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SAMPLING ANDMEASUREMENT UNCERTAINTY

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To make an objective, wise, fair, and impartial decision,

a valid information of the intended population is required

The information could be qualitative or quantitative,

therefore scientific data generated through valid

measurements is required.

Sampling is the first part of the whole process of

measurement. Without representative sample with good

protection of its integritytherefore sampling is an

important part in making decision

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Measurements and decisions

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Wise decision are based on scientific information obtained by

valid measurements. Examples :

Acceptance of consignments

Testing for batch releases

Control of raw materials Control of in process products

Finished product controls

Release of non-conforming products

Legal disputes

Inter laboratory trials

The credibility of these decisions depends on the uncertainty ofthe measurement results the most important parameterthatdescribes the quality of measurements is uncertainty of

measurement

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It is impossible to analyze the entire bulk of the material to becharacterized a measurement always involves the processof taking a representative sample the uncertaintyassociated with the sampling process will contribute to theuncertainty associated with the reported result Theuncertainty arising from the sampling process musttherefore be evaluated.

Since analytical and sampling processes contribute to theuncertainty in the result, the uncertainty can only be estimated ifthere is an understanding of the complete process.

Sampling planners and analytical scientists optimize thewhole measurement procedure, and plan a strategy to estimatethe uncertainty reliable decisions based upon themeasurements for the customer .

Sampling and Measurements

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for making reliable interpretation of measurements, andjudging theirfitness for purpose, it is important to know thetotal uncertainty in a measurement

To estimate the uncertainty of measurement, arising from theprocesses of sampling and the physical preparation ofsamples, it takes a holistic view of the measurement process,including all of the sampling steps as well as the analyticalprocess, describing the effects and errors that causeuncertainty in the final measurement.

Sampling and uncertainty ofMeasurements

Sampling is not just delivering the sample tothe laboratory.

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Sampling target

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Sampling target is

Portion of material (the whole of a batch, lot or consignment),at a particular time, that the sample is intended to representto be characterized

Notes:1. The sampling target should be defined prior to designing the

sampling plan.2. The sampling target may be defined by Regulations:

composition of a whole batch sampling target: whole batch

3. If the interest are the properties and characteristics of thecertain area or period, then each location will be a separatesampling target.

sampling : a procedure whereby a part of a substance,material or product is taken to provide for testing or

calibration a representative sample of the whole. (ISO/IEC10725)

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Uncertainty of Primary sample andComposite sample

The whole process of measurement begins with the taking of theprimary sample from a sampling target.

Primary sample: The collection of one or more incrementsor units initially taken from a population.

Note: The term primary, in this case, does not refer to thequality of the sample, rather the fact that the sample wastaken during the earliest stage of measurement.

primary samples are often combined to form a compositesample before a measurement is made the uncertainty of this

single composite sampleThe value of this uncertainty will beaffected by the number of primary samples taken. The resulting sample goes through intermediary steps, such as

transportation and preservation of samples, prior to theanalytical determination. Each steps contribute to the uncertainty

of measurement in the final result,

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measurement

process

diagram

Eurachem/EURO

LAB/CITAC/Nordt

est Guide, April

2006

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Sampling in the measurement process

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Sampling contribute to the uncertainty of measurement

describes the quality of measurements

The sampling target that we are studying is not homogenous and

the properties vary significant cause of uncertainty bothsampling and analysis is associated with uncertainty

the goal of sampling is to select and obtain a test portion of

the material in some manner, such that the sub-sample is

representative of the entire experimental unit.

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General

Sampling should be performed by persons trained in thetechniques of sample collection

Each lot that is to be examined must be clearly defined.

The appropriate Codex Commodity Committee should stipulate

how a consignment should be handled in instances where no lotdesignation exists

A lot is a definite quantity of some commodity manufacturedor produced under conditions, which are presumed uniform.

For the goods presumed heterogeneous, sampling can only be

achieved on each homogeneous part of this heterogeneous lot.In that case, the final sample is called a stratified sample

A consignment is a quantity of some commodity delivered atone time. It may consist in either a portion of a lot, either a setof several lots.

