References Materials for Chemical Analysis || Proper Usage of Reference Materials

Reference Materials for Chemical Analysis Certification, Availability, and Proper Usage

Edited by Markus Stoeppler, Wayne R. WOK PeterJjenks

0 Wiley-VCH Verlag GmbH, 2001

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Proper Usage o f Reference Materials PeterJ Jenks and RolfZeisler

7.1 Selection, Use, and Abuse of RMs

Since the early 1970’s there has been a growing belief that chemical measurements must not only be done correctly, but that data, the product of the measurement process, must be seen to be accurate, precise, and reliable. Analpcal data have become another manufactured product and like all manufactured products, the customers demand that Quality Assurance (QA) must be built in.

There is an abundance of references defining and describing the role played by QA, Quality Control (QC) and Total Quality Management (TQM) in a modem commercial analytical laboratory. The role played by reference materials (RMs) and certified reference materials (CRMs) in the pursuit of analytical measurement accuracy is also well documented.

It has become an accepted wisdom that the use of RMs or CRMs will help to improve the accuracy and precision of an analybcal process. This belief has led to a rapid growth in the use of RMs and CRMs in commercial laboratories. The authors and many analysts the world over support this view, but also recognize that in far too many cases inexperience and carelessness conspire together with the result that error accumulates and often unreliable data are produced.

In this Chapter we highlight the practical considerations that must be understood by all users of RMs and CRMs; we look at some of the issues of traceability and make the CRM user aware of the uncertainty budgets that need to be considered with the use of CRMs. No attempt will be made to advise CRM users on the proper use of statistics in the analytical measurement process and no statistical approaches on the establishment of measurement uncertainty will be given. There are a number of good texts on the subject which should be consulted. These are listed in the “Further Reading” section of the references at the end of this Chapter and include Miller and Miller (1993) and Taylor’s work for NIST (Taylor 1985).

I 237 7.1 Selection, Use, and Abuse of RMs

7.1.1 Conventional “Proper” Uses of RMs

There are many applications in which RMs and CRMs are used, but those that are relevant to analytical chemistry, including environmental, industrial, bio-medical, and forensic applications and that directly influence Total Quality Management (TQM) can briefly be grouped into the main categories listed below.

7.1.1.1 Method Development and Evaluation

Evaluation of field methods

Evaluation and verification of the precision and accuracy of test methods Development of reference test methods

Validation of methods for specific uses, and developing new or improved techniques and methods.

7.1.1.2 Assurance of Measurement Compatibility Direct calibration of methods and instrumentation; i.e. ensuring that an analytical device is giving a correct reading. For some types of direct solid sample analysis, sample results can be calibrated using several CRMs with suitable matrices (Kur- fiirst 1998); see also Section 4.4.

Internal (intra-laboratory) quality assurance External (inter-laboratory) quality assurance Demonstration of the integrity and performance of a complete analytical system, from initial sampling to data manipulation

7.1.1.3 Establishment of Measurement Traceability

Direct laboratory use

Development and implementation of traceability protocols Development of secondary (in-house) standards

Verification of laboratory competence to satisfy organizational or customer needs

There are a number of prerequisites for properly using CRMs in these tasks, including established quality control of the laboratory’s analytical measurement operations and proven statistical control of the analytical measurement process. Publications describing the use of RMs and CRMs are not as plentiful as those on how CRMs are made but, in addition to the ISO/REMCO Guides 30-35, the ISO/REMCO publication “The role of reference materials in achieving quality in analytical chemistry”(IS0 9000 1987). the NIST Handbook for SRM users (Taylor 1995), and the various LGC-VAM publications listed under “Further Reading” should all be consulted.

238 7 Proper Usage of Reference Materials

7.1.2 Mis-Use and Other Causes of Errors

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Despite all that has been written about the requirements of carefully performed analytical quality control including the usage of CRMs, there are still many laboratories that either have neither proper internal QC nor CRMs, or perhaps do not want to admit to using them!

Many more common problems start because the new users do not really understand their analytical systems, a problem described as the "NintendoTM scientist" syn- drome by Jenks (1995). Inexperienced scientists are often not sufficiently discriminating in their selection and use of CRMs. But incorrect choice can also be due to the unavailability of suitable matched matrix CRMs, or surprisingly often because the laboratory believes it cannot afford the ideal product.

We have described a selection of uses of CRMs in Chapters 4, 5, and 6. There are copious additional references in the literature, as we explain in Chapter 8. But most of these references do not explain how the CRM was used, only why. The authors assume that the reader knows how to use a CRM. We have discovered from a number of years experience in advising on the use of CRMs that the same mistakes and mis-understandings occur time and time again. The main causes of error can be conveniently grouped together, as follows:

7.1.2.1 Documentation Errors Such errors include mis-reading certificate or report data supplied by the producers, using secondary information from catalogs and/or literature listings, and reporting incorrect data. For the correct use of a RM/CRM, it is essential to read the information that accompanies the product once an appropriate RM/CRM has been obtained. The only reliable source of information is the Certificate of Analysis or Report of Assigned Values issued with the RM/CRM, and it must be the most up to date version available. Failure to follow a producer's recommendation will invariably result in error.

Unfortunately, these rather basic errors are distressingly common, yet cause much unnecessary dissatisfaction. No printer is perfect, and relying on catalog data can result in the publication of incorrect data in a paper. This occurred, e.g. in 1994 when data was taken from an out-of-date NIST catalog, rather than the appropriate certificate. Published in the Journal of Analytical Atomic Spectroscopy, the paper by Soares et al. (1994) cited a certified value for Cr in NIST SRM 1548, when consulta- tion of the Certificate would have shown that for several technical reasons the ele- ment value reported could not be certified.

Supplementary information on many RMs/CRMs may also exist in the form of publications in the open literature, describing their production and recommended uses. That information should also be consulted by the users. But users must be aware that some values reported in these publications are indicative only and may lack the complete evaluation of data that eventually forms the certified value. Even more caution is advisable when data compilations are used in lieu of certified values. For many years a cobalt value in SRM 1577, Bovine Liver, was propagated in this way at about 120 % of the true value.


