Mastitis adversely affects profit and animal welfare in the Australian dairy industry. Electrical conductivity (EC) is increasingly used to detect mastitis, but with variable results. The aim of the present study was to develop and evaluate a range of indexes and algorithms created from quarter-level EC data for the early detection of clinical mastitis at four different time windows (7 days, 14 days, 21 days, 27 days). Historical longitudinal data collected (4-week period) for 33 infected and 139 healthy quarters was used to compare the sensitivity (Se; target >80%), specificity (Sp; target >99%), accuracy (target >90%) and timing of ‘alert’ by three different approaches. These approaches involved the use of EC thresholds (range 7.5– 10 mS/cm), testing of over 250 indexes (created ad hoc), and a statistical process-control method. The indexes were developed by combining factors (and levels within each factor), such as conditional rolling average increase, percentage of variation, mean absolute deviation, mean error %; infected to non-infected ratio, all relative to the rolling average (3–9 data points) of either the affected quarter or the average of the four quarters. Using EC thresholds resulted in Se, Sp and accuracy ranging between 47% and 92%, 39% and 92% and 51% and 82% respectively (threshold 7.5 mS/cm performed best). The six highest performing indexes achieved Se, Sp and accuracy ranging between 68% and 84%, 60% and 85% and 56% and 81% respectively. The statistical process-control approach did not generate accurate predictions for early detection of clinical mastitis on the basis of EC data. Improved Sp was achieved when the time window before treatment was reduced regardless of the test approach. We concluded that EC alone cannot provide the accuracy required to detect infected quarters. Incorporating other information (e.g. milk yield, milk flow, number of incomplete milking) may increase accuracy of detection and ability to determine early onset of mastitis.
Quality Control Analysis of Belt Conveyor with Statistical Process Control Approach: A Case Study in Fabrication Manufacturing Industry
