Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory

Abstract

Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility. However, maintaining quality control without constant human oversight remains a critical challenge. Here, we present a novel machine learning framework for automated anomaly detection in High-Performance Liquid Chromatography (HPLC) experiments conducted in a cloud lab. Our system specifically targets air bubble contamination—a common yet challenging issue that typically requires expert analytical chemists to detect and resolve. By leveraging active learning combined with human-in-the-loop annotation, we trained a binary classifier on approximately 25 000 HPLC traces. Prospective validation demonstrated robust performance, with an accuracy of 0.96 and an F1 score of 0.92, suitable for real-world applications. Beyond anomaly detection, we show that the system can serve as a sensitive indicator of instrument health, outperforming traditional periodic qualification tests in identifying systematic issues. The framework is protocol-agnostic, instrument-agnostic, and, in principle, vendor-neutral, making it adaptable to various laboratory settings. This work represents a significant step toward fully autonomous laboratories by enabling continuous quality control, reducing the expertise barrier for complex analytical techniques, and facilitating proactive maintenance of scientific instrumentation. The approach can be extended to detect other types of experimental anomalies, potentially transforming how quality control is implemented in self-driving laboratories (SDLs) across diverse scientific disciplines.