A detection system and method for meat products

Abstract

The present invention relates to the technical field of meat product detection, and particularly to a detection system and method for meat products, including an aging and cutting module, an image acquisition module, a preliminary detection module, a laser detection module, and an aging analysis module; used to determine whether to cut aged beef into aged steaks according to the determined deterioration characterization state of the aged beef, and conduct preliminary detection and laser detection on the aged steaks to determine the structural characterization state and lipid characterization state of the internal structure of the aged steaks, and determine whether the aged steaks are qualified through the structural characterization state and lipid characterization state. The present invention effectively improves the quality control and safety of beef products, reduces human error, and improves the detection efficiency and accuracy by evaluating the internal structure, lipid state, and surface deterioration of the aged steaks.

Description

Technical Field

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

[0002] Wet aging of beef is a method that utilizes vacuum packaging technology and the natural enzymes of beef itself during refrigerated transportation or storage to decompose and age proteins in an environment of 0-4 degrees Celsius for more than 20 days. This technology can improve the tenderness and juiciness of beef, with relatively low losses and affordable prices. In recent years, studies have explored the impact of wet aging on beef quality through techniques such as metabolomics, revealing the biochemical changes in beef exudates during the aging process and their potential for indicating meat quality.

[0003] Chinese Patent Publication No. CN115069578B discloses an automatic detection and screening system for steaks, belonging 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 installed 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 collection device for collecting steaks is arranged on the workbench; a driving device for driving the conveyor belt to rotate is arranged on the workbench. In this application, when a steak passes through the pressure sensor, the weight of the steak can be detected, and steaks that do not meet the quality requirements can be screened out through the screening device, thereby reducing the possibility of substandard steaks entering the next process and improving the ex-factory quality of steaks. However, this invention has the following problems:

[0004] This invention does not consider the problem that aged beef is prone to deterioration and does not consider how to detect its aging effect. Summary of the Invention

[0005] Therefore, the present invention provides a detection system and method for meat products to overcome the problems in the prior art that do not consider the perishability of aged beef and do not consider how to detect its aging effect.

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

[0007] An aging and cutting module for wet aging a number of beef blocks to form aged beef, cutting the aged beef into aged steaks with equal thickness, and respectively selecting a number of pre-determined initial inspection aged steaks and fat inspection aged steaks;

[0008] An image acquisition module connected to the aging and cutting module, including an image photographing unit for acquiring the surface image of the aged beef and a video shooting unit for acquiring the laser irradiation video of the fat inspection aged steaks;

[0009] A preliminary detection module, which is connected to the aging and cutting module, is used to take out each of the preliminarily inspected and aged steaks after soaking them in the marinade box for a preset time, determine the infiltration amount of each preliminarily inspected and 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;

[0010] A laser detection module, which is respectively connected to the aging and cutting module and the image acquisition module, is used to irradiate the surface of each of the lipid-inspected and aged steaks with laser, 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 beads in the last frame of a single laser irradiation video, and determine the oil bead 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 bead distribution;

[0011] An aging analysis module, which is respectively connected to the aging and cutting module, the image acquisition module, the preliminary detection module and the laser detection module, is used to divide the beef surface image into several regional surface images to determine the chromaticity values of each regional surface image, determine the spoilage characterization state (dominant spoilage state / hidden spoilage phenomenon) of the corresponding aged beef according to whether the chromaticity values of each regional surface image meet the spoilage characterization conditions, determine whether to conduct preliminary detection and laser detection on the aged steak according to the spoilage characterization state, and determine the qualification of the aged steak according to the structural characterization state and the lipid characterization state.

[0012] Further, the preliminary detection module determines the infiltration amount of each preliminarily inspected and aged steak according to the mass difference of each marinade box before and after soaking, and determines the average infiltration amount according to the average value of the infiltration amounts of each preliminarily inspected and aged 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] Among them, the structural characterization state includes a standard characterization state and a loose characterization 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 that the chromaticity value of the real-time frame of a single laser irradiation video meets 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 a preset duration;

[0018] Among them, the Maillard reaction trend includes a fast reaction trend and a slow reaction trend.

