national measurement system vam bulletin - lgc ltd...issue 34 – spring 2006 vam bulletin 5 guest...

Control charts in chemical analysis

Using reproducibility info to estimate uncertainty

Traceable reference standards for PT

Alcohol standards & UK ‘drink-drive’ laws

Spring 2006 Issue 34

VAM BulletinNational Measurement System

2 I S S U E 3 4 – S P R I N G 2 0 0 6V A M B U L L E T I N

ContentsC O N T E N T S

Front cover photograph by Andrew Brookes

Editorial

Facts and figures ...........................................................................................................3

Guest column

Measurement uncertainty in chemical analysis................................................................4

Statistical focus

Using reproducibility information in measurement uncertainty estimation .......................9

Resampling statistics....................................................................................................12

Control charting in chemical analysis ...........................................................................14

Case study

Provision of traceable reference values for proficiency testing ........................................19

Contributed articles

Nanoscale analysis of microfibres .................................................................................23

Breath alcohol standards underpinning UK ‘drink-drive’ legislation...............................25

Chemical nomenclature

Lists of chemical substances ........................................................................................27

VAM in education

Introduction to measurement.......................................................................................28

Proficiency testing update

New joint venture for proficiency testing ......................................................................29

VAM helpdesk

VAM at your service....................................................................................................30

Forthcoming events

Achieving reliable mass spectrometry data ....................................................................32

Current topics in method validation .............................................................................32

Chemistry in action .....................................................................................................33

Mass spectrometry measurement research in the VAM programme ...............................33

LGC’s analytical training programme...........................................................................34

Contact points ................................................................................................................36

Keith MarshallEditor020 8943 7614

General enquiries about VAM to:VAM helpdesk020 8943 [email protected]

LGC’s address:LGC, Queens RoadTeddingtonMiddlesex, TW11 0LY

ISSN 0957 1914

The DTI VAMprogramme:Analytical measurements are vital to the UKeconomy. The VAM programme underpinstheir reliability by supporting the developmentand maintenance of reference methods andstandards, and providing laboratories withthe ‘tools’ needed to implement bestpractice and produce reliable results. It issupported by the DTI National MeasurementSystem (NMS), which is responsible forstimulating good measurement practiseand enabling business to make accurateand traceable measurements.

The VAM Principles (below) alloworganisations to deliver reliable results firsttime, every time, thus achieving bottomline improvements through increasedefficiency and reduction in risk.

1. Analytical measurements should bemade to satisfy an agreed requirement.

2. Analytical measurements should bemade using methods and equipment,which have been tested to ensure theyare fit for their purpose.

3. Staff making analytical measurementsshould be both qualified andcompetent to undertake the task.

4. There should be a regular independentassessment of the technicalperformance of a laboratory.

5. Analytical measurements made in onelocation should be consistent withthose elsewhere.

6. Organisations making analyticalmeasurements should have welldefined quality control and qualityassurance procedures.

The VAM Bulletin is produced by LGC under contract withthe UK Department of Trade and Industry as part of theNational Measurement System Valid AnalyticalMeasurement Programme. No liability is accepted for theaccuracy of information published and the views expressedare not necessarily those of the Editor, LGC or DTI.

mailto:[email protected]

http://www.vam.org.uk

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3I S S U E 3 4 – S P R I N G 2 0 0 6 V A M B U L L E T I N

Keith MarshallLGC

When I began my career as a

laboratory scientist, one of the first

projects I was given to do was to critically

assess a method for estimating uncertainty of

chemical analytical procedures. At the time,

uncertainty was very much in the domain of

the metrologists and was not in common use

in analytical laboratories. However, today,

the estimation of uncertainty is now routine

in most analytical laboratories and has

become a part of the chemistry curriculum

in schools, colleges and universities in

the UK.

Statistical information has always had an

important role in confirming the reliability of

analytical data. However, providing that

information can, at times, be a baffling

process. So to counteract this, the VAM

programme provides advice and information

to analysts, through the publication of

guidance documents and workbooks and the

provision of free advice on specific problems

via the VAM helpdesk. However, many

readers who took part in the VAM Bulletin

readership survey last year highlighted that

they would like to see more written on

statistical issues in future issues of the

Bulletin. So, this issue focuses on statistics,

by popular demand.

Our statistical focus concentrates mainly on

measurement uncertainty and begins with

our guest columnist, P.S. Ramanathan

(Gharda Chemicals, India), who gives a

broad overview of the topic and its

importance in chemical analysis; including

possible sources, its evaluation, its role in

regulation and the guidance available to

analysts. LGC’s Steve Ellison, discusses

the use of reproducibility information in

uncertainty estimation. And for all of those

who thought that the ‘Monte Carlo method’

had something to do with ‘breaking the

bank’ at a famous European casino, LGC’s

Michael Griffiths, will demonstrate

that the said method can, in fact, be

used to estimate uncertainty using

resampled data.

Control charting is one of the most powerful

and common ways of monitoring the

performance of an analytical procedure

over a period of time. Emad Edaddu

(Royal Scientific Society, Jordan)

concludes our statistical focus by discussing

the various types of control charts in

common use in chemical analysis, their

construction and their uses.

As well as the statistical focus of this

issue, we hope to include a regular

statistics article in future issues of the

VAM Bulletin.

Also in this issue

• Mike Sargent and Gill Holcombe

(LGC) describe how LGC provided

reference values for the analysis of

tin in a sample of tomato paste

and how that led to further work looking

at the problems incurred when

analysing tin in food matrices.

• Chris Brookes (NPL) describes the role

of ethanol gas standards developed by NPL

in ensuring that roadside breath alcohol

measurements are accurate and reliable.

Any volunteers?

A special thanks goes to P.S. Ramanathanand Emad Edaddu for contacting the VAMBulletin and offering to submit articles forthis issue.

The VAM Bulletin welcomes articlesfrom all authors and organisations,providing the topic is VAM related andrelevant to Bulletin readers. If you areone of the 40% of readers who said thatthey were willing to submit an article 1

(or even if you are not) and you have anidea for an article for a future issue,please contact me.

Keith MarshallEditor, VAM BulletinLGC

Tel: 020 8943 [email protected]

REFERENCES

1. VAM Bulletin, 33, pp 28-29, 2005.

Facts and figuresE D I T O R I A L

Focus on statistics.


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P.S.RamanathanGharda ChemicalsLtd, India

Introduction

Quality has now become the basicconsumer decision factor in many

products and services. It leads to businesssuccess/growth, enhances competitiveposition and improves a nation’s economy.Analytical chemistry has always been used tomeasure the quality of manufacturedproducts, particularly in chemical industry.It is one of the duties of a good analyticalchemist to assess the reliability of results,prior to reporting them.

The concepts of‘measurement uncertainty’

and ‘error’

Most measurements are subject to errorsthat are not perfectly quantifiable. Hence, there is ‘uncertainty’ associated withthe results of such measurements. Ameasurement result is, therefore, incompletewithout a statement of the corresponding‘measurement uncertainty’.

Most measurements aresubject to errors that are not

perfectly quantifiable.

Uncertainty is a parameter associated with

the result of a measurement (e.g., a

calibration or test), that defines the range of

the values that could reasonably be

attributed to the measured quantity. The

parameter may be, for example, a standard

deviation (or a given multiple of it) or half-

width of an interval, having a stated level of

confidence 1. When uncertainty is evaluated

and reported in a specified way, it indicates

the level of confidence that the value actually

lies within the range, defined by the

uncertainty interval.

‘Error’ is defined as the difference betweenan individual result and the ‘true’ or ‘mostprobable’ value of the measurand. As such, it is a single value. ‘Error’ is anidealised concept. It cannot be knownexactly, as it is not possible to arrive at the ‘true value’ of any analyte throughexperiments. One can only arrive at the‘most probable value’.

On the other hand, ‘uncertainty’ is reportedin the form of a range. If estimated for ananalytical procedure, and defined sampletype, it may apply to all determinations sodescribed. While the value of a known errorcan be applied as a correction to the result,the value of the uncertainty, in general,cannot be used to correct a measured result.

Whenever possible, we try to correct for anyknown errors: for example, by applyingcorrections from calibration certificates. Butany error whose value we do not know, is a source of uncertainty. Unlike error,‘uncertainty’ is a quantification of the doubtabout the measurement result.

All analytical results actually take the form ‘a ± 2u’ or ‘a ± U’, where:

a = the best estimate of the true value of the

concentration of the measurand (the

analytical result).

u = standard uncertainty

U = expanded uncertainty (= 2u, where 2 is

the coverage factor, used for an approximate

confidence level of 95%).

Measurement uncertainty in chemical analysis

G U E S T C O L U M N

After a brief stint as a lecturer in 1956,

Dr. P.S. Ramanathan worked as a

government chemist until 1960.

Thereafter, he worked as scientist in

the Analytical Chemistry Division of

the Atomic Energy Establishment,

Bombay, until 1982. Since then, he has

been working for Gharda Chemicals

Ltd. in Bombay, where he is

currently the Director Corporate

Analytical Operations.

Figure 1: Best estimate of the true value and uncertainty in the result.



‘4u’ is the range in which the value

is estimated, with a high probability,

to fall. The value of ‘U’ or 2u is called

‘measurement uncertainty’. Figure 1

illustrates this aspect.

Measurement uncertainty does not implydoubt about the validity of a result. On the contrary, knowledge of the uncertaintyimplies increased confidence in the validityof a measurement result. David Layden 2

stated: “Good measurements must be defined by their accuracy, precision and need. Of course, measurement capabilities should fit the need, but new measurement capabilities can, often, redefine those needs”. It has also been stated that absolute certainty in measurement is a privilege of uneducated minds and fanatics and that it is an unattainable ideal for scientists and technologists.

Measurement uncertaintydoes not imply doubt about

the validity of a result.

The topic of uncertainty is the most widely

talked subject, but probably the least well

understood. It is now accepted that the

subject of uncertainty is a complex one.

Because the nature of the effect of an

uncertainty component can change, the

‘Comite International des Poids et Mesures’

(CIPM) has advocated the grouping of

uncertainty components according to the

method used to estimate their numerical

values 3: Type A (Those evaluated by

statistical methods.) and Type B (Those

evaluated by other means.)

Sources of uncertainty

Uncertainty may arise from a number of

sources. All these sources of uncertainty must

be examined carefully before arriving at the

uncertainty limits for the measurement made.

In practice, a preliminary study of the

sources of uncertainty will quickly show the

predominant ones, which almost entirely

control the combined uncertainty. A good

estimate of uncertainty can be made by

concentrating effort on the largest

contributions. Figure 2 illustrates the

relative contributions made by the four

sources of uncertainty in the volumetric

assay of oxyclozanide 4. In this example,

the volume and normality of potassium

hydroxide (KOH) appear to be the

dominant sources contributing to the

observed uncertainty.

Evaluation of measurement uncertainty

Uncertainty is a consequence of the unknown

sign of random effects and the limited ability to

correct for systematic effects. It is, therefore,

expressed as a quantity, i.e. as an interval about

the result 5. Four options available to the

analyst for measuring uncertainty are described

by William Horwitz6. They are:

1. Calculation of the equivalent of a

confidence interval from the ‘t’ factor

applied to the standard deviation of

replicates;

2. The ‘bottom up’ approach, in which one

estimates the expected variation that each

factor will contribute to the final value;

3. An interlaboratory study on a standard

method;

4. Applying the Horwitz formula to make a

rough calculation.

Uncertainty is a consequence of the unknownsign of random effects and

the limited ability to correctfor systematic effects.

Figure 2: Uncertainty contributions in the assay of oxyclozanide. For the confidence level 95%, the coverage factor, k, is 2. Hence Expanded uncertainty = 0.2960 x 2 = 0.5912.The assay value of Oxyclozanide including the expanded uncertainty = 98.2 ± 0.5912%.



6 V A M B U L L E T I N

Guides for uncertainty evaluations assume

that the analytical methods used are

implemented using fully documented

procedures. Uncertainties estimated using

ISO guidelines are not intended to allow

for the possibility of spurious errors. The

term ‘uncertainty’ is attached to a result, not

to a method, i.e., measurement uncertainty

is being discussed, not method uncertainty 6.

Guidelines from ISO/IEC 17025 and other international documents

In many sectors of analytical chemistry it

is now a formal (frequently legislative)

requirement for laboratories to introduce

quality assurance measures to ensure that

they are capable of, and are providing

data of the required quality. With the

introduction of ISO and other guidelines on

uncertainty estimation, the accuracy

available from analytical methods is

increasingly characterised in terms of

measurement uncertainty. To make a

decision on the compliance or non-

compliance of a product with its

specifications, one needs information on

test results, and the measurement

uncertainty. In many cases, the uncertainty

has a bearing on the compliance statement.

The need for an internationally accepted

procedure for expressing measurement

uncertainty led to several efforts to initiate

procedures and develop guides for carrying

out these measurements. ISO/IEC 17025

stresses the requirement to provide

information on measurement uncertainty.

As per this, laboratories are encouraged to

have an understanding of the variability of

the results, when possible7.

The 1993 ISO document ‘Guide to

the Expression of Uncertainty in

Measurement’ 8 sets out the general

principles of measurement uncertainty. The

Eurachem/CITAC guide 1 shows how the

concepts in the ISO guide may be applied in

chemical measurement. The second edition

of the Eurachem/CITAC guide was

prepared in the light of practical experience

of uncertainty estimation in chemistry

laboratories, and the even greater awareness

of the need to introduce formal quality

assurance procedures by laboratories. This

stresses that the procedures introduced by a

laboratory to estimate its measurement

uncertainty should be integrated with

existing quality assurance measures, since

these measures frequently provide much of

the information required to evaluate the

measurement uncertainty.

Some common areas in which chemical

measurements are needed, and in which the

principles of this guide may be applied are1:

• quality control and quality assurance in

manufacturing industries;

• testing for regulatory compliance;

• testing using an agreed method;

• calibration of standards and equipment;

• measurements associated with the

development and certification of

reference materials;

• research and development.

Importance of measurementuncertainty calculations

Many decisions are based on the results of

quantitative chemical analysis. In these

cases, it is important to have some indication

of the quality of the results: i.e. the extent to

which they are fit for purpose. Users of the

results of chemical analysis, particularly in

those areas concerned with international

trade, are coming under increasing pressure

to eliminate the replication of effort,

frequently expended in obtaining them.

