Poultry feed raw material metabolic energy in-vitro prediction method, device, medium and equipment

Abstract

The invention discloses a poultry feed raw material metabolic energy in-vitro prediction method and device, a medium and equipment, and relates to the technical field of information. The method comprises the steps that measured values of all prediction indexes of feed raw materials and measured values of metabolic energy of the feed raw materials for poultry are obtained, and all the prediction indexes comprise the physical properties of the feed raw materials and subdivided components of different levels; analyzing a correlation coefficient between each prediction index and the metabolic energy based on the measured value of each prediction index and the measured value of the metabolic energy; screening candidate prediction indexes from the prediction indexes according to the correlation coefficients; based on the candidate prediction indexes, constructing a target metabolic energy prediction equation of the feed raw materials for the poultry; and predicting the metabolic energy in the new feed raw materials based on the target metabolic energy prediction equation to obtain a predicted value of the metabolic energy. The method can improve the prediction precision of the metabolic energy value of the feed raw materials for poultry.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to an in vitro prediction method, device, medium and equipment for metabolizable energy of poultry feed raw materials. Background Art

[0002] Energy is the primary nutrient to be considered in feed formulation, and energy-supplying feed raw materials (feed raw materials + protein raw materials) account for about 90% of the feed formula. Accurately evaluating the metabolic energy of feed raw materials is of great significance for economically formulating feed, reducing feed costs, and increasing breeding income. For example, hulled barley is an important feed raw material. Due to its rough shell, unlike naked barley, it is not suitable for direct use in human food, but it can be used as a good energy feed raw material for poultry (such as geese). However, the nutritional composition of hulled barley varies greatly, and there may be differences in metabolic energy values ​​between different batches. It is difficult to accurately formulate poultry (such as geese) feed formulas using the reference metabolic energy values ​​of the standard database, so it is necessary to use in vitro prediction technology that can dynamically predict its metabolic energy value in real time.

[0003] The evaluation methods of poultry metabolizable energy mainly include emptying force feeding method, endogenous excretion energy correction method, nitrogen correction method, and rapid determination method. Among them, the first three methods need to be based on animal experiments, which have high requirements for sites and animals, and are cumbersome to operate, expensive and laborious; the fourth method includes in vitro methods, etc., which can quickly, simply and economically evaluate the metabolizable energy of poultry feed.

[0004] At present, the traditional in vitro method for predicting the metabolizable energy of poultry feed raw materials is usually based on the determination of conventional nutrient components, which is reflected in the use of conventional crude fiber (CF), crude protein (CP), crude fat (EE), gross energy (GE), neutral detergent fiber (NDF), acid detergent fiber (ADF) and crude fiber (CF) to predict the metabolizable energy of poultry. However, conventional nutrients, in the early years, referred to approximate nutrients. Later, ADF, NDF and other components that simply decomposed the fiber components were added on the basis of approximate nutrients, and their correlation with metabolizable energy was limited.

[0005] In addition to the content of fat, carbohydrates, protein and other nutrients in raw materials affecting the utilization of metabolic energy, the content of components at different levels and the three-dimensional structure also have a great impact on metabolic energy. Therefore, it is difficult to accurately predict the metabolic energy value of feed raw materials for poultry by traditional in vitro methods. Therefore, it is necessary to divide traditional nutrients according to different structural types, measure the content of the main structural components, and use them to establish in vitro prediction methods for metabolic energy. Summary of the invention

[0006] In view of this, the present application provides an in vitro prediction method, device, medium and equipment for the metabolic energy of poultry feed raw materials, the main purpose of which is to improve the prediction accuracy of the metabolic energy value of feed raw materials for poultry.

[0007] According to the first aspect of the present application, a method for predicting the metabolizable energy of poultry feed raw materials in vitro is provided, the method comprising:

[0008] Obtaining measured values ​​of various prediction indicators of the feed raw material and measured values ​​of metabolizable energy of the feed raw material for poultry, wherein the various prediction indicators include physical properties of the feed raw material and components of different levels of dissection;

[0009] Based on the measured values ​​of the respective prediction indicators and the measured value of the metabolic energy, analyzing the correlation coefficients between the respective prediction indicators and the metabolic energy;

[0010] According to the correlation coefficient, selecting candidate prediction indicators from the various prediction indicators;

[0011] Based on the candidate prediction index, constructing a target metabolizable energy prediction equation for the feed raw material for the poultry;

[0012] Metabolic energy prediction is performed based on the target metabolic energy prediction equation to obtain the predicted value of the metabolic energy. According to the second aspect of the present application, an in vitro prediction device for metabolic energy of poultry feed raw materials is provided, the device comprising:

[0013] An acquisition unit, used for acquiring measured values ​​of various prediction indicators of the feed raw material and measured values ​​of metabolizable energy of the feed raw material for poultry, wherein the various prediction indicators include physical properties of the feed raw material and dissection components at different levels;

[0014] an analyzing unit, configured to analyze the correlation coefficients between the respective prediction indicators and the metabolic energy based on the measured values ​​of the respective prediction indicators and the measured value of the metabolic energy;

[0015] A screening unit, for screening candidate prediction indicators from the various prediction indicators according to the correlation coefficient; a construction unit, for constructing a target metabolic energy prediction equation of the feed raw material for the poultry based on the candidate prediction indicators;

[0016] A prediction unit is used to perform metabolic energy prediction based on the target metabolic energy prediction equation to obtain a predicted value of the metabolic energy.

[0017] According to a third aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned in vitro prediction method of metabolizable energy of poultry feed raw materials is implemented.

[0018] According to the fourth aspect of the present application, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the program, the above-mentioned in vitro prediction method of metabolizable energy of poultry feed raw materials is implemented.

[0019] By means of the above technical scheme, the present application provides a poultry feed raw material metabolic energy in vitro prediction method, device, medium and equipment. Compared with the prior art, by analyzing the correlation coefficient between each prediction index of the feed raw material and the metabolic energy, a candidate prediction index with a high correlation with the metabolic energy is screened out from each prediction index, and based on the candidate prediction index, a target metabolic energy prediction equation for poultry of the feed raw material is constructed, which can predict the metabolic energy in vitro. Since each prediction index in the present application includes different levels of dissection components, compared with the prior art method of only analyzing the primary general nutrient, the present application can dig out prediction factors with a high correlation with metabolic energy, thereby improving the prediction accuracy of metabolic energy, and then can provide a more reliable method for promoting the in vitro rapid determination of the metabolic energy of feed raw materials, the real-time dynamic and accurate preparation of poultry feed, and the reduction and substitution of corn.

