CN111118109B - A Method for Evaluating Individual Protein Nutrition Status of Pigs Using Blood Biochemical Indexes - Google Patents

Abstract

The invention discloses a method for evaluating the individual protein nutritional state of pigs by using blood biochemical indexes. The method evaluates the individual protein nutritional state of pigs by simultaneously quantitatively detecting at least one of the blood biochemical indexes.

Description

A Method for Evaluating Individual Protein Nutrition Status of Pigs Using Blood Biochemical Indexes

technical field

The invention belongs to the technical field of animal nutrition, and in particular relates to a method for evaluating the protein nutrition state of individual pigs by using blood biochemical indexes.

Background technique

In recent years, the shortage of feed raw materials has intensified the competition between humans and animals for food; the supply of feed exceeding nutritional needs will also increase the pressure on the environment of livestock and poultry farming. In order to improve feed remuneration, it is urgent to carry out precise nutrition supply. Precision nutrition is an animal nutrition strategy that accurately connects the feed nutrient supply and the nutritional needs of farmed animals to supply nutrients according to different nutrient supply states of individuals. The nutritional status of an individual animal is a comprehensive expression of the interaction between feed, body and environment, and reflects the animal's ability to meet its nutritional needs. At present, the nutritional status of live pigs is mainly determined by external indicators, such as the direct method of body shape and appearance, the method of body condition score evaluation and the method of body mass index, etc. When the nutrition of pigs is unbalanced, the loss will be difficult to make up and correct.

Protein is the primary nutrient for farmed animals. How to accurately assess the changes in protein nutritional requirements of individuals or subgroups and their protein nutritional status has become the basis of precision nutrition. Under the current intensive farming conditions, due to the influence of factors such as individual genetic background variation, environmental differences, and different health status, pigs fed the same protein level diet will have different individual requirements for dietary protein nutrition, resulting in Different individuals have different levels of protein nutrition satisfaction, which leads to large differences in actual growth performance or protein nutrition status of pigs after eating diets with the same protein nutrition level.

Blood biochemical indicators refer to some enzymes and proteins in the blood, which play an important role in body metabolism, immune regulation, energy transfer and animal growth and development, and can be obtained through minimally invasive ways, which are ideal protein nutrition The organization where the status monitoring target is located. At present, there is no report on the establishment of a dynamic regression model using blood biochemical indicators that are significantly related to the nutritional status of pig protein to accurately evaluate the nutritional status and nutritional requirements of pig protein.

Contents of the invention

The present invention aims to provide a method for evaluating the nutritional status of pig individual protein by using blood biochemical indexes aiming at the deficiencies of the current prior art.

In order to achieve the above object, the technical solution provided by the invention is:

The method for evaluating the nutritional status of pig individual protein by using blood biochemical indicators is to evaluate the nutritional status of individual pig protein by simultaneously quantitatively detecting at least one of the following blood biochemical indicators in the blood: urea, total protein, blood ammonia, alanine aminotransferase, Aspartate aminotransferase, pancreatic amylase, creatinine, glucose, total cholesterol, high-density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin M, triglycerides, low-density lipoprotein, immunoglobulin g.

Preferably, the method quantitatively detects the following blood biochemical indicators of group A, or blood biochemical indicators of group B, or blood biochemical indicators of group C, or blood biochemical indicators of group D, or blood biochemical indicators of group E in the blood , or the blood biochemical indicators of group F, to evaluate the individual protein nutritional status of pigs:

Group A: urea;

Group B: total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine, glucose, total cholesterol, high-density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immune globulin Protein M;

Group C: total protein, alanine aminotransferase, urea, glucose, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G, immunoglobulin M;

Group D: triglycerides, total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein, cholinesterase;

Group E: urea, glucose, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G, immunoglobulin M;

Group F: total protein, alanine aminotransferase, aspartate aminotransferase, urea, glucose, total cholesterol, immunoglobulin G, low-density lipoprotein, high-density lipoprotein.

