CN111118109B - Method for evaluating protein nutrition status of individual live pigs by using blood biochemical indexes - Google Patents
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Abstract
The invention discloses a method for evaluating the protein nutrition status of individual live pigs by utilizing blood biochemical indexes, which evaluates the protein nutrition status of individual live pigs by simultaneously and quantitatively detecting at least one of the blood biochemical indexes.
Description
Technical Field
The invention belongs to the technical field of animal nutrition, and particularly relates to a method for evaluating the protein nutrition status of individual live pigs by using blood biochemical indexes.
Background
In recent years, the shortage of feed raw materials and the grain contending condition of people and livestock are aggravated; feed supplies that exceed nutritional requirements can also exacerbate the environmental stress of livestock and poultry farming. In order to increase feed return, developing accurate nutrient supply becomes urgent. The accurate nutrition is an animal nutrition strategy for accurately connecting feed nutrition supply and animal nutrition demand for breeding according to different nutrition supply states of individuals. The nutrition state of the animal individuals is the comprehensive expression of the interaction among the feed, the organism and the environment, and the capability of the animal to meet the nutrition requirement is reflected. At present, the nutrition state of the live pigs is mainly measured through external indexes, such as a body shape and appearance visual method, a body condition score evaluation method, a body mass index method and the like, but the loss of the live pigs is difficult to compensate and correct when the nutrition imbalance of the live pigs is found due to the defects of subjectivity, insensitivity, statics and the like of the indexes.
Protein is the first nutrient substance for cultured animals, and the basis of accurate nutrition is how to accurately evaluate the protein nutrition demand change of individuals or sub-populations and the protein nutrition state of the individuals or sub-populations. Under the current intensive culture conditions, due to the influences of factors such as individual genetic background variation, environmental differences and different health conditions, different individuals can have different demands on daily ration protein nutrition when feeding pigs with the same protein level daily ration, so that different individual protein nutrition satisfaction degrees are different, and the actual growth performance or protein nutrition state of the pigs after feeding the same protein nutrition level daily ration is greatly different.
The blood biochemical indexes refer to some enzymes and proteins in blood, play important roles in the aspects of organism metabolism, immunoregulation, energy transfer, animal growth and development and the like, can be obtained by a minimally invasive way, and are tissues where ideal protein nutrition state monitoring targets are located. At present, no report of accurately evaluating the protein nutrition state and the nutrition demand change of the live pigs by establishing a dynamic regression model by utilizing blood biochemical indexes obviously related to the protein nutrition state of the live pigs exists.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art at present and provides a method for evaluating the protein nutrition status of individual live pigs by using blood biochemical indexes.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
the method for evaluating the protein nutrition status of the individual live pigs by utilizing the blood biochemical indexes is to evaluate the protein nutrition status of the individual live pigs by simultaneously and quantitatively detecting at least one of the following blood biochemical indexes in blood: urea, total protein, blood ammonia, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, pancreatic amylase, creatinine, glucose, total cholesterol, high density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase, immunoglobulin M, triglycerides, low density lipoprotein, and immunoglobulin G.
Preferably, the method evaluates the protein nutritional status of individual pigs by quantitatively detecting the following group a blood biochemical indicators, or group B blood biochemical indicators, or group C blood biochemical indicators, or group D blood biochemical indicators, or group E blood biochemical indicators, or group F blood biochemical indicators in the 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.
Preferably, the specific steps of the method include:
(1) Establishing a regression model of growth performance of growing pigs and protein level in the 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) A dynamic regression model of blood biochemical index content (in the invention, the content units of blood biochemical index total protein, cholinesterase, immunoglobulin G and immunoglobulin M are G/L, the content units of urea, glucose, total cholesterol, triglyceride, low density lipoprotein and high density lipoprotein are mmol/L, the content units of blood ammonia and creatinine are mu mol/L, and the content units of glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, pancreatic amylase, creatine kinase and lactic dehydrogenase are U/L) and daily ration protein level is constructed, wherein the regression model is as follows:
a regression model:
CP＝-0.2039x 2 +4.5507x-0.6947; wherein x is the content of urea 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 said x 1 To x 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 -0.304x 6 +0.2573x 7 +0.2926x 8 +0.0066x 9 -0.0391x 10 ) X 100; wherein said x 1 To x 10 Respectively total protein, glutamic-pyruvic transaminase, urea, glucose, triglyceride, total cholesterol, high density lipoprotein and low density in bloodLipoprotein, immunoglobulin G and immunoglobulin M content;
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 said x 1 To x 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 said x 1 To x 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 -0.002x5-0.0014x 6 -0.00093x 7 -0.00057x 8 -0.00053x 9 ) X 100; wherein said x 1 To x 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.
