ASTM E1790-04(2016)e1

This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the
Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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Designation: E1790 − 04 (Reapproved 2016)
Standard Practice for
Near Infrared Qualitative Analysis
This standard is issued under the fixed designation E1790; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
ε NOTE—Editorial change was made in Subsection 6.6.3 in April 2016.
1. Scope 3. Terminology
1.1 This practice covers the use of near-infrared (NIR) 3.1 Definitions—For definitions of general terms and sym-
spectroscopy for the qualitative analysis of liquids and solids. bols pertaining to NIR spectroscopy and statistical
The practice is written under the assumption that most NIR computations, refer to Terminology E131.
qualitative analyses will be performed with instruments de- 3.2 Definitions of Terms Specific to This Standard:
signed specifically for this region and equipped with comput-
3.2.1 interactance, n—the phenomenon whereby radiant
erized data handling algorithms. In principle, however, the energy entering the surface of a material is scattered by the
practice also applies to work with liquid samples using
material back to the surface, but at a different portion of the
instruments designed for operation over the ultraviolet (UV), surface.
visible, and mid-infrared (IR) regions if suitable data handling
3.2.1.1 Discussion—This differs from diffuse reflectance,
capabilities are available. Many Fourier Transform Infrared
where the returning radiation exits the same portion of the
(FTIR) (normally considered mid-IR instruments) have NIR
surface of the material as the illuminating radiation entered.
capability, or at least extended-range beamsplitters that allow
3.2.2 training sample (otherwise called a “reference
operation to 1.2 µm; this practice also applies to data from
sample” or “standard”), n—a quantity of material of known
these instruments.
composition or properties, or both, presented to an instrument
1.2 The values stated in SI units are to be regarded as for measurement in order to find relationships between the
standard. No other units of measurement are included in this measurements and the composition or properties, or both, of
standard. the sample.
3.2.2.1 Discussion—This term is typically used in conjunc-
1.3 This standard does not purport to address all of the
tion with computerized methods for ascertaining the relation-
safety concerns, if any, associated with its use. It is the
ships.
responsibility of the user of this standard to establish appro-
Training samples for quantitative analysis (also called
priate safety and health practices and determine the applica-
“calibration samples,” as in Practices E1655) have different
bility of regulatory limitations prior to use.
requirements than training samples used for qualitative
analysis.
2. Referenced Documents
2.1 ASTM Standards:
4. Significance and Use
E131 Terminology Relating to Molecular Spectroscopy
4.1 NIR spectroscopy is a widely used technique for quan-
E1252 Practice for General Techniques for Obtaining Infra-
titative analysis, and it is also becoming more widely used for
red Spectra for Qualitative Analysis
the identification of organic materials, that is, qualitative
E1655 Practices for Infrared Multivariate Quantitative
analysis. In general, however, the concept of qualitative analy-
Analysis
sis as used in the NIR spectral region differs from that used in
the mid-IR spectral region in that NIR qualitative analysis
refers to the process of automated comparison of the spectra of
This practice is under the jurisdiction of ASTM Committee E13 on Molecular
unknown materials to the spectra of known materials in order
Spectroscopy and Separation Science and is the direct responsibility of Subcom-
mittee E13.11 on Multivariate Analysis.
to identify the unknown. This approach constitutes a library
Current edition approved April 1, 2016. Published June 2016. Originally
search method in which each user generates his own library.
approved in 1996. Last previous edition approved in 2010 as E1790 – 04(2010).
DOI: 10.1520/E1790-04R16E01.
