Identifying data anomalies in milk component measurements from partial-day milking records

High-quality milk and milk component data are crucial for accurate genetic evaluations and effective herd management. However, data recording errors can compromise the validity of downstream decisions. In a recent study, we proposed using intraclass correlation coefficients as a herd-level metric to assess the consistency of milk components from single milkings, thereby effectively identifying farms with potential data quality concerns. A key challenge, however, is whether potentially erroneous records can be detected at the cow-day level. In this study, we introduce a novel metric-individual-level intraclass correlations-to assess data consistency at the cow-day level and evaluate its performance against 3 commonly used anomaly-detection methods. We further introduce a 2-step approach to estimate percentile thresholds for flagging outliers. The results demonstrate the superior performance of this new metric over the conventional univariate and multivariate methods in identifying anomalies in correlated partial daily milk component data. In addition, the negative impact of data shuffling was examined. Together, these methods provide robust and practical tools for detecting suspect milk component records at the individual cow-day level.