7.1.2.2 Selection Errors

Choosing an inappropriate RM/CRM, or failing to understand the matrix effect is another common source of error. In many types of environmental and biological reference materials the effect of the matrix can be profound, especially when using routine sample preparation and analysis methods. Nevertheless, it should not be forgotten that some direct analysis procedures, e.g. instrumental neutron activation analysis (INAA), are much less sensitive to matrix mis-match because of their matrix-independence and dynamic range. The use of such direct procedures in the certification of a RM has to be taken into consideration when matching matrix and RM. The issue of matrix-matching is considered in more detail later in this Chapter.

The literature includes a number of mis-matches, the following standing as examples for the many! The use of bovine liver and other animal tissues for QC in the analysis of human body fluids should not be considered by analysts. The matrix and the levels of trace elements do not match the levels to be analyzed, which may lead to serious errors. An even more severe mis-use was recently reported by Schuhma- cher et al. (1996) for NIST SRMm1577a Bovine Liver, which was used for QC in the analysis of trace elements in plant materials and soil samples in the vicinity of a municipal waste incinerator. Also recently, Cheung and Wong (1997) described how the quality control for the analysis of trace elements in clams (shellfish) and sediments was performed with the same material NIST SRM 1646, Estuarine sediment. Whilst the selected SRM was appropriate for sediments, its usefulness as a QC tool for clams is difficult to prove; see also Chapter 8. This inappropriate use is the more mystifying because a broad selection of suitable shellfish RMs from various producers is available.

How critically interdependent matrix and analybcal methods can be is illustrated in the example of the analysis of a soil sample. Table 7.1 shows the method dependent certified values for some common trace elements. The soil had been subjected to a multi-national, multi-laboratory comparison on a number of occasions (Houba et al. 1995) which provided extensive data. The data was subjected to a rigorous statistical program, developed for the USEPA by Kadafar (1982). This process allowed the calculation of certified values for a wide range of inorganic analytes. Uniquely, for the soil there are certified values for four very different sample preparation methods, as follows:

Acid Extraction - Aqua regia extraction is comparable with DIN 38 414 part 7, NEN 6465 and many other European routine procedures. Calcium Chloride Extraction - A 0.01 M calcium chloride solution extraction. This method has been shown to be a step forward in the development of a universal extractant for nutrients and metals by Erp et al. (1998). Nitric Extraction - A 2 M hot nitric acid. This method is comparable with the EPA 3050A extraction procedure. Total - The complete dissolution of the matrix by methods such as hydrofluo- ric acid, or measurement by nondestructive methods such as INAA.


Tab. 7.1

(Data extracted from Certificate for CRM RTH 912, Loess Soil from Switzerland, produced by Pro- mochem CmbH, Germany, with permission). All units mg/kg

The Influence of analytical technique on analytical values I

Cadmium Chromium Cobalt Copper Iron Lead

13.4 127.0 35 600 71.6

Confidence Interval Prediction Interval

Acid Extraction Confidence Interval Prediction Interval

Nitric Acid Extraction Confidence Interval Prediction Interval

Chloride Extraction Confidence Interval Prediction Interval

103-120 12.4-14.3 123-132 34 200-37 000 63.8-79.4

62.3-160 8.61-18.1 98.2-156 26 800-44 400 27.9-115

1.20 67.5 12.2 120.0 32 500 64.2

1.16-1.24 65.7-69.3 12.0-12.5 118-121 31 900-33 000 63.2-65.3

0.67-1.73 40.2-94.8 9.48-15.0 100-139 25 300-39 600 47.5-81.0

1.22 36.6 9.53 117.0 No Data 64.6

1.19-1.24 35.3-37.9 9.28-9.78 115-118 63.3-66.0

0.90-1.53 22.9-50.3 6.90-12.2 99.7-133 49.6-79.7

(0.124) (0.122) No Data 0.492 1.13 (3.03)

0.413-0.571 0.74-1.53

0.0062-0.949 0.00-3.23

The enormous difference in certified values between methods and between analytes illustrates well how much care is needed in matrixlmethod matching. Further evidence of the importance of matrix matching is provided by an interlaboratory study on trace elements in soil reported by Maier et al. (1983) and the certification of a sewage sludge described by Maaskant et al. (1998).

In trace organic analysis there is usually an extraction or clean-up process, rather than a sample dissolution. Here not only must the matrix effect be considered, but also the recovery yield of the extraction. Frequently an external spike standard is added, but there is often no way of knowing if the recovery of the spike standard matches the analyte in question. There is considerable evidence that the US EPA method for VOA analysis (Minnich 1993) is subject to such error, as reported by Schumacher and Ward (1997). The analyst must always consider the possibility of such an error, especially when using CRMs to control methods that are applied in routine mode.

7.7 Selection, Use, and Abuse of RMs

7.1 2 . 3

As mentioned before, RM producers go to great lengths, as are required by the I S 0 Guide 31 (1996) to prove homogeneity and stability, and to establish the best sample size and storage conditions for an optimal shelf life. This information is normally provided in the RM certificate. Nevertheless, users must pay particular attention to a number of procedures in the use of RMs to avoid invalid results.

Handling and Use Errors

7.1 2.4 Storage The stability of individual analytes within a matrix material is often quite variable. A good example is shown by NIST SRM 96% fat soluble vitamins and cholesterol in serum. The material must be shipped and stored at -80°C. The SRM is certified for a range of vitamins, most of which are quite stable at -2o"C, or even +4"C, but the beta-carotene and other components are not. It is therefore essential to ensure the material, if the carotene components are of interest, is shipped and stored correctly.

