Meat product detection system and method

Abstract

The invention relates to the technical field of meat product detection, in particular to a meat product detection system and method.The meat product detection system comprises a ripening cutting module, an image acquisition module, a preliminary detection module, a laser detection module and a ripening analysis module; the detection module is used for determining whether the ripened beef is cut into ripened steak according to the judged deterioration characterization state of the ripened beef, and performing preliminary detection and laser detection on the ripened steak to determine the structural characterization state and lipid characterization state of the internal structure of the ripened steak; and determining whether the cooked beefsteak is qualified or not according to the structure characterization state and the lipid characterization state. By evaluating the internal structure, the lipid state and the surface deterioration condition of the cooked beef steak, the quality control and safety of the beef product are effectively improved, the personal error is reduced, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meat product detection, and in particular to a meat product detection system and method. Background Art

[0002] Wet aging of beef is a method of using vacuum packaging technology and the natural enzymes of beef to decompose protein and mature beef in an environment of 0 to 4 degrees for more than 20 days during refrigerated transportation or storage. This technology can improve the tenderness and juiciness of beef, with low loss and relatively affordable price. In recent years, studies have explored the effect of wet aging on beef quality through metabolomics and other technologies, revealing the biochemical changes of beef exudates during aging and their potential for indicating meat quality.

[0003] Chinese patent announcement number CN115069578B discloses an automatic steak detection and screening system, which belongs to the technical field of steak production equipment, including a workbench, a conveyor belt, an MCU and a pressure sensor. The conveyor belt is rotatably mounted on the workbench, and the pressure sensor is arranged on the conveyor belt; the output end of the pressure sensor is electrically connected to the input end of the MCU; a screening device for responding to the MCU is arranged on the workbench; a collecting device for collecting steaks is arranged on the workbench; and a driving device for driving the conveyor belt to rotate is arranged on the workbench. The present application can detect the weight of the steak when the steak passes through the pressure sensor, and screen out the steak that does not meet the quality requirements through the screening device, thereby reducing the possibility of steaks that do not meet the quality standards entering the next process, and further improving the quality of the steaks leaving the factory. It can be seen that the invention has the following problems:

[0004] This invention does not take into account the fact that aged beef is prone to spoilage and does not consider how to detect the effect of its aging. Summary of the invention

[0005] To this end, the present invention provides a meat product detection system and method to overcome the problem in the prior art that the aged beef is prone to spoilage and how to detect its aging effect is not considered.

[0006] To achieve the above object, on the one hand, the present invention provides a meat product detection system, comprising:

[0007] An aging and cutting module, for wet aging a plurality of beef blocks to form aged beef, cutting the aged beef into aged steaks of equal thickness, and selecting a plurality of preset numbers of primary inspection aged steaks and fat inspection aged steaks respectively;

[0008] An image acquisition module, which is connected to the aging and cutting module, and includes an image photographing unit for acquiring an image of the surface of the aged beef and a video shooting unit for acquiring a laser irradiation video of the fat-inspected aged steak;

[0009] a preliminary detection module connected to the ripening and cutting module, for soaking each of the initially inspected ripened steaks in a marinade box for a preset time and then taking them out, determining the infiltration amount of each initially inspected ripened steak according to the quality difference of each marinade box before and after soaking to determine the average infiltration amount, and determining the structural characterization state of the internal structure of the ripened steak according to the average infiltration amount;

[0010] a laser detection module, which is connected to the aging cutting module and the image acquisition module respectively, and is used to irradiate the surface of each fat-inspected aged steak with a laser and shoot the laser irradiation video of each fat-inspected aged steak, determine the stop time of the laser irradiation and the end shooting time of the laser irradiation video according to a single laser irradiation video, determine the Maillard reaction trend according to the average duration of each laser irradiation video, determine the expected diameter and expected density of the aged steak according to the average diameter and density of the oil droplets in the last frame of a single laser irradiation video, determine the oil droplet distribution of the aged steak according to the expected diameter and expected density, and determine the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution;

[0011] The aging analysis module is respectively connected to the aging cutting module, the image acquisition module, the preliminary detection module and the laser detection module, and is used for dividing the beef surface image into a plurality of regional surface images to determine the chromaticity value of each regional surface image, determining the deterioration characterization state (dominant deterioration state / latent deterioration phenomenon) of the corresponding aged beef according to whether the chromaticity value of each regional surface image meets the deterioration characterization condition, determining whether to perform preliminary detection and laser detection on the aged steak according to the deterioration characterization state, and determining the eligibility of the aged steak according to the structural characterization state and the lipid characterization state.

[0012] Furthermore, the preliminary detection module determines the infiltration amount of each initially inspected mature steak according to the quality difference of each marinade box before and after soaking, and determines the average infiltration amount according to the average value of the infiltration amount of each initially inspected mature steak.