Preferably, the specific steps of the method include:

(1) Establish the regression model of the growth performance of growing pigs and the protein level in the diet, determine the protein level of the diet when the growth performance is the best, and set the protein level of the diet ± 1% as the reference standard for the protein nutritional status of growing pigs ;

(2) Construct the blood biochemical index content (in the present invention, the content unit of blood biochemical index total protein, cholinesterase, immunoglobulin G and immunoglobulin M is g/L reflecting growth pig protein nutritional state, urea, The unit of glucose, total cholesterol, triglyceride, low-density lipoprotein and high-density lipoprotein is mmol/L, the unit of blood ammonia and creatinine is μmol/L, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine The content unit of acid kinase and lactate dehydrogenase is U/L) and the dynamic regression model of dietary protein level, and described regression model is as follows:

A regression model:

CP=-0.2039x ² +4.5507x-0.6947; wherein, the x is the content of urea in the blood;

B regression model:

CP＝14.3304+0.3498x ₁ +0.7335x ₂ -0.0110x ₃ -0.1162x ₄ -0.0400x ₅ +0.0003x ₆ -0.0448x ₇ -0.5111x ₈ -3.5789x ₉ +1.1543x ₁₀ -0.0045x ₁₁ +0.0019x ₁₂ +0.0028x ₁₃ -5.2590x ₁₄ ; wherein, _x1 to _x14 are respectively total protein in blood, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine, glucose, total cholesterol, high Density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin M content;

C regression model:

CP＝(0.2497+0.0021x ₁ +0.0012x ₂ +0.0086x ₃ -0.0069x ₄ +0.1081x ₅ -0.304x ₆ +0.2573x ₇ +0.2926x ₈ +0.0066x ₉ -0.0391x ₁₀ )×100; Said _x1 to _x10 are respectively the content of total protein, alanine aminotransferase, urea, glucose, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G and immunoglobulin M in the blood ;

D regression model:

CP＝(0.5349x ₁ ² +0.0073x ₂ ² +0.0307x ₃ ² +0.0206x ₄ ² +0.0207x ₅ -0.3931x ₁ -0.1839x ₂ +0.1008x ₆ +0.0179x ₃ -0.1119x ₇ -0.0449x ₄ ) × 100; wherein, said _x1 to _x7 are respectively the content of triglyceride, total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein, and cholinesterase in blood;

E regression model:

17.75CP＝0.06786+0.0033x ₁ -0.0014x ₂ -0.0057x ₃ -0.003x ₄ -0.003x ₅ -0.0035x ₆ -0.0012x ₇ +0.02x ₈ ; wherein, said x ₁ to x ₈ are respectively Contents of urea, glucose, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G, and immunoglobulin M;

F regression model:

CP=(0.001x ₁ -0.0018x ₂ +0.0006x ₃ +0.01x ₄ -0.002x5-0.0014x ₆ -0.00093x ₇ -0.00057x ₈ -0.00053x ₉ )×100; wherein, the x ₁ to x ₉ Respectively, the mass percentages of total protein, alanine aminotransferase, aspartate aminotransferase, urea, glucose, total cholesterol, immunoglobulin G, low-density lipoprotein, and high-density lipoprotein in blood;

The CP is the protein level of the diet, and the protein level of the diet refers to the protein content in the diet, and the unit is %;

(3) Determination of the content of group A blood biochemical indicators in the blood of the individual pigs to be tested, and substitute the measured results into the A regression model; Substitute into the B regression model; or, measure the content of the C group blood biochemical index in the blood of the individual pig to be tested, and substitute the measured result into the C regression model; or, measure the content of the blood biochemical index of the D group in the blood of the individual pig to be tested, and The measured results are substituted into the D regression model; or, the content of the blood biochemical indicators of the E group in the blood of the individual pigs to be tested is determined, and the measured results are substituted into the E regression model; or, the blood biochemical indicators of the F group in the blood of the individual pigs to be tested are determined Substitute the measured results into the F regression model; calculate and obtain the protein level of the diet fed by the individual pigs to be tested, and combine the protein level of the diet fed by the individual pigs to be tested with the protein nutritional status of the growing pigs obtained in step (1) Compared with the reference standard, if it is within the value range of the reference standard for the protein nutritional status of growing pigs, it means that the nutritional status of the individual pig to be tested is the best.

Preferably, when detecting blood, an automatic biochemical analyzer, an enzyme-linked immunosorbent assay kit, etc. are used to measure the biochemical indicators in the blood.

