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Near-infrared (NIR) spectroscopy is a branch of spectroscopy that shares many of the principles that apply to other spectroscopic measurements discussed in Spectrophotometry and Light-Scattering 851. The NIR spectral region includes two subranges. The short-wavelength or Herschel range extends from approximately 750 to 1100 nm (~13,333–9000 cm–1), while longer wavelengths between 1100–2500 nm comprise the traditional NIR region. In common with other spectrophotometric measurements, NIR is used for both qualitative and quantitative assessment of the chemical composition of samples. It may also be sensitive to physical properties of the sample. Measurements can be made directly on in-situ samples, in addition to standard sampling and testing procedures. Typical applications of NIR spectra utilize both wavelength and wavenumber units.
Vibrational spectroscopy in the NIR region is dominated by overtones and combinations that are much weaker than the fundamental mid-IR vibrations from which they originate. Because molar absorptivities in the NIR range are low, radiation typically penetrates several millimeters into materials, including solids. Furthermore, many materials such as glass are relatively transparent in this region. Fiber-optic technology is readily implemented in the NIR range, which allows monitoring of processes in inaccessible, remote, and challenging environments.
The tests and criteria given in this chapter may not be appropriate for all instrument configurations, particularly on-line process analytical technology measurements. In such cases, alternative instrument qualification and performance checks should be scientifically justified.
Transmittance and Reflectance
Two different measurements commonly performed in the NIR spectral range are transmittance and reflectance.
TRANSMITTANCE, T, measures the decrease in radiation intensity as a function of wavelength when radiation is passed through the sample. The sample is placed in the optical beam between the source and the detector. This arrangement is similar to many conventional spectrophotometers, and the results can be presented directly in terms of absorbance. A variation of this technique, transflectance, places a reflector behind the sample so as to double the path length. This configuration can be adapted to share the same instrument geometry with reflectance or fiber-optic probe systems where the source and the detector are on the same side of the sample.
REFLECTANCE, R, measures the ratio of the intensity of light reflected from the sample, I, to that reflected from a background or reference reflective surface, Ir . NIR radiation can penetrate a substantial distance into the sample, where it can be absorbed by the vibrational combinations and overtones of the analyte species present in the sample. Nonabsorbed radiation is reflected back from the sample to the detector. NIR reflectance spectra are accessed by calculating and plotting log (1/R) versus wavelength. This logarithmic form is commonly called absorbance.
Factors Affecting Quantitation
The following list, although not exhaustive, includes many of the major factors affecting spectral response.
Sample Temperature— This parameter is most important for aqueous solutions, where a difference of a few degrees can result in significant spectral changes. Temperature is also an important parameter for solids and powders containing water. Various methods exist to calculate the appropriate temperature correction.
Moisture and Residual Solvents— Moisture present in the sample and analytical system will contribute to bands in the NIR region. Other residual solvents may also contribute to the spectrum.
Sample Thickness— Sample thickness is a known source of spectral variability and must be understood and/or controlled. For example, in reflectance, the sample must be “infinitely” thick, or thinner samples of constant thickness must have a stable, diffusely reflecting backing material of constant, and preferably high, reflectivity.
Sample Optical Properties— In solids, both surface and bulk scattering properties of calibration standards and analytical samples must be taken into account. Spectra of physically, chemically, or optically heterogeneous samples may require sample averaging by increasing the beam size or examining multiple samples. Certain factors, such as differing degree of compaction or particle size in powdered materials and surface finish of samples, can cause significant spectral differences.
Polymorphism— Because NIR reflectance can be measured directly for solid crystalline substances, variations in crystalline structure (polymorphism) influence the spectra. Hence, polymorphs, as well as the amorphous form of a solid, may be distinguished from one another on the basis of their NIR spectra. Where multiple polymorphs can coexist in an otherwise chemically pure bulk drug substance, care must be taken to ensure that the calibration standards have a distribution of polymorphs relevant to the intended application.
Age of Samples— Samples may exhibit changes in their chemical, physical, or optical properties over time. Care must be taken to ensure that samples for NIR analysis are representative of those used for calibration. If samples of different age are to be analyzed, potential differences in properties must be accounted for in the calibration sample set.
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All NIR measurements are based on passing light radiation through or into a sample and measuring the attenuation of the emerging (transmitted, scattered, or reflected) beam. There are a variety of spectrophotometers available based on different operating principles.