Confidence in data obtained outside the

user’s own organisation is a prerequisite to

meeting this objective.

Previously, most countries adopted ‘official

methods’ to fulfil legislative and trading

requirements, as more emphasis was being

laid on the precision of results using a

specified method. Their traceability to a

defined standard or SI unit was not

adequately stressed. However, as there is

now a formal requirement to establish the

confidence of results, it has become essential

that measurement results be traceable to a

defined reference such as a SI unit, reference

material or (where applicable) a defined or

empirical method. Internal quality control

procedures, proficiency testing and

accreditation help in establishing evidence of

traceability to a given standard1.

As a consequence of these requirements,

chemists are coming under increasing

pressure to demonstrate the quality of their

results. A major criterion to be satisfied is

the degree to which a result would be

expected to agree with other results,

normally, irrespective of the analytical

methods used. Measurement uncertainty

turns out to be an invaluable tool for this 1.

Confidence in the comparability of results

can help to reduce barriers to trade.

Regulators’ responsibilities

It is now recognised that there are a numberof actions that may be taken by thoseresponsible for the enforcement of legislationin various countries, which directly affectdecisions as to whether or not a sample, orbatch from which a sample is taken, is incompliance with the specifications9.

Before any specification is laid down in

legislation, it must be understood that a

specific parameter will depend on the

procedure used for sampling and estimation.

In the absence of uniform criteria for these,

and their common application and

interpretation, different judgements may

be arrived at by different people in different

countries, regarding the compliance of a

particular batch. For example, some

countries may correct analytical results for

recovery, whereas others may not. Some

may take into account the measurement

uncertainty associated with the analytical

results. Because of these basic variations, a

material for which there is a statutory

limit of 4 µg kg -1 for a contaminant, might

have been interpreted as containing

3 µg kg -1 on analysis in one country, but as

10 µg kg-1 in another country. This emphasises

the need for uniform interpretation of

legislation, which, in turn, needs consistent

interpretation of analytical results, leading to

equivalence across the various countries.

It is highly recommended that measurement uncertainty be used whenassessing compliance. An analytical resultmight be quite different if theextraction/dissolution/fusion methods arealtered, or – equally bothersome – if theresult is corrected for recovery 9.



Substance analysed Value + Expanded uncertainty

Carbon (Atomic weight) [IUPAC] 12.0107 ± 0.0008

Hydrogen (Atomic weight) [IUPAC] 1.00794 ± 0.00007

Standardisation of KMnO4 solution of about 0.1 N (Volumetry) 10a 0.1004 ± 0.000284 N

Determination of CaO content in cement, by gravimetry10a 57.20 ± 0.322%

Percentage of tin in copper base alloys10b 5.28 ± 0.015%

Estimation of formaldehyde in textiles10c 35.24 ± 0.5 ppm

Estimation of the saponification value of the vegetable oil 10d 193.97 ± 0.864 mg KOH g-1

Polyester composition on blended fabric10e 66.73 ± 1.23%

Density estimation10f 1.037 ± 0.0037 g cm-3

Ash/filler content10f 14.1 ± 0.299%

Assay of cypermethrin(GC) technical 4 94.45 ± 0.59%

Assay of alphamethrin(HPLC) technical 4 98.2 ± 1.019%

Assay of isoproturon(HPLC) technical 4 98.2 ± 0.854%

Estimation of the suspensibility of isoproturon 75% WP formulation (Major component)4 89.1 ± 2.15%

Assay of oxyclozanide (Volumetry) (Purity) 4 98.2 ± 0.591%

Assay of cypermethric acid chloride (Volumetry) (Purity) 4 98.9 ± 1.32%

Preparation of a calibration Cd standard solution11 1002.7 ± 1.8 mg L-1

Preparation of standard sodium hydroxide solution (0.1 N)11 0.1021 ± 0.0002 N

Standardisation of HCl using NaOH11 0.1014 ± 0.0004 N

Determination of organophosphorus pesticides in bread (Extraction and GC)11 Uop = 0.68 x Pop, for

organophosphorus pesticides (mg kg-1)

Cadmium release from ceramic ware11 0.036 ± 0.007 mg dm-3

Crude fibre in animal feeding stuffs11 2.5 ± 0.62% w/w

5 ± 0.8% w/w

10 ± 0.12% w/w

Lead in water using double isotope dilution and ICP MS11 0.0537± 0.00036 µµmol g -1

Analysis of 2,4-D herbicide in urine(GC-ECD)(version 1) 12 20 ± 11 µµg L-1

40 ± 13 µµg L-1

60 ± 16 µµg L-1

80 ± 20 µµg L-1

100 ± 24 µµg L-1

Lead in blood by graphite furnace atomic absorption spectroscopy (AAS) U =1.668 x 10-1 x X,

Calibrating with a commercially available standard (version 1) 12 Where X = concentration (µµmol L-1)

Table 1.


The uncertainty of the average test result is

dependent on the number of samples. When

the number of samples increases, the average

test result will be a better approximate of the

true value. Therefore, the number of test

samples should be noted in the report 9.

It is highly recommended that measurement

uncertainty be used whenassessing compliance.

Only the declaration of the measurement

uncertainty will enable one to make a correct

assessment about the reliability of a

measurement and of its result. Erroneous or

missing information on uncertainty may give

rise to wrong decisions.

Some typical uncertainty values

Table 1 (page 7) gives typical values ofuncertainties reported in the literature, andfrom our laboratory, for certain types ofanalyses carried out.

One must realise that measurements ofatomic and molecular weights, which formthe very foundation of chemistry and relatedscientific disciplines, are themselvessubjected to minor uncertainties. It can beseen that the percentage of uncertainty withrespect to the best estimate of the true valueof the concentration of the measurandcomes down, in general, as the analyticalevaluation goes from nano – to micro – tominor component and major componentestimations. This is because theexperimental variables affect the trace andultra trace level estimations to a greaterextent than the analytical results of the majorcomponent. One must also realise that theextent of uncertainty, in general, increaseseven in micro level estimations, withdecrease in the concentration of the analyteof interest 12a.

Conclusion

Analytical chemists must understand the

principles involved in measurement

uncertainty, know how to calculate

measurement uncertainty and apply this

knowledge while conducting routine

analysis. To achieve success in these

areas, the following are indispensable.

• a knowledge of statistics;

• an understanding sources of uncertainty;

• the ability to convert uncertainty related

information to standard uncertainty and

combined uncertainty, and report the

results to the client.

The expanded uncertainties for various

concentration ranges encountered in food

and feed analysis, and the range of

acceptable concentrations, have been

published by the EU8.

The AOAC manual for the Peer Verified

Methods Program includes a table with

estimated precision data as a function of

analyte concentration, and another with

estimated recovery data as a function of

analyte concentration13.

REFERENCES

1. Ellison, S.L.R., Roesslein, M., Williams,

A. (Eds), Eurachem/CITAC guide,

“Quantifying Uncertainty in Analytical

Measurement” , Eurachem/CITAC,

p 4, 2000.

2. Layden, D., “Quality Progress”,

February 2001.

3. “The Expression of Uncertainty and

Confidence in Measurement”, Document

M-3003, UKAS, 1st Ed, December 1997.

4. Data from author’s laboratory.

5. “Measurement Uncertainty”, UKAS,

September, 2003.

6. Horwitz, W., JOAC, 86, pp 109-111,

2003.

7. ISO/IEC 17025:1999, “General

Requirements for the Competence of

Calibration and Testing Laboratories”,

ISO, Geneva, 1999 (revised 2005).

8. “Guide to the Expression of Uncertainty

in Measurement”, ISO, Geneva(1993)

9. “Report on the Relationship between

Analytical Results, Measurement

Uncertainty, Recovery factors, and the

Provision of EU Food and Feed

Legislation, with particular reference to

Community legislation concerning

Contaminants in Food (Council

Regulation (EEC) No. 315/93 of 8-2-93),

laying down Community Process

Contaminants in Food and Undesirable

substances in Feed (Directive

2002/32/EC of the European Parliament

and the Council of 7/5/2002) on

Undesirable substances in animal feed”.

10. NABL News, NABL, Dept. of Science

and Technology, Government of India,

New Delhi.

(a) Issue No. 29, January, 2003

(b) Issue No. 30, April, 2003

(c) Issue No. 34, April, 2004

(d) Issue No.21, January, 2001

(e) Issue No. 26, April, 2002

(f) Issue No. 36, October, 2004

11. Ell ison, S.L.R., Roesslein, M.,

Williams, A. (Eds), Eurachem/CITAC

guide, “Quantifying Uncertainty

in Analytical Measurement”, Eurachem,

appendices A1 – A7, 2000.

12. O’Donnell, G., Lab Services Unit,

Sydney, Australia

13. “AOAC Peer Verified Methods Program,

Manual on Policies and Procedures”,

AOAC, Arlington, VA, November 1993.



Steve EllisonLGC

Laboratories undertaking chemical

omeasurement and testing now have

access to a variety of relevant and

internationally accepted guidance

documents on the estimation of

measurement uncertainty. These include the

fundamental ISO guide (“the GUM”) 1,

the Eurachem/CITAC guide, which is

tailored to analytical measurement 2,

general guidance from Eurolab 3 and from

accreditation bodies such as EA 4 and,

in the UK, UKAS 5. Many of these

guidance documents allow laboratories to

use interlaboratory reproducibility data

as a basis for uncertainty estimation; that is,

data obtained in interlaboratory validation

studies of standard methods of analysis.

ISO 17025, the principal documentary

standard for laboratory accreditation,

also acknowledges that reproducibility data

can provide the basis for uncertainty

estimation in testing. This is very helpful

to laboratories, as reproducibility

information is commonly available for

standard methods in analytical chemistry,

and using reproducibility data can

drastically simplify the process of uncertainty

estimation. Most international documents,

however, concentrate on ‘modelling’

approaches to uncertainty, and give

relatively little detail as to how

reproducibility data may be used.

This gap has recently been filled by the

publication of new ISO guidance for

the use of reproducibility data in

uncertainty estimation, ISO TS 21748 6. In

this article, I will summarise the main

principles and provisions of this new ISO

Technical Specification.

Uncertainty estimation using reproducibility data

The basic principles of uncertainty

estimation are well documented. An

analytical result typically provides an

estimate of the concentration or proportion

of some substance in a test material. Because

of factors such as run to run variation,

uncertainties in calibration solutions and

(usually) other effects which are often hard

to characterise, there is always some

uncertainty about the actual concentration;

the observed analytical result will be

consistent with a range of possible values or,

more accurately, a dispersion of possible

values. Measurement uncertainty itself is a

parameter – such as a standard deviation –

that is intended to characterise the width of

that distribution. To do that job well, we

must take into account all the factors which

might reasonably affect the result.

The ‘modelling’ approach taken by the ISO

Guide to the expression of uncertainty in

measurement achieves this by starting from a

mathematical description that describes,

quantitatively, how all the known ‘input’

effects can reasonably alter the result.

Uncertainties in ‘inputs’ then lead, through

mathematical manipulation, to a combined

uncertainty for the result (Figure 1). This

works well for well-understood systems,

particularly where calibration uncertainties

dominate the uncertainty; the principal

weakness is that it relies on a well-developed

and comprehensive mathematical model of

the measurement system and is consequently

prone to underestimation due to

missing effects.

In principle, the same outcome can be

achieved experimentally by varying the

‘inputs’ randomly across the range implied

by their uncertainty (Figure 2, page 10).

This can be done computationally, as

described by a current draft supplement to

the GUM, which uses Monte Carlo methods

to obtain combined uncertainties7. It can also,

in principle, be achieved experimentally.

Usually this is considered uneconomic for

individual effects. However, as Figure 2

shows, taking a selection of analysts (perhaps

in different laboratories) at random will tend

to generate a random selection from the

possible values of each input effect. This is,

of course, exactly the principle of

collaborative method evaluation studies; that

the important factors affecting the results

will vary naturally within their normal ranges

Using reproducibility information inmeasurement uncertainty estimationA new ISO guide

S T A T I S T I C A L F O C U S

Figure 1: Uncertainty estimation using modelling.

1 0 I S S U E 3 4 – S P R I N G 2 0 0 6V A M B U L L E T I N


if a selection of laboratories is asked to

perform the same analysis using the same

method. The practical effect is that the

reproducibility standard deviation sR

should be a direct estimate of the

uncertainty u(y). Indeed, comparison of

interlaboratory study reproducibility is

among the most powerful tests of

measurement uncertainty estimates

formed from mathematical models.

Interlaboratory study reproducibility can

accordingly be taken as the basis for a

measurement uncertainty estimate,

providing that certain conditions are met.

The experimental approach has the

advantage of including significant

unknown sources of variation; its principal

weakness is the possibility of unsuspected

consistent bias. In the most general

standard for interlaboratory studies of

method performance, ISO 5725, this is

addressed principally by assessment of

trueness using certified reference materials,

leading to an estimate of trueness expressed

as bias for a particular material or

materials. A typical collaborative study

conducted according to ISO 5725

accordingly provides three principal items

of information. Precision data include

the reproducibility standard deviation,

representing performance across laboratories,

and the repeatability standard deviation,

which describes expected precision within a

single laboratory. In many cases,

the trial data include some information

on trueness, usually in the form of

a mean bias for a reference material.

In addition to these three, the

study usually furnishes some data on the

relationship between precision and

concentration, which is important in

estimating precision for different materials.

Implementation in TS 21748

ISO TS 21748 relies on this data, and

applies it to form a detailed methodology for

uncertainty estimation in testing. It

calculates an estimated uncertainty based on

the reproducibility standard deviation,

combined with additional terms for

uncertainties associated with method bias

(trueness) and for effects which would not

vary during the particular collaborative

study. These are principally effects such as

sampling, sample preparation, and matrix

effects (Figure 3). But this approach relies

on two fundamental assumptions: First, the

laboratory’s implementation of the

method must be consistent with the

performance of laboratories in the particular

collaborative study. Second, the sample type

must be consistent with the scope of the

method. Because these are critical

assumptions, TS 21748 provides specific

tests for each, and corrections for

deviations from either assumption.