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 A schematic diagram of a process for predicting in vitro metabolizable energy of poultry feed raw materials provided in an embodiment of the present application is shown;

[0023] Figure 2 A schematic diagram of a process for constructing a target metabolic energy prediction equation provided in an embodiment of the present application is shown;

[0024] Figure 3 A schematic diagram of the structure of an in vitro prediction device for the metabolic energy of poultry feed raw materials provided in an embodiment of the present application is shown;

[0025] Figure 4 A schematic structural diagram of another in vitro prediction device for the metabolizable energy of poultry feed raw materials provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0027] Since conventional nutrients (approximate nutrients) have a low correlation with metabolizable energy, it is difficult to accurately predict the metabolizable energy value of feed ingredients for poultry using traditional in vitro methods.

[0028] In order to solve the above problems, the embodiment of the present invention provides a method for predicting the metabolizable energy of poultry feed raw materials in vitro. Figure 1 As shown, the method includes:

[0029] Step 10: Obtain the measured values ​​of various prediction indicators of the feed raw materials, and the measured values ​​of the metabolizable energy of the feed raw materials for poultry.

[0030] Wherein, the feed raw material includes at least one raw material that can supply energy in the poultry feed formula, such as corn, hulled barley and cottonseed meal, etc., and the poultry includes chickens, ducks, geese, turkeys, ostriches, quails, pigeons and other poultry. It should be noted that the feed raw material in the embodiment of the present invention is not limited to hulled barley, but can also be other feed raw materials. Similarly, the poultry in the embodiment of the present invention is not limited to geese, but can also be other poultry. In addition, the various prediction indicators include the physical properties of the feed raw materials and the different levels of decomposition components (decomposition nutrients, decomposition anti-nutritional factors), and the different levels of decomposition components include: the top component of the feed raw materials, the first-level decomposition general component, the second-level decomposition structural component and the third-level decomposition subdivision component. Wherein, the top component includes gross energy and dry matter; the first-level decomposition general component includes water, nitrogen-free extract, crude fat, crude protein, crude ash, crude fiber, and various conventional anti-nutritional factors. The secondary decomposition structural components include calcium, phosphorus, crude fat, total starch, amylose and amylopectin, soluble sugar, insoluble sugar, albumin, gluten, globulin, alcohol-soluble protein, neutral detergent fiber, acid detergent fiber, lignin, β-glucan, arabinoxylan, free cottony phenol, bound cottony phenol, hydrolyzed tannin and condensed tannin and other decomposed anti-nutritional factors of different structures; the tertiary decomposition subdivided components include more trace components further decomposed on the basis of the secondary decomposition structural components, including various specific amino acids, various specific fatty acids, various specific monosaccharides, disaccharides, oligosaccharides, polysaccharides, various major elements and trace elements, and various different trace decomposed anti-nutritional factors.

[0031] The embodiment of the present invention is mainly applicable to the scenario of in vitro prediction of metabolic energy. The execution subject of the embodiment of the present invention is a device or equipment capable of in vitro prediction of metabolic energy.

[0032] In the process of explaining the following technical solution, the embodiment of the present invention is described by taking husk barley and goose as examples. For the embodiment of the present invention, in order to analyze the correlation coefficient between each prediction index and metabolic energy and to mine the prediction factor, it is necessary to determine the content of each prediction index in husk barley of different origins, processing methods and batches, as well as the metabolic energy value of the husk barley to the goose by experimental methods in advance, thereby obtaining the measured values ​​of each prediction index of husk barley and the measured value of the metabolic energy of husk barley to the goose, wherein the metabolic energy specifically includes apparent metabolic energy and / or true metabolic energy.

[0033] In some implementations, in order to ensure the mining accuracy of prediction factors, embodiments of the present invention may expand the scope of prediction indicators, that is, the prediction indicators include not only different levels of dissection components, but also different levels of anti-nutritional factors.

[0034] The following is a brief introduction to several methods for determining the content of prediction indicators.

[0035] For bulk density, it can be measured using a bulk density instrument according to GB / T 5498-2013.

[0036] Fat EE can be determined by Soxhlet extraction method according to the national standard GB / T 6433-2006 using FoodALYTRD-40 fat analyzer.

[0037] Calcium was determined according to Feed Analysis and Testing edited by He Jianhua (2011).

[0038] For total starch, 80% ethanol can be used to separate the soluble sugar and starch in the sample, and then the amylopectin content can be determined using dual-wavelength colorimetry. The relevant determination can use the Nanjing Jiancheng Bioengineering Research Institute Kit (Cat. No.: A152-2-1).

[0039] For amylose, 80% ethanol can be used to separate the soluble sugar and starch in the sample, and then the complex formed by amylose and iodine has an absorption peak at 620nm, and then the amylopectin content is determined by colorimetry. The relevant determination can use the Nanjing Jiancheng Bioengineering Research Institute Kit (Article No.: A152-1-1). For amylopectin: It can be calculated based on the measurement results of total starch and amylose, that is, it can be obtained using the formula amylopectin = total starch - amylose.

[0040] [1]. For albumin: The first step is to extract albumin from hulled barley. That is, the hulled barley is crushed and sieved, and 0.2g of hulled barley powder is placed in a 10ml centrifuge tube. 3-4mL of water is first added, mixed on a vortex oscillator, and water is added to 10ml. Then, the mixture is placed in a 50℃ ultrasonic cleaner for 30min, centrifuged at 4000r / min for 8min, and the supernatant is transferred to a new centrifuge tube and fixed to 10ml for quantitative analysis of albumin. The second step is quantitative analysis of albumin. That is, an accurate and reliable protein quantitative analysis kit is used for protein analysis. Since albumin has been extracted in the previous step, the protein content obtained in this step is the albumin content. In the embodiment of the present invention, the protein concentration can be measured by a modified Bradford method, and the spectrophotometric method or the ELISA plate method is used to measure the protein concentration using the modified Bradford method protein concentration determination kit (product number: C503041) of Sangon Biotech (Shanghai) Co., Ltd.

[0041] [2]. For globulin: The first step is to extract globulin from the hulled barley. Add 3-4 ml of 10% sodium chloride solution to the residue after centrifugation during the albumin extraction process, mix well on a vortex oscillator, then add 10% sodium chloride to make the volume 10 ml, then place it in an ultrasonic cleaner at 50°C for 30 minutes and heat at 4000r.