[0019] Furthermore, the laser detection module respectively determines the expected average diameter and the expected density according to the average diameter and the density of the oil droplets in the last frame of each laser irradiation video, determines the expected diameter and the expected density of the aged steak according to the expected average diameter and the expected density, and determines that the oil droplet distribution of the aged steak is a normal oil distribution according to the fact that the expected diameter and the expected density meet the oil irradiation conditions;

[0020] Among them, the oil irradiation conditions are that the expected diameter is less than or equal to a preset diameter and the expected density is greater than or equal to a preset density, and the oil droplet distribution includes a normal oil distribution and an abnormal oil 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, it is determined that the lipid characterization state is 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, it is determined that the lipid characterization state is a defective characterization state.

[0024] Furthermore, the aging analysis module determines that the aging characterization state of the corresponding aged beef is an unaged state according to the fact that the chromaticity values of the surface images of each region do not meet the deterioration characterization conditions, and determines to perform preliminary detection and laser detection on the aged steak.

[0025] Furthermore, the aging analysis module determines that the aged steak is qualified according to the fact that the structure characterization state is a standard characterization state and the lipid characterization state is a standard characterization state.

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

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

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

[0029] Step S3, determine the spoilage characterization status of the corresponding aged beef based on whether the chromaticity values of the surface images of each region meet the spoilage characterization conditions to determine whether to perform preliminary detection and laser detection on the aged steak, including,

[0030] Do not perform preliminary detection and laser detection on the aged steak, and determine that the aged beef is unqualified;

[0031] Or, after cutting the aged beef into aged steaks with equal thickness, respectively select a number of preset initial inspection aged steaks and fat inspection aged steaks, and continue with step S4;

[0032] Step S4, perform preliminary detection on the initial inspection aged steaks, including,

[0033] Step S41, soak each of the initial inspection aged steaks in the marinade box for a preset time and then take them out;

[0034] Step S42, determine the infiltration amount of each initial inspection aged steak based on the mass difference of each marinade box before and after soaking the initial inspection aged steaks to determine the average infiltration amount;

[0035] Step S43, determine the structural characterization status of the internal structure of the aged steak based on the average infiltration amount;

[0036] Step S5, perform laser detection on the fat inspection aged steaks, including,

[0037] Step S51, perform laser irradiation on the surface of each of the fat inspection aged steaks and take videos of the laser irradiation of each fat inspection aged steak;

[0038] Step S52, determine the stop time of the laser irradiation and the end shooting time of the laser irradiation video based on a single laser irradiation video;

[0039] Step S53, determine the Maillard reaction trend based on the average duration of each laser irradiation video;

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

[0041] Step S55, determine the lipid characterization status based on the Maillard reaction trend and the oil droplet distribution;

[0042] Step S6, determine the qualification of the aged steak based on the structural characterization status and the lipid characterization status.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows. The meat product detection system provided by the present invention realizes a comprehensive automated process for beef from wet aging, cutting to quality inspection by integrating multiple functional modules. This 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 enhance the detection efficiency and accuracy.

[0044] Furthermore, the preliminary detection module determines the infiltration amount of the preliminarily 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 and compares it with the preset infiltration amount, so as to effectively evaluate 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 the judgment of beef quality; in particular, this module fully considers the texture difference between wet-aged steak and non-wet-aged steak, and can accurately reflect the aging effect by reasonably setting the preset infiltration amount, ensuring the quality and safety of beef products.

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

[0046] Furthermore, the laser detection module can accurately judge the Maillard reaction trend of the aged steak and distinguish it into a fast reaction trend and a slow reaction trend by comparing the average duration of each laser irradiation video with the preset duration. This method not only considers the difference in the Maillard reaction duration between the aged steak and the non-aged steak, but also considers the influence of laser power and steak thickness on the reaction time. By scientifically setting the preset duration, the accurate evaluation of the Maillard reaction process is achieved; 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 realizes the accurate evaluation of the oil droplet distribution state of the aged steak by determining the expected diameter and expected density of the aged steak and judging whether the oil droplet distribution is normal oil distribution or abnormal oil distribution, not only considering the dual factors of oil droplet size and density. This intelligent detection method based on laser technology provides a more refined and reliable basis for the quality control and quality evaluation of beef products, helping to improve the overall quality of the product.

[0048] Furthermore, the aging analysis module determines the spoilage characterization status by comprehensively evaluating the chromaticity values of the surface images of each area of the aged steak, effectively differentiating between the non-spoiled state and the obvious spoilage state, avoiding further detection of unqualified products, and improving the detection efficiency.

