CN114446465B - Method for evaluating heat stress state of chicken - Google Patents
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Abstract
The invention discloses an evaluation method of chicken heat stress state, which collects rectal temperature data RT and blood samples of partial chickens in hot weather, detects biochemical indexes of the blood samples: ALB, AST and blood gas indices: and Cl and Hb are substituted into a Fisher discriminant function: y1=2.1176 + RT +0.0094 + AST-0.2045 + ALB-0.1322 + Cl-0.1466 + Hb-70.8139; y2=1.2204 RT-0.0091 AST +0.2348 ALB +0.0975 Cl +0.4176 Hb-68.2245; when Y1 is less than 0, it is judged as no heat stress, when Y1 is greater than 0 and Y2 is less than 0, it is judged as light heat stress, and when Y1 is greater than 0 and Y2 is greater than 0, it is judged as heavy heat stress. And the feeding management scheme is adjusted immediately, the negative influence of heat stress on production is reduced, and the economic loss is reduced.
Description
Technical Field
The invention detects the heat stress state of the chicken and evaluates the heat stress state in a grading way.
Background
With the continuous development of the physical life of people, the demand of livestock and poultry products such as meat, eggs and milk is continuously increased, and the livestock and poultry breeding industry is continuously developed in the process. Modern poultry mostly undergoes high-intensity breeding, the production performance is excellent, the metabolism rate is high, the body surface of the poultry is covered by feathers, and compared with other poultry, the body surface of the poultry does not have sweat glands, is more easily affected by heat stress, causes the reduction and even death of the production performance, and causes unnecessary economic loss. Meanwhile, the countryside is vast, but most areas have the characteristic of high temperature in summer, the temperature in a hot summer period can even reach more than 38 ℃, and under the condition of incomplete feeding management, poultry is very easy to generate heat stress.
In high-temperature weather, most of the farms adopt a unified mode for feeding management, the maintenance cost is high, specific conditions of different chicken flocks are not considered, and finally obtained effects are different in level. In order to reduce the influence caused by heat stress as much as possible and better ensure the welfare and health of the chicken flocks and the production and benefit of enterprises, the feeding management scheme should be adjusted in time according to the specific situations such as whether the chicken flocks have heat stress and the severity of the heat stress. Although a great deal of research is carried out on chicken heat stress at present, index systems used in different researches are different, physiological indexes such as rectal temperature are used in some researches, blood biochemical indexes such as hormone levels are used in some researches, however, the blood gas indexes are not reported to the chicken heat stress research, and the analysis of the blood gas indexes can reflect the respiratory function and the acid-base balance state of an organism instantly and accurately. In addition, no unified standard for grading the heat stress state of the chicken is formed at present. Therefore, the invention aims to establish a method capable of accurately judging the heat stress degree of the chicken by establishing a standard heat stress model and integrating and screening various indexes.
Disclosure of Invention
The invention aims to solve the technical problem of grading the heat stress state of chicken, adopts the technical means of establishing a standard heat stress model, dividing the state of the chicken into a non-heat stress state, a light heat stress state and a heavy heat stress state, then combining physiological indexes and biochemical indexes which have been researched at present and blood gas indexes which are not used in poultry research, finding out the indexes which have obvious and key changes in the heat stress process by a statistical analysis method, and establishing a Fisher discriminant function by using the indexes, wherein the technical scheme is as follows:
a method for evaluating heat stress state of chickens includes collecting rectal temperature data RT and blood samples of partial chickens under the condition of hot weather, and detecting biochemical indexes of the blood samples: ALB, AST and blood gas indices: and Cl and Hb are substituted into a Fisher discriminant function:
Y1＝2.1176*RT+0.0094*AST-0.2045*ALB-0.1322*Cl-0.1466*Hb-70.8139
Y2＝1.2204*RT-0.0091*AST+0.2348*ALB+0.0975*Cl+0.4176*Hb-68.2245
if Y1 is less than 0, it is judged as non-heat stress, if Y1 is greater than 0 and Y2 is less than 0, it is judged as light heat stress, and if Y1 is greater than 0 and Y2 is greater than 0, it is judged as heavy heat stress.
