1、Designation: D6299 131An American National StandardStandard Practice forApplying Statistical Quality Assurance and Control ChartingTechniques to Evaluate Analytical Measurement SystemPerformance1This standard is issued under the fixed designation D6299; the number immediately following the designati
2、on indicates the year oforiginal 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.1NOTESection A1.9 was corrected editorially in
3、March 2015.1. Scope*1.1 This practice covers information for the design andoperation of a program to monitor and control ongoing stabilityand precision and bias performance of selected analyticalmeasurement systems using a collection of generally acceptedstatistical quality control (SQC) procedures
4、and tools.NOTE 1A complete list of criteria for selecting measurement systemsto which this practice should be applied and for determining the frequencyat which it should be applied is beyond the scope of this practice.However, some factors to be considered include (1) frequency of use ofthe analytic
5、al measurement system, (2) criticality of the parameter beingmeasured, (3) system stability and precision performance based onhistorical data, (4) business economics, and (5) regulatory, contractual, ortest method requirements.1.2 This practice is applicable to stable analytical measure-ment systems
6、 that produce results on a continuous numericalscale.1.3 This practice is applicable to laboratory test methods.1.4 This practice is applicable to validated process streamanalyzers.1.5 This practice is applicable to monitoring the differencesbetween two analytical measurement systems that purport to
7、measure the same property provided that both systems havebeen assessed in accordance with the statistical methodology inPractice D6708 and the appropriate bias applied.NOTE 2For validation of univariate process stream analyzers, see alsoPractice D3764.NOTE 3One or both of the analytical systems in 1
8、.5 can be laboratorytest methods or validated process stream analyzers.1.6 This practice assumes that the normal (Gaussian) modelis adequate for the description and prediction of measurementsystem behavior when it is in a state of statistical control.NOTE 4For non-Gaussian processes, transformations
9、 of test resultsmay permit proper application of these tools. Consult a statistician forfurther guidance and information.2. Referenced Documents2.1 ASTM Standards:2D3764 Practice for Validation of the Performance of ProcessStream Analyzer SystemsD5191 Test Method for Vapor Pressure of Petroleum Prod
10、-ucts (Mini Method)D6708 Practice for Statistical Assessment and Improvementof Expected Agreement Between Two Test Methods thatPurport to Measure the Same Property of a MaterialD6792 Practice for Quality System in Petroleum Productsand Lubricants Testing LaboratoriesD7372 Guide for Analysis and Inte
11、rpretation of ProficiencyTest Program ResultsE177 Practice for Use of the Terms Precision and Bias inASTM Test MethodsE178 Practice for Dealing With Outlying ObservationsE456 Terminology Relating to Quality and StatisticsE691 Practice for Conducting an Interlaboratory Study toDetermine the Precision
12、 of a Test Method3. Terminology3.1 Definitions:3.1.1 accepted reference value, na value that serves as anagreed-upon reference for comparison and that is derived as (1)a theoretical or established value, based on scientific principles,(2) an assigned value, based on experimental work of somenational
13、 or international organization, such as the U.S. Na-tional Institute of Standards and Technology (NIST), or (3)aconsensus value, based on collaborative experimental workunder the auspices of a scientific or engineering group. E177,E4561This practice is under the jurisdiction of ASTM Committee D02 on
14、 PetroleumProducts, Liquid Fuels, and Lubricants and is the direct responsibility of Subcom-mittee D02.94 on Coordinating Subcommittee on Quality Assurance and Statistics.Current edition approved Oct. 1, 2013. Published October 2013. Originallyapproved in 1998. Last previous edition approved in 2010
15、 as D6299 102. DOI:10.1520/D6299-13E01.2For referenced ASTM standards, visit the 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.*A Summary of Changes
16、section appears at the end of this standardCopyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States13.1.2 accuracy, nthe closeness of agreement between anobserved value and an accepted reference value. E177, E4563.1.3 assignable cause, na fact
17、or that contributes to varia-tion and that is feasible to detect and identify. E4563.1.4 bias, na systematic error that contributes to thedifference between a population mean of the measurements ortest results and an accepted reference or true value. E177, E4563.1.5 control limits, nlimits on a cont
18、rol chart that areused as criteria for signaling the need for action or for judgingwhether a set of data does or does not indicate a state ofstatistical control. E4563.1.6 lot, na definite quantity of a product or materialaccumulated under conditions that are considered uniform forsampling purposes.