[0049] Furthermore, the aging analysis module combines the evaluation results of the structural characterization status and the lipid characterization status, ensuring that it is only determined to be qualified when the structure of the aged steak is standard and the oil distribution is normal. This comprehensive and delicate detection method improves the quality control and safety guarantee of meat products. Brief Description of the Drawings

[0050] Figure 1 Connection diagram of the detection system for meat products in an embodiment of the present invention;

[0051] Figure 2 Step diagram of the detection method for meat products in an embodiment of the present invention;

[0052] Figure 3 Step diagram of the preliminary detection method in an embodiment of the present invention;

[0053] Figure 4 Step diagram of the laser detection method in an embodiment of the present invention. Detailed Embodiments

[0054] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be 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 will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0056] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only 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, and therefore cannot be understood as a limitation to the present invention.

[0057] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "linkage" 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 an indirect connection through an intermediate medium, and it can be the communication inside 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 situations.

[0058] Please refer to Figure 1 as shown, which is the connection diagram of the detection system for meat products in an embodiment of the present invention. An embodiment of the present invention provides a detection system for meat products, including:

[0059] An aging and cutting module for wet-aging a number of beef blocks to form aged beef, cutting the aged beef into aged beef steaks with equal thickness (the thickness of the aged beef steaks in the present invention is 1.5 cm to 3 cm), and respectively selecting a number of preset initial inspection aged beef steaks and fat inspection aged beef steaks; in implementation, the preset number is determined according to the number of aged beef steaks. Generally, the ratio of the preset number to the number of aged beef steaks satisfies 1% to 5%, and preferably, the ratio of the preset number to the number of aged beef steaks satisfies 2%; it can be understood that through standardized wet-aging and precise cutting processes, the thickness consistency and quality stability of the aged beef steaks are ensured, and at the same time, the initial inspection and fat inspection samples are reasonably selected according to the quantity ratio, providing a reliable basis for subsequent detection;

[0060] An image acquisition module connected to the aging and cutting module, including an image photographing unit for collecting the beef surface image of the aged beef and a video shooting unit for collecting the laser irradiation video of the fat inspection aged beef steaks; it can be understood that the image acquisition module provides data support for subsequent quality evaluation;

[0061] A preliminary detection module connected to the aging and cutting module for soaking each of the initial inspection aged beef steaks in a marinade box for a preset time (in implementation, the preset time is generally set to 5 min to 10 min, and more often set to 10 min), then taking them out, determining the infiltration amount of each initial inspection aged beef steak according to the mass 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 aged beef steak according to the average infiltration amount; it can be understood that the marinade in each marinade box has the same ratio and mass (the marinade in a single marinade box submerges the initial inspection aged beef steak); it can be understood that by soaking the marinade and measuring the mass difference, the internal structure state of the aged beef steak can be quickly and accurately evaluated, providing an important basis for judging the quality of beef;

[0062] A laser detection module, which is respectively connected to the aging cutting module and the image acquisition module, is used to irradiate the surface of each aged and inspected steak with laser, determine the stop time of laser irradiation and the end shooting time of the laser irradiation video according to a single laser irradiation video, determine the Maillard reaction trend (including fast reaction trend and 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 beads in the last frame of a single laser irradiation video, and determine the oil bead distribution of the aged steak (including normal oil distribution and abnormal oil distribution) according to the expected diameter and expected density, and determine the lipid characterization status (including standard characterization status and defect characterization status) according to the Maillard reaction trend and the oil bead distribution; In practice, the laser detection module is equipped with a machine learning model to determine the chromaticity value of each frame of the laser irradiation video and the average diameter and density of the oil beads in the last frame of the laser irradiation video; It can be understood that by deeply analyzing the laser irradiation video in combination with the machine learning model, not only can the Maillard reaction trend be accurately judged, but the oil bead distribution state can also be precisely evaluated, so as to comprehensively evaluate the lipid characterization status of the aged steak;

[0063] An aging analysis module, which is respectively connected to the aging cutting module, the image acquisition module, the preliminary detection module and the laser detection module, 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 status (including obvious deterioration status / hidden deterioration phenomenon) of the corresponding aged beef according to whether the chromaticity value of each regional surface image meets the deterioration characterization condition, determine whether to conduct preliminary detection and laser detection on the aged steak according to the deterioration characterization status, and determine the qualification of the aged steak according to the structural characterization status and the lipid characterization status when it is determined to conduct preliminary detection and laser detection on the aged steak. In practice, the aging analysis module is also equipped 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 analyzing the chromaticity value of the beef surface image through the machine learning model, the deterioration characterization status of the beef can be detected in time, and the qualification of the aged steak can be determined according to the comprehensive detection results, ensuring the safety and quality of beef products.