Manufacturers of CRMs go to considerable lengths to ensure that the CRMs are stable, therefore attention must be paid to specified storage conditions and shelf life. It must not be forgotten that in most cases the shelf life stated by the producer refers to an unopened unit, and that once opened shelf life is often not guaranteed. This applies especially to CRMs packed under a protective atmosphere and stored at reduced temperatures; see Sections 2.1, 3.1, and 7.1.2.5 for further consideration of these issues.

Unfortunately not all producers provide specific storage instructions, and even for those that do it is unlikely that they will have updated certificates produced 5-10 years ago. Therefore, in the absence of clear guidelines for storage after opening, one approach is to divide the material into a number of aliquots, taking into consideration the recommended minimum sample size, and to re-seal each aliquot before storage at appropriate temperatures (for sensitive materials in deep freezers at e.g. -2o"C, for less sensitive, e.g. gamma-radiation sterilized biological matrices, storage at + 4 T may suffice) under controlled humidity and protected from light. Reports on evaluations of long-term storage of biological and environmental materials under several conditions can be found in the literature (Zeisler et al. 1988; Mackey et al.

1999).

7.1 2.5 Most matrix reference materials are regarded stable for their application within a certain time frame; see also Section 2.2. They are usually produced in large batches designed to ensure that the same material is available for a number of years as well as to spread the high cost of production over as many units as is possible. The stability is closely monitored from initial production by the producer; lot numbers or even individual unit numbers are allocated and the producers closely monitor, by regular analysis, the condition and quality of their reference materials over time. Because of such careful control, and to minimize waste, the tendency has been for producers to give a "usable life from receipt" to the customer, commonly 12-24 months. HOW- ever, the producers can give this shelf life expectancy only for unopened units,

Shelf Life and Expiration Dates


stored as recommended by the producer. Accordingly, users are advised to buy fresh CRMs when required and not to store them for a prolonged period.

The most recent revision of I S 0 Guide 31(1ggG) includes a recommendation that each certificate include a clear expiration date. This change reflects increasing demands by laboratory quality management procedures for all consumable materials to have a clear use by date. Industries have welcomed this move; and a large user sector has reacted by claiming that when there is a change to an I S 0 Guide, the RM producers should quickly up-date all their certificates. Funding does not always exist to do so, therefore certificates are sometimes up-dated when RMs are re-certified, or new information is added to the certificate. The authors have found that industries are unwilling to understand this approach and also contradict such requirements with the demand for long shelf lives and usage periods. Other industries, especially the highly regulated pharmaceutical industry, believe that CRMs are designed to be taken by the scientific community as of the highest quality on a metrological basis and they should therefore be in perfect compliance with all appropriate rules at all times. Compliance with such demands may force producers to increase production and certification capacities beyond reasonable limits and contribute to dwindling variety in available RMs.

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7.1.2.6 Sampling and Preparation o f RMs for Analysis An important point for achieving results that can be compared to the certified values is the use of the appropriate sample size. The required amount is usually specified by the producer. However, some users take either a smaller amount (sometimes much smaller as required by the analytical technique; see Sections 4.3 and 4.4). and many may even take larger amounts. These users must be aware of two problems:

(I) The specified amount is, in many instances, just a piece of guesswork since expensive studies of sampling behavior were not incorporated in the certification process. However, the certification process has established that the recommended amount provides sufficient analyte for a reproducible value with many of the analytical techniques operating at an optimal level. Devia- tion in sample size may change the optimum and reproducible response of some analytical techniques.

(2 ) If a different amount is taken, other than which is specified in the certificate, then this has a significant impact on the confidence interval for the certified value in that particular sample. Extrapolation of uncertainty to different sample sizes, in particular uncertainties due to inhomogeneity at smaller sample size, is not possible without extensive sampling studies. Even so, RM producers should support analysis procedures that require different sample sizes by supplying sampling information such as sampling constants; see also Sec- tion 4.3.

Correct determination of the sample mass is critical. Usually drying conditions are specified by the producer. The user has to be aware that there is an amazing variety of recommended drying conditions presented in the various certificates. Since the optimum conditions are very dependent on matrix and composition, it is of utmost


importance that the user follows the instructions, even if they are not a standard procedure in the user’s laboratory. The influence of different drying conditions on the uncertainty of the mass determination, as well as on the content of certain analytes is generally unknown; so serious errors can result from a non-validated sample preparation.

Another common error is caused by contaminating the RM during removal of samples. Contamination can be caused by other samples in the laboratory, but also by bacteria, fungi and dust. Once removed for later use, RM aliquots should never, ever, be returned to the original RM container after sampling, but must be discarded. Contamination may be the source of many apparently subtle sources of error, especially in the use of matrix CRMs. Users may wish to exceed the normal precautions taken against contamination of their analytical samples, and only open and weigh out CRMs in a laminar flow hood, or other “clean bench” environment, to assure long-term validity of the RM unit.

7.1.2.7 Sample Characteristics

Discontinuity between the physical form of the sample and reference material used can lead to error. This is another manifestation of the matrix effect, but one which has to be considered when analyzing biological and environmental samples. There is no easy answer to the relationship between particle size and homogeneity. It is a popular assumption that the smaller the particle size the less the degree of heterogeneity. In some cases this may be true but there are a number of considerations.

(I) Reducing particle size does nothing to make the individual particles the same, it only makes more of them and increases total surface area, with the probability of higher exchange factors for constituents leaving or entering the sample.

(2) The resultant material in many cases is not “flowable”, which means it tends to cake or agglomerate. It is then difficult to get good particle distribution which increases the tendency toward heterogeneity and means mixing of the material before sampling is critical.

( 3 ) The importance of particle size is directly proportional to the sub-sample size recommended by the analytical method. The larger the sub-sample size the larger the acceptable particle size. For sub-sample sizes of Ig or greater a soil sieved through a Imm screen is generally acceptable. Therefore if the sample is relatively coarse, e.g up to zmm particles and the matrix CRM is an uni- form sub-micron powder, it may be necessary to use a much larger sample from the material under test than for the CRM.

