ASTM D6122-2006e1 Standard Practice for Validation of the Performance of Multivariate Process Infrared Spectrophotometers《多工艺红外分光光度计性能确认的标准实施规范》.pdf

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1、Designation: D 6122 06e1An American National StandardStandard Practice forValidation of the Performance of Multivariate ProcessInfrared Spectrophotometers1This standard is issued under the fixed designation D 6122; the number immediately following the designation indicates the year oforiginal adopti

2、on or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon (e) indicates an editorial change since the last revision or reapproval.e1NOTEUpdated Fig. 4 editorially in November 2006.INTRODUCTIONOperation of a process

3、stream analyzer system typically involves four sequential activities.(1) Analyzer CalibrationWhen an analyzer is initially installed, or after major maintenance hasbeen performed, diagnostic testing is performed to demonstrate that the analyzer meets themanufacturers specifications and historical pe

4、rformance standards.These diagnostic tests may requirethat the analyzer be adjusted so as to provide predetermined output levels for certain referencematerials. (2) CorrelationOnce the diagnostic testing is completed, process stream samples areanalyzed using both the analyzer system and the correspo

5、nding primary test method (PTM). Amathematical function is derived that relates the analyzer output to the primary test method (PTM).The application of this mathematical function to an analyzer output produces a predicted primary testmethod result (PPTMR). (3) Probationary ValidationOnce the relatio

6、nship between the analyzeroutput and PTMRs has been established, a probationary validation is performed using an independentbut limited set of materials that were not part of the correlation activity. This probationary validationis intended to demonstrate that the PPTMRs agree with the PTMRs to with

7、in user-specifiedrequirements for the analyzer system application. (4) General and Continual ValidationAfter anadequate number of PPTMRs and PTMRs have been accrued on materials that were not part of thecorrelation activity, a comprehensive statistical assessment is performed to demonstrate that the

8、PPTMRs agree with the PTMRs to within user-specified requirements. Subsequent to a successfulgeneral validation, quality assurance control chart monitoring of the differences between PPTMR andPTMR is conducted during normal operation of the process analyzer system to demonstrate that theagreement be

9、tween the PPTMRs and the PTMRs established during the General Validation ismaintained. This practice deals with the third and fourth of these activities.1. Scope1.1 This practice covers requirements for the validation ofmeasurements made by online, process near- or mid-infraredanalyzers, or both, us

10、ed in the calculation of physical, chemi-cal, or quality parameters (that is, properties) of liquid petro-leum products. The properties are calculated from spectro-scopic data using multivariate modeling methods. Therequirements include verification of adequate instrument per-formance, verification

11、of the applicability of the calibrationmodel to the spectrum of the sample under test, and verificationof equivalence between the result calculated from the infraredmeasurements and the result produced by the primary testmethod used for the development of the calibration model.When there is adequate

12、 variation in property level, the statis-tical methodology of Practice D 6708 is used to providegeneral validation of this equivalence over the completeoperating range of the analyzer. For cases where there isinadequate property variation, methodology for level specificvalidation is used.1.2 Perform

13、ance Validation is conducted by calculating theprecision and bias of the differences between results from theanalyzer system (or subsystem) produced by application of themultivariate model, (such results are herein referred to asPredicted Primary Test Method Results (PPTMRs), versus thePrimary Test

14、Method Results (PTMRs) for the same sampleset. Results used in the calculation are for samples that are not1This practice is under the jurisdiction of ASTM Committee D02 on PetroleumProducts and Lubricants and is the direct responsibility of Subcommittee D02.25 onPerformance Assessment and Validatio

15、n of Process Stream Analyzer Systems forPetroleum and Petroleum Products.Current edition approved July 1, 2006. Published August 2006. Originallyapproved in 1997. Last previous edition approved in 2001 as D 612201.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken,

16、PA 19428-2959, United States.used in the development of the multivariate model. Thecalculated precision and bias are statistically compared touser-specified requirements for the analyzer system applica-tion.1.2.1 For analyzers used in product release or productquality certification applications, the

17、 precision and bias re-quirement for the degree of agreement are typically based onthe site or published precision of the Primary Test Method.NOTE 1In most applications of this type, the PTM is the specification-cited test method.1.2.2 This practice does not does not describe proceduresfor establish

