protocol-agnostic, instrument-agnostic, and 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.