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Standard Format and Guidance forAOAC Standard Method Performance Requirement (SMPR) Documents(Version 12.1; 31-Jan-11)

AOAC SMPR 2010.XXX

Method Name: must include the analyte(s), matrix(-es), and analytical technique (unless theSMPR is truly intended to be independent of the analytical technology). The method name may

refer to a common name (e.g. Kjeldahl method).Approved by: Stakeholder Panel or Expert Review Panel nameFinal version date: dateEffective date: date

1. Intended Use: Additional information about the method and conditions for use.

2. Applicability: List matrixes if more than one. Provide details on matrix such as specificspecies for biological analytes, or IUPAC

1nomenclature andCAS

2registry number for

chemical analytes. Specify the form of the matrix such as raw, cooked, tablets, powders,etc.

3. Analytical Technique: Provide a detailed description of the analytical technique if the

SMPR is to apply to a specific analytical technique; or state that the SMPR applies to anymethod that meets the method performance requirements.

4. Definitions: List and define terms used in the performance parameter table (see table 2 forlist of standard terms)

5. Method Performance Requirements : List the performance parameters and acceptancecriteria appropriate for each method/analyte/matrix. See table 1 for appropriate performancerequirements.

If more than one analyte/matrix, and if acceptance criteria differ for analyte/matrixcombinations then organize a table listing each analyte/matrix combination and its minimumacceptance criteria for each performance criteria.

6. System suitability tests and/or analytical quality control: Describe minimum systemcontrols and QC procedures.

7. Reference Material(s):Identify the appropriate reference materials if they exist, or state thatreference materials are not available. Refer to Appendix D Development and Use of In-House Reference Materialsif a reference material cannot be identified

8. Validation Guidance: Qualify the method into one of the method classifications in Table 1.which provides general recommendations regarding validation of the method. Validationstudy protocols should be provided as an annex to the SMPR. Identify which studies shouldbe carried out at the method developers site (single laboratory validation); and theindependent laboratory (for the Performance Tested Methodsprogram).

9. Maximum Time-To-Determination: Maximum allowable time to complete an analysisstarting from the test portion preparation to final determination or measurement.

1International Union of Pure and Applied Chemistry (IUPAC)

2Chemical Abstracts Service

MPR Document 5


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Annex I: Validation Study Protocols

(Required for all SMPRs)

Introduction: Provide basic information about the type of methods that are applicableto the validation outline; and general information about the levels of validation required

for the SMPR method.

Level 1: Method Developer Validation Study Protocol . Describe the studies that amethod developer must carry out to demonstrate that a candidate method meets theperformance parameters specified in an SMPR. Refer to Table 3 for the recommendedstudies.

Level 2: Independent Laboratory Validation Study Protocol. Describe the studiesthat an independent laboratory must carry out to demonstrate that a candidate methodmeets the performance parameters specified in an SMPR. Refer to Table 3 for therecommended studies

Level 3: Collaborative Study Protocol. Describe the collaborative study that aMethod Developer / Study Director must carry out to demonstrate that a candidatemethod meets the performance parameters specified in an SMPR.

ALL VALIDATION PROTOCOLS MUST INLCUDE THE FOLLOWING STATEMENTS:

Method developers seeking to submit data to the Official Methods orPerformance Tested Methods programs must prepare, submit, andobtain approval of their individualized validation study protocol.

An approved protocol is valid for one year from its approval date.Validation protocols with approval dates more than one year must besubmitted for re-approval.


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Annex II: Inclusivi ty/Select ivi ty Panel

(Required for qualitative and identification method SMPRs)

Annex III: Exclusivi ty/Specificity Panel


Annex IV: Envi ronmental Materials Panel



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Table 1: Performance Requirements

Notes:1. 100 g/kg2.


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Table 2: Recommended Defini tions

Bias

The difference between the expectation of the test results and anaccepted reference value. Bias is the total systematic error ascontrasted to random error. There may be one or more systematicerror components contributing to the bias.

EnvironmentalInterference

Ability of the assay to detect target organism in the presence ofenvironmental substances and to be free of cross reaction fromenvironmental substances.

ExclusivityStrains or isolates or variants of the target agent(s) that the methodmust not detect.