Checking laboratoryperformance

Since a typical laboratory has not taken part

in the particular collaborative exercise used

to validate a standard method, validation

Figure 2: Uncertainty estimation by random variation.

Figure 3: Forming an uncertainty budget based oncollaborative study reproducibility.

1 1I S S U E 3 4 – S P R I N G 2 0 0 6 V A M B U L L E T I N


data are used to check consistency of

performance. A variety of evidence can be

used. First, one can (and should) check that

within-laboratory precision for the method is

consistent with the repeatability standard

deviation found in the study. This is a

normal part of verification for a standard

method; it can be done either by in-house

validation studies or, if precision criteria are

specified in the standard method, by

demonstrating that the precision criteria are

consistently met. Where a laboratory’s

repeatability differs from the repeatability in

the collaborative study, the Specification

provides a method for substituting the

laboratory’s own repeatability and

calculating a revised reproducibility estimate

(though of course, much poorer precision

will be a matter of concern that may need

investigation and remedy). Second, a check

on laboratory bias must be performed. This

usually relies on reference material studies;

the laboratory’s bias must be within that

expected from the between-laboratory term

found from the study. However, spiking and

similar recovery studies also provide

evidence; so, too, does continued successful

performance in other interlaboratory

exercises such as proficiency testing.

Adjusting for sample type and concentration

Once the laboratory has confirmed that its

performance is consistent with the

collaborative study data, attention turns to

the test sample. Here, the most important

feature is usually the concentration of

analyte; it is very common to find that

precision depends on analyte level. Usually,

the standard deviation is approximately

proportional to concentration when well

above the detection limits, that is, the %CV

is roughly constant. TS 21748 provides

methods for adjusting the repeatability and

reproducibility to accommodate differences

in concentration, based on the previously

published models in ISO 5725. Where the

reproducibility varies with concentration, the

simplest model is a straightforward constant

%CV approach, which is often applicable

when well away from the detection limit and

is essentially what many laboratories already

use in practice.

Finally, it is left to the laboratory to make

allowances for any additional effects which

were not represented by the collaborative

study conditions. For example, changes

in sample type are likely to cause additional

bias or dispersion. Differences in calibration

material or glassware specification may

also need checking, though these are

usually small and easily checked using

spreadsheet methods for uncertainty

estimation 2,8,9. In general, laboratories

adjust methodology to render these effects

small, and will often find that they are

negligible compared to the reproducibility

standard deviation.

Future development of ISO TS 21748

ISO TS 21748 is currently a Technical

Specification. These are intended for rapid

development and implementation, but have

a relatively short lifetime; ISO require that a

TS is reviewed after two years and

either withdrawn or taken as draft Standards

and progressed through the normal

standardisation route to become full ISO

standards. In 2005, ISO TC/69, the

committee responsible for TS 21748,

resolved that 21748 would, when it

fell due for review, be taken forward to

standardisation, ensuring its continued

availability in the long term. The current TS

will remain in force during development of

the standard.

Conclusions

ISO 21748 provides a detailed methodology

for estimating uncertainty using published

reproducibility data for standard methods of

analysis. Application depends on checking

laboratory performance using familiar

validation methods, and the results can be

extended to any material within the scope

of the method. The approach usually

reduces to use of the reproducibility standard

deviation when matrix and homogeneity

effects are negligible or already accounted

for, saving considerable time and effort for

the laboratory.

ISO TS 21748 is available in the UK

from the British Standards Institute

(BSI), and internationally via national

standards bodies in the same way as

other ISO documents.

REFERENCES

1. “ISO/IEC Guide to the expression of

uncertainty in measurement”,

International organization for

Standardization, Geneva, 1995.


A. (eds), Eurachem/CITAC guide,


Measurement”, Eurachem, 2000.

3. Eurolab technical report 1/2002,

“Measurement uncertainty in testing”,

Eurolab aisbl, 2002, www.eurolab.org.

4. EA-4/16, “EA guidelines on the

expression of uncertainty in quantitative

testing”, 2004.

5. UKAS LAB12, “The Expression of

Uncertainty in Testing”, United Kingdom

Accreditation Service, Feltham,

Middlesex, UK, 2000.

6. ISO TS 21748:2005, “Guidance for the

use of repeatability, reproducibility and

trueness estimates in measurement

uncertainty estimation”, International

Organization for Standardization,

Geneva, 2005.

7. Cox, M., Harris, P., “Measurement

Techniques”, 48(4), pp 336-345, 2005.

See also BIPM, Joint Committee on

Guides in Metrology (JCGM), Working

Group 1, www.bipm.fr, GUM Supplement

1: “Propagation of distributions using a

Monte Carlo method”.

8. Kragten, J., Analyst, 119, pp 2161-2166,

1994.

9. Ellison, S.L.R., Accred. Qual. Assur.

10(7), pp 338-343, 2005.

http://www.eurolab.org

http://www.bipm.fr

krbm

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MichaelGriffiths LGC

What is resampling?

The es t ima t i on o f unce r t a in t yis usual ly accomplished where a

well-defined algebraic relationship exists andwhere reasonable assumptions regarding the underlying population distribution can be made. For example, for a dataset whosedistribution is approximately normal, estimationof the sample standard deviation of the samplemedian is a relatively straightforward procedurebecause the underlying population distribution isknown and a known algebraic relationship existsto calculate such a value from the standarddeviation of the data. But what happens whenwe don’t know the underlying distribution, orwhere such an algebraic relationship does notexist? The choices are:

1. derive a relationship capable ofestimating the uncertainty.

2. ignore any associated uncertainty.

3. find an alternative to conventionalstatistical theory.

The problem is that option 1 may not always bepossible whereas option 2 is obviously onlyrecommended where it is known that theuncertainty is negligible. Fortunately there existsa branch of statistics that falls under option 3,commonly known as resampling. Resampling isa computationally intensive statistical techniquein which multiple new samples are drawn(generated) from the data sample or from thepopulation inferred from the data. Certainstatistics (or estimates) of interest (e.g., thesample median) are then calculated for each ofthese new samples, and the resulting multiplecalculated values of the statistics are thenanalysed in order to investigate and estimatevarious properties (e.g., sampling distribution,error, bias, etc.) of the statistics.

Two of the more common resamplingtechniques are Monte Carlo Simulation (MCS)and the Bootstrap. Bootstrapping has beendiscussed in a recent AMC brief 1. Althoughmany such methods fall under the heading ofresampling, MCS and the Bootstrap are by farthe more thorough in a statistical sense.

What is Monte CarloSimulation (MCS) and how does it differ from

the Bootstrap?

The Bootstrap, when used to find thestandard error of a parameter estimate, reliesupon the generation of a large number of newdata sets from x, where x = (x1,...,xn). Eachnew data set, each the same size as theoriginal, is generated by sampling x at randomwith replacement. Sampling with replacementmeans that, if any member of the original setis chosen as the first value of the bootstrapsample it may also be chosen as any of thesuccessive values. But what can be done whenthe analyst only has only a very limitedamount of information? In such situations theBootstrap may only be able to generate asmall number of new data sets, giving a poorestimate of the parameter’s standard error.

MCS is based on the following considerations.The expected value of a measured result y isconventionally obtained by calculating y fromthe estimated values (x1,...,xn) of the inputquantities, which are quite often mean valuesthemselves. However, since each inputquantity can be described by a probabilitydensity function (pdf) rather than a single

number, a value as legitimate as its mean canbe obtained by drawing a value at randomfrom this pdf. MCS operates in the followingmanner, based on this consideration.

Generate a value at random from the pdf foreach input quantity and recalculate y usingthese values as input quantities. Repeat thisprocess many times, to obtain in all say, M,estimates of y. According to the central limittheorem, the mean value y of the M, estimatesof the output quantity obtained in this mannerconverges as 1/√M, if the standard uncertaintyu(y) exists. Irrespective of the number of inputquantities, it is only necessary to quadruple Min order to halve the expected uncertainty in theestimate of u(y). Thus, the basic concept ofMCS has reasonable convergence properties.Calculation of the square root of the variance ofthe M estimates of y gives the Monte Carloestimate of u(y), i.e. the standard uncertainty ofthe measurement result, y. If the distribution ofthe generated measurement values is normal,u(y) can be used to obtain U(y) = ku(y) as isusual, if not, calculation of the 2.5th and 97.5th

percentile yields a direct estimate of the 95%confidence interval whose accuracy is much lessdependent on the shape of the distribution andis therefore recommended where the exactnature of the distribution is unknown.

Advantages of Monte CarloSimulation (MCS) compared

to the Bootstrap

One of the obvious advantages of this

technique is that the estimated standard

error can be obtained from input values and

their uncertainties when the number of

observations is low; provided that an

estimate of the mean and standard deviation

are given. All parameter estimates are

derived from their respective distributions,

thus allowing the propagation of

distributions2 through the model. If the

resulting model estimate distribution is

asymmetric then the shortest coverage

interval can be calculated, this is not possible

when using the Bootstrap.

Resampling statisticsMonte Carlo Simulation (MCS) and its application to theevaluation of measurement uncertainty


The Monte Carlo with which most readers will be familiar. [Ed]



A simple example: Acid-base titration

In this simple example the concentration ofhydrochloric acid (HCl) is determined bytitration with sodium hydroxide (NaOH),which has been standardised againstpotassium hydrogen phthalate (KHP). TheMonte Carlo Simulation method is used toobtain an estimate of the standard uncertaintyof an acid concentration and compare thiswith the algebraically derived value.

Values for the input quantities of the acid-basetitration are given in Table 1. Also shown inTable 1 are the standard uncertainties associatedwith each quantity and the distribution. Noticethat MCS uses the actual distribution, and notjust u(x). Estimates of each of the inputquantities are then sampled from their respectivedistributions using x and u(x). These valueswere then placed into Equation 1. In thisparticular example, M = 2,000 resulting in thehistogram of cHCl shown in Figure 1.

Where:

cHCl = concentration of HCl

MKHP = mass of KHP

PKHP = purity of KHP

VT1 = volume of NaOH for KHP titration

VT2 = volume of NaOH for HCl titration

VHCl = HCl aliquot for NaOH titration

rep = repeatability

From Figure 1, it is clear that the mean of theMonte Carlo cHCl simulations 0.1013894, is anextremely good approximation of thealgebraically derived value of 0.1013872. Inthis particular example it would not have beenpossible to use the bootstrap to calculate theuncertainty because the number of replicatesfor each model variable was only one. Wherenumber of replicate values for an input variableis insufficient to generate at least 1,000 newdata sets, Monte Carlo Simulation can be usedunder the constraints already mentioned.

Using the ‘law of propagation of uncertainties’3

principles of error propagation, U(cHCl) = 2 x0.00018 = 0.0004 mol L-1, giving a 95% lowerand upper confidence interval of 0.1010 and

0.1018 mol L-1 respectively. For this particularMonte Carlo simulation, if we assume a normaldistribution for cHCl, which turns out to be areasonable assumption looking at Figure 1, thelower and upper 95% confidence limits of theresampled cHCl values are 0.10101 and 0.10177mol L-1 respectively. Results using thepercentile method are very close to these values;the lower and upper 95% confidence limitsbeing 0.10099 and 0.10175 mol L-1

respectively, which is not surprising given thecloseness of this distribution to that of thenormal distribution.

Further reading

Tutorial-style information about Monte Carlosimulation, plus examples, software etc., canbe found in the website of Resampling Stats

at www.resample.com. An Excel add-in willbe available on the AMC website shortly.

REFERENCES

1. “The Bootstrap: A Simple approach to

Estimating Standard Errors and

Confidence Intervals when Theory

Fails”, Analytical Methods Committee,

8, RSC, August 2001.





3. “ISO/IEC Guide to the expression of

uncertainty in measurement”,

International Organization for

Standardization, Geneva, 1995.

Standard DistributionParameters Value (x) uncertainty u(c)

Repeatability 1.00 0.001 Normal

Weight of KHP (g) 0.39 0.00012 Rectangular

Purity of KHP 1.00 0.00029 Rectangular

Volume of NaOH for HCl titration (mL) 14.89 0.014 Triangular

Volume of NaOH for KHP titration (mL) 18.64 0.015 Triangular

Molar mass of KHP (g mol -1) 204.22 0.0038 Rectangular

HCl aliquot for NaOH titration (mL) 15.00 0.011 Triangular

cHCl (mol L-1) 0.1014

Table 1: Acid-base titration values, uncertainties and theirassociated distribution.

Figure 1:Histogram of 2,000 Monte Carlo simulations of cHCl.Based on the estimates of the parameter value, standard uncertainty and probabilitydensity function (pdf) as shown in Table 1. A normal distribution (red) is overlaidwith the same mean and standard deviation as that of the Monte Carlo estimates.

cHCl =1000MKHP .PKHP .VT2

VT1.MKHP .VHCl

x rep

Equation (1)

cHCl

Freq

uenc

y

http://www.resample.com



Emad EddaduRoyal ScientificSociety, Jordan

Introduction

According to quality systems related to laboratory analysis, such as ISO

17025, a laboratory shall have qualitycontrol procedures for monitoring thevalidity of tests and calibrations undertaken.Trends are detectable and, wherepracticable, statistical techniques should be applied for reviewing the results 1. VAM Principle number 6 (Page 2) statesthat organisations making analyticalmeasurements should have well-definedquality control and assurance procedures.

One quality procedure that demonstrates that asystem of analysis is under statistical control(statistics of course does not control thesystem!) is control charting; a conceptdeveloped in the 1930s for the manufacturingindustry by Shewart2. The concept is based onthe process of continually sampling from the production line, measuring themanufactured property value (weight of aspirin tablet for example) and plotting the results on a chart which shows the measured values as a function of time. Three horizontal lines are plotted on the chart (Figures 1&2): one in the middle representing the mean, and upper and lower control lines calculated respectivelyby adding and subtracting three standard deviations to/from the mean. It isassumed that 99.73% of the data should lie between those two control lines todemonstrate that the manufacturing process isunder control.

Principles of control chartingin chemical analysis

In manufacturing industry, the measurementprocess is considered as ‘error free’ or atleast the error associated with measurementis very low compared to that associated with

the manufacturing process capability. In thelaboratory, the measurement error (eithersystematic or random) should be evaluatedand monitored. Control charts were firstemployed in chemical analysis by theworkers of NBS3.