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Representative sampling The representative sampling is a procedure used for

drawing or forming a representative sample

A representative sample is a sample in which the

characteristics of the lot from which it is drawn are

maintained.

It is in particular the case of a simple random sample

where each of the items or increments of the lot has

been given the same probability of entering the

sample.

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Random sampling

Random sampling involves the collection ofn items

from a lot of N items in such a way that all possible

combinations ofn items have the same probability of

being collected. The randomness can be obtained byuse of table of

random number which can be generated by using

computer software.

In order to avoid any dispute over therepresentativeness of the sample, a random sampling

procedure should be chosen,

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Part of Random

sampling numbers(ISO 2859-0: table

3)

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stratified random sampling

If the lot is heterogeneous, a random sample may not berepresentative of the lot stratified sampling may be asolution.

Stratified sampling consists ofdividing the lot into

different strata or zones, each stratum being morehomogenous than the original lot. Then a random sampleis drawn from each of these strata, following specifiedinstructions which may be drafted by the Codex productcommittees.

Each stratum can then be inspected by random samplingwhich usually includes from 2 to 20 items or incrementsper sample.

it is necessary, where appropriate, to refer to the specificinstructions of the Codex product committees.

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When it is not possible to sample atrandom, it is mandatory:

1. To avoid preferentially choosing items which are more

easily accessible or which can be differentiated by avisible characteristic.

2. In the case ofperiodic phenomena, to avoid sampling

every k seconds or every kth package, or every kth

centimetres, to take an unit from every nth palette, pre-

package,

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Reliable analytical information

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Reliable results can only be obtained from samples takenaccording to the objectives of the study

The samples must be representative enable to apply theanalytical result to the entire experimental unit.

Utmost attention should be given to the selection ofsampling methods, handling (packing, labelling, shippingand storage) of samples.

Valid analytical results can only be obtained if the sampleshave been properly taken, dispatched and stored before

analysis. The study should be designed to assure the integrity of the

whole chain of activities.

Sampling plans

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sampling plan IUPAC (1990), ISO 11074-2 (1998), AMC (2005)

Predetermined procedure for the selection, withdrawal,preservation, transportation and preparation of the portionsto be removed from a population as a sample.

ISO 2859-1(1999) combination of sample size(s) to be used and associated lotacceptability criteria

NOTE 1 A single sampling plan is a combination of sample size

and acceptance and rejection numbers. A doublesampling plan is a combination of two sample sizes andacceptance and rejection numbers for the first sampleand for the combined sample.

2 A sampling plan does not contain the rules on how todraw the sample.

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Primary Samples A primary sample is the portion of product collected from a lot

during the first stage of the samplingprocess, and will normallybe in the form of:

an item (if collected from a lot of prepacked products) or of

an increment (if collected from a bulk lot). However, an increment may be considered to be an item if

measurements are made on individual increments.

Note: Consider the

Nature of the lot

Bulk or pre-packed commodities Size, homogeneity and distribution concerning the

characteristic to control

Nature of the characteristic to control: Qualitative orQuantitative

Nature of the control: Characteristic of individual item or the

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precautions

As far as is practicable, primary samples should be takenthroughout the lot

departures from this requirement should be recorded.

Sufficient primary samples of similar size should be collectedto facilitate laboratory analysis

maintain sample integrity sampling error ssampling

i.e., avoid contamination or any other changes

adversely affect the amount of residues or the analytical

determinations, or

make the laboratory sample not representative of the

composite sample from the lot.

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composite sample When required by the sampling plan, a composite

sample is produced by carefully mixing the primary

samples

items from a lot ofpre-packaged products; or

increments from a bulk (not pre-packaged) lot.

Except foreconomical reasons, this sampling technique

is not to be recommended given the loss of information

on sample-to-sample variation due to the combination ofprimary samples.