InclusivityStrains or isolates or variants of the target agent(s) that the methodcan detect.

Laboratory Probabilityof Detection

The overall fractional response (mean POD = CPOD) for the methodcalculated from the pooled PODjresponses of the individuallaboratories (j = 1, 2, ..., L).

1 See Appendix A.

Limit of Detection (LOD)The minimum concentration or mass of analyte that can be detected in a

given matrix with no greater than 5% false positive risk and 5% false

negative risk.

Limit of Quantitation(LOQ)

The minimum concentration or mass of analyte in a given matrixthat can be reported as a quantitative result

Maximum Level (ML) Maximum or minimum or normative level. The expected level orconcentration of the analyte.

POD(0)The probability of the method giving a (+) response when the sampleis truly without analyte

POD (c)The probability of the method giving a (-) response when the sampleis truly without analyte

Probability of Detection(POD)

The proportion of positive analytical outcomes for a qualitativemethod for a given matrix at a given analyte level or concentration.Consult Appendix A for a definition.

1AOAC INTERNATIONAL Methods Committee Guidelines for Validation of Biological Threat Agent Methods

and/or Procedures.Appendix E. Calculation of CPOD and dCPOD Values from Qualitative MethodCollaborative Study Data.


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Precision (repeatability)

The closeness of agreement between independent test resultsobtained under stipulated conditions. The measure of precision isusually expressed in terms of imprecision and computed as astandard deviation of the test results.

2

Recovery

Fraction or percentage of the analyte that is recovered when the testsample is analyzed using the entire method is the recovery. Thereare two types of recovery: (1) Total recovery based on recovery ofthe native plus added analyte, and (2) marginal recovery based onlyon the added analyte (the native analyte is subtracted from both thenumerator and denominator).

3

Repeatability Precision under repeatability conditions.

Repeatability ConditionsConditions where independent test results are obtained with the samemethod on identical test items in the same laboratory by the sameoperator using the same equipment within short intervals of time.

Reproducibility Precision under reproducibility conditions

ReproducibilityConditions

Conditions where independent test results are obtained with the samemethod on identical test items in different laboratories with differentoperators using different equipment.

2ISO 5725-1-19943Appendix D: Guidelines for Collaborative Study Procedures To Validate Characteristics of a Method ofAnalysis, Official Methods of Analysis Manual; AOAC ; 2002.


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Table 3: Recommendations for Evaluation

Accuracy

The closeness of agreement between a test result and the acceptedreference value. The term accuracy, when applied to a set of testresults, involves a combination of random components and acommon systematic error or bias component.

Bias (if a referencematerial is available)

The difference between the expectation of the test results and anaccepted reference value. Bias is the total systematic error ascontrasted to random error. There may be one or more systematicerror components contributing to the bias.

EnvironmentalInterference

Analyze test portions containing a specified concentration of oneenvironmental materials panel member. Materials may be pooled.Consult with AOAC statistician.

ExclusivityAnalyze one test portion containing a specified concentration of oneexclusivity panel member. More replicates can be used. Consultwith AOAC statistician.

InclusivityAnalyze one test portion containing a specified concentration of oneinclusivity panel member. More replicates can be used. Consultwith AOAC statistician.

Limit of Detection (LOD)

Measure 10 blank samples.Calculate the mean average and standard deviation of the resultsLOD

4 = average (blank) + z s0(blank);

wheres0 = standard deviationz = 2 x Gaussian critical value = 2 x 1.645 = 3.3

Limit of Quantitation(LOQ)

Estimate the LOQ = average (blank) + 10 * s0 (blank);Measure blank samples with analyte at the estimated LOQ.Calculate the mean average and standard deviation of the results

Guidance5:

For ML 100 ppm (0.1 mg/kg): LOD = ML * 1/5For ML < 100 ppm (0.1 mg/kg): LOD = ML * 2/5

POD(0)

POD (c)

Use data from collaborative study

Repeatability

Prepare and homogenize 3 unknown samples at differentconcentrations to represent the full, claimed range of the method.