[0013] Further, the preliminary detection module determines the structural characterization state of the internal structure of the aged steak according to the magnitude relationship between the average infiltration amount and the preset infiltration amount;

[0014] The structural representation state includes a standard representation state and a loose representation state.

[0015] Further, the laser detection module determines the stop time of the laser irradiation and the end shooting time of the laser irradiation video according to the chromaticity value of the real-time frame of the single laser irradiation video satisfying the chromaticity change condition;

[0016] Among them, the chromaticity change condition is that the L value is less than 60, the a value is less than 50 and the b value is greater than 10.

[0017] Furthermore, the laser detection module determines the Maillard reaction trend according to the relationship between the average duration of each laser irradiation video and the preset duration;

[0018] The Maillard reaction trend includes a fast reaction trend and a slow reaction trend.

[0019] Further, the laser detection module determines an expected average diameter value and an expected density value according to the average diameter and density of the oil droplets in the last frame of each laser irradiation video, respectively, determines an expected diameter and an expected density of the aged steak according to the expected average diameter value and the expected density value, and determines that the oil droplet distribution of the aged steak is a normal oil distribution according to the expected diameter and the expected density satisfying the oil irradiation condition;

[0020] The grease irradiation condition is that the expected diameter is less than or equal to the preset diameter and the expected density is greater than or equal to the preset density, and the oil droplet distribution includes normal grease distribution and abnormal grease distribution.

[0021] Furthermore, the laser detection module determines the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution, including:

[0022] If the Maillard reaction trend is a fast reaction trend and the oil droplet distribution is a normal oil distribution, the lipid characterization state is determined to be a standard characterization state;

[0023] If the Maillard reaction trend is a slow reaction trend and / or the oil droplet distribution is an abnormal oil distribution, the lipid characterization state is determined to be a defective characterization state.

[0024] Furthermore, the ripening analysis module determines that the spoilage characterization state of the corresponding aged beef is not spoiled based on the fact that the chromaticity values ​​of the surface images of each area do not meet the spoilage characterization conditions, and determines to perform preliminary detection and laser detection on the aged steak.

[0025] Furthermore, the ripening analysis module determines that the ripened steak is qualified based on the structural characterization state being the characterization state and the lipid characterization state being the normal fat distribution.

[0026] On the other hand, the present invention also provides a method for detecting meat products, comprising:

[0027] Step S1, collecting a surface image of aged beef;

[0028] Step S2, dividing the beef surface image into a plurality of regional surface images and determining the chromaticity value of each regional surface image;

[0029] Step S3, determining the deterioration characterization state of the corresponding aged beef according to whether the chromaticity value of the surface image of each area meets the deterioration characterization condition to determine whether to perform preliminary detection and laser detection on the aged beef, including:

[0030] Failure to conduct preliminary testing and laser testing on aged steaks and to determine that the aged beef was unqualified;

[0031] Or, after cutting the aged beef into aged steaks of equal thickness, a preset number of initial inspection aged steaks and fat inspection aged beef are selected respectively, and the process continues to step S4;

[0032] Step S4, performing preliminary testing on the initially inspected aged steak, including:

[0033] Step S41, soaking each of the initially inspected matured steaks in a marinade box for a preset time and then taking them out;

[0034] Step S42, determining the infiltration amount of each initially inspected matured steak according to the quality difference of each marinade box before and after soaking the initially inspected matured steak to determine the average infiltration amount;

[0035] Step S43, determining the structural representation state of the internal structure of the aged steak according to the average infiltration amount;

[0036] Step S5, performing laser detection on the fat-tested aged steak, including:

[0037] Step S51, irradiating the surface of each fat-inspected aged steak with a laser and shooting a laser irradiation video of each fat-inspected aged steak;

[0038] Step S52, determining the stop time of the laser irradiation and the end shooting time of the laser irradiation video according to the single laser irradiation video;

[0039] Step S53, determining the Maillard reaction trend according to the average duration of each of the laser irradiation videos;

[0040] Step S54, determining the expected diameter and expected density of the aged steak according to the average diameter and density of the oil droplets in the last frame of the single laser irradiation video to determine the distribution of the oil droplets of the aged steak;

[0041] Step S55, determining the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution;

[0042] Step S6, determining the eligibility of the aged steak according to the structural characterization state and the lipid characterization state.

[0043] Compared with the prior art, the beneficial effect of the present invention is that the meat product detection system provided by the present invention realizes a comprehensive automated process from wet aging, cutting to quality inspection of beef by integrating multiple functional modules. The system can accurately evaluate the internal structure, lipid state and surface deterioration of aged steaks, effectively improving the quality control and safety of beef products, reducing human errors, and improving detection efficiency and accuracy.