The Fisher discriminant function is obtained by the following steps:
(1) Establishing a standard heat stress model, carrying out heat stress treatment on a plurality of chickens only under the conditions that the environment humidity is 60-70%, the environment temperature is 32 +/-1 ℃ and 36 +/-1 ℃, starting at 11 am and ending at 17 pm for 6 hours, and recovering the state in a time of more than two weeks between two temperature gradients;
(2) Collecting rectal temperature data and blood sample of chicken before and after each heat stress treatment, and detecting blood gas indexes such as Na, cl, K, and TCO 2 、Glu、Hct、pH、PCO 2 、HCO 3 ALT, AST, BUN, CK, CA, GLU, LDH, TCHO, TG, TP, ALB, GLB;
(3) Dividing the obtained data into a non-heat stress group and a heat stress group according to whether the collected sample is before heat stress treatment or after heat stress treatment, analyzing the data by adopting SPSS25, performing independent sample T test, and screening out an index, namely P, which has obvious change before and after heat stress<0.05, randomly combining the indexes with obvious change into a plurality of models with different index numbers p, carrying out stepwise regression analysis on each model and the state of the chicken, and selecting the model with the minimum index number p, R 2 >80% and the model of the index composition is significant, i.e. P<0.001；
(4) The data of the heat stress group are grouped according to the temperature of the experiment, the data before stress is set as the non-stress group, and 32The data of the temperature is set as a mild heat stress group, the data of the temperature of 36 ℃ is set as a severe heat stress group, and the sample size of each group is n DEG C i And (3) establishing a Fisher discriminant function when the total sample size is n, firstly calculating the mean value of each group, wherein i is a different group, i → 1 is a non-heat stress group, i → 2 is a light heat stress group, and i → 3 is a heavy heat stress group:
wherein i is different groups, and the overall mean value is calculated according to the mean values of the groups:
calculating each group of covariance matrix S after obtaining the overall mean value i And the covariance matrix S in the joint group p An intra-group SSCP matrix W and an inter-group SSCP matrix B, where X ij For the jth sample of the ith group:
wherein the "'" symbol is a transposed matrix, a characteristic root lambda of the discriminant function is calculated according to the obtained W and B, the quantity t of the root is min (p, g-1), namely the number of the discriminant function:
(W -1 B-λI)E＝0 (7)
wherein I and E are unit matrixes, and after lambda is obtained, the coefficient a of each index in the discriminant function is calculated based on the formula (8) t ：
(W -1 B-λ t T)a t =0 and (a) t S p a t )＝1 (8)
To obtain a t Constant term c of post-calculation discriminant function t ：
Finally, the discriminant function y is obtained t ：
y t ＝x 1 *a t1 +x 2 *a t2 +…+x p *a tp +c t (10)
(5) The obtained discriminant function is used for carrying out retrospective verification on the data
: and (5) detecting the rectal temperature data RT and the blood sample of the chicken grouped in the steps (3) to (4) according to biochemical indexes of the blood sample: ALB, AST and blood gas indices: and (4) substituting Cl and Hb into the step (4) to obtain a Fisher discriminant function, judging as non-heat stress if Y1 is less than 0, judging as light heat stress if Y1 is greater than 0 and Y2 is less than 0, judging as heavy heat stress if Y1 is greater than 0 and Y2 is greater than 0, and counting the number of the non-heat stress group, the light heat stress group and the heavy heat stress group predicted by the discriminant function: n is 1 ’，n 2 ’，n 3 ', calculate and judge the rate of accuracy again: eta 1 ＝n 1 ’/n 1 ，η 2 ＝n 2 ’/n 2 ，η 3 ＝n 3 ’/n 3 ；
(6) If the accuracy rate of all the groups is more than 80%, selecting the groups as discriminant functions;
if not, randomly extracting the data grouped in the steps (3) to (4) by 80 percent for multiple times, reestablishing the discrimination function for the data extracted each time according to the step (4) to obtain multiple discrimination function results, and averaging the coefficients of the results obtained multiple times to obtain a new discrimination function;
(7) And (4) retrospectively verifying the discriminant function obtained in the step (6) again according to the steps (5) to (6) until the accuracy of all the groups is more than 80%.
Under the condition of hot weather, samples of partial chickens are collected, the heat stress state of the chickens is judged through a discrimination function, the heat stress state of the whole chicken flock is further deduced, the feeding management scheme can be improved in a more targeted manner after the state of the chicken flock is known, the negative influence caused by heat stress is reduced, and the economic loss is reduced.