Analyze each unknown sample by the candidate method 7 times,beginning each analysis from weighing out the test portion through tofinal result with no additional replication (unless stated to do so in the

4ISO 16140:2003

5Codex AlimentariusCodex Procedure Manual


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method). All of the analyses for one unknown sample should beperformed within as short period of time as is allowed by themethod. The second and third unknowns may be analyzed inanother short time period. Repeat for each claimed matrix.

Probability of Detection(POD)

Determine the desired Probability of Detection at a criticalconcentration. Consult with table 7 to determine the number of testportions required to demonstrate the desired Probability of Detection.

Recovery

Determined from spiked blanks or samples with at least 7independent analyses per concentration level at a minimum of 3concentration levels covering the analytical range. Independentmeans at least at different times. If no confirmed (natural) blank isavailable, the average inherent (naturally containing) level of theanalyte should be determined on at least 7 independent replicates.

Marginal % recovery = (Cf- Cu) x 100/ CA

Total % recovery = 100(Cf)/(Cu+ CA)

Where Cf = concentration of fortified samples,Cu = concentration of unfortified samples; andCA= concentration of analyte added to the

test sample.6

Usually total recovery is used unless the native analyte is present inamounts greater than about 10% of the amount added, in which caseuse the method of addition.

7

Relative standarddeviation (RSD)

RSD = six 100/

Quantitativemethods

Recruit 10-12 collaboratorsMust have 8 valid data sets2 blind duplicate replicates at five concentrations foreach analyte/matrix combination to eachcollaborator

Reproducibility(Collaborative study)

Qualitativemethods

Recruit 12-15 collaboratorsMust have 10 valid data sets6 replicates at five concentrations for eachanalyte/matrix combination to each collaborator

Standard deviation ( si) s

i= [(x

i ,)

2

/n]0.5

6Appendix D: Guidelines for Collaborative Study Procedures To Validate Characteristics of a Method ofAnalysis, Official Methods of Analysis Manual; AOAC ; 2002.

7AOAC Guidelines for Single Laboratory Validation of Chemical Methods for DietarySupplements and Botanicals


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Table 4: Expected Precision (repeatability) as a Funct ion of Analyte Concentration

Analyte % Analyte Ratio Uni t RSD%

100 1 100% 1.3

10 10-1

10% 1.9

1 10-2 1% 2.7

0.01 10-3

0.1% 3.7

0.001 10-4

100 ppm (mg/kg) 5.3

0.0001 10-5

10 ppm (mg/kg) 7.3

0.00001 10-6

1 ppm (mg/kg) 11

0.000001 10-7

100 ppb (g/kg) 15

0.0000001 10-8

10 ppb (g/kg) 21

0.00000001 10-9

1 ppb (g/kg) 30

Table excerpted from:AOAC Peer-Verified Methods Program, Manual on policies and procedures,Arlington, Va., USA (1998).

The precision of a method is the closeness of agreement between independent test resultsobtained under stipulated conditions. Precision is usually expressed in terms of imprecision andcomputed as a relative standard deviation of the test results. The imprecision of a methodincreases as the concentration of the analyte decreases. This table provides targets RSDs for arange of analyte concentrations.

Table 5: Expected Recovery as a Function of Analyte Concentration

Analyte % Analyte Rat io Uni tMean Recovery

(%)

100 1 100% 98-10210 10

-1 10% 98-102

1 10-2

1% 97-103

0.01 10-3

0.1% 95-105

0.001 10-4

100 ppm 90-107

0.0001 10-5

10 ppm 80-110

0.00001 10-6

1 ppm 80-110

0.000001 10-7

100 ppb 80-110

0.0000001 10-8

10 ppb 60-115

0.00000001 10-9

1 ppb 40-120

Table excerpted from:AOAC Peer-Verified Methods Program, Manual on policies and procedures,Arlington, Va., USA (1998).

Recovery is defined as the ratio of the observed mean test result to the true value. The range ofthe acceptable mean recovery expands as the concentration of the analyte decreases. This tableprovides target mean recovery ranges for analyte concentrations from 100% to 1 ppb.


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Table 6: Predicted Relative Standard Deviation of Reproducibili ty (PRSDR)

Concentration, C Mass fraction, C PRSD(R) (%)

100 % 1.0 2

1 % 0.01 4

0.01% 0.0001 8

1 ppm 0.000001 16

10 ppb 0.00000001 32

1 ppb 0.000000001 45

Table excerpted from: Definitions And Calculations Of Horrat Values From Intralaboratory Data,AOAC INTERNTIONAL, Horrat for SLV.doc, 2004-01-18.