4.2 Historically, NIR spectroscopy as practiced with classi-
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
cal UV-VIS-NIR instruments using methods similar to those
contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
described in Practice E1252 was not considered to be a strong
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. technique for qualitative analysis. Although the positions and
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
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E1790 − 04 (2016)
intensities of absorption bands in specific wavelength ranges 5.1.1 The technique is applicable to liquids, solids, and
were used to confirm the presence of certain functional groups, gases. For analysis of gases, multipath vapor cells capable of
the spectra were not considered to be specific enough to allow
achievingupto100-mpathlengthsmayberequired.Spectraof
unequivocal identification of unknown materials.Afew impor-
vapors and gases may be sensitive to the total sample pressure,
tant libraries of NIR spectra were developed for qualitative
and this has to be determined for each type of sample.
purposes, but the lack of suitable data handling facilities
5.1.2 Unknownsamplestobeidentifiedmaybeprescreened
limited the scope of qualitative analysis severely. Furthermore,
based on criteria other than their NIR spectra (for example,
earlier work was limited almost entirely to liquid samples.
visual inspection). The training samples (that is, the “knowns”
4.3 Currently, the mid-IR procedure of deducing the struc- used to teach the algorithm what different materials look like)
ture of an unknown material by method of analysis of the may also be similarly prescreened and grouped into libraries of
locations, strengths, and positional shifts of individual absorp-
similar materials (for example, liquids and solids). The un-
tion bands is generally not used in the NIR.
known is then compared with only those materials in the
appropriate library. The prescreening will help reduce the
4.4 With the development of specialized NIR instruments
chance of false identification, although care must be taken that
and mathematical algorithms for treating the data, it became
an unknown material not in the library is not identified as a
possible to obtain a wealth of information from NIR spectra
similar material that is in the library.
that had hitherto gone unused. While the mathematical algo-
rithms described in this practice can be applied to spectral data
5.1.3 Measurements may be made by method of
in any region, this practice describes their application to the
transmission, reflection, or any other optical setup suitable for
NIR.
collecting NIR spectra. In practice, only transmission and
diffuse reflection have been in common use.
4.5 The application of NIR spectroscopy to qualitative
analysis in the manner described is relatively new, and proce-
5.1.4 Determination of the relationships between absor-
dures for this application are still evolving. The application of
bances at different wavelengths for a set of materials and
chemometric methods to spectroscopy has limitations, and the
consolidation of these relationships into a set of criteria for
limitations are not all defined yet since the techniques are
identifying those materials requires the use of computerized
relatively new. One area of concern to some scientists is the
learning algorithms. These algorithms can also take into
effect of low-level contaminants. Any analytical methodology
account extraneous variations such as are found, for example,
has its detection limits, and NIR is no different in this regard,
when measurements are made on powdered solids.
but neither would we expect it to be any worse. Since the
5.1.5 Instrumentation is commercially available for making
relatively broad character of NIR bands makes it unlikely that
suitable measurements in the NIR spectral region. Manufac-
a contaminant would not overlap any of the measured
turer’s instructions should be followed to ensure correct
wavelengths, the question would only be one of degree:
operation,optimumaccuracy,andsafetybeforecollectingdata.
whether a given amount of contaminant could be detected.The
5.1.6 NIR spectroscopy has, as one of its paradigms, that
user must be aware of the probable contaminants he is liable to
little or no sample preparation be required. In conformance
run into and account for the possibility of this occurring,
with that paradigm, sample preparation steps in other spectro-
perhaps by including deliberately contaminated samples in the
scopic technologies are replaced with sample presentation
training set.
methodologies in NIR analysis. The most common sample
5. General
presentation methods are the following:
5.1 NIR qualitative analysis is conducted by comparison of
5.1.6.1 Diffuse Reflectance—Solid materials are ground into
NIR absorption spectra of unknown materials with those of
powder (or used as-is, if already in suitably fine powder form)
knownreferencematerials.Sincetheabsorptionbandsofmany
and packed into a cup, which allows the surface of the sample
substances of interest are less distinctive in the NIR than in the
to be illuminated and the reflected radiant power measured.