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Final Sample The bulk or bulked sample should, if possible, constitute the

final sample and be submitted to the laboratory for analysis.

If the bulk/bulked sample is too large, the final sample

may be prepared from it by a suitable method ofreduction.

In this process, however, individual items must not be cut

or divided.

National legislative needs may require that the final sample be

subdivided into two or more portions for separate analysis.

Each portion must be representative of the final sample.

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Packaging and Transmission ofLaboratory Samples

laboratory sample : The sample submitted to the laboratory

the form of either the final sample or a representative portion ofthe final sample.

should be kept in such a manner that the controlledcharacteristic is not modified (e.g., for microbiological controls,mandatory use of a sterile and cooled container).

should be placed in a clean inert containeroffering adequateprotection from external contamination and protection against

damage to the sample in transit. The container should be

sealed in such a manner that unauthorised opening is detectable,

sent to the laboratory as soon as possible taking any necessaryprecautions against leakage or spoilage, e.g., frozen foods should

be kept frozen and perishable samples should be kept cooled orfrozen, as appropriate.

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Sampling reports Every sampling act implies the drafting of a sampling report and

indicating in particular the reason for sampling, the origin of the sample,

the sampling method and the date and place of sampling, together with any additional

information likely to be of assistance to the analyst, such astransport time and conditions.

The samples, in particular the ones for the laboratory, shall be

clearly identified. In case of any departure from the recommended sampling

procedure necessary to append another detailed report on thedeviating procedure which has been actually followed However , this decision is to be taken by the responsible

authorities.

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ESTIMATION ERRORS The total standard deviation is given by the formula:

where s is the sampling standard-deviation,

m

the measurement standard-deviation

- the most frequent one:

the analytical error

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Uncertainty

The uncertainty arises from a variety of sources, and thesecan be categorized in different ways

Uncertainty is related to other concepts, such as accuracy,error, trueness, bias and precision.

Uncertainty is a range of values attributable on the basisof the measurement result and other known effects,whereas erroris a single difference between a result anda true (or reference) value

Uncertainty includes allowances forall effects that mayinfluence a result (i.e. both random and systematic

errors); precision only includes the effects that varyduring the observations (i.e. only some random errors).

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Sources of uncertainty

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Sampling Sample preparation

Heterogeneity (or inhomogeneity)

Effects of specific sampling strategy (e.g.random, stratified random, proportional etc.)

Effects of movement of bulk medium(particularly density selection)

Physical state of bulk (solid, liquid, gas)

Temperature and pressure effects

Effect of sampling process on composition(e.g. differential adsorption in samplingsystem).

Contamination

Transportation and preservation of sample

Homogenisation and/or sub-

sampling effects Drying

Milling

Dissolution

Extraction

Contamination Derivatisation (chemical effects)

Dilution errors

(Pre-)Concentration

Control of speciation effects

Both sampling and analysis is associated with uncertainty

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Cause-effect (fish-bone) diagram of possiblesources contributing to the uncertainty

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Systematic error in sampling

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The systematic effects in sampling are caused by theheterogeneity of the sampling target combined with aninability of the sampling method to properly reflect thisheterogeneity.

The heterogeneity can in turn be divided into the inherent heterogeneity of the material, caused by

e.g. different size, shape and composition of the particlesin a solid sample or different molecules in liquid samples,and

distribution heterogeneity caused by e.g. poor mixing,which may allow particles or molecules of differentcharacteristics to segregate in the target.

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Reducing the systematic effects

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Select methods for sampling and sample preparation thatmatch the sampling target and its properties

Increase the sample size give a better representation ofthe whole target

Reduce the particle size of either the whole target or arelatively large sample, then collecting a sub-sample

Mixing. This will reduce the segregation, However, in somespecial cases mixing may induce the segregation In thesecases mixing should be avoided

Proper storage or transportation should be carried out toreduce chemically and/or microbiologically changes ofsample composition prior to the analysis

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Random error in sampling

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Random effects are mainly caused by

variations in the composition of the sample in space or intime

sampling method

Sampling procedure or the handling of the sample, e.g.caused by different persons being involved

The sampling equipment and the way in which theequipment works

Approach to reduce the random effects is to increase thenumber of samples taken smaller standard deviation of themean

Th d li t th d A b l d

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The duplicate method: A balanceddesign

Appropriate ANOVA generates estimates of s2between-target , s2sampling ,

and s2analytical

The balanced design will only give the repeatability standarddeviation of the analytical measurements, while the estimation ofanalytical bias can be obtained from the well-matched

certified reference materials

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Implications for planning samplingand measurement strategies.

