1、Designation: E1655 05 (Reapproved 2012)Standard Practices forInfrared Multivariate Quantitative Analysis1This standard is issued under the fixed designation E1655; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last re
2、vision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () 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 ch
3、emical characteristics 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- a
4、nd near-infrared 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 sp
5、ectroscopy may 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 ca
6、libration model 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 specif
7、ied by the manufacturer.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
8、computer for datacollection 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 sufficien
9、tly small that 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
10、herein, specifically 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 The values stated in SI units are to be regarded asstandard. No other units of mea
11、surement are included in thisstandard.1.8 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 determine the applica-bility of regulatory li
12、mitations prior to use.2. Referenced Documents2.1 ASTM Standards:2D1265 Practice for Sampling Liquefied Petroleum (LP)Gases, Manual MethodD4057 Practice for Manual Sampling of Petroleum andPetroleum ProductsD4177 Practice for Automatic Sampling of Petroleum andPetroleum ProductsD4855 Practice for Co
13、mparing Test Methods3D6122 Practice for Validation of the Performance of Mul-tivariate Online, At-Line, and Laboratory Infrared Spec-trophotometer Based Analyzer SystemsD6299 Practice for Applying Statistical Quality Assuranceand Control Charting Techniques to Evaluate AnalyticalMeasurement System P
14、erformanceD6300 Practice for Determination of Precision and BiasData for Use in Test Methods for Petroleum Products andLubricantsE131 Terminology Relating to Molecular SpectroscopyE168 Practices for General Techniques of Infrared Quanti-tative AnalysisE275 Practice for Describing and Measuring Perfo
15、rmanceof Ultraviolet and Visible Spectrophotometers1These practices are under the jurisdiction of ASTM Committee E13 onMolecular Spectroscopy and Separation Science and are the direct responsibility ofSubcommittee E13.11 on Multivariate Analysis.Current edition approved April 1, 2012. Published May
16、2012. Originallyapproved in 1997. Last previous edition approved in 2005 as E1655 05. DOI:10.1520/E1655-05R12.2For 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 st
17、andards Document Summary page onthe ASTM website.3Withdrawn. The last approved version of this historical standard is referencedon www.astm.org.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.E334 Practice for General Techniques of I
18、nfrared Micro-analysisE456 Terminology Relating to Quality and StatisticsE691 Practice for Conducting an Interlaboratory Study toDetermine the Precision of a Test MethodE932 Practice for Describing and Measuring Performanceof Dispersive Infrared SpectrometersE1421 Practice for Describing and Measuri
19、ng Performanceof Fourier Transform Mid-Infrared (FT-MIR) Spectrom-eters: Level Zero and Level One TestsE1866 Guide for Establishing Spectrophotometer Perfor-mance TestsE1944 Practice for Describing and Measuring Performanceof Laboratory Fourier Transform Near-Infrared (FT-NIR)Spectrometers: Level Ze
20、ro and Level One Tests3. Terminology3.1 DefinitionsFor terminology related to molecular spec-troscopic methods, refer to Terminology E131. For terminol-ogy relating to quality and statistics, refer to TerminologyE456.3.2 Definitions of Terms Specific to This Standard:3.2.1 analysis, nin the context
21、of this practice, the processof 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 c
22、reating a model that relates componentconcentrations 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
23、.4 calibration samples, nthe set 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 cali
24、bration.3.2.5 estimate, nthe value 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 th
25、e model and the reference method, 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 moretha
26、n one wavelength or frequency.3.2.8 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 calibr
27、ation or validation samples whichare 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 ob
28、tainprecision consistent with that 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 sur
29、rogate method, na standard test 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 referen
30、ce method),and are compared to those 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 ca
31、libration model is ap-plied to the 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
32、mathematical techniques that are 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 thedev
33、elopment of the calibration model. 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 calibra
34、tion model is performed byusing 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 Statistic
35、al tests are applied to detect when 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. Signif
36、icance and Use5.1 These practices 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 theE1655 05 (2012)2calibration model should be c
37、overed separately (see for ex-ample, Practice D6122).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 ofnear-inf
38、rared spectroscopic analysis. While sampling 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 a
39、pplicable to basic aspects of the technique, 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 a
40、re adequate forproviding meaningful estimates 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
41、 Selecting the Calibration SetThis set 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 th
42、e statisticalterms necessary to define 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
43、method inorder to accurately calibrate 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
44、, and prediction (un-known) samples for 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
45、 and infrared spectralmeasurements in Section 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 techn
46、iques are used to evaluate andoptimize 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
47、 the model be applied for the analysis 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.
48、1.6 Application of the Model for the Analysis 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
49、 Section 16).6.1.7 Routine Analysis and 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 or 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 D6122. Theuse of
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