[0044] Furthermore, the preliminary detection module determines the infiltration amount of the initially inspected aged steak by measuring the mass difference of each marinade box before and after immersion, and calculates the average infiltration amount based on this for comparison with the preset infiltration amount, thereby effectively evaluating the internal structure state of the aged steak; this method is not only scientific and accurate, but also can distinguish between the standard characterization state and the loose characterization state, providing an important basis for judging the quality of beef; in particular, this module fully considers the texture difference between wet-aged steak and non-wet-aged steak, and by reasonably setting the preset infiltration amount, it can accurately reflect the aging effect and ensure the high quality and safety of beef products.

[0045] Furthermore, the laser detection module monitors the real-time frame chromaticity values ​​of the laser irradiation video in real time, and intelligently determines the stop time of laser irradiation and the end time of video shooting according to the preset chromaticity change conditions. This method can accurately capture the Maillard reaction process in which the color of the beef surface changes from red to brown, effectively avoiding errors in human judgment and improving detection efficiency and accuracy.

[0046] Furthermore, the laser detection module can accurately determine the Maillard reaction trend of the aged steak and distinguish it into fast reaction trend and slow reaction trend by comparing the average duration of each laser irradiation video with the preset duration. This method not only takes into account the difference in the Maillard reaction time between aged steak and uncooked steak, but also takes into account the influence of laser power and steak thickness on the reaction time. By scientifically setting the preset duration, it realizes the accurate evaluation of the Maillard reaction process; this intelligent and automated detection method not only improves the detection efficiency, but also provides an important basis for the quality control and quality evaluation of beef products.

[0047] Furthermore, the laser detection module determines the expected diameter and expected density of the aged steak, and judges whether the oil droplet distribution is normal or abnormal based on this. It not only takes into account the dual factors of oil droplet size and density, but also realizes the accurate assessment of the fat distribution state of the aged steak. This intelligent detection method based on laser technology provides a more sophisticated and reliable basis for the quality control and quality assessment of beef products, which helps to improve the overall quality of the product.

[0048] Furthermore, the ripening analysis module determines the spoilage status of the mature steak by comprehensively evaluating the chromaticity values ​​of the surface images of various areas of the mature steak, effectively distinguishing between the unspoiled state and the overtly spoiled state, avoiding further testing of unqualified products and improving detection efficiency.

[0049] Furthermore, the aging analysis module combines the evaluation results of the structural characterization status and the lipid characterization status to ensure that the aged steak is only judged as qualified if the structure is standard and the fat distribution is normal. This comprehensive and detailed testing method improves the quality control and safety of beef products. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a connection diagram of the meat product detection system according to an embodiment of the present invention;

[0051] Figure 2 This is a step diagram of a method for detecting meat products according to an embodiment of the present invention;

[0052] Figure 3 A diagram showing the steps of a preliminary detection method according to an embodiment of the present invention;

[0053] Figure 4 1 is a step diagram of a laser detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0056] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0057] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] See also Figure 1 As shown, it is a connection diagram of a meat product detection system according to an embodiment of the present invention. An embodiment of the present invention provides a meat product detection system, comprising:

[0059] The aging and cutting module is used to wet-age several beef blocks to form mature beef, cut the aged beef into mature steaks of equal thickness (the thickness of the mature steaks in the present invention is 1.5 cm to 3 cm), and select several preset numbers of primary inspection mature steaks and fat inspection mature steaks respectively; in implementation, the preset number is determined according to the number of mature steaks, generally, the ratio of the preset number to the number of mature steaks satisfies 1% to 5%, preferably, the ratio of the preset number to the number of mature steaks satisfies 2%; it can be understood that the thickness consistency and quality stability of the mature steaks are ensured through standardized wet aging and precise cutting processes, and at the same time, the primary inspection and fat inspection samples are reasonably selected according to the quantity ratio, which provides a reliable basis for subsequent detection;

[0060] An image acquisition module, which is connected to the aging and cutting module, includes an image taking unit for acquiring an image of the surface of the aged beef and a video shooting unit for acquiring a laser irradiation video of the fat-inspected aged beef steak; it can be understood that the image acquisition module provides data support for subsequent quality assessment;

[0061] A preliminary detection module is connected to the aging and cutting module, and is used to soak each of the initially inspected aged steaks in a marinade box for a preset time (in practice, the preset time is generally set to 5 minutes to 10 minutes, and is often set to 10 minutes) and then take them out, determine the infiltration amount of each initially inspected aged steak according to the mass difference of each marinade box before and after soaking to determine the average infiltration amount, and determine the structural characterization state of the internal structure of the aged steak according to the average infiltration amount; it can be understood that the proportion and mass of the marinade in each of the marinade boxes are equal (the marinade in a single marinade box does not exceed the initially inspected aged steak); it can be understood that by soaking the marinade and measuring the mass difference, the internal structure state of the aged steak can be quickly and accurately evaluated, which provides an important basis for judging the quality of beef;

