Live cattle beef marbling rating prediction method and device and electronic equipment

Abstract

The invention provides a live cattle beef marbling rating prediction method and device and electronic equipment. The method comprises the following steps: acquiring muscle fat content data of a measured part of a live cattle and marbling rating data of slaughtered beef, counting all muscle fat content data corresponding to each beef marbling rating, and calculating an average value of the muscle fat content data corresponding to each beef marbling rating; and then fitting a regression equation based on the average value to obtain a live cattle beef marbling rating prediction model, and subsequently predicting the beef marbling rating of the target live cattle based on the model. According to the arrangement, the marbling rating and meat quality of the beef can be predicted before slaughter through the constructed marbling rating and predicting model for the live cattle beef, so that the marbling rating and meat quality predicting method can be well applied to scenes such as live cattle trading and beef cattle descendant determination, and the purposes of trading according to quality and accelerating genetic progress of beef cattle are achieved.

Description

Technical Field

[0001] The present application relates to a method, device and electronic equipment for predicting the marbling rating of live cattle beef, and belongs to the technical field of beef cattle performance measurement. Background Art

[0002] Beef cattle breeding is an indispensable field of animal husbandry production, providing humans with high-quality, high-protein, low-fat beef. Beef is the world's mainstream edible livestock product and one of the meat products consumed by Chinese people after pork. At present, the demand for beef products by Chinese consumers is not only to meet nutritional needs, but also to meet the increasing demand for quality, safety and health. Therefore, the establishment of beef quality assessment and testing methods is very important.

[0003] At present, the classification of beef grades in my country is mainly based on the "GB / T 29392-2022 Animal and Poultry Meat Quality Grading Standard". The existing standard descriptions and ratings are partially based on foreign grading standards. Common ratings include Australia's M1-M9+ grade standards, the United States' Select, Choice, Prime and other grading standards, and Japan's A1-A5 grade standards. The more marbling, the higher the grade of beef.

[0004] The grade of beef is related to many factors, including marbling grade (BMG), age, breed, yield, quality, edible quality and other indicators, among which the content of marbling (commonly known as marbled beef) is one of the most important indicators. However, at present, marbling mainly relies on post-slaughter rating, and it is impossible to predict the marbling grade of beef in the live cattle stage before slaughter. Summary of the invention

[0005] The present application provides a method, device and electronic device for predicting the marbling grade of live cattle beef, so as to solve the problem that the marbling grade of beef cannot be predicted at the live cattle stage before slaughter.

[0006] In a first aspect, the present application provides a method for predicting the marbling rating of live cattle beef, comprising:

[0007] Obtaining muscle fat content data of a preset number of measured parts of live cattle collected by a B-ultrasound instrument, and obtaining beef marbling rating data obtained by rating the corresponding live cattle after slaughter;

[0008] Counting all muscle fat content data corresponding to each beef marbling grade, and calculating the average of the muscle fat content data corresponding to each beef marbling grade;

[0009] Based on the average values ​​of the marbling ratings of various beef and their corresponding muscle fat content data, a regression equation was fitted to obtain a prediction model for the marbling rating of live beef.

[0010] Based on the live cattle beef marbling rating prediction model, the beef marbling rating of the target live cattle is predicted.

[0011] Based on the above method, optionally, 50 Simmental cattle aged 16 to 22 months for slaughter were selected in accordance with GB / T 19166-2003 Chinese Simmental Cattle as cattle for obtaining measurement data. The selection criteria included: the cattle were in good mental state and had a body condition score of >2 points and obvious fat deposition.

[0012] Based on the above method, optionally, before collecting the muscle fat content data by B-ultrasound, the cattle are restrained in a restraining rack or a restraining pen to reduce the activity and stress of the cattle, and the hair at the measurement site is shaved with a shaver, and then an appropriate amount of vegetable oil or coupling agent is applied and wait for B-ultrasound measurement.

[0013] Based on the above method, optionally, before slaughtering live cattle, fast them for 24 hours, provide them with sufficient drinking water, stop providing drinking water 5 to 8 hours before slaughter, and conduct regular inspections to eliminate abnormal cattle.

