CN108344779B - Method for rapidly detecting cow recessive mastitis grade based on dielectric spectrum technology - Google Patents
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Abstract
The invention discloses a method for rapidly detecting the level of recessive mastitis of a dairy cow based on a dielectric spectrum technology. The method comprises the following steps: collecting a batch of milk samples of dairy cows with recessive mastitis grades of negative, weak positive, positive and strong positive, and measuring the dielectric spectrum of the milk samples by using a dielectric spectrum measuring instrument; dividing a correction set and a prediction set, and extracting characteristic dielectric variables; establishing a linear or nonlinear model for detecting the recessive mastitis grade based on the full spectrum or characteristic dielectric variable; verifying the accuracy of the model, and determining an error back propagation network model based on a continuous projection algorithm as an optimal model through comparison; and (3) collecting milk samples of the dairy cattle with unknown recessive mastitis grade, measuring dielectric spectrum of the milk samples, and substituting the numerical value of the characteristic dielectric variable into the optimal model to obtain the recessive mastitis grade of the dairy cattle. The method is based on the dielectric spectrum technology, and has the advantages of convenience and rapidness in detection, simplicity in operation, high precision and capability of realizing on-line and real-time detection.
Description
Technical Field
The invention belongs to the technical field of cow mastitis diagnosis, and particularly relates to a method for rapidly detecting cow recessive mastitis grade based on a dielectric spectrum technology.
Background
Cow milk is deeply loved by consumers due to the fact that cow milk is rich in various nutrient substances such as protein, fat, mineral substances and the like. The recessive mastitis of dairy cattle is a recognized problem all over the world, and the prevention and treatment problems of the recessive mastitis of dairy cattle have attracted high attention of society. When a dairy cow suffers from recessive mastitis, the nutrient content and quality of the dairy cow are changed, even the dairy cow produces less milk, and huge economic loss is caused to the dairy industry. Although some large dairy enterprises can seriously consider the quality of dairy products, small dairy enterprises and farmers often blend cow milk of dairy cows with subclinical mastitis into normal milk for sale. In addition, it is difficult for many dairy cattle producers to determine whether a cow has mastitis, particularly subclinical mastitis. In order to promote the healthy development of the dairy industry and provide qualified cow milk for consumers, the problem of rapidly detecting the recessive mastitis grade of the dairy cows is very important.
Since the increase of the number of somatic cells in milk is caused when the bovine has recessive mastitis, the number of somatic cells is taken as an international standard for diagnosing the recessive mastitis of the dairy cows and is also taken as one of the standards for purchasing the bovine milk in almost all the countries in which the dairy industry is developed. The detection method for the somatic cell count of the cow milk specified by the standard is suitable for laboratory detection, and has the defects of complex operation, more required chemical reagents, time consumption, lag result and the like. Although some scientific researchers research detection methods of bovine somatic cell number according to conductivity, pH value and the like, the accuracy of the methods for predicting the recessive mastitis grade of the dairy cows is not high.
Dielectric spectroscopy is an instrumental measurement of the dielectric properties of a substance over a range of frequencies. Its advantages are high speed, multiple dielectric property parameter spectrums can be obtained by one-time measurement, and no need of pretreatment of liquid sample. Dielectric spectroscopy has been widely used in food tissue constituent detection applications, such as moisture content, salt content, fat, protein, sugar, etc. However, no research report for detecting the level of the recessive mastitis of the dairy cattle based on the dielectric spectrum technology is found at present.
Disclosure of Invention
The invention aims to overcome the defects of complicated detection steps, time consumption, more required chemical reagents, delayed results and the like of the traditional laboratory measurement method for detecting the grade of the cow recessive mastitis, provides a method for quickly detecting the grade of the cow recessive mastitis based on a dielectric spectrum technology, and lays a foundation for the development of a quick, efficient, accurate, real-time and on-line detection instrument for the cow recessive mastitis. The technical problem to be solved by the invention is as follows: how to quickly diagnose the mastitis grade of dairy cows.
The method of the invention can also be used for rapidly detecting the recessive mastitis grade of the ewe.
The method for rapidly detecting the recessive mastitis grade of the dairy cow based on the dielectric spectrum technology is characterized by comprising the following steps:
(1) collecting a sample: collecting milk samples of a batch of dairy cows with different recessive mastitis grades; the sample is stored at low temperature, and the collection of the dielectric spectrum of the sample is completed within 24 hours; the somatic cell number of each cow milk sample was measured according to the method prescribed by the national standard, and the bovine recessive mastitis grade was classified based on the somatic cell number.
