Comparing Methods for Detecting Anomalous Values in Automated Laboratory Processes

Outlier detection is used in many domains. In automated laboratory processes, detecting anomalous values is critical for ensuring the reliability of experimental results. This study compares various outlier detection methods, including traditional statistical approaches like Mahalanobis distance, Median and mean absolute deviation (MAD), as well as modern machine learning techniques such as Isolation Forest, Angle Based Outlier Detection (ABOD), and Local Outlier Factor (LOF). The performances of these methods were evaluated using simulated multivariate data, with different types of outliers and levels of contamination. Comparisons are made using sensitivity, precision, and mainly the F2 score, a weighted metric of sensitivity and precision that gives more weight to precision. The results show that in univariate settings, the Median MAD method works consistently well. For multivariate scenarios, Mahalanobis methods with Minimum Covariance Determinant estimates and Minimum Volume Ellipsoid estimates work well even for high contamination percentages. This study highlights the importance of selecting an appropriate outlier detection method for the situation.