ASTM E1655-2005 Standard Practices for Infrared Multivariate Quantitative Analysis《红外多变量定量分析的标准规程》.pdf

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1、Designation: E 1655 05Standard Practices forInfrared Multivariate Quantitative Analysis1This standard is issued under the fixed designation E 1655; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last revision. A number

2、 in parentheses indicates the year of last reapproval. Asuperscript epsilon (e) indicates an editorial change since the last revision or reapproval.1. Scope1.1 These practices cover a guide for the multivariatecalibration of infrared spectrometers used in determining thephysical or chemical characte

3、ristics of materials. These prac-tices are applicable to analyses conducted in the near infrared(NIR) spectral region (roughly 780 to 2500 nm) through themid infrared (MIR) spectral region (roughly 4000 to 400cm1).NOTE 1While the practices described herein deal specifically withmid- and near-infrare

4、d analysis, much of the mathematical and proceduraldetail contained herein is also applicable for multivariate quantitativeanalysis done using other forms of spectroscopy.The user is cautioned thattypical and best practices for multivariate quantitative analysis using otherforms of spectroscopy may

5、differ from practices described herein for mid-and near-infrared spectroscopies.1.2 Procedures for collecting and treating data for develop-ing IR calibrations are outlined. Definitions, terms, and cali-bration techniques are described. Criteria for validating theperformance of the calibration model

6、 are described.1.3 The implementation of these practices require that theIR spectrometer has been installed in compliance with themanufacturers specifications. In addition, it assumes that, atthe times of calibration and of validation, the analyzer isoperating at the conditions specified by the manu

7、facturer.1.4 These practices cover techniques that are routinelyapplied in the near and mid infrared spectral regions forquantitative analysis. The practices outlined cover the generalcases for coarse solids, fine ground solids, and liquids. Alltechniques covered require the use of a computer for da

8、tacollection and analysis.1.5 These practices provide a questionnaire against whichmultivariate calibrations can be examined to determine if theyconform to the requirements defined herein.1.6 For some multivariate spectroscopic analyses, interfer-ences and matrix effects are sufficiently small that

9、it is possibleto calibrate using mixtures that contain substantially fewerchemical components than the samples that will ultimately beanalyzed. While these surrogate methods generally make useof the multivariate mathematics described herein, they do notconform to procedures described herein, specifi

10、cally withrespect to the handling of outliers. Surrogate methods mayindicate that they make use of the mathematics describedherein, but they should not claim to follow the proceduresdescribed herein.1.7 This standard does not purport to address all of thesafety concerns, if any, associated with its

11、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 Standards:2D 1265 Practice for Sampling Liquefied Petroleum (LP)Gases (Manual Method

12、)D 4057 Practice for Manual Sampling of Petroleum andPetroleum ProductsD 4177 Practice for Automatic Sampling of Petroleum andPetroleum ProductsD 4855 Practices for Comparing Test MethodsD 6122 Practice for Validation of Multivariate Process In-frared SpectrophotometersD 6299 Practice for Applying S

13、tatistical Quality AssuranceTechniques to Evaluate Analytical Measurement SystemPerformanceD 6300 Practice for Determination of Precision and BiasData for Use in Test Methods for Petroleum Products andLubricantsE 131 Terminology Relating to Molecular SpectroscopyE 168 Practices for General Technique

14、s of Infrared Quanti-tative AnalysisE 275 Practice for Describing and Measuring Performanceof Ultraviolet, Visible, and Near Infrared Spectrophotom-etersE 334 Practice for General Techniques of Infrared Mi-croanalysisE 456 Terminology Relating to Quality and StatisticsE 691 Practice for Conducting a

15、n Interlaboratory Study to1These practices are under the jurisdiction of ASTM Committee E13 onMolecular Spectroscopy and Chromatography and are the direct responsibility ofSubcommittee E13.11 on Chemometrics.Current edition approved Feb. 1, 2005. Published April 2005. Originallyapproved in 1997. Las

16、t previous edition approved in 2004 as E 1655 04.2For 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 Document Summary page onthe ASTM website.1Copyright A