Some examples of currently available spectrophotometers are the following: filter and grating-based dispersive, acousto-optical tunable filter (AOTF), Fourier-transform (FT-NIR), and liquid crystal tunable filters (LCTF) systems. Silicon, lead sulfide, indium gallium arsenide and deuterated triglycine sulphate are commonly used detector materials. Conventional cuvette sample holders, fiber-optic probes, transmission dip cells, and spinning or traversing sample holders are some of the more common sampling arrangements.
The selection of the equipment should be based on the intended application, with particular attention being paid to the suitability of the sampling device for the type of sample to be analyzed.
Near-Infrared Reflectance References
NIR references, by providing a known stable measurement against which other measurements can be compared, are used to eliminate instrumental variations that would affect the measurements.
Transmittance Mode— The measurement of transmittance is dependent on a background transmittance spectrum for its calculation. A transmittance reference can be air, an empty cell, a solvent blank, or in special cases, a reference sample.
Reflectance Mode— The measurement of reflectance is dependent on a background reflectance spectrum for its calculation. Most measurements are performed in single-beam instruments; the reflectance of a background reference is scanned to obtain a baseline, and then the reflectance of one or more analytical samples is measured. Common reflectance references are ceramic, perfluorinated polymers, and gold; other suitable materials may be used. Only spectra measured against a background possessing the same optical properties can be directly compared with one another.
Qualification of NIR Instruments
Elements of Qualification— The qualification of an NIR instrument can be divided into three elements:
  • Installation Qualification (IQ)
  • Operational Qualification (OQ)
  • Performance Qualification (PQ)
Installation Qualification— The IQ requirements help ensure that the hardware and software are installed according to vendor and safety specifications at the desired location.
Operational Qualification— In operational qualification, the instrument's performance is controlled with respect to external certified standards to verify that the system operates within target specifications. The purpose of operational qualification is to ensure that an instrument is suitable for its intended application. Because there are so many different approaches to measuring NIR spectra, operational qualification with traceable external standards that can be used on any instrument is desired. The most important property of a reference material is its stability. For example, the commonly employed internal polystyrene-film reference may be subject to aging and attack by solvents and vapors in the laboratory environment. The use of external traceable reference standards does not imply the omission of the instrument's internal quality control procedures. Similiar to any spectrophotometric device, NIR instruments need to be qualified for both wavelength and photometric scale. Maximum and reduced light-flux noise tests are also included.
Peformance Qualification— In performance qualification, a quality of fit to an initial scan or group of scans included in the operational qualification is employed. In such an analysis, it is assumed that reference standard spectra collected on a new or a newly repaired, properly operating instrument represent the best ones available. Comparisons of spectra taken over time on the identical reference standards form the basis for evaluating the long-term stability of an NIR measurement system. The objective is to ensure that no wavelength calibration shift or change in sensitivity occurs during ongoing analysis.
Previous operational qualification has shown that the equipment is acceptable for use; therefore, a single performance qualification standard can be used to reverify performance on a continuing basis. The user may have a method-specific reference sample to perform this kind of control, provided the sample is stable.
Test Details— The specific tests and how frequently they are performed for each level of qualification is dependent on the instrument and intended application.
Wavelength Uncertainty— Potential problems with internal calibration schemes are avoided by specifying appropriate independent external wavelength standards. For the reflectance mode, USP Near-Infrared Calibrator RS1 USP29 and NIST SRM 20352 used in the transflectance mode are available. The nature and type of background reference standard must be specified. In transmittance measurements, NIST SRM 2035 rare earth oxide in glass standard, or NIST SRM 2036,3 USP29 is available. Alternative standards may be used with appropriate justification.
Take one spectrum (with the same spectral resolution used to obtain the certified value) and measure the position of at least three peaks to cover the entire available range. The acceptance limits for USP Near-Infrared Calibrator RSUSP29 are reported in Table 1.
