Near-infrared spectroscopy and imaging: Basic principles

Transcription

Advanced Drug Delivery Reviews 57 (2005) 1109 – 1143
www.elsevier.com/locate/addr
Near-infrared spectroscopy and imaging: Basic principles
and pharmaceutical applications
Gabriele ReichT
Institute for Pharmacy and Molecular Biotechnology, Department of Pharmaceutical Technology and Pharmacology,
University of Heidelberg, Im Neuenheimer Feld 366, D-69120 Heidelberg, Germany
Received 17 December 2003; accepted 19 January 2005
Abstract
Near-infrared (NIR) spectroscopy and imaging are fast and nondestructive analytical techniques that provide chemical and
physical information of virtually any matrix. In combination with multivariate data analysis these two methods open many
interesting perspectives for both qualitative and quantitative analysis. This review focuses on recent pharmaceutical NIR
applications and covers (1) basic principles of NIR techniques including chemometric data processing, (2) regulatory issues, (3)
raw material identification and qualification, (4) direct analysis of intact solid dosage forms, and (5) process monitoring and
process control.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Noninvasive qualitative and quantitative analysis; Calibration and validation; Chemometrics; Raw material identification and
characterization; Quality control of intact dosage forms; Process analytical technologies (PAT); Process monitoring
Contents
1.
2.
3.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Basic principles of near-infrared (NIR) spectroscopy . . . . . . . . . .
2.1. Origin and characteristics of NIR absorption bands . . . . . . .
2.2. Instrumentation and sample presentation . . . . . . . . . . . .
Theory and practice of chemometric data processing. . . . . . . . . .
3.1. Data pretreatments . . . . . . . . . . . . . . . . . . . . . . . .
3.2. Reduction of variables by principal component analysis (PCA) .
3.3. Multivariate calibration for quantitative analysis . . . . . . . .
3.4. Multivariate classification for qualitative analysis . . . . . . . .
T Tel.: +49 6221 548335; fax: +49 6221 545971.
E-mail address: gabriele.reich@urz.uni-heidelberg.de.
0169-409X/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.addr.2005.01.020
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G. Reich / Advanced Drug Delivery Reviews 57 (2005) 1109–1143
4.
Regulatory aspects. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1. Actual status of pharmaceutical NIR analysis . . . . . . . . . . .
4.2. NIR spectroscopy in view of the U.S.F.D.A. initiative on PAT . .
5. Pharmaceutical applications . . . . . . . . . . . . . . . . . . . . . . . .
5.1. Identification and qualification of raw materials and intermediates
5.1.1. Library approach . . . . . . . . . . . . . . . . . . . . .
5.1.2. Conformity approach . . . . . . . . . . . . . . . . . . .
5.1.3. Quantitative calibration models . . . . . . . . . . . . . .
5.2. Analysis of intact dosage forms . . . . . . . . . . . . . . . . . .
5.2.1. Tablets. . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.2. Capsules . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.3. Lyophilized products . . . . . . . . . . . . . . . . . . .
5.2.4. Polymeric implants and microspheres . . . . . . . . . . .
5.3. Process monitoring and process control . . . . . . . . . . . . . .
5.3.1. Powder blending . . . . . . . . . . . . . . . . . . . . .
5.3.2. Drying. . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.3. Granulation . . . . . . . . . . . . . . . . . . . . . . . .
5.3.4. Pelletization . . . . . . . . . . . . . . . . . . . . . . . .
5.3.5. Tabletting and capsule-filling . . . . . . . . . . . . . . .
5.3.6. Film coating . . . . . . . . . . . . . . . . . . . . . . . .
5.3.7. Packaging . . . . . . . . . . . . . . . . . . . . . . . . .
6. NIR imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1. Basic principles and instrumentation . . . . . . . . . . . . . . . .
6.2. Analytical targets and strengths . . . . . . . . . . . . . . . . . .
6.3. Pharmaceutical applications . . . . . . . . . . . . . . . . . . . .
7. Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
Near-infrared spectroscopy (NIRS) is a fast and
nondestructive technique that provides multi-constituent analysis of virtually any matrix. It covers the
wavelength range adjacent to the mid infrared and
extends up to the visible region. Historically, the
discovery of the NIR region in 1800 is ascribed to
Herschel who separated the electromagnetic spectrum
with a prism and found out that the temperature
increased markedly towards and beyond the red, i.e. in
the region that is now called the near-infrared.