Predicted relative standard deviation= PRSD(R) or PRSDR. The reproducibility relative standard

deviation calculated from the Horwitz formula:

PRSD(R) = 2C-0.15 ;

where C is expressed as a mass fraction.

This table provides the calculated PRSD)R) for a range of concentrations. See Appendix C foradditional information.


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Table 7: POD and and Number of Test Portions

Table excerpted from: Technical Report TR308; Sampling plans to verify the proportion of anevent exceeds of falls below a specified value; LaBudde, Robert; June 4, 2010. Not published.

This table was produced as part of an informative report for the Working Group for Validationof Identity Methods for Botanical Raw Materialscommissioned by the AOACINTERNATIONAL Presidential Task Force on Dietary Supplements. The project wasfunded by the Office of Dietary Supplements, National Institute for Health.


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Figure 1: Relationship between Precision versus Trueness (Bias)

Figure 1 illustrates the relationship between trueness (bias) and precision. Trueness is reportedas bias. Bias is defined as the difference between the test results and an accepted referencevalue.

Figure 2: Relationship between LOD and LOQ

Figure 2 illustrates the relationship between the Limit of Detection (LOD) and Limit of Quantitation(LOQ). LOD is defined as the lowest quantity of a substance that can be distinguished from theabsence of that substance (a blank value) within a stated confidence limit. LOQ is the levelabove which quantitative results may be obtained with a stated degree of confidence.


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Figure 3: Horwitz Curve

J. AOAC Int. 89, 1095 (2006)

Figure 3 illustrates the exponential increase in the coefficient of variation as the concentration ofthe analyte decreases.


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Appendix A . Understanding the POD Model

Excerpted from: AOAC INTERNATIONAL Methods Committee Guidelines for Validation of

Biological Threat Agent Methods and/or Procedures, 2010, AOAC INTERNATIONAL.

The Probability of Detection (POD) model is a way of characterizing the performance ofa qualitative (binary) method. A binary qualitative method is one that gives a result asone of two possible outcomes, either positive or negative, or presence/absence or +/-.

The single parameter of interest is the Probability of Detection (POD), which is definedas the probability at a given concentration of getting a positive response by the detectionmethod. POD is assumed to be dependent on concentration, and generally, theprobability of a positive response will increase as concentration increases.

For example, at very low concentration, the expectation is that the method will not besensitive to the analyte, and at very high concentration, we desire a high probability ofgetting a positive response. The goal of method validation is to characterize how

method response transitions from low concentration/low response to highconcentration/high response.

POD is always considered to be dependent upon analyte concentration. The POD curveis a graphical representation of method performance where the probability is plotted as afunction of concentration (see, for example, Figure A1).

Figure A1: Theoretical POD Curve for a qualitative detection method

The POD Model is designed to allow an objective description of method responsewithout consideration to an a priori expectation of the probabilities at givenconcentrations. The model is general enough to allow comparisons to any theoreticalprobability function.


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The POD Model is also designed to allow for an independent description of methodresponse without consideration to the response of a reference method. The model isgeneral enough to allow for comparisons between reference and candidate methodresponses, if desired.

Older validation models have used the terms Sensitivity, Specificity, False Positive and

False Negative to describe method performance. The POD model has incorporated allof the performance concepts of these systems into a single parameter, POD.

For example, False Positive has been defined by some models as the probability of apositive response, given the sample is truly negative (concentration = 0). The equivalentpoint on the POD curve for this performance characteristic is the value of the curve atConc = 0.

Similarly, False Negative has sometimes been defined as the probability of a negativeresponse when the sample is truly positive (concentration >0). In the POD curve, thiswould always be specific to a given sample concentration, but would be represented asthe distance from the POD curve to the POD = 1 horizontal top axis at all concentrations

except C=0.

The POD model has incorporated all these method characteristics into a singleparameter, which is always assumed to vary by concentration. In other models, theterms False Positive, False Negative, sensitivity, and specificity have been definedin a variety of ways, usually not conditional on concentration. For these reasons, theseterms are obsolete under this model.

