ASTM D6122-2009 781 Standard Practice for Validation of the Performance of Multivariate Process Infrared Spectrophotometers.pdf

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1、Designation: D 6122 09Standard Practice forValidation of the Performance of Multivariate ProcessInfrared Spectrophotometer Based Analyzer Systems1This standard is issued under the fixed designation D 6122; the number immediately following the designation indicates the year oforiginal adoption or, in

2、 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.INTRODUCTIONOperation of a process stream analyzer system typically involves four sequential ac

3、tivities.(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 performance standards.These diagnostic tests may requirethat t

4、he 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 corresponding primary test method (PTM). Amathematical function is d

5、erived 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 relationship between the analyzeroutput and PTMRs has been establis

6、hed, 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 within user-specifiedrequirements for the analyzer system applic

7、ation. (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 thePPTMRs agree with the PTMRs to within user-specified require

8、ments. 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 between the PPTMRs and the PTMRs established during the Genera

9、l 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, used in the calculation of physical, chemi-cal, or quality par

10、ameters (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 of the applicability of the calibrationmodel to the spectrum

11、 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 variation in property level, the statis-tical methodology o

12、f 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 Performance Validation is conducted by calculating theprecision and

13、 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 Method Results (PTMRs) for the same sampleset. Results used

14、in the calculation are for samples that are notused in the development of the multivariate model. The1This 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 Validation of P

15、rocess Stream Analyzer Systems.Current edition approved June 1, 2009. Published July 2009. Originally approvedin 1997. Last previous edition approved in 2006 as D 6122061.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.calculated pre

16、cision 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 precision and bias re-quirement for the degree of agreement are typically based onthe site or pu

17、blished 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 establishing precision and bias requirements for analyzersystem applications. Such requirements must be ba

18、sed 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 thevalidation procedures described herein.1.3 This practice does not cover procedures for establishingth

19、e 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 novices, this practice will serve as an overview oftechniques used to verify instrument performanc

20、e, 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 measurement.1.5 This practice teaches and recommends appropriate sta-tistical tools, outlier det

21、ection 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 infrared measure-ment results in a valid property or parameter estimate.1.6 The outlier detection

22、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 determine that aspectral measurement lies outside the calibration, but will nothave specific info

23、rmation 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 response.1.7 This practice is not intended as a quantitative perfor-mance standard for the compariso

24、n 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 applicable to at-line andlaboratory infrared analyzers which employ multivariate mod-els.1.9 This standar

25、d 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 determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM

26、 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 Sampling of Petroleum andPetroleum ProductsD 4177 Practice for Automatic Sampling of Petroleum andPetro

27、leum ProductsD 6299 Practice for Applying Statistical Quality Assuranceand Control Charting Techniques to Evaluate AnalyticalMeasurement System PerformanceD 6708 Practice for Statistical Assessment and Improve-ment of Expected Agreement Between Two Test Methodsthat Purport to Measure the Same Proper

28、ty of a MaterialE 131 Terminology Relating to Molecular SpectroscopyE 275 Practice for Describing and Measuring Performanceof Ultraviolet and Visible SpectrophotometersE 456 Terminology Relating to Quality and StatisticsE 932 Practice for Describing and Measuring Performanceof Dispersive Infrared Sp

29、ectrometersE 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 QuantitativeAnalysisE 1866 Guide for Establishing Spectrophotometer Perfor-mance TestsE 1944 Practice

30、 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 Definitions:3.1.1 For definitions of terms and symbols relating to IRspectroscopy, refer to Terminol

31、ogy 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 6299 and Terminology E 456.3.1.4 control limits, nlimits on a control chart which areused as

32、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 quantitativeexpression of the random error associated with the differencebetween two results obtained by differen

33、t operators usingdifferent apparatus and applying the two methods X and Y,respectively, each obtaining a single result on an identical test2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume

34、 information, refer to the standards Document Summary page onthe ASTM website.3Available from ASTM International Headquarters. Order Adjunct No.ADJD6708.D6122092sample, when the methods have been assessed and an appro-priate bias-correction has been applied in accordance with thispractice; it is def

35、ined 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 instrumentperformance test, beyond which the analyzer is expected toproduce potentially invalid

36、 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 calibration, nsee multivariate calibration.3.2.4 analyzer intermediate precision, na statistical mea-sure

37、 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 repeatability, na statistical measure of theexpected short-term variability of results produced by theana

38、lyzer 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 calibration model to the spectral data collected by theanalyzer.3.2.8 analyzer validation test, nsee va

39、lidation 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 instrument standardization.3.2.10 check sample, na single, pure liquid hydrocarboncompound or a known, r

40、eproducible 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 chart based on the exponentially weightedaverage of individual observations from a system; the

41、 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 system; the observa-tions may be the differences between the analyzer result andthe result from

42、 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 multivariate calibration space, which is sparselypopulated.3.2.15 in-line probe, na spectropho

43、tometer 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 cell and, if utilized,transfer optics.3.2.17 instrument standardization, na procedure for stan-dar

44、dizing 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 the spectrophotometerhardware or by way of mathematical treatment of the collectedspectra.3.2.1

45、8 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 through the analyzer is ofuniform quality and the analyzer result is essentially constant.3.2.19 mov

46、ing 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 test method.3.2.20 multivariate calibration, nan analyzer calibrationthat relates the spectrum at

47、 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 qualityparameters from the measured infrared spectrum.3.2.22 nearest neighbor distance inlier, na sp

48、ectrum 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 under test, typically obtained by measur-ing the radiation transmitted through the spectrophot

49、ometercell 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 of an outlier detection method to a spectrum,beyond which the spectrum represents an extrapol