ASTM D6122-2019 Standard Practice for Validation of the Performance of Multivariate Online At-Line and Laboratory Infrared Spectrophotometer Based Analyzer Syst.pdf

《ASTM D6122-2019 Standard Practice for Validation of the Performance of Multivariate Online At-Line and Laboratory Infrared Spectrophotometer Based Analyzer Syst.pdf》由会员分享，可在线阅读，更多相关《ASTM D6122-2019 Standard Practice for Validation of the Performance of Multivariate Online At-Line and Laboratory Infrared Spectrophotometer Based Analyzer Syst.pdf（51页珍藏版）》请在麦多课文档分享上搜索。

1、Designation: D6122 18D6122 19Standard 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 y

2、ear 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.INTRODUCTIONOperation of a laboratory or process stream analyzer

3、system typically involves five sequentialactivities. (1) CorrelationPrior to the initiation of the procedures described in this practice, amultivariate model is derived which relates the spectrum produced by the analyzer to the Primary TestMethod Result (PTMR). (1a) If the analyzer and Primary Test

4、Method (PTM) measure the samematerial, then the multivariate model directly relates the spectra to PTMR collected on the samesamples. Alternatively (1b) if the analyzer measures the spectra of a material that is subjected totreatment prior to being measured by the PTM, then the multivariate model re

5、lates the spectra ofthe untreated sample to the PTMR for the same sample after treatment. (2) Analyzer QualificationWhen an analyzer is initially installed, or after major maintenance has been performed, or after themultivariate model has been changed, diagnostic testing is performed to demonstrate

6、that the analyzermeets the manufacturers specifications and historical performance standards. These diagnostic testsmay require that the analyzer be adjusted so as to provide predetermined output levels for certainreference materials (3) Local ValidationA local validation is performed using an indep

7、endent butlimited set of materials that were not part of the correlation activity. This local validation is intendedto demonstrate that the agreement between the Predicted Primary Method Test Results (PPTMRs) andthe PTMRs are consistent with expectations based on the multivariate model. (4) GeneralV

8、alidationAfter an adequate number of PPTMRs and PTMRs have been accrued on materials thatwere not part of the correlation activity and which adequately span the multivariate modelcompositional space, a comprehensive statistical assessment can be performed to demonstrate that thePPTMRs agree with the

9、 PTMRs to within user-specified requirements. (5) Continual ValidationSubsequent to a successful local or general validation, quality assurance control chart monitoring ofthe differences between PPTMR and PTMR is conducted during normal operation of the processanalyzer system to demonstrate that the

10、 agreement between the PPTMRs and the PTMRs establishedduring the GeneralValidation is maintained.This practice deals with the third, fourth, and fifth of theseactivities.“Correlation where analyzer measures a material which is subjected to treatment before beingmeasured by the PTM” as outlined in t

11、his practice can be applied to biofuels where the biofuelmaterial is added at a terminal or other facility and not included in the process stream material sampledby the analyzer at the basestock manufacturing facility. The “treatment” shall be a constant percentageaddition of the biofuels material t

12、o the basestock material. The correlation is deemed valid only forthe specific percentage addition and type of biofuel material used in its development.1. Scope*1.1 This practice covers requirements for the validation of measurements made by laboratory or process (online or at-line) near-or mid-infr

13、ared analyzers, or both, used in the calculation of physical, chemical, or quality parameters (that is, properties) of liquid1 This practice is under the jurisdiction of ASTM Committee D02 on Petroleum Products, Liquid Fuels, and Lubricants and is the direct responsibility of SubcommitteeD02.25 on P

14、erformance Assessment and Validation of Process Stream Analyzer Systems.Current edition approved July 1, 2018Jan. 1, 2019. Published January 2019February 2019. Originally approved in 1997. Last previous edition approved in 20152018 asD6122 15.D6122 18. DOI: 10.1520/D6122-18. 10.1520/D6122-19.This do

15、cument is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Becauseit may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions a