[0042] / min centrifuge for 8 minutes, transfer the supernatant to a new centrifuge tube, and dilute to 10ml with 10% sodium chloride solution for quantitative analysis of globulin. Step 2, quantitative analysis of globulin. Use an accurate and reliable protein quantitative analysis kit for protein analysis. Since the globulin has been extracted in the previous step, the protein content obtained in this step is the globulin content. In the embodiment of the present invention, the modified Bradford method can be used to measure the protein concentration, using spectrophotometry or ELISA plate method, and using the modified Bradford method protein concentration determination kit of Sangon Biotech (Shanghai) Co., Ltd. (Product No.: C503041).

[0043] Step 20: Analyze the correlation coefficients between each prediction index and the metabolic energy based on the measured values ​​of each prediction index and the measured value of the metabolic energy.

[0044] For the embodiment of the present invention, in order to determine the correlation coefficient between each prediction index and the metabolic energy, the measured value of each prediction index and the measured value of the metabolic energy can be input into the preset statistical analysis software for correlation analysis to obtain the correlation coefficient between each prediction index and the metabolic energy. Wherein, the preset statistical analysis software can be specifically SPASS statistical software, or other statistical analysis software, which is not specifically limited in the embodiment of the present invention. The analyzed correlation coefficients are shown in Table 1, which are the correlation coefficients between the metabolic energy of skin barley and conventional nutrients, secondary decomposition structure components (nutrients + anti-nutritional factors), and physical properties.

[0045] Table 1

[0046]

[0047] Among them, DM represents dry matter, Ash represents crude ash, EE represents crude fat, Ca represents calcium, TP represents total phosphorus, CF represents crude fiber, NDF represents neutral detergent fiber, ADF represents acid detergent fiber, L* brightness, a* and b* represent yellowness, AME represents apparent metabolizable energy, and TME represents true metabolizable energy. In addition, "**" indicates that the correlation is significant at a confidence level of 99%, and "*" indicates that the correlation is significant at a confidence level (bilateral) of 95%.

[0048] It should be noted that the correlation coefficients in Table 1 are written in a Western way, omitting the zero before the decimal point. Step 30: Select candidate prediction indicators from the prediction indicators according to the correlation coefficients. The number of candidate prediction indicators is at least one.

[0049] For the embodiment of the present invention, after analyzing the correlation coefficients between the components of the different levels of dissection and the physical properties and the metabolic energy, the correlation coefficient can be used to screen out candidate prediction indicators with a higher correlation with the metabolic energy from the various prediction indicators. For this process, step 30 specifically includes: based on the correlation coefficient, screening the prediction indicators with the correlation coefficient greater than the preset correlation coefficient from the various prediction indicators; determining the prediction indicators with the correlation coefficient greater than the preset correlation coefficient as the candidate prediction indicators. Since the subsequent establishment is a multivariate and most likely not a univariate linear regression equation, the equation fit may be improved as more prediction indicators are added even if the correlation coefficient does not reach a significant level. Among them, the preset correlation coefficient can be set according to actual business needs, and the embodiment of the present invention does not specifically limit this. Compared with the conventional nutrient analysis method, the embodiment of the present invention can mine prediction factors that are more related to metabolic energy by analyzing the correlation coefficients between the top component, the first-level dissection general component, the second-level dissection structural component, the third-level dissection subdivision component and the physical property and the metabolic energy, thereby improving the prediction accuracy of metabolic energy. Step 40, based on the candidate prediction indicators, construct the target metabolic energy prediction equation of the feed raw material for the poultry.

[0050] The number of target metabolic energy prediction equations may be one, two or more.

[0051] According to the embodiment of the present invention, after the candidate prediction indicators are screened, a target metabolic energy prediction equation of skin barley for geese can be constructed based on the candidate prediction indicators. For this process, Figure 2 As shown, the method includes:

[0052] Step 41: Perform stepwise regression analysis based on the candidate prediction indicators to construct multiple metabolic energy prediction equations, wherein different metabolic energy prediction equations correspond to different combinations of candidate prediction indicators.

[0053] According to the embodiment of the present invention, the selected candidate prediction indicators can be analyzed step by step, that is, the candidate prediction indicators are added or increased one by one to form a combination of different candidate prediction indicators, and linear fitting is performed to construct multiple metabolic energy equations.

[0054] Step 42: Evaluate the determination coefficients corresponding to the plurality of metabolic energy prediction equations.

[0055] According to the embodiment of the present invention, after constructing multiple metabolic energy equations, the determination coefficients of the multiple metabolic energy equations are evaluated respectively. The specific calculation formula of the determination coefficient is as follows:

[0056]

[0057] Among them, R 2 is the coefficient of determination, is the predicted value of metabolizable energy, y i is the true value of metabolizable energy, is the average value of the true value of metabolic energy, and n represents the number of samples. Finally, multiple metabolic energy prediction equations and their corresponding determination coefficients can be obtained.

[0058] The specific calculation formula of the adjusted determination coefficient is as follows:

[0059]

[0060] Among them, R 2 adj is the adjusted coefficient of determination, which is more reliable than R 2 ; P is significance; n represents the sample size.

[0061] In actual applications, the implementation entity can choose to use R according to specific needs. 2 or R adj 2 This is not defined here. In this embodiment, R 2 adj As shown in Tables 2 and 3, Table 2 is a metabolic energy prediction equation constructed based only on conventional nutrients, and Table 3 is a metabolic energy prediction equation constructed based on conventional nutrients and secondary decomposition structural components.

[0062] Table 2

[0063]

[0064] Among them, AME is the apparent metabolizable energy, TME is the true metabolizable energy; R 2 is the coefficient of determination, also called R square; R 2 adj is the adjusted determination coefficient, also called adjusted R-square; P is significance.

[0065] As shown in Table 2, based on the conventional nutrient content of hulled barley (DM, Ash, CP, EE, Ca, total phosphorus, CF, GE, etc.), eight metabolic energy regression equations with high fitting degrees can be obtained through stepwise regression analysis. When there is only one predictor, the AME prediction equation is 0.305 and the TME prediction equation is 0.306. As the number of predictors entering the equation increases, the coefficient of determination of the equation also increases. The R of the prediction equation with the highest AME fitting degree (method 8) is 2 adj The R of the prediction equation with the best TME fit was only 0.510. 2 adj Only 0.506.

[0066] Table 3

[0067]

[0068]

[0069] Among them, AME is the apparent metabolizable energy, TME is the true metabolizable energy; R 2 is the coefficient of determination, also called R square; R 2 adj is the adjusted determination coefficient, also called adjusted R-square; P is significance.

