1、Designation: E1655 05 (Reapproved 2012)E1655 17Standard 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
2、 last revision. 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 multivariate calibration of infrared spectrometers used in determining the phys
3、ical orchemical characteristics of materials. These practices are applicable to analyses conducted in the near infrared (NIR) spectralregion (roughly 780 to 2500 nm) through the mid infrared (MIR) spectral region (roughly 4000 to 400 cm1).NOTE 1While the practices described herein deal specifically
4、with mid- and near-infrared analysis, much of the mathematical and procedural detailcontained herein is also applicable for multivariate quantitative analysis done using other forms of spectroscopy. The user is cautioned that typical andbest practices for multivariate quantitative analysis using oth
5、er forms of spectroscopy may differ from practices described herein for mid- andnear-infrared spectroscopies.1.2 Procedures for collecting and treating data for developing IR calibrations are outlined. Definitions, terms, and calibrationtechniques are described. Criteria for validating the performan
6、ce of the calibration model are described.1.3 The implementation of these practices require that the IR spectrometer has been installed in compliance with themanufacturers specifications. In addition, it assumes that, at the times of calibration and of validation, the analyzer is operatingat the con
7、ditions specified by the manufacturer.1.4 These practices cover techniques that are routinely applied in the near and mid infrared spectral regions for quantitativeanalysis. The practices outlined cover the general cases for coarse solids, fine ground solids, and liquids. All techniques coveredrequi
8、re the use of a computer for data collection and analysis.1.5 These practices provide a questionnaire against which multivariate calibrations can be examined to determine if theyconform to the requirements defined herein.1.6 For some multivariate spectroscopic analyses, interferences and matrix effe
9、cts are sufficiently small that it is possible tocalibrate using mixtures that contain substantially fewer chemical components than the samples that will ultimately be analyzed.While these surrogate methods generally make use of the multivariate mathematics described herein, they do not conform topr
10、ocedures described herein, specifically with respect to the handling of outliers. Surrogate methods may indicate that they makeuse of the mathematics described herein, but they should not claim to follow the procedures described herein.1.7 The values stated in SI units are to be regarded as standard
11、. No other units of measurement are included in this standard.1.8 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 safety, health, and healthenvironmental practi
12、ces and determine theapplicability of regulatory limitations prior to use.1.9 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 Reco
13、mmendations issuedby the World Trade Organization Technical Barriers to Trade (TBT) Committee.2. Referenced Documents2.1 ASTM Standards:2D1265 Practice for Sampling Liquefied Petroleum (LP) Gases, Manual MethodD4057 Practice for Manual Sampling of Petroleum and Petroleum ProductsD4177 Practice for A
14、utomatic Sampling of Petroleum and Petroleum Products1 These practices are under the jurisdiction ofASTM Committee E13 on Molecular Spectroscopy and Separation Science and are the direct responsibility of SubcommitteeE13.11 on Multivariate Analysis.Current edition approved April 1, 2012Dec. 1, 2017.
15、 Published May 2012January 2018. Originally approved in 1997. Last previous edition approved in 20052012 asE1655 05.E1655 05(2012). DOI: 10.1520/E1655-05R12.10.1520/E1655-17.2 For referencedASTM standards, visit theASTM website, www.astm.org, or contactASTM Customer Service at serviceastm.org. For A
16、nnual Book of ASTM Standardsvolume information, refer to the standards Document Summary page on the ASTM website.This document 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
17、be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current versionof the standard as published by ASTM is to be considered the official document.Copyright ASTM International, 100 Barr Harbor Dri
18、ve, PO Box C700, West Conshohocken, PA 19428-2959. United States1D4855 Practice for Comparing Test Methods (Withdrawn 2008)3D6122 Practice for Validation of the Performance of Multivariate Online, At-Line, and Laboratory Infrared SpectrophotometerBased Analyzer SystemsD6299 Practice for Applying Sta
19、tistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measure-ment System PerformanceD6300 Practice for Determination of Precision and Bias Data for Use in Test Methods for Petroleum Products and LubricantsE131 Terminology Relating to Molecular SpectroscopyE168 Practices
20、for General Techniques of Infrared Quantitative AnalysisE275 Practice for Describing and Measuring Performance of Ultraviolet and Visible SpectrophotometersE334 Practice for General Techniques of Infrared MicroanalysisE456 Terminology Relating to Quality and StatisticsE691 Practice for Conducting an
21、 Interlaboratory Study to Determine the Precision of a Test MethodE932 Practice for Describing and Measuring Performance of Dispersive Infrared SpectrometersE1421 Practice for Describing and Measuring Performance of Fourier Transform Mid-Infrared (FT-MIR) Spectrometers: LevelZero and Level One Tests
22、E1866 Guide for Establishing Spectrophotometer Performance TestsE1944 Practice for Describing and Measuring Performance of Laboratory Fourier Transform Near-Infrared (FT-NIR)Spectrometers: Level Zero and Level One Tests3. Terminology3.1 DefinitionsFor terminology related to molecular spectroscopic m
23、ethods, refer to Terminology E131. For terminologyrelating to quality and statistics, refer to Terminology E456.3.2 Definitions of Terms Specific to This Standard:3.2.1 analysis, nin the context of this practice, the process of applying the calibration model to a spectrum, preprocessed asrequired, s
24、o as to estimate a component concentration value or property.3.2.2 calibration, na process used to create a model relating two types of measured data. In the context of this practice, aprocess for creating a model that relates component concentrations or properties to spectra for a set of known refe
25、rence samples.3.2.3 calibration model, nthe mathematical expression or the set of mathematical operations that relates componentconcentrations or properties to spectra for a set of reference samples.3.2.4 calibration samples, nthe set of reference samples used for creating a calibration model. Refer
26、ence componentconcentration or property values are known (measured by reference method) for the calibration samples and a calibration modelis found which relates these values to the spectra during the calibration.3.2.5 estimate, nthe value for a component concentration or property obtained by applyi