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

[0065] Specifically, the preliminary detection module determines the infiltration amount of each preliminarily detected aged steak based on the mass difference of each marinade box before and after soaking, and determines the average infiltration amount based on the average value of the infiltration amounts of each preliminarily detected aged steak.

[0066] It can be understood that the greater the mass difference, the greater the infiltration amount, indicating that the more marinade is absorbed by the preliminarily detected aged steak.

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

[0068] Among them, the structural characterization state includes a standard characterization state and a loose characterization state.

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

[0070] It can be understood that due to different textures, the pickling infiltration degrees of wet-aged steaks and non-wet-aged steaks will be different in the same period of time. Non-wet-aged steaks usually have a higher infiltration degree: (1) After a period of sealed storage, the natural enzymes in the beef of wet-aged steaks act on the muscle fibers under specific temperature and humidity conditions, making the meat texture softer; at the same time, due to relatively less water loss, 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 within the same period of time; although the surface muscle fibers have softened, the internal structure is still relatively firm, hindering the quick infiltration of the marinade; therefore, within the same pickling time, the infiltration degree of wet-aged steaks is relatively low. (2) The meat of steaks that have not been wet-aged is relatively hard and the muscle fibers are relatively rough; however, its internal structure is relatively loose and the water content may also be high; therefore, the relatively loose structure and high water content make it easier for the marinade to penetrate into the steak within the same period of time. The marinade can reach deeper parts faster as it diffuses with the water in the meat, so non-wet-aged steaks have a relatively high infiltration degree within the same pickling time.

[0071] Therefore, the smaller the average infiltration amount, the better the aging effect of the aged steak. Thus, in practice: if the average infiltration amount is greater than or equal to the preset infiltration amount, it is determined that the structural characterization state is the loose characterization state; if the average infiltration amount is less than the preset infiltration amount, it is determined that the structural characterization state is the standard characterization state.

[0072] It is understandable 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 and compares it with the preset infiltration amount, so as to effectively evaluate the internal structure state of the aged steak; this method is not only scientific and accurate, but also can distinguish between the standard representation state and the loose representation state, providing an important basis for the judgment of beef quality; in particular, this module fully considers the texture difference between the wet-aged steak and the 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 the laser irradiation and the end shooting time of the laser irradiation video according to the chromaticity value of the real-time frame of a single laser irradiation video satisfying the chromaticity change condition; it is understandable that if the chromaticity value of the real-time frame of a single laser irradiation video does not satisfy the chromaticity change condition, it is determined that the stop time of the laser irradiation and the end shooting time of the laser irradiation video are reached, so it is necessary to continue the laser irradiation and continue to shoot 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, the chromaticity value is an important index used to describe colors, and the CIELab* color space is usually used to represent it. The three axes of this color space respectively represent: (1) The L value is the brightness, 0 is black, and 100 is white; (2) The a value is the hue from green to red, negative values are green, and positive values are red; (3) The b value is the hue from blue to yellow, negative values are blue, and positive values are yellow.

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

[0077] It can be understood that the laser detection module intelligently judges the stop time of laser irradiation and the end time of video shooting by real-time monitoring the chromaticity values of the real-time frames of the laser irradiation video and 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 the errors of manual judgment and improving the detection efficiency and accuracy.

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

[0079] Among them, 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 reaction trend; if the average duration is less than the preset duration, it is judged as a fast reaction trend.

[0081] It can be understood that for cooked steaks, thin water vapor can usually be observed within a few seconds after laser irradiation, and the stage of changing from red to brown is generally about 10 seconds to 30 seconds; for uncooked steaks, the surface water evaporation is relatively slow, and it may take 5 seconds to 10 seconds or even longer to form water vapor, and the stage of changing the color from red to brown may take about 20 seconds to 60 seconds (because the meat quality of uncooked steaks is relatively uneven and the color change may be inconsistent, so the time span is relatively large); that is, the duration for cooked beef to produce the Maillard reaction and turn the beef surface brown is shorter, and the duration for uncooked beef to produce the Maillard reaction and turn the beef surface brown is longer; at the same time, if the laser power is large and the steak is thin, the time may be shorter, and vice versa, the time may be extended. Therefore, the value range of the preset duration is generally 10s to 30s, and preferably set to 25s.