18、ing precision and bias requirements for analyzersystem applications. Such requirements must be based on thecriticality of the results to the intended business application andon contractual and regulatory requirements. The user mustestablish precision and bias requirements prior to initiating thevali

19、dation procedures described herein.1.3 This practice does not cover procedures for establishingthe calibration model (correlation) used by the analyzer.Calibration procedures are covered in Practices E 1655 andreferences therein.1.4 This practice is intended as a review for experiencedpersons. For n

20、ovices, this practice will serve as an overview oftechniques used to verify instrument performance, to verifymodel applicability to the spectrum of the sample under test,and to verify equivalence between the parameters calculatedfrom the infrared measurement and the results of the primarytest method

21、 measurement.1.5 This practice teaches and recommends appropriate sta-tistical tools, outlier detection methods, for determiningwhether the spectrum of the sample under test is a member ofthe population of spectra used for the analyzer calibration. Thestatistical tools are used to determine if the i

22、nfrared measure-ment results in a valid property or parameter estimate.1.6 The outlier detection methods do not define criteria todetermine whether the sample or the instrument is the cause ofan outlier measurement. Thus, the operator who is measuringsamples on a routine basis will find criteria to

23、determine that aspectral measurement lies outside the calibration, but will nothave specific information on the cause of the outlier. Thispractice does suggest methods by which instrument perfor-mance tests can be used to indicate if the outlier methods areresponding to changes in the instrument res

24、ponse.1.7 This practice is not intended as a quantitative perfor-mance standard for the comparison of analyzers of differentdesign.1.8 Although this practice deals primarily with validation ofonline, process infrared analyzers, the procedures and statisti-cal tests described herein are also applicab

25、le to at-line andlaboratory infrared analyzers which employ multivariate mod-els.1.9 This standard does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and de

26、termine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2D 1265 Practice for Sampling Liquefied Petroleum (LP)Gases, Manual MethodD 3764 Practice for Validation of the Performance of Pro-cess Stream Analyzer SystemsD 4057 Practice for Manual Sampli

27、ng of Petroleum andPetroleum ProductsD 4177 Practice for Automatic Sampling of Petroleum andPetroleum ProductsD 6299 Practice for Applying Statistical Quality AssuranceTechniques to Evaluate Analytical Measurement SystemPerformanceD 6708 Practice for Statistical Assessment and Improve-ment of Expect

28、ed Agreement Between Two Test Methodsthat Purport to Measure the Same Property of a MaterialE 131 Terminology Relating to Molecular SpectroscopyE 275 Practice for Describing and Measuring Performanceof Ultraviolet, Visible, and Near-Infrared Spectrophotom-etersE 456 Terminology Relating to Quality a

29、nd StatisticsE 932 Practice for Describing and Measuring Performanceof Dispersive Infrared SpectrometersE 1421 Practice for Describing and Measuring Performanceof Fourier Transform Mid-Infrared (FT-MIR) Spectrom-eters: Level Zero and Level One TestsE 1655 Practices for Infrared Multivariate Quantita

30、tiveAnalysisE 1866 Guide for Establishing Spectrophotometer Perfor-mance TestsE 1944 Practice for Describing and Measuring Performanceof Laboratory Fourier Transform Near-Infrared (FT-NIR)Spectrometers: Level Zero and Level One Tests2.2 ASTM Adjuncts:Software Program CompTM33. Terminology3.1 Definit

31、ions:3.1.1 For definitions of terms and symbols relating to IRspectroscopy, refer to Terminology E 131.3.1.2 For definitions of terms and symbols relating tomultivariate calibration, refer to Practices E 1655.3.1.3 For definitions of terms relating to statistical qualitycontrol, refer to Practice D

32、6299 and Terminology E 456.3.1.4 control limits, nlimits on a control chart which 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. E 4563.1.5 cross-method reproducibility (RXY), na quantitativeexpressi

33、on of the random error associated with the differencebetween two results obtained by different operators usingdifferent apparatus and applying the two methods X and Y,2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual

34、Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.3Available from ASTM International Headquarters. Order Adjunct No.ADJD6708.D612206e12respectively, each obtaining a single result on an identical testsample, when the methods have been assessed