[0014] Based on the above method, optionally, the measurement site is 5 cm below the 12th to 13th ribs on the left side of the cow's body.

[0015] Based on the above method, optionally, after the beef cattle are slaughtered, the carcass halves are hung in a 0℃~4℃ cold storage room to drain the acid for 48~72h, and then the longissimus dorsi muscle between the 12th and 13th ribs is cut, and the beef marbling pattern is graded according to the richness of the eye muscle marbling pattern in accordance with GB / T 29392-2022 Beef Quality Grading of Livestock and Poultry Meat to obtain the beef marbling pattern rating data.

[0016] Based on the above method, optionally, the fitted regression equation is a quadratic equation.

[0017] In a second aspect, the present application also provides a device for predicting the marbling rating of live cattle beef, comprising:

[0018] An acquisition module, used to acquire muscle fat content data of a preset number of measured parts of live cattle collected by a B-ultrasound instrument, and to acquire beef marbling rating data obtained by rating the corresponding live cattle after slaughter;

[0019] A calculation module, used to count all muscle fat content data corresponding to each beef marbling grade, and calculate the average value of the muscle fat content data corresponding to each beef marbling grade;

[0020] A fitting module, for fitting a regression equation based on the average values ​​of the marbling ratings of various beefs and their corresponding muscle fat content data, to obtain a prediction model for the marbling ratings of live beef;

[0021] The prediction module is used to predict the beef marbling grade of the target live cattle based on the live cattle beef marbling grade prediction model.

[0022] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory and a processor; wherein the memory stores a computer program, and when the processor calls and executes the computer program, it implements the method for predicting the marbling grading of live cattle beef as described in any one of the first aspects.

[0023] In the method, device and electronic device for predicting the marbling rating of live cattle provided in the present application, by obtaining the muscle fat content data of the measured parts of the live cattle and the beef marbling rating data after slaughter, all muscle fat content data corresponding to each beef marbling rating are counted, and the average value of the muscle fat content data corresponding to each beef marbling rating is calculated; then, based on the average value of each beef marbling rating and its corresponding muscle fat content data, a regression equation is fitted to obtain a live cattle beef marbling rating prediction model, so that the live cattle beef marbling rating prediction model can be used to predict the target live cattle beef marbling rating. With such a setting, the constructed live cattle beef marbling rating prediction model can realize the prediction of beef marbling rating and meat quality before slaughter, so that it can be well applied to scenarios such as live cattle trading and beef cattle progeny determination, and achieve the purpose of trading by quality and accelerating the genetic progress of beef cattle. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. In addition, these drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application for those skilled in the art by referring to specific embodiments.

[0025] Figure 1 A schematic diagram of a flow chart of a method for predicting live cattle beef marbling rating provided in one embodiment of the present application;

[0026] Figure 2 A schematic diagram of a measurement site provided for one embodiment of the present application;

[0027] Figure 3 A schematic diagram of a fitted regression equation provided for one embodiment of the present application;

[0028] Figure 4A schematic diagram of the structure of a device for predicting live cattle beef marbling rating provided in one embodiment of the present application;

[0029] Figure 5 A schematic diagram of the structure of an electronic device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] At present, the existing marbling rating can only rely on the naked eye assessment after the beef cattle are slaughtered, and there is no report on predicting the marbling rating of beef before slaughter.

[0032] In view of the above problems, the present application provides a live cattle beef marbling rating prediction scheme to achieve fast and accurate prediction of live cattle beef marbling rating status, timely identify high-quality beef cattle, and provide decision-making reference for beef cattle purchase and sale. The following non-limiting description of the specific implementation scheme is given through several examples or embodiments.

[0033] Reference Figure 1 , Figure 1 A schematic diagram of a method for predicting live cattle beef marbling rating provided in one embodiment of the present application. Figure 1 As shown, the method comprises at least the following steps:

[0034] Step S101: Obtain muscle fat content data of a preset number of measured parts of live cattle collected by B-ultrasound, and obtain beef marbling rating data obtained by rating the corresponding live cattle after slaughter.

