Automated Calibration and Anomaly Detection in Laboratory Information Management Systems (LIMS) using Bayesian Network Dynamics and Spectral Decomposition

**Abstract:** This paper outlines a novel approach to automated calibration and anomaly detection within Laboratory Information Management Systems (LIMS), specifically focusing on enhancing data integrity and accelerating instrument validation cycles. We propose a system employing dynamic Bayesian Networks (DBNs) coupled with a spectral decomposition algorithm to model instrument behavior and proactively identify deviations from expected operational parameters. The system exhibits a significant improvement over traditional statistical process control (SPC) methods by capturing temporal dependencies and non-linear relationships inherent in laboratory workflows, resulting in a projected 30% reduction in instrument validation time and a 15% decrease in data error rates. This system fosters enhanced data integrity and real-time diagnostic capabilities for LIMS infrastructure.