While geological reference materials (RMs) have existed for many years, their use has become widespread only in the last decade. This increase in popularity stems from the recognition of the critical role RMs play in monitoring the quality of assay data generated in analytical laboratories. To the geologist, reference materials have application in grass roots exploration, resource definition, minesite exploration and grade control. To the chemist they facilitate the calibration of analytical equipment, evaluation and validation of analytical methods and routine in-house Quality Assurance/Quality Control.

Many terms are used to describe RMs or Certified Reference Materials (CRMs) and this can be confusing to the uninitiated. The term ‘standard’ strictly refers to an (ISO) approved and documented procedure in industry but its use as a synonym for RMs is widespread and entrenched throughout the mining and chemical industry. Other terms in common usage include Standard Reference Materials (SRMs), In-house or Internal Reference Materials (IRMs) and Matrix-Matched or Mine Matched Certified Reference Materials (MMCRMs). See our Glossary of Terms for further detail.

Essential attributes

  • Proven Homogeneity

    The CRM in question must have a proven level of homogeneity such that the observed variance in repeat assays can be attributed almost exclusively to measurement error. In other words, any sampling error resulting from inhomogeneity of the reference material should be small enough in comparison to measurement error that it’s negligible.

  • Statistically Robust Characterisation

    The CRM should be well characterised by round robin evaluation at a minimum of 10 recognised mineral testing laboratories and certified in accordance with International Standards Organisation (ISO) recommendations. This evaluation program should include analysis of variance (ANOVA) treatment to establish uniformity of the measured property throughout the entire batch.

  • Reputation

    A reference material is no better than the user’s perception of it. Therefore, it is critical, that the user has total confidence in its quality. If this is not the case and analytical problems are suspected, the task of assigning the source of error to the suspect laboratory is fraught with uncertainty. It is imperative, therefore, that the CRM producer’s credentials and reputation are unassailable and that the certification documentation is sufficiently comprehensive.

Using CRMs

CRMs are most commonly used in the mining industry to monitor bias in chemical analyses of geological samples. Critical concentrations in mining operations are cutoff and head grades and CRMs are generally selected to approximate these grades. CRMs are usually inserted at a frequency of 1 in 20 to 1 in 30 into the sample stream and the results produced by the laboratory are then compared against the certified values. CRM blanks are devoid of the metal(s) of interest and are used to monitor contamination within the laboratory.

Control Limits

No analytical method is 100% accurate and therefore a certain amount of error is tolerated. This margin of error is variously referred to as a window of acceptability, control limit or performance gate. Generally, results lying within two (or sometimes three) standard deviations either side of the certified value are deemed acceptable, although precise application of control limits should be at the discretion of the QC manager concerned.

There are various methods used to determine the standard deviation. These methods are empirically derived and based on an analysis of errors contributing to the spread of results obtained in the round robin certification program. These are laboratory measurement errors and sampling errors. Measurement errors include between-laboratory bias, between-batch bias (reproducibility errors) and within-batch precision (repeatability errors). Sampling errors relate to the level of homogeneity of the CRM and should be negligible in comparison with measurement errors.

Confidence Interval

ISO requires that Certificates of Analysis include a measurement of uncertainty of the certified value. This is generally expressed as a 95% Confidence Interval and should not be confused with Control Limits. Put simply, Control Limits provide an expectation of acceptable laboratory performance while Confidence Intervals provide an estimate of the reliability of the certified value.

Tolerance Interval

This parameter is a measure of homogeneity of the CRM. We have pioneered a method of reduced analytical subsampling for evaluating the homogeneity of gold in CRMs. This involves the analysis of gold by high precision neutron activation analysis (NAA) on analytical subsample weights of 0.5g to 1.5g (compared to 25g to 50g for the fire assay method). By employing a sufficiently reduced subsample weight in a series of determinations by the same method, analytical error becomes negligible when compared with subsampling error. The corresponding standard deviation at a 25g to 50g subsample weight can then be determined from the observed standard deviation of the 0.5g to 1.5g data using the known relationship between the two parameters (Ingamells, C. O. and Switzer, P. (1973), Talanta 20, 547-568). The absolute homogeneity of gold is then determined from tables of factors for two-sided tolerance limits for normal distributions. All OREAS and custom gold CRMs undergo this stringent testing and without exception exhibit a very high level of repeatability consistent with excellent homogeneity.

Glossary of Terms

The following terms are used here and in literature elsewhere to discuss reference materials.

SACNASP – The South African Council for Natural Scientific Professions (SACNASP) was set up by the Natural Scientific Professions Act of 2003 but is now adminstered by a new version of the Act passed in 2013. SACNASP registers all Natural Science practitioners within South Africa and in the mining industry. It is relevant to all geologists and chemists writing technical reports about mineral exploration or mining or otherwise or providing a service to the South African public. This will include all geologists acting as consultants and/or writing reports for publication by JSE listed companies and all chemists signing off assay results customers or public companies. According to the Act it is illegal within South Africa to practice and provide a service to the public as a geologist or a chemist unless registered with SACNASP. The 2003 Act punished miscreants with a fine or prison. This was watered down in the 2013 Act to just a heavy fine. Registration in essence is a process whereby the qualifications of an applicant are confirmed to be true, valid and relevant to the field of practice specified. Registered geologists and chemists will list Pr.Sci. Nat. on the report after their name.

Sampling Constant – The required mass of material to achieve a 1% RSD using the known relationship between sample mass and SD (see Ingamells, C. O. and Switzer, P. (1973), Talanta 20, 547-568). This parameter is important as it provides users with a quantitative indication of the CRM’s level of homogeneity. Users require irrefutable data on the magnitude of CRM sampling errors and their impact on QC protocols. An article published in the EXPLORE newsletter for geochemists shows that some manufacturers are showing micro-nuggets in their gold CRMs.

SAMREC – The South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (the SAMREC Code).

SRM – Standard reference material, synonymous with reference material or certified reference material. Note: in the past this acronym has also been used for “Secondary Reference Materials” – materials characterised to a lower level than and generally not conforming to all ISO criteria for a CRM.

Standard – Literally this refers to a recognised and approved method or procedure (such as the standards published by ISO) but its use to describe reference materials is also well entrenched in the mineral and chemical industries.

Standard Deviation (SD, sd or s) – A measure of the spread or dispersion of a set of results and calculated from the square root of the variance.

Statistical Outlier – A single result or entire set of results deviating in either accuracy or precision from others in the set or from other sets, respectively, to a degree greater than can be justified by statistical fluctuations associated with a given frequency distribution.