[0082] In implementation, for the cooked steaks with a thickness of 1.5 cm to 3 cm in the present invention, a laser beam with a laser power of about 100 watts should be used.

[0083] It can be understood that the laser detection module can accurately judge the Maillard reaction trend of cooked steaks and distinguish it into a fast reaction trend and a slow reaction trend by comparing the average duration of each laser irradiation video with the preset duration. This method not only considers the difference in the Maillard reaction duration between cooked steaks and uncooked steaks, but also considers the influence of laser power and steak thickness on the reaction time. By scientifically setting the preset duration, the accurate evaluation of the Maillard reaction process is realized; 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 the expected average diameter and the expected density of oil droplets based on the average diameter and the density of oil droplets in the last frame of each laser irradiation video, determines the expected diameter and the expected density of the aged steak according to the expected average diameter and the expected density, and determines that the oil droplet distribution of the aged steak is a normal oil distribution according to whether the expected diameter and the expected density meet the oil irradiation conditions;

[0085] Among them, the oil irradiation conditions are 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 oil distribution and abnormal oil distribution. In practice, 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 each oil droplet in this frame, and the diameter of a single oil droplet is the contact diameter between the oil droplet and the steak surface.

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

[0088] In practice, (1) the oil droplets may be very small, with a diameter approximately between 0.1 mm and 1 mm. Preferably, the preset diameter is set to 0.1 mm. (2) The density of the oil droplets may vary from a few to dozens per square centimeter. If the aging degree is high, the fat content is rich, and the laser action 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 can be understood that the laser detection module determines the lipid characterization state by determining the expected diameter and the expected density of the aged steak and judging whether the oil droplet distribution is a normal oil distribution or an abnormal oil distribution based on this. It not only considers the dual factors of the size and density of the oil droplets, but also realizes the accurate evaluation of the lipid distribution state of the aged steak. This intelligent detection method based on laser technology provides a more refined and reliable basis for the quality control and quality evaluation of beef products, and helps to improve the overall quality of the products.

[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, it is determined that the lipid characterization state is a standard characterization state; that is, it is initially detected that the aging process is standard and the aging effect meets the 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, it is initially detected that the aging process may not be standard and the aging effect does not meet expectations.

[0093] Specifically, the aging analysis module determines that the deterioration characterization state of the corresponding aged beef is the non-deteriorated state based on the fact that the chromaticity values of the surface images of each region do not meet the deterioration characterization conditions, and determines to perform a preliminary detection and a laser detection on the aged steak.

[0094] It can be understood that the color of a normal wet-aged steak should be relatively uniform, showing a dark red or dark brown color. If obvious discoloration is found on the surface, such as the appearance of green, gray or black spots, it may be a sign of mold or deterioration.

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

[0096] In practice, the aging analysis module determines that the deterioration characterization state of the corresponding aged beef is the obvious deterioration state based on the fact that the chromaticity value of the surface image of any one region meets the deterioration characterization conditions, and determines not to perform a preliminary detection and a laser detection on the aged steak (that is, the aging is unqualified).

[0097] It can be understood that the aging analysis module judges its deterioration characterization state by comprehensively evaluating the chromaticity values of the surface images of each region of the aged steak, effectively distinguishing the non-deteriorated state and the obvious deterioration state, avoiding the further detection of unqualified products, and improving the detection efficiency.

[0098] Specifically, the aging analysis module determines that the aged steak is qualified based on the fact that the structure characterization state is the standard characterization state and the lipid characterization state is the standard characterization state.

[0099] It can be understood that the aging analysis module combines the evaluation results of the structure characterization state and the lipid characterization state, ensuring that it is only determined to be qualified when the structure of the aged steak is standard and the oil distribution is normal. This comprehensive and refined detection method improves the quality control and safety guarantee of beef products.