Table A1: Terminology

TraditionalTerminology

ConceptPOD

EquivalentComment

False Positive

The probability of themethod giving a (+)response when the sampleis truly without analyte

POD(0)POD at Conc =

0

The POD curve value atConc = 0 The Y-intercept of the POD curve

Specificity

The probability of themethod giving a (-)response when the sampleis truly without analyte

1-POD(0)

The distance along thePOD axis from POD=1 tothe POD curve value.

False Negative(at a given

concentration)

The probability of a (-)response at a givenconcentration

1-POD(c)

The distance from the PODcurve to the POD = 1 topaxis in the vertical direction

Sensitivity

(at a givenconcentration)

The probability of a (+)

response at a givenconcentration

POD(c)

The value of the POD curve

at any given concentration

True Negative

A sample that contains noanalyte C = 0

Point on concentration axiswhere c = 0

True Positive

A sample that containsanalyte at some positiveconcentration

C > 0

Range of concentrationwhere c > 0


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Figure A2: Comparison of POD Model Terminology to Other Obsolete Terms

The terms Sensitivity, Specificity, False Positive and False Negative are obsolete

under the POD model.


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Appendix B: Classi ficat ion o f Methods

The following guidance may be used to determine which performance parameters in Table 1apply to different classifications of methods.

AOAC INTERNATIONAL does not recognize the term semi-quantitative as a method

classification. Methods that have been self-identified as semi-quantitative will be classifiedinto one of the following five types:

Type I: Quantitative MethodsCharacteristics: Generates a continuous number as a result.

Recommendation: Use Performance Requirements specified for QuantitativeMethod (main or trace component). Use recovery range andmaximum precision variation in tables 4 and 5.

In some cases and for some purposes, methods with lessaccuracy and precision than recommended in tables 4 and 5may be acceptable. Method developers should consult with

the appropriate method committee to determine if therecommendations in tables 4 and 5 do or do not apply to theirmethod.

Type II: Methods that Report RangesCharacteristics: Generates a range indicator such as 0, low, moderate, and

high.Recommendation: Use Performance Requirements specified for Qualitative

Methods (main component). Specify a range of POD for eachrange range indicator.

Type III: Methods w ith Cut Off valuesCharacteristics: Method may generate a continuous number as an interim

result (such as a CT value for a PCR method) which is notreported but converted to a qualitative result (presence/absence) with the use of a cutoff value.

Recommendation: Use Performance Requirements specified for QualitativeMethods.

Type IV: Qualitative MethodsCharacteristics: Method of analysis whose response is either the presence or

absence of the analyte detected either directly or indirectly ina specified test portion.

Recommendation: Use Performance Requirements specified for QualitativeMethods.

Type V: Identification MethodsCharacteristics: Method of analysis whose purpose is to determine the identity

of an analyte.

Recommendation: Use Performance Requirements specified for IdentificationMethods.


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Appendix C: Defin it ions And Calcu lat ions Of Horrat Values From Int ralaboratoryData

Excerpted from: Definitions And Calculations Of Horrat Values From IntralaboratoryData, AOAC INTERNTIONAL, Horrat for SLV.doc, 2004-01-18.

1. Definitions:

1.1 Replicate data are data developed under common conditions in the samelaboratory: simultaneous performance, or, if necessary to obtain sufficientvalues, same series, same analyst, same day. Such data providesrepeatability statistical parameters.

1.2 Pooled data are replicate data developed in the same laboratory underdifferent conditions but considered sufficiently similar that for the purpose ofstatistical analysis they may be considered together. These may includedifferent runs, different instruments, different analysts, and different days.

1.3Average = 0 = sum of the individual values, xi, divided by the number of

individual values, n.

0 = (xi)/n

1.4 Standard deviation = si= [(x

i ( ))

2

/n]0.5

1.5 Relative standard deviation = RSD = six 100/ .

1.5.1 Repeatability relative standard deviation = RSD(r) or RSDr

The relative standard deviation calculated from within-laboratory data.

1.5.2 Reproducibility relative standard deviation = RSD(R) or RSDR

The relative standard deviation calculated from among-laboratory data.

