Discover the important features of CRMs, how they should be used and understand the various associated terminologies.
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.
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.
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.
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.
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.
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.
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.
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.
The following terms are used here and in literature elsewhere to discuss reference materials.
Certified Reference Materials (CRMs) – Reference materials that are characterised by metrologically valid procedures for one or more specified properties and which are accompanied by a certificate providing the value of the specified property, its associated uncertainty, and a statement of metrological traceability. ISO Guide 30:2015 recommends terms and definitions that should be assigned to them when used in connection with reference materials, with particular attention to terms that are used in reference material certificates and corresponding certification reports. The certificate must contain a statement of traceability indicating the principles and procedures on which the property values (together with their measurement uncertainties) are based. In the case of mineral CRM's this will be method specific, by inter-laboratory testing, with operationally defined property values using a network of competent laboratories employing methods which have been independently validated (ISO 17034:2016).
CIM Code – The Canadian Institute of Mining, Metallurgy and Petroleum (CIM). The CIM Definition Standards for Mineral Resources and Mineral Reserves are one of the CRIRSCO-style reporting Codes. It was originally based on the Australasian JORC Code . It is the document underlying the Canadian Stock Exchanges NI 43-101.
Commutability – Reference material producers should ensure that a reference material is suited for its intended use. For calibrators and quality control materials this usually includes verification that the raw material selection and processing procedures result in a material with the same behaviour as routine samples in the relevant measurement procedures. The assessment of commutability is part of the demonstration that such a reference material is fit for the intended use.
Confidence Interval – A range of values within which the Recommended Value is expected to lie. The magnitude of the confidence interval is inversely proportional to the number of participating laboratories and inter-laboratory agreement. It is a measure of the reliability of the recommended value; the narrower the confidence interval the greater the certainty in the recommended value.
Control Charts – Schewart or Levey-Jennings control charts are used to monitor analytical processes. In the context of monitoring QC data for CRMs, these charts contain a centreline and the CRM’s ±2 and ±3 SD window control limits are plotted. The user’s own data obtained for the CRM by a laboratory being monitored is then progressively plotted and the data should follow a normal distribution. By definition, 4.5% of data falls outside the 2SD window and 0.3% of data will fall outside the 3SD window. This means approx. 1 in 22 analyses will naturally fall outside 2SDs and approx. 1 in 333 analyses will naturally fall outside 3SDs. Westgard Rules can be used to determine when intervention should be instigated due to QC failures. A CRMs statistics quoted in certs is linked to the round robin program and is at best, a first principle guide to what a lab may be able to perform within. Each lab has its own unique operators, equipment, reagents and processes all of which contribute to a repeatable and reproducible level of variability. This means each lab has its own inherent SD linked to the particular method carried out and this may or may not be a good match to the SD quoted in a CRM’s certificate. For this reason some CRMP’s prefer not to provide SD’s in certificates and recommends that users monitor the precision over time and attribute their own empircally derived SD. The obvious weakness of this is that it takes a while to accumulate a critical mass of analyses and if the analytical process has poor precision or bias the laboratory won’t be held accountable.
Control Limits – A window of acceptability for results obtained by a laboratory for a reference material and generally calculated from multiples of the standard deviation (SD) of the certification data. The SD for each analyte’s certified value reported in OREAS’ certificates is calculated from the same filtered data set used to determine the certified value, i.e. after removal of any individual, lab dataset (batch) and 3SD outliers (single iteration). These outliers can only be removed after the absolute homogeneity of the CRM has been independently established, i.e. the outliers must be confidently deemed to be analytical rather than arising from inhomogeneity of the CRM. The standard deviation is then calculated for each analyte from the pooled accepted analyses generated from the certification program.
In the application of SD’s in monitoring performance it is important to note that not all laboratories function at the same level of proficiency and that different methods in use at a particular laboratory have differing levels of precision. Each laboratory has its own inherent SD (for a specific concentration level and analyte-method pair) based on the analytical process and this SD is not directly related to the round robin program.
The majority of data generated in the round robin program was produced by a selection of world class laboratories. The SD’s thus generated are more constrained than those that would be produced across a randomly selected group of laboratories. To produce more generally achievable SD’s the ‘pooled’ SD’s provided in this report include inter-lab bias. This ‘one size fits all’ approach may require revision at the discretion of the QC manager concerned following careful scrutiny of QC control charts.
CRIRSCO – The International Reporting Template, first published in 2006, is a document that represents the best of the CRIRSCO-style codes, previously referred to as JORC-style codes; reporting standards that are recognised and adopted world-wide for market-related reporting and financial investment.
Custom Reference Materials – Synonymous with Matrix-Matched CRMs (MMCRMs also mine-matched or site-specific CRMs) manufactured out of source materials supplied by clients; usually for mines and advanced reserve-definition stage projects but can also be made from materials sourced from exploration projects or downstream metallurgical products (feed, tails and concentrate). Matrix-Matched Reference Materials provide the highest degree of assurance of the entire analytical process being in control and completely avoid issues of commutability.
Each quarter, we will keep you up to date with news on the latest OREAS CRMs and QA/QC best practices in the analytical industry.