1、Designation: E1790 04 (Reapproved 2010)Standard Practice forNear Infrared Qualitative Analysis1This standard is issued under the fixed designation E1790; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last revision. A
2、number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice covers the use of near-infrared (NIR)spectroscopy for the qualitative analysis of liquids and solids.The practice is writ
3、ten under the assumption that most NIRqualitative analyses will be performed with instruments de-signed specifically for this region and equipped with comput-erized data handling algorithms. In principle, however, thepractice also applies to work with liquid samples usinginstruments designed for ope
4、ration over the ultraviolet (UV),visible, and mid-infrared (IR) regions if suitable data handlingcapabilities are available. Many Fourier Transform Infrared(FTIR) (normally considered mid-IR instruments) have NIRcapability, or at least extended-range beamsplitters that allowoperation to 1.2 m; this
5、practice also applies to data fromthese instruments.1.2 The values stated in SI units are to be regarded asstandard. No other units of measurement are included in thisstandard.1.3 This standard does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsib
6、ility 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:2E131 Terminology Relating to Molecular SpectroscopyE1252 Practice for General Techniques for Obt
7、aining Infra-red Spectra for Qualitative AnalysisE1655 Practices for Infrared Multivariate QuantitativeAnalysis3. Terminology3.1 DefinitionsFor definitions of general terms and sym-bols pertaining to NIR spectroscopy and statistical computa-tions, refer to Terminology E131.3.2 Definitions of Terms S
8、pecific to This Standard:3.2.1 interactance, nthe phenomenon whereby radiantenergy entering the surface of a material is scattered by thematerial back to the surface, but at a different portion of thesurface.3.2.1.1 DiscussionThis differs from diffuse reflectance,where the returning radiation exits
9、the same portion of thesurface of the material as the illuminating radiation entered.3.2.2 training sample (otherwise called a “referencesample” or “standard”), na quantity of material of knowncomposition or properties, or both, presented to an instrumentfor measurement in order to find relationship
10、s between themeasurements and the composition or properties, or both, ofthe sample.3.2.2.1 DiscussionThis term is typically used in conjunc-tion with computerized methods for ascertaining the relation-ships.Training samples for quantitative analysis (also called “calibrationsamples,” as in Practices
11、 E1655) have different requirements thantraining samples used for qualitative analysis.4. Significance and Use4.1 NIR spectroscopy is a widely used technique for quan-titative analysis, and it is also becoming more widely used forthe identification of organic materials, that is, qualitativeanalysis.
12、 In general, however, the concept of qualitative analy-sis as used in the NIR spectral region differs from that used inthe mid-IR spectral region in that NIR qualitative analysisrefers to the process of automated comparison of the spectra ofunknown materials to the spectra of known materials in orde
13、rto identify the unknown. This approach constitutes a librarysearch method in which each user generates his own library.4.2 Historically, NIR spectroscopy as practiced with classi-cal UV-VIS-NIR instruments using methods similar to thosedescribed in Practice E1252 was not considered to be a strongte
14、chnique for qualitative analysis. Although the positions andintensities of absorption bands in specific wavelength ranges1This practice is under the jurisdiction of ASTM Committee E13 on MolecularSpectroscopy and Separation Science and is the direct responsibility of Subcom-mittee E13.11 on Multivar
15、iate Analysis.Current edition approved March 1, 2010. Published April 2010. Originallyapproved in 1996. Last previous edition approved in 2004 as E1790 04. DOI:10.1520/E1790-04R10.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org
16、. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.were used to confirm the presence of certain functional groups,the
17、 spectra were not considered to be specific enough to allowunequivocal identification of unknown materials. A few impor-tant libraries of NIR spectra were developed for qualitativepurposes, but the lack of suitable data handling facilitieslimited the scope of qualitative analysis severely. Furthermo
18、re,earlier work was limited almost entirely to liquid samples.4.3 Currently, the mid-IR procedure of deducing the struc-ture of an unknown material by method of analysis of thelocations, strengths, and positional shifts of individual absorp-tion bands is generally not used in the NIR.4.4 With the de