[0070] In order to optimize the prediction method, the embodiment of the present invention performs a secondary splitting of the energy, protein and fiber of the hulled barley, and uses amylose, amylopectin, albumin, globulin, alcohol-soluble protein, gluten, NDF and ADF as substitutes. With amylose, amylopectin, DM, Ash, EE, Ca, total phosphorus, NDF, ADF, albumin, globulin, alcohol-soluble protein and gluten as independent variables, a stepwise regression analysis is performed again to obtain the prediction method in Table 3.

[0071] As shown in Table 3, through stepwise regression analysis, the AME prediction equation with the highest fitting degree was 0.701, P < 0.001, which was much higher than the R of the best prediction equation based on conventional nutrients. 2 adj (0.510, see Table 2). At the same time, the TME prediction equation was 0.699, P < 0.001, which was much higher than the R of the best prediction equation based on conventional nutrients. 2 adj (0.506, see Table 2). It can be seen from the above data that, compared with the conventional nutrient analysis method, the embodiment of the present invention can improve the accuracy of metabolic energy prediction by constructing a prediction equation through secondary decomposition of conventional nutrients. Further, in order to upgrade the prediction method, the embodiment of the present invention adds physical properties, namely bulk density, thousand-grain weight, color (L*, a*, b*), etc. on the basis of amylose, amylopectin, DM, Ash, EE, Ca, total phosphorus, NDF, ADF, albumin, globulin, alcohol-soluble protein, and glutenin to establish a prediction equation, as shown in Table 4.

[0072] Table 4

[0073]

[0074]

[0075] Among them, AME is the apparent metabolizable energy, TME is the true metabolizable energy; R 2 is the coefficient of determination, also called R square; R 2 adj is the adjusted determination coefficient, also called adjusted R-square; P is significance.

[0076] Table 4 shows that the more predictive factors are added to the equation, the better the fit of the equation. After stepwise regression of metabolic energy with conventional nutrients, dissection components (nutrients + anti-nutritional factors), and physical properties, the R of the AME equation with the highest fit is 2 adj 2 =0.779, P < 0.001, which is higher than the R of the best prediction equation in Table 3 2 adj (0.701). At the same time, the R 2 adj =0.782, P < 0.001, which is higher than the R of the best prediction equation in Table 3 2 adj (0.699).

[0077] Step 43: select the target metabolic energy prediction equation from the multiple metabolic energy prediction equations according to the determination coefficient, and determine the candidate prediction indicators involved in the target metabolic energy equation as prediction factors. The target metabolic energy equation includes the apparent metabolic energy equation and / or the true metabolic energy equation.

[0078] For the embodiment of the present invention, a prediction equation with a determination coefficient greater than a preset determination coefficient can be selected from multiple metabolic energy prediction equations as a target metabolic energy equation. The preset determination coefficient can be set according to actual business needs, and the embodiment of the present invention does not specifically limit this.

[0079] For example, a prediction equation with a determination coefficient greater than 0.64 is screened out from Table 4 as the target metabolic energy prediction equation, and the candidate prediction indicators involved in the target metabolic energy equation are determined as the final prediction factors.

[0080] The embodiment of the present invention finally screened out 10 metabolic energy prediction equations, specifically including 5 apparent metabolic energy equations and 5 true metabolic energy equations.

[0081] Specifically, when the predictors include calcium, globulin, and crude fat, the apparent metabolizable energy equation is: AME = 13.372-21.009×calcium+0.182×globulin-0.609×crude fat

[0082] Wherein, the AME is the apparent metabolizable energy.

[0083] When the prediction factors include calcium, globulin, crude fat, and albumin, the apparent metabolizable energy equation is: AME=13.925-18.847×calcium+0.162×globulin-0.590×crude fat-0.093×albumin, wherein the AME is the apparent metabolizable energy.

[0084] When the predictors include calcium, globulin, crude fat, albumin, and amylose, the apparent metabolizable energy equation is:

[0085] AME = 13.683-19.676×calcium+0.162×globulin-0.597×crude fat-0.110×albumin+0.048×amylose

[0086] Wherein, the AME is the apparent metabolizable energy.

[0087] When the predictive factors include calcium, globulin, crude fat, and bulk density, the apparent metabolizable energy equation is: AME = 8.101-24.047×calcium+0.150×globulin-0.856×crude fat+0.008×bulk density

[0088] Wherein, the AME is the apparent metabolizable energy.

[0089] When the predictive factors include calcium, globulin, crude fat, bulk density, and amylopectin, the apparent metabolizable energy equation is:

[0090] AME = 7.555-24.955×calcium+0.180×globulin-0.931×crude fat+0.011×bulk density-0.033×branch starch

[0091] Wherein, the AME is the apparent metabolizable energy.

[0092] When the predictors include calcium, globulin, and crude fat, the true metabolizable energy equation is: TME = 14.389-21.027×calcium+0.182×globulin-0.608×crude fat

[0093] Wherein, the TME is the true metabolic energy equation.

[0094] When the prediction factors include calcium, globulin, crude fat, and albumin, the true metabolic energy equation is: TME=14.930-18.910×calcium+0.162×globulin-0.590×crude fat-0.091×albumin, wherein the TME is the true metabolic energy equation.

[0095] When the predictors include calcium, globulin, crude fat, albumin, and amylose, the true metabolizable energy equation is:

[0096] TME=14.686-19.746×calcium+0.163×globulin-0.597×crude fat-0.109×albumin+0.049×amylose

[0097] Wherein, the TME is the true metabolic energy equation.

[0098] When the predictive factors include calcium, globulin, crude fat, and bulk density, the true metabolizable energy equation is: TME = 9.084-24.085×calcium+0.150×globulin-0.856×crude fat+0.009×bulk density

[0099] Wherein, the TME is the true metabolic energy equation.

[0100] When the predictors include calcium, globulin, crude fat, bulk density, and amylopectin, the true metabolizable energy equation is:

[0101] TME=8.526-25.010×calcium+0.180×globulin-0.933 crude fat+0.11×bulk density-0.034×branched starch, wherein the TME is the true metabolizable energy equation.

[0102] For the embodiment of the present invention, after determining the above-mentioned target metabolic energy prediction equation, the target metabolic energy prediction equation is embedded in the poultry raw material metabolic energy prediction applet, and the content of the prediction factor is entered into the poultry raw material metabolic energy prediction applet, and the metabolic energy can be calculated.

[0103] Step 50: Perform metabolic energy prediction based on the target metabolic energy prediction equation to obtain a predicted value of the metabolic energy.

[0104] For the embodiment of the present invention, when predicting the metabolic energy of a new raw material, the content of the prediction factor in the feed raw material is first obtained, and then the content of the prediction factor is input into the target metabolic energy equation to predict the metabolic energy, thereby obtaining the predicted value of the metabolic energy.