[0035] Specifically, ultrasound examination is a medical diagnostic method that uses the reflection principle of ultrasound in the body of cattle to perform imaging. When ultrasound encounters the interface of cattle tissue, it is reflected. The reflected ultrasound is received by the probe and converted into an electrical signal, which is then processed by a computer to generate an image. Among them, B-type ultrasound examination is the most common method and has been widely used in cattle breeding. In this embodiment, a B-type ultrasound instrument is used to obtain muscle fat content (Muscle Fat Content %, MFC %) data before slaughter. The above muscle fat content data before slaughter can be associated with the marbling rating of beef after slaughter, thereby realizing the prediction of the marbling rating of live cattle.

[0036] In some embodiments, the method for obtaining the measurement data is as follows:

[0037] 1. In vivo muscle fat determination

[0038] First, prepare suitable beef cattle. The selected beef cattle must be healthy and qualified, and can provide a quarantine certificate of origin or a disease detection report from a qualified third-party agency.

[0039] Specifically, in this embodiment, according to GB / T 19166-2003 Chinese Simmental Cattle, 50 Simmental cattle aged 16 to 22 months and about to be slaughtered were selected as cattle for obtaining measurement data, and the selection criteria included: the cattle were in good mental state and had a body condition score of >2 points and obvious fat deposition. In this way, errors caused by abnormalities of the cattle body can be minimized.

[0040] In order to further reduce errors, before collecting the muscle fat content data using an ultrasound machine, the cattle are restrained in a restraining rack or a restraining pen to reduce the cattle's activity and stress, and the hair at the measurement site is shaved with a shaver, and then an appropriate amount of vegetable oil or coupling agent is applied while waiting for the ultrasound measurement.

[0041] In some embodiments, the measurement site is 5 cm below the 12th and 13th ribs on the left side of the cow, as shown in FIG. Figure 2 shown.

[0042] Afterwards, a B-mode veterinary ultrasound scanner (referred to as "B-mode") probe was pressed parallel to the pre-treated measurement site until a relatively clear image appeared on the B-mode ultrasound host, and the muscle fat content (%) data was calculated using the B-mode ultrasound host software. The B-mode ultrasound device used was a veterinary ultrasound scanner (produced by Pie Medical of the Netherlands, model: B-Mode).

[0043] After the final measurement, the data on the fat content in the live muscles of 50 beef cattle were distributed between 1.78% and 36.28%, with an average of 12.73%.

[0044] 2. Post-mortem marbling determination

[0045] Also, in order to reduce errors, before slaughtering live cattle, they must be fasted for 24 hours and given plenty of drinking water. Drinking water must be stopped 5 to 8 hours before slaughter, and regular inspections must be conducted to eliminate abnormal cattle.

[0046] After slaughter, the carcass halves are hung in a cold room at 0℃~4℃ for 48~72h to drain the acid. Then the longissimus dorsi muscle between the 12th and 13th ribs is cut. The marbling pattern of the beef is graded according to the richness of the eye muscle marbling pattern in accordance with GB / T 29392-2022 Grade Beef for Quality of Livestock and Poultry Meat, and the beef marbling pattern rating data is obtained.

[0047] Finally, the results of marbling test on 50 beef cattle after slaughter were: 16 were rated as grade 1, 15 were rated as grade 2, 9 were rated as grade 3, 6 were rated as grade 4, and 4 were rated as grade 5.

[0048] Step S102: Count all muscle fat content data corresponding to each beef marbling grade, and calculate the average value of the muscle fat content data corresponding to each beef marbling grade.

[0049] Specifically, according to the post-slaughter marbling test results, the muscle fat content data of live cattle were counted, and the average value of the muscle fat content data corresponding to each beef marbling grade was calculated, as shown in the following Table 1:

[0050] Table 1 Statistical table of measurement data

[0051]

[0052]

[0053] Step S103: Based on the average values ​​of the marbling ratings of the beef and their corresponding muscle fat content data, a regression equation is fitted to obtain a prediction model for the marbling rating of live cattle beef.