Has the advantages that:
1) Firstly, the blood gas index is applied to the research of the chicken, and the blood gas index is well combined with the heat stress problem of the chicken;
2) The evaluation result is reliable: according to the invention, the characteristic that physiological indexes, biochemical indexes and blood gas indexes of the chicken change along with the intensity of heat stress is grasped, and a plurality of key indexes are combined, so that the formed discriminant function can accurately evaluate the heat stress state of the chicken;
3) The operation is simple and convenient: the field feeding manager only needs to collect rectal temperature data and blood samples of the chickens.
Drawings
FIG. 1 original discriminant function regression verification results: judging that the points falling in the second quadrant and the third quadrant are non-heat stress, the points falling in the first quadrant are severe heat stress, and the points falling in the fourth quadrant are mild heat stress;
fig. 2 final discriminant function regression verification results: the points falling in the second and third quadrants were judged as non-heat stress, the points falling in the first quadrant were judged as severe heat stress, and the points falling in the fourth quadrant were judged as mild heat stress.
Detailed description of the preferred embodiments
Example 1
1) 90 Hailan brown laying hens and 90 Hailan laying hens in the egg producing period are selected respectively, and in order to eliminate the stress influence caused by long-distance transportation and environmental change, the laying hens are fed at the room temperature for two weeks and then are subjected to an adaptive normal-temperature feeding test. Normally, chicken flocks are bred in a room with the environment temperature of 18-25 ℃, the illumination scheme adopts 16-hour illumination and 8-hour darkness, automatic timing switch control is adopted, sufficient drinking water and feed are given all day long, the water quality meets the requirement of NY5027-2008 pollution-free food, livestock and poultry drinking water quality, and the feed component composition and the nutrient level meet the requirement of ' Chinese chicken breeding standard ' (NY/T2004) (Ministry of agriculture of the people's republic of China, 2004);
2) And (3) reforming a heating ring control cabin: install 100 mm's polyurethane heated board additional at the furred ceiling, adopt polyurethane foam to fill in door and window gap department, increase the holistic thermal insulation performance in room. Heating plates and heating fans are arranged on two sides of a room to heat the room, and a temperature control switch is adopted to control a heating device, so that the temperature of the room is kept at a target temperature;
3) Establishing a standard heat stress model: normally, raising the chicken flocks in a proper environment at the temperature of 18-25 ℃, then transferring the chicken flocks to a heating environmental control cabin, carrying out heat stress treatment under the conditions of 60-70% of environmental humidity and the environmental temperatures of 32 +/-1 ℃ and 36 +/-1 ℃, and giving two weeks between two temperature gradients to restore the chicken flocks to a normal state. When heat stress treatment is carried out, the chicken flocks are transferred from a normal feeding environment to an environment-controlled cabin which is modified to have stable and uniform temperature and humidity for 6 hours, so that the chicken flocks generate heat stress reaction;
4) Before heat stress treatment of chicken flocks and 6 hours after heat stress treatment, 3.5 ml of blood is respectively collected by veins under the left and right wings, 0.5 ml of the blood is placed in a lithium heparin negative pressure tube for anticoagulation and used for subsequent blood gas analysis, 3 ml of the blood is placed in a negative pressure tube without additives, after 3500g of the blood is subsequently centrifuged for 20min, supernatant is taken, and the separated serum is used for subsequent biochemical analysis of the serum. Simultaneously, a Rectal thermometer for animals is extended into the rectum for about 4cm to collect Rectal Temperature (RT) data before and after heat stress of the chicken;
5) Blood sample detection: and slightly inverting the anticoagulation solution from top to bottom, shaking up, adding 90 mu L of the anticoagulation solution into an EC8+ blood gas detection plate, and then inserting the anticoagulation solution into an Atpeii STAT 300G blood gas analyzer for detection. Adding 500 μ L of serum into a full-automatic biochemical analyzer (BX-4000, sysmex, japan), and detecting the contents of glutamic-oxaloacetic transaminase (AST), glutamic-pyruvic transaminase (ALT), total Protein (TP), albumin (ALB), globulin (GLB), urea nitrogen (BUN), total Cholesterol (TCHO), triglyceride (TG), glucose (GLU), creatine Kinase (CK), lactate Dehydrogenase (LDH), calcium (CA) and other biochemical indexes in the serum. Detecting the content of tetraiodothyronine (T4), cortisol (Cortisol), superoxide dismutase (SOD) and immunoglobulin G (IgG) in serum by using an enzyme-linked immunosorbent assay (ELISA) kit;
6) Data preprocessing: dividing the data of each index under the two temperature gradients into four groups according to the conditions of the breeds (Xinhua laying hens and Hailan brown laying hens) and the breeds (before and after heat stress), carrying out box diagram inspection on the data of each group by using statistical software SPSS25, marking and removing abnormal values, and finally obtaining 169 data before and after heat stress treatment at 32 ℃ and 184 data before and after heat stress treatment at 36 ℃;
7) And (3) screening the obvious difference indexes: setting index data before heat stress treatment of two temperature gradients of 32 ℃ and 36 ℃ as a non-heat stress group, setting index data after heat stress treatment of the two temperature gradients of 32 ℃ and 36 ℃ as a heat stress group, then carrying out independent sample T test on the index data of the heat stress group and the non-heat stress group, discharging samples according to specific analysis on deficiency values, selecting indexes with significant changes before and after heat stress, wherein group statistical results are shown in a table 1, and independent sample T test results are shown in a table 2.