1. Expertise and consultation

the sampling and analytical processes cover a rangeof activities all of those involved will have goodknowledge of some part of the process, but few are

able to advise on the complete process sampleplanners should involve analytical chemists,statistician, decision makers and other experts

2. Avoiding sampling bias, include possible biasassociated with differential sampling

3. Planning for uncertainty estimationat least somereplicated samples and measurements to assess theuncertainty of the results.

I li ti f l i li

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4. Fitness-for-purpose criteria, include theestablishment of clear fitness-for-purpose criteria,taking into account the relative costs and uncertaintiesof sampling and analysis where they are known or can

reasonably be determined in advance.5. Use of prior validation data, it should be noted that

the variability observed during a relatively short seriesof analyses is rarely sufficient as an estimate ofuncertainty. Long-term continuing validity studies are

generally more reliable.6. Acceptability of sampling uncertainty should be

evaluated

Implications for planning samplingand measurement strategies (contd)

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Competence requirements

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To plan and perform qualified sampling and to make a reliableestimate of the measurement uncertainty require competencein

the issue and the sampling target

Theoretical and practical knowledge about the samplingmethod and the sampling equipment

Sample analytical point of view e.g. stability, conservation,moisture uptake, how to avoid contamination and analyteloss etc.

analytical method used, e.g. interferences, memory effects,sample amount needed, calibration strategy

uncertainty in general

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quality assurance of sampling

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Sampling protocols are never perfect in that they cannever describe the action required by the sampler forevery possible eventuality that may arise in the realworld in which sampling occurs.

quality assurance of sampling, including The required competence,

validation and quality control of sampling methods,

documentation of sampling.

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Illustration of the combined use ofvalidation and quality control of sampling

One method used at

many sites

One method used

repeatedly at one site

Validation Initial validation yielding

generic performance data

On site validation yielding

the performance data for

the specific target

Quality control Extensive quality control

with site specific

verification of generic

performance data

Spot quality control

verifying the performance

data consistency over

time

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Summary of sampling documentation

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Sampling methodA generic description of the operations used for sampling

Sampling procedureA specific and detailed description of the operations used for

sampling after a defined principle and with defined equipment.

Sampling field report The detailed notes on the sampling details as noted in the

field

Chain of custody reportA written record of the handling of the sample from sampling

to analysis including transport and storage conditions. Sampling report

Report summarizing the sampling results including targetdefinition, reference to applied method and procedure,relevant notes from field and chain of custody report and

uncertainty.

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Sampling procedure and Sampling protocols

Sampling procedureISO 3534-1: 4.5 (1993), ISO 11704-2,AMC (2005)

Operational requirements and/or instructions relating to the

use of a particular sampling plan; i.e. the planned method of

selection, withdrawal and preparation of sample(s) from alot to yield knowledge of the characteristic(s) of the lot.

Sampling protocols describe the recommended procedure for

the sampling of innumerable types of material and for many

different chemical components.

These protocols are sometimes specified in regulation or ininternational agreements (i.e. CAC/GL 33)

These procedures rarely identify the relative contributions of

sampling and chemical analysis to the combined

uncertainty.

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Representative Samples

Sampling theory has developed largely independently ofanalytical chemistry and chemical metrology.

Sampling quality has generally been addressed insampling theory by the selection of a correct sampling

protocol, appropriate validation, and training of samplingpersonnel (i.e. samplers) to ensure that the protocolis applied correctly

It is then assumed that the samples will berepresentative and unbiased, and the variance will bethat predicted by the model.