A reference material (RM) is used to constructa control chart by analysing it many times (at least 7 but preferably 20), on different days, by different analysts, employing differentcalibrations, instruments, etc. The referencematerial should:

• be stable to at least the time span that it

will be employed;

• be homogeneous to at least the quantity

of the sample which will be tested;

• resemble the matrix of samples and alsocontain the analyte at a concentrationsimilar to the samples that are routinelyanalysed in the laboratory;

• be available in sufficiently large amounts.

Certified reference materials (CRMs) arevery expensive, so it is acceptable to use anuncertified RM on a day-to-day basis,provided it is analysed regularly against therelevant CRM, to ensure its stability, thereliability of the control chart and themeasurement results. By continuouslymeasuring the RM, a mean control chart(Shewart Chart) can be constructed. It isassumed that the results of analysis followthe normal distribution (inverted bell orGaussian distribution), (Figure 3).

Control charting in chemical analysis

Figure 1: Typical Shewart Control Chart (process is under control).

Figure 2: Typical Shewart Control Chart (process is out of control).

Typical Shewart Control Chart (mean chart)

Typical Shewart Control Chart (mean chart)



The normal distribution has the followingcharacteristics:

• Mean = Mode = Median;

• 68.27% of the area under curve is within

� 1 standard deviation of the mean;

• 95.45% is within � 2 standard deviations;

• 99.73% is within � 3 standard deviations.

The mean (x ) is calculated by addingtogether all of the data and dividing by thenumber of results i.e.:

The sample standard deviation (s) iscalculated by taking the difference betweeneach measurement and the mean, squaringthis difference, dividing the sum of thesquared differences by their number minusone, and, finally taking the square root ofthis figure:

(x ): the average (arithmetic mean)

xi: the individual results

n: number of results

Mean (x) control charts

After collecting the data from analysing theRM, the mean control chart can beconstructed as follows (Figure 4):

• Upper control limit (line), UCL =

mean +3s;

• Upper warning limit (line), UWL =

mean +2s;

• Central line = mean;

• Lower warning limit (line), LWL =

mean -2s;

• Lower control limit (line), LCL = mean -3s .

The boundary lines are used to controlfuture lab performance, according to thecriteria that will be discussed later.

What should a mean control chart look like?

Depending on the probability theory and theproperties of the normal distribution, a meancontrol chart should have the following features:

• Half of the points should lie above thecentral line, and the other half should liebelow;

It is the same idea as if you toss a coin for alarge number of trials. You would expect that50% of the trials would be one of the twofaces of the coin, while the probability thatthe other face will appear will also be 50%.

• about 68% of the points should lie withinthe mean � one standard deviation;

Although not usually part of the meancontrol chart, two lines can be added tomonitor this.

• about 95% of the points should lie withinthe two warning lines (i.e. mean � 2s);

• 99.73% (virtually all of the points)should lie within the two control lines(i.e. mean � 3s);

• the points should not follow a trend(either increasing or decreasing).

After a control chart’s lines are known and

drawn, the RM should be analysed frequently

(e.g. every day, every batch of analysis or every

10-20 samples, depending on the cost,

importance of the test, and time available),

and its result plotted on the control chart. The

results should be statistically analysed on a

regular basis (e.g. monthly) using special

statistical tests (F-test for comparison of the

spread, Student-t for the two means of the two

groups, say this and the previous months).

Figure 3: Normal distribution.

x = xi / nΣi = 1

n

(xi – )Σ i = 1

n

x�� 2

n – 1

s =

Figure 4: Mean control chart (Shewart control chart, with twoadditional lines for UWL & LWL).



Mean control chart as an

effective tool for assessment of

lab technical performance

Providing the above steps are followed, control

charts can be used by analysts to identify when

the measurement system is no longer under

control and detect potential errors before they

occur. In the event of any of the following

scenarios, the analysis should be stopped and

the RM reanalysed. If there are no problems

with the RM, investigate for the possible

source of that error, rectify the problem and

resume your analysis.

Those are some rules which should help in

deciding a possible lack of control 4:

One point outside the

control lines (Figure 5)

The probability of one point being either

higher or lower than the UCL = 0.27%.

This equates to about three chances in 1000!

In other words, if you analyse one batch

per day and work 250 days per year, you

would expect to have three occurrences in

four years.

Two successive points are outside

the upper warning line (Figure 6)

Probability of occurrence of one point

between the upper control and warning lines:

= 4.55% ÷ 2 = 2.275%.

Probability of two points in the exact

same region:

= ( 2.275% )2 = (2.275/100)2 = 0.0005

= 0.05%

Four successive points exceed

the mean by more than one

standard deviation (Figure 7)

Probability of occurrence of a point higher

than 1s:

= 100% – 68.27% = 31.73%;

But on one of the two sides:

= 31.73% ÷ 2 = 15.865%

Probability of 4 points higher than 1s from

one side:

= (15.865%)4 = (15.865/100)4

= 0.00063 = 0.063%

Figure 5: Last point abnormally exceeds the upper control line.

Figure 6: Last two successive points are above UWL.

Figure 7: Last four successive points are outside one standard deviation.



Eight successive points are on the same side of the mean (Figure 8)

Probability of this event:

= (50%)8 = (50/100)8 = 0.0039

= 0.39%

Trending: six points are in decreasing or increasing order (Figure 9)

This scenario is unlikely to happen bychance and indicates a gradual drift in themeasurement system.

Other control charts

Mean of means (X ) control charts

The difference between X and X controlchart is that you do replicate analysis for theRM in the latter and the boundary lines have

a revised criteria. The warning and controllines are narrowed by a factor of 1/√k, wherek is the number of replicates.

Consider, for example, the duplicate analysisof the RM (k=2):

Where:

xi is the individual average of the replicates

X is the average of the averages.

n is the number of averages.

In other words, the average of replicates in eachanalysis is calculated, and when a suitablenumber of data have been obtained (say 20averages) the control chart is established basedon the average of those 20 averages.

The pooled standard deviation should alsobe calculated. With duplicates, the pooledstandard deviation is calculated using the formula:

Where:

R i = individual ranges of the duplicates

N= is the number of duplicates

Range (R) control charts

We can utilise the values of ranges from theX control chart and establish criteria for theinterpretation of the difference (range)between the duplicates (or any number ofreplicates). Such an interpretation is an Rcontrol chart that will help the analyst decidewhether the difference between the results ofthe two analytical trials for the RM isacceptable.

An R control chart can be constructed usingthe following equations, which are applicablefor analyses done in duplicate:

Control line (CL) = 3.27 R

Warning line (WL) = 2.456 R

Middle line (50%L) = 0.845 R

Where: R = the average of the absolute valuesof ranges for the RM analysed in duplicate.

The constants shown are applicable forduplicate analyses. For analyses with more thantwo replicates, refer to special statistical tables.

There are no lower warning and controllines. Zero is the lowest value that can beobtained (identical results of the duplicates).Also, the middle (50%) line is not theaverage line. This distribution was proposedby Youden and Steiner5. A typical R chart isshown in Figure 10 (page 18).

Figure 8: Last eight successive points are on the same side ofthe mean line.

Figure 9: Six successive points are in increasing order.


An R control chart can be used in parallel

with an X control chart. The R chart

controls the batch (short-term) precision,

and the X chart, controls the bias of the

average of the two results and the long term

precision (different analysts, instruments,

calibrations, lab ambient environment, etc).

R charts can also be used separately

where it is difficult to guarantee a stable

analyte value for an RM, such as moisture in

foodstuffs and suspended solids

in wastewater6.

Moving average control charts

This is similar to the mean (X) control

chart, except that the average of last m

results is calculated, where:

m = the number of results underconsideration by the analyst.

The higher the value of m, the smoother will be the behaviour of the curve, making trends and shifts in the data more easily and clearly identifiable. Warning and control lines are reduced by afactor of 1/√m.

Figure 11 shows an example where the last

four points were averaged. In this case,

values for the warning and control

lines were divided by √4.

Control charting as an aid in estimating measurement uncertainty

According to the Eurachem guide 7,measurement uncertainty in chemical

analysis can be estimated by either

evaluating the uncertainty arising from

each individual source and combining

them, or by determining directly the

combined contribution to the uncertainty

on the result from some or all of the

sources using method performance data.

Such performance data could be

obtained from control charting, which

would give us an important factor:

precision. Besides ‘bias’, these are the

two main factors that determine

method performance.

If uncertainty is estimated by evaluating

each individual source and combining

them together, the calculated standard

uncertainty (standard deviation) could be

used to construct a control chart 8,

monitoring method performance. This

would be the absolute minimum

performance! 7 This is different from our

previous way of constructing a control

chart. The performance of the lab should

satisfy or be better than control

and warning lines based on the

uncertainty estimation or something better.

Otherwise, if the performance is worse,

then it can be said that the uncertainty

estimation did not take into consideration

some uncertainty sources, or the lab

performance has deteriorated and needs to

be improved.


Figure 10: R control chart.

Figure 11: 'Mean control' and 'Moving average' charts. Mean control chart (a) was recalculated as a moving average chart (b) where each point represents the average of the last fourresults. Warning and control lines were recalculated accordingly.


Use of computers in control charting

Many labs deal with a large amount of

samples requiring a long list of analysis. If

control charts have to be applied, the

process will of course be laborious and time-

consuming. PCs have been proven to be an

effective tool for tabulating, manipulating,

treating, interpreting and presenting data.

The charts in this article have been

produced using MS Office EXCEL 2003.

However, other specialist software is also

available including, for example, ‘Control

Chart! Pro Plus ’.9

REFERENCES

1. “General Requirements for the

Competence of Calibration and Testing

Laboratories”, ISO/IEC 17025:1999,

ISO, Geneva, 1999.

2. Shewart, W. A., “Statistical Methods

from the Viewpoint of Quality Control”,

Washington DC, 1939.

3. Pontius, P. E., Cameron, J. M., “RealisticUncertainties and the MassMeasurement Process”, NBS Monograph

103, National Bureau of Standards, 1967.

4. “Manual on Presentation of Data and

Control Chart Analysis”, ASTM, 7th Edition.

5. Youden, W. J., Steiner, E. H., “Statistical

Manual of the AOAC”, AOAC, 1975.

6. Dux, J. P., “Handbook of Quality

Assurance for the Analytical Chemistry

Laboratory”, Second Edition, 1991.

7. “Quantifying Uncertainty in Analytical

Measurement” , Second Edition,

EURACHEM/CITAC, 2000.

8. Rowley, A. G., “Evaluating uncertainty

for Laboratories – a practical guide and

handbook”, Version 1.1, January 2001.

9. www.chemsw.com


Mike Sargentand Gill HolcombeLGC

Many analytical laboratory managers

will be aware of the increasing

demand from purchasers of data and by

regulators for proven comparability of

measurements. Increasing numbers of

measurements are used in support of

regulations, for which there is an

expanding requirement for rigorously

proven reliability and good agreement

between different laboratories. In addition,

ever-expanding international trade

depends on laboratories around the world

being able to provide measurement

data to a common basis no matter where

they are located. Finally, the recent

increase in the use of sub-contracted

analytical measurements requires both

conformity of the contractors to quality

systems and demonstration that data

from different contractors are equivalent.

Participation in proficiency testing (PT)

schemes is by far the most common

means for laboratories to evaluate and

demonstrate their capability to perform

specific analyses or to apply a specific

measurement method. These schemes

have also played a key role for many

years in demonstrating the degree of

comparability of data from a group

of laboratories.

It is also becoming increasingly common

for regulatory bodies, accreditation

authorities, customers, and other interested

parties to accept data only from laboratories

which have demonstrated successful

participation in a relevant PT scheme.

In the United Kingdom, for example, the

value of PT schemes has long been

recognised by government agencies in the

defence, clinical, environmental and

food sectors. In some cases, this recognition

has been extended to legislation such

as the MCERTS programmes initiated by

the Environment Agency (EA) for

airborne pollution monitoring and

contaminated land analysis. These

programmes require participating

laboratories to successfully participate in

PT schemes in order to gain and

maintain their accreditation. Similarly,

the Food Standards Agency (FSA) sets

strict data quality requirements for

contractors undertaking surveys or

research projects. Contracts frequently

include compulsory participation in

one or more relevant PT schemes, with

acceptable performance over an extended

period. Similar requirements are

also widely set by purchasers of analytical

data from industry or elsewhere

in the private sector, such as retailing.

It is clear, therefore, that the ability

of a laboratory to demonstrate successful

participation is frequently a source

of competitive advantage and that

Provision of traceable reference valuesfor proficiency testing

C A S E S T U D Y

http://www.chemsw.com

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Highlight


C A S E S T U D Y

the converse is equally true. Hence, it is vital

that the performance scores provided to

participants by organisers are a true

reflection of capability. Otherwise

laboratories could well be unfairly penalised

and UK regulations might be enforced with

erroneous data.

Participation in PTschemes is by far the most

common means forlaboratories to evaluate anddemonstrate their capabilityto perform specific analyses

or to apply a specificmeasurement method.

This situation has substantially altered theethos of PT schemes, many of whichoriginated as collaborative activities with theaim of helping laboratories to identifysources of error in their measurementmethods. One aspect of the schemes has,however, scarcely altered as they have grownin size, number and commercial importance;

namely the way in which they derive anagreed value (i.e. the ‘correct answer’)against which to assess each participant’sperformance. In the vast majority of schemesthis remains the well-known concept ofcalculating a consensus mean for eachmeasurand in each round, by applying arecognised statistical procedure to the resultssubmitted by all participants. This concepthas several advantages: it avoids the need toidentify expert laboratories as the arbiters ofthe agreed value, the additional cost to theorganisers is small, and the value is usuallybased on a large pool of data. In most casesthe agreed values derived in this way areboth cost-effective and fit for purpose. Forexample, well-established applications wheremost participants use industry-standardmethods generally give reliable consensusvalues. Problems are more likely to arisewith difficult applications (e.g. traceanalysis) or where instrumentation andmethodology are changing rapidly. Thesituation may be particularly acute in newareas where appropriate matrix certifiedreference materials (CRMs) are few or non-existent. If a sufficient number ofparticipants use the same, or similar, methodwith a significant bias the consensus value

may be seriously skewed. At best this willunfairly penalise any participants having anunbiased method. At worst it may set a longterm trend as participants seek to ‘correct’their methods in order to achieve anacceptable performance score. This situationis sometimes revealed when an opportunityarises to assess the comparability of two ormore separate PT schemes, wherebyparticipants take part in a common PTscheme or laboratory comparison exercise.