Test results show that the rectal temperature before and after heat stress has obvious difference, biochemical indexes such as ALT, AST, BUN, CK, CA, GLU, LDH, TCHO, TG, TP, ALB, GLB and the like have obvious difference, blood and gas indexes such as Na, K, cl, TCO2, hct, pH, PCO2, HCO3, angap, hb and the like have obvious difference, and immune index IgG and hormone index T4 have obvious difference (P)<0.05). Then carrying out stepwise regression analysis on indexes with obvious difference before and after heat stress and heat stress states (heat stress and non-heat stress), and selecting the index with the least number p and the least R 2 >80% and the model of index composition is significant, i.e. P<0.001, regression analysis results are shown in tables 3 and 4, model 6 (f) can explain 80% or more of R by using 5 indexes of RT, cl, ALB, AST, and Hb 2 And the model is extremely significant (P)<0.001)；
And (3) establishing a discriminant function: the discriminant is established by using 5 indexes of RT, cl, ALB, AST and Hb. Screening samples with complete RT, cl, ALB, AST and Hb5 index data, regrouping the samples, and setting sample data before heat stress treatment of two temperature gradients of 32 ℃ and 36 ℃ as a non-heat stress group: 188 chickens, sample data after 32 ℃ heat stress was set as mild heat stress group: 83 chickens, sample data after 36 ℃ heat stress was set as severe heat stress group: and (4) establishing Fisher discriminant functions for 62 chickens, wherein the sample amount of each group is ni, and the total sample amount is n.
First according to
Calculate the mean of each group, where i is the different group, i → 1 is the non heat-stressed group, i → 2 is the mild heat-stressed group, i → 3 is the severe heat-stressed group, and the grouped data are shown in table 8:
and then calculating the overall mean value according to the group mean values:
calculating each group of covariance matrix S after obtaining the overall mean value i And the covariance matrix S in the joint group p An intra-group SSCP matrix W and an inter-group SSCP matrix B, where X ij Is the jth of the ith groupSample preparation:
the "'" symbol is a feature root λ of a discriminant function calculated by taking a transposed matrix and obtaining W and B, where the number of roots t is min (p, g-1), i.e., the number of discriminant functions, p is an index number having strong correlation with the heat stress state, and p =5, g =3.
Then according to (W) -1 B- λ I) E =0, calculating a characteristic root, I and E both being identity matrices:
λ 1 ＝4.618，λ 2 ＝0.363；
finally, calculating the coefficient a of each index in the discriminant function after obtaining the lambda t ：
According to (W) -1 B-λ t T)a t =0 and (a) t S p a t ) =1 and
the obtained discriminant function:
finally, the discriminant function y is obtained t ：
y t ＝x 1 *a t1 +x 2 *a t2 +…+x p *a tp +c t (8)
Y1＝2.141*RT+0.008*AST-0.200*ALB-0.133*Cl-0.153*Hb-71.459，
Y2＝1.229*RT-0.008*AST+0.247*ALB+0.102*Cl+0.407*Hb-69.418。
And substituting each index of the sample into a discriminant function, judging as a non-heat stress state if the obtained Y1 value is less than 0, judging as a light heat stress state if the obtained Y1 value is more than 0 and the Y2 value is less than 0, and judging as a heavy heat stress state if the obtained Y1 value is more than 0 and the Y2 value is more than 0.
