Key Takeaways

  • QA/QC monitoring — tracking lab standards, blanks, and duplicates for drift or contamination — is a natural home for statistical anomaly detection.
  • ALS QCPro and ioGAS’s QAQC modules are the standard tools, both now adding statistical/ML anomaly flagging.
  • This is an “Emerging” category: the underlying QC discipline is decades-old and well-standardized (ISO/IEC 17025), the ML layer on top is the new part.
  • The goal isn’t replacing QA/QC protocols — it’s catching drift or contamination faster than a human reviewing control charts manually would.

TL;DR

Use a platform like ALS QCPro or ioGAS’s QAQC module to track your standards/blanks/duplicates automatically, and lean on their newer statistical/ML anomaly-flagging features to catch lab drift or contamination issues faster than manual control-chart review.

How Do I Monitor QA/QC With AI?

QA/QC in geochemistry is fundamentally a monitoring problem: you insert known standards, blanks, and duplicate samples into every batch, then watch for drift, bias, or contamination that would undermine confidence in your assay results. It’s a mature, standardized discipline — ALS Geochemistry, for instance, runs every site under a single Global Geochemistry Quality Manual compliant with ISO/IEC 17025 — but the actual review of QC charts has traditionally been a manual, eyeball-the-plot exercise.

ALS QCPro digitizes and centralizes this: it lets you monitor your QAQC program independently of the lab (with client-side encryption so ALS itself can’t see your private meta-data), generates control charts automatically, and issues Process Control Alerts and Overlimit Alerts when values drift outside expected bounds. ioGAS’s QAQC modules serve a similar function within the broader ioGAS geochemical analysis environment, letting you fold QC review into the same tool you’re already using to interpret assay results.

The “AI” framing here should be taken with a grain of salt — this is largely statistical process control (control charts, Overlimit Alerts) rather than deep learning, with newer ML-based anomaly detection features appearing in recent releases to catch subtler drift patterns that simple threshold rules would miss. If you’re setting this up, the win is automating the monitoring loop so QC issues surface within a batch cycle rather than being discovered weeks later during a data review.

Try It With Geocluster

Understanding what “normal” QC variance looks like for your specific deposit type and lab, versus a genuine data-quality problem, benefits from pulling in comparable case studies and lab-method literature — the kind of research task Geocluster is built to handle.