1、Designation: D6091 07 (Reapproved 2014)Standard Practice for99 %/95 % Interlaboratory Detection Estimate (IDE) forAnalytical Methods with Negligible Calibration Error1This standard is issued under the fixed designation D6091; the number immediately following the designation indicates the year oforig
2、inal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice establishes a standard for computing a99 %/95 % I
3、nterlaboratory Detection Estimate (IDE) and pro-vides guidance concerning the appropriate use and application.The calculations involved in this practice can be performedwith DQCALC, Microsoft Excel-based software availablefrom ASTM.21.2 The IDE is computed to be the lowest concentration atwhich ther
4、e is 90 % confidence that a single measurement froma laboratory selected from the population of qualified labora-tories represented in an interlaboratory study will have a truedetection probability of at least 95 % and a true nondetectionprobability of at least 99 % (when measuring a blank sample).1
5、.3 The fundamental assumption of the collaborative studyis that the media tested, the concentrations tested, and theprotocol followed in the study provide a representative and fairevaluation of the scope and applicability of the test method aswritten.When properly applied, the IDE procedure ensures
6、thatthe 99 %/95 % IDE has the following properties:1.3.1 Routinely Achievable IDE ValueMost laboratoriesare able to attain the IDE detection performance in routineanalyses, using a standard measurement system, at reasonablecost. This property is needed for a detection limit to bepractically feasible
7、. Representative laboratories must be in-cluded in the data to calculate the IDE.1.3.2 Routine Sources of Error Accounted ForThe IDEshould realistically include sources of bias and variation whichare common to the measurement process. These sourcesinclude, but are not limited to: intrinsic instrumen
8、t noise, sometypical amount of carryover error, plus differences inlaboratories, analysts, sample preparation, and instruments.1.3.3 Avoidable Sources of Error ExcludedThe IDEshould realistically exclude avoidable sources of bias andvariation, that is, those which can reasonably be avoided inroutine
9、 field measurements. Avoidable sources would include,but are not limited to: modifications to the sample, measure-ment procedure, or measurement equipment of the validatedmethod, and gross and easily discernible transcription errors(provided there was a way to detect and either correct oreliminate t
10、hem).1.3.4 Low Probability of False DetectionThe IDE is a trueconcentration consistent with a measured concentration thresh-old (critical measured value) that will provide a highprobability, 99 %, of true nondetection (a low probability offalse detection, = 1 %). Thus, when measuring a blanksample,
11、the probability of not detecting the analyte would be99 %. To be useful, this must be demonstrated for the particularmatrix being used, and not just for reagent water.1.3.5 Low Probability of False NondetectionThe IDEshould be a true concentration at which there is a highprobability, at least 95 %,
12、of true detection (a low probabilityof false nondetection, = 5 %, at the IDE), with a simultane-ous low probability of false detection (see 1.3.4). Thus, whenmeasuring a sample at the IDE, the probability of detectionwould be at least 95 %.To be useful, this must be demonstratedfor the particular ma
13、trix being used, and not just for reagentwater.NOTE 1The referenced probabilities, and , are key parameters forrisk-based assessment of a detection limit.1.4 The IDE applies to measurement methods for whichcalibration error is minor relative to other sources, such aswhen the dominant source of varia
14、tion is one of the following(with comment):1.4.1 Sample Preparation, and calibration standards do nothave to go through sample preparation.1.4.2 Differences in Analysts, and analysts have little oppor-tunity to affect calibration results (such as with automatedcalibration).1.4.3 Differences in Labor
15、atories, for whatever reasons,perhaps difficult to identify and eliminate.1.4.4 Differences in Instruments (measurement equipment),which could take the form of differences in manufacturer,model, hardware, electronics, sampling rate, chemical process-ing rate, integration time, software algorithms, i
16、nternal signalprocessing and thresholds, effective sample volume, and con-tamination level.1This practice is under the jurisdiction of ASTM Committee D19 on Water andis the direct responsibility of Subcommittee D19.02 on Quality Systems,Specification, and Statistics.Current edition approved Jan. 15,