Preferably, the regression model is a linear regression model, a nonlinear regression model, a principal component regression model, a least squares model and a partial least squares regression model.

Preferably, the regression analysis F-test significance value of the regression model is p<0.05, and the coefficient of determination R ² >0.6.

The present invention will be further described below:

The present invention provides 6 assessment models of individual protein nutrition status of pigs and a reference standard of protein nutrition status (the dietary protein content is 16.3-18.3%), by measuring the blood biochemical index content of individual pigs to be tested and substituting them into the assessment model of pig individual protein nutrition status Obtain the protein content of the diet, and compare it with the reference standard (16.3-18.3%) of the dietary protein content when the individual protein nutritional status of the pig is the best to realize the evaluation of the nutritional status of the pig protein; this is obtained through the evaluation model of the individual protein nutritional status of the pig The adjusted value of dietary protein content required for the individual pigs to be tested to obtain the optimal nutritional status.

The present invention first uses the blood of each individual of animal samples to test, and the animal samples are growing pigs grouped as follows according to the crude protein content in the feeding ration: nitrogen-free ration group (CPO), the ration protein level is 5 % group (CP5), dietary protein level of 9% (CP9), dietary protein level of 12% group (CP12), dietary protein level of 16% group (CP16), dietary protein level of 17% group ( CP17), dietary protein level of 18% group (CP18), dietary protein level of 21% group (CP21), dietary protein level of 25% group (CP25) and dietary protein level of 30% group (CP30) ; Feeding in the diet, except that the crude protein content is different, the nutritional level of other substances is the same; the feeding conditions of the pig group are all the same except the diet; the blood biochemical indicators of each individual in the pig group The content is tested to obtain the data of the blood biochemical index content of each individual, and the data are grouped into CP0, CP5, CP9, CP12, CP16, CP17, CP18, CP21, CP25 according to the grouping of the animal samples and the data set of the CP30 group;

Then the data set is analyzed, and the blood biochemical indicators for evaluating the protein nutritional status of growing pigs are screened: use IBM SPSS Statistics 25 software to carry out one-way ANOVA analysis of variance on the data set, and obtain a single blood biochemical index significantly affected by the dietary protein level. Indicators; use IBM SPSS Statistics 25 software to conduct nonlinear correlation analysis on the data set, and screen out a single blood biochemical index significantly related to dietary protein level, which are total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, total Cholesterol, high-density lipoprotein, creatine kinase and lactate dehydrogenase, use IBM SPSS Statistics 25 software to carry out regression analysis and screening on these indicators to obtain a single blood biochemical indicator reflecting the nutritional status of growing pig protein, which is urea; use Matlab software to analyze the data Set up R language programming solution to obtain combined blood biochemical indicators reflecting the protein nutritional status of growing pigs; they are ① total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine, glucose, total cholesterol, high Density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin M, ② total protein, alanine aminotransferase, urea, glucose, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein Protein, immunoglobulin G, immunoglobulin M, ③ triglyceride, total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein, cholinesterase, ④ urea, glucose, triglyceride , total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G, immunoglobulin M, ⑤ total protein, alanine aminotransferase, aspartate aminotransferase, urea, glucose, total cholesterol, immunoglobulin G, low-density lipoprotein protein, high-density lipoprotein;

Then establish the regression model ADG (g/day)=35.5+35.9×protein level-1.51×protein level ² +0.0182×protein level ³ in the growth performance of growing pigs and the protein level in the diet (coefficient of determination r ² =0.945) The protein content of the diet was 17.3% when the growth performance was obtained by solving the equation. According to the reasonable fluctuation range of 1% protein level in the actual production, the protein level of this diet ± 1% was set as the dietary protein level. The optimum proportion of protein added is 16.3-18.3%. In this range, the protein nutritional status of growing pigs is the best, and this dietary protein level is set as the reference standard for protein nutritional status of growing pigs;

Utilize IBM SPSS Statistics 25 software curve estimation to construct the unary nonlinear regression model of the single blood biochemical index of described evaluation growth pig protein nutritional status and described dietary protein level; Utilize Matlab software R language programming to solve and construct described evaluation growth pig protein The multiple regression model of the blood biochemical index content of nutritional state and described dietary protein level; Can select corresponding optimal pig protein nutritional state evaluation regression model according to model determination coefficient ^R2 :