(2) Collection of dielectric spectrum: before measurement, the sample is warmed to room temperature, and the collection of dielectric spectrum is completed by adopting a dielectric spectrum measuring instrument at 25 ℃; preheating and calibrating a measuring device before acquisition, and setting acquisition software; measuring dielectric spectrums of the cow milk samples in a radio frequency/microwave range, wherein the dielectric spectrums comprise a relative dielectric constant spectrum and a dielectric loss factor spectrum.
(3) Sample division: dividing the milk samples under each grade into a correction set and a prediction set by adopting a kennard-Stone method; dividing the sample proportion of the correction set and the prediction set according to 2:1 or 3:1 or 4: 1; the correction set and the prediction set respectively contain cow milk of negative, weak positive, positive and strong positive mastitis grade cows, and the proportion of the cow milk in the correction set and the prediction set of each grade is consistent with the proportion of samples in the total correction set and the prediction set.
(4) Extracting characteristic dielectric variable: based on the correction set sample, adopting a continuous projection algorithm, an information-free variable elimination method and a competitive adaptive reweighting algorithm to reduce the dimension of the dielectric spectrum of the cow milk sample, and extracting characteristic dielectric variables capable of distinguishing the cow recessive mastitis grade from all dielectric spectrum data; the characteristic dielectric variable can be extracted from the relative dielectric constant spectrum or the dielectric loss factor spectrum independently, or can be extracted from the relative dielectric constant spectrum and the dielectric loss factor spectrum simultaneously.
(5) Establishing a dairy cow recessive mastitis grade qualitative detection model: and (4) taking the characteristic dielectric variable extracted in the step (4) as an input parameter, taking four grades of negative mastitis, weak mastitis, positive mastitis and strong positive mastitis as output parameters, and establishing a linear or nonlinear model for detecting the mastitis grade of the dairy cow by respectively using partial least square discriminant analysis, a support vector machine, an extreme learning machine and an error back propagation network algorithm based on the cow milk sample in the correction set.
(6) And (3) verification of the model: and (3) comparing and determining the error back propagation network model based on the continuous projection algorithm as the optimal model by using the performance of the plurality of cow recessive mastitis grade detection models established in the step (5) of concentrated forecast cow sample data inspection and taking the correct recognition rate as the standard for evaluating the quality of the model.
(7) Detection of unknown recessive mastitis grade cows: and (3) for the dairy cattle with unknown recessive mastitis grade, collecting a cow milk sample, finishing the collection of dielectric spectrums according to the step (2), and substituting the dielectric spectrum data, which are identical to the characteristic dielectric variable determined by the continuous projection algorithm in the step (4), in the dielectric spectrum data into the optimal model determined in the step (6), so that the unknown dairy cattle recessive mastitis grade can be quickly detected.
The negative grade of the recessive mastitis in the invention means that the number of somatic cells of cow producing cow milk is less than 50 ten thousand per mL. The recessive mastitis grade is weakly positive, which means that the number of somatic cells of cow producing cow milk is more than 50 ten thousand/mL and less than 150 ten thousand/mL. The recessive mastitis grade is positive, which means that the cow produces cow milk with the somatic cell number more than 150 ten thousand/mL and less than 500 ten thousand/mL. The recessive mastitis grade is strong positive, which means that the cow produces cow milk with the somatic cell number of more than 500 ten thousand per mL.
The invention has the following advantages: the method comprises the steps of firstly obtaining a dielectric spectrum of cow milk, extracting characteristic dielectric variables expressing cow recessive mastitis grade from the dielectric spectrum, further establishing a linear or nonlinear detection model of the cow recessive mastitis grade, and taking the model with the highest concentrated correct recognition rate as an optimal model. The cow recessive mastitis grade can be quickly detected by measuring the characteristic dielectric variable value of the cow milk with unknown recessive mastitis grade and substituting the characteristic dielectric variable value into the established optimal model. The method provides a method for the rapid, accurate, real-time, on-site and on-line detection of the recessive mastitis grade of the dairy cattle.
Drawings
FIG. 1: a flow chart of a method for rapidly detecting the recessive mastitis grade of a dairy cow based on a dielectric spectrum technology;
FIG. 2: correcting centralized average risk value under continuous projection algorithmGA curve that varies with the number of characteristic dielectric variables contained in the model.