19、 E4563.1.7 precision, nthe closeness of agreement between testresults obtained under prescribed conditions. E4563.1.8 repeatability conditions, nconditions where mutu-ally independent test results are obtained with the same testmethod in the same laboratory by the same operator with thesame equipmen
20、t within short intervals of time, using testspecimens taken at random from a single sample of material.3.1.9 reproducibility conditions, nconditions under whichtest results are obtained in different laboratories with the sametest method, using test specimens taken at random from thesame sample of ma
21、terial.3.2 Definitions of Terms Specific to This Standard:3.2.1 analytical measurement system, na collection of oneor more components or subsystems, such as samplers, testequipment, instrumentation, display devices, data handlers,printouts or output transmitters, that is used to determine aquantitat
22、ive value of a specific property for an unknownsample in accordance with a test method.3.2.1.1 DiscussionA standard test method (for example,ASTM, ISO) is an example of an analytical measurementsystem.3.2.1.2 DiscussionAn analytical measurement systemmay comprise multiple instruments being used for
23、the sametest method provided there is no statistically observable biasand precision differences between the multiple instruments.3.2.2 blind submission, nsubmission of a check standardor quality control (QC) sample for analysis without revealingthe expected value to the person performing the analysi
24、s.3.2.3 check standard, nin QC testing, a material having anaccepted reference value used to determine the accuracy of ameasurement system.3.2.3.1 DiscussionA check standard is preferably a mate-rial that is either a certified reference material with traceabilityto a nationally recognized body or a
25、material that has anaccepted reference value established through interlaboratorytesting. For some measurement systems, a pure, single com-ponent material having known value or a simple gravimetric orvolumetric mixture of pure components having calculablevalue may serve as a check standard. Users sho
26、uld be awarethat for measurement systems that show matrix dependencies,accuracy determined from pure compounds or simple mixturesmay not be representative of that achieved on actual samples.3.2.4 common (chance, random) cause, nfor quality as-surance programs, one of generally numerous factors, indi
27、vidu-ally of relatively small importance, that contributes tovariation, and that is not feasible to detect and identify.3.2.5 double blind submission, nsubmission of a checkstandard or QC sample for analysis without revealing the checkstandard or QC sample status and expected value to the personperf
28、orming the analysis.3.2.6 in-statistical-control, adja process, analytical mea-surement system, or function that exhibits variations that canonly be attributable to common cause.3.2.7 proficiency testing, ndetermination of a laboratorystesting capability by participation in an interlaboratory cross-
29、check program.3.2.7.1 DiscussionASTM Committee D02 conducts pro-ficiency testing among hundreds of laboratories, using a widevariety of petroleum products and lubricants.3.2.8 quality control (QC) sample, nfor use in qualityassurance programs to determine and monitor the precision andstability of a
30、measurement system, a stable and homogeneousmaterial having physical or chemical properties, or both,similar to those of typical samples tested by the analyticalmeasurement system. The material is properly stored to ensuresample integrity, and is available in sufficient quantity forrepeated, long te
31、rm testing.3.2.9 site expected value (SEV), nfor a QC sample this isan estimate of the theoretical limiting value towards which theaverage of results collected from a single in-statistical-controlmeasurement system under site precision conditions tends asthe number of results approaches infinity.3.2
32、.9.1 DiscussionThe SEV is associated with a singlemeasurement system; for control charts that are plotted inactual measured units, the SEV is required, since it is used asa reference value from which upper and lower control limits forthe control chart specific to a batch of QC material areconstructe
33、d.3.2.10 site precision (R), nthe value below which theabsolute difference between two individual test results obtainedunder site precision conditions may be expected to occur witha probability of approximately 0.95 (95 %). It is defined as 2.77times the standard deviation of results obtained under
34、siteprecision conditions.3.2.11 site precision conditions, nconditions under whichtest results are obtained by one or more operators in a singlesite location practicing the same test method on a singlemeasurement system which may comprise multipleinstruments, using test specimens taken at random fro
35、m thesame sample of material, over an extended period of timespanning at least a 15 day interval.3.2.11.1 DiscussionSite precision conditions should in-clude all sources of variation that are typically encounteredduring normal, long term operation of the measurement sys-tem. Thus, all operators who
36、are involved in the routine use ofthe measurement system should contribute results to the siteprecision determination. If multiple results are obtained withina 24h period, then it is recommended that the number ofD6299 1312results used in site precision calculations be increased tocapture the longer
37、 term variation in the system.3.2.12 site precision standard deviation, nthe standarddeviation of results obtained under site precision conditions.3.2.13 validation audit sample, na QC sample or checkstandard used to verify precision and bias estimated fromroutine quality assurance testing.3.3 Symbo
38、ls:3.3.1 ARVaccepted reference value.3.3.2 EWMAexponentially weighted moving average.3.3.3 Iindividual observation (as in I-chart).3.3.4 MRmoving range.3.3.5 MRaverage of moving range.3.3.6 QCquality control.3.3.7 Rsite precision.3.3.8 SEVsite expected value.3.3.9 Rsite precision standard deviation.