The obvious way to minimise any tendencyof schemes towards a bias introduced by theparticipants is to replace or augment theconsensus value with a reference value whichis completely independent of the schemeparticipants. This could be done, forexample, as an occasional spot check orwhere there is reason to believe a bias mayexist. Adoption of this approach has,however, been very limited with theexception of the European Union’s IMEPscheme 1 (which is infrequent and not,strictly speaking, a PT scheme). There aretwo reasons for this situation. The first is therelatively high additional cost, since most PTschemes are now commercial and operate ina competitive environment. The secondreason stems from a reluctance by mostparticipants to accept that specificlaboratories working in their field aresomehow more expert than others and canbe used to provide a reference value in thisway. The cost issue is a serious concerngiven the need for reference values with asmall uncertainty and a high degree ofreliability, similar to the requirements forcertification of a reference material. It can beaddressed by limiting the provision of suchvalues to situations where a problem issuspected or, as suggested above, spotchecks. Even then many scheme organiserswould maintain that additional funding isneeded, as with IMEP or the VAM activitiesdescribed here.

The second issue with independentreference values, credibility of the referencelaboratories which provide them, is morecomplex and is frequently cited by PTscheme organisers as a major obstacle totheir use. There is, however, a long-established and international recognisedmeans of addressing the credibility issue,namely the international measurementsystem which has underpinned the SI unitsfor well over a century. This system is based


C A S E S T U D Y

on the development of reference standardsand methods, which offer rigorouslydetermined levels of uncertainty, andcollaboration between national measurementinstitutes (NMIs) to ensure their integrityand comparability. For most of its historythis international system has focussed onphysical measurements but in 1993 theInternational Committee for Weights andMeasures (CIPM) decided to establish acollaborative programme of work inchemistry 2,3. This programme is organisedthrough the CIPM’s ConsultativeCommittee on Amount of Substance(CCQM). The CCQM members areworking to resolve the practical difficulties ofachieving widely accepted chemicalreference measurements and to provide aninternational structure which enablesnational and regional laboratories todemonstrate the equivalence of theirmeasurement data. This is being donethrough a series of key comparisons, whichreflect applications relevant to industry,trade, health, environment, etc, as well as bya requirement for appropriate qualitymanagement systems. The formalarrangements for this collaboration are setout in a Mutual Recognition Arrangement(MRA) co-ordinated by the InternationalBureau of Weights and Measures (BIPM) inParis. In the case of chemistry, links to thekey comparisons are being achieved by theNMIs through provision of traceable CRMs,standards, and calibration services and byencouraging PT schemes to use traceablereference values where appropriate.

This case study provides an example of using

an independent reference value for a food

analysis PT scheme. The reference analysis

method for tin, using ICP-MS with isotope

dilution (IDMS)4,5, was developed as one of

LGC’s activities for the VAM programme.

There is an EU regulatory limit of 200 mg

kg -1 for tin in canned food and proficiency

testing for this analysis is undertaken on a

regular basis by the UK Central Science

Laboratory (CSL) as part of their FAPAS®

scheme6. This has highlighted problems with

the measurement of tin in food matrices by

field laboratories, leading to inconsistent

data between laboratories. The samples for

each FAPAS® round of this type are

prepared by adding tin to a natural material

(spiking) to bring the level within the region

of the regulatory limit. This is done on an

approximate basis but the expected

concentration was typically so far above the

consensus value that a problem with the

participants’ results was suspected. It is well

known that low results may be obtained for

tin analysis due to the difficulty of keeping the

element in solution after acid digestion of

samples. In addition to this, the use of multi-

element techniques for food applications is

increasing rapidly and errors arise because the

multi-element digestion or extraction

procedures most commonly used are

frequently unsuitable for the determination of

tin. In particular, an HCl concentration of

around 5% is usually recommended but this

can cause interferences in ICP-MS* methods.

...proficiency testing hashighlighted problems with themeasurement of tin in food

matrices by field laboratories,leading to inconsistent data

between laboratories.

In view of the suspected problem, LGCprovided a reference value using IsotopeDilution Mass Spectrometry (IDMS), for atomato paste sample (FAPAS® series 07round 38)7 after that study had beenconcluded. The reference value indicated abias of over 20% in the consensus value,potentially sufficient to affect enforcement of

the 200 mg kg -1 regulatory limit. Table 1shows a summary of the results and how theperformance of the participants would havechanged if the agreed value were based onIDMS rather than a consensus. It can be seenthat changing the agreed value in this waywould have affected a significant number ofparticipants. In cases such as this, where thereis a problem with a widely-used routinemethod, comparison with a reliable,independent reference value gives a betterindication of competency. The IDMSreference method used by LGC is toolaborious for routine use but overcomes manyof the sample loss problems frequentlyencountered with tin analysis. It is alsoaccompanied by a full uncertainty budget andis based upon exhaustive testing of instrumentconditions and sample-spike equilibration.Consequently, the end result provides a preciseand accurate ‘target’ permitting reliablerecovery (bias) estimations to be made forroutine techniques. This is best achieved whenthe routine method used by field laboratoriesalso has a proper uncertainty budget so thatcomparison is rigorously made by means of a t-test or similar statistical approach.

LGC provided an IDMSreference value for a tomato

paste sample...

Following the initial reference value

measurement described above, discussions

between LGC, the FAPAS® organisers and

Table 1: FAPAS® Round 0738 (tin in tomato puree). Change in tin performance using an IDMS reference value instead of a consensusvalue. The participants’ results were in the range 48 – 330 mg kg-1.

Agreed value Concentration Standard Uncertainty (b)

Deviation (a)

1. Consensus value 204 mg kg-1 45.5 mg kg-1

(22.3%)

2. IDMS reference value 247.8 mg kg-1 1.6 mg kg-1

(0.65%)

Effect on performance of changing No. of Percentage of allagreed value from 1 to 2. labs affected labs affected

‘Acceptable’ >> ‘Unacceptable’ 17 37

‘Unacceptable’ >> ‘Acceptable’ 9 20

No change to rating 20 43

* Inductively coupled plasma – mass spectrometry.

(a) Arithmetic standard deviation based on 46 participants.(b) Expanded uncertainty based on a standard uncertainty multiplied by a coverage factor k=2, providing a

level of confidence of approximately 95%.


C A S E S T U D Y

the FSA led to additional work within the

VAM chemical metrology projects to address

the problem of tin analysis. In addition to

providing further tin reference values for

FAPAS® series 07 (rounds 045 8 and 054 9),

LGC has organised several additional

studies based on the FAPAS® sample

prepared for series 07, round 54. These

include a CCQM key comparison (CCQM-

K45) to confirm validation of the IDMS

reference method at an international level, a

wider international comparison involving

additional expert laboratories (CCQM-P72)

and an LGC comparison as part of the

production of a matrix CRM (LGC 7161).

Use of the same sample for all these

purposes and a FAPAS® round has allowed

a thorough evaluation of the tin analysis

problem. Figures 1 to 4 show some of the

results from these exercises. It can be seen

from Figure 2 that the simple consensus

mean from the FAPAS® round shows a

smaller bias than with round 038 but is still

significant. The result of the LGC

comparison (Figure 4), which also required

participants to analyse a quality control

sample, is similar to that for the FAPAS®

round. This also illustrates the care needed

in certifying reference materials on the basis

of laboratory comparisons. These are

effective in most cases but in recent years it

has become less likely that participants will

be using several completely independent

methods, increasing the risk of a biased

result. LGC 7161 will be certified for tin

(and also cadmium and lead) on the basis of

IDMS data.

This case study has provided a clear

illustration of the benefits of using a reliable,

independent reference value for PT schemes

where a problem is suspected with routine

methods. Unfortunately, as mentioned

above, reference methods such as IDMS, as

used by LGC, are expensive and

time-consuming. Hence it is not feasible to

provide such measurements for all analytes

in all matrices currently involved in PT

schemes. However, by carefully selecting

those applications most prone to

measurement problems, or where a high

degree of confidence in routine results is of

greatest concern, the maximum benefit can

be achieved with the available resources.

Figure 3: Results of an international comparison (CCQM-P72) for theanalysis of tin in a tomato paste sample (FAPAS® series 07 round 54).The triangles show laboratories which also participated in CCQM-K45 (Figure 1).The error bars are expanded uncertainties (k=2).

Figure 2: PT scheme participants’ results for the analysis of tin in atomato paste sample (FAPAS® series 07 round 54).Graph shows the consensus mean and reference value of the CCQM-K45 study usingthe same sample.

Figure 1: Results of an international key comparison (CCQM-K45) forthe analysis of tin in a tomato paste sample (FAPAS® series 07 round 54).The error bars are expanded uncertainties (k=2). The horizontal lines show thecalculated reference value for the study and its uncertainty. Lab 1 detected an errorin its method and was excluded from the calculation of the reference value.


C A S E S T U D Y

REFERENCES

1. www.imep.ws

2. Sargent, M., VAM Bulletin, 17 ,

pp 11-12, 1997.

3. Catterick, T., Fairman, B., Sargent, M.,

Webb, K., VAM Bulletin, 17, pp 13-15,

1997.

4. Sargent, M., VAM Bulletin, 18, pp 10-

11, 1998.

5. Sargent, M., Harte, R., Harrington, C.,

“Guidelines for achieving high accuracy

in isotope dilution mass spectrometry

(IDMS)”, RSC, Cambridge, UK, 2002.

6. “Protocol for the Food Analysis

Performance Assessment Scheme,

Organisation and Assessment of Data”,

6th Ed., FAPAS®, Central Science

Laboratory, York, UK, 2002.

7. FAPAS® report 0738, Central Science

Laboratory, York, UK, September 2002.


Laboratory, York, UK, January 2004.


Laboratory, York, UK, 2005.

ACKNOWLEDGEMENTS

The work described in this case study was

carried out in close collaboration with FAPAS®

staff at CSL; in particular Linda Owen, Joanne

Croucher and Amanda Earnshaw. The work

was supported by VAM and by the Food

Standards Agency. We are also grateful to the

many laboratories, both in the UK and overseas,

which participated in the studies.

Figure 4: Results of an LGC-led laboratory comparison for the analysis of tin in a tomato paste sample (FAPAS® series 07round 54).The bars show the standard deviation of the replicate results submitted by eachparticipant (5 in most cases) with the mean value plotted.

Ian Gilmore,NPL and Ian Fletcher,ICI MeasurementScience Group

NPL together with partners Unilever,ICI and the University of Sheffield

have recently started a DTI funded microand nano-technology project to develop thenanoscale analysis of microfibres. The projectbuilds on essential foundations developedunder VAM and links to projects in thecurrent and future programmes.

Fibres present massive challenges and

opportunities for micro- and nano-

technologies (MNT). These challenges are

not in the manufacturing of the fibres, but in

the control of their behaviour. The sale of

products manufactured to control the

behaviour of a variety of types of fibre

contributes extensively to the UK’s GDP.

Products such as fabric detergents and

conditioners as well as hair shampoos and

conditioners are familiar in everyday life, but

the technology behind them is fast-changing

and challenging. Examples of novel

technologies include modified surface

nanotexture, polymer and nanoparticle

surface coating, nanoscale lubrication and

polymer micro-welding of fibres to change

fabric mechanical properties.

In the case of hair conditioner, for example,

the product modifies the surface friction of

hairs as they slide against each other.

This project will help to address the

enormous technical challenges that confront

scientists developing innovative surface

treatments for fibres. The four key thrusts

are to:

1. develop robust industry measurement

methods for fibre surfaces using an

Atomic Force Microscope (AFM) for

friction force, chemical force and

modulus measurement;

2. validate methods for cotton, synthetic

and human hair fibres with formulated

organic coatings and surface treatments;

Nanoscale analysis of microfibres using SSIMS

C O N T R I B U T E D A R T I C L E S

http://www.imep.ws

krbm

Highlight



3. provide reliable and powerful methodsto analyse nanoscale surface chemicalanalysis data from X-Ray PhotoelectronSpectroscopy (XPS) and StaticSecondary Ion Mass Spectrometry(SSIMS);

4. correlate nanoscale analysis of fibresurface physico-chemical properties withproduct performance.

Here, we briefly introduce one of the topics

and the approach being developed.

SSIMS is a powerful analytical technique

for the chemical analysis of surfaces with

excellent sensitivity, high specificity and in

modern instruments excellent spatial

resolution of up to 100 nm. This is

illustrated using SSIMS images of a human

hair with a multi-component formulated

treatment (Figure 1). Figure 1(a) shows the

total ion image, the sum of all the secondary

ions for each pixel, i l lustrating the

complex topography of hair. Figure 1(b) is a

chemical map of three separate

chemical components identified from

the mass spectra shown as an overlay

of three colours. The localisation,

quantification and identification of all

components, both initially and as a function

of the effects of washing and other

processes, is absolutely critical for

formulators to understand the mechanisms

that influence performance (e.g. migration,

competitive segregation and desorption).

From these data, improved products withmore environmental and economic benefitmay be designed.

The effect of surface topography is a major

issue for all surface chemical analytical

techniques and analysts have identified this

as a high priority for the next VAM

programme. Another high priority is the

valid and reliable analysis of the

overwhelming volume of data in

high-resolution molecular images, supplied

at increasingly faster rates by modern

instrumentation. For example, Figure 1(a)

is derived from a 3-dimensional matrix,

as shown in Figure 2, where at each

pixel (i,j) there exists an entire mass

spectrum. The high-resolution mass

spectrum may contain some 1x10 5 data

values but may usually be reduced to

typically 300 peak intensities. The size of the

3-dimensional matrix representing the data

is then 2x10 7 voxels (elements of the

3-dimensional matrix). The information

content is spread over many variables so that

the intensity in any voxel is low. For

analysts, this makes data interpretation

difficult and slow and can lead to low

intensity and to important components

not being identified.