Although a number of NIR experiments were carried
out in the early 1920s, it was not before the mid to late
1960s that NIR spectroscopy was practically used. It
was Karl Norris from the U.S. Department of
Agriculture who recognized the potential of this
analytical technique and introduced bmodern NIRSQ
into industrial practice [1]. From then on, the breakthrough of the method as an industrial quality- and
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process-control tool proceeded in jumps coinciding
with the introduction of efficient chemometric data
processing techniques and the development of novel
spectrometer configurations based on fiber optic
probes.
In recent years, NIR spectroscopy has gained wide
acceptance within the pharmaceutical industry for raw
material testing, product quality control and process
monitoring. The growing pharmaceutical interest in
NIR spectroscopy is probably a direct result of its
major advantages over other analytical techniques,
namely, an easy sample preparation without any
pretreatments, the possibility of separating the sample
measurement position and spectrometer by use of
fiber optic probes, and the prediction of chemical and
physical sample parameters from one single spectrum.
This paper is dedicated to pharmaceutical applications of NIR spectroscopy. To fully appreciate the
analytical versatility of this spectroscopic technique, a
short introduction into the principles of the method is
helpful. To this end, the author provides the reader
with a short introduction into the theoretical fundamentals of the technique (Section 2.1), the equipment
it uses (Section 2.2), and the mathematical and
statistical tools that are needed to process recorded
signals and extract the relevant information for
qualitative or quantitative analysis (Section 3). Section 4 focuses on regulatory aspects that are critical
for pharmaceutical NIR analyses. Important current
and possible future pharmaceutical applications of
NIR spectroscopy, including raw material identification and characterization, analysis of intact dosage
forms and process monitoring, are discussed in
Section 5. Section 6 briefly emphasizes the pharmaceutical potential of NIR imaging techniques.
2. Basic principles of near-infrared (NIR)
spectroscopy
2.1. Origin and characteristics of NIR absorption
bands
The American Society of Testing and Materials
(ASTM) defines the NIR region of the electromagnetic spectrum as the wavelength range of 780–
2526 nm corresponding to the wave number range
12820–3959 cm1. The most prominent absorption
bands occurring in the NIR region are related to
overtones and combinations of fundamental vibrations of –CH, –NH, –OH (and –SH) functional
groups. The key issues which determine the occurrence and spectral properties, i.e. frequency and
intensity of NIR absorption bands are anharmonicity
and Fermi resonance, the physical basis of which will
be briefly described in this section. For a more
comprehensive treatise the reader is referred to some
excellent textbook chapters on the subject matter [2,3].
Since the energy curve of an oscillating molecule is
affected by intramolecular interactions, vibrations
around the equilibrium position are non-symmetric
and the spacings between energy levels that the
molecule can attain are not identical, but rather
decrease with increasing energy. This situation
resembles the quantum mechanical model of an
anharmonic oscillator. Since quantum mechanical
selection rules do not rigorously exclude transitions
with Dt N 1 for anharmonic systems, transitions
1111
between vibrational states of Dt = 2 or 3 are possible,
although their probability decreases with an increase
in the vibrational quantum number t. These multilevel energy transitions are the origin of NIR overtone
bands that occur at multiples of the fundamental
vibrational frequency. For most chemical bonds the
wave numbers of overtones can be estimated from
their fundamental vibrations with an anharmonicity
constant v of 0.01–0.05 by the following equation:
mx ¼ Dy m0 ð1 DyvÞ
ð1Þ
where m x = wave number of x overtone, m 0 = wave
number of fundamental vibration, v = anharmonicity
constant.
Combination bands appearing between 1900 nm
and 2500 nm are the result of vibrational interactions,
i.e. their frequencies are the sums of multiples of each
interacting frequency. A special type of configuration
interaction, called Fermi resonance, leads to the
feature that two NIR absorption bands of a polyatomic
molecule with the same frequency do not simply
overlay and sum up, but split in two peaks of
somewhat higher and lower frequencies than the
expected unperturbed position. Furthermore, intermolecular hydrogen bondings and dipole interactions
have to be considered, since they alter vibrational
energy states, thus shifting existing absorption bands
and/or giving rise to new ones. This effect allows
crystal forms, for instance, to be determined by NIR
spectroscopy.
In conclusion, NIR absorption bands are typically
broad, overlapping and 10–100 times weaker than
their corresponding fundamental mid-IR absorption
bands. These characteristics severely restrict sensitivity in the classical spectroscopic sense and call for
chemometric data processing to relate spectral information to sample properties (see Section 3). The low
absorption coefficient, however, permits high penetration depth and, thus, an adjustment of sample
thickness. This aspect is actually an analytical
advantage, since it allows direct analysis of strongly
absorbing and even highly scattering samples, such as
turbid liquids or solids in either transmittance or
reflectance mode without further pretreatments.