(4) The more a source material is processed the less it behaves and reacts like a typical field sample, and if a real-world contaminated soil is ground to reduce the particle size the heat of frictionlshearing may alter the composition and constituents may volatilize.

244 7 Proper Usage ofReference Materials

7.1.3 Continuity

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Lack of continuity in the supply of a matrix CRM can often be a cause of error: this can be either discontinuity caused by the re-certification of a CRM or advances in analytical technology rendering certified values unreliable. Once a particular batch of a given matrix CRM is sold out, there may be a considerable delay before a replacement is available. Replacements often differ in analyte profile and value, although the producers generally attempt to hold these differences to a minimum. Analysts must be aware of changes in the composition which sometimes may be subtle; at other times, however, they may be significant. For example, a change in the milling technique such as using a zirconia mill will introduce significant levels of Zr and Hf to the sample, possibly interfering with the analytical procedure. Standard solution RMs are normally replaced with an identical product and without a significant break in supply. However, the supplier may have changed the solution matrix in pH or composition to assure better stability. This may cause a significantly different response in the analytical technique used.

Evolution of analytical techniques can cause data, once considered to be "state of the art" to be shown to be unreliable. A good example is provided by the work of Houba et al. (1995). who demonstrated that a number of older methods for the determination of trace levels of boron in plant materials were subject to the interference by high levels of copper. This and other evidence suggest that older data, even when presented on a certificate, have to be viewed critically; see also Section 3.2. The analyst must stay aware of developments and be ready to disregard certified values if the date of certification of the CRM predates the release of new developments and the certification authority con- cerned cannot confirm that the certified value is good in the light of the new knowledge.

7.1.4 Artifacts

In certain areas, particularly the rapidly developing area of organo-metallic speciation, concern has been expressed that artifacts may lead to false results. One example are the doubts about the accuracy and suspicion of possible artifact formation of methylmercury (MeHg) during analytical procedures, mainly distillation and alkaline dissolution, which were expressed for the first time at the Conference "Mercury as a Global Pollu- tant" in 1996 (Hintelmann and Evans 1997; Hintelmann et al.1997).

Stable isotope dilution ICP-MS was used to study the accuracy of mercury speciation analysis. It was found that, on spiking sediments with relatively high quantities of inorganic mercury (Hg2+ in acidic aqueous solution) enriched with 202Hg isotope, an increase of "'MeHg was observed after water vapor distillation, suggesting in situ formation of MeHg from inorganic Hg. This was also proved by conventional analytical techniques, described by Bloom et al. (1997). Although the amount of MeHg formed was very small, varying from about 0.005 % to a maximum of 0.1 % of the spiked Hg2+, it could represent an important proportion of MeHg in samples that normally contain low concentrations of MeHg (a factor up to 1.5 was reported).

I 245 7. I Selection, Use, and Abuse of RMs

The controversy was serious enough for the European Commission to finance a Worltshop which was held in Wiesbaden, Germany on 28-29 May 1998.

Reports of this work led many laboratories to doubt the value of the CRMs they use for their quality control, but at this stage the findings on artifact formation of MeHg are not considered to give sufficient ground to claim that all MeHg data produced worldwide were overestimated (Quevauviller and Horvat 1999). Even so the experience serves as a salutary warning that assumptions based on experience can so easily be overturned with the arrival of new, more revealing, methodologies.

7.1.5 Data Interpretation Errors

Users expect “certified values” to be correct - with a probability of 95 % - within the stated uncertainty intervals. They assume, perhaps naively, that all statements of uncertainty are the same. In practice the stated uncertainties may have quite different meanings because they have been based on quite different principles. This issue is discussed in more detail below. But for most users neither the differences nor the consequences of the differences are always evident, or understood. I S 0 Guide 33 (1989) recommends that “CRMs are used on a regular basis to ensure reliable measurements”. In reality, the expression “to ensure reliable measurements” can have a wide range of interpretations, including:

“to transfer information on property values” “to assure traceability to unit scales or to standards” “to assess precision and/or trueness of measurement processes”

Although the user will require differing types of information from the producer to properly use the CRM for each applications, there is a tendency to provide only a certified value and an uncertainty value, which is generally said to be a 95 % confidence interval, or something similar. The relevance of this was made clear by Jor- hem (1998), but it is not always evident from the supplied documentation.

One of the most common complaints from the inexperienced user is that the result obtained in the routine laboratory does not fall in the confidence interval. Pau- wels (1999) makes considerable reference to this problem, which he calls the ‘‘Jor- hem Paradox”. Even though Pauwels goes on to explain this paradox, in doing so he highlights the problem when he states “two results (the certified value and the sub- sequent laboratory determination) which both claim to contain the most probable mean value of the material with a probability of 95 % do, effectively, not overlap”.

How can this situation arise? It is because most certification bodies are not in a position to consider other uncertainty components than those associated with the certification process.

The proper application of the I S 0 Guide to the expression of uncertainty in measurement requires that all sources of uncertainty are included. In practical terms this means an “uncertainty budget” has to be developed also by the user. The development of an uncertainty budget, and the consequences for both analysts and producers is described later in Section 7.2.


Considering the different uses of RMs in the estimation of uncertainty would mean that more routine results overlap the certified value, but it would also make the certified values given by some agencies less useful for the broad user community. So there needs to be a better way of expressing uncertainty, possibly by showing the existing uncertainty and a wider total uncertainty.

A form of this approach has long been followed by RT Corporation in the USA. In their certification of soils, sediments and waste materials they give a certified value, a normal confidence interval and a “prediction interval”. A rigorous statistical process is employed, based on that first described by Kadafar (1982), to produce the two intervals: the prediction interval (PI) and the confidence interval (CI). The prediction interval is a wider range than the confidence interval. The analyst should expect results to fall “19 times out of 20” into the prediction interval. In real-world QC procedures, the PI value is of value where Shewhart (1931) charts are used and batch, daily, or weekly QC values are recorded; see Section 4.1. Provided the recorded value falls inside the PI gs % of the time, the method can be considered to be in control. So occasional abnormal results, where the accumulated uncertainty of the analytical procedure cause an outlier value, need no longer cause concern.