35、and an appro-priate bias-correction has been applied in accordance with thispractice; it is defined as the 95 % confidence limit for thedifference between two such single and independent results.D 67083.2 Definitions of Terms Specific to This Standard:3.2.1 action limit, nthe limiting value from an

36、instrumentperformance test, beyond which the analyzer is expected toproduce potentially invalid results.3.2.2 analyzer, nall piping, hardware, computer, software,instrumentation and calibration model required to automati-cally perform analysis of a process or product stream.3.2.3 analyzer calibratio

37、n, nsee multivariate calibration.3.2.4 analyzer intermediate precision, na statistical mea-sure of the expected long-term variability of analyzer resultsfor samples whose spectra are neither outliers, nor nearestneighbor inliers.3.2.5 analyzer model, nsee multivariate model.3.2.6 analyzer repeatabil

38、ity, na statistical measure of theexpected short-term variability of results produced by theanalyzer for samples whose spectra are neither outliers nornearest neighbor inliers.3.2.7 analyzer result, nthe numerical estimate of aphysical, chemical, or quality parameter produced by applyingthe calibrat

39、ion model to the spectral data collected by theanalyzer.3.2.8 analyzer validation test, nsee validation test.3.2.9 calibration transfer, na method of applying a mul-tivariate calibration developed on one analyzer to a differentanalyzer by mathematically modifying the calibration model orby instrumen

40、t standardization.3.2.10 check sample, na single, pure liquid hydrocarboncompound or a known, reproducible mixture of liquid hydro-carbon compounds whose spectrum is constant over time suchthat it can be used in a performance test.3.2.11 exponentially weighted moving average controlchart, na control

41、 chart based on the exponentially weightedaverage of individual observations from a system; the obser-vations may be the differences between the analyzer result, andthe result from the primary test method.3.2.12 individual observation control chart, na controlchart of individual observations from a

42、system; the observa-tions may be the differences between the analyzer result andthe result from the primary test method.3.2.13 inlier, nsee nearest neighbor distance inlier.3.2.14 inlier detection methods, nstatistical tests whichare conducted to determine if a spectrum resides within aregion of the

43、 multivariate calibration space, which is sparselypopulated.3.2.15 in-line probe, na spectrophotometer cell installedin a process pipe or slip stream loop and connected to theanalyzer by optical fibers.3.2.16 instrument, nspectrophotometer, associated elec-tronics and computer, spectrophotometer cel

44、l and, if utilized,transfer optics.3.2.17 instrument standardization, na procedure for stan-dardizing the response of multiple instruments such that acommon multivariate model is applicable for measurementsconducted by these instruments, the standardization beingaccomplished by way of adjustment of

45、the spectrophotometerhardware or by way of mathematical treatment of the collectedspectra.3.2.18 line sample, na process or product sample which iswithdrawn from a sample port in accordance with PracticesD 1265, D 4057,orD 4177, whichever is applicable, during aperiod when the material flowing throu

46、gh the analyzer is ofuniform quality and the analyzer result is essentially constant.3.2.19 moving range of two control chart, na control chartthat monitors the change in the absolute value of the differencebetween two successive differences of the analyzer resultminus the result from the primary te

47、st method.3.2.20 multivariate calibration, nan analyzer calibrationthat relates the spectrum at multiple wavelengths or frequen-cies to the physical, chemical, or quality parameters.3.2.21 multivariate model, na multivariate, mathematicalrule or formula used to calculate physical, chemical, or quali

48、typarameters from the measured infrared spectrum.3.2.22 nearest neighbor distance inlier, na spectrum re-siding within a gap in the multivariate calibration space, theresult for which is subject to possible interpolation error.3.2.23 optical background, nthe spectrum of radiationincident on a sample

49、 under test, typically obtained by measur-ing the radiation transmitted through the spectrophotometercell when no sample is present, or when an optically thin ornonabsorbing liquid is present.3.2.24 optical reference filter, nan optical filter or otherdevice which can be inserted into the optical path in thespectrophotometer or probe producing an absorption spectrumwhich is known to be constant over time, such that it can beused in place of a check or test sample in a performance test.3.2.25 outlier detection limits, nthe limiting value forapplication