[0100] Please refer to Figures 2 to 4 shown, which are respectively the step diagrams of the detection method of meat products in the embodiments of the present invention, the step diagrams of the preliminary detection method in the embodiments of the present invention, and the step diagrams of the laser detection method in the embodiments of the present invention. The present invention also provides a detection method for meat products, which is characterized by including,

[0101] Step S1, collect the beef surface image of the aged beef;

[0102] Step S2, divide the beef surface image into several regional surface images and determine the chromaticity values of each regional surface image;

[0103] Step S3, determine the spoilage characterization state of the corresponding aged beef according to whether the chromaticity values of each regional surface image meet the spoilage characterization conditions, so as to determine whether to conduct preliminary detection and laser detection on the aged steak, including,

[0104] Do not conduct preliminary detection and laser detection on the aged steak, and determine that the aged beef is unqualified, that is, no subsequent steps are carried out;

[0105] Or, cut the aged beef into aged steaks with equal thickness, and then select several preset numbers of initially inspected aged steaks and lipid inspected aged steaks respectively, and continue with Step S4;

[0106] Step S4, conduct preliminary detection on the initially inspected aged steaks, including,

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

[0108] Step S42, determine the infiltration amount of each initially inspected aged steak according to the mass difference of each marinade box before and after soaking the initially inspected aged steaks, so as to determine the average infiltration amount;

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

[0110] Step S5, conduct laser detection on the lipid inspected aged steaks, including,

[0111] Step S51, irradiate the surface of each of the lipid inspected aged steaks with laser and shoot the laser irradiation video of each lipid inspected aged steak;

[0112] Step S52, 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;

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

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

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

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

[0117] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the 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 are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within 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; an image acquisition module connected to the aging and cutting module, comprising an image photographing unit for capturing an image of the surface of the aged beef and a video shooting unit for capturing a laser irradiation video of the fat-inspected aged steak; a preliminary inspection module connected to the aging and cutting module, configured to soak each of the initially inspected aged steaks in a marinade box for a preset time, then remove the steaks, determine an average infiltration amount of each initially inspected aged steak based on a mass difference between the marinade boxes before and after soaking, and determine a structural characterization state of the internal structure of the aged steak based on the average infiltration amount; a laser detection module, connected to the aging and cutting module and the image acquisition module, respectively, for irradiating the surface of each fat-inspected aged steak with a laser, determining a stop time for the laser irradiation and an end time for capturing the laser irradiation video based on a single laser irradiation video, determining a Maillard reaction trend based on an average duration of each laser irradiation video, determining an expected diameter and an expected density of the aged steak based on an average diameter and density of oil droplets in the last frame of a single laser irradiation video, determining an oil droplet distribution of the aged steak based on the expected diameter and expected density, and determining a lipid characterization state based on the Maillard reaction trend and the oil droplet distribution; an aging analysis module, which is respectively connected to the aging cutting module, the image acquisition module, the preliminary detection module and the laser detection module, and is used to divide the beef surface image into a plurality of regional surface images to determine the chromaticity value of the surface image of each region, determine the deterioration characterization state of the corresponding aged beef according to whether the chromaticity value of the surface image of each region meets the deterioration characterization condition, determine whether to perform preliminary detection and laser detection on the aged steak according to the deterioration characterization state, and determine 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 value of the 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 based on 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 stopping time of the laser irradiation and the ending 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 meets the chromaticity change condition; 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 based on 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 based on 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 based on the expected average diameter value and the expected density value, and determines that the oil droplet distribution of the aged steak is normal based on the expected diameter and the expected density satisfying the oil irradiation conditions; 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 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 ripe steak.

9. The meat product detection system according to claim 1, characterized in that: The aging analysis module determines that the aged steak is qualified based on the structural characterization state being the standard characterization state and the lipid characterization state being the standard characterization state.

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 based on 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; Alternatively, the aged beef is cut into aged steaks of equal thickness, and a predetermined number of initial inspection aged steaks and fat inspection aged steaks are selected respectively, and the process continues to step S4; Step S4, performing preliminary testing on the initially aged steak, including: Step S41, soaking each of the initially inspected mature steaks in a marinade box for a preset time and then taking them out; Step S42, determining the soaking amount of each initially inspected aged steak based on the quality difference between each marinade box before and after soaking the initially inspected aged steak to determine the average soaking amount; Step S43, determining a structural representation state of the internal structure of the aged steak based on the average infiltration amount; Step S5, performing laser detection on the fat-aged steak, including: Step S51, irradiating the surface of each fat-inspected aged steak with a laser and capturing a laser irradiation video of each fat-inspected aged steak; Step S52, determining the laser irradiation stop time and the laser irradiation video end shooting time 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 based on the average diameter and density of the oil droplets in the last frame of the laser irradiation video to determine the distribution of the oil droplets in 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 based on the structural characterization state and the lipid characterization state.

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