Key Takeaways

  • Geochemical QA/QC monitoring, which tracks 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 and 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

To automate geochemical QA/QC, load your standards, blanks and duplicates into a platform like ALS QCPro or ioGAS’s QAQC module. Let it build control charts and raise alerts on every batch. Then use its newer anomaly flagging to catch subtle drift within a batch cycle, not weeks later.

What Is Geochemical QA/QC?

Quality assurance (QA) is the set of procedures that keeps sampling and assaying reliable. Quality control (QC) is the checking that proves it worked. In geochemistry, the two come together as a monitoring problem.

You insert known control samples into every batch sent to the lab. Certified reference materials (standards) test accuracy. Blanks test for contamination. Duplicates test precision at each stage, from field splitting to lab pulp. Then you 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. However, the actual review of QC charts has traditionally been a manual, eyeball-the-plot exercise.

How Do I Monitor Geochemical QA/QC With AI?

The workflow runs from centralizing the data, to automated alerts, to anomaly detection on top.

Centralize geochemical QA/QC data

ALS QCPro digitizes and centralizes QC review. It lets you monitor your QAQC program independently of the lab. It also uses client-side encryption, so ALS itself can’t see your private metadata. ioGAS’s QAQC modules serve a similar function within the broader ioGAS geochemical analysis environment. As a result, you fold QC review into the same tool you already use to interpret assay results.

Automate control charts and alerts

Next, let the software generate control charts automatically. QCPro issues Process Control Alerts and Overlimit Alerts when values drift outside expected bounds. That means each batch is checked on arrival rather than at a quarterly data review. It also creates a clear record of which batches passed, which failed and what was re-assayed.

Add anomaly detection to geochemical QA/QC

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. Newer ML-based anomaly detection features are appearing in recent releases. They aim to catch subtler drift patterns that simple threshold rules would miss, such as a slow bias across several batches.

Why Fast QC Matters for Everything Downstream

If you’re setting this up, the win is automating the monitoring loop. QC issues then surface within a batch cycle rather than being discovered weeks later during a data review. By then, the affected assays may already sit in models and reports.

Every downstream use depends on this. Field XRF analysis needs its own standards and duplicates to catch instrument drift. Lab analytical methods need checks on digestion and detection limits.

Finally, statistical analysis and domaining assumes the assays behind it are trustworthy. Poor QC quietly undermines all of them.

FAQs

What control samples does geochemical QA/QC use?

Most programs use certified reference materials (standards) for accuracy, blanks for contamination and duplicates for precision. They are inserted at a set rate into every batch. The exact rate depends on your program and reporting code.

Does AI replace QA/QC protocols?

No. AI and statistical tools automate the monitoring of control samples, not the protocol itself. You still need to insert the right controls, investigate failures and decide on re-assays.

What is the difference between ALS QCPro and ioGAS for QC?

ALS QCPro is a dedicated QC monitoring platform that runs independently of the lab. ioGAS is a broader geochemical analysis tool with QAQC modules built in. Both generate control charts and flag values outside expected bounds.

How quickly should geochemical QA/QC issues be caught?

Ideally within the same batch cycle, before results enter your database and models. Automated alerts make that realistic. Manual reviews often find problems weeks later, when fixing them costs far more.

Try It With Geocluster

Understanding what “normal” QC variance looks like for your deposit type and lab, versus a genuine data-quality problem, takes context. That means pulling in comparable case studies and lab-method literature. It’s the kind of research task Geocluster is built to handle.