[0062] a laser detection module, which is connected to the aging cutting module and the image acquisition module respectively, and is used to irradiate the surface of each fat-inspected aged steak with a laser and shoot the laser irradiation video of each fat-inspected aged steak, determine the stop time of the laser irradiation and the end time of the laser irradiation video according to a single laser irradiation video, determine the Maillard reaction trend (including the fast reaction trend and the slow reaction trend) according to the average duration of each laser irradiation video, determine the expected diameter and expected density of the aged steak according to the average diameter and density of the oil droplets in the last frame of a single laser irradiation video, and determine the expected diameter and expected density of the aged steak according to the expected diameter and expected density. The oil droplet distribution of the aged steak (including normal oil distribution and abnormal oil distribution), and the determination of the lipid characterization state (including standard characterization state and defect characterization state) according to the Maillard reaction trend and the oil droplet distribution; in implementation, the laser detection module is installed with a machine learning model to determine the chromaticity value of each frame of the laser irradiation video and determine the average diameter and density of the oil droplets in the last frame of the laser irradiation video; it is understandable that the in-depth analysis of the laser irradiation video in combination with the machine learning model can not only accurately determine the Maillard reaction trend, but also accurately evaluate the oil droplet distribution state, thereby comprehensively evaluating the lipid characterization state of the aged steak;

[0063] The ripening analysis module is connected to the ripening cutting module, the image acquisition module, the preliminary detection module and the laser detection module respectively, and is used to divide the beef surface image into several regional surface images to determine the chromaticity value of each regional surface image, determine the deterioration characterization state (including explicit deterioration state / hidden deterioration phenomenon) of the corresponding ripe beef according to whether the chromaticity value of each regional surface image meets the deterioration characterization condition, determine whether to perform preliminary detection and laser detection on the ripe steak according to the deterioration characterization state, and when determining to perform preliminary detection and laser detection on the ripe steak, determine the qualification of the ripe steak according to the structural characterization state and the lipid characterization state. In implementation, the ripening analysis module is also installed with a machine learning model to determine the chromaticity value of each regional surface image of the beef surface image; it can be understood that by performing chromaticity value analysis on the beef surface image through the machine learning model, the deterioration characterization state of the beef can be discovered in time, and the qualification of the ripe steak can be determined according to the comprehensive detection results, thereby ensuring the safety and quality of the beef products.

[0064] It can be understood that the meat product detection system provided by the present invention realizes a comprehensive automated process from wet aging, cutting to quality inspection of beef by integrating multiple functional modules. The system can accurately evaluate the internal structure, lipid state and surface deterioration of aged steaks, effectively improve the quality control and safety of beef products, reduce human errors, and improve detection efficiency and accuracy.

[0065] Specifically, the preliminary detection module determines the infiltration amount of each initially inspected mature steak according to the quality difference of each marinade box before and after soaking, and determines the average infiltration amount according to the average value of the infiltration amount of each initially inspected mature steak.

[0066] It is understandable that the greater the quality difference, the greater the amount of infiltration, which means that more marinade has been absorbed by the primary inspection mature steak.

[0067] Specifically, the preliminary detection module determines the structural representation state of the internal structure of the aged steak according to the magnitude relationship between the average infiltration amount and the preset infiltration amount;

[0068] The structural representation state includes a standard representation state and a loose representation state.

[0069] In practice, the mass of marinade that can enter into every 100g of wet-aged steak is 0.5g to 1g; therefore, the preset infiltration amount of the unit mass (100g) of wet-aged steak is set to 1g, and the preset infiltration amount in the present invention is the ratio of the mass of a single initially inspected aged steak to the unit mass.

[0070] It is understandable that due to their different textures, wet-aged steaks and non-wet-aged steaks will have different degrees of marinating penetration in the same period of time, and non-wet-aged steaks will usually have a higher degree of penetration: (1) After a period of sealed storage, the natural enzymes in the beef act on the muscle fibers under specific temperature and humidity conditions, making the meat softer; at the same time, due to the relatively small loss of water, the structure of the meat is relatively compact. This soft but compact texture makes it difficult for the marinade to quickly penetrate deep into the steak in the same period of time; although the muscle fibers on the surface have been softened, the internal structure is still relatively compact, which hinders the rapid penetration of the marinade; therefore, within the same marinating time, the wet-aged steak has a relatively low degree of penetration. (2) The texture of steak that has not been wet-aged is relatively hard and the muscle fibers are relatively rough; however, its internal structure is relatively loose and the moisture content may also be higher. Therefore, the looser structure and higher moisture content make it easier for the marinade to penetrate into the steak in the same amount of time. As the marinade diffuses in the meat, it can reach deeper parts faster, making the non-wet-aged steak have a relatively high degree of wetness within the same marinating time.