The dual dependence of the analytical signal on the
chemical and physical properties of the sample,
resulting from absorption and scatter effects, can be
favorably used to perform chemical and physical
1112
analysis from one single measurement. However, if
not the analytical target, scatter effects in NIR spectra,
resulting from physical sample variations, may also
pose more or less severe analytical problems. In these
situations, they have to be considered in the calibration process as dinterfering parametersT, as will be
discussed in Section 3. More detailed information on
the theory of absorption and scatter effects in diffuse
reflectance and transmittance NIR spectroscopy can
be found elsewhere [4,5].
2.2. Instrumentation and sample presentation
A NIR spectrometer is generally composed of a
light source, a monochromator, a sample holder or a
sample presentation interface, and a detector, allowing
for transmittance or reflectance measurements (Fig. 1).
The light source is usually a tungsten halogen
lamp, since it is small and rugged [6]. Detector types
include silicon, lead sulfide (PbS) and indium gallium
arsenide (InGaAs) [6]. Silicon detectors are fast, lownoise, small and highly sensitive from the visible
region to 1100 nm. PbS detectors are slower, but very
popular since they are sensitive from 1100 to 2500 nm
and provide good signal-to-noise properties. The most
expensive InGaAs detector combines the speed and
size characteristics of the silicon detector with the
wavelength range of the PbS detector.
A number of optical configurations exist that can
be used to separate the polychromatic NIR spectral
region into dmonochromaticT frequencies. A detailed
description of the different principles can be found in
various textbooks [7–9]. Here the basic principles and
main differences will be shortly discussed from a
practical point of view. Broadband, discrete filter
photometers or light-emitting diode (LED)-based
instruments provide selected frequencies, thus, covering only a narrow spectral range of 50–100 nm.
Diffraction grating, interferometer, diode-array or
acousto-optic tunable filter (AOTF)-based instruments
provide full spectral coverage. Selection of the
appropriate technology is usually based upon the
required analyte sensitivity, reliability, ease of use,
calibration transferability and implementation needs.
The latter aspect requires laboratory and process
analyzers to be differentiated.
Laboratory analyzers are intended for off-line or
at-line measurements in quality control, research and
plant laboratories, i.e. high analyte sensitivity and
reliability are required, while speed is of lower
importance. Optimum sample presentation to the
instrument, high signal-to-noise ratio, instrument
stability, and sufficient resolution are the most
important aspects for analysis. Presently, grating and
interferometer-based instruments are mainly in use for
this purpose. The appropriate NIR measuring mode
will be dictated by the optical properties of the
samples (Fig. 2). Transparent materials are usually
measured in transmittance (Fig. 2A). Turbid liquids or
semi-solids and solids may be measured in diffuse
transmittance (Fig. 2B), diffuse reflectance (Fig. 2C)
or transflectance (Figs. 2D/E), depending on their
absorption and scattering characteristics. In any case,
absorbance (A) values relative to a standard reference
material are measured, with A corresponding to log 1/
R and log 1/T for reflectance and transmittance
spectra, respectively.
To measure good NIR spectra, the proper sample
presentation is of utmost importance, especially when
measuring solid samples, since scatter effects and
Detector
Diffuse Reflectance
Light Source
Monochromator
Sample
Fig. 1. Basic NIR spectrometer configurations.
Detector
Transmittance
Transmittance
Transflectance
Diffuse
Reflectance
(A)
(D)
(C)
(B)
(E)
Fig. 2. NIR measuring modes—(A/B) transmittance, (C) diffuse
reflectance and (D/E) transflectance.
stray light induced by variations in packing density of
powders or sample positioning of tablets or capsules
may cause large sources of error in the spectra [10].
Therefore, several types of sample cells, such as
quartz cuvettes with defined optical path length for
liquids, specifically designed sample cells with quartz
windows for semi-solids and powders, and adjusted
sample holders for tablets and capsules have been
developed [11]. Temperature control and sample
movement are other options that have been realized.
Process analyzers are intended for in-line or online measurements to provide real-time process
information while operating in harsh conditions. This
requires fast and rugged instruments with no moving
parts, such as AOTF-based instruments, allowing for
numerous readings per second without being sensitive
to vibrations. AOTF-based instruments choose wavelengths by using radio-frequency signals to alter the
refractive index of a birefringent crystal (usually
TeO2). Wavelength scans can, thus, be performed
much more rapidly than with other configurations.