1.6 Mass fraction. Concentration, C, expressed as a decimal fraction. Forcalculating and reporting statistical parameters, the data may be expressed inany convenient units (e.g., %, ppm, ppb, mg/g, g/g; g/kg; g/L, g/L, etc.).For reporting HORRAT values, the data must be reported as a mass fractionwhere the units of the numerator and denominator are the same: e.g., for 100%(pure materials), the mass fraction C = 1.00; for 1 g/g (ppm), C = 0.000001 =(E-6). See table, C1for other examples.

1.7 Predicted relative standard deviation = PRSD(R) or PRSDR. The

reproducibility relative standard deviation calculated from the Horwitz formula:

PRSD(R) = 2C-0.15

Where C is expressed as a mass fraction. See table, C1.


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In spreadsheet notation: PRSD(R) = 2 * C ^(-0.15).

1.8 HORRAT value. The ratio of the reproducibility relative standard deviationcalculated from the data to the PRSD(R) calculated from the Horwitz formula:

HORRAT = RSD(R) / PRSD(R)

To differentiate the usual HORRAT value calculated from reproducibility datafrom the HORRAT value calculated from repeatability data, attach an R for theformer and an r for the latter. But note that the denominator always uses thePRSDR calculated from reproducibility data because this parameter is morepredictable than the parameter calculated from repeatability data:

HORRAT(R) = RSDR

/ PRSD(R)

HORRAT(r) = RSDr/ PRSD(R)

Some expected, predicted relative standard deviations are given in the following

summary table:

Table C1: Predicted Relative Standard Deviations

2.0Acceptab le HORRAT values

2.1 For interlaboratory studies:

HORRAT(R): The original data developed from interlaboratory (among-laboratory) studies assigned a HORRAT value of 1.0 with limits of acceptability of0.5 to 2.0. The corresponding within-laboratory relative standard deviations werefound to be typically one half to two thirds the among-laboratory relative standarddeviations.

Concentration, C Mass fraction, C PRSD(R) (%)

100 % 1.0 2

1 % 0.01 4

0.01% 0.0001 8

1 ppm 0.000001 16

10 ppb 0.00000001 32

1 ppb 0.000000001 45


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2.1.1 LimitationsHORRAT values do not apply to method-defined (empirical) analytes(moisture, ash, fiber, carbohydrates by difference, etc.), physical propertiesor physical methods (pH, viscosity, drained weight, etc.), and ill-definedanalytes (polymers, products of enzyme reactions).

2.2 For intralaboratory studies:

2.2.1 RepeatabilityWithin-laboratory acceptable predicted target values for repeatability aregiven in the following table at 1/2 of PRSDR, which represents the bestcase.

Table C2: Predicted Relative Standard Deviations

2.2.2 HORRAT(r)Based on experience and for the purpose of exploring the extrapolation ofHORRAT values to single-laboratory validation (SLV) studies, take as theminimum acceptability one half of the lower limit (0.5 x 0.5 0.3) and asthe maximum acceptability two thirds of the upper limit (0.67 x 2.0 1.3).

Calculate HORRAT(r) from the SLV data:

HORRAT(r) = RSD(r) / PRSD(R)

Acceptable HORRAT(r) values are 0.3 1.3. Values at the extremesmust be interpreted with caution. With a series of low values check forunreported averaging or prior knowledge of the analyte content; with aseries of high values, check for method deficiencies such as unrestrictedtimes, temperatures, masses, volumes, and concentrations; unrecognizedimpurities (detergent residues on glassware, peroxides in ether);

Concentration, C PRSD(R) (%) PRSD(r) (%)

100 % 2 1

1 % 4 2

0.01% 8 4

1 ppm 16 8

10 ppb 32 16

1 ppb 45 22


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incomplete extractions and transfers and uncontrolled parameters inspecific instrumental techniques.

2.3 Other lim itations and extrapolationsThe HORRAT value is a very rough but useful summary of the precision in

analytical chemistry. It overestimates the precision at the extremes, predictingmore variability than observed at the high end of the scale (C >ca 0.1; i.e.,>10%) and at the low end of the scale (C


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Appendix D: Development and Use of In-House Reference Materials

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