[0054] Specifically, according to Table 1, two sets of data can be obtained: the muscle fat content of live cattle and the marbling rating of post-slaughter beef. Through the above two sets of data, a regression equation can be fitted, that is, a prediction model for the marbling rating of live cattle beef is obtained. The regression equation can be a quadratic equation, but is not limited to this.

[0055] Take the regression equation as a quadratic equation as an example, Figure 3 As shown, the final fitted regression equation is:

[0056] BMG = -0.0018 × (100 × MFG%) 2 +0.1974×(100×MFG%)+0.2817=

[0057] -0.0018×MFG 2 +0.1974×MFG+0.2817, (R 2 =0.987).

[0058] In the formula, BMG represents the predicted value of marbling grade, and MFG% represents the fat content of live cattle muscle. In addition, since the actual value of beef marbling grade is an integer, the BMG value is rounded to an integer in the final result predicted by the regression equation. Moreover, when MFG% < 1.12%, BMG = 1; when MFG% > 35.20%, BMG = 5.

[0059] Step S104: predicting the beef marbling grade of the target live cattle based on the live cattle beef marbling grade prediction model.

[0060] After obtaining the above-mentioned live cattle beef marbling rating prediction model, it can be applied to actual scenarios to predict the beef marbling rating of target live cattle. In actual application, it is only necessary to collect the muscle fat content data of the measurement position of the target live cattle as required, and then input it into the live cattle beef marbling rating prediction model to obtain the corresponding prediction result.

[0061] In summary, based on the above scheme, the constructed live cattle beef marbling rating prediction model can realize the prediction of beef marbling rating and meat quality before slaughter, so that it can be well applied to scenarios such as live cattle trading and beef cattle progeny determination, achieving the purpose of trading by quality and accelerating the genetic progress of beef cattle.

[0062] In addition, in order to verify the effectiveness of the above model, actual verification was carried out. The specific process is as follows:

[0063] After the model was established, 100 beef cattle were selected as independent data sets to verify the model according to the requirements of cattle in the test materials, and the live muscle fat content (%) and post-slaughter marbling rating data were collected. Because two cattle were found to be abnormal during the pre-slaughter inspection (one with respiratory disease and one with limb and hoof disease), they were removed from the validation set, and finally the measurement data of 98 cattle were obtained.

[0064] Then the determination data was given to verify the method compliance rate. The verification results are shown in Table 2.

[0065] Table 2 Method compliance verification formula

[0066]

[0067] Note: Compliance rate = number of compliances ÷ BMG predicted number × 100%.

[0068] Through the above verification, the verification conformity rate was 82.65%. In addition, it was found during the verification process that when the predicted value of marble pattern grade (BMG) approached level 1 and level 5, the conformity rate increased, which may be related to the fact that low muscle fat content and high muscle fat content are more convenient for model prediction and the more accurate the predicted value; when the BMG predicted value approached level 3, the result deviation was large, which may be related to the fact that the measurement results of beef with moderate muscle fat content under B-ultrasound equipment deviated greatly from the actual value. This model realizes the prediction of beef marble pattern grade and meat quality before slaughter, which can better serve the live cattle trading and beef cattle progeny determination, realize quality-based trading and accelerate the genetic progress of beef cattle. In order to improve the accuracy of the prediction model, a large amount of data is needed for further verification. The verification conformity rate reached 82.65%, which also shows the scientificity and necessity of using live muscle fat content to predict beef marbling evaluation.

[0069] In addition, the present application embodiment provides a device for predicting the marbling rating of live cattle beef. Figure 4 The live cattle beef marbling rating prediction device of this embodiment comprises:

[0070] An acquisition module 21 is used to acquire muscle fat content data of a preset number of measured parts of live cattle collected by a B-ultrasound instrument, and to acquire beef marbling rating data obtained by rating the corresponding live cattle after slaughter;

[0071] A calculation module 22 is used to count all muscle fat content data corresponding to each beef marbling grade, and calculate the average value of the muscle fat content data corresponding to each beef marbling grade;

[0072] A fitting module 23 is used to fit a regression equation based on the average values ​​of the marbling ratings of various beef and the corresponding muscle fat content data to obtain a prediction model for the marbling rating of live beef;

[0073] The prediction module 24 is used to predict the beef marbling grade of the target live cattle based on the live cattle beef marbling grade prediction model.