Preferably, when detecting blood, an automatic biochemical analyzer, an enzyme-linked immunosorbent assay kit and the like are used for measuring biochemical indexes in the blood.
Preferably, the regression model is a linear regression model, a nonlinear regression model, a principal component regression model, a least square method model, and a partial least square regression model.
Preferably, regression analysis F in the regression model examines the significance value p<0.05, determining coefficient R 2 >0.6。
The invention is further described below:
the invention provides 6 individual protein nutrition state assessment models of live pigs and protein nutrition state reference standards (daily ration protein content is 16.3-18.3%), the daily ration protein content is obtained by measuring the blood biochemical index content of the individual live pigs to be tested and substituting the blood biochemical index content into the individual protein nutrition state assessment models of live pigs, and the daily ration protein content is compared with the daily ration protein content reference standards (16.3-18.3%) when the individual protein nutrition state of the live pigs is optimal, so that the assessment of the protein nutrition state of the live pigs is realized; and obtaining a daily ration protein content adjustment value required by the individual pig to be tested for obtaining the optimal nutrition state through the individual pig protein nutrition state evaluation model.
Firstly, blood of each individual is tested by using an animal sample, wherein the animal sample is a growing pig which is divided into the following groups according to the crude protein content in feeding ration: nitrogen-free ration group (CP 0), ration protein level 5% group (CP 5), ration protein level 9% (CP 9), ration protein level 12% group (CP 12), ration protein level 16% group (CP 16), ration protein level 17% group (CP 17), ration protein level 18% group (CP 18), ration protein level 21% group (CP 21), ration protein level 25% group (CP 25) and ration protein level 30% group (CP 30); feeding the daily ration, wherein the nutrition levels of other substances are the same except for the difference of the crude protein content; the raising conditions of the live pig groups except for daily ration are the same; detecting the blood biochemical index content of each individual in the live pig population, obtaining data of the blood biochemical index content of each individual, and counting the data into a data set of groups of CP0, CP5, CP9, CP12, CP16, CP17, CP18, CP21, CP25 and CP30 according to the grouping of the animal samples;
and then analyzing the data set, and screening to obtain blood biochemical indexes for evaluating the nutritional status of the growing pig protein: performing one-way ANOVA variance analysis on the data set by using IBM SPSS Statistics software to obtain a single blood biochemical index which is obviously affected by the daily ration protein level; carrying out nonlinear correlation analysis on the data set by utilizing IBM SPSS Statistics software, screening to obtain single blood biochemical indexes which are obviously correlated with the daily ration protein level, wherein the single blood biochemical indexes are respectively total protein, urea, blood ammonia, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, total cholesterol, high density lipoprotein, creatine kinase and lactic dehydrogenase, and carrying out regression analysis and screening on the indexes by utilizing IBM SPSS Statistics software to obtain single blood biochemical indexes which reflect the nutrition state of the growing pig protein, wherein the single blood biochemical indexes are urea; carrying out R language programming solution on the data set by Matlab software to obtain a combined blood biochemical index reflecting the nutrition state of the growing pig protein; respectively (1) total protein, urea, blood ammonia, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, pancreatic amylase, creatinine, glucose, total cholesterol, high density lipoprotein, cholinesterase, creatine kinase, lactic dehydrogenase, immunoglobulin M, (2) total protein, glutamic pyruvic transaminase, 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, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, urea, glucose, total cholesterol, immunoglobulin G, low density lipoprotein, high density lipoprotein;
regression model ADG (g/day) =35.5+35.9×protein level-1.51×protein level of growth performance of growing pigs and protein level in the ration was re-established 2 +0.0182 x protein levels 3 (determining coefficient r) 2 =0.945), solving the equation to obtain the protein content of the daily ration with optimal growth performance, setting the protein level of the daily ration to be the optimal protein adding proportion of the daily ration, namely 16.3-18.3% according to the reasonable fluctuation range of 1% of the protein level of the daily ration in production practice, wherein the protein level of the daily ration is set as the reference standard of the protein nutrition state of the growing pig;