16、s appropriate. In all cases only the current versionof the standard as published by ASTM is to be considered the official document.*A Summary of Changes section appears at the end of this standardCopyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. Unit

17、ed States1petroleum products and fuels. The properties are calculated from spectroscopic data using multivariate modeling methods. Therequirements include verification of adequate instrument performance, verification of the applicability of the calibration model tothe spectrum of the sample under te

18、st, and verification that the degree of agreement between the results calculated from the infraredmeasurements and the results produced by the PTM used for the development of the calibration model meets user-specifiedrequirements. Initially, a limited number of validation samples representative of c

19、urrent production are used to do a localvalidation. When there is an adequate number of validation samples with sufficient variation in both property level and samplecomposition to span the model calibration space, the statistical methodology of Practice D6708 can be used to provide generalvalidatio

20、n of this equivalence over the complete operating range of the analyzer. For cases where adequate property andcomposition variation is not achieved, local validation continues to be used.1.1.1 For some applications, the analyzer and PTM are applied to the same material. The application of the multiv

21、ariate modelto the analyzer output (spectrum) directly produces a PPTMR for the same material for which the spectrum was measured. ThePPTMRs are compared to the PTMRs measured on the same materials to determine the degree of agreement.1.1.2 For other applications, the material measured by the analyz

22、er system is subjected to a consistent treatment prior to beinganalyzed by the PTM. The application of the multivariate model to the analyzer output (spectrum) produces a PPTMR for thetreated material. The PPTMRs based on the analyzer outputs are compared to the PTMRs measured on the treated materia

23、ls todetermine the degree of agreement.1.2 Multiple physical, chemical, or quality properties of the sample under test are typically predicted from a single spectralmeasurement. In applying this practice, each property prediction is validated separately. The separate validation procedures foreach pr

24、operty may share common features, and be affected by common effects, but the performance of each property predictionis evaluated independently. The user will typically have multiple validation procedures running simultaneously in parallel.1.3 Results used in analyzer validation are for samples that

25、were not used in the development of the multivariate model, andfor spectra which are not outliers or nearest neighbor inliers relative to the multivariate model.1.4 When the number, composition range or property range of available validation samples do not span the model calibrationrange, a local va

26、lidation is done using available samples representative of current production. When the number, composition rangeand property range of available validation samples becomes comparable to those of the model calibration set, a general validationcan be done.1.4.1 Local Validation:1.4.1.1 The calibration

27、 samples used in developing the multivariate model must show adequate compositional and propertyvariation to enable the development of a meaningful correlation, and must span the compositional range of samples to be analyzedusing the model to ensure that such analyses are done via interpolation rath

28、er than extrapolation. The Standard Error of Calibration(SEC) is a measure of how well the PTMRs and PPTMRs agree for this set of calibration samples. SEC includes contributionsfrom spectrum measurement error, PTM measurement error, and model error. Sample (type) specific biases are a part of the mo

29、delerror. Typically, spectroscopic analyzers are very precise, so that spectral measurement error is small relative to the other types oferror.1.4.1.2 During initial analyzer validation, the compositional range of available samples may be small relative to the range ofthe calibration set. Because of

30、 the high precision of the spectroscopic measurement, the average difference between the PTMRsand PPTMRs may reflect a sample (type) specific bias which is statistically observable, but which are less than the 95 %uncertainty of PPTMR, U(PPTMR). Therefore, the bias and precision of the PTMR/PPTMR di

31、fferences are not used as the basisfor local validation.1.4.1.3 Based on SEC, and the leverage statistic, a 95 % uncertainty for each PPTMR, U(PPTMR) is calculated. Duringvalidation, for each non-outlier sample, a determination is made as to whether the absolute difference between PPTMR and PTMR,|,

32、is less than or equal to U(PPTMR). Counts are maintained as to the total number of non-outlier validation samples, and thenumber of samples for which | is less than or equal to U(PPTMR). Given the total number of non-outlier validation samples,an inverse binomial distribution is used to calculate th

