Neitzel Anne-Christin, Stamer Eckhard, Junge Wolfgang, Thaller Georg
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, D-24098 Kiel, Germany.
TiDa Tier und Daten GmbH, D-24259 Westensee, Germany.
Springerplus. 2014 Dec 22;3:760. doi: 10.1186/2193-1801-3-760. eCollection 2014.
The aim of the paper was to estimate the accuracy of the metrology of an installed indirect on-line sensor system based on the automated California Mastitis Test (CMT) with focus on the prior established device-dependent variation. A sensor calibration was implemented. Therefore, seven sensors were tested with similar trials on the dairy research farm Karkendamm (Germany) on two days in July 2011 and January 2012. Thereby, 18 mixed milk samples from serial dilutions were fourfold recorded at every sensor. For the validation, independent sensor records with corresponding lab somatic cell score records (LSCS) in the period between both trials were used (n = 1,357). From these records for each sensor a polynomial regression function was calculated. The predicted SCS (PSCS) was obtained for each sensor with the previously determined regression coefficients. Pearson correlation coefficients between PSCS and LSCS were established for each sensor and ranged between r = 0.57 and r = 0.67. Comparing the results with the correlation coefficients between the on-line SCS (OSCS) and the LSCS (r = 0.20 to 0.57) for every sensor, the calibration showed the tendency to improve the installed sensor system.
本文旨在评估基于自动加州乳房炎检测(CMT)的已安装间接在线传感器系统的计量准确性,重点关注先前确定的与设备相关的变化。实施了传感器校准。因此,2011年7月和2012年1月在德国卡尔肯丹姆奶牛研究农场对七个传感器进行了两次类似试验。在此过程中,对来自系列稀释的18个混合牛奶样本在每个传感器上进行了四次记录。为了进行验证,使用了两次试验期间独立的传感器记录以及相应的实验室体细胞评分记录(LSCS)(n = 1357)。从每个传感器的这些记录中计算出多项式回归函数。利用先前确定的回归系数为每个传感器获得预测体细胞评分(PSCS)。为每个传感器建立了PSCS与LSCS之间的皮尔逊相关系数,范围在r = 0.57至r = 0.67之间。将结果与每个传感器的在线体细胞评分(OSCS)和LSCS之间的相关系数(r = 0.20至0.57)进行比较,校准显示出改善已安装传感器系统的趋势。