39、3.3.10 VAvalidation audit.3.3.11 2chi squared.3.3.12 lambda.4. Summary of Practice4.1 QC samples and check standards are regularly analyzedby the measurement system. Control charts and other statisticaltechniques are presented to screen, plot, and interpret testresults in accordance with industry-ac
40、cepted practices to as-certain the in-statistical-control status of the measurementsystem.4.2 Statistical estimates of the measurement system preci-sion and bias are calculated and periodically updated usingaccrued data.4.3 In addition, as part of a separate validation auditprocedure, QC samples and
41、 check standards may be submittedblind or double-blind and randomly to the measurement systemfor routine testing to verify that the calculated precision andbias are representative of routine measurement system perfor-mance when there is no prior knowledge of the expected valueor sample status.5. Sig
42、nificance and Use5.1 This practice can be used to continuously demonstratethe proficiency of analytical measurement systems that areused for establishing and ensuring the quality of petroleum andpetroleum products.5.2 Data accrued, using the techniques included in thispractice, provide the ability t
43、o monitor analytical measurementsystem precision and bias.5.3 These data are useful for updating test methods as wellas for indicating areas of potential measurement system im-provement.6. Reference Materials6.1 QC samples are used to establish and monitor theprecision of the analytical measurement
44、system.6.1.1 Select a stable and homogeneous material havingphysical or chemical properties, or both, similar to those oftypical samples tested by the analytical measurement system.NOTE 5When the QC sample is to be utilized for monitoring a processstream analyzer performance, it is often helpful to
45、supplement the processanalyzer system with a subsystem to automate the extraction, mixing,storage, and delivery functions associated with the QC sample.6.1.2 Estimate the quantity of the material needed for eachspecific lot of QC sample to (1) accommodate the number ofanalytical measurement systems
46、for which it is to be used(laboratory test apparatuses as well as process stream analyzersystems) and (2) provide determination of QC statistics for auseful and desirable period of time.6.1.3 Collect the material into a single container and isolateit.6.1.4 Thoroughly mix the material to ensure homog
47、eneity.6.1.5 Conduct any testing necessary to ensure that the QCsample meets the characteristics for its intended use.6.1.6 Package or store QC samples, or both, as appropriatefor the specific analytical measurement system to ensure thatall analyses of samples from a given lot are performed onessent
48、ially identical material. If necessary, split the bulkmaterial collected in 6.1.3 into separate and smaller containersto help ensure integrity over time. (WarningTreat thematerial appropriately to ensure its stability, integrity, andhomogeneity over the time period for which it is to stored andused.
49、 For samples that are volatile, such as gasoline, storage inone large container that is repeatedly opened and closed canresult in loss of light ends. This problem can be avoided bychilling and splitting the bulk sample into smaller containers,each with a quantity sufficient to conduct the analysis.Similarly, samples prone to oxidation can benefit from splittingthe bulk sample into smaller containers that can be blanketedwith an inert gas prior to being sealed and leaving them sealeduntil the sample is needed.)6.2 Check stan
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