Since process analyzers are dedicated to performing a
particular analysis on a specific sample type, the
process sample interface depends on the sample type
and the process conditions, with NIR light being
transferred via fiber optics. In-line analysis of clear to
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opaque liquids and solids is typically carried out by
contact transmission and reflectance probes, while
non-contact reflectance measurements are performed
on materials transported in hoppers or conveyor belts.
3. Theory and practice of chemometric data
processing
Since NIR spectra are typically composed of broad
overlapping and, thus, ill-defined absorption bands
containing chemical and physical information of all
sample components, the analytical information is
multivariate in nature and, therefore, hardly selective.
To perform qualitative or quantitative NIR analysis,
i.e. to relate spectral variables to properties of the
analyte, mathematical and statistical methods (i.e.
chemometrics) are required that extract brelevantQ
information and reduce birrelevantQ information, i.e.
interfering parameters.
In the following sections, the most frequently used
mathematical data pretreatments and their specific
purpose (Section 3.1), reduction of variables with
principal component analysis (Section 3.2), multivariate calibration methods for quantitative analysis
(Section 3.3), and multivariate classification techniques for qualitative analysis (Section 3.4) will be
discussed. Different methods for calibration transfer
between instruments, an important economic and
regulatory issue for qualitative and quantitative
pharmaceutical NIR analysis, have recently been
commented on by Blanco et al. [12] and will, thus,
not be considered here in detail.
3.1. Data pretreatments
Interfering spectral parameters, such as light
scattering, path length variations and random noise,
resulting from variable physical sample properties or
instrumental effects, call for mathematical corrections,
so-called data pretreatments, prior to multivariate
modeling in order to reduce, eliminate or standardize
their impact on the spectra. Since careful selection of
data pretreatments can significantly improve the
robustness of a calibration model, the most commonly
used methods are briefly discussed with respect to the
effect they are able to correct. A detailed description
of the techniques can be found elsewhere [13].
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method is principal component analysis (PCA). PCA
is a mathematical procedure that resolves the spectral
data into orthogonal components whose linear combinations approximate the original data. The new
variables, called principal components (PC), eigenvectors or factors, correspond to the largest eigenvalues of the covariance matrix, thus, accounting for the
largest possible variance in the data set. The first PC
represents maximum variance amongst all linear
combinations and each successive variable accounts
for as much of the remaining variability as possible.
The transformation procedure is visualized schematically in Fig. 3 on the basis of three original variables,
i.e. three wavelengths per spectrum. For real spectra
with p wavelengths the transformation leads to a pdimensional space.
In pharmaceutical NIR analysis, it is often possible
to compress most of the spectral variability to only a
few principal components, i.e. factors with only a
rather small loss of information. A number of multivariate calibration and classification methods, therefore, rely on PCA data (see Sections 3.3 and 3.4). For
further details on PCA, interested readers are referred
to the excellent and comprehensive treatise of Howard
Mark [14].
Mathematical treatments used to compensate for
scatter-induced baseline offsets include multiplicative scatter correction (MSC) and standard normal
variate (SNV). Both methods have originally been
developed to process reflectance spectra, but they
are also applied to transmittance spectra. Baseline
shifts and intensity differences resulting from variable positioning or path length variations may be
reduced or eliminated by normalization algorithms.
Derivatives can be applied to improve the resolution
of overlapping bands. In addition, they are able to
reduce baseline offsets. Since spectral noise is also
amplified by derivation, derivatives are usually
combined with Taylor or Savitzky Golay smoothing
algorithms.
3.2. Reduction of variables by principal component
analysis (PCA)
Since multivariate NIR spectral data contain a huge
number of correlated variables (= collinearity), there is
a need for reduction of variables, i.e. to describe data
variability by a few uncorrelated variables containing
the relevant information for calibration modeling. The
best known and most widely used variable-reduction
Intensity
λ3
λ2
λ1
λ2
λ1
λ3
F2
λ3
λ3
λ2
F3
λ2
F1
F3
F2
F1
λ1
λ1
Fig. 3. Transformation of a spectrum with three variables, i.e. wavelengths (a) to a new coordinate system with one axis for each wavelength
thereby converting the spectrum to a single point in a three-dimensional space (b), cloud formation of several spectra (c), mean centering (d),
and determination of principal