17、STM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.Determine the Precision of a Test MethodE 932 Practice for Describing and Measuring Performanceof Dispersive Infrared SpectrometersE 1421 Practice for Describing and Measuring Performanceof Fourier

18、 Transform Mid-Infrared (FT-IR) Spectrometers:Level Zero and Level One TestsE 1866 Guide for Establishing Spectrophotometer Perfor-mance TestsE 1944 Practice for Describing and Measuring Performanceof Fourier Transform Near-Infrared (FT-NIR) Spectrom-eters: Level Zero and Level One Tests3. Terminolo

19、gy3.1 DefinitionsFor terminology related to molecular spec-troscopic methods, refer to Terminology E 131. For terminol-ogy relating to quality and statistics, refer to TerminologyE 456.3.2 Definitions of Terms Specific to This Standard:3.2.1 analysis, nin the context of this practice, the processof

20、applying the calibration model to a spectrum, preprocessedas required, so as to estimate a component concentration valueor property.3.2.2 calibration, na process used to create a modelrelating two types of measured data. In the context of thispractice, a process for creating a model that relates com

21、ponentconcentrations or properties to spectra for a set of knownreference samples.3.2.3 calibration model, nthe mathematical expression orthe set of mathematical operations that relates componentconcentrations or properties to spectra for a set of referencesamples.3.2.4 calibration samples, nthe set

22、 of reference samplesused for creating a calibration model. Reference componentconcentration or property values are known (measured byreference method) for the calibration samples and a calibrationmodel is found which relates these values to the spectra duringthe calibration.3.2.5 estimate, nthe val

23、ue for a component concentrationor property obtained by applying the calibration model for theanalysis of an absorption spectrum.3.2.6 model validation, nthe process of testing a calibra-tion model with validation samples to determine bias betweenthe estimates from the model and the reference method

24、, and totest the agreement between estimates made with the model andthe reference method.3.2.7 multivariate calibration, na process for creating amodel that relates component concentrations or properties tothe absorbances of a set of known reference samples at morethan one wavelength or frequency.3.

25、2.8 reference method, nthe analytical method that isused to estimate the reference component concentration orproperty value which is used in the calibration and validationprocedures.3.2.9 reference values, nthe component concentrations orproperty values for the calibration or validation samples whic

26、hare measured by the reference analytical method.3.2.10 spectrometer/spectrophotometer qualification,nthe procedures by which a user demonstrates that theperformance of a specific spectrometer/spectrophotometer isadequate to conduct a multivariate analysis so as to obtainprecision consistent with th

27、at specified in the method.3.2.11 surrogate calibration, na multivariate calibrationthat is developed using a calibration set which consists ofmixtures which contain substantially fewer chemical compo-nents than the samples which will ultimately be analyzed.3.2.12 surrogate method, na standard test

28、method that isbased on a surrogate calibration.3.2.13 validation samplesa set of samples used in vali-dating the model. Validation samples are not part of the set ofcalibration samples. Reference component concentration orproperty values are known (measured by reference method),and are compared to t

29、hose estimated using the model.4. Summary of Practices4.1 Multivariate mathematics is applied to correlate thespectra measured for a set of calibration samples to referencecomponent concentrations or property values for the set ofsamples. The resultant multivariate calibration model is ap-plied to t

30、he analysis of spectra of unknown samples to providean estimate of the component concentration or property valuesfor the unknown sample.4.2 Multilinear regression (MLR), principal componentsregression (PCR), and partial least squares (PLS) are examplesof multivariate mathematical techniques that are

31、 commonlyused for the development of the calibration model. Othermathematical techniques are also used, but may not detectoutliers, and may not be validated by the procedure describedin these practices.4.3 Statistical tests are applied to detect outliers during thedevelopment of the calibration mode

32、l. Outliers include highleverage samples (samples whose spectra contribute a statisti-cally significant fraction of one or more of the spectralvariables used in the model), and samples whose referencevalues are inconsistent with the model.4.4 Validation of the calibration model is performed byusing

33、the model to analyze a set of validation samples andstatistically comparing the estimates for the validation samplesto reference values measured for these samples, so as to test forbias in the model and for agreement of the model with thereference method.4.5 Statistical tests are applied to detect w