A regression model:

CP=-0.2039x ² +4.5507x-0.6947 (x is blood urea content);

B regression model:

CP＝14.3304+0.3498x ₁ +0.7335x ₂ -0.0110x ₃ -0.1162x ₄ -0.0400x ₅ +0.0003x ₆ -0.0448x ₇ -0.5111x ₈ -3.5789x ₉ +1.1543x ₁₀ -0.0045x ₁₁ +0.0019x ₁₂ +0.0028x ₁₃ -5.2590x ₁₄ (x ₁ -x ₁₄ are total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine, glucose, total cholesterol, high-density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin M content);

C regression model:

CP＝(0.2497+0.0021x ₁ +0.0012x ₂ +0.0086x ₃ -0.0069x ₄ +0.1081x ₅ -0.304x ₆ +0.2573x ₇ +0.2926x ₈ +0.0066x ₉ -0.0391x ₁₀ )×100(x ₁ -x ₁₀ are the contents of total blood protein, alanine aminotransferase, urea, glucose, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G and immunoglobulin M respectively);

D regression model:

CP＝(0.5349x ₁ ² +0.0073x ₂ ² +0.0307x ₃ ² +0.0206x ₄ ² +0.0207x ₅ -0.3931x ₁ -0.1839x ₂ +0.1008x ₆ +0.0179x ₃ -0.1119x ₇ -0.0449x ₄ )×100 (x ₁ -x ₇ are blood triglyceride, total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein, cholinesterase content respectively);

E regression model:

17.75CP＝0.06786+0.0033x ₁ -0.0014x ₂ -0.0057x ₃ -0.003x ₄ -0.003x ₅ -0.0035x ₆ -0.0012x ₇ +0.02x ₈ (x ₁ -x ₈ are blood urea, glucose, glycerol Triesters, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G, immunoglobulin M content);

F regression model:

CP＝(0.001x ₁ -0.0018x ₂ +0.0006x ₃ +0.01x ₄ -0.002x ₅ -0.0014x ₆ -0.00093x ₇ -0.00057x ₈ -0.00053x ₉ )×100(x ₁ -x ₉ are blood respectively Total protein, alanine aminotransferase, aspartate aminotransferase, urea, glucose, total cholesterol, immunoglobulin G, low-density lipoprotein, high-density lipoprotein content);

The CP is the protein level of the diet, in %;

Determine the content of blood biochemical indicators of group A in the blood of individual pigs to be tested, and substitute the measured results into regression model A; or measure the content of biochemical indicators of group B blood in the blood of individual pigs to be tested, and substitute the measured results into regression model B Or, measure the content of C group blood biochemical indicators in the blood of the individual pig to be tested, and substitute the measured results into the C regression model; or, measure the content of the blood biochemical indicators of the D group in the individual pig blood to be measured, and use the measured results The results are substituted into the D regression model; or, the determination of the content of the blood biochemical indicators of the E group in the blood of the individual pigs to be tested is performed, and the measured results are substituted into the E regression model; or, the content of the blood biochemical indicators of the F group in the blood of the individual pigs to be measured is Substitute the measured results into the F regression model; calculate and obtain the protein content of the diet fed by the individual pigs to be tested, and compare it with the dietary protein content reference standard (16.3～18.3%) when the individual protein nutritional status of the pigs is the best Yes, if it is within the range, it means that the nutritional status of the individual pig to be tested is the best, and if it is not within the range, it means that the nutritional status of the individual pig to be tested is not in the best state, and the protein nutrition level needs to be adjusted; according to the prediction The formula is calculated to obtain the protein content of the diet fed by the individual pig to be tested and the reference standard (16.3-18.3%) for the protein content of the diet when the individual pig's protein nutritional status is optimal, and compares it to obtain the optimal nutritional status of the pig individual The desired dietary protein level adjustment.

In the present invention, the significance value of single factor variance ANOVA analysis is p<0.05; the significance value of correlation analysis F test is p<0.05 and the correlation coefficient r>0.5; the significance value of regression analysis F test is p<0.05 and the coefficient of determination R ² > 0.6.