All data are reviewed and verified, and the verification result is shown in fig. 1, the non-heat-stress group and the heat-stress group are distinguished accurately, and the mild heat-stress group and the severe heat-stress group are mixed more. The group statistics of the retrospective validation and the leave-one validation are shown in table 5, i.e. rectal temperature data RT of the chicken and the blood sample, biochemical indicators of the blood sample were measured: ALB, AST and blood gas indices: substituting Cl and Hb into a Fisher discriminant function, judging as non-heat stress if Y1 is less than 0, judging as light heat stress if Y1 is greater than 0 and Y2 is less than 0, judging as severe heat stress if Y1 is greater than 0 and Y2 is greater than 0, and counting the number of the non-heat stress group, the light heat stress group and the severe heat stress group predicted by the discriminant function: n is 1 ’＝187，n 2 ’＝67，n 3 ' =47, recalculation determination accuracy: eta 1 ＝n 1 ’/n 1 ＝99.5％，η 2 ＝n 2 ’/n 2 ＝80.7％，η 3 ＝n 3 ’/n 3 ＝75.8％。
The result shows that the judgment accuracy of the discriminant function on the non-heat stress group in the training set data is 99.5%, the judgment accuracy on the light heat stress group is 80.7%, the judgment accuracy on the heavy heat stress group is 75.8%, and the overall accuracy is 90.4%. And the accuracy of one verification is up to 89.5%, which indicates that the extrapolation of the discriminant function is good.
8) And (3) improving a discriminant function: and (4) randomly extracting all training set data by 80 percent for 10 times, reestablishing the discriminant function according to the step (7) for the data extracted each time, repeating the steps for 10 times, and obtaining 10 times of discriminant function results as shown in the table 6. Averaging the coefficients of the results obtained 10 times to obtain a final discrimination function, wherein Y1=2.1176 × RT +0.0094 × AST-0.2045 × ALB-0.1322 × Cl-0.1466 × Hb-70.8139, Y2=1.2204 × RT-0.0091 × AST +0.2348 × ALB +0.0975 × Cl +0.4176 × Hb-68.2245. Retrospective validation was performed on all data, the results of which are shown in fig. 2. The retrospective verification group statistical results are shown in table 7, and the results show that the judgment accuracy of the discriminant function on the non-heat-stress group, the mild heat-stress group and the severe heat-stress group in the training set data is 97.9%, 80.7% and 82.3% respectively, the judgment accuracy on each state after improvement is over 80%, and the total accuracy is 90.6%.
The invention provides a method for graded evaluation of heat stress state of chickens in high-temperature weather. In hot weather with high temperature, randomly extracting partial chickens from the chicken flock, collecting physiological indexes and blood biochemical index data of the chickens, substituting the physiological indexes and the blood biochemical index data into a discrimination function, predicting the heat stress state of the chickens, deducing the heat stress state of the chicken flock, immediately adjusting a feeding management scheme, reducing the negative influence of heat stress on production, and reducing economic loss.
TABLE 1 statistics of the respective indices of Heat stress group and non-Heat stress group
TABLE 2 test results of independent samples of each index of heat stress group and non-heat stress group
TABLE 3 regression analysis results of significant difference index and heat stress status
TABLE 4 regression model significance results
TABLE 5 original discriminant function prediction set statistics
TABLE 6 discrimination function results sampled 10 times randomly
TABLE 7 Final discriminant function prediction group statistics
TABLE 8 statistics of non-heat-stressed, mild and severe heat-stressed groups
Claims (1)
1. The method for evaluating the heat stress state of the chicken is characterized in that under the condition of hot weather, rectal temperature data RT and blood samples of partial chicken are collected, and biochemical indexes of the blood samples are detected: ALB, AST and blood gas indices: and Cl and Hb are substituted into a Fisher discriminant function:
Y1＝2.1176*RT+0.0094*AST-0.2045*ALB-0.1322*Cl-0.1466*Hb-70.8139
Y2＝1.2204*RT-0.0091*AST+0.2348*ALB+0.0975*Cl+0.4176*Hb-68.2245
when Y1 is less than 0, it is judged as no heat stress, when Y1 is greater than 0 and Y2 is less than 0, it is judged as light heat stress, and when Y1 is greater than 0 and Y2 is greater than 0, it is judged as heavy heat stress.
