We designed our automatic workflow (see Figure ) in a data-driven manner rather than relying on rule-based or hardware-based approaches. This strategy makes the system adaptable and generalizable to other rare pitfalls that become observable as the database of HPLC experiments accumulates at scale. Our workflow began with the collection of approximately 25,000 HPLC experiments (see Methods for details), a dataset large enough to capture infrequent events like air bubble contamination. An initial subset of this data was reviewed by a human expert, who observed and annotated anomalous cases, resulting in an initial pool of 93 HPLC experiments affected by air bubble contamination. The initial pool of affected cases is relatively small due to infrequency of occurrence (a conservative a priori estimation was ≈ 1%) coupled with infeasibility of explicit annotation of the whole dataset due to its size. Although air bubble contamination is a common issue in HPLC, the low observed frequency is expected in a well-maintained system. Such an imbalance can be challenging for an ML model and may introduce bias. To address this, we employed Stochastic Negative Addition (SNA), which stochastically adds negative ("unaffected") examples to the training set to ensure balanced representation while minimizing further annotation effort (see Methods). SNA has been successfully applied as a balancing strategy in other data-driven domains .