[0105] In the traditional in vivo determination method, it is necessary to first collect barley husk samples from different provinces and cities as well as barley husk samples from abroad, that is, collect a total of 30 barley husk samples from different sources, and then crush the barley husk samples and preserve them for force-feeding for metabolic energy determination.

[0106] The experimental animals selected were healthy adult male Taihu geese with a body weight close to (3.46±0.39kg). Each type of skin barley constituted a treatment group, with a total of 30 skin barley groups, 6 replicates in each group, and 1 goose in each replicate. A total of 180 adult male geese (times) were required. During the informal experiment, the experimental geese were free to eat and drink water. It should be noted that in the specific experiment, the types of skin barley are not limited to 30, and can also be other numbers. The embodiment of the present invention does not make specific limitations on this. The fewer the sample types, the lower the credibility of the prediction model (target metabolic energy prediction equation).

[0107] The test adopts the force feeding method, which is also called the starvation method. It is a rapid determination technology of metabolic energy proposed and established by Sibbald. The specific operation refers to the method of Sheng Dongfeng (2005). The husk barley sample to be tested is pre-fed one day before the formal period. After the pre-feeding, the test geese are fasted for 24 hours to empty the residual contents of the intestine, and each group of test geese is weighed. After that, 40g of husk barley is accurately force-fed using a force feeder, and the force feeding time is recorded. 24 hours after force feeding, the excrement on the dung board is collected, and a new dung board is replaced, and the excrement within 24 hours is collected for the determination of endogenous loss. Pollutants such as feathers and feed are picked out from the excrement. 10ml of 10% hydrochloric acid is added to every 100g of excrement for nitrogen fixation. The feces sample is placed in a 65℃ oven for 48 hours, taken out after drying, rehumidified in a natural state for 24 hours, and weighed. After the fecal samples were completely crushed and passed through a 40-mesh sieve, the gross energy (GE) was determined using an automatic oxygen bomb calorimeter (PARR-6100, PARR Company).

[0108] The metabolic energy values ​​obtained by the above in vivo determination method are shown in Table 5. The determination process needs to be carried out by more than two professionals, and it takes 180 days from test preparation to test completion, and requires farm rental fees, farm management fees, test animals, feed, breeding staff and test staff employment fees, energy meter and other equipment purchase fees of about 75,000 yuan, which consumes a lot of manpower, financial resources and time.

[0109] Table 5

[0110] Metabolizable energy assessment method project Evaluation cycle Approximate cost (RMB) Animal testing Apparent metabolizable energy + true metabolizable energy 180 days 75000 In vitro prediction Apparent metabolizable energy + true metabolizable energy 3-5 days 2000-3000

[0111] By constructing the target metabolic energy prediction equation and determining the prediction factors in the first use of the embodiment of the present invention, the key prediction factors in 3-6 kinds of husk barley need to be determined in the subsequent determination, so that the predicted value of metabolic energy can be directly calculated without animal experiments. If the same 30 kinds of husk barley samples are determined by the same two professionals using the method of the embodiment of the present invention, it only takes about 3-5 days, and the cost of the detection personnel, wages, and reagents (kits) is only 2-3 thousand yuan, which is 1.7%-3% of the time required for the traditional in vivo method and 3%-4% of the cost, which greatly saves manpower, material resources, financial resources and time.

[0112] Compared with the traditional in vivo method, the in vitro determination method of the embodiment of the present invention does not need to use animal experiments, saves farm rental fees, animal purchase and feeding costs, avoids the harm of force feeding to animal welfare, greatly increases the number of sample evaluations per unit time, saves evaluation cycles, and thus greatly saves manpower, material resources, financial resources and time. In addition, since the prediction index range screened by the embodiment of the present invention is wide, the prediction accuracy of metabolic energy is significantly improved.

[0113] In addition, in the embodiment of the present invention, for the method of analyzing by three-level decomposition and subdivision (as shown in Table 6), the instrument is expensive to purchase, and professional personnel are required for determination, and the determination reagents are expensive and the operation is cumbersome. The measured value is not significantly correlated with the metabolic energy due to the excessively fine nutrient decomposition (P>0.05), and a prediction equation with high credibility cannot be established. However, in other poultry and other raw materials, the three-level decomposition and subdivision of the components still has the possibility of improving the credibility of the equation, and the user can decide whether to perform the determination of the subdivision components by weighing the cost and effect.

[0114] Table 6

[0115] project average value Variation range Coefficient of variation Threonine 0.38 0.18～0.47 13.40 Valine 0.48 0.23～0.60 13.35 Leucine 0.75 0.18～0.93 16.45 Isoleucine 0.40 0.33～0.51 9.76 Phenylalanine 0.63 0.20～0.78 15.80 Lysine 0.41 0.14～0.52 16.45 Glycine 0.45 0.22～0.57 13.61 Histidine 0.18 0.003～0.29 44.51 Serine 0.45 0.21～0.56 13.18 Glutamate 0.06 0.03～0.07 15.62 Alanine 0.45 0.22～0.58 13.38 Cystine 0.12 0.07～0.15 13.17 Tyrosine 0.24 0.19～0.35 11.76 Aspartic acid 0.66 0.33～0.83 13.24

[0116] Compared with the analysis method of three-level segmentation and subdivision, the prediction method of the embodiment of the present invention reduces the number of prediction factors and workload, as well as the working time and cost of chemical analysis. In addition, the prediction method of the embodiment of the present invention is simple and easy to implement. Compared with the traditional Excel calculation method, after the prediction equation is embedded in the poultry raw material metabolic energy prediction applet, it can be widely promoted by carrying the WeChat applet or other mobile phone applications and computer software, and has high economic value.