constructing a unitary nonlinear regression model of the single blood biochemical index evaluating the nutritional status of the growing pig protein and the daily ration protein level by utilizing IBM SPSS Statistics software curve estimation; utilizing Matlab software R language programming to solve and construct a multiple regression model for evaluating the blood biochemical index content of the growing pig protein nutrition state and the daily ration protein level; the coefficient R can be determined according to the model 2 Selecting a corresponding optimal pig protein nutrition state evaluation regression model:
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 the contents of 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 -0.304x 6 +0.2573x 7 +0.2926x 8 +0.0066x 9 -0.0391x 10 )×100(x 1 -x 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 )×100(x 1 -x 7 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 blood urea, glucose, triglycerides, total cholesterol, high density lipoprotein, low density lipoprotein, immunoglobulin G, immunoglobulin M content, 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 )×100(x 1 -x 9 the total protein, glutamic-pyruvic transaminase, glutamic-oxaloacetic transaminase, urea, glucose, total cholesterol, immunoglobulin G, low density lipoprotein and high density lipoprotein content of blood respectively;
the CP is the protein level of the ration in units of;
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 and obtaining the daily ration protein content fed by the individual live pigs to be tested, comparing the daily ration protein content with a daily ration protein content reference standard (16.3-18.3%) when the protein nutrition status of the individual live pigs is optimal, wherein the condition that the individual live pigs to be tested are optimal in the range is indicated, and the condition that the individual live pigs to be tested are not in the optimal status is indicated when the individual live pigs to be tested are not in the range, and the protein nutrition level is required to be adjusted; and calculating and obtaining the daily ration protein content fed by the individual live pigs to be tested according to a prediction equation, and comparing the daily ration protein content fed by the individual live pigs to a daily ration protein content reference standard (16.3-18.3%) for obtaining the daily ration protein level adjustment value required by the individual live pigs to obtain the optimal nutritional state.
In the invention, a significance value p is analyzed by a one-way variance ANOVA<0.05; correlation analysis F test significance value p<0.05 and correlation coefficient r>0.5; regression analysis F test significance value p<0.05 and a determination coefficient R 2 >0.6。
The animals in the invention are pigs, and the invention can also be pushed to other animals and humans.
Compared with the prior art, the invention has the beneficial effects that:
the daily ration protein content can be obtained by measuring the blood biochemical index content of the protein nutrition state of the individual blood sample of the live pig, evaluating the blood biochemical index content of the protein nutrition state of the individual blood sample of the live pig singly or in combination, substituting the blood biochemical index content into a prediction equation of a protein nutrition state evaluation model of the individual live pig, comparing the daily ration protein content with a daily ration protein content reference standard (16.3-18.3%) when the protein nutrition state of the individual live pig is optimal, and obtaining a daily ration protein content adjustment value required by the individual live pig to obtain the optimal protein nutrition state; the individual protein nutrition state evaluation model of the live pigs can be utilized to rapidly master the change of the protein nutrition state of the live pigs, and the nutrition demand of animals can be known so as to take countermeasures in time; has important theoretical and practical significance for body health, animal accurate feeding and accurate management.
Drawings
FIG. 1 is a graph showing the effect of daily gain of growing pigs on daily protein levels; in the figure: ADG: daily gain; CP: daily ration protein level (%). The different superscripts between the groups indicate significant differences (p <0.05, n=6).
Detailed Description
The experimental methods, materials and reagents used in the examples described below were conventional and commercially available unless otherwise specified.
1. Materials and methods
1) Test animals
About 35kg of binary hybrid (long white x about gram) growing pigs with no significant difference in weight were selected for 60 groups of 6 heads (n=6) at random, and fed for 30 days in single columns with free water but fed uniformly and quantitatively (average daily feeding is 1.5 kg).