34、hen values esti-mated using the model represent extrapolation of the calibra-tion.4.6 Statistical expressions for calculating the repeatabilityof the infrared analysis and the expected agreement betweenthe infrared analysis and the reference method are given.5. Significance and Use5.1 These practice

35、s can be used to establish the validity ofthe results obtained by an infrared (IR) spectrometer at the timethe calibration is developed. The ongoing validation of esti-mates produced by analysis of unknown samples using thecalibration model should be covered separately (see for ex-ample, Practice D

36、6122).5.2 These practices are intended for all users of infraredspectroscopy. Near-infrared spectroscopy is widely used forquantitative analysis. Many of the general principles describedin these practices relate to the common modern practices ofE1655052near-infrared spectroscopic analysis. While sam

37、pling methodsand instrumentation may differ, the general calibration meth-odologies are equally applicable to mid-infrared spectroscopy.New techniques are under study that may enhance thosediscussed within these practices. Users will find these practicesto be applicable to basic aspects of the techn

38、ique, to includesample selection and preparation, instrument operation, anddata interpretation.5.3 The calibration procedures define the range over whichmeasurements are valid and demonstrate whether or not thesensitivity and linearity of the analysis outputs are adequate forproviding meaningful est

39、imates of the specific physical orchemical characteristics of the types of materials for which thecalibration is developed.6. Overview of Multivariate Calibration6.1 The practice of infrared multivariate quantitative analy-sis involves the following steps:6.1.1 Selecting the Calibration SetThis set

40、is also termedthe training set or spectral library set. This set is to represent allof the chemical and physical variation normally encounteredfor routine analysis for the desired application. Selection of thecalibration set is discussed in Section 17, after the statisticalterms necessary to define

41、the selection criteria have beendefined.6.1.2 Determination of Concentrations or Properties, orBoth, for Calibration SamplesThe chemical or physicalproperties, or both, of samples in the calibration set must beaccurately and precisely measured by the reference method inorder to accurately calibrate

42、the infrared model for predictionof the unknown samples. Reference measurements are dis-cussed in Section 9.6.1.3 The Collection of Infrared SpectraThe collection ofoptical data must be performed with care so as to presentcalibration samples, validation samples, and prediction (un-known) samples for

43、 analysis in an alike manner. Variation insample presentation technique among calibration, validation,and prediction samples will introduce variation and error whichhas not been modeled within the calibration. Infrared instru-mentation is discussed in Section 7 and infrared spectralmeasurements in S

44、ection 8.6.1.4 Calculating the Mathematical ModelThe calcula-tion of mathematical (calibration) models may involve avariety of data treatments and calibration algorithms. The morecommon linear techniques are discussed in Section 12.Avariety of statistical techniques are used to evaluate andoptimize

45、the model. These techniques are described in Section15. Statistics used to detect outliers in the calibration set arecovered in Section 16.6.1.5 Validation of the Calibration ModelValidation ofthe efficacy of a specific calibration model (equation) requiresthat the model be applied for the analysis

46、of a separate set oftest (validation) samples, and that the values predicted for thesetest samples be statistically compared to values obtained by thereference method. The statistical tests to be applied forvalidation of the model are discussed in Section 18.6.1.6 Application of the Model for the An

47、alysis ofUnknownsThe mathematical model is applied to the spectraof unknown samples to estimate component concentrations orproperty values, or both, (see Section 13). Outlier statistics areused to detect when the analysis involves extrapolation of themodel (see Section 16).6.1.7 Routine Analysis and

48、 MonitoringOnce the efficacyof one or more calibration equations is established, the equa-tions must be monitored for continued accuracy and precision.Simultaneously, the instrument performance must be moni-tored so as to trace any deterioration in performance to eitherthe calibration model itself o

49、r to a failure in the instrumentationperformance. Procedures for verifying the performance of theanalysis are only outlined in Section 22. For petrochemicals,these procedures are covered in detail in Practice D 6122. Theuse of Practice D 6122 requires that a quality control procedurebe established at the time the model is developed. The QCcheck sample is discussed in Section 22. For practices tocompare reference methods and analyzer methods, refer toPractices D 4855.6.1.8 Transfer of CalibrationsTransferable calibrationsare equations that can be transferred fr