ASTM D6122-2010 1250 Standard Practice for Validation of the Performance of Multivariate Online At-Line and Laboratory Infrared Spectrophotometer Based Analyzer Systems《多变量联机和实验室基于.pdf

1、Designation: D6122 10Standard Practice forValidation of the Performance of Multivariate Online, At-Line, and Laboratory Infrared Spectrophotometer BasedAnalyzer Systems1This standard is issued under the fixed designation D6122; the number immediately following the designation indicates the year ofor

2、iginal 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.INTRODUCTIONOperation of a laboratory or process stream analyzer system t

3、ypically involves four sequentialactivities. (1) Analyzer CalibrationWhen an analyzer is initially installed, or after majormaintenance has been performed, diagnostic testing is performed to demonstrate that the analyzermeets the manufacturers specifications and historical performance standards. The

4、se diagnostic testsmay require that the analyzer be adjusted so as to provide predetermined output levels for certainreference materials. (2) CorrelationOnce the diagnostic testing is completed, process streamsamples are analyzed using both the analyzer system and the corresponding primary test meth

5、od(PTM). A mathematical 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 predictedprimary test method result (PPTMR). (3) Probationary ValidationOnce the relationship betweenthe analyz

6、er output and PTMRs has been established, a probationary validation is performed using anindependent but limited set of materials that were not part of the correlation activity. This probationaryvalidation is intended to demonstrate that the PPTMRs agree with the PTMRs to within user-specifiedrequir

7、ements 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 thePPTMRs agree with the P

8、TMRs 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 between the PPTMRs and th

9、e PTMRs established during the General Validation ismaintained. This practice deals with the third and fourth of these activities.1. Scope*1.1 This practice covers requirements for the validation ofmeasurements made by laboratory or process (online or at-line)near- or mid-infrared analyzers, or both

10、, used in the calculationof physical, chemical, or quality parameters (that is, properties)of liquid petroleum products. The properties are calculatedfrom spectroscopic data using multivariate modeling methods.The requirements include verification of adequate instrumentperformance, verification of t

11、he 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 var

12、iation in property level, the statis-tical methodology of Practice D6708 is used to provide generalvalidation of this equivalence over the complete operatingrange of the analyzer. For cases where there is inadequateproperty variation, methodology for level specific validation isused.1.2 Performance

13、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 Metho

14、d 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 Validation of

15、Process Stream Analyzer Systems.Current edition approved May 1, 2010. Published May 2010. Originallyapproved in 1997. Last previous edition approved in 2009 as D612209. DOI:10.1520/D6122-10.1*A Summary of Changes section appears at the end of this standard.Copyright ASTM International, 100 Barr Harb

16、or Drive, PO Box C700, West Conshohocken, 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 prod

17、uctquality certification applications, the 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

18、 describe procedures for estab-lishing precision and bias requirements for analyzer systemapplications. Such requirements must be based on the critical-ity of the results to the intended business application and oncontractual and regulatory requirements. The user must estab-lish precision and bias r

19、equirements prior to initiating thevalidation 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 E1655 andreferences therein.1.4 This practice is intended as

20、a review for experiencedpersons. For novices, 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

21、 the results of the primarytest method 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. Thestatistica

22、l tools are used to determine if the infrared 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

23、a routine basis will find criteria to 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 arerespo

24、nding to changes in the instrument response.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 ofinfrared analyzers, the procedures and statistical tests describedhere

25、in are also applicable to other types of analyzers whichemploy multivariate models.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

26、determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2D1265 Practice for Sampling Liquefied Petroleum (LP)Gases, Manual MethodD3764 Practice for Validation of the Performance of Pro-cess Stream Analyzer SystemsD4057 Practice for Manual Samplin

27、g of Petroleum andPetroleum ProductsD4177 Practice for Automatic Sampling of Petroleum andPetroleum ProductsD6299 Practice for Applying Statistical Quality Assuranceand Control Charting Techniques to Evaluate AnalyticalMeasurement System PerformanceD6708 Practice for StatisticalAssessment and Improv

28、ementof Expected Agreement Between Two Test Methods thatPurport to Measure the Same Property of a MaterialE131 Terminology Relating to Molecular SpectroscopyE275 Practice for Describing and Measuring Performanceof Ultraviolet and Visible SpectrophotometersE456 Terminology Relating to Quality and Sta

29、tisticsE932 Practice for Describing and Measuring Performanceof Dispersive Infrared SpectrometersE1421 Practice for Describing and Measuring Performanceof Fourier Transform Mid-Infrared (FT-MIR) Spectrom-eters: Level Zero and Level One TestsE1655 Practices for Infrared Multivariate QuantitativeAnaly

30、sisE1866 Guide for Establishing Spectrophotometer Perfor-mance TestsE1944 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 Definitions:3.1.1

31、For definitions of terms and symbols relating to IRspectroscopy, refer to Terminology E131.3.1.2 For definitions of terms and symbols relating tomultivariate calibration, refer to Practices E1655.3.1.3 For definitions of terms relating to statistical qualitycontrol, refer to Practice D6299 and Termi

32、nology E456.3.1.4 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. E4563.1.5 cross-method reproducibility (RXY), na quantitativeexpression of the random

33、 error associated with the differencebetween two results obtained by different 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

34、 Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.3Available from ASTM International Headquarters. Order Adjunct No.ADJD6708.D6122 102sample, when the methods have been assessed and an appro-pria

35、te 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.D67083.2 Definitions of Terms Specific to This Standard:3.2.1 action limit, nthe limiting value from an instrumentperforma

36、nce 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 calibration, nsee multivaria

37、te 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 repeatability, na statistica

38、l 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 calibration model to the s

39、pectral 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 instrument standardization.

40、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 chart based on th

41、e 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 system; the observ

42、a-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 multivariate cali

43、bration 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 cell and, if utilized

44、,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 the spectrophotome

45、terhardware 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 PracticesD1265, D4057,orD4177, whichever is applicable, during aperiod when the material flowing through the analyzer is of

46、uniform 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 test method.3.2.20 mult

47、ivariate 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 qualityparameters from the

48、 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 under test, typicall

49、y 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 of an outlier detec