2) Test ration
According to NRC (2012) standards, 10 groups of different protein flat diets were designed, a nitrogen-free diet group (CP 0), a diet protein level 5% group (CP 5), a diet protein level 9% group (CP 9), a diet protein level 12% group (CP 12), a diet protein level 16% group (CP 16), a diet protein level 17% group (CP 17), a diet protein level 18% group (CP 18), a diet protein level 21% group (CP 21), a diet protein level 25% group (CP 25) and a diet protein level 30% group (CP 30), each group having the same diet energy. The composition and nutritional ingredients of the diets are shown in Table 1.
Table 1 test ration composition and nutrient level (dry matter basis)
a Premix composition (%): monocalcium phosphate, 31.575; stone powder 15; calcium lactate, 30; salt, 10; choline chloride (50%), 2.5; 2.5 parts of mildew preventive; an antioxidant, 1.25;436 multidimensional (porcine multidimensional), 1; cuSO4.5H2O, 0.75; ferrous sulfate FeSO4.H2O, 0.75; zinc sulfate ZnSO4.H2O, 0.5; manganese sulfate MnSO4.H2O, 0.25; organochromium (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; complex enzyme (888), 0.75; coated VC (90%), 0.25; vitamin E powder (50%), 0.125; and 0.5 of bacillus subtilis (microecological preparation).
b Nutritional ingredients (calculated): standard digestible phosphorus STTD P (%), 0.28; sodium (%), 0.16; chlorine (%), 0.25; salt (%), 0.41; copper coppers (ppm), 75.6; iron ion (ppm), 90; zinc (ppm), 71; manganese 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 pig was measured separately. Average daily gain was calculated. Blood is collected after animal feeding tests are finished, and the contents of total protein, urea, blood ammonia, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, glutamyl aminotransferase, pancreatic amylase, creatinine, glucose, triglyceride, total cholesterol, high density lipoprotein, low density lipoprotein, cholinesterase, creatine kinase, lactic dehydrogenase, immunoglobulin G and immunoglobulin M in the blood are measured by an automatic biochemical analyzer.
4) Data analysis
Data were subjected to differential analysis using IBM SPSS Statistics software: using One-way ANOVA analysis of variance, CONTRAST result comparison and Duncan multiple comparison analysis, wherein the results are expressed in the form of mean value + -standard error, the different letter superscripts represent that the difference is significant, and the difference is from big to small; p <0.05 represents the difference is significant, and p <0.001 represents the difference is extremely significant;
performing univariate correlation analysis on the data by using IBM SPSS Statistics software, wherein the correlation coefficient is 0.4-0.7, and the independent variable is obviously correlated with the dependent variable; r <1, 0.7, represents that the independent variable is highly correlated with the dependent variable, and using curve correlation regression analysis, F test significance value p <0.05 represents that the independent variable has significant regression relation with the dependent variable;
multiplex regression analysis is carried out on the data by Matlab software, and the significance value p is checked by F<0.05 represents that the independent variable and the dependent variable have obvious regression relation and the coefficient R is determined 2 >0.6 represents that all independent variables can account for 60% of the dependent variable variation, the closer to 1 the model the better the goodness of fit;
3. test results
1) Screening of evaluation indexes of nutritional status of growing pig protein
The effect of daily ration protein levels on biochemical indicators of growing pig blood is shown in Table 2. As a result, it was found that the daily protein level significantly affected the total protein, urea, serum ammonia, glutamic-pyruvic transaminase, glutamic-oxaloacetic transaminase, pancreatic amylase, creatinine, glucose, total cholesterol, high density lipoprotein, cholinesterase, creatine kinase, lactate dehydrogenase and immunoglobulin M content (p < 0.05) in blood. Analysis of correlation of the content of biochemical indicators of pig blood and protein level of daily ration under feeding of different protein levels is carried out, and as shown in table 3, the content of total protein, urea, blood ammonia, glutamic-pyruvic transaminase, glutamic-oxaloacetic transaminase, total cholesterol, high density lipoprotein, creatine kinase and lactic dehydrogenase in the biochemical indicators of blood has obvious correlation with the protein level of daily ration (p <0.05, r > 0.5). And finally obtaining blood biochemical indexes sensitive to the change of the daily ration protein level, wherein the blood biochemical indexes comprise total protein, urea, blood ammonia, glutamic-pyruvic transaminase, glutamic-oxaloacetic transaminase, total cholesterol, high-density lipoprotein, creatine kinase and lactate dehydrogenase.