Detailed Description
The method has good universality for detecting the recessive mastitis grade of various dairy cattle, and is also suitable for detecting the recessive mastitis grade of the dairy sheep. The method only takes the dairy cattle breed which is mainly bred in China, namely Holstein dairy cattle, as an embodiment, and the detection of the recessive mastitis grade of other dairy cattle can be carried out by referring to the method of the embodiment.
The invention is further illustrated by the following figures and examples.
The flow of the embodiment of the invention is shown in fig. 1, wherein the specific implementation method comprises the following steps:
(1) a batch of fresh cow milk with known negative recessive mastitis grade, weak positive recessive mastitis grade and positive recessive mastitis grade is collected from different dairy cows (except within one month after delivery and in the late lactation period) in the dairy cattle farm of a livestock station of northwest agriculture and forestry science and technology university, wherein the number of samples of the batch of cow milk is 153, and the total number of the samples of the negative recessive mastitis grade cow milk, the weak positive recessive mastitis grade cow milk and the positive recessive mastitis grade cow milk is not greatly different. The number of fresh cows produced by the holstein cows with the negative recessive mastitis grade, the weak positive recessive mastitis grade and the positive recessive mastitis grade selected in the embodiment is respectively 48, 68 and 43.
(2) The relative permittivity spectrum and the dielectric dissipation factor spectrum of the batch of milk samples were collected. This example measured the dielectric spectrum of a sample using an Agilent E5l071C vector network analyzer and 85070E-020 coaxial probe. The conditions for dielectric spectrum acquisition are: the frequency range is 20-4500 MHz, the number of collected points at equal intervals under logarithmic coordinates is 201, and the temperature of a sample is 25 ℃ during dielectric spectrum collection.
(3) Sample division: the samples were divided into the correction set and prediction set using the Kennard-Stone method at a 2:1 ratio. The correction set comprises 102 samples, and the number of the negative recessive mastitis grade cow milk, the number of the weak positive recessive mastitis grade cow milk and the number of the positive recessive mastitis grade cow milk samples are respectively 32, 41 and 29; the prediction set comprises 51 samples, and the number of the samples of the negative recessive mastitis grade cow milk, the weak positive recessive mastitis grade cow milk and the positive recessive mastitis grade cow milk is 16, 21 and 14 respectively. The calibration set contained samples with a minimum and maximum somatic cell count.
(4) By taking a correction set sample as an object, extracting characteristic dielectric variables expressing the somatic cell number of cow milk from a relative dielectric constant spectrum with 201 points and a dielectric loss factor number spectrum (402 dielectric parameter values) with 201 points by adopting a continuous projection algorithm, an information-free variable elimination method and a competitive adaptive re-weighting algorithm.
When the characteristic dielectric variable is extracted by adopting a continuous projection algorithm, setting the range of the extracted characteristic dielectric variable number to be 1-15, and calculating the average risk value of a correction set under each characteristic dielectric variable numberGAccording to the smallest correction concentrationGThe values determine the optimum number of characteristic dielectric variables. Correction set under different characteristic dielectric variablesGThe calculation results of the values are shown in fig. 2. The results show that the correction set is when the number of characteristic dielectric variables is 9GFor this embodiment, 9 characteristic dielectric variables were extracted using the alternative projection algorithm, namely the relative dielectric constants at frequencies 34.4, 46.7, 163.6, and 256.1MHz and the dielectric dissipation factors at frequencies 22.9, 167.2, 2994.5, 3370.9, and 3465.0MHz, respectively.
(5) And (3) taking the characteristic dielectric variable or the full dielectric spectrum (the relative dielectric constant at 201 points and the dielectric loss factors of 201) extracted in the step (4) as input variables, taking negative, weak positive and positive grades as output parameters, and establishing a partial least squares discriminant analysis, a support vector machine, an extreme learning machine and an error back propagation network model based on the correction set sample.
(6) And (4) comparing the correct recognition rate of the built models to the prediction set samples by using the models built in the prediction set sample detection step (5), and taking the model with the maximum correct recognition rate to the prediction set samples as the optimal model for detecting the recessive mastitis grade of the dairy cow. For the embodiment, the error back propagation network model established based on the continuous projection algorithm has the highest correct recognition rate on the cow recessive mastitis grade, and the correct recognition rate on the concentrated cow recessive mastitis grade is 94.12%.