mid-IR spectral region, the analytical capability of the tech-
5.1.6.2 “Transflectance”—Clear or scattering liquids are
nique relies heavily on the accuracy of the absorption mea-
placed in a cup containing a transparent window with a
surements and the relationship of the relative absorbances at
diffusely reflecting material behind the sample. Any radiant
different wavelengths. Materials to be identified are measured
energy passing through the sample is reflected diffusely by the
by a NIR spectrometer, and the spectral data thus generated are
backingmaterial,sothenetmeasurementisjustlikethediffuse
saved in an auxiliary computer attached to the spectrometer
reflectance measurement of powdered solids.
proper. One of the several algorithms described in Section 6 is
5.1.6.3 Transmission—Liquids or solids are placed in cells
then applied to the data in order to generate classification
with two transparent windows and measured by transmission.
criteria, which can then be applied to data from unknown
5.1.6.4 Fiber Probes—Illuminating and collecting fibers are
samples in order to classify (or identify) them as being the
brought in parallel to the sample. A variety of optical configu-
same as one of the previously seen materials. Good chemical
rations are used to couple the radiant energy from the fibers to
laboratory practice should be followed to help ensure repro-
the sample and back again, in an optical “head” of some sort.
ducible results for each material. The preparation and presen-
tation of samples to the instrument should be consistent within Transmittance, reflectance, and interactance have all been used
at the sample end of the fiber to couple the radiation to the
a library, and unknowns should be treated the same way that
the training samples were. sample.Interactancemeasurementsaresometimesmadebythe
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E1790 − 04 (2016)
simple expedient of pressing the end of a fiber bundle materials in a given library, and that procedure should be
containing mixed illuminating and receiving fibers against the specified as part of the method.
sample surface.
5.3.3 The unknowns must also be treated in the same
manner as the training samples. It is particularly important that
5.2 To connect the mathematics with the spectroscopy used,
if the samples must be ground, the unknown samples should be
the procedure can be generally described as follows:
groundtothesameparticlesizeastheknownsamplesincluded
(1) The spectral measurements define some multidimen-
in the library.
sional space. The axes in that space are the absorbances at the
various wavelengths, or some mathematical transformation
6. Algorithms Used
thereof.
(2) Groups of spectra for the same material define some
6.1 This section describes some of the computerized algo-
region in the multidimensional space.
rithms that have been found effective for qualitative analysis in
(3) The analysis involves determining which region the
the NIR spectral region. This section is mainly for reference.
unknown falls in.
Descriptionsofmultivariatemethodsofstatisticaldataanalysis
5.2.1 Problems with this type of analysis include the fol-
tend to be inherently abstract mathematically and resistant to
lowing:insufficientseparationofthegroupsinthemultidimen-
reduction to words. A number of books exist in both the
sional space to allow for classification (indicating insufficient
statistical and chemometric literature that describe methods of
differences among the spectra of the materials involved),
multivariate analysis at varying levels of mathematical abstrac-
inadequate representation of measurement variability within
tion (see, for example, Refs (1-5), a useful starting point but
groups during training (indicating an insufficient number or
far from exhaustive list); most of the algorithms used for NIR
variety of training samples), or poor detection limits for minor
qualitative analysis are relatively straightforward applications
contaminants.
of these methods.
5.2.2 To optimize the methods against these potential prob-
6.1.1 Implementations of these algorithms are available in
lem areas, generation of a method occurs in three stages. In the
standard generic statistical software packages. Software pro-
first, or training stage, known samples are presented to the
grams designed for analysis of spectroscopic data may also
instrument. The data collected are then presented to one of the
contain implementations of these algorithms. In addition, the
various algorithms and are thus used to “train” the algorithm to
manufacturers of modern NIR spectrometers include imple-
recognize the various different materials.
mentations of these algorithms in their proprietary software
5.2.3 In the second, or validation stage, the ability of the
packages that run on the auxiliary computers supplied with the
algorithm to correctly recognize materials not in the training
spectrometers; this approach has the advantage that the soft-
set of samples is tested. Samples measured during the valida-
ware matches the format and nature of t
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