19、velopment of specialized NIR instrumentsand mathematical algorithms for treating the data, it becamepossible to obtain a wealth of information from NIR spectrathat had hitherto gone unused. While the mathematical algo-rithms described in this practice can be applied to spectral datain any region, th
20、is practice describes their application to theNIR.4.5 The application of NIR spectroscopy to qualitativeanalysis in the manner described is relatively new, and proce-dures for this application are still evolving. The application ofchemometric methods to spectroscopy has limitations, and thelimitatio
21、ns are not all defined yet since the techniques arerelatively new. One area of concern to some scientists is theeffect of low-level contaminants. Any analytical methodologyhas its detection limits, and NIR is no different in this regard,but neither would we expect it to be any worse. Since therelati
22、vely broad character of NIR bands makes it unlikely thata contaminant would not overlap any of the measured wave-lengths, the question would only be one of degree: whether agiven amount of contaminant could be detected. The user mustbe aware of the probable contaminants he is liable to run intoand a
23、ccount for the possibility of this occurring, perhaps byincluding deliberately contaminated samples in the training set.5. General5.1 NIR qualitative analysis is conducted by comparison ofNIR absorption spectra of unknown materials with those ofknown reference materials. Since the absorption bands o
24、f manysubstances of interest are less distinctive in the NIR than in themid-IR spectral region, the analytical capability of the tech-nique relies heavily on the accuracy of the absorption mea-surements and the relationship of the relative absorbances atdifferent wavelengths. Materials to be identif
25、ied are measuredby a NIR spectrometer, and the spectral data thus generated aresaved in an auxiliary computer attached to the spectrometerproper. One of the several algorithms described in Section 6 isthen applied to the data in order to generate classificationcriteria, which can then be applied to
26、data from unknownsamples in order to classify (or identify) them as being thesame as one of the previously seen materials. Good chemicallaboratory practice should be followed to help ensure repro-ducible results for each material. The preparation and presen-tation of samples to the instrument should
27、 be consistent withina library, and unknowns should be treated the same way thatthe training samples were.5.1.1 The technique is applicable to liquids, solids, andgases. For analysis of gases, multipath vapor cells capable ofachieving up to 100-m path lengths may be required. Spectra ofvapors and ga
28、ses may be sensitive to the total sample pressure,and this has to be determined for each type of sample.5.1.2 Unknown samples to be identified may be prescreenedbased on criteria other than their NIR spectra (for example,visual inspection). The training samples (that is, the “knowns”used to teach th
29、e algorithm what different materials look like)may also be similarly prescreened and grouped into libraries ofsimilar materials (for example, liquids and solids). The un-known is then compared with only those materials in theappropriate library. The prescreening will help reduce thechance of false i
30、dentification, although care must be taken thatan unknown material not in the library is not identified as asimilar material that is in the library.5.1.3 Measurements may be made by method of transmis-sion, reflection, or any other optical setup suitable for collect-ing NIR spectra. In practice, onl
31、y transmission and diffusereflection have been in common use.5.1.4 Determination of the relationships between absor-bances at different wavelengths for a set of materials andconsolidation of these relationships into a set of criteria foridentifying those materials requires the use of computerizedlea
32、rning algorithms. These algorithms can also take intoaccount extraneous variations such as are found, for example,when measurements are made on powdered solids.5.1.5 Instrumentation is commercially available for makingsuitable measurements in the NIR spectral region. Manufac-turers instructions shou
33、ld be followed to ensure correctoperation, optimum accuracy, and safety before collecting data.5.1.6 NIR spectroscopy has, as one of its paradigms, thatlittle or no sample preparation be required. In conformancewith that paradigm, sample preparation steps in other spectro-scopic technologies are rep
34、laced with sample presentationmethodologies in NIR analysis. The most common samplepresentation methods are the following:5.1.6.1 Diffuse ReflectanceSolid materials are ground intopowder (or used as-is, if already in suitably fine powder form)and packed into a cup, which allows the surface of the sa