Fortunately, multivariate methods provide

a powerful way to reduce the data

complexity by defining a new data space that

more efficiently accounts for variation

(information) in the data set. This method

may typically reduce the complexity of the

data by a factor of 100 so that, in the case of

Figure 1, only three images would be

required to account for the information out

of the possible 300 images in the original

data. However, at present the methods are

not clear to analysts and are surrounded in

jargon and ill-defined terms. Additionally,

the effects of different data normalisation

and scaling methods can lead to major

inconsistencies. In this project, and in the

VAM programme, NPL is developing a

reliable and validated procedure for

analysts to use.

Figure 1:Secondary Ion Mass Spectrometry images of human hair.(a) Total ion image showing the complex topography.(b) Chemical map overlay showing the distribution of 3 chemical components.

Figure 2:A SIMS image represented by a 3-dimensional matrix.

The i rows and j columns form the plane of the image, while the third dimension (k)contains the mass spectrum.



Chris Brookes,NPL

As long ago as the 1967 Road Safety Act,ithe concept of the amount of alcohol in

a person’s breath being representative of theamount of alcohol in that person’s blood wasaccepted. (Alcohol in this case refers to ethylalcohol, or ethanol: the alcohol present inintoxicating drinks). This Act first entailedroadside testing that was, at that point,carried out using a blow-in bag, containingcrystals which changed colour in thepresence of alcohol. If deemed to be ‘overthe limit’, the driver was taken to a policestation to have a blood or urine sample takenfor more accurate evidential analysis. Thelegal limit in the UK was (and still is) 80 mgof alcohol per 100 mL of blood (0.08%).

Initially, blood samples had to be taken by a

police surgeon. These were often measured

several days later. Due to the expense and

time delays incurred using this process, as

well as other operational difficulties (including

objections to the sampling of bodily fluids),

the Transport Act of 1981 required that the

amount of alcohol present in a vehicle

driver’s body should be determined through

the measurement of the alcohol content of

the drivers breath. Electronic roadside

screening devices were then introduced and

more accurate evidential breath alcohol

measuring instruments were installed into

police stations. These screening devices are

generally simple fuel cell devices, whereas the

evidential instruments work on the principle

of infrared absorption or a combination of

the two different techniques. The results are

obtained as soon as the breath sample is

taken, so there is clearly no need for a police

surgeon to be present, and also no

requirement to take body fluid samples.

In order to be sure that all the evidentialbreath alcohol-measuring instruments in theUK give the correct results, their calibrationis checked before and after a person blows a

breath sample into the instrument. Thegaseous calibration checks are carried outoperationally by injecting an ethanol in airgas standard into the instrument. Thesechecks require the use of accurate nationallytraceable gas standards as mandated by theHome Office. Operational gas standards areprepared by commercial companies, using UKAS-agreed quality controlled procedures and referenced to nationally traceable reference gas standards using agreed protocols.

VAM underpins the production andverification of the UK’s Primary GasStandards of ethanol in air, and maintainsthese to enable UK government, and thepublic, to have confidence in the accuracyand international comparability of theresults. These Primary Standards are used tocertify traceable secondary standardsproduced by NPL for use by commercialcompanies as reference standards. Theamount concentration of ethanol in airrepresentative of 80 mg of ethanol per 100 mL of blood is 35 µg of ethanol per 100 mL of breath. This translates to arequirement for standards with an amountfraction of 191.4 µmol mol -1 (ppm) ethanol in air.

Preparation

The first methodology for preparingethanol/air gas standards involvedequilibrating ethanol vapour intoelectropolished stainless steel spheresspecially designed at NPL (Figure 1). Thesewere weighed before and after the ethanol vapour was transferred into anevacuated gas cylinder. The cylinder wasthen filled with a known mass ofhydrocarbon-free synthetic air. Specialinternally passivated gas cylinders thatminimise reactions or adhesion to thecylinder walls are used to contain these gasstandards. Environmental tests were carriedout to ensure the cylinder contents werestable and not affected by temperaturechanges, long term storage etc.

More recently, under VAM, NPL developed

a more efficient and more accurate method

for preparing these standards. The method

involves the injection of a known mass of

high purity ethanol directly into a selected

cylinder. NPL has also developed a

low-volume connector as part of this work to

ensure that all of the ethanol is transferred

into the cylinder, and none is lost by absorption

onto, for example, the cylinder valve

(Figure 2). This method is now also being

used in the preparation of many of the UK’s

other national gas standards giving improved

accuracy and more efficient production. UK

companies are now commercially exploiting

the connector technology.

Breath alcohol standards underpinningUK drink-driving measurements

Figure 1: Sphere containingequilibrated ethanol vapour.

Figure 2: Low volumeconnector attached to gas cylinder.


Figure 3:Degrees of equivalence resulting from the CCQM k4and EUROMET 580 intercomparisons of ethanol in air.


Demonstrating the accuracy of ethanol in

air gas standards

It is important that the primary ethanol in airgas standards made at NPL, andsubsequently disseminated, are accurate andstable in concentration since the results formthe basis on which the level of drink-drivingpenalties are decided. Confidence in thestandards is achieved both through in-housevalidation checks and through internationalcomparisons carried out under the BIPMCCQM and EUROMET protocols. ABIPM Key Comparison has been completedwhich involved intercomparisons betweenrecognised national measurement institutesthroughout the world. This establishedNPL’s expertise in the field of ethanol in airpreparation and analysis (Figure 3), andshowed that the standards prepared at NPLare the most accurate available. This has lednot only to NPL maintaining the PrimaryGas Standards used to underpin all UKmeasurements of breath-alcohol, but also tothe provision of gas standards that underpinother countries’ breath-alcohol legislation.

Interfering substances

The establishment of expertise in preparingethanol in air gas standards resulted in NPLbeing commissioned to carry out type-approval testing to ensure that the evidentialbreath-alcohol measuring instruments do

not give erroneous results due to vapoursubstances other than ethanol that may bepresent in breath samples. This testing iscarried out before each type of instrument isdeployed in a police station.

The instruments are type-tested by

providing them with known concentrations

of a wide range of gaseous substances in the

presence of ethanol, in order to ensure there

was no significant interference with the

ethanol measurement result (Table 1). These

‘interfering substances’ are prepared in a

similar manner to the ethanol in air gas

standards, to ensure their accuracy

before use.

Following this first type-testing procedureand the installation of the instruments in police stations, there is an ongoingrequirement to check that the presence of

certain of the ‘interfering substances’ do not

cause erroneous results. This is done on a

routine basis when the instruments undergo

their periodical service and maintenance checks.

A subset of ‘interfering substances’ (Table 1) are

selected for regular checks. Selected commercial

companies prepare gas standards of these

substances, mixed with ethanol, and their

concentrations are certified by NPL in a similar

manner to the ethanol in air standards.

The VAM programme supports the

UK facility at NPL for preparing primary

gas standards for ethanol in air and

interfering substances.

Future gas standards for breath-alcoholmeasurements

In addition to the requirement to maintainthese Primary Gas Standards at the currentUK legislative levels, and to provide a rangeof concentrations to underpin requirementsof the UK’s Home Office and ForensicScience Service, the newly introducedRailways and Transport Act 2003 requires alower permitted level of alcohol in the breathof Safety Critical Aircrew and Air TrafficControllers. This corresponds to a level of 9 µg of ethanol per 100 ml of breath, andnew Primary Gas Standards will be preparedand maintained at this level.

It is also proposed to introduce mobileevidential breath-alcohol measuring instrumentswithin the next two years, and these will requiretype-testing to verify their performance beforeuse. This will require accurate gas standards to cover the operating range of the instruments, and also a new range of standardsfor interfering substances.

Table 1: ‘Interfering substances’ injected into the breath-alcohol measuring instruments.

water toluene n-pentane

carbon dioxide xylene n-hexane

acetone benzene n-heptane

acetaldehyde perchloroethylene n-octane

methanol methyl ethlyl ketone diethylether

isopropanol ethyl acetate i+n-butane

carbon monoxide methane


C H E M I C A L N O M E N C L A T U R E

Lists of chemical substances

Kevin ThurlowLGC

There are a number of international lists

of chemicals associated with trade or

legislation. International Nonproprietary

Names (INN), for pharmaceutical

substances, are produced by the World

Health Organization (WHO) to avoid using

systematic names, which are frequently

somewhat lengthy. Although there are

linguistic variations, the (INN) suffix allows

people to look up the name and identify the

chemical rapidly. An international

committee examines applications for new

INNs, bearing in mind that the INN should

not be ‘inconveniently long and should not

be liable to confusion with names in

common use.’ This obviously makes sense!

If the INN is similar to another INN, trade

name or chemical name, it could cause

confusion. However, they also require that

the name reflects any structural or

pharmacological similarities with existing

INNs. This means that any INN ending

‘-azepam’ will be similar to diazepam (INN),

with a benzodiazepine structure. The

‘-diazepine’ means that there is an aromatic

seven-membered ring (‘epine’) with two

nitrogens (‘diaz’) in it, so this name

transmits some structural information.

Anything ending ‘-caine’ will be a local

anaesthetic. The only drawback with this is

that eventually you run out of prefixes and it

is difficult to distinguish different chemicals.

This is understandable – requiring a name to

be distinctive and similar at the same time

can cause a few headaches. WHO do a fine

job in sorting out these problems, and others

which might not immediately be apparent.

The name prefix ‘bala-’ was proposed for an

antipsychotic drug, which seems reasonable

enough, until someone from Germany

pointed out that ‘bala’ is associated with the

meaning ‘mad’ in German, and this was not

an appropriate name for such a drug. The

committee made a minor amendment and

all was well. WHO rely on interested parties

pointing out possible trademark clashes.

A similar procedure is used for ISO pesticidenames. An expert committee examinesapplications for ISO names, to decide theirsuitability for publication in ISO 1750. Thistakes into account the basic guidelines forformation of names published in ISO 257.The committee actively looks for trademarkclashes so it includes a trademark expert, whopoints out any similar names. The committeedecides if they are important. The committeedoes not amend names that are deemedunacceptable, but refers them back to thestandards organisation of the country oforigin, which contacts the manufacturer. Thereply goes by the reverse route, for thecommittee to consider the new application.This policy can cause serious delays. If aname is acceptable to the committee,participating member bodies are permitted afew months to comment. If a seriousobjection is raised, e.g. a trademark clash, itmay be necessary to refer it back to thesponsor again, but once the participatingcountries are happy, a batch of new namesgoes to ‘letter ballot’. If anyone objects at thisstage, the name is published with a footnotethat it is not acceptable in that particularcountry. However it becomes the commonpesticide name in the other countries. Owingto the likelihood of delays in producing thisname, a regrettable tendency has arisen forcompanies to launch the product before anISO common name has been approved. Thiscan cause problems if the proposed commonname has to be amended.

ISO has a similar policy on name stems toWHO. Carbamates and thiocarbamates willhave ‘carb’ somewhere in the name, and thesuffix ‘-uron’ tells you urea is in the structuresomewhere.

Figure 1 is flazasulfuron (ISO). The ‘fl-’implies a fluorine, the ‘-aza-’ a nitrogen, ‘-sulf-’ a sulfur, and the ‘-uron’ an urea. Sothe ISO name gives some information,without accurately describing the structure.Care is taken that names do not sound toomuch like a systematic chemical name. BothWHO and ISO have independentnomenclature experts on their committees.

The European Commission established aninventory and common nomenclature ofingredients used in cosmetic products in1996. It is decreed that the InternationalNomenclature Cosmetic Ingredient (INCI)names used therein shall constitute thecommon nomenclature for purposes of theCosmetics Directive. This INCI is not to beconfused with the IUPAC/NIST ChemicalIndicator, which has now been renamedINChI. INCI names are decided by a tradeassociation, rather than an independentpanel of experts, and the style of names doesvary considerably. The INCI name foracetanilide is ‘acetanilid’, which is veryslightly shorter, but the INCI name foracetylcysteine is ‘acetyl cysteine’, which islonger. Colour Index names are used, whichis understandable as ‘Acid Blue 1’ is considerably shorter than its systematic

Figure 1: Flazasulfuron (ISO).


Vicki Barwick LGC

There are some words that are used ineveryday conversation that have

specific meanings in the context ofmeasurement results. Terms such as‘precise’ and ‘accurate’ are sometimes usedinterchangeably and the word ‘error’ is oftentaken to mean ‘mistake’. Students oftenstruggle to understand the ‘scientific’definitions of words that they have alsoencountered away from their science lessons.The specifications for GCSE science and A-level chemistry courses require students to

have an understanding of factors which canaffect the reliability of scientific data and tobe familiar with terms such as ‘precision’and ‘accuracy’. Previously, the VAMprogramme has supported the production oftwo booklets, aimed at A-level students,covering the topics of measurementuncertainty and the terminology used todescribe measurements.1,2 These booklets have proved very popular.However, feedback from teachers hasindicated that it would also be useful to haveshorter documents or posters whichintroduce these topics; the booklets can then be used by students wishing topursue the subjects in greater depth. Withthis in mind, we have produced a series

of four A2 full colour posters aimed atGCSE and A-level students which introducekey issues relating to measurementterminology. The posters cover the following topics:

• How do I obtain a sample for testing?

The amount of material that is tested in thelaboratory is often relatively small. How isa representative laboratory sample obtainedfrom a much larger amount of material?This poster introduces the importance ofsampling and key sampling terminology.

• What’s in my sample?

This poster explains the difference betweena quantitative and a qualitative test.

Introduction to measurement New resources for schools and colleges

2 8 V A M B U L L E T I N

C H E M I C A L N O M E N C L A T U R E

equivalent. INCI names are a mixture ofsemi-systematic and common names, sothere is a rather inconsistent approach, butat least you can look them up on the list.

The European Customs Inventory ofChemical Substances (ECICS – or the‘Inventory’) provides unique referencenumbers for common items of trade andutilises INN, ISO, INCI and IUPACsystematic names. If a Customs Officer seesthat an item of trade has the Inventorynumber 14691, he can easily look it up andsee what it is and what the TariffClassification should be. Later IUPACnames are suspect as they were just taken

from catalogues, not checked by anomenclature expert.