[0071] Therefore, the smaller the average infiltration amount, the better the aging effect of the aged steak. Therefore, in implementation: if the average infiltration amount is greater than or equal to the preset infiltration amount, the structural characterization state is judged to be a loose characterization state; if the average infiltration amount is less than the preset infiltration amount, the structural characterization state is judged to be a standard characterization state.

[0072] It can be understood that the preliminary detection module determines the infiltration amount of the initially inspected aged steak by measuring the mass difference of each marinade box before and after soaking, and calculates the average infiltration amount based on this for comparison with the preset infiltration amount, thereby effectively evaluating the internal structure state of the aged steak; this method is not only scientific and accurate, but also can distinguish between the standard characterization state and the loose characterization state, providing an important basis for judging the quality of beef; in particular, this module fully considers the texture difference between wet-aged steak and non-wet-aged steak, and by reasonably setting the preset infiltration amount, it can accurately reflect the aging effect and ensure the quality and safety of beef products.

[0073] Specifically, the laser detection module determines the stop time of laser irradiation and the end time of shooting the laser irradiation video according to whether the chromaticity value of the real-time frame of the single laser irradiation video satisfies the chromaticity change condition; it can be understood that if the chromaticity value of the real-time frame of the single laser irradiation video does not satisfy the chromaticity change condition, it is determined that the stop time of laser irradiation and the end time of shooting the laser irradiation video have been reached, so it is necessary to continue laser irradiation and continue shooting the laser irradiation video;

[0074] Among them, the chromaticity change condition is that the L value is less than 60, the a value is less than 50 and the b value is greater than 10.

[0075] It is understandable that in color science, chromaticity value is an important indicator used to describe color, and is usually represented by the CIELab* color space. The three axes of this color space represent: (1) L value is brightness, 0 is black, and 100 is white; (2) a value is hue from green to red, negative value is green, and positive value is red; (3) b value is hue from blue to yellow, negative value is blue, and positive value is yellow.

[0076] In practice, when red turns to brown, the chromaticity value usually changes as follows: (1) L value: the brightness value of red is usually higher and the brightness value of brown is lower; usually, the L value of bright red is between 60 and 80, and the L value of brown is between 30 and 60, and the darker the brown, the smaller the L value; (2) a value: the a value of red is usually positive and high, while the a value of brown will decrease; usually, the a value of red can be between 50 and 70, and the a value of brown may drop to 30 to 50 due to the decrease of red component caused by the increase of other colors (such as yellow or black); (3) b value: the b value of red is usually low, and when red turns to brown, the b value may increase, showing a more obvious yellow component; usually, the b value of red is between 5 and 10, and the b value of brown is between 10 and 30.

[0077] It can be understood that the laser detection module monitors the real-time frame chromaticity values ​​of the laser irradiation video in real time, and intelligently determines the stop time of laser irradiation and the end time of video shooting according to the preset chromaticity change conditions. This method can accurately capture the Maillard reaction process in which the color of the beef surface changes from red to brown, effectively avoiding errors in human judgment and improving detection efficiency and accuracy.

[0078] Specifically, the laser detection module determines the Maillard reaction trend according to the relationship between the average duration of each laser irradiation video and the preset duration;

[0079] The Maillard reaction trend includes a fast reaction trend and a slow reaction trend.

[0080] In implementation, if the average duration is greater than or equal to the preset duration, it is judged as a slow response trend; if the average duration is less than the preset duration, it is judged as a fast response trend.

[0081] It is understandable that a thin layer of water vapor can usually be observed forming on a mature steak within a few seconds after laser irradiation, and the stage of changing from red to brown generally takes about 10 to 30 seconds; the evaporation of water on the surface of an uncooked steak is relatively slow, and it may take 5 to 10 seconds or even longer to form water vapor, and the color may change from red to brown in about 20 to 60 seconds (because the texture of an uncooked steak is relatively uneven, the color change may be at different times, so the time span is relatively large); that is, the Maillard reaction of mature beef turns the surface of the beef brown in a shorter time, while the Maillard reaction of uncooked beef turns the surface of the beef brown in a longer time; at the same time, if the laser power is large and the steak is thinner, the time may be shorter, otherwise the time may be longer. Therefore, the value range of the preset time length is generally 10s to 30s, and is preferably set to 25s.

