A rapid method of identifying mastitis degrees of bovines based on dielectric spectra of raw milk

【摘要】 Bovine mastitis is the most complex and costly disease in the dairy industry worldwide. Somatic cell count(SCC) is accepted as an international standard for diagnosing mastitis in cows, but most instruments used to detect SCC are expensive, or the detection speed is very low. To develop a rapid method for identifying mastitis degree, the dielectric spectra of 301 raw milk samples at three mastitis grades, i.e., negative, weakly positive, and positive grades based on SCC, were obtained in the frequency range of 20–4500 MHz using coaxial probe technology. Variable importance in the projection method was used to select characteristic variables, and principal component analysis(PCA) and partial least squares(PLS) were used to reduce data dimension. Linear discriminant analysis, support vector classification(SVC), and feed-forward neural network models were established to predict the mastitis degrees of cows based on 22 principal components and 24 latent variables obtained by PCA and PLS, respectively. The results showed that the SVC model with PCA had the best classification performance with an accuracy rate of 95.8% for the prediction set. The research indicates that dielectric spectroscopy technology has great potential in developing a rapid detector to diagnose mastitis in cows in situ or online.更多还原

【Abstract】 Bovine mastitis is the most complex and costly disease in the dairy industry worldwide. Somatic cell count(SCC) is accepted as an international standard for diagnosing mastitis in cows, but most instruments used to detect SCC are expensive, or the detection speed is very low. To develop a rapid method for identifying mastitis degree, the dielectric spectra of 301 raw milk samples at three mastitis grades, i.e., negative, weakly positive, and positive grades based on SCC, were obtained in the frequency range of 20–4500 MHz using coaxial probe technology. Variable importance in the projection method was used to select characteristic variables, and principal component analysis(PCA) and partial least squares(PLS) were used to reduce data dimension. Linear discriminant analysis, support vector classification(SVC), and feed-forward neural network models were established to predict the mastitis degrees of cows based on 22 principal components and 24 latent variables obtained by PCA and PLS, respectively. The results showed that the SVC model with PCA had the best classification performance with an accuracy rate of 95.8% for the prediction set. The research indicates that dielectric spectroscopy technology has great potential in developing a rapid detector to diagnose mastitis in cows in situ or online.更多还原