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error and uncertainty inmeasurement

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uncertainty of measurement Uncertainty of measurement, or measurement uncertainty

(MU), is defined in metrological terminology (ISO 1993) as:

Parameter, associated with the result of a measurement,thatcharacterises the dispersion of the values that couldreasonably be attributed to the measurand.

The term value of the measurand is closely related to thetraditional concept of true value in classical statisticalterminologyuncertainty has also been defined [ISO 3534-1: 1993] as:

An estimate attached to a test result which characterisesthe range of values within which the true value isasserted to lie

Uncertainty is related to other concepts, such as accuracy,error, trueness, bias and precision.

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The act of taking a sample introduces uncertainty into the reported

measurement result wherever the objective of the measurement is

defined in terms of the analyte concentration.

Sampling protocols are never perfect . Sampling protocols never

describe the action required by the sampler for every possible

eventuality that may arise in the real world in which sampling occurs.

Heterogeneity always gives rise to uncertainty. If the test portion is a

few microgramsnearly all material will be heterogeneous the

sampling step will contribute to the uncertainty in the measurement of

an analyte concentration

processes of physical preparation (e.g. transportation, preservation,

comminution, splitting, drying, sieving, homogenisation) introduce

errors from a range of mechanisms, such as loss of analyte, loss of

fine particles, or contamination from equipment or previous samples.

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ERRORS

Presentation of Quantitative results should beaccompanied by some estimate of the random(unpredictable) and systematic(predictable) errors in them.

Random errors affect the precision of the result,

systematic errors affect accuracy Sampling plans are associated with two types of error:

sampling error(caused by the samplefailing toaccurately represent the population from which itwascollected); and

measurement error(caused by the measured value ofthe characteristicfailing to accurately representthetrue value of the characteristic within the sample).

the analysis should be quantified and minimised.

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Systematic and random effects

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The uncertainties causedby the sampling step canbe divided systematiceffects (bias) and randomeffects (precision), eachbeing caused by a definedset of sources.

Generally speaking thesystematic effects arehard to quantify but oftenpossible to avoid, whereasthe random effects areeasier to quantify butharder to avoid.

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Terms

Accuracy: The closeness of agreement between a test result andthe accepted reference value related to systematic error.

Bias: The difference between the expectation of the test resultand an accepted reference value.

Note: Bias is a measure of the total systematic erroras

contrasted to random error. Precision: The closeness of agreement between independent

test results obtained under stipulated conditions. Notes:

1. Precision depends only on the distribution ofrandom errors

2. The measure of precision usually is expressed in terms ofimprecision and computed as a standard deviation of thetest results.

3. Independent test results means results obtained in amanner not influenced byany previous resulton the same orsimilar test object

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Source Description

Fundamental sampling error (FSE) A result of the constitutional heterogeneity (the particles being

chemically or physically different)

Grouping and segregation error (GSE) A result of the distributional heterogeneity

Long-range point selection error (PSE1) Trends across space or over time

Periodic point selection error (PSE2) Periodic levels across space or over time

Increment delimitation error (IDE) Identifying the correct sample to take. Considers the volumeboundaries of a correct sampling device

Increment extraction error (IXE) Removing the intended sample. Considers the shape of the sampling

device cutting edges

Increment and sample preparation error (IPE) Contamination (extraneous material in sample):

Losses (adsorption, condensation, precipitation etc.):

Alteration of chemical composition (preservation):

Alteration of physical composition (agglomeration, breaking ofparticles, moisture etc.):

Weighting error (SWE) The result of errors in assigning weights to different parts of an

unequal composite sample

Sources of sampling uncertainty in sampling theory of Gy 1992

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Fitness For Purpose Fitness for purpose is the degree to which data produced

by a measurement process enables a userto maketechnically and administratively correct decisions for a statedpurpose.

The fitness for purpose of measurement results can only bejudged by having reliable estimates of theiruncertainty.