Table 1.Recommended Near-IR Instrument Specificationsa
Wavelength Uncertainty USP Near-Infrared Calibrator RSUSP29 peaksb occur at 1261, 1681, and 1935 nm
Tolerances ±1 nm at 1200 nm or ±8 cm1 at 8300 cm1
±1 nm at 1600 nm or ±4 cm1 at 6250 cm1
±1.5 nm at 2000 nm or ±4 cm1 at 5000 cm1
Photometric Linearity AOBS vs AREF at 1200, 1600, and 2000 nm;c
slope = 1.0 ± 0.05; intercept = 0.0 ± 0.05
Spectrophotometric Noise measured for 100-nm (300 cm1) segments between 1200 and 2200 nm (8300 and 4500 cm1)
Average RMS for measurements
at high-light flux
less than 0.3 × 103; no RMS noise greater than 0.8 × 103
Average RMS for measurements
at low-light flux
less than 1 × 103; no RMS noise greater than 2.0 × 103
a  A maximum nominal instrument bandwidth of 10 nm at 2500 nm or 16 cm1 at 4000 cm1 is appropriate for most applications.
b  The nominal 1935-nm peak is sensitive to instrument bandwidth. Use the wavelength value supplied with USP Near-Infrared Calibrator RSUSP29 at the appropriate instrument bandwidth to determine wavelength uncertainty.
c  AOBS is the observed absorbance, and AREF is the tabulated absorbance of the reference reflectors at each of the three specified wavelengths.
Photometric Linearity— Verification of photometric linearity is demonstrated with a set of transmission standards of known relative transmittance or reflectance standards of known relative reflectance, usually expressed as percent transmittance or reflectance. For reflectance measurements, traceable carbon-doped polymer standards are available. Spectra obtained from reflectance standards are subject to variability as a result of the difference between the experimental conditions under which they were factory-calibrated and those under which they are subsequently put to use. Hence, the percent reflectance values supplied with a set of calibration standards may not be useful in the attempt to establish an “absolute” calibration for a given instrument. Provided that (1) the standards do not change chemically or physically, (2) the same reference background is used as was used to obtain the certified values, and (3) the instrument measures each standard under identical conditions (including precise sample positioning), the reproducibility of the photometric scale will be established over the range of standards used. Subsequent measurements on the identical set of standards give information on long-term stability. Use at least four reference standards in the range 10% to 90%. [NOTE—A typical set of four reflectance references might be 10%, 20%, 40%, and 80% with 1.0, 0.7, 0.4, and 0.1 as their respective absorbances.] If the system is used for analytes with absorbances higher than 1.0, add a 2% or a 5% standard, or both, to the set. The specifications are reported in Table 1.
Spectrophotometric Noise— NIR instrument software may include built-in procedures to automatically determine system noise and to provide a statistical report of noise or signal-to-noise ratio over its operating range. As previously discussed, it is desirable to supplement such checks with measurements that do not rely directly on manufacturer-supplied procedures. If the qualification procedures in the NIR software do not comply with the contents of this chapter, it is recommended to supplement such checks with measurements that do not rely directly on manufacturer-supplied procedures. The method involves measuring spectra of high- and low-reflectance traceable reference materials. For transmittance modules there are no standards for the low-flux noise test at this time, so it is only possible to perform the high-flux noise test.
HIGH-FLUX NOISE— The instrument noise is evaluated at high-light flux by measuring reflectance or transmittance of the reference standard, with the reference material (e.g., 99%, reflectance standard) acting as both the sample and the background reference. The analysis is performed by tabulating RMS noise levels in successive nominal 100-nm (300 cm–1) spectral segments across the instrument’s range. The limits are reported in Table 1.
LOW-FLUX NOISE— The same procedure may be used with a lower-reflectivity reference material (e.g., 10% reflectance standard) to determine system noise at reduced light flux. The source, optics, detector, and electronics make significant contributions to the noise under these conditions. The limits are reported in Table 1.

The objective of the validation of an NIR method, as in the case with the validation of any analytical procedure, is to demonstrate that it is suitable for its intended purpose. Quantitation by NIR is performed by reference to data obtained from a primary method or a calibration set of samples having known composition.
Although NIR is somewhat different from conventional analytical techniques such that validation is generally achieved through the assessment of specialized chemometric parameters, these parameters can still be related to the fundamental validation characteristics required for any analytical method.
Data pretreatment is often a vital step in the chemometric analysis of NIR spectral data. It can be defined as the mathematical transformation of the NIR spectral data to enhance spectral features and/or remove or reduce unwanted sources of variation prior to the development of the calibration model. Calibration is the process of constructing a mathematical model to relate the response from an analytical instrument to the properties of samples. Many suitable chemometric algorithms for data pretreatment and calibration exist; the selection should be based on suitability for the intended use. Any available data transformation or algorithm that can be clearly defined in an exact mathematical expression and gives suitable results can be used.
