Subclinical mastitis is a costly disease of the dairy industry and is presently diagnosed by somatic cell counts. Current detection methods for cell counts include automated machines at remote laboratory sites and subjective farm tests. An alternate method was developed to quantify somatic cells using a rapid assay and sensor that were designed to be easily amenable to an on-line sensor. The detection was based on fluorescence from PicoGreen labelled deoxyribonucleic acids (DNA), which was extracted from cells by diluting milk 1:10 with a high pH buffer and a non-ionic detergent. The high pH buffer served to reduce milk opacity and eliminate enzymes that degrade DNA, and the non-ionic detergent served to lyse cells and release DNA into solution. A sensor was designed to measure fluorescence based on dye peak excitation of 480 nm and peak emission of 520 nm. A light-emitting diode with a peak wavelength of 470 nm was used in conjunction with a 480 nm short-pass filter to excite PicoGreen, and a photodiode with peak sensitivity of 565 nm was used in conjunction with a 520 nm long-pass filter to detect fluorescence. The sensor was calibrated with calf thymus DNA in buffer and with DNA extracted from milk using a commercial kit and the detergent extraction method. The regression of sensor output on calf thymus DNA concentration produced a positive linear response, demonstrating the ability of the sensor to detect double-stranded DNA in a linear range up to 6 μg/ml. Sensor output based on DNA extracted from milk with the commercial kit and detergent extraction were regressed against microscopic somatic cell counts. The standard errors of prediction for the commercial kit and detergent extraction were 84 and 406 kcell/ml, respectively. The calibrated system was tested by classifying predicted cell counts into low (<200 kcell/ml), medium (200-750 kcell/ml), and high (>750 kcell/ml) categories and comparing with the actual classes based on microscopic analysis. Commercial kit classifications were correct for nine out of ten samples. Detergent extraction classifications were correct for six out of 15 samples, but improved to ten out of 15 when based on logistic regression. For a low-high classification with counts below and above 200 kcell/ml, detergent extractions were correct 12 out of 15 times when based on either linear or logistic regression.
|Original language||English (US)|
|Number of pages||9|
|State||Published - Feb 2005|
ASJC Scopus subject areas
- Agronomy and Crop Science
- Control and Systems Engineering