A United Nations Committee produces a listof ‘Proper Shipping Names’ to aidinternational transport of dangerous goods.This includes some synonyms and is ofcourse a good idea, but some of the namesare ambiguous, and many are out of date orinconsistent. Indeed, a fellow nomenclatortook one look at the entry ‘amyl mercaptan’,and christened the book, ‘The AncientMariners’ Chemical Substances List’. ‘Amylmercaptan’ might be taken to mean pentane-1-thiol, but is more likely to be a mixture of3-methylbutane-1-thiol and 2-methylbutane-

1-thiol, unless it is impure, when it couldcontain other isomers as well (Figure 2).

Certainly IUPAC abandoned ‘mercaptan’and ‘amyl’ many years ago. This is not aproblem for the UN, as long as thesechemicals have the same level of hazard.

Proper listings of chemicals preferably withsynonyms and structures are necessary forthe scientific community, and all these listsoffer benefits to their users.

For advice on chemical nomenclaturecontact the Chemical NomenclatureAdvisory Service (CNAS) at the VAM helpdesk.

Figure 2: ‘Amyl mercaptan’.

E D U C A T I O N A N D T R A I N I N G

2 9I S S U E 3 4 – S P R I N G 2 0 0 6 V A M B U L L E T I N2 9 V A M B U L L E T I N

E D U C A T I O N A N D T R A I N I N G

• How reliable is my experiment?

This poster explains what is meant by‘precision’, ‘bias’ and ‘accuracy’?

• How reliable are my results?

The final poster in the series introducesthe concepts of error and uncertaintyand explains why it is important toknow something about the uncertaintyassociated with measurement results.

The posters have been distributed to all theschools participating in this year’sproficiency testing competition, and receivedmany favourable comments from delegatesat the Association for Science Education(ASE) annual conference in January.

In addition to the posters, we have producedan A4 leaflet describing the issue of ‘Errorsand Uncertainty’ in more detail. These aretopics which teachers often struggle toconvey to their students, and which studentsoften have difficulty in grasping. The leafletcovers the following areas:

• ‘What is measurement uncertainty?’

• ‘Why is measurement uncertaintyimportant?’

• ‘Sources of measurement uncertainty’

• ‘Sources of error’.

The leaflet explains the difference between‘uncertainty’ and ‘error’ and explains theconcepts of random and systematic errors.Sources of uncertainty are illustrated byconsidering the example of a titrationexperiment to determine the concentrationof an acid. The leaflet also contains aglossary of key terms.

Sets of the posters and copies of theleaflet can be ordered, free of charge,via the VAM website.

REFERENCES

1. Barwick, V., Prichard, E., “Introducing

Measurement Uncertainty”, LGC, 2003,

ISBN 0 948926 19 8

2. Prichard, E., “Introduction toMeasurement Terminology”, LGC, 2004,ISBN 0 948926 21 X

Education and training events for 2006

VAM and Eurachem-UK Seminar:Current topics in method validation

Scarman HouseUniversity of Warwick20 June 2006Delegate fee: £55 + VAT

Chemistry in Action: A one-dayworkshop for teachers on analyticaltechniques

Loughborough University29 June 2006Delgate fee: £40 + VAT

Further details about these events can befound on pages 32 and 33.

Three of the UK’s leading providers

have formed a joint venture dedicated

to proficiency testing (PT). By bringing

together the PT schemes of LGC, Quality

Management (QM) and Aquacheck, this

new joint venture will encompass over 4000

participating laboratories across the world,

making it a major international provider.

With over twenty years of experience, in

each of the three companies, covering all

aspects concerned with the provision of PT

services, the joint venture aims to provide a

centre of excellence in PT for the analytical

community. A comprehensive portfolio

of PT schemes for chemistry and

microbiology are available, serving a wide

number of industries including meat,

dairy & other food sectors; water,

soil & other environmental sectors;

brewing, distilling, malting, sugar,

forensic, consumer safety and

pharmaceutical. The schemes are accredited

by the United Kingdom Accreditation

Service (UKAS).

For further information contact:

Phil Smith

Quality Management Ltd

Tel: 0161 762 9240

[email protected]

Eurachem PT workshop in Slovenia

Following the success of previous workshops

the Eurachem Proficiency Testing Working

Group organised the 5th Workshop on

‘Proficiency Testing in Analytical Chemistry,

Microbiology and Laboratory Medicine’. 120

delegates from 28 different countries met in

Portoroz, Slovenia, in September last year.

Through a series of lectures and working

group discussions, key topics on proficiency

testing were addressed, which included:

• current practice and future directions of

PT/EQA;

• accreditation of PT/EQA providers;

• performance criteria used in PT/EQA;

• PT/EQA and laboratory accreditation;

• comparability of PT/EQA schemes;

• PT/EQA and the internet;

• pre and post analytical PT/EQA schemes.

The lectures and working group discussions

were accompanied by over 29 posters

addressing a range of issues concerned with

proficiency testing. Over 60 delegates also

attended a training course on the practical

implementation of uncertainty in PT, which

was held in conjunction with the workshop.

The lively debate and fruitful discussions

coupled with good positive feedback

reflected the benefits of the workshop. A

special issue of Accreditation and Quality

Assurance (Springer Verlag) is being

prepared to cover keynote lectures, working

group discussions and poster contributions.

The issue is expected during spring 2006.

Copies of the speakers’ presentations will be

available at www.eurachem.com.

New joint venture for proficiency testing

ˆ

P R O F I C I E N C Y T E S T I N G


http://www.eurachem.com

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V A M H E L P D E S K

A k e y a c t i v i t y o f t h e V A M

programme is the provision of

free advice and information, to help

diagnose and solve measurement-

related problems. The range of advice

available is wide and varied but falls

mainly into five main areas:

• Reference materials (including

availability and their correct use);

• Proficiency testing (including

availability, selection, interpretation

of data, establishing your own

scheme);

• Analytical quality assurance

(including accreditation and quality

systems);

• Chemical nomenclature (including

naming structures, the use of

scientific units and abbreviations);

• Statistics (including advice on all

statistical tests, measurement

uncertainty, method validation).

Below are examples of actual enquiries

received by the VAM helpdesk,

highlighting the variety of enquiries

received and the advice available.

Analytical quality assurance

Q. “What are the differences between

ISO/IEC 17025:1999 and the new

ISO/IEC 17025:2005?”

A. When ISO 17025:1999 was published it

made reference to ISO 9001:1994. This

caused a problem when shortly after ISO

17025:1999 was published ISO

9001:1994 was replaced by ISO

9001:2000. This meant that the 17025

standard was referencing a standard

which no longer existed.

The revision of ISO 17025 has

addressed this issue. There are no

changes to the technical requirements

(i.e. section 5) in ISO 17025:2005. The

changes have occurred in section 4

(management requirements) to bring it

into line with ISO 9001:2000. The main

change in the management section is the

explicit requirement for a continual

improvement of the management system

and communication with the customer.

Q. “How do you qualify gas

chromatographs?”

A. There is no VAM literature available for

the qualification of gas chromatographs.

However, “The Development and

Application of Guidance on Equipment

Qualification of Analytical Instruments”1

gives details about the basic processes

required for the qualification of all

analytical instruments. This guide is

available to download from the VAM

website at www.vam.org.uk.

A guide looking at the qualification of

the HPLC systems 2, which is also

available from the VAM website, may

also be of some help in this case. Advice

is available for any specific issues

regarding the qualification of gas

chromatographs from the VAM helpdesk.

The VAM helpdesk often receive

enquiries where the sender asks a

general, non-specific question. We

endeavour to answer all enquiries and

provide advice and information that is

clear and relevant to the user. To ensure

that you receive the most appropriate

advice, it helps to be as specific and

give as much information as possible.

Statistics

Q. “What is the standard practice for quantifying measurementuncertainty, in the chemical analysisof metals using Optical EmissionSpectrometry (OES)?”

A. In order to obtain an estimate of thecombined uncertainty for anymeasurement, including metals determinedusing ICP-OES, all contributinguncertainties need to be combined in aspecific manner. Start by obtaining a list ofall those potential uncertainties that arerelevant to the method of analysis. Forexample if the metal is first weighed andthen dissolved in, for example, HCl/HN03,then a good starting point would be theuncertainty associated with the balance andalso in the measurement of the amount ofHCl/HNO3 used. This would typicallyentail obtaining information from the flaskcalibration certificate, usually in the form ofa standard uncertainty. Other sources ofuncertainty would typically be humanerror, ICP-OES instrument repeatabilityand reproducibility, etc.

The Eurachem/CITAC guide,“Quantifying Uncertainty in AnalyticalMeasurement”3, has step-by-stepinstructions on how to obtain a combineduncertainty estimate. This guide can bedownloaded free of charge fromwww.eurachem.com. Hard copies can bepurchased from the VAM helpdesk.

Q. “I have pesticide concentrations forenvironmental data. How do yourecommend that I treat data belowLOD (Limit of Detection)?”...

...“It seems in the literature thatdealing with this ‘left censored data’can be treated in a variety of ways. Atmost I have 10% of samples that are<LOD. I am defining LOD as thelowest standard that has a signal/noiseratio (SN) >= 3 (I am using ion trapmass spectrometry, in MS/MS). SN 3 is

VAM at your serviceAdvice and information from the VAM helpdesk



http://www.eurachem.com

QA

V A M H E L P D E S K

taken from the literature for thisparticularly sensitive method. Myapproach, based on USEPA guidancefor data quality assessment, would beto replace data <LOD with 1/10 LOD,as I have less than 15% of sampleswhich fall below LOD. This is unlikelyto produce biased estimates of themean and standard deviation, andmakes no assumptions about thedistribution of the data.”

The general recommendation we give for

<LOD data is not to truncate in the first

place but to use the raw data in

preference. Thus, if you have the raw data

and are yourself assigning values as below

LOD, we would recommend that you do

not mark data as <LOD at all, but use the

raw data itself: even if it is negative. This

is less biased than truncating at an

arbitrary LOD and then assuming a value.

If you have been handed data including<LOD statements, and cannot obtainthe unbiased raw data, we know of noconsistent approach for treating the dataquantitatively. This is because the effectof any assumption or modificationwould depend heavily on the subsequentcalculations and use of the resulting numbers.

For example, were you to take a simple

mean and standard deviation, any

assumption made about the actual values

below the quoted LOD would introduce a

bias. Its significance would depend on the

actual mean and dispersion of the

remainder of the data. If we were

considering an assumed value, we would

explore the practical effect of the different

assumptions by recalculating our mean

and SD using 0 and the LOD as the

extremes. If the change is irrelevant to any

reported value, any assumption in this

range would clearly be safe. On the other

hand, if other estimators are sufficient for

your purpose, these may avoid the

problem. For example, if less than 25%

of the data are ‘<LOD’ the median will

be entirely unaffected by the assumed

value and an interquartile range based

estimate of dispersion would also remain

valid. You could also use many of the

nonparametric tests appropriate for

ordinal data. The disadvantage is that

the median has higher variance than the

mean, and nonparametric tests typically

have less power than those based on

valid distribution assumptions.

Failing all that, we would pick the

most authoritative recommendation

reasonably appropriate for our purpose

(you mention the EPA guidance; ideally,

we would seek guidance from our

‘customer’), state the authority in our

report, assess the practical effect of different

assumptions and report the value obtained

using our selected guidance, with due

caveats for any effect of our assumptions.

With more information about your

particular application, we may be able to

be a little more definite. But in general,

the ‘right’ answer from a statistical

viewpoint is to get the raw data if you can.

Chemical nomenclature

Below are two of the most frequently askedquestions under this topic. The answers toquestions like these usually generateconsiderable debate. However the responseswe give are usually those of IUPAC andinternationally recognised bodies.

Q. “Is the symbol for litre ‘l’ or ‘L’?”

A. Nearly all units have symbols whichmust be lower case or must be uppercase. The symbol for litre is a rareexception, as IUPAC allow either ‘l’ or ‘L’. Some people think that uppercase letters are reserved for symbolswhich are named after a person, but thisis not the case. Some organisations do insist on ‘l’ for litre, but it has the clear disadvantage that it may easily be confused with ‘1’ (one) or ‘I’ (uppercase ‘i’). Accordingly, the useof ‘L’ is recommended. Having said that,neither is incorrect, and authors must beguided by the policy of the publicationto which they are submitting work.

Q “How do you spell sulfur/sulphur?”

This question has generated some debate inthe UK, where the common spelling is witha ‘ph’: i.e. ‘sulphur’. However, the VAMBulletin, adopted the ‘f’ spelling (i.e.sulfur) some years ago. In January lastyear, the publication, Laboratory News 4,faced criticism from a reader who accused it

of “consistently using the US spelling ofsulphur!” in an article they had recentlypublished. Before responding, LaboratoryNews turned to VAM for guidance. Belowis the ‘definitive’ answer.

A. There is very little etymological

justification for the use of the English

spelling sulphur, which seems to have

arisen from the mistaken belief that the

word was of Greek origin, and that ‘ph’

should replace the Greek ‘phi’ (Φ). In

fact the word is of Roman origin, and

the Romans spelt it three ways: sulpur,

sulphur and sulfur. Most languages

subsequently used ‘f’: e.g. French

(soufre), Portuguese (enoxfre), Spanish

(azufre), Italian (zolfo), German

(Schwefel) Dutch (solfer). The Middle

English word was ‘soufre’. So there is no

compelling reason to use ‘ph’ rather than

‘f”. This historical survey draws on

information supplied by the Associate

Editor of Oxford Dictionaries. IUPAC

standardised on ‘sulfur’ in 1979, and

ISO have followed suit. The Royal

Society of Chemistry has recommended

the ‘f’ spelling since 1992.

If you need advice or information on

anything to do with VAM or analytical

measurement, please contact:

The VAM helpdesk

LGC

Tel: 020 8943 7393

[email protected]

www.vam.org.uk

REFERENCES

1. Bedson, P., Sargent, M., Accred. Qual.

Assur., 1, 1996, pp 265 – 274.

2. Bedson, P., “Guidance on equipment

qualification of analytical instruments:

High Performance Liquid Chromatography

(HPLC)”, LGC, LGC/VAM/1998/026,

1998.





4. “Letters to the Editor”, Laboratory

News, January 2005, p 3.




