1、Designation: E1790 04 (Reapproved 2016)1Standard 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.1NOTEEditorial change was made in Subsection 6.6.3 in April 2016.1. Scope1.1 This practice covers the use of near-infrared (NIR)spectroscopy for t
3、he qualitative analysis of liquids and solids.The practice is written 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 app
4、lies to work with liquid samples usinginstruments designed for operation 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 leas
5、t extended-range beamsplitters that allowoperation to 1.2 m; this 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 thesa
6、fety 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 limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2E131 Terminology Relating to M
7、olecular SpectroscopyE1252 Practice for General Techniques for Obtaining 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 statisticalco
8、mputations, refer to Terminology E131.3.2 Definitions of Terms Specific 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 diffe
9、rs from diffuse reflectance,where the returning radiation exits 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, present
10、ed to an instrumentfor measurement in order to find relationships 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 quantita
11、tive analysis (also called“calibration samples,” as in Practices E1655) have differentrequirements than training samples used for qualitativeanalysis.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 ide
12、ntification of organic materials, that is, qualitativeanalysis. 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 spectr
13、a ofunknown materials to the spectra of known materials in orderto 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 those
14、described in Practice E1252 was not considered to be a strongtechnique for qualitative analysis. Although the positions and1This 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 Multiv
15、ariate Analysis.Current edition approved April 1, 2016. Published June 2016. Originallyapproved in 1996. Last previous edition approved in 2010 as E1790 04(2010).DOI: 10.1520/E1790-04R16E01.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at servi
16、ceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States1intensities of absorption bands in specific wavelength ra
17、ngeswere used to confirm the presence of certain functional groups,the 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 f
18、acilitieslimited the scope of qualitative analysis severely. Furthermore,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 individ
19、ual absorp-tion bands is generally not used in the NIR.4.4 With the development 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 descr
20、ibed in this practice can be applied to spectral datain any region, this 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 o
21、fchemometric methods to spectroscopy has limitations, and thelimitations 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
22、regard,but neither would we expect it to be any worse. Since therelatively broad character of NIR bands makes it unlikely thata contaminant would not overlap any of the measuredwavelengths, the question would only be one of degree:whether a given amount of contaminant could be detected. Theuser must
23、 be aware of the probable contaminants he is liable torun into and account for the possibility of this occurring,perhaps by including deliberately contaminated samples in thetraining set.5. General5.1 NIR qualitative analysis is conducted by comparison ofNIR absorption spectra of unknown materials w
24、ith those ofknown reference materials. Since the absorption bands of 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 re
25、lative absorbances atdifferent wavelengths. Materials to be identified 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 orde
26、r to generate classificationcriteria, which can then be applied to 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. Th
27、e preparation and presen-tation of samples to the instrument should 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 ofachievin
28、g up to 100-m path lengths may be required. Spectra ofvapors and gases 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 inspe
29、ction). The training samples (that is, the “knowns”used to teach the 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 theappropri
30、ate library. The prescreening will help reduce thechance of false identification, 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 oftransmission, reflection, or any other opt
31、ical setup suitable forcollecting NIR spectra. In practice, only transmission anddiffuse reflection 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 for
32、identifying those materials requires the use of computerizedlearning 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 measuremen
33、ts in the NIR spectral region. Manufac-turers instructions should 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
34、 preparation steps in other spectro-scopic technologies are replaced 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
35、 form)and packed into a cup, which allows the surface of the sampleto 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 radiantenerg
36、y passing through the sample is reflected diffusely by thebacking 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 Probe
37、sIlluminating and collecting fibers arebrought in parallel to the 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 sa
38、mple end of the fiber to couple the radiation to thesample. Interactance measurements are sometimes made by theE1790 04 (2016)12simple 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 s
39、pectroscopy used,the procedure can be generally described as follows:(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
40、someregion in the multidimensional space.(3) The analysis involves 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 insufficientd
41、ifferences among the spectra of the materials involved),inadequate 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 pote
42、ntial prob-lem areas, generation of a method occurs in three stages. 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 materi
43、als.5.2.3 In the second, or validation stage, the ability of thealgorithm 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 trai
44、ning stage.5.2.4 In the third, or use stage, unknown samples arepresented 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 classified
45、as whichever material gives the closest agreement to the data.5.2.5 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
46、be identified during the use stage must be inthe same phase and 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 wi
47、th a given unknown, andbe specified as part of the method. While 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
48、 as the training samples. If asolution is used, the amount of dilution 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 li
49、brary, and that procedure should bespecified as part of the method.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