Pauwels (1999) argues that the certified values of CRMs should be presented in the form of an expanded combined uncertainty according to the I S 0 Guide on the expression of uncertainty in measurement, so that coverage factor should always be clearly mentioned in order to allow an easy recalculation of the combined standard uncertainty. This is needed for uncertainty propagation when the CRM is used for calibration and the I S 0 Guide should be revised accordingly. The use of the expanded uncertainty has been policy in certification by NIST since 1993 (Taylor and Kuyatt 1994).

There are a number of other problems relating to the manipulation and interpretation of data that cause difficulty. The most common are: (I) uncertainty about the number of replicate results required for proper comparison of the certified reference value, and (2) the actual analytical result and how gross outlier results should be handled. These issues and how to deal with data that falls outside the confidence limit are reviewed in detail by Walker and Lumley (~ggg) , who conclude that whilst customer requirements may provide answers the judgement of the analyst must always be the final arbiter in any decision!

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7.1.6 Reporting Errors

In his survey of the use of CRMs in food related publications on the subject of trace elements for the years 1990-1996, Jorhem (1998) checked 82 papers published in five international journals. He found that in 42 papers there was no mention of CRM results and assumed that no CRMs were used. He wrote: “Since the importance of incorporating CRMs in the AQA-activities today is well recognized, it is sur- prising that firstly so many laboratories still do not use CRMs and secondly that scientific journals accept papers describing analytical results without the use of reference materials, as part of the verification of the analytical results”.

I 247 7.2 Statistical Consequences in the Assurance of Measurement Compatibility

Of these 4 2 papers, 13 came from countries within the EU, eight from European countries outside the EU, and 12 from North America. He mentioned that the papers dealing with the use of CRMs did not make reference to any user guide such as I S 0 Guide 33 (1989) (see Section 1.2) and further that “the comparison between the found results and the certified means and intervals are often presented in rather vague, or non statistical terms”.

The reasons for such vagueness may lie in a combination of factors, the lamenta- ble level of proper understanding of statistics amongst many analysts and, as mentioned above, the inconsistent and complex manner in which many certification bodies use statistics to produce their certified values and the willingness of journals to accept papers that lack proper validation of results and do not describe the proper use of CRMs.

7.2 Statistical Consequences in the Assurance of Measurement Compatibility

7.2.1 Uncertainty

In this Section we aim to make the CRM user aware of the uncertainty budgets that need to be considered with the use of CRMs. Certified values in CRMs are the property values (mass fraction, concentration, or amount of substance) and their uncertainty, the uncertainty being in many instances a specified confidence interval for the certified property. As we discussed before, this uncertainty value is not always a complete uncertainty budget for an analytical process from sampling to production of data. But even when disregarding the subtle differences in the certificates, the way a CRM is used has serious consequences on the uncertainty budget that has to be applied to a user’s result. This is summarized in Table 7.2. These uses may affect accuracy claims as well as traceability claims. It is the user’s obligation to establish com-

Tab. 7.2 Overview on uncertainties (variances for more convenient formalism) with different CRM uses

Investigated Material CRM Use Uncertainty of User Result

Matrix Match Calibration u,Z = urn2 + ( u C R M ) ~

Matrix Related ~ , 2 = urn2 + urn,: + ( u ~ R M ) ~

Matrix Relation Inferred Matrix Not Related

u,Z = urn2 + n urn,: + ( u ~ R , ) ~

Only useable with truly matrix- U,” = Urnz + ( UCRM)’ independent procedures

Matrix Match Control Measurement u,Z = 2( urn)’ + k( U c R M ) 2

etc. Rapidly Increasing Uncertainties

U,: Combined Uncertainty Urn: Measurement Uncertainty Urn,,: Uncertainty in Materials Properties (n= potential multiplier for differences) k Coverage factor to be considered for normally fewer measurements of CRM


plete uncertainty budgets and statistical control for each user component of the analytical process and be certain that the recipient of the data understands the consequences of the stated uncertainty. The authors experience is that this is often not the case.

From the formulae presented it is clearly evident that the uncertainty of the CRM may become a strong component in the user’s combined uncertainty, but it is not the only component.

To this effect, the user’s result will always have a larger uncertainty than the uncertainty stated for the CRM.

Only a direct matrix match of sample and CRM, and the CRM’s use as a direct calibrant will allow the user to demonstrate accuracy and subsequently traceability close to the uncertainties established during the CRM certification ( note: matrix- matching may not be necessary with matrix-independent techniques). This reality places a significant burden on the CRM producers, since large uncertainties in the certified values may degrade the perceived value of the CRM. On most occasions CRMs are used as Quality Control materials, rather than as “cali- brations”. As outlined above, this common application adds significantly to the user’s uncertainty budget, since at a minimum it is necessary to consider at least two independent measurement events (Urn), so increasing the combined uncertainty of the results. Again this process rapidly increases the combined uncertainty with increasing complexity of the analytical system; and so the usefulness of a control analysis may be downgraded when a correct uncertainty budget is formulated.

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7.2.2 Indicative Approach to Quantifying Uncertainty

Mention has already been made of the EPA recommended use of both confidence interval and prediction interval. However, many users and their customers may be satisfied by some simplistic comparisons. Two methods for comparing experimental results with certified values are presented here. The users are referred to statistical handbooks for comparing results of sets of determinations such as usingftest, etc.

Users may conclude that the analyhcal procedure gives correct results if the difference between the analyst’s experimental mean(s) (xe) and the certified value(s) (xc) is less than the combined uncertainty (IS standard deviation) of the experimental and certified means (Equation. 7-I), with s, and s, representing the estimates of the respective standard deviations.

In practice this evaluation is difficult to apply because the standard deviation of the certified value is usually neither stated in the certificate nor can it be derived from the quoted confidence interval.