35、mpleto be illuminated and the reflected radiant power measured.5.1.6.2 “Transflectance”Clear or scattering liquids areplaced in a cup containing a transparent window with adiffusely reflecting material behind the sample. Any radiantenergy passing through the sample is reflected diffusely by thebacki
36、ng material, so the net measurement is just like the diffusereflectance measurement of powdered solids.5.1.6.3 TransmissionLiquids or solids are placed in cellswith two transparent windows and measured by transmission.5.1.6.4 Fiber ProbesIlluminating and collecting fibers arebrought in parallel to t
37、he sample. A variety of optical configu-rations are used to couple the radiant energy from the fibers tothe sample and back again, in an optical “head” of some sort.Transmittance, reflectance, and interactance have all been usedat the sample end of the fiber to couple the radiation to thesample. Int
38、eractance measurements are sometimes made by thesimple expedient of pressing the end of a fiber bundlecontaining mixed illuminating and receiving fibers against thesample surface.5.2 To connect the mathematics with the spectroscopy used,the procedure can be generally described as follows:E1790 04 (2
39、010)2(1) The spectral measurements define some multidimen-sional space. The axes in that space are the absorbances at thevarious wavelengths, or some mathematical transformationthereof.(2) Groups of spectra for the same material define someregion in the multidimensional space.(3) The analysis involv
40、es determining which region theunknown falls in.5.2.1 Problems with this type of analysis include the fol-lowing: insufficient separation of the groups in the multidimen-sional space to allow for classification (indicating insufficientdifferences among the spectra of the materials involved),inadequa
41、te representation of measurement variability withingroups during training (indicating an insufficient number orvariety of training samples), or poor detection limits for minorcontaminants.5.2.2 To optimize the methods against these potential prob-lem areas, generation of a method occurs in three sta
42、ges. In thefirst, or training stage, known samples are presented to theinstrument. The data collected are then presented to one of thevarious algorithms and are thus used to “train” the algorithm torecognize the various different materials.5.2.3 In the second, or validation stage, the ability of the
43、algorithm to correctly recognize materials not in the trainingset of samples is tested. Samples measured during the valida-tion stage should preferably be in the same phase and physicalcondition as the known samples were during the training stage.5.2.4 In the third, or use stage, unknown samples are
44、presented to the instrument, which then compares the data soobtained to the data from the known samples and decideswhether the data from the unknown agrees with the data fromany of the known materials. The unknown material is classifiedas whichever material gives the closest agreement to the data.5.
45、2.5 Optionally, the algorithm may provide for the case inwhich the data from the unknown does not agree with that fromany of the knowns sufficiently well to permit identification, andrefuse to identify the unknown sample.5.3 Samples to be identified during the use stage must be inthe same phase and
46、physical condition as the known sampleswere during the validation stage.5.3.1 Liquids may be run neat or in solution. In either case,the optical pathlength of the sample cell should be fixed, be thesame for all liquids to be compared with a given unknown, andbe specified as part of the method. While
47、 an algorithm may betrained on data incorporating variations in these characteristics,greater accuracy will be achieved when extraneous variationsare reduced. The unknown, of course, should also be run in acell under the same conditions as the training samples. If asolution is used, the amount of di
48、lution should also bespecified.5.3.2 Some solids may be run as-is if they have one or moresuitably flat surfaces; others may need to be ground. If solidsamples are ground, the same procedure should be used for allmaterials in a given library, and that procedure should bespecified as part of the meth
49、od.5.3.3 The unknowns must also be treated in the samemanner as the training samples. It is particularly important thatif the samples must be ground, the unknown samples should beground to the same particle size as the known samples includedin the library.6. Algorithms Used6.1 This section describes some of the computerized algo-rithms that have been found effective for qualitative analysis inthe NIR spectral region. This section is mainly for reference.Descriptions of multivariate methods of statistical data analysistend to be inherently abstract mathematically and resis
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