33、e minimum number of results for which | must be less than U(PPTMR).If the number of results for which | is less than U(PPTMR) is greater than or equal to this minimum, then the results are consistentwith the expectations of the multivariate model, and the analyzer passes local validation. The calcul

34、ations involved are describedin detail in Section 11 and Annex A4.1.4.1.4 The user must establish that results that are consistent with the expectations based on the multivariate model will beadequate for the intended application. A 95 % probability is recommended for the inverse binomial distributi

35、on calculation. Theuser may adjust this based on the criticality of the application. See Annex A4 for details.1.4.2 General Validation:1.4.2.1 When the validation samples are of sufficient number, and their compositional and property ranges are comparable tothat of the model calibration set, then a

36、General Validation can be done.1.4.2.2 General Validation is conducted by doing a D6708 based assessment of the differences between results from the analyzersystem (or subsystem) produced by application of the multivariate model, (such results are herein referred to as PPTMRs), versusthe PTMRs for t

37、he same sample set. The calculated precision and bias are statistically compared to user- specified requirementsfor the analyzer system application.D6122 1921.4.2.3 For analyzers used in product release or product quality certification applications, the precision and bias requirement forthe degree o

38、f agreement are typically based on the site or published precision of the PTM.NOTE 1In most applications of this type, the PTM is the specification-cited test method.1.4.2.4 This practice does not describe procedures for establishing precision and bias requirements for analyzer systemapplications. S

39、uch requirements must be based on the criticality of the results to the intended business application and oncontractual and regulatory requirements. The user must establish precision and bias requirements prior to initiating the validationprocedures described herein.1.4.3 For analyzers used in produ

40、ct release or product quality certification applications, the precision and bias requirement forthe degree of agreement are typically based on the site or published precision of the PTM.1.4.4 This practice does not describe procedures for establishing precision and bias requirements for analyzer sys

41、temapplications. Such requirements must be based on the criticality of the results to the intended business application and oncontractual and regulatory requirements. The user must establish precision and bias requirements prior to initiating the validationprocedures described herein.NOTE 1In most a

42、pplications of this type, the PTM is the specification-cited test method.1.5 This practice does not cover procedures for establishing the calibration model (correlation) used by the analyzer. Calibrationprocedures are covered in Practices E1655 and references therein.1.6 This practice is intended as

43、 a review for experienced persons. For novices, this practice will serve as an overview oftechniques used to verify instrument performance, to verify model applicability to the spectrum of the sample under test, and toverify equivalence between the parameters calculated from the infrared measurement

44、 and the results of the primary test methodmeasurement.1.7 This practice teaches and recommends appropriate statistical tools, outlier detection methods, for determining whether thespectrum of the sample under test is a member of the population of spectra used for the analyzer calibration. The stati

45、stical toolsare used to determine if the infrared measurement results in a valid property or parameter estimate.1.8 The outlier detection methods do not define criteria to determine whether the sample or the instrument is the cause of anoutlier measurement. Thus, the operator who is measuring sample

46、s on a routine basis will find criteria to determine that a spectralmeasurement lies outside the calibration, but will not have specific information on the cause of the outlier. This practice doessuggest methods by which instrument performance tests can be used to indicate if the outlier methods are

47、 responding to changesin the instrument response.1.9 This practice is not intended as a quantitative performance standard for the comparison of analyzers of different design.1.10 Although this practice deals primarily with validation of infrared analyzers, the procedures and statistical tests descri

48、bedherein are also applicable to other types of analyzers which employ multivariate models.1.11 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibilityof the user of this standard to establish appropriate safety, health, and env

49、ironmental practices and determine the applicability ofregulatory limitations prior to use.1.12 This international standard was developed in accordance with internationally recognized principles on standardizationestablished in the Decision on Principles for the Development of International Standards, Guides and Recommendations issuedby the World Trade Organization Technical Barriers to Trade (TBT) Committee.2. Referenced Documents2.1 ASTM Standards:2D86 Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric PressureD1265 Practice for Sa