[0117] The use effect of the method described in the embodiment of the present invention on other poultry (chicken) and raw materials (cottonseed meal) is verified as follows:

[0118] The experiment selected 144 Hy-Line brown laying hens of 33 weeks of age, similar body weight and good health, and determined the apparent metabolizable energy of 10 kinds of cottonseed meal raw materials from 10 different geographical sources. The candidate prediction indicators of cottonseed meal and excrement at different decomposition levels, such as moisture, dry matter (DM), crude protein (CP), crude fat (EE), crude fiber (CF), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude ash (Ash), gross energy (GE), albumin, globulin, alcohol-soluble protein, gluten, amylose, and amylopectin, were determined. The best prediction equation for the apparent metabolizable energy (AME) of cottonseed meal raw materials based on conventional nutrients is: Apparent metabolizable energy = -33.419 + 2.446 * gross energy (R 2 adj =0.719, relative deviation (RSD) =3.74); the introduction of dissected nutrients as prediction factors improved the fitting of the prediction equation. The prediction equation for cottonseed meal raw material AME was: apparent metabolizable energy = -32.526 + 2.260 × total energy + 11.639 × globulin (R 2 adj=0.892, RSD=2.05). In this embodiment, a secondary decomposition structure component is introduced on the basis of the conventional prediction method, among which globulin is introduced into the prediction equation because it can significantly improve the equation fitting degree. The determination coefficient of 0.892 after the introduction of the secondary decomposition structure component is much higher than 0.719 obtained by the conventional prediction method, and the relative deviation between the predicted value and the true value is also reduced from 3.74 to 2.05. Hulled barley is an energy raw material that can provide energy, and the cottonseed meal in this embodiment is a protein raw material that can provide energy. This embodiment proves that the metabolic energy prediction method of the present invention can be used for the metabolic energy evaluation of different types of poultry and different types of energy raw materials in addition to the metabolic energy evaluation of barley for geese.

[0119] The correlation coefficient of the above analysis of the embodiment of the present invention specifically refers to the Pearson correlation coefficient, that is, the linear correlation coefficient, but in fact the Pearson correlation coefficient can only indicate whether there is a linear relationship between two variables. The low correlation between some prediction indicators and metabolic energy determined by the Pearson correlation coefficient can only indicate that there is no linear relationship between the two, but it cannot rule out the possibility that there is a nonlinear relationship between the prediction indicator and the metabolic energy. Based on this, after determining that there is no linear relationship between some prediction indicators and metabolic energy, the Spearman correlation coefficient between the prediction indicator and the metabolic energy can be calculated to represent the nonlinear relationship between the two. When specifically calculating the Spearman correlation coefficient, the original data of the prediction indicator and the metabolic energy can be ranked in order of size and assigned a grade, and then the grade difference between the prediction indicator and the metabolic energy is calculated, and then the square sum of the grade difference is calculated, and the Spearman correlation coefficient is calculated using the following formula:

[0120] r _S =1-6Σd 2 / (n(n2-1))

[0121] Among them, r _S is the Spearman correlation coefficient, n is the sample size, Σd 2 is the sum of squares of grade differences. The final calculated r _SThe value range of is used to judge the nonlinear correlation between the prediction index and the metabolic energy. An in vitro prediction method for the metabolic energy of poultry feed raw materials provided in an embodiment of the present invention, compared with the prior art, is to analyze the correlation coefficient between each prediction index and the metabolic energy of different levels of the feed raw materials, screen out candidate prediction indexes with a high correlation with the metabolic energy from each prediction index, and construct a target metabolic energy prediction equation for poultry based on the candidate prediction index, so as to predict the metabolic energy in vitro. Since each prediction index in the embodiment of the present invention includes different levels of dissection components, compared with the prior art method of only analyzing the primary general nutrients, the embodiment of the present invention can dig out prediction factors with a high correlation with the metabolic energy, thereby improving the prediction accuracy of the metabolic energy, and further can provide a more reliable method for promoting the rapid in vitro determination of the metabolic energy of the feed raw materials, the real-time dynamic and accurate preparation of poultry feed, and the reduction and substitution of corn.

[0122] Further, as Figure 1 and Figure 2 The specific implementation of the method shown in the embodiment provides an in vitro prediction device for the metabolizable energy of poultry feed raw materials, such as Figure 3 As shown, the device includes: an acquisition unit 31, an analysis unit 32, a screening unit 33, a construction unit 34 and a prediction unit 35.

[0123] The acquisition unit 31 can be used to acquire the measured values ​​of various prediction indicators of the feed raw material and the measured values ​​of the metabolizable energy of the feed raw material for poultry, wherein the various prediction indicators include the physical properties of the feed raw material and the dissection components at different levels.

[0124] The analysis unit 32 may be configured to analyze the correlation coefficients between the respective prediction indicators and the metabolic energy based on the measured values ​​of the respective prediction indicators and the measured value of the metabolic energy.

[0125] The screening unit 33 may be configured to screen candidate prediction indicators from the various prediction indicators according to the correlation coefficient.

[0126] The construction unit 34 may be used to construct a target metabolizable energy prediction equation of the feed raw material for the poultry based on the candidate prediction index.

[0127] The prediction unit 35 may be used to perform metabolic energy prediction based on the target metabolic energy prediction equation to obtain a predicted value of the metabolic energy.

[0128] Among them, the in vitro prediction device for metabolic energy of poultry feed raw materials has a prediction model import port, through which the target metabolic energy prediction equation can be directly called to perform metabolic energy prediction.

[0129] In some embodiments, the different levels of decomposition components include: the top component, the first-level decomposition general component, the second-level decomposition structural component and the third-level decomposition detailed component of the feed raw material; and / or the feed raw material includes at least one raw material that can supply energy in the poultry feed formula, such as hulled barley, cottonseed meal and other energy-supplying raw materials; the poultry includes chickens, ducks, geese, turkeys, ostriches, quails, pigeons and other poultry.

[0130] In some embodiments, the top component includes gross energy and dry matter; the primary decomposition components include water, nitrogen-free extract, crude fat, crude protein, crude ash, crude fiber, and various conventional anti-nutritional factors.

[0131] In some embodiments, the secondary decomposition structural components include calcium, phosphorus, crude fat, total starch, amylose and amylopectin, soluble sugars, insoluble sugars, albumin, gluten, globulin, alcohol-soluble protein, neutral detergent fiber, acid detergent fiber, lignin, β-glucan, arabinoxylan, free cottonpol, bound cottonpol, hydrolyzed tannin and condensed tannin, and other decomposed anti-nutritional factors of different structures. In some embodiments, the tertiary decomposition subdivided components include further decomposed more trace components based on the secondary decomposition structural components, including various specific amino acids, various specific fatty acids, various specific monosaccharides, disaccharides, oligosaccharides, polysaccharides, various major elements and trace elements, and various different trace decomposed anti-nutritional factors.

[0132] In some embodiments, the analysis unit 32 can be specifically used to input the measured values ​​of the various prediction indicators and the measured values ​​of the metabolic energy into a preset statistical analysis software for correlation analysis to obtain the correlation coefficients between the various prediction indicators and the metabolic energy.

[0133] In some embodiments, the screening unit 33 can be specifically used to screen the prediction indicators whose correlation coefficient is greater than the preset correlation coefficient from the various prediction indicators based on the correlation coefficient; and determine the prediction indicators whose correlation coefficient is greater than the preset correlation coefficient as the candidate prediction indicators.