Validation Parameters
Analytical performance characteristics that should be considered for demonstrating the validation of NIR methods are similar to those required for any analytical procedure. A discussion of the general principles that apply is found in Validation of Compendial Methods 1225. These principles should be considered typical for NIR procedures, but exceptions should be dealt with on a case-by-case basis. For qualitative NIR methods, refer to Analytical Performance Characteristics for Category IV assays under Validation of Compendial Methods 1225; quantitative NIR methods will correspond to the Analytical Performance Characteristics for Category I and Category II assays in the chapter. Specific acceptance criteria for each validation parameter must be consistent with the intended use of the method. The samples used for validation should be independent of the calibration set.
Specificity— The extent of specificity testing is dependent on the intended application. Lack of specificity of the NIR method can be compensated by other supporting analytical procedures.
Demonstration of specificity in NIR methods may be accomplished by using the following approaches:
  • Potential challenges should be presented to the spectral reference library. These can be materials received on site that are similar to library members in visual appearance, chemical structure, or by name. These challenges must fail identification. Independent samples of materials represented in the library, but not used to create it (i.e., different batches, blends), must give positive identifications when analyzed.
  • Wavelengths used in the calibration model can be compared to the known bands of the analyte of interest and those of the matrix to verify that the bands of the analyte of interest are being used in the calibration.
  • Wavelengths used for the calibration (e.g., for multiple linear regression models (MLR) or the loadings for the factors used (e.g., for partial least squares [PLS] or principal component regression [PCR] models) can be examined to verify that the actual spectroscopic information results from the analyte of interest.
  • For PLS and PCR calibrations, the coefficients can be plotted and the regions of large coefficients compared with the spectrum of the analyte.
  • Variations in the sample matrix may be shown to have no significant effect on quantitation of the analyte within the specified method range.
Linearity— The validation of NIR linearity involves the demonstration of correlated NIR response to samples distributed throughout the defined range of the calibration model.
Demonstration of linearity in NIR methods may be accomplished using the following approaches:
  • The slope and y-intercept (bias) for the predicted validation set can be used together with a plot of the data. Many statistical methods are available for evaluation of the significance of the slope and bias. Other applicable statistics may be used as appropriate.
  • Statistical tests such as Durbin–Watson are available for the determination of linearity. Other applicable statistics may be used as appropriate.
The correlation coefficient, r, is not a true measure of linearity, but is rather a measure of the fraction of variation in the data that is adequately modeled by the equation. It is dependent on the standard error of the calibration equation (and hence the reference method) and on the range of the calibration data.
Range— The range of analyte reference values in the validation set defines the range of the NIR method. The range of analyte reference values also effectively defines the quantitation limits for an NIR method. Controls must be in place to ensure that results outside the validated range are not accepted. In certain circumstances, it may not be possible or desirable to extend the validated range to cover the specifications or expected process variability for the entire life cycle of the process. Examples of situations in which only a limited sample range may be available are samples from a controlled manufacturing process and in-process samples. A limited sample set does not preclude the use of an NIR method.
The validation procedure for a quantitative NIR method should generate an outlier result when a sample containing analyte outside of the calibration range is measured. This outlier result does not necessarily indicate an out-of-specification result. An outlier result from the NIR measurement indicates that further testing of the sample is required. If subsequent testing of the sample by an appropriate method indicates that the analyte content is within specifications, then the sample should be considered to have met those specifications. Thus, measurement of a sample by NIR may generate an outlier result and the sample may still meet specifications for the analyte of interest. For qualitative methods, a batch of material may be outside the space of the original calibration data and fail the identification specification. Acceptable identification of the material must then be established by other appropriate methods.
Accuracy— Accuracy for NIR methods is demonstrated by correlation of NIR results with analytical reference data.
Demonstration of accuracy in NIR methods may be accomplished using the following approaches:
  • Accuracy can be indicated by how close the standard error of prediction (SEP) is to the standard error of the reference method used for validation. The error of the reference method may be known on the basis of historical data, or a determination of standard error of the laboratory (SEL) may be carried out.
  • Several statistical comparison methods can be applied to the predicted validation set and reference values to determine if there is any statistical difference between the results of each method at a specified confidence limit (e.g., paired t-test, bias evaluation).