Another assessment is based on the presumption that there is no significant difference between the certified values and the experimental results when the confidence intervals of the two values overlap. Using this method, the analyst needs a

I 249 7.3 Traceability

data set of n independent measurements from which a confidence interval must be calculated (Equation 7-2) with t,05 being the student’s t value at the 95 % probability level with (n-I) degrees of freedom.

If this range overlaps the stated confidence interval of the certified value, then the analytical procedure may be assumed to be under satisfactory statistical control. Discussion about the accepted degree of overlap may inevitably occur; so when reporting results it is good practice that the certified value of the CRM should be within the experimentally determined confidence interval.

The disadvantage of both tests is that the user obtains only a small set of test data compared to the certification measurements that lead to the certified value and its uncertainty. But this is a fact of life in the world of the routine, rather than research, laboratory . Therefore the user of the data is always forced to compare differently obtained values and uncertainties.

7.3 Traceability

7.3.1 Definition

To the users of CRMs, the concept of “traceability” is very closely related to the statistical considerations in the measurement process and the quality of the measurements in the users’ laboratory. Traceability is defined in the international vocabulary on metrology (VIM) as:

“The property of a result of a measurement or standard whereby it can be related to stated reference, usually national or international standards, through an unbroken chain of comparisons all having stated uncertainties”

(VIM 1993). Traceability is at the core of the answers to one of the key questions for analytical

determinations: How reliable is the data?

The answers can be rather complex and range from qualitative statements, such as:

“the specificity of a certain chemical measurement for the chemical of interest”

to quantitative statements, such as:

“the accuracy and uncertainty associated with measured value of the mass fraction or amount of substance determined”.

250 7 Proper Usage ofkference Materials

It is in the declaration of the latter where a certain quality of the measured value can be described with traceability. This relation to measurement quality has been expressed in the literature for some time:

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“Traceability to designated standards (national, international, or well-charac- terized reference standards based upon fundamental constants of nature) is an attribute of some measurements. Measurements have traceability to the designated standards if and only if scientifically rigorous evidence is produced on a continuing basis to show that the measurement process i s produ- cing measurement results (data) for which the total measurement uncertainty relative to a national or otherwise designated standard is quantified (Belanger 1980).

Although there are many similar definitions for traceability, the essence of traceability is an unbroken pathway to the definition of the accepted units used to express the measurement result and a measurement process in which quality assurance is an integral component.

Considering the proper use of a CRM in a measurement process, the user may wish for a recipe to establish traceability, so that the data produced can be claimed to be of the highest quality, or to satisfy regulatory requirements, contractual agree- ments, and to comply with the conditions of written standards. In view of these issues, it should be noted that there i s a component of “legal traceability” that expands the obligations of a measurement laboratory beyond the demonstration of “good laboratory practice” that commonly support a laboratory’s claims for accuracy in their measurements. Regardless of these extensions, in this section we review some uses of CRMs that can support claims on the traceability of an analytical chemist’s result. But in every instance, the user of a CRM must understand, as we have explained above, that the uncertainty of the CRM used in the assessment will expand the uncertainty of the result.

7.3.2 Practical Aspects

It should be recognized that, in some cases, it is not difficult to set up a traceable measurement system. The best examples of this are in physical metrology where traceability i s often based on “direct” measurements of the SI units. There i s also general agreement that a similar SI link is highly desirable in the case of chemical measurements, but, for a variety of reasons, direct “chemical” traceability i s difficult to achieve in most of the analytical chemistry applications. Only a very few analytical chemistry procedures exhibit a direct measurement capability that allows the set-up of a traceable measurement pathway such as in physical metrology measurements; most of these procedures have been accepted as primary methods if carried out under certain constraints (CCQM 1998).

The majority of analytical chemistry procedures, especially those in commercial laboratories, depend on a matrix of controllable variables that include sampling,

I 251 7.3 Traceability

calibration, chemical yields and instrumental efficiencies, specificity of chemical and/or instrument responses, and the material and/or matrix in which the analysis is conducted. Therefore, demonstration of traceability for analytical chemistry procedures requires a complex approach in the determination and validation of the afore- mentioned matrix of variables. Whilst CRMs can be used in many of these assess- ments and the degree to which traceability will be demonstrated may correspond with the degree of demonstrated accuracy for a certain measurement, or may be only inferred through comparisons and established knowledge of the processes involved. Nevertheless, there are a few elementary pathways through which the use of a CRM may establish traceability to the declared reference, e.g. the certified mass fraction of a chemical in the CRM. The possible pathways include the use of a CRM as a calibrant in the analytical process, the comparison of measurement results with results obtained in measuring a CRM under essentially equal conditions, or the quality of performance of the analytical procedure on a number of CRMs over time. In discussing these pathways and considering the broad spectrum of analytical chemistry applications in contrast to the limited supply of CRMs, one has to keep in mind that the unbroken chain of comparisons may be literally demonstrated in only a few instances and that for a long time to come data are accepted by inferred quality, including traceability.

The use of a CRM as calibrant in an analytical procedure is probably the simplest way to comply with the VIM definition. CRM producers may give guidance to the users for the proper use of the CRM, e.g. the certificate for SRM@ 2031a, Metal on Fused Silica Filters for Spectrophotometry, states: “To demonstrate that a user’s measurements are traceable within acceptable limits to the accuracy transferred by SRM@ 2031a, the user must first determine the required tolerances or acceptable uncertainty for the application in question. It is recommended that a number of replicate measurements be made for each filter and wavelength, with removal and replacement of the filter between replicate measurements. The user should then compare each mean value and the user-defined tolerance with the NIST certified value and expanded uncertainty (given in the certificate). An acceptable level of agreement between a user’s measurement and the certified value is demonstrated if any part of the range defined by the NIST certified value and its expanded uncertainty overlaps any part of the user’s tolerance band defined by the measured mean and the user-defined level of acceptable uncertainty” (NIST 1994; SRMP 1997).