[0134] In some embodiments, the construction unit 34, such as Figure 4 As shown, it includes: a construction module 341, an evaluation module 342 and a screening module 343.

[0135] The construction module 341 can be used to perform stepwise regression analysis based on the candidate prediction indicators to construct multiple metabolic energy prediction equations, wherein different metabolic energy prediction equations correspond to different combinations of candidate prediction indicators.

[0136] The evaluation module 342 may be used to evaluate the determination coefficients corresponding to the multiple metabolic energy prediction equations.

[0137] The screening module 343 may be used to screen out the target metabolic energy prediction equation from the multiple metabolic energy prediction equations according to the determination coefficient, and determine the candidate prediction indicators involved in the target metabolic energy equation as prediction factors.

[0138] In some embodiments, the prediction unit 35 can be specifically used to obtain the content of the prediction factor in the feed raw material; input the content of the prediction factor into the target metabolic energy equation to perform metabolic energy prediction, and obtain the predicted value of the metabolic energy.

[0139] In some embodiments, when the feed raw material is hulled barley and the poultry is goose, the feed raw material is hulled barley, the poultry is goose, and the target metabolic energy equation includes an apparent metabolic energy equation and / or a true metabolic energy equation; when the predictor includes calcium, globulin, and crude fat, the apparent metabolic energy equation of hulled barley on goose is:

[0140] AME = 13.372-21.009×calcium+0.182×globulin-0.609×crude fat

[0141] Wherein, the AME is the apparent metabolizable energy; and / or when the predicting factors include calcium, globulin, crude fat, and albumin, the apparent metabolizable energy equation of braised barley on geese is:

[0142] AME=13.925-18.847×calcium+0.162×globulin-0.590×crude fat-0.093×albumin, wherein AME is the apparent metabolizable energy; and / or when the predicting factors include calcium, globulin, crude fat, albumin, and amylose, the apparent metabolizable energy equation of braised barley on geese is:

[0143] AME = 13.683-19.676×calcium+0.162×globulin-0.597×crude fat-0.110×albumin+0.048×amylose

[0144] Wherein, the AME is the apparent metabolizable energy; and / or when the predicting factors include calcium, globulin, crude fat, and bulk density, the apparent metabolizable energy equation of hulled barley on geese is:

[0145] AME = 8.101-24.047×calcium+0.150×globulin-0.856×crude fat+0.008×bulk density

[0146] Wherein, the AME is the apparent metabolizable energy; and / or when the predicting factors include calcium, globulin, crude fat, bulk density, and amylose, the apparent metabolizable energy equation of hulled barley on geese is:

[0147] AME = 7.555-24.955×calcium+0.180×globulin-0.931×crude fat+0.011×bulk density-0.033×branch starch

[0148] Wherein, the AME is the apparent metabolizable energy; and / or when the predictor includes calcium, globulin, and crude fat, the true metabolizable energy equation of braised barley on geese is:

[0149] TME=14.389-21.027×calcium+0.182×globulin-0.608×crude fat

[0150] Wherein, the TME is the true metabolizable energy equation; and / or when the predicting factors include calcium, globulin, crude fat, and albumin, the true metabolizable energy equation of braised barley on geese is:

[0151] TME=14.930-18.910×calcium+0.162×globulin-0.590×crude fat-0.091×albumin, wherein the TME is the true metabolizable energy equation; and / or when the predicting factors include calcium, globulin, crude fat, albumin, and amylose, the true metabolizable energy equation of braised barley on geese is:

[0152] TME=14.686-19.746×calcium+0.163×globulin-0.597×crude fat-0.109×albumin+0.049×amylose

[0153] Wherein, the TME is the true metabolizable energy equation; and / or when the predicting factors include calcium, globulin, crude fat, and bulk density, the true metabolizable energy equation of hulled barley on geese is:

[0154] TME = 9.084-24.085×calcium+0.150×globulin-0.856×crude fat+0.009×bulk density

[0155] Wherein, the TME is the true metabolizable energy equation; and / or when the predictive factors include calcium, globulin, crude fat, bulk density, and amylopectin, the true metabolizable energy equation of hulled barley on geese is:

[0156] TME=8.526-25.010×calcium+0.180×globulin-0.933 crude fat+0.11×bulk density-0.034×branched starch, wherein the TME is the true metabolizable energy equation.

[0157] It should be noted that for other corresponding descriptions of the functional units involved in the in vitro prediction device for poultry feed raw material metabolic energy provided by the embodiment of the present invention, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0158] Based on the above Figure 1 and Figure 2 The method shown in the embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The in vitro prediction method for metabolizable energy of poultry feed ingredients is shown.

[0159] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0160] Based on the above Figure 1 and Figure 2 The method shown, and Figure 3 and Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, which can be a personal computer, a tablet computer, a server, or other network equipment, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The in vitro prediction method for metabolizable energy of poultry feed ingredients is shown.

[0161] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0162] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0163] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0164] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0165] Since the various prediction indicators in the embodiments of the present invention include different levels of dissection components, compared with the prior art method of only analyzing the primary general nutrients, the embodiments of the present invention can dig out prediction factors with a high correlation with metabolizable energy, thereby improving the prediction accuracy of metabolic energy, and further providing a more reliable method for promoting the rapid in vitro determination of metabolic energy of feed raw materials, real-time dynamic and accurate formulation of poultry feed, and reducing the amount of corn substitution.

[0166] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0167] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for predicting the metabolizable energy of poultry feed raw materials in vitro, characterized in that: include: Obtaining measured values ​​of various prediction indicators of the feed raw material and measured values ​​of metabolizable energy of the feed raw material for poultry, wherein the various prediction indicators include physical properties of the feed raw material and components of different levels of dissection; Based on the measured values ​​of the respective prediction indicators and the measured value of the metabolic energy, analyzing the correlation coefficients between the respective prediction indicators and the metabolic energy; According to the correlation coefficient, selecting candidate prediction indicators from the various prediction indicators; Based on the candidate prediction index, constructing a target metabolizable energy prediction equation for the feed raw material for the poultry; Metabolic energy prediction is performed based on the target metabolic energy prediction equation to obtain the predicted value of the metabolic energy.

2. The method according to claim 1, characterized in that The different levels of decomposition components include: the top components of the feed raw materials, the primary decomposition general components, the secondary decomposition structural components and the tertiary decomposition detailed components; and / or The feed raw material includes at least one raw material that can supply energy in a poultry feed formula; the poultry includes at least one poultry selected from the group consisting of chicken, duck, goose, turkey, ostrich, quail and pigeon.