17、 2014. Published February 2014. Originallyapproved in 1997. Last previous edition approved in 2007 as D6091 07. DOI:10.1520/D6091-07R14.2Available fromASTM International Headquarters. OrderAdjunct No. ADJDQ-CALC. Original adjunct produced in 2007.Copyright ASTM International, 100 Barr Harbor Drive,
18、PO Box C700, West Conshohocken, PA 19428-2959. United States11.5 Alternative Data Quality ObjectivesOther values for, confidence, etc. may be chosen for calculating an IDE;however, this procedure addresses only the 99 %/95 % IDE.2. Referenced Documents2.1 ASTM Standards:3D2777 Practice for Determina
19、tion of Precision and Bias ofApplicable Test Methods of Committee D19 on Water2.2 ASTM Adjuncts:DQCALC Microsoft Excel-based software for the Interlabo-ratory Quantitation Estimate (IQE)23. Terminology3.1 Definitions:3.1.1 99 %/95 % Interlaboratory Detection Estimate (99 %/95 % IDE, also denoted LD
20、for Limit of Detection in accor-dance with Currie (1)4The lowest concentration at whichthere is 90 % confidence that a single measurement from alaboratory selected from the population of qualified laborato-ries represented in an interlaboratory study will have a truedetection probability of at least
21、 95 % and a true nondetectionprobability of at least 99 %.3.2 Definitions of Terms Specific to This Standard:3.2.1 Censored MeasurementA measurement that is notreported numerically nor is reported missing but as a nondetector a less-than, for example, “less than 0.1 ppb.” The formermeans that an alg
22、orithm in the measurement system deter-mined that the measurement should not be reported numeri-cally for one of two reasons: either it was considered notsufficiently precise or accurate, or the identification of theanalyte was suspect. A reported less-than may have the samemeaning, but it also impl
23、ies (perhaps erroneously) that anyconcentration greater than or equal to the accompanying value(for example, 0.1 ppb) can be measured and will be reportednumerically.3.2.2 Detection Limit (DL) or Limit of Detection (LD)Anumerical value, expressed in physical units or proportion,intended to represent
24、 the lowest level of reliable detection (alevel which can be discriminated from zero with high prob-ability while simultaneously allowing high probability ofnondetection when blank samples are measured.NOTE 2In some cases, the discrimination may be from a value otherthan zero, such as a background l
25、evel. Note also that a DL also dependson other characteristics of the measurement and detection process, such asdescribed in 1.3.2. The IDE is an example of a DL.3.2.3 Probability of False DetectionThe false positiveprobability, denoted , that a single measurement of a blanksample will result in a d
26、etection. (See Fig. 1.) This probabilityis often referred to as the Type 1 error probability and dependson the analyte, measurement system, analytical method,matrix, analyst, and measurement (recovery) threshold (mea-surement critical value) used to decide whether detection hasoccurred. This definit
27、ion can be generalized to refer to un-wanted detection from a single measurement of a sample at anynonzero concentration of the analyte rather than a blanksample, provided that the nonzero concentration is less than thedetection limit or IDE.3.2.4 Probability of False NondetectionThe false negativep
28、robability, denoted or (T), that a single measurement of asample containing a nonzero concentration, T, of an analyte ofinterest will result in a nondetection. This is the complement ofthe probability of true detection. (See Fig. 1.) This probabilityfunction is often referred to as the Type 2 error
29、probabilityfunction, and it depends explicitly on the concentration ( T). Itdepends implicitly on the analyte, measurement system, ana-lytical method, matrix, analyst, and critical value for detection.3.2.5 Probability of True DetectionThe probability, de-noted 1- or 1- (T), that a single measuremen
30、t of a samplecontaining a nonzero concentration, T, of an analyte of interestwill result in a detection. (See Fig. 1.) This probability is oftenreferred to as statistical power or the power of detection, and itdepends explicitly on the concentration (T). It depends implic-itly on the analyte, measur
31、ement system, analytical method,matrix, analyst, and critical value for detection.3.2.6 Probability of True NondetectionThe true negativeprobability, denoted 1-, that a single measurement of a blanksample will result in a nondetection. This is the complement of3For referenced ASTM standards, visit t
32、he ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.4The boldface numbers in parentheses refer to the list of references at the end ofthis standard.FIG.