In a different example, traceability in the amount-of-substance analysis of natural potassium, thorium, and uranium by the method of passive gamma-ray spectrometry was demonstrated by Nir-El (1997). For an absolute quantitative determination, accurate values of two parameters were required: (I) the emission probability of a gamma-ray in the decay of the respective indicator radionuclides, and (2) the detection efficiency of that gamma-ray. This work employed a number of CRMs in the critical calibration of the detection efficiency of the gamma-ray spectrometer and the establishment of precise emission probabilities. The latter results compared well with literature values and provided smaller uncertainties for several gamma-rays that were critical for the traceability claim. The amount-of-substance analytical results of the long lived naturally occurring radionuclides 40K, 232Th, 235U, and 238U


were shown to be traceable to radioactivity CRMs. It must be noted, however, that in many instances the known nuclear and atomic parameters may have substantial uncertainties and, in most cases, a CRM will provide only a single point calibration, whose extension to a broader bandwidth of mass fractions or concentration levels may be erroneous.

In their broadest application, CRMs are used as “controls” to verify in a direct comparison the accuracy of the results of a particular measurement; parallel with this verification, traceability may be demonstrated. Under conditions demonstrated to be equal for sample and CRM, agreement of results, e.g. as defined above, is proof. Since such possibilities for a direct comparison between samples and a CRM are rare, the user’s claims for accuracy and traceability have to be made by inference. Naturally, the use of several CRMs of similar matrix but different analyte content will strengthen the user’s inference. Even so, the user still has to assess and account for all uncertainties in this comparison of results. These uncertainty calculations must include beyond the common analytical uncertainty budget: (I) a component that reflects material matrix effects, (2) a component that reflects differences in the amount of substance determined, ( 3 ) the uncertainty of the certified or reference value(s) used, and 4) the uncertainty of the comparison itself. All this information certainly supports the assertion of accuracy in relation to the CRM. However, the requirement of the “unbroken chain of comparisons” will not be formally fulfilled.

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7.4 Conclusions

We have shown that CRMs can have a positive influence and help identify sources of error when used as the producer intended and when the user understands the limitations of the CRM in the particular application.

If users are to benefit from the implementation and/or verification of traceability in analytical chemistry the unbroken pathway of references must be kept short. The uncertainty of the references (CRMs) used may significantly widen the uncertainty a user must attach to the result of his measurement when addressing accuracy and traceability through comparison with a CRM. These comparisons should be only considered in a first or second level step as to keep the uncertainties of the results within limits fit for the purpose. The producers of CRMs must keep their uncertainties sufficiently small to allow introduction of the CRM at different points in the analytical pathway, without limiting the usefulness of results through unduly expanded uncertainties.

The producers need to establish, in general terms or for specific applications of CRMs, clear instructions for the user on how to establish traceability to the stated reference and (implied in the use of CRMs of national metrology orga- nizations) the SI. But the authors’ experience is that, as the workload in commercial analytical laboratories increases (as analytical laboratories seek to demonstrate their competence and quality to an increasingly discriminating

7.5 References I 2 5 3

marketplace) the pressures on analysts to produce product increases. One result is that, in an attempt to meet pressure from both management and customers, corners are cut. CRMs are used inappropriately and without fully understanding the various traceability and uncertainty issues, leading to: Accumulation of unstated error = incorrect data = useless product = unhappy customers!

7.5 References

BELANCER BC (1980) Traceabilty an evolving concept. Standardization News 822-28. BLOOM NS, COLMAN JA and BARBER L (1997) Artifact formation of methyl mercury during aque-

ous distillation and alternative techniques for the extraction of methylmercury from environmental samples. Fresenius J Anal Chem 358371377,

CCQM (1998) Consultative Committee on the Quantity of Material, Bureau International des Poids et Mesures (BIPM), Minutes of Fifth Meeting, S k e s , France.

CHEUNG YH and WONG MH (1997) Depuration and bioaccumulation of heavy metals by clams from To10 harbour, Hongkong. Toxic01 Environ Chem 58:103-116.

ERP P VAN, HOUBA V and BEUSICHEM M VAN (1998) One Hundredth Molar Calcium Chloride Extraction Procedure Part I. Commun Soil Sci Plant Anal 29:1603-16~3.

HINTELMANN H and EVANS RD (1997) Application of stable isotopes in environmental tracer studies - measurement of monomethylmercury (CH,Hg+) by isotope dilution ICP-MS and detection of species transformation. Fresenius J Anal Chem 358:378-385.

HINTELMANN H, FALTER R, ILGEN G and EVANS RD (1997) Determination of artifactual formation of monomethylmercury (CH,Hgf) in environmental samples using stable Hg2+ isotopes with ICP-MS detection: calculation of contents applying species specific isotope addition. Fresenius J Anal Chem 358:363-370.

HOUBA V, UITTENBOGAARD J and PELLEN P (1995) Wageningen Evaluating Programmes for Ana- lytical Laboratories (WEPAL) Organisation and Purpose. Commun Soil Sci Plant Anal 27:421-

431. HOUBA V, NOVOZAMSKY I and LEE J VAN D E R (1995) Influence of Storage of Plant Samples on the

Chemical Composition. Sci Total Environ 176:73-79. I S 0 9000 (1987) Quality management and quality assurance standards - Guidelines for selection

and use. I S 0 Publications, Casa Postale $5, CH 1211 Geneva 20, Switzerland. I S 0 Guide 31 (1996) Contents of certificates of reference materials, and Document N 382, revi-

sion of I S 0 GUIDE 31. I S 0 Publications, Casa Postale 56, CH 1211 Geneva 20, Switzerland. I S 0 Guide 33 (1989) Uses of certified reference materials (under revision). IS0 Publications,

Casa Postale 56, CH 1211 Geneva 20, Switzerland. JENKS PJ (1995) Editorial in Fresenius J Anal Chem 352:3-4. JORHEM L (1998) Non-use and misinterpretation of CRMs. Can the situation be improved? Frese-

KADAFAR K (1982) A bi-weight approach to the one sample problem. J Am Sta Assn 77 No

KURFURST U, ed. (1998) Solid sample analysis. direct and slurry sampling using GF-AAS and ETV-ICP. Springer Berlin Heidelberg New York.

