A well set up quality assurance program for assay data should more than pay for itself through significant savings from process improvement, or additional value from auditable mineral resource categorization.
If your economic element control samples show a <0.1% bias, a CV 90% detection of critical systematic error and a false rejection rate of <5%; then the company might be advised to save money by increasing the ratio of samples to controls. Alternatively if you have no control results, or control samples are showing that analytical results are poor; then you need to place a higher priority on error prevention. You will probably discover, by looking at real numbers, you can stop chasing some ghosts You might even be able to start some method improvement.
Anybody sending samples to an assay lab has to set up a quality assurance (QA) program over and above any QA program run by the lab. Both the customer and the lab QA program have one goal (to check the lab) and both programs must be auditable.
The customer QA program should comprise at least:
1. Prior testing the primary and secondary labs for accuracy and bias.
2. Submission of routine QC samples, including reference materials and blanks, either as field samples or inserted in the lab as pulps.
3. Use of random secondary reference materials to check the labs treatment of the primary reference material.
4. Monthly QC meetings with the lab.
5. Actioning of QC failures.
6. Reporting of QC actions.