TABLE 2 Effect of daily protein levels on biochemical indicators of growing pig blood
Note that: CP: daily ration protein level (%). The different superscripts between the groups indicate significant differences (p <0.05, n=6)
TABLE 3 analysis of correlation of daily ration protein levels and biochemical indicators of growing pig blood
Note that: r is a correlation coefficient, and p is a significance value.
2) Setting of reference standard for nutrient status of growing pig protein
As can be seen from fig. 1, the daily ration protein level significantly affected the growth performance of growing pigs (p<0.05 The daily gain of the growing pigs is in a trend of rising and then falling along with the increase of the protein level of the daily ration; correlation analysis also shows that there is a significant correlation between daily gain of growing pigs and daily protein level of ration (r=0.952), and a regression model of growth performance of growing pigs and daily protein level of ration ADG (g/day) =35.5+35.9×protein level-1.51×protein level is established 2 +0.0182 x protein levels 3 (determining coefficient R) 2 =0.945), the protein level of the daily ration is 17.3% when the growth performance of the growing pig is optimal, 17.3+/-1% is set as the optimal protein adding proportion in the daily ration according to the reasonable fluctuation range of 1% of the protein level of the daily ration in production practice, namely 16.3-18.3%, the nutrition state of the growing pig protein is optimal in the range, and therefore the protein level of the daily ration is set as the reference standard of the nutrition state of the growing pig protein.
3) Construction of growth pig protein nutrition state evaluation model
Performing unitary regression analysis and multiple regression analysis by using the growth pig protein nutrition status evaluation index obtained by screening to study the relationship between the daily ration protein nutrition level and the protein nutrition status evaluation index, wherein table 4 lists the growth pig protein nutrition status prediction regression equation after regression analysis, and the construction standard is regression model F for checking significance value p<0.05, determining coefficient R 2 >0.6. Determination of coefficient R by urea regression model 2 Up to 0.8314, the method can be used as a predictor in a unitary predictive equation to evaluate the nutrition state of the growing pig protein; the establishment of the multiple regression equation can improve the accuracy of the prediction equation, and the optimal multiple regression model in tables 2-3 determines the coefficient R 2 0.9636 can be reached, the predictive equation is cp= (0.5349 x 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 The contents of triglyceride, total cholesterol, low density lipoprotein, immunoglobulin G, urea, high density lipoprotein and cholinesterase in blood, respectively).
TABLE 4 growth pig protein nutrient status predictive regression equation
Note that: x is the biochemical index content of blood, CP is the protein level (%) of ration, R 2 To determine the coefficient, p is the displayAnd (5) a value of the copybook.
4. Conclusion(s)
The blood biochemical index can be used as a marker for evaluating the protein nutrition status of individual pigs, wherein the single blood biochemical index is urea; the biochemical indexes of the combined blood are (1) total protein, urea, blood ammonia, glutamic pyruvic transaminase, glutamic oxaloacetic transaminase, pancreatic amylase, creatinine, glucose, total cholesterol, high density lipoprotein, cholinesterase, creatine kinase, lactic dehydrogenase, immunoglobulin M, (2) total protein, glutamic pyruvic transaminase, 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, glutamic pyruvic transaminase, urea, glucose, total cholesterol, immunoglobulin G, low density lipoprotein and high density lipoprotein respectively.
The blood biochemical indexes can well predict the daily ration protein level of the live pigs to reflect the influence of protein nutrition on the bodies of the live pigs, and the markers are used as predictors to construct a live pig protein nutrition state assessment model, wherein the optimal unitary prediction equation is CP= -0.2039x 2 +4.5507x-0.6947(R 2 =0.8314, x is the blood urea content) and the optimal multivariate prediction equation is cp= (0.5349 x) 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 The contents of triglyceride, total cholesterol, low density lipoprotein, immunoglobulin G, urea, high density lipoprotein and cholinesterase in blood, respectively).