[0082] In practice, a laser beam with a laser power of about 100 watts should be used for the 1.5 cm to 3 cm thick aged steak of the present invention.

[0083] It is understandable that the laser detection module can accurately determine the Maillard reaction trend of the matured steak and distinguish it into fast reaction trend and slow reaction trend by comparing the average duration of each laser irradiation video with the preset duration. This method not only takes into account the difference in the Maillard reaction time between matured steak and uncooked steak, but also takes into account the influence of laser power and steak thickness on the reaction time. By scientifically setting the preset duration, it realizes the accurate evaluation of the Maillard reaction process; this intelligent and automated detection method not only improves the detection efficiency, but also provides an important basis for the quality control and quality evaluation of beef products.

[0084] Specifically, the laser detection module determines an expected average diameter value and an expected density value according to the average diameter and density of the oil droplets in the last frame of each laser irradiation video, respectively, determines an expected diameter and an expected density of the aged steak according to the expected average diameter value and the expected density value, and determines that the oil droplet distribution of the aged steak is a normal oil distribution according to the expected diameter and the expected density satisfying the oil irradiation condition;

[0085] The grease irradiation condition is that the expected diameter is less than or equal to the preset diameter and the expected density is greater than or equal to the preset density, and the oil droplet distribution includes normal grease distribution and abnormal grease distribution. In implementation, the expected value of the average diameter is the expected diameter, and the expected value of the density is the expected density.

[0086] It can be understood that the average diameter of the oil droplets in the last frame is determined according to the average value of the diameters of the oil droplets in the frame, and the diameter of a single oil droplet is the contact diameter of the oil droplet with the steak surface.

[0087] It is understandable that the fat and protein of aged steak will decompose during the aging process, and after laser irradiation, small and dense oil droplets should be formed on the surface of the steak; while uncooked steak will not form small and dense oil droplets.

[0088] In practice, (1) the oil droplets may be very small, with a diameter of about 0.1 mm to 1 mm, and preferably the preset diameter is set to 0.1 mm. (2) The density of the oil droplets may range from a few to dozens per square centimeter. If the degree of ripening is high, the fat content is rich, and the laser effect is relatively uniform, the oil droplets may be relatively dense, otherwise they will be relatively sparse; preferably, the preset density is set to 15 per square centimeter.

[0089] It is understandable that the laser detection module determines the expected diameter and expected density of the aged steak, and judges the oil droplet distribution as normal or abnormal, taking into account the dual factors of oil droplet size and density, and achieves an accurate assessment of the fat distribution state of the aged steak. This intelligent detection method based on laser technology provides a more sophisticated and reliable basis for the quality control and quality assessment of beef products, which helps to improve the overall quality of the product.

[0090] Specifically, the laser detection module determines the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution, including:

[0091] If the Maillard reaction trend is a fast reaction trend and the oil droplet distribution is a normal oil distribution, the lipid characterization state is determined to be a standard characterization state; that is, the preliminary test determines that its ripening process is standard and the ripening effect is in line with expectations;

[0092] If the Maillard reaction trend is a slow reaction trend and / or the oil droplet distribution is an abnormal oil distribution, the lipid characterization state is determined to be a defective characterization state; that is, the preliminary test determines that its ripening process may not be standard and the ripening effect does not meet expectations.

[0093] Specifically, the ripening analysis module determines that the spoilage characterization state of the corresponding aged beef is not spoiled based on the fact that the chromaticity values ​​of the surface images of each area do not meet the spoilage characterization conditions, and determines to conduct preliminary inspection and laser inspection on the aged steak.

[0094] Understandably, a normal wet-aged steak should be a fairly uniform dark red or dark brown in color. If you notice noticeable discoloration on the surface, such as green, gray, or black spots, it could be a sign of mold or spoilage.

[0095] In practice, the chromaticity value of green is usually: L value is about 30 to 80, a value is negative value between about -20 and -80, and b value is usually low, about -10 to 30; the chromaticity value range of gray is relatively wide: L value is about 20 to 80, a value is close to 0, and b value is close to 0; the chromaticity value of black is usually: L value, a value and b value are all close to 0. Therefore, the deterioration characterization condition is L less than 60, a value less than 5 and b value less than 30.

[0096] In implementation, the ripening analysis module determines that the spoilage characterization state of the corresponding ripe beef is an obvious spoilage state based on the chromaticity value of the surface image of any area satisfying the spoilage characterization condition and determines not to perform preliminary inspection and laser inspection on the ripened steak (i.e., the ripening is unqualified).

[0097] It can be understood that the ripening analysis module determines the spoilage characterization state of the ripe steak by comprehensively evaluating the chromaticity values ​​of the surface images of various areas of the ripe steak, effectively distinguishing between the unspoiled state and the overtly spoiled state, avoiding further detection of unqualified products and improving detection efficiency.