The animals described in the present invention are pigs, and can also be extended to other animals and humans.

Compared with prior art, the beneficial effect of the present invention is:

The content of blood biochemical indicators that can evaluate the nutritional status of protein by measuring any single or combination of individual pig blood samples and substituting it into the prediction equation of the individual protein nutritional status evaluation model of pigs to obtain the dietary protein content, and when the nutritional status of individual pigs is optimal The dietary protein content reference standard (16.3-18.3%) is compared to realize the evaluation of the protein nutritional status of pigs, and obtain the dietary protein content adjustment value required for the pig individual to obtain the best protein nutritional status; use this pig The individual protein nutritional status assessment model can quickly grasp the changes in the nutritional status of pig protein and understand the nutritional requirements of animals, so as to take timely countermeasures; it has important theoretical and practical significance for body health, animal precision feeding and refined management.

Description of drawings

Figure 1 is the effect of dietary protein level on the daily gain of growing pigs; in the figure: ADG: daily gain; CP: dietary protein level (%). Different superscripts between groups indicate significant differences (p<0.05, n=6).

Detailed ways

Unless otherwise specified, the experimental methods, materials and reagents used in the following examples are conventional methods, materials and reagents, all of which can be obtained from commercial sources.

1. Materials and Methods

1) Test animals

Select 60 binary hybrid (Landrace × large gram) growing pigs with no significant difference in body weight, and divide them into 10 groups randomly, with 6 pigs in each group (n=6), and raise them in a single pen for 30 days, with free access to water but uniform ration Feed intake (average daily intake is 1.5kg).

2) Test diet

According to the NRC (2012) standard, 10 groups of diets with different protein levels were designed, nitrogen-free diet group (CP0), diet protein level 5% group (CP5), diet protein level 9% group (CP9), The dietary protein level was 12% group (CP12), the dietary protein level was 16% group (CP16), the dietary protein level was 17% group (CP17), the dietary protein level was 18% group (CP18), the dietary The protein level was 21% group (CP21), the dietary protein level was 25% group (CP25) and the dietary protein level was 30% group (CP30), and the dietary energy of each group was the same. The composition and nutrient composition of the diet are shown in Table 1.

Table 1 Composition and nutritional level of experimental diet (dry matter basis)

^a Premix composition (%): calcium dihydrogen phosphate, 31.575; stone powder, 15; calcium lactate, 30; salt, 10; choline chloride (50%), 2.5; ; 436 multidimensional (pig multidimensional), 1; CuSO4 5H2O, 0.75; ferrous sulfate FeSO4 H2O, 0.75; zinc sulfate ZnSO4 H2O, 0.5; manganese sulfate MnSO4 H2O, 0.25; organic chromium (0.2%), 0.375; Calcium iodate (1% iodine), 0.05; organic selenium (0.2%), 0.375; aureomycin (15%), 1.25; high temperature resistant phytase 10000U, 0.25; compound enzyme (888), 0.75; enveloped VC (90%), 0.25; Vitamin E powder (50%), 0.125; Bacillus subtilis (probiotics), 0.5.

^b Nutrient composition (calculated value): Standard digestible phosphorus STTD P (%), 0.28; Sodium (%), 0.16; Chlorine (%), 0.25; Salt (%), 0.41; Copper (ppm), 75.6; Iron (ppm), 90; Zinc (ppm), 71; Manganese (ppm), 29.5; Chromium (ppm), 0.3; Iodine (ppm), 0.2; Selenium (ppm), 0.3.

3) Test method

At the beginning and end of the experiment, the body weight of each pen was measured separately. Calculate average daily weight gain. Blood was collected after the animal feeding experiment, and the total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, glutamyl aminotransferase, pancreatic amylase, creatinine, glucose, triglyceride, Contents of total cholesterol, high-density lipoprotein, low-density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin G, and immunoglobulin M.