MAASKANT J, BOEKHOLT A, JENKS P and RUCINSKI R (1998) An international interlaboratory study for the production of a sewage sludge certified reference material for routine use in inorganic quality control. Fresenius J Anal Chem 360:406-409.

nius J Anal Chem 360:370-373.

378416- 424.

254 7 Proper Usage ofReference Materials

MACICEY EA, DEMIRALP R, FITZPATRICK IW, PORTER BJ, WISE SA, BECICER PR and GREENBERG RR (1999) Quality assurance in analysis of cryogenically stored liver tissue specimens from the NJST National Biomonitoring Specimen Bank (NBSB). Sci Total Environ 226:165-176.

MAIER E, GRIEPINK B, MUNTAU H and VERCOUTERE J< (1993) Report EUR 15283 EN. European Commission, Luxembourg.

MINNICH M (1993) Behavior and determination of volatile organic compounds in soil: A literature review. USEPA, Las Vegas, NV, EPA/6oo/R-93/140 U.S. Government Printing Office, Washington, DC

NIR-EL Y (1997) Traceability in the amount-ofisubstance analysis of natural potassium, thorium and uranium by the method of passive gamma-ray spectrometry. Accred Qua1 Assur 2:193-

NJST (1994) Guidelines for the expression of uncertainties o f NJST measurement results. NJST Tech. Note 1297, Gaithersburg, MD, USA.

PAUWELS J (1999) How to use matrix certified reference materials? Examples of materials produced by JRMM’s reference materials unit. In: FAJGELJ A and PARKANY M, eds. The use of matrix reference materials in environmental analytical processes, pp 31-45. Royal Society o f Chemistry, Cambridge.

QUEVAUVILLER PH and HORVAT M (1999) Artifact formation of methylmercury in sediments. Let- ter to the Editor. Anal Chem ~ I : I ~ ~ A - I ~ G A .

SCHUHMACHER M, GRANERO S, BELL& M, LLOBET JM and DOMINGO J L (1996) Levels of metals in soils and vegetation in the vicinity of a municipal solid waste incinerator. Toxic01 Environ Chem 56:119-132.

SCHUMACHER BA and WARD SE (1997) Quantitation reference compounds and VOC recoveries from soils by purge-and-trap GC/MS. Environ Sci Techno1 31:2287-2291.

SHEWHART W (1931) Economic Control of Quality of Manufactured Products. Van Nostrand, New York.

SOARES M, BASTOS M and FERRERIA M (1994) Determination of Total Chromium and Chromium (IV) in Animal Feeds by Electrothermal AAS. J Anal Atom Spectrom 9:1zGg-1272.

SRMP (1997) Certificate Standard Reference Material@ 2031a, SRM@ Program, Gaithersburg, MD, USA.

TAYLOR BN and KUYATI CE (1994) Guidelines for evaluating and expressing the uncertainty of NIST measurement results. NIST Technical Note 1297, U.S. Government Printing Ofice, Washington, DC.

TAYLOR JK (1995) Handbook for SRM Users. NBS Special Publication 260-100, US. Department of Commerce.

VIM (1993) International vocabulary of basic and general terms in metrology 2nd edition. ISO, Geneva, Switzerland

WALKER R, LUMLEY J (1999) Pitfalls in terminology and use of reference materials. Trends Anal Chem 18:594-616.

ZEISLER R, GREENBERG RR, STONE SF, SULLIVAN TM (1988) Long-term stability of the elemental composition of biological materials. Fresenius 2 Anal Chem jp:G1~-615.

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198.

7.6 Further Reading

BRM and BERM special issues of Fresenius J Anal Chem (available issues: BRM-2, 1987; BRM-3, 1988; BERM-4, 1990; BERM-5, 1993; BERM-6, 199s and BERM-7, 1998; for an overview of these symposia see Chapter 8.

CSUROS M (1997) Environmental Sampling and Analysis, Laboratory Manual. CRC Press. DOERFFEL I< (1994) Assuring trueness of analytical results. Fresenius J Anal Chem 348x83-184

I 255 7. G Further Reading

GUNZLER H (1995) Accreditation and Quality Assurance in Analytical Chemistry. Springer, Ger-

ISO/REMCO (1997) The role of reference materials in achieving quality in analytical chemistry.

KOHL H (1994) Qualitatsmanagement im Labor. Springer, Germany; LGC - VAM Publications: (I) The Fitness for Purpose of Analytical Methods, A Laboratory Guide

to Method Validation and Related Topics, (2) Practical Statistics for the Analytical Scientist: A Bench Guide By TJ Farrant, ( 3 ) Trace Analysis: A structured Approach to Obtaining Reliable Results By E Pritchard, (4) Quantifying Uncertainty in Analytical Measurement, and (5) Qual- ity in the Analytical Chemistry Laboratory. LGC/RSC Publications, London, England.

many.

I S 0 Publications, Casa Postale 56, CH 1211 Geneva 20, Switzerland

MESLEY RJ et al. (1991) Analytical Quality Assurance - A Review. Analyst 116:975-990. MILLER JC and MILLER J N (1993) Statistics for Analytical Chemistry, 3rd edition. Ellis Honvood

TAYLOR J K (1985) Principles of Quality Assurance of Chemical Measurements. NBS (now NIST)

THOMPSON M (1997) Comparability and Traceability in Analytical Measurements and Reference

YOUDEN WJ (1991) Experimentation and Measurement. NIST Special Publication 672, Reprint of

Prentice Hall Series in Analytical Chemistry, Prentice Hall.

U.S. Department of Commerce.

Materials. Analyst I ~ Z : I Z O I - I ~ O ~ .

1961, U.S. Department of Commerce.

Documents

References Materials for Chemical Analysis || Proper Usage of Reference Materials