Require an effective procedures for estimating theuncertainties arising from all parts of the measurementprocess, includes uncertainties arising from any relevant

sampling and physical preparation. understanding uncertainty in sampling provides

procedures that allow their practical implementation

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Approaches to uncertainty estimation Both sampling and analysis contribute to measurement

uncertainty.

There are two main approaches to the estimation ofuncertainty

The empirical approach (top-down ) uses repeatedsampling and analysis, under various conditions, to quantifythe effects caused by factors such as the

(1) heterogeneity of the analyte in the sampling target, (2) variations in the application of sampling protocols

The modelling approach (bottom-up) uses a predefinedmodel that identifies each of the component parts of theuncertainty, making estimates of each component, andsums them in order to make an overall estimate.

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Empirical approach (top-down)

intended to obtain a reliable estimate of the uncertainty, withoutnecessarily knowing any of the sources individually. It relies onoverall reproducibility estimates from either in-house or inter-organisational measurement trials.

It is possible to describe the general type of source, such as

random or systematic effects, and to subdivide these as thosearising from the sampling process or the analytical process.

Estimates of the magnitude ofeach properties of themeasurement methods separately

sampling precision (for random effects arising fromsampling)

analytical bias (for systematic effects arising from chemicalanalysis

These estimates can be combined to produce an estimate ofthe uncertainty in the measurement result.

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measured and true values

the relationship between the measured single measurement ofanalyte concentration (x), on one sample (composite or single),

from one particular sampling target and true values of analyte

concentration :

Xtrue is the true value of the analyte concentration in the

sampling target, The total error due to sampling is sampling

and the total analytical error is analysis

if the sources of variation are independent, the measurement

variance

If statistical estimates of variance (s2) are used

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Estimating Standard uncertainty

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Both sampling and analysis contribute to measurementuncertainty The standard deviation ofthe measurement

The ssamplingcan then be obtained by

The random partof the uncertainty is described by thestandard deviation The standard uncertainty (u) can beestimated using smeas

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Concentration variation between the targets

In a survey across several sampling targets recommend theadditional term targetrepresents the variation of concentration

between the targetsand has variance 2between-target The

total variance 2total

replicate method

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Combined Standard Uncertainty (u)

The combined standard uncertainty, u, is calculated based onstandard deviations of replicate measurements, x. It may, ormay not, include contributions from systematic effects toget the combined standard uncertainty(u) of sampling andanalysis, estimates ofsystematic effects should be included.

the systematic errors (bias) cannot be easily obtained whenduplicate design is used, but some approaches to this aregiven

The bias of analysis can be estimated by using certified

reference materials (CRM) or participating in laboratoryproficiency tests.

E d d U t i t (U)

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Expanded Uncertainty (U)

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The combined standard uncertainty, u, is calculated based onstandard deviations ofreplicate measurements,x. It may, ormay not, include contributions from systematic effects

In any case, as it is based on one single standard deviation ( X = x u ) will mean that the probability that the reportedrange contains the "true value" is only 67% (a 67%confidence interval).

In most cases, it is therefore more useful to the persons

evaluating the data to use the expanded uncertainty, Uthe

expanded uncertainty, U, obtained from replicatemeasurements, x, applying a coverage factor of 2 U = 2 u

the result, x, reported as X = x U , giving the range of the

true value, X, with 95% confidence.

Calculation of uncertainty and its

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Calculation of uncertainty and itscomponents

The values ofssamp and sanalfrom the ANOVA are estimates ofsampling precision and analytical precision respectively. The

random component of the measurement uncertainty is

calculated by the combination of these two estimates

The expanded uncertainty, for approximately 95% confidence forexample, requires this value to be multiplied by a coverage

factor of 2.