Precision— Precision of an NIR method expresses the closeness of agreement between a series of measurements under the prescribed conditions. There are two levels of precision that may be considered: repeatability and intermediate precision. The precision of an NIR method is typically expressed as the relative standard deviation of a series of predictions and should be equivalent or better than the precision of the reference method used for validation.
Demonstration of precision in NIR methods may be accomplished using the following approaches:
  • Statistical evaluation of a number of replicate measurements of the same sample without variation in sample position.
  • Statistical evaluation of multiple sample positionings or aliquots, as appropriate.
Intermediate Precision
  • Statistical evaluation of a number of replicate measurements by different analysts on different days.
Robustness— The challenges performed in this category will vary depending on the application and sampling technique. Some of the challenges may be covered as part of the development of the method.
Typical challenges are the following:
  • Effect of environmental conditions (e.g., temperature, humidity)
  • Effect of sample temperature
  • Sample handling (e.g., probe depth, compression of material, sample depth/thickness, sample position)
  • Influence of instrument changes (e.g., lamp change, warm-up time)
Ongoing Model Evaluation
NIR models validated for use should be the subject of ongoing performance evaluation, which may include the monitoring of accuracy, precision, or other suitable parameters. If unacceptable performance is indicated, corrective action will be necessary. This will involve initial investigations into the cause of the discrepancy and may indicate that the calibration model is not performing satisfactorily. Maintenance of the model will then be required and may involve revalidation of the model. The degree of revalidation required depends on the nature of the changes. Appropriate change controls should be established to cover these procedures.
Revalidation of a qualitative model may be necessary as a result of the following:
  • Addition of a new material to the spectral reference library
  • Changes in the physical properties of the material
  • Changes in the source of supply
  • Coverage of a wider range of characteristics of a material
Revalidation of a quantitative model may be necessary as a result of the following:
  • Changes in the composition of the finished product
  • Changes in the manufacturing process
  • Changes in the sources or grades of raw materials
  • Changes in the reference analytical method
  • Major changes to the instrument hardware
Model Transfer
The model for an NIR method is developed, stored and applied in electronic form as part of an appropriate instrument/software package. When a model is transferred to another instrument, procedures and criteria must be applied to demonstrate that the model remains valid on the second instrument. In general, electronic model transfer is only recommended for another instrument of the same type and configuration. A number of model transfer procedures exist and can be applied as appropriate. Procedures involve the use of various chemometric (mathematical and statistical) approaches with appropriate validation.

ABSORBANCE, A, is represented by the equation:
A = –log T = log (1/T) or A = log (I/R)
in which T and R are the transmittance and the reflectance, respectively.
BACKGROUND SPECTRUM is also referred to as a reference spectrum or background reference. A ratio of this spectrum to that of the sample radiation intensities produces a transmittance or reflectance spectrum. For example, in reflectance measurements, a highly reflective standard reference material is used.
CALIBRATION MODEL is a mathematical expression to relate the response from an analytical instrument to the properties of samples.
DIFFUSE REFLECTANCE is that portion of radiated light penetrating the sample surface, interacting with the analyte material, and being reflected back to the detector. This is the component of the overall reflectance that produces the absorbance spectrum of the sample.
DURBIN–WATSON is a method of testing the linearity of a calibration by comparing the sum of squares of successive calibration residuals to the sum of squares of the calibration residuals around their mean. The expected value of the Durbin–Watson statistic for random, independent, normally distributed residuals is two.
FIBER-OPTIC PROBES consist of two components: optical fibers, which may vary in length and in the number of fibers, and a terminus, which contains specially designed optics for examination of the sample matrix.
INSTRUMENT BANDWIDTH is a measure of the ability of a spectrometer to separate radiation of similar wavelengths.
MULTIPLE LINEAR REGRESSION is a calibration algorithm used to relate the response from an analytical instrument to the properties of samples. The distinguishing feature of this algorithm is the use of a limited number of independent variables. Linear-least-squares calculations are performed to establish a relationship between these independent variables and the properties of the samples.
OPERATIONAL QUALIFICATION is the process by which it is demonstrated and documented that the instrument performs according to specifications, and that it can perform the intended task. This process is required following any significant change such as instrument installation, relocation, major repair, etc.
PARTIAL LEAST SQUARES (PLS) is a calibration algorithm used to relate instrument responses to the properties of samples. The distinguishing feature of this algorithm is that, while similar to PCR, this algorithm includes data concerning the properties of the samples used for calibration in the calculation of the factors used to describe the instrument responses.
