and to the left of the pulverizing component, a stirring component installed inside the lid, and a collection box fixedly installed at the bottom of the digestion tank. The stirring component includes a drive housing fixedly installed in the middle of the top of the digestion tank, a stirring shaft rotatably connected to the inside of the lid, and a transmission box installed outside the stirring shaft, with the stirring shaft rotatably connected to the transmission box. This utility model, through its design, improves the mixing effect, resulting in more uniform solid-liquid contact and ensuring the uniformity of meat sample size. The pulverized meat sample can fully contact the digestion liquid, shortening the reaction time and thus improving the digestion efficiency.

The utility model relates to the technical field of beef sample detection, and discloses a yak meat sample detection device which comprises detection equipment, a display screen is arranged on the outer wall of the detection equipment, an adjusting knob is movably connected to the outer wall of the detection equipment, a lifting button is arranged on the outer wall of the detection equipment, and the lifting button is movably connected with the display screen. The top of the detection equipment is movably connected with a sealing cover, an electric telescopic machine is installed in an inner cavity of the detection equipment, a power output shaft of the electric telescopic machine is connected with a detection table, a loader is placed at the top of the detection table, a transparent cover is placed on the outer wall of the loader, and a temperature cavity is formed in the inner cavity of the detection equipment. According to the device for detecting the yak meat sample, the heating pipe on the device is matched with an adjusting knob, a display screen and a camera module for use, so that the device can completely record the change of the sample and the freshness of the meat at different temperatures and time.

The invention belongs to the technical field of heterogeneous chicken breast quality detection, and provides a chicken breast fibrosis degree online detection method and system in order to adapt to industrial production line low-cost and large-batch real-time detection of chicken breast samples. The established graph-spectrum coupling chicken breast fibrosis degree discrimination model accurately classifies lignified chicken breast, Italian meat and normal chicken breast, meets the requirements of low complexity and high robustness, and realizes real-time detection on low-computing-power equipment.

This invention discloses a pH colorimetric membrane for indicating meat freshness, obtained through the following steps: First, konjac glucomannan and polyvinyl alcohol are dissolved separately in distilled water and heated and stirred until dissolved; then, the konjac glucomannan aqueous solution and the polyvinyl alcohol aqueous solution are mixed to obtain a film-forming solution; curcumin and alizarin are added to the film-forming solution, homogenized using a homogenizer, and then ultrasonically treated to remove air bubbles; the solution is poured into a plastic petri dish and dried in an incubator to obtain the pH colorimetric membrane. This invention uses a 3:1 mass ratio of curcumin and alizarin as colorants, resulting in stronger pH sensitivity and more obvious color changes. Furthermore, all raw materials used are derived from natural substances, are safe and non-toxic, and edible. No complex processing of meat samples is required, making the operation simple and low-cost. It has great application potential in meat freshness monitoring and intelligent packaging, and can be applied to the freshness monitoring of various meat products.