27、ng the calibration model for theanalysis of an absorption spectrum.3.2.6 model validation, nthe process of testing a calibration model with validation samples to determine bias between theestimates from the model and the reference method, and to test the agreement between estimates made with the mod
28、el and thereference method.3.2.7 multivariate calibration, na process for creating a model that relates component concentrations or properties to theabsorbances of a set of known reference samples at more than one wavelength or frequency.3.2.8 reference method, nthe analytical method that is used to
29、 estimate the reference component concentration or propertyvalue which is used in the calibration and validation procedures.3.2.9 reference values, nthe component concentrations or property values for the calibration or validation samples which aremeasured by the reference analytical method.3.2.10 s
30、pectrometer/spectrophotometer qualification, nthe procedures by which a user demonstrates that the performance ofa specific spectrometer/spectrophotometer is adequate to conduct a multivariate analysis so as to obtain precision consistent withthat specified in the method.3.2.11 surrogate calibration
31、, na multivariate calibration that is developed using a calibration set which consists of mixtureswhich contain substantially fewer chemical components than the samples which will ultimately be analyzed.3.2.12 surrogate method, na standard test method that is based on a surrogate calibration.3.2.13
32、validation samplesa set of samples used in validating the model.Validation samples are not part of the set of calibrationsamples. Reference component concentration or property values are known (measured by reference method), and are compared tothose estimated using the model.3 The last approved vers
33、ion of this historical standard is referenced on www.astm.org.E1655 1724. Summary of Practices4.1 Multivariate mathematics is applied to correlate the spectra measured for a set of calibration samples to reference componentconcentrations or property values for the set of samples. The resultant multi
34、variate calibration model is applied to the analysis ofspectra of unknown samples to provide an estimate of the component concentration or property values for the unknown sample.4.2 Multilinear regression (MLR), principal components regression (PCR), and partial least squares (PLS) are examples ofmu
35、ltivariate mathematical techniques that are commonly used for the development of the calibration model. Other mathematicaltechniques are also used, but may not detect outliers, and may not be validated by the procedure described in these practices.4.3 Statistical tests are applied to detect outliers
36、 during the development of the calibration model. Outliers include high leveragesamples (samples whose spectra contribute a statistically significant fraction of one or more of the spectral variables used in themodel), and samples whose reference values are inconsistent with the model.4.4 Validation
37、 of the calibration model is performed by using the model to analyze a set of validation samples and statisticallycomparing the estimates for the validation samples to reference values measured for these samples, so as to test for bias in themodel and for agreement of the model with the reference me
38、thod.4.5 Statistical tests are applied to detect when values estimated using the model represent extrapolation of the calibration.4.6 Statistical expressions for calculating the repeatability of the infrared analysis and the expected agreement between theinfrared analysis and the reference method ar
39、e given.5. Significance and Use5.1 These practices can be used to establish the validity of the results obtained by an infrared (IR) spectrometer at the time thecalibration is developed. The ongoing validation of estimates produced by analysis of unknown samples using the calibrationmodel should be
40、covered separately (see for example, Practice D6122).5.2 These practices are intended for all users of infrared spectroscopy. Near-infrared spectroscopy is widely used for quantitativeanalysis. Many of the general principles described in these practices relate to the common modern practices of near-
41、infraredspectroscopic analysis. While sampling methods and instrumentation may differ, the general calibration methodologies are equallyapplicable to mid-infrared spectroscopy. New techniques are under study that may enhance those discussed within these practices.Users will find these practices to b
42、e applicable to basic aspects of the technique, to include sample selection and preparation,instrument operation, and data interpretation.5.3 The calibration procedures define the range over which measurements are valid and demonstrate whether or not thesensitivity and linearity of the analysis outp
43、uts are adequate for providing meaningful estimates of the specific physical or chemicalcharacteristics of the types of materials for which the calibration is developed.6. Overview of Multivariate Calibration6.1 The practice of infrared multivariate quantitative analysis involves the following steps
44、:6.1.1 Selecting the Calibration SetThis set is also termed the training set or spectral library set. This set is to represent all ofthe chemical and physical variation normally encountered for routine analysis for the desired application. Selection of thecalibration set is discussed in Section 17,
45、after the statistical terms necessary to define the selection criteria have been defined.6.1.2 Determination of Concentrations or Properties, or Both, for Calibration SamplesThe chemical or physical properties,or both, of samples in the calibration set must be accurately and precisely measured by th
46、e reference method in order to accuratelycalibrate the infrared model for prediction of the unknown samples. Reference measurements are discussed in Section 9.6.1.3 The Collection of Infrared SpectraThe collection of optical data must be performed with care so as to present calibrationsamples, valid
47、ation samples, and prediction (unknown) samples for analysis in an alike manner. Variation in sample presentationtechnique among calibration, validation, and prediction samples will introduce variation and error which has not been modeledwithin the calibration. Infrared instrumentation is discussed
48、in Section 7 and infrared spectral measurements in Section 8.6.1.4 Calculating the Mathematical ModelThe calculation of mathematical (calibration) models may involve a variety of datatreatments and calibration algorithms. The more common linear techniques are discussed in Section 12. A variety of st
49、atisticaltechniques are used to evaluate and optimize the model. These techniques are described in Section 15. Statistics used to detectoutliers in the calibration set are covered in Section 16.6.1.5 Validation of the Calibration ModelValidation of the efficacy of a specific calibration model (equation) requires that themodel be applied for the analysis of a separate set of test (validation) samples, and that the values predicted for these test samplesbe statistically compared to values obtained by the reference method. The statistical tests to be applied f
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