U can also be expressed relative to the reported value x and

expressed in terms of apercentage,

l ti d d t i t

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relative expanded uncertainty

The relative expanded uncertainty for the sampling oranalysis alone can similarly be expressed as

Examples of tools for the estimation

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Examples of tools for the estimationof uncertainty contributions

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Random (precision) Systematic (bias)

Analysis Replicate analyses Certified reference materials

Laboratory proficiency test

Reference analytical method

Sampling Replicate samples Reference sampling targetSampler proficiency test,

Inter-method comparisons

Known theoretical value of

sampling target

Reference sampling method

The modelling approach

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The modelling approach

(bottom-up)

Initially, identifies all of the sources of uncertainty,

such as the form of a cause-and-effect (fish-

bone), quantifies the contributions from each

source, and then combines all of the contributions

to give an estimate of the combined standard

uncertainty.

The uncertainty of measurement generated by

each of these steps is estimated independently,then calculated by combining the uncertainty from

all of the steps by appropriate methods. This

approach is well established for analytical

methods [1]

Sampling theory for estimation of

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Sampling theory for estimation ofuncertainty

This approach relies on the use of a theoretical model (PierreGy),

Most sampling errors, except the preparation errors, are

due to the material heterogeneity, which can be divided

into two classes: 1) constitution heterogeneity (CH), and

2) distribution heterogeneity (DH).

Both heterogeneities can be mathematically defined

and experimentally estimated.

t t l t d d d i ti

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total standard deviation

The total standard deviation is given by the formula:

where s is the sampling standard-deviation, m the measurement standard-deviation

- the most frequent one:

the analytical error

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Principles of quality assurance insampling

Relationship between validation and

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Relationship between validation andquality control

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After the required uncertainty which makes the measurements fitfor purpose has been established the sampling and analyticalprocedures proposed to meet those purposes should beevaluated tools required are:

validation and

continuous quality control.. Sampling validation comprises a one-time estimation determined

under conditions expected in the routine use of the samplingprocedure demonstrates what can be achieved but not yetshows the conformation to fitness-for-purpose requirements

For sampling, where the degree of heterogeneity may varymarkedly from one target to the next the larger part of theuncertainty component stems from the heterogeneity of thetarget validation alone cannot ensure that routine results areindeed fit for purpose

Methods of internal quality control

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q yof sampling

we need to see whether results for individual sampling targetsare fit for purpose,

Bias is difficult to address in validation and almost impossiblein internal quality control. The focus of interest is the precisionaspect The principal tool is replication

minimally executed by taking two samples from each targetby a complete (and suitably randomised) duplication of thesampling protocol Each sample is analyzed once.

the difference between the results

the standard deviation of measurement

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If the validated uncertainties of sampling and analysis are usand ua respectively, the combined standard uncertainty is

a one sided range control chart can be constructed with a

control limit (at the 95% confidence interval) of 2.83umeas

and an action limit (at the 99% confidence interval) of

3.69umeas

Example of a range control chart for

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p gquality control of sampling

Central line: CL = 1.128* smeasurement Warning limit: WL = 2.83* smeasurement (not exceeded in

95% of control result) Action limit: AL = 3.69* smeasurement (not exceeded in 99%

of control result)

Judging fitness for purpose of

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g gmeasurements using uncertainty

A proper understanding of uncertainty from sampling must beembedded in the broader perspective of fitness for purpose.

Three approaches have been suggested for setting fitness-for-purpose criteria.

First : to set an arbitrary limit on the maximum value of

uncertainty that is considered acceptable. does not relate tointended purpose for which the user requires the measurement.

Second: to compare the variance generated by themeasurement (sampling and analysis) to the variance of thedifferent sampling targets, such as in mineral exploration sets

the fitness-for-purpose criterion so that the measurementvariance < 20% to the total variance

third, to judge the fitness for purpose of measurements consider the effect of the measurement support a decision. Adecision can be either correct or incorrect, an incorrect decision

is more likely if the uncertainty is higher.

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need the quality requirements for sampling and theuncertainty associated with between target variability.

If the uncertainty of measurements is underestimated, i.e.

sampling uncertainty is not taken into account erroneous

decisions may be made

large financial, health andenvironmental consequences.