1、Designation: D6299 102An 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.1NOTECorrected definition references and figure
3、 numbering editorially in March 2011.2NOTECorrected Table A1.8 footnote editorially in March 2012.1. Scope1.1 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
4、 using a collection of generally acceptedstatistical quality control (SQC) procedures 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 pract
5、ice.However, some factors to be considered include (1) frequency of use ofthe analytical 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
6、requirements.1.2 This practice is applicable to stable analytical measure-ment systems 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 t
7、o monitoring the differencesbetween two analytical measurement systems that purport tomeasure 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 str
8、eam analyzers, see alsoPractice D3764.NOTE 3One or both of the analytical systems in 1.5 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
9、is in a state of statistical control.NOTE 4For non-Gaussian processes, transformations 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 o
10、f Pro-cess Stream Analyzer SystemsD5191 Test Method for Vapor Pressure of Petroleum Prod-ucts (Mini Method)D6708 Practice for StatisticalAssessment and Improvementof Expected Agreement Between Two Test Methods thatPurport to Measure the Same Property of a MaterialD6792 Practice for Quality System in
11、 Petroleum Productsand Lubricants Testing LaboratoriesD7372 Guide for Analysis and Interpretation 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 St
12、atisticsE691 Practice for Conducting an Interlaboratory Study toDetermine the Precision 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
13、scientific principles,(2) an assigned value, based on experimental work of somenational 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 engine
14、ering group.E177, E4561This practice is under the jurisdiction of ASTM Committee D02 on PetroleumProducts and Lubricants and is the direct responsibility of Subcommittee D02.94 onCoordinating Subcommittee on Quality Assurance and Statistics.Current edition approved March 1, 2010. Published June 2010
15、. Originallyapproved in 1998. Last previous edition approved in 2009 as D629909. DOI:10.1520/D6299-10.2For 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
16、Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.3.1.2 accuracy, nthe closeness of agreement between anobserved value and an accepted reference value. E177, E4563.1.3 assignable cause, na facto
17、r that contributes tovariation 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 control
18、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. E45
19、63.1.7 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 equipment wi
20、thin 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 materi
21、al.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 aquantitative
22、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 the
23、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 analysis.3.
24、2.3 check standard, nin QC testing, a material havingan accepted reference value used to determine the accuracy ofa measurement 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 mate
25、rial 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 should
26、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, individu
27、-ally of relatively small importance, that contributes to varia-tion, 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 personperfor
28、ming 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-ch
29、eck 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 me
30、asurement 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 term
31、 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.3.2.9
32、.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 areconstructed.
33、3.2.10 site precision (R8), 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 s
34、iteprecision 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 multiple instru-ments, using test specimens taken at random fr
35、om the samesample of material, over an extended period of time spanningat 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 ofresults used in site precision calculations be increased tocapture the longer term var
37、iation in the system.D6299 10223.2.12 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 Symb
38、ols: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.MR average of moving range.3.3.6 QCquality control.3.3.7 R8site precision.3.3.8 SEVsite expected value.3.3.9 sR8site precision standard deviation.3
39、.3.10 VAvalidation audit.3.3.11 x2chi squared.3.3.12 llambda.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-a
40、ccepted 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 an
41、d 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. Si
42、gnificance 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
43、to 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 aprocess stream analyzer performance, it is often helpful to
45、 supplement theprocess analyzer 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 homo
47、geneity.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 onessen
48、tially 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. Simi-larly, samples prone to oxidation can benefit from splitting thebulk sample into smaller containers that can be blanketed withan inert gas prior to being sealed and leaving them sealed untilthe sample is needed.)6.2 Check standard