[0098] Specifically, the ripening analysis module determines that the ripened steak is qualified based on the structural characterization state being the characterization state and the lipid characterization state being the normal fat distribution.

[0099] It can be understood that the aging analysis module combines the evaluation results of the structural characterization status and the lipid characterization status to ensure that the aged steak is only judged as qualified if the structure is standard and the fat distribution is normal. This comprehensive and detailed testing method improves the quality control and safety of beef products.

[0100] See also Figures 2 to 4 As shown, they are respectively a step diagram of the meat product detection method according to an embodiment of the present invention, a step diagram of the preliminary detection method according to an embodiment of the present invention, and a step diagram of the laser detection method according to an embodiment of the present invention. The present invention also provides a meat product detection method, characterized in that it comprises:

[0101] Step S1, collecting a surface image of aged beef;

[0102] Step S2, dividing the beef surface image into a plurality of regional surface images and determining the chromaticity value of each regional surface image;

[0103] Step S3, determining the deterioration characterization state of the corresponding aged beef according to whether the chromaticity value of the surface image of each area meets the deterioration characterization condition to determine whether to perform preliminary detection and laser detection on the aged beef, including:

[0104] Not conducting preliminary testing and laser testing on aged steaks, and judging that the aged beef is unqualified, no further steps will be taken;

[0105] Or, after cutting the aged beef into aged steaks of equal thickness, a preset number of initial inspection aged steaks and fat inspection aged beef are selected respectively, and the process continues to step S4;

[0106] Step S4, performing preliminary testing on the initially inspected aged steak, including:

[0107] Step S41, soaking each of the initially inspected matured steaks in a marinade box for a preset time and then taking them out;

[0108] Step S42, determining the infiltration amount of each initially inspected matured steak according to the quality difference of each marinade box before and after soaking the initially inspected matured steak to determine the average infiltration amount;

[0109] Step S43, determining the structural representation state of the internal structure of the aged steak according to the average infiltration amount;

[0110] Step S5, performing laser detection on the fat-tested aged steak, including:

[0111] Step S51, irradiating the surface of each fat-inspected aged steak with a laser and shooting a laser irradiation video of each fat-inspected aged steak;

[0112] Step S52, determining the stop time of the laser irradiation and the end shooting time of the laser irradiation video according to the single laser irradiation video;

[0113] Step S53, determining the Maillard reaction trend according to the average duration of each of the laser irradiation videos;

[0114] Step S54, determining the expected diameter and expected density of the aged steak according to the average diameter and density of the oil droplets in the last frame of the single laser irradiation video to determine the distribution of the oil droplets of the aged steak;

[0115] Step S55, determining the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution;

[0116] Step S6, determining the eligibility of the aged steak according to the structural characterization state and the lipid characterization state.

[0117] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A meat product detection system, characterized in that: include: An aging and cutting module, for wet aging a plurality of beef blocks to form aged beef, cutting the aged beef into aged steaks of equal thickness, and selecting a plurality of preset numbers of primary inspection aged steaks and fat inspection aged steaks respectively; An image acquisition module, which is connected to the aging and cutting module, and includes an image photographing unit for acquiring an image of the surface of the aged beef and a video shooting unit for acquiring a laser irradiation video of the fat-inspected aged steak; a preliminary detection module connected to the ripening and cutting module, for soaking each of the initially inspected ripened steaks in a marinade box for a preset time and then taking them out, determining the infiltration amount of each initially inspected ripened steak according to the quality difference of each marinade box before and after soaking to determine the average infiltration amount, and determining the structural characterization state of the internal structure of the ripened steak according to the average infiltration amount; a laser detection module, which is connected to the aging cutting module and the image acquisition module respectively, and is used to irradiate the surface of each fat-inspected aged steak with a laser and shoot the laser irradiation video of each fat-inspected aged steak, determine the stop time of the laser irradiation and the end shooting time of the laser irradiation video according to a single laser irradiation video, determine the Maillard reaction trend according to the average duration of each laser irradiation video, determine the expected diameter and expected density of the aged steak according to the average diameter and density of the oil droplets in the last frame of a single laser irradiation video, determine the oil droplet distribution of the aged steak according to the expected diameter and expected density, and determine the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution; A ripening analysis module is respectively connected to the ripening cutting module, the image acquisition module, the preliminary detection module and the laser detection module, and is used for dividing the beef surface image into a plurality of regional surface images to determine the chromaticity value of each regional surface image, determining the deterioration characterization state of the corresponding aged beef according to whether the chromaticity value of each regional surface image meets the deterioration characterization condition, determining whether to perform preliminary detection and laser detection on the aged steak according to the deterioration characterization state, and determining the eligibility of the aged steak according to the structural characterization state and the lipid characterization state.