PERFORMANCE QUALIFICATION is the process of using one or more well-characterized and stable reference materials to verify consistent instrument performance. Qualification may employ the same or different standards for different performance characteristics.
PHOTOMETRIC LINEARITY, also referred to as photometric verification, is the process of verifying the response of the photometric scale of an instrument.
PRINCIPAL COMPONENT REGRESSION (PCR) is a calibration algorithm used to relate the response from an analytical instrument to the properties of samples. This algorithm, which expresses a set of independent variables as a linear combination of factors, is a method of relating those factors to the properties of the samples for which the independent variables were obtained.
REFERENCE SPECTRUM—See Background Spectrum.
REFLECTANCE is described by the equation:
R = I/Ir
in which I is the intensity of radiation reflected from the surface of the sample; and Ir is the intensity of radiation reflected from a background reference material and its incorporated losses due to solvent absorption, refraction, and scattering.
ROOT-MEAN-SQUARE (RMS) NOISE is calculated by the equation:
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in which N is the number of points per segment; Ai is the absorbance for each data point; and bar(A) is the mean absorbance over the spectral segment.
SPECTRAL REFERENCE LIBRARY is a collection of spectra of known materials used for the purpose of comparison with unknown materials. The term is commonly used in connection with qualitative methods of spectral analysis (e.g., identification of materials).
STANDARD ERROR OF THE LABORATORY (SEL) is a calculation based on repeated readings of one or more samples to estimate the precision and/or accuracy of the reference laboratory method, depending on how the data was collected.
STANDARD ERROR OF PREDICTION (SEP) is a measure of accuracy of an analytical method based on applying a given calibration model to the spectral data from a set of samples different from but similar to those used to calculate the calibration model. The SEP is the standard deviation of the residuals obtained from comparing the values from the reference laboratory to those from the method under test, for the specified samples. The SEP provides a measure of the accuracy expected when measuring future samples.
SURFACE REFLECTANCE, also known as specular reflection, is that portion of the radiation not interacting with the sample but simply reflecting back from the sample surface layer (sample-air interface).
TRANSFLECTANCE is a transmittance measurement technique in which the radiation traverses the sample twice, the second time after being reflected from a surface behind the sample.
TRANSMITTANCE is represented by the equation:
T = I/I0 or T = 10A
in which I is the intensity of the radiation transmitted through the sample; I0 is the intensity of the radiant energy incident on the sample and includes losses due to solvent absorption, refraction, and scattering; and A is the absorbance.

1  USP Near-Infrared Calibrator RS is a mixture of dysprosium, holmium, erbium, and talc and may be obtained from USP. “Accurate Wavelength Measurements of a Putative Standard for Near-Infrared Diffuse Reflection Spectrometry,” by Tomas Isaksson, Husheng Yang, Gabor J. Kemeny, Richard S. Jackson, Qian Wang, M. Kathleen Alam, and Peter R. Griffiths (Department of Chemistry, University of Idaho, Moscow, Idaho 83844-2343, USA) Appl Spectrosc 2003, 57(2) 176–185. This reference material exhibits peaks in both the 700- to 1100-nm and 1100- to 2500-nm ranges.
2  SRM 2035, a rare earth oxide in glass, is intended for transmission wavelength qualification and has been certified recently by NIST. This standard consists of samarium, ytterbium, holmium, and neodymium rare earth oxide (REO).USP29 “Production and Verification of SRM 2035. Near Infrared Transmission Wavelength Standard”, NIST Special Publication 1999, 260-102 (in preparation). This standard may be used in transflectance mode, but it is not currently certified for such use.
3  SRM 2036, a rare earth oxide in glass, is intended for NIR diffuse reflectance wavelength qualification and is available from NIST. This standard also consists of samarium, ytterbium, holmium, and neodymium rare earth oxide (REO). In addition, SRM 2036 contains a piece of sintered polytetrafluoroethylene (PTFE) that provides a nearly ideal diffuse reflector.

Auxiliary Information—
Staff Liaison : Gary E. Ritchie, M.Sc., Scientific Fellow
Expert Committee : (GC05) General Chapters 05
USP29–NF24 Page 2979
Pharmacopeial Forum : Volume No. 30(6) Page 2137
Phone Number : 1-301-816-8353