4) Data Analysis

Use IBM SPSS Statistics 25 software to analyze the difference of data: use One-way ANOVA analysis of variance, CONTRAST result comparison and Duncan multiple comparison analysis, the results are expressed in the form of mean ± standard error, and the superscripts of different letters indicate significant differences, from a to f Indicates that the difference is from large to small; p<0.05 indicates that the difference is significant, and p<0.001 indicates that the difference is extremely significant;

Use IBM SPSS Statistics 25 software to conduct univariate correlation analysis on the data. The correlation coefficient 0.4≤r<0.7 indicates that the independent variable is significantly correlated with the dependent variable; 0.7≤r<1 indicates that the independent variable is highly correlated with the dependent variable, and curve correlation regression analysis is used , the significance value of F test p<0.05 indicates that there is a significant regression relationship between the independent variable and the dependent variable;

Using Matlab software to conduct multiple regression analysis on the data, the significance value of F test p<0.05 indicates that there is a significant regression relationship between the independent variable and the dependent variable, and the coefficient of determination R ² >0.6 indicates that the percentage of all independent variables that can explain the change of the dependent variable is 60%. The closer to 1, the better the model fit;

3. Test results

1) Screening of evaluation indicators for protein nutritional status of growing pigs

The effects of dietary protein levels on blood biochemical indicators of growing pigs are shown in Table 2. The results showed that the level of dietary protein significantly affected the levels of total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine, glucose, total cholesterol, high-density lipoprotein, cholinesterase, creatine The content of kinase, lactate dehydrogenase and immunoglobulin M (p<0.05). Carry out the correlation analysis of blood biochemical index content and dietary protein level of growing pigs fed with different protein level diets, as shown in Table 3, blood biochemical index in total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, There were significant correlations between total cholesterol, high-density lipoprotein, creatine kinase and lactate dehydrogenase and dietary protein levels (p<0.05, r>0.5). Based on the screening results of the two aspects, the blood biochemical indicators that are sensitive to changes in dietary protein levels are finally obtained as total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, total cholesterol, high-density lipoprotein, creatine kinase, and lactate dehydrogenation enzyme.

Table 2 Effect of dietary protein level on blood biochemical indicators of growing pigs

Note: CP: dietary protein level (%). Different superscripts between groups indicate significant differences (p<0.05, n=6)

Table 3 Correlation analysis between dietary protein level and blood biochemical indexes of growing pigs

Note: r is the correlation coefficient, and p is the significance value.

2) Setting of reference standards for protein nutritional status of growing pigs

It can be seen from Figure 1 that the dietary protein level significantly affects the growth performance of growing pigs (p<0.05), and the daily gain of growing pigs increases first and then decreases with the increase of dietary protein level; correlation analysis also shows that growth There is a significant correlation between pig daily gain and dietary protein level (r=0.952), and the regression model ADG(g/day)=35.5+35.9×protein level-1.51×protein level is established for the growth performance of growing pigs and dietary protein level ² +0.0182×protein level ³ (coefficient of determination R ² ＝0.945), through the solution of the equation, the dietary protein level for the best growth performance of growing pigs is 17.3%, which is set according to the reasonable fluctuation range of 1% dietary protein level in actual production Set 17.3±1% as the optimal ratio of protein in the diet, that is, 16.3 to 18.3%, and the protein nutritional status of growing pigs is the best in this range, so this dietary protein level is set as the reference standard for protein nutritional status of growing pigs.

3) Construction of the evaluation model for protein nutritional status of growing pigs

Utilize the evaluation index of protein nutritional state of growing pigs obtained by screening to carry out single regression analysis and multiple regression analysis to study the relationship between dietary protein nutritional level and protein nutritional state evaluation index. Table 4 lists the protein nutrition of growing pigs after regression analysis For state prediction regression equation, the construction standard is regression model F-test significance value p<0.05, coefficient of determination R ² >0.6. Urea can be used as a predictor in the unary predictive equation to evaluate the protein nutritional status of growing pigs because of its regression model determination coefficient ^R2 reaching 0.8314; and the establishment of multiple regression equations can improve the accuracy of the predictive equation, the most in Table 2-3 The coefficient of determination R ² of the excellent multiple regression model can reach 0.9636, and the prediction equation is CP=(0.5349x ₁ ² +0.0073x ₂ ² +0.0307x ₃ ² +0.0206x ₄ ² +0.0207x ₅ -0.3931x ₁ -0.1839x _{2 +} 0.1008x ₆ +0.0179x ₃ -0.1119x ₇ -0.0449x ₄ )×100(x ₁ -x ₇ are blood triglyceride, total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein, respectively protein and cholinesterase content).