(7) And (3) acquiring a relative dielectric constant frequency spectrum and a dielectric loss factor frequency spectrum of any cow with unknown cow recessive mastitis grade at 201 points between 20MHz and 4500MHz according to the step (2), combining the characteristic dielectric variable extracted by the continuous projection algorithm of the step (4), and substituting the obtained value of the characteristic dielectric variable into the optimal model determined in the step (6) to quickly calculate the cow recessive mastitis grade of the cow.
According to the embodiments, the method can realize the rapid detection of the cow recessive mastitis grade by using the dielectric spectrum technology, and has high detection precision.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (3)
1. The method for rapidly detecting the recessive mastitis grade of the dairy cow based on the dielectric spectrum technology is characterized by comprising the following steps:
(1) collecting a sample: collecting milk samples of a batch of dairy cows with different recessive mastitis grades; the sample is stored at low temperature, and the collection of the dielectric spectrum of the sample is completed within 24 hours; measuring the somatic cell number of each cow milk sample according to a method specified by national standard, and classifying the recessive mastitis grade of the cows on the basis of the somatic cell number;
(2) collection of dielectric spectrum: before measurement, the sample is warmed to room temperature, and the collection of dielectric spectrum is completed by adopting a dielectric spectrum measuring instrument at 25 ℃; preheating and calibrating a measuring device before acquisition, and setting acquisition software; measuring the dielectric spectrum of the cow milk sample in a radio frequency/microwave range, wherein the dielectric spectrum comprises a relative dielectric constant spectrum and a dielectric loss factor spectrum;
(3) sample division: dividing the milk samples under each grade into a correction set and a prediction set by adopting a kennard-Stone method; dividing the sample proportion of the correction set and the prediction set according to 2:1 or 3:1 or 4: 1; the correction set and the prediction set respectively contain cow milk of negative, weak positive, positive and strong positive mastitis grade cows, and the proportion of the cow milk in the correction set and the prediction set of each grade is consistent with the proportion of samples in the total correction set and the prediction set;
(4) extracting characteristic dielectric variable: based on the correction set sample, adopting a continuous projection algorithm, a non-information face-changing elimination method and a competitive self-adaptive re-weighting algorithm to reduce the dimension of the dielectric spectrum of the cow milk sample, and extracting characteristic dielectric variables capable of distinguishing the cow recessive mastitis grade from all dielectric spectrum data; the characteristic dielectric variable is extracted from a relative dielectric constant spectrum or a dielectric loss factor spectrum independently or simultaneously;
(5) establishing a dairy cow recessive mastitis grade qualitative detection model: taking the characteristic dielectric variable extracted in the step (4) as an input parameter, taking four grades of negative mastitis, weak mastitis, positive mastitis and strong positive mastitis as output parameters, and respectively establishing a linear or nonlinear model for detecting the mastitis grade of the dairy cow by using partial least square discriminant analysis, a support vector machine, an extreme learning machine and an error back propagation network algorithm based on the cow milk sample in the correction set;
(6) and (3) verification of the model: the performance of a plurality of cow recessive mastitis grade detection models established in the step (5) is detected by using the concentrated predicted cow sample data, the correct recognition rate is used as the standard for evaluating the quality of the models, and the error back propagation network model based on the continuous projection algorithm is determined as the optimal model through comparison;
(7) detection of unknown recessive mastitis grade cows: and (3) for the dairy cattle with unknown recessive mastitis grade, collecting a cow milk sample, finishing the collection of dielectric spectrums according to the step (2), and substituting the dielectric spectrum data, which are identical to the characteristic dielectric variable determined by the continuous projection algorithm in the step (4), in the dielectric spectrum data into the optimal model determined in the step (6), so that the unknown dairy cattle recessive mastitis grade can be quickly detected.
2. The method for rapidly detecting the level of recessive mastitis of dairy cattle based on the dielectric spectroscopy technology as claimed in claim 1, wherein the method can be used for rapidly detecting the level of mastitis of dairy sheep.
3. The method for rapidly detecting the bovine subclinical mastitis grade based on the dielectric spectroscopy technology as claimed in claim 1, wherein in the step (7), the dielectric spectroscopy of the cow sample with unknown subclinical mastitis grade is acquired, so that the development of a special dielectric spectroscopy instrument for characteristic dielectric variables is not excluded to replace the dielectric spectroscopy instrument in the step (2), only the characteristic dielectric variables are measured according to the step (2), and the measured values are substituted into the optimal model determined in the step (6), thereby achieving the rapid detection of the bovine mastitis grade.
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