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。
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB9225711D0 (en) * | 1992-12-09 | 1993-02-03 | Univ Guelph | Methodology for developing a superior line of domesticated animals |
| CN102948616A (en) * | 2012-11-19 | 2013-03-06 | 浙江大学 | Growing-finishing pig low-protein daily feed capable of improving meat quality and feeding method thereof |
| CN103087963A (en) * | 2013-01-31 | 2013-05-08 | 武汉工业学院 | Method for screening probiotics |
| CN103149280A (en) * | 2011-12-07 | 2013-06-12 | 中国农业大学 | Method for evaluating animal individual nutriture by metabonomics |
| CN103548774A (en) * | 2013-11-21 | 2014-02-05 | 中国科学院亚热带农业生态研究所 | Dynamic nutrition feeding method for improving nutrition metabolism of pig organisms |
| CN107328947A (en) * | 2017-08-22 | 2017-11-07 | 湖北省农业科学院畜牧兽医研究所 | A kind of evaluation method, restorative procedure and the feed of transgene pig exogenous origin gene integrator position effect and expression product physiological function |
| CA3071597A1 (en) * | 2017-07-31 | 2019-02-07 | Myriad Genetics, Inc. | Adjusted multi-biomarker disease activity score for inflammatory disease assessment |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100212031A1 (en) * | 2002-05-21 | 2010-08-19 | Foley Leigh Shaw Marquess | Method for improving efficiencies in livestock production |
| US8450074B2 (en) * | 2009-01-26 | 2013-05-28 | W. Jean Dodds | Multi-stage nutrigenomic diagnostic food sensitivity testing in animals |
| CN103525908A (en) * | 2013-08-13 | 2014-01-22 | 南京吉泰生物科技有限公司 | Method for rapidly detecting chicken, duck and pig blood components in blood jelly |
| CN109662062A (en) * | 2019-01-18 | 2019-04-23 | 中国科学院亚热带农业生态研究所 | The method for building up of high casein daily ration induction weanling pig trophism diarrhea model |
-
2020
- 2020-01-03 CN CN202010003889.4A patent/CN111118109B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB9225711D0 (en) * | 1992-12-09 | 1993-02-03 | Univ Guelph | Methodology for developing a superior line of domesticated animals |
| CN103149280A (en) * | 2011-12-07 | 2013-06-12 | 中国农业大学 | Method for evaluating animal individual nutriture by metabonomics |
| CN102948616A (en) * | 2012-11-19 | 2013-03-06 | 浙江大学 | Growing-finishing pig low-protein daily feed capable of improving meat quality and feeding method thereof |
| CN103087963A (en) * | 2013-01-31 | 2013-05-08 | 武汉工业学院 | Method for screening probiotics |
| CN103548774A (en) * | 2013-11-21 | 2014-02-05 | 中国科学院亚热带农业生态研究所 | Dynamic nutrition feeding method for improving nutrition metabolism of pig organisms |
| CA3071597A1 (en) * | 2017-07-31 | 2019-02-07 | Myriad Genetics, Inc. | Adjusted multi-biomarker disease activity score for inflammatory disease assessment |
| CN107328947A (en) * | 2017-08-22 | 2017-11-07 | 湖北省农业科学院畜牧兽医研究所 | A kind of evaluation method, restorative procedure and the feed of transgene pig exogenous origin gene integrator position effect and expression product physiological function |
Non-Patent Citations (3)
| Title |
|---|
| 发酵床饲养对育肥猪血液生化指标的影响;周玉刚;宁康健;刘树全;许百年;谢俊龙;;安徽农业大学学报(02);51-56 * |
| 营养水平对保育期美系长白猪血清生化指标的影响;李秀宝;张恒博;黄郁萱;谢植;齐俊勇;何若钢;;畜牧与兽医(07);70-74 * |
| 雷山小香羊母羊部分血液生化指标测定分析;田兴贵;朱红刚;杨正梅;王家鹏;杨云;主性;罗终菊;唐静;吴飞;刘若余;;西南农业学报(02);45-48 * |
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