3. The method according to claim 2, characterized in that The top components include total energy and dry matter; the primary fractionation components include water, nitrogen-free extract, crude fat, crude protein, crude ash, crude fiber, and various conventional anti-nutritional factors; and / or The secondary decomposition structural components include calcium, phosphorus, crude fat, total starch, amylose and amylopectin, soluble sugars, insoluble sugars, albumin, gluten, globulin, alcohol-soluble protein, neutral detergent fiber, acid detergent fiber, lignin, β-glucan, arabinoxylan, free cottony phenol, bound cottony phenol, hydrolyzed tannins and condensed tannins and other decomposed anti-nutritional factors of different structures; and / or the tertiary decomposition subdivision components include more trace components further decomposed on the basis of the secondary decomposition structural components, containing various specific amino acids, various specific fatty acids, various specific monosaccharides, disaccharides, oligosaccharides, polysaccharides, various major elements and trace elements, and various different trace decomposed anti-nutritional factors.

4. The method according to claim 1, characterized in that The step of analyzing the correlation coefficients between the respective prediction indicators and the metabolic energy based on the measured values ​​of the respective prediction indicators and the measured value of the metabolic energy comprises: Inputting the measured values ​​of each prediction index and the measured value of the metabolic energy into a preset statistical analysis software for correlation analysis to obtain the correlation coefficients between each prediction index and the metabolic energy; and / or The selecting candidate prediction indicators from the various prediction indicators according to the correlation coefficient includes: selecting a prediction indicator having a correlation coefficient greater than a preset correlation coefficient from the various prediction indicators based on the correlation coefficient; The prediction indicator whose correlation coefficient is greater than the preset correlation coefficient is determined as the candidate prediction indicator.

5. The method according to any one of claims 1 to 4, characterized in that: The step of constructing a target metabolizable energy prediction equation for the poultry based on the candidate prediction index comprises: Performing stepwise regression analysis based on the candidate prediction indicators to construct multiple metabolic energy prediction equations, wherein different metabolic energy prediction equations correspond to different combinations of candidate prediction indicators; evaluating the determination coefficients corresponding to the plurality of metabolic energy prediction equations respectively; The target metabolic energy prediction equation is screened out from the multiple metabolic energy prediction equations according to the determination coefficient, and the candidate prediction indicators involved in the target metabolic energy equation are determined as prediction factors.

6. The method according to claim 5, characterized in that The step of performing metabolic energy prediction based on the target metabolic energy prediction equation to obtain a predicted value of the metabolic energy includes: Obtaining the content of the prediction factor in the feed raw material; The content of the prediction factor is input into the target metabolic energy equation to perform metabolic energy prediction to obtain the predicted value of the metabolic energy.

7. The method according to claim 6, characterized in that When the feed raw material is hulled barley and the poultry is goose, the target metabolizable energy equation includes an apparent metabolizable energy equation and / or a true metabolizable energy equation; when the predictive factors include calcium, globulin, and crude fat, the apparent metabolizable energy equation of hulled barley on goose is: AME = 13.372-21.009×calcium+0.182×globulin-0.609×crude fat Wherein, the AME is the apparent metabolizable energy; and / or When the predictors include calcium, globulin, crude fat, and albumin, the apparent metabolizable energy equation of braised barley on geese is: AME=13.925-18.847×calcium+0.162×globulin-0.590×crude fat-0.093×albumin, wherein the AME is the apparent metabolizable energy; and / or When the predictors include calcium, globulin, crude fat, albumin, and amylose, the apparent metabolizable energy equation of braised barley on geese is: AME = 13.683-19.676×calcium+0.162×globulin-0.597×crude fat-0.110×albumin+0.048×amylose Wherein, the AME is the apparent metabolizable energy; and / or When the predictive factors include calcium, globulin, crude fat, and bulk density, the apparent metabolizable energy equation of braised barley on geese is: AME=8.101-24.047×calcium+0.150×globulin-0.856×crude fat+0.008×bulk density, wherein the AME is the apparent metabolizable energy; and / or When the predictive factors include calcium, globulin, crude fat, bulk density, and amylose, the apparent metabolizable energy equation of braised barley on geese is: AME = 7.555-24.955×calcium+0.180×globulin-0.931×crude fat+0.011×bulk density-0.033×branch starch Wherein, the AME is the apparent metabolizable energy; and / or When the predictors include calcium, globulin, and crude fat, the true metabolizable energy equation of braised barley on geese is: TME=14.389-21.027×calcium+0.182×globulin-0.608×crude fat Wherein, the TME is the true metabolic energy equation; and / or When the predictors include calcium, globulin, crude fat, and albumin, the true metabolizable energy equation of braised barley on geese is: TME=14.930-18.910×calcium+0.162×globulin-0.590×crude fat-0.091×albumin, wherein the TME is the true metabolizable energy equation; and / or When the predictors include calcium, globulin, crude fat, albumin, and amylose, the true metabolizable energy equation of braised barley on geese is: TME=14.686-19.746×calcium+0.163×globulin-0.597×crude fat-0.109×albumin+0.049×amylose Wherein, the TME is the true metabolic energy equation; and / or When the predictors include calcium, globulin, crude fat, and bulk density, the true metabolizable energy equation of hulled barley on geese is: TME=9.084-24.085×calcium+0.150×globulin-0.856×crude fat+0.009×bulk density, wherein the TME is the true metabolizable energy equation; and / or When the predictors include calcium, globulin, crude fat, bulk density, and amylopectin, the true metabolizable energy equation of hulled barley on geese is: TME=8.526-25.010×calcium+0.180×globulin-0.933crude fat+0.11×bulk density-0.034×pulse starch Wherein, the TME is the true metabolic energy equation.

8. An in vitro prediction device for poultry feed raw material metabolizable energy, characterized in that: include: An acquisition unit, used for acquiring measured values ​​of various prediction indicators of the feed raw material and measured values ​​of metabolizable energy of the feed raw material for poultry, wherein the various prediction indicators include physical properties of the feed raw material and dissection components at different levels; an analyzing unit, configured to analyze the correlation coefficients between the respective prediction indicators and the metabolic energy based on the measured values ​​of the respective prediction indicators and the measured value of the metabolic energy; A screening unit, for screening candidate prediction indicators from the various prediction indicators according to the correlation coefficient; a construction unit, for constructing a target metabolic energy prediction equation of the feed raw material for the poultry based on the candidate prediction indicators; A prediction unit is used to perform metabolic energy prediction based on the target metabolic energy prediction equation to obtain a predicted value of the metabolic energy.

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

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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