Table 4 Regression equation for predicting protein nutritional status of growing pigs

Note: x is the blood biochemical index content, CP is the dietary protein level (%), R ² is the determination coefficient, and p is the significance value.

4 Conclusion

Blood biochemical indicators can be used as markers to evaluate individual protein nutritional status of pigs. The single blood biochemical indicator is urea; the combined blood biochemical indicators are ① total protein, urea, blood ammonia, alanine aminotransferase, aspartate aminotransferase, pancreatic amylase, creatinine , glucose, total cholesterol, high-density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin M, ② total protein, alanine aminotransferase, urea, glucose, triglyceride, total cholesterol, high Density lipoprotein, low-density lipoprotein, immunoglobulin G, immunoglobulin M, ③ triglyceride, total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein, cholinesterase, ④ Urea, glucose, triglyceride, total cholesterol, high-density lipoprotein, low-density lipoprotein, immunoglobulin G, immunoglobulin M, ⑤ total protein, alanine aminotransferase, aspartate aminotransferase, urea, glucose, total cholesterol, immune Globulin G, low-density lipoprotein, high-density lipoprotein.

These blood biochemical indicators can well predict the dietary protein level of pigs to reflect the impact of protein nutrition on the pig body, and use these markers as predictors to build a pig protein nutritional status evaluation model, in which the optimal one-variable prediction equation is CP =-0.2039x ² +4.5507x-0.6947 (R ² =0.8314, x is blood urea content), the optimal multivariate prediction equation is CP=(0.5349x ₁ ² +0.0073x ₂ ² +0.0307x ₃ ² +0.0206x ₄ ² +0.0207x ₅ -0.3931x ₁ -0.1839x ₂ +0.1008x ₆ +0.0179x ₃ -0.1119x ₇ -0.0449x ₄ )×100(R ² =0.9636, x ₁ -x ₇ are triglycerides in blood respectively , total cholesterol, low-density lipoprotein, immunoglobulin G, urea, high-density lipoprotein and cholinesterase).

Claims (4)

1. A method for evaluating the protein nutrition status of individual pigs by using blood biochemical indexes, which is characterized in that the method evaluates the protein nutrition status of individual pigs by quantitatively detecting the following blood biochemical indexes of A group, B group, C group, D group, E group or F group in blood:

group A: urea;

group B: total protein, urea, blood ammonia, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, pancreatic amylase, creatinine, glucose, total cholesterol, high density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, and immunoglobulin M;

group C: total protein, glutamic pyruvic transaminase, urea, glucose, triglyceride, total cholesterol, high density lipoprotein, low density lipoprotein, immunoglobulin G, immunoglobulin M;

group D: triglyceride, total cholesterol, low density lipoprotein, immunoglobulin G, urea, high density lipoprotein, cholinesterase;

group E: urea, glucose, triglycerides, total cholesterol, high density lipoproteins, low density lipoproteins, immunoglobulin G, immunoglobulin M;

group F: total protein, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, urea, glucose, total cholesterol, immunoglobulin G, low density lipoprotein, and high density lipoprotein;

the method comprises the following specific steps:

(1) Establishing a regression model of growth performance of growing pigs and protein level in daily ration, determining the protein level of the daily ration when the growth performance is optimal, and setting +/-1% of the protein level of the daily ration as a reference standard of protein nutrition state of the growing pigs;

(2) Constructing a dynamic regression model reflecting the blood biochemical index content and daily ration protein level of the protein nutrition state of the growing pig, wherein the regression model is as follows:

a regression model:

CP= - 0.2039x ² + 4.5507x-0.6947; wherein the saidxIs the urea content in blood;

b regression model:

CP=14.3304+0.3498x ₁ +0.7335x ₂ -0.0110x ₃ -0.1162x ₄ -0.0400x ₅ +0.0003x ₆ -0.0448x ₇ -0.5111x ₈ -3.5789x ₉ +1.1543x ₁₀ -0.0045x ₁₁ +0.0019x ₁₂ +0.0028x ₁₃ -5.2590x ₁₄ the method comprises the steps of carrying out a first treatment on the surface of the Wherein the saidx ₁ To the point ofx ₁₄ The total protein, urea, blood ammonia, glutamic pyruvic transaminase, amylopsin, creatinine, glucose, total cholesterol, high density lipoprotein, cholinesterase, creatine kinase, lactic dehydrogenase and immunoglobulin M in blood;

c regression model:

CP=(0.2497 + 0.0021x ₁ + 0.0012x ₂ + 0.0086x ₃ - 0.0069x ₄ + 0.1081x ₅ - 0.304 x ₆ + 0.2573 x ₇ + 0.2926 x ₈ + 0.0066 x ₉ - 0.0391x ₁₀ ) X 100; wherein the saidx ₁ To the point ofx ₁₀ The contents of total protein, glutamic pyruvic transaminase, urea, glucose, triglyceride, total cholesterol, high density lipoprotein, low density lipoprotein immunoglobulin G and immunoglobulin M in blood respectively;

d regression model:

CP=(0.5349x ₁ ² +0.0073x ₂ ² +0.0307x ₃ ² +0.0206x ₄ ² +0.0207x ₅ -0.3931x ₁ -0.1839x ₂ +0.1008x ₆ +0.0179x ₃ -0.1119x ₇ -0.0449x ₄ ) X 100; wherein the saidx ₁ To the point ofx ₇ The contents of triglyceride, total cholesterol, low density lipoprotein, immunoglobulin G, urea, high density lipoprotein and cholinesterase in blood;

e regression model:

17.75CP=0.06786+0.0033x ₁ -0.0014x ₂ -0.0057x ₃ -0.003x ₄ -0.003x ₅ -0.0035x ₆ -0.0012x ₇ + 0.02x ₈ the method comprises the steps of carrying out a first treatment on the surface of the Wherein the saidx ₁ To the point ofx ₈ The contents of urea, glucose, triglyceride, total cholesterol, high density lipoprotein, low density lipoprotein, immunoglobulin G and immunoglobulin M in blood respectively;

f regression model:

CP=(0.001x ₁ -0.0018x ₂ +0.0006x ₃ +0.01x ₄ -0.002x ₅ -0.0014x ₆ -0.00093x ₇ -0.00057x ₈ -0.00053x ₉ ) X 100; wherein the saidx ₁ To the point ofx ₉ The total protein, glutamic-pyruvic transaminase, glutamic-oxaloacetic transaminase, urea, glucose, total cholesterol, immunoglobulin G, low density lipoprotein and high density lipoprotein in blood respectively;

the CP is a daily ration protein level, wherein the daily ration protein level refers to the content of protein in daily ration, and the unit is;

(3) Measuring the content of the biochemical indexes of the blood of the group A in the blood of the live pig individual to be measured, and substituting the measured result into an A regression model; or, determining the content of the B group blood biochemical index in the blood of the live pig individual to be detected, and substituting the measured result into a B regression model; or, determining the content of the biochemical indexes of the group C blood in the blood of the live pig individual to be detected, and substituting the measured result into a C regression model; or, determining the content of the biochemical indexes of the group D blood in the blood of the live pig individual to be detected, and substituting the measured result into a D regression model; or, determining the content of the biochemical indexes of the E group blood in the blood of the live pig individual to be detected, and substituting the measured result into an E regression model; or, determining the content of the biochemical indexes of the F group blood in the blood of the live pig individual to be detected, and substituting the measured result into an F regression model; calculating to obtain the daily ration protein level fed by the individual live pigs to be detected, comparing the daily ration protein level fed by the individual live pigs to be detected with the reference standard of the protein nutrition state of the growing pigs obtained in the step (1), and if the protein level is within the reference standard numerical range of the protein nutrition state of the growing pigs, indicating that the nutrition state of the individual live pigs to be detected is optimal.

2. The method of claim 1, wherein the biochemical markers in the blood are measured using an automated biochemical analyzer or an enzyme-linked immunosorbent assay.

3. The method of claim 1, wherein the regression model is a linear regression model, a nonlinear regression model, a principal component regression model, a least squares regression model, and a partial least squares regression model.

4. The method of claim 1, wherein regression analysis in the regression modelFChecking significance valuesp< 0.05Determining the coefficient R ² >0.6。