33、 1 Simplest Case of Reliable DetectionD6091 07 (2014)2the probability of false detection. (See Fig. 1.) This probabilityalso depends on the analyte, measurement system, analyticalmethod, matrix, analyst, and response threshold. The probabil-ity of true nondetection can be similarly generalized: it c
34、anapply to a single measurement of a sample at any nonzeroconcentration less than the detection limit or IDE.3.2.7 100(1-) %Confidence Statistical Tolerance Limitfor 100(1-) % of a Population (also known as a One-SidedStatistical Tolerance Interval)Astatistically determined limitthat will, with 100(
35、1-) % confidence, exceed (or fall below)100(1-) % of the population (the 100(1-) % quantile). SeeHahn and Meeker (2) for further explanation and tables ofvalues.4. Summary of Practice4.1 EveryASTM D19 test method is evaluated to determineprecision and bias by conducting a collaborative study inaccor
36、dance with Practice D2777. That study, or a similarcollaborative study, can also be used to evaluate the lowestconcentration level of reliable detection for a test method,referred to herein as the Interlaboratory Detection Estimate.Such a study must include concentrations suitable for modelingthe un
37、certainty of mean recovery of interlaboratory measure-ment (preferably without extrapolation). It must also beplanned and conducted to allow the known, routine sources ofmeasurement variability to be observed at typical levels ofinfluence. After it is conducted, outlying laboratories andindividual m
38、easurements should be eliminated using anaccepted, scientifically based procedure for outlier removal,such as found in Practice D2777. The IDE computations mustbe based on retained data from at least six independentlaboratories at each concentration level.4.2 Retained data are analyzed to identify a
39、nd fit one ofthree proposed interlaboratory standard deviation (ILSD) mod-els which describe the relationship between the interlaboratorystandard deviation of measurements and the true concentration.The identification process involves evaluating the models inorder, from simplest to most complex: con
40、stant, straight-line,or exponential (all with respect to true concentration, T).Evaluation includes statistical significance and residual analy-sis.4.3 The chosen model is used to predict interlaboratorymeasurement standard deviation at any true concentrationwithin the study concentration range. If
41、interlaboratory stan-dard deviation is not constant, the predictions are used togenerate weights for fitting the mean recovery relationship (thestraight-line relationship between measured concentration andtrue concentration), using weighted least squares (otherwise,ordinary least squares is used). T
42、he mean recovery curve isevaluated for statistical significance and lack of fit and usingresidual analysis. An ILSD model prediction is also used toestimate the interlaboratory standard deviation of measure-ments of blanks. This estimate is used to compute YC,ameasurement critical value for detectio
43、n (see 6.4.1). The YC isthe value that with approximately 90 % confidence will not beexceeded by 99 % of all measurements of blanks made byqualified laboratories as represented in the study. The LCcomputed from YC is the true concentration with expectedmeasurement equal to YC (see 6.4.2). The model
44、is also used topredict interlaboratory standard deviation at nonzero concen-trations. The IDE is directly or iteratively computed to be thetrue concentration that with approximately 90 % confidencewill produce measurements that will exceed YC at least 95 %of the time and simultaneously not exceed mo
45、re than 1 % ofthe time when blank samples are measured.5. Significance and Use5.1 Appropriate application of this practice should result inan IDE achievable by most laboratories properly using the testmethod studied. This IDE provides the basis for any prospec-tive use of the test method by qualifie
46、d laboratories for reliabledetection of low-level concentrations of the same analyte as theone studied in this practice and same media (matrix).5.2 The IDE values may be used to compare the detectionpower of different methods for analysis of the same analyte inthe same matrix.5.3 The IDE provides hi
47、gh probability (approximately95 %) that result values of the method studied which exceedthe IDE represent presence of analyte in the sample and highprobability (approximately 99 %) that blank samples will notresult in a detection.5.4 The IDE procedure should be used to establish theinterlaboratory d
48、etection capability for any application of amethod where interlaboratory detection is important to datause. The intent of IDE is not to set reporting limits.6. Procedure6.1 The procedure described as follows has stages describedin the following sections: IDE Study Plan, Design and Protocol(6.2); Con
49、duct the IDE Study, Screen the Data, and Choose aModel (6.3); and Compute the IDE (6.4). A flowchart of theprocedure is shown in Fig. 2.6.2 IDE Study Plan, Design, and Protocol:6.2.1 Choose Analyte, Matrix, and MethodAt least oneanalyte of interest is selected, typically one for which there isinterest in trace levels of concentration, such as toxic materialsthat are controlled and regulated. For each analyte, an approxi-mate maximum true concentration is selected based on thefollowing considerations:6.2.1.1 The anticipated IDE should be excee