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F O R T H C O M I N G E V E N T S

Society of Chemical Industry (SCI)London, 6 June 2006

Achieving reliable data in mass

spectrometry, as with any technique,

requires staff who have a good

understanding of two key areas: the basic

concepts and instrumental characteristics of

the technique, and the practical problems

which arise in applying mass spectrometry

for specific purposes. Such an understanding

usually comes through a combination of

good training and first-hand experience. The

enormous growth in the application of mass

spectrometry has meant, however, that data

are increasingly being generated by staff who

have an insufficient understanding in both

respects. This seminar provides an overview

of potential data reliability problems. More

importantly, it highlights solutions to key

issues arising from recent rapid advances in

mass spectrometry instrumentation and

inadequate training.

The first part of the seminar addresses

more general data reliability issues

in mass spectrometry and will provide advice

on possible solutions. The remainder

of the programme brings together some

specific practical considerations found

to be important over a wide range

of mass spectrometry applications.

Finally, a discussion session with

an expert panel will address topics

of common interest raised by participants.

A small exhibition will provide a

further opportunity during the day for

discussion and to learn of recent

developments in instrumentation.

This important topic should be of direct

concern to laboratory managers,

particularly in the pharmaceutical, chemical

and life sciences sectors, and to those

providing relevant training courses or other

material. The latter include instrument

suppliers and others offering short courses in

mass spectrometry and its applications.

Much of the material is also highly relevant

to chemistry and other undergraduate and

post-graduate courses which include training

in mass spectrometry. The specialist topics

being addressed should be of key concern

to any organisation offering mass

spectrometry services or requiring reliable

qualitative or quantitative data based on

mass spectrometry.

The seminar will be chaired by LGC’s

Chief Metrologist, Dr Mike Sargent, who

will also give a presentation entitled ‘Data

reliability – the wider view’. The list of

speakers includes LGC’s Vicki Barwick, who

will be looking at two VAM training guides

entitled ‘Mass spectrometry for beginners’

and ‘Making AccMass measurements’.

For further information, contact:

AAMG-RSC

PO Box 174

Manchester

M24 4XZ

[email protected]

Achieving reliable mass spectrometry dataBack to basics

Scarman HouseUniversity of Warwick20 June 2006

Method validation is recognised as an

essential part of good measurement practice.

It cannot be guaranteed that analytical data

will be fit-for-purpose unless the

performance of the test method has been

studied and demonstrated to be adequate.

Quality standards such as ISO/IEC 17025,

‘General requirements for the competence of

testing and calibration laboratories’, place

considerable emphasis on the validation of

test methods. Laboratories often spend a

substantial amount of time and effort on

method validation but it is not always a

straightforward activity. This seminar will

discuss a range of topics relating to the

assessment of the performance of chemical

methods, in particular addressing issues

that can cause problems during method

validation.

Organised in conjunction with Eurachem-

UK, this VAM seminar will be of value to

analytical chemists, laboratory managers and

anyone with an interest in ensuring that

analytical data are fit-for-purpose.

The programme includes presentations on:

• assessing limits of detection;

• matrix effects in chemical analysis;

• validation of qualitative test methods;

• evaluating uncertainties associated with

sampling;

• using method validation data in the

evaluation of measurement uncertainty.

Further information is available on the

VAM website. To reserve a place, contact:

Carolyn Osgerby

LGC

Tel: 020 8943 8441

[email protected]

Current topics in method validation



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Loughborough University29 June 2006

Analytical techniques such as chromatographyand spectroscopy feature in the specifications formany science courses. However, due to theexpensive nature of the equipment, schools andcolleges are often unable to offer students orteachers experience of their use. As a result,many teachers have to teach a range of analyticaltechniques, without having up to dateexperience of their application. LGC, inconjunction with Loughborough University andwith support from the RSC AnalyticalChemistry Trust Fund, is organising a one-dayworkshop for teachers which provides anopportunity to gain hands on experience ofmodern analytical techniques. During theworkshop, delegates work as part of a team anduse a range of techniques – such as gaschromatography, high performance liquidchromatography (HPLC), infrared spectroscopyand UV/visible spectroscopy – to solve a‘murder mystery’ case. The workshop aims to:

• give teachers an opportunity to gainpractical experience of a range ofmodern analytical techniques;

• provide information on the importance of

quality in analytical measurements and

examples of how this is achieved

in practice;

• demonstrate that analytical chemistry is

essentially a problem solving activity;

• provide ideas about how chemistry

can be taught in ways that will

motivate students.

The workshop will be of benefit to teachers whoare interested in finding out more about modernanalytical techniques and their applications.

Further information is available on theVAM website. To reserve a place, contact:

Carolyn OsgerbyLGC

Tel: 020 8943 [email protected].

Chemistry in actionA one day workshop for teachers on analytical techniques

Gaining hands-on experience with HPLC at the last Loughborough workshop in 2004.

NPL, Teddington7 July 2006

Research into analytical measurements

using mass spectrometry has been a key

part of the VAM programme for many

years. The work within the programme covers

applications from field use of mass

spectrometry for emissions measurement to

high accuracy molecular mass determination.

NPL is hosting a one-day conference to present

key developments in mass spectrometry

measurement science developed in the VAM

programme. Topics to be covered include:

• calibration of mass spectrometers;

• data interpretation and handling;

• structural identification.

To register your interest in this event, please contact:

Stacy SkangosNPL

Tel: 020 8943 [email protected]

A full programme of talks will be sent outin May to all those who have registeredan interest.

Mass spectrometry measurementresearch in the VAM programme

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Laboratory management –role of the Quality Managerand technical management

25-27 April 200613-15 June 200617-19 October

This training course covers the integration ofthe requirements of the laboratoryaccreditation standard, ISO/IEC 17025within laboratory business processes. Thiscourse, provided by UKAS, will help stafffrom accredited or applicant laboratorieswho have responsibility for the technical orquality management, to:

• understand how the business processesof your laboratory can be combinedeffectively with the requirements ofISO/IEC 17025;

• understand the roles of the qualitymanager and technical management.

This course will be run at Sunningdale Park(Ascot, Berkshire).

Quality systems in testing laboratories

26 April 2006

This course provides an introduction toquality systems appropriate for use in atesting laboratory. The main standards inuse, GLP, ISO/IEC 17025 and ISO 9001will be described and their selection andimplementation discussed. Their similaritiesand differences will be highlighted. Thiscourse will help laboratory staff and quality managers:

• understand the benefits of a qualitysystem;

• identify the factors to consider whenselecting the most appropriate qualitysystem to meet your needs;

• develop procedures to minimiseduplication when more than onestandard is in use in your organisation;

• prepare for the implementation of aquality system.

Traceability in chemicaltesting – meeting accreditation requirements

10 May 2006

Reliable measurements depend on

competent staff, validated methods and

equipment, comprehensive quality systems

and traceability to appropriate measurement

standards. Laboratory accreditation to

ISO/IEC 17025 is a demonstration that

these requirements have been met.

Establishing traceability is a new

requirement of accreditation and in

recent European directives. To achieve

comparability of results over time or from

one location to another, it is essential to link

all the results to some common, stable

reference or measurement standard. The

results can be compared through their

relationship to that reference. Traceability is

the process of linking results to a reference.

Traceability is required for results obtained

from all types of method; standard, in-house


LGC’s analytical training programme

Why should you attend an analyticalquality training course?

Analytical quality is of paramount importanceto anyone making chemical measurementsand to those making decisions based on theresults from such measurements. There areincreasing burdens on companies to meetregulatory, trade and quality requirements andthis has resulted in greater emphasis onmethod validation, measurement uncertaintyand traceability. This is endorsed by theinternational accreditation standard ISO/IEC17025, used in the UK by UKAS as a basisfor laboratory accreditation. The standardcontains detailed requirements for thesetopics. Evidence of training and competencein the topics mentioned is a requirement of thestandard and of customers of analytical results.

LGC’s range of courses

The range of courses offered under LGC’sanalytical training programme is designedto meet the increasing need for laboratorymanagers and analysts to demonstrate

competence and to keep abreast withquality assurance issues and practises.

Collaboration with UKAS

In order to make maximum use of theirrespective expertise and provide an evengreater range of training, LGC and theUnited Kingdom Accreditation Service(UKAS) have agreed a programme ofcollaboration. This involves co-operation inthe publication of each other’s training andknowledge transfer activities as well as thejoint development and running of newcourses and events.

What do the LGC courses consist of?

All the courses consist of lectures and

workshop sessions, providing opportunities

for group discussions and to practise the

newly acquired knowledge. To ensure

maximum benefit from each course,

delegates work in small groups for the

workshop sessions, with a tutor present for

each group. They are run mainly in

South West London, either at LGC’s

facilities at Teddington or at the Lensbury

Conference Centre, Teddington Lock.

Courses tailored to your requirements

LGC recognises that the needs of eachindividual company may differ and thus acustomised solution is often required. LGCcan provide a course on analytical quality tomeet your in-house training needs. Suchcourses can be run at LGC’s trainingvenues or at your own site. LGC providinga tailor-made course enables you toschedule the course at a convenient dateand at a more economical cost per delegate.

For further information on LGC’sAnalytical Quality Training Programmeplease contact:

Lorraine DidinalLGC

Tel: 020 8943 7631Fax: 020 8943 [email protected]


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and those where the results is defined by the

method (empirical method). This course will

help laboratory staff:

• gain a better understanding oftraceability;

• meet the traceability requirements ifISO/IEC 17025;

• understand how reference materials canbe used to achieve traceability;

• select appropriate reference materialsand plan how to use them;

• develop procedures to ensure traceabilityis maintained.

Laboratory internal audit

6-7 June 200612-13 September 200614-15 November 2006

This training course covers the processesinvolved in undertaking an internal audit.The course is run on an interactive basis andprovides an opportunity for delegates toperform an internal audit through role-play.This course, provided by UKAS, will helpanyone involved with, or interested in theuse of internal audit, new quality managersand new laboratory staff. It will help them:

• understand the internal audit process

and its value;

• gain hands-on experience of the auditing

process.

This course will be run at Sunningdale Park

(Ascot, Berkshire).

Statistics for analytical chemists

18 July 20064 October 2006

The quality of analytical data is a vital aspect of

the work of an analytical chemist. The

application of statistics is central to the

assessment of data quality and an understanding

of statistics is essential to the interpretation of

analytical results. The application of statistics is

required for method validation and

measurement uncertainty calculations, and is

thus essential for meeting ISO/IEC 17025

accreditation requirements. This computer-

based course provides an introduction to the

basic statistical tools that analytical chemists

need for their work. It will help them:

• understand some of the most importantstatistical concepts used by analyticalchemists;

• calculate the most common statistics;

• apply significance testing;

• use linear regression in calibration.

Further statistical tools for analytical chemists

13 June 20067 November 2006

The ever increasing amounts of data generatedin the course of analytical measurements meansthere is a greater need to use statistical tools toassess the quality of the data and to assist withits interpretation. Appropriate use of these toolsimproves the chances of making correctdecisions. This course builds on the material inthe ‘Statistics for analytical chemists’ course. Itwill help analytical chemists:

• deal with normal and non-normaldistributions;

• identify cases of normally distributeddata with outliers;

• calculate statistical parameters in thepresence of probable outliers;

• identify where two-way ANOVA isappropriate;

• use some of the more advancedregression tools.

Principles and practice ofmeasurement uncertainty inchemical testing laboratories

14-15 June 20068-9 November 2006

The ability to estimate measurementuncertainty is now a requirement of testinglaboratories accredited to ISO/IEC 17025.

This course is in line with ISO principlesand with the Eurachem/CITAC guide‘Quantifying Uncertainty in AnalyticalMeasurement’. The first day introduces theprinciples of evaluating uncertainty and thesecond day goes on to provide the tools foridentifying uncertainties and using validationdata. This course, aimed at analyticalchemists who have limited knowledge ofmeasurement uncertainty, will help you:

• give your clients confidence in your results;

• determine the fitness for purpose of your results;

• demonstrate compliance with regulatorylimits and contract specifications;

• make valid comparisons between resultsobtained at different times and places;

• meet ISO/IEC 17025 accreditationrequirements.

Method validation

11-13 July 20065-7 December 2006

Method validation is the process that providesevidence that a given analytical method, whencorrectly applied, produces results that are fitfor purpose. No matter how well a methodperforms elsewhere, analysts need to confirmthat the method is valid when applied in theirlaboratory. There is now a much greateremphasis on method validation in theISO/IEC 17025 accreditation standard.Through a number of workshops, delegatesbuild a validation protocol for a method oftheir choice. This three-day course will alsohelp analytical chemists and potential orexisting laboratory managers who are involvedin method development and validation to:

• understand method validation and itsrequirements;

• select and apply the statistics requiredduring method validation;

• select and use the appropriate types ofmethod validation studies;

• appreciate and understand the links withmeasurement uncertainty.


3 6 I S S U E 3 4 – S P R I N G 2 0 0 6V A M B U L L E T I NV A M B U L L E T I N

Further information about the VAM programme

The VAM helpdesk

LGC

Tel: 020 8943 7393

[email protected]

www.vam.org.uk

Main contractors

LGC

Queens Road

Teddington

Middlesex, TW11 0LY

Tel: 020 8943 7000

[email protected]

www.lgc.co.uk

National Physical Laboratory (NPL)

Hampton Road

Teddington

Middlesex, TW11 0LW

Tel: 020 8943 6880 (NPL Helpline)

[email protected]

www.npl.co.uk

Produced by Horrex Davis Design Associates (HDDA) 04/06. www.hdda.co.uk

© LGC Limited, 2006. All rights reserved.

Subject to Crown licence, no part of this publication may be reproduced or transmitted in any form or by any means, electronicor mechanical, including photocopying, recording or any retrievalsystem, without the written permission of the copyright holder.

Further information about the National Measurement System

National Measurement System

Department of Trade and Industry

151 Buckingham Palace Road

London

SW1W 9SS

[email protected]

www.dti.gov/nms

C O N T A C T S

Contact points




http://www.lgc.co.uk


http://www.npl.co.uk

http://www.hdda.co.uk


http://www.dti.gov/nms

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national measurement system vam bulletin - lgc ltd...issue 34 – spring 2006 vam bulletin 5 guest...

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