The Fisher discriminant function is obtained by the following steps:
(1) Establishing a standard heat stress model, carrying out heat stress treatment on a plurality of chickens only under the conditions that the environmental humidity is 60-70%, the environmental temperature is 32 +/-1 ℃ and 36 +/-1 ℃, carrying out heat stress treatment for 6 hours, and recovering the state of the chickens within two weeks between two temperature gradients;
(2) Collecting rectal temperature data and blood sample of chicken before and after each heat stress treatment, and detecting blood gas indexes such as Na, cl, K, and TCO 2 、Glu、Hct、pH、PCO 2 、HCO 3 ALT, AST, BUN, CK, CA, GLU, LDH, TCHO, TG, TP, ALB, GLB; wherein ALB represents albumin, AST represents glutamic-pyruvic transaminase, RT represents rectal temperature data of chicken, BUN represents urea nitrogen, CK represents creatine kinase, CA represents calcium, GLU represents glucose, LDH represents lactate dehydrogenase, TCHO represents total cholesterol, TG represents triglyceride, TP represents total protein, and GLB represents globulin;
(3) Dividing the acquired data into a non-heat stress group and a heat stress group according to whether the acquired sample is before heat stress treatment or after heat stress treatment, analyzing the data by adopting SPSS25, firstly carrying out independent sample T test, screening out indexes with significant changes before and after heat stress, randomly combining the indexes with significant changes into a plurality of models with different p, carrying out stepwise regression analysis on each model and the state of the chicken, and selecting R with the least p and R 2 >80% and index groupThe resulting model is significant, i.e. P<0.001; 5 indexes are obtained: a model consisting of RT, cl, ALB, AST and Hb, wherein p is an index number with strong correlation with the heat stress state;
(4) Screening samples with complete index data of RT, cl, ALB, AST and Hb, regrouping, grouping the data of heat stress groups according to the temperature of the experiment, setting the data before stress as a non-stress group, setting the data at 32 ℃ as a mild heat stress group, setting the data at 36 ℃ as a severe heat stress group, and setting the sample amount of each group as n i And (3) establishing a Fisher discriminant function, firstly calculating the mean value of each group, wherein i is a different group, i is a non-heat stress group 1, i is a light heat stress group 2, and i is a heavy heat stress group 3:
wherein i is different groups, the overall mean value is calculated according to the mean value of each group, and x (j) is index data of RT, cl, ALB, AST and Hb of the jth sample in each group:
calculating each group of covariance matrix S after obtaining the overall mean value i And the covariance matrix S in the joint group p An intra-group SSCP matrix W and an inter-group SSCP matrix B, where X ij For the jth sample of the ith group:
wherein the "'" symbol is a transposed matrix, a characteristic root λ of the discriminant function is calculated according to the obtained W and B, the number t of the roots is min (p, 3-1), namely the number of the discriminant function:
(W -1 B-λI)E＝0 (7)
wherein I and E are both unit matrixes, and after lambda is obtained, the coefficient a of each index in the discriminant function is calculated t ：
To obtain a t Constant term c of post-calculation discriminant function t ：
Finally, a discriminant function y is obtained t ：
(5) The obtained discriminant function is used for carrying out retrospective verification on the data: detecting the rectal temperature data RT and the blood sample of the chicken grouped in the steps (3) to (4) to detect the biochemical indexes of the blood sample: ALB, AST and blood gas indices: and (4) substituting Cl and Hb into the step (4) to obtain a Fisher discriminant function, judging as non-heat stress if Y1 is less than 0, judging as light heat stress if Y1 is greater than 0 and Y2 is less than 0, judging as heavy heat stress if Y1 is greater than 0 and Y2 is greater than 0, and counting the number of the non-heat stress group, the light heat stress group and the heavy heat stress group predicted by the discriminant function: n is 1 ’ ，n 2 ’ ，n 3 ’ And then calculating and judging the accuracy: eta 1 ＝n 1 ’ /n 1 ，η 2 ＝n 2 ’ /n 2 ，η 3 ＝n 3 ’ /n3；
(6) If the accuracy rate of all the groups is more than 80%, selecting the groups as discriminant functions;
if not, randomly extracting the data grouped in the steps (3) to (4) by 80 percent for multiple times, reestablishing the discrimination function for the data extracted each time according to the step (4) to obtain multiple discrimination function results, and averaging the coefficients of the results obtained multiple times to obtain a new discrimination function;
(7) And (4) retrospectively verifying the discriminant function obtained in the step (6) again according to the steps (5) to (6) until the accuracy of all the groups is more than 80%.
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