2. The meat product detection system according to claim 1, characterized in that: The preliminary detection module determines the infiltration amount of each initially inspected mature steak according to the quality difference of each marinade box before and after soaking, and determines the average infiltration amount according to the average infiltration amount of each initially inspected mature steak.

3. The meat product detection system according to claim 2, characterized in that: The preliminary detection module determines the structural representation state of the internal structure of the aged steak according to the magnitude relationship between the average infiltration amount and the preset infiltration amount; The structural representation state includes a standard representation state and a loose representation state.

4. The meat product detection system according to claim 1, characterized in that: The laser detection module determines the stop time of the laser irradiation and the end time of the shooting of the laser irradiation video according to whether the chromaticity value of the real-time frame of the single laser irradiation video satisfies the chromaticity change condition; Among them, the chromaticity change condition is that the L value is less than 60, the a value is less than 50 and the b value is greater than 10.

5. The meat product detection system according to claim 4, characterized in that: The laser detection module determines the Maillard reaction trend according to the relationship between the average duration of each laser irradiation video and the preset duration; The Maillard reaction trend includes a fast reaction trend and a slow reaction trend.

6. The meat product detection system according to claim 1, characterized in that: The laser detection module determines an expected average diameter value and an expected density value according to the average diameter and density of the oil droplets in the last frame of each laser irradiation video, respectively, determines an expected diameter and an expected density of the aged steak according to the expected average diameter value and the expected density value, and determines that the oil droplet distribution of the aged steak is a normal oil distribution according to the expected diameter and the expected density satisfying the oil irradiation condition; The grease irradiation condition is that the expected diameter is less than or equal to the preset diameter and the expected density is greater than or equal to the preset density, and the oil droplet distribution includes normal grease distribution and abnormal grease distribution.

7. The meat product detection system according to claim 1, characterized in that: The laser detection module determines the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution, including: If the Maillard reaction trend is a fast reaction trend and the oil droplet distribution is a normal oil distribution, the lipid characterization state is determined to be a standard characterization state; If the Maillard reaction trend is a slow reaction trend and / or the oil droplet distribution is an abnormal oil distribution, the lipid characterization state is determined to be a defective characterization state.

8. The meat product detection system according to claim 1, characterized in that: The ripening analysis module determines that the spoilage characterization state of the corresponding ripe beef is not spoiled according to the fact that the chromaticity values ​​of the surface images of each area do not meet the spoilage characterization conditions, and determines to perform preliminary detection and laser detection on the ripe steak.

9. The meat product detection system according to claim 1, characterized in that: The ripening analysis module determines that the ripened steak is qualified according to the structural characterization state being the characterization state and the lipid characterization state being the normal fat distribution.

10. A method for detecting meat products applied to the meat product detection system according to any one of claims 1 to 9, characterized in that: include, Step S1, collecting a surface image of aged beef; Step S2, dividing the beef surface image into a plurality of regional surface images and determining the chromaticity value of each regional surface image; Step S3, determining the deterioration characterization state of the corresponding aged beef according to whether the chromaticity value of the surface image of each area meets the deterioration characterization condition to determine whether to perform preliminary detection and laser detection on the aged beef, including: Failure to conduct preliminary testing and laser testing on aged steaks and to determine that the aged beef was unqualified; Or, after cutting the aged beef into aged steaks of equal thickness, a preset number of initial inspection aged steaks and fat inspection aged beef are selected respectively, and the process continues to step S4; Step S4, performing preliminary testing on the initially inspected aged steak, including: Step S41, soaking each of the initially inspected matured steaks in a marinade box for a preset time and then taking them out; Step S42, determining the infiltration amount of each initially inspected matured steak according to the quality difference of each marinade box before and after soaking the initially inspected matured steak to determine the average infiltration amount; Step S43, determining the structural representation state of the internal structure of the aged steak according to the average infiltration amount; Step S5, performing laser detection on the fat-tested aged steak, including: Step S51, irradiating the surface of each fat-inspected aged steak with a laser and shooting a laser irradiation video of each fat-inspected aged steak; Step S52, determining the stop time of the laser irradiation and the end shooting time of the laser irradiation video according to the single laser irradiation video; Step S53, determining the Maillard reaction trend according to the average duration of each of the laser irradiation videos; Step S54, determining the expected diameter and expected density of the aged steak according to the average diameter and density of the oil droplets in the last frame of the single laser irradiation video to determine the distribution of the oil droplets of the aged steak; Step S55, determining the lipid characterization state according to the Maillard reaction trend and the oil droplet distribution; Step S6, determining the eligibility of the aged steak according to the structural characterization state and the lipid characterization state.

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