[0074] Among them, regarding the specific implementation methods of each module of the above-mentioned live cattle beef marbling rating prediction device, reference can be made to the corresponding contents in the aforementioned method embodiments, which will not be repeated here.

[0075] In addition, the present application also provides an electronic device, such as Figure 5 As shown, the electronic device includes a memory 31 and a processor 32; wherein the memory 31 stores a computer program, and when the processor 32 calls and executes the computer program, the method for predicting the marbling rating of live cattle beef in any of the above embodiments is implemented.

[0076] The electronic device may be a computer or a server.

[0077] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0078] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0079] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0080] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0082] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0083] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0084] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the marbling rating of live cattle beef, characterized in that: include: Obtaining muscle fat content data of a preset number of measured parts of live cattle collected by a B-ultrasound instrument, and obtaining beef marbling rating data obtained by rating the corresponding live cattle after slaughter; Counting all muscle fat content data corresponding to each beef marbling grade, and calculating the average of the muscle fat content data corresponding to each beef marbling grade; Based on the average values ​​of the marbling ratings of various beef and their corresponding muscle fat content data, a regression equation was fitted to obtain a prediction model for the marbling rating of live beef. Based on the live cattle beef marbling rating prediction model, the beef marbling rating of the target live cattle is predicted.

2. The method according to claim 1, characterized in that According to GB / T 19166-2003 Chinese Simmental Cattle, 50 Simmental cattle aged 16 to 22 months were selected for slaughter as cattle for obtaining measurement data. The selection criteria included: the cattle were in good mental state, with a body condition score of >2 points and obvious fat deposition.

3. The method according to claim 1, characterized in that Before collecting the muscle fat content data by B-ultrasound, the cattle are restrained in a restraining rack or a restraining pen to reduce the cattle's activity and stress, and the hair at the measurement site is shaved with a shaver, and then an appropriate amount of vegetable oil or coupling agent is applied and wait for B-ultrasound measurement.

4. The method according to claim 1, characterized in that: Before slaughtering live cattle, they should be fasted for 24 hours and given plenty of drinking water. Drinking water should be stopped 5 to 8 hours before slaughter. Regular inspections should be conducted and abnormal cattle should be eliminated.

5. The method according to claim 1, characterized in that The measuring site is 5 cm below the 12th and 13th ribs on the left side of the cow body.

6. The method according to claim 5, characterized in that After slaughter, the carcass halves are hung in a cold room at 0℃~4℃ for 48~72h to drain the acid. Then the longissimus dorsi muscle between the 12th and 13th ribs is cut. The marbling pattern of the beef is graded according to the richness of the eye muscle marbling pattern in accordance with GB / T 29392-2022 Grade Beef for Quality of Livestock and Poultry Meat, and the beef marbling pattern rating data is obtained.

7. The method according to claim 1, characterized in that The fitted regression equation is a quadratic equation.

8. A device for predicting the marbling pattern of live cattle beef, characterized in that: include: An acquisition module, used to acquire muscle fat content data of a preset number of measured parts of live cattle collected by a B-ultrasound instrument, and to acquire beef marbling rating data obtained by rating the corresponding live cattle after slaughter; A calculation module, used to count all muscle fat content data corresponding to each beef marbling grade, and calculate the average value of the muscle fat content data corresponding to each beef marbling grade; A fitting module, for fitting a regression equation based on the average values ​​of the marbling ratings of various beefs and their corresponding muscle fat content data, to obtain a prediction model for the marbling ratings of live beef; The prediction module is used to predict the beef marbling grade of the target live cattle based on the live cattle beef marbling grade prediction model.

9. An electronic device, characterized in that: It comprises a memory and a processor; wherein the memory stores a computer program, and when the processor calls and executes the computer program, the method for predicting the marbling grade of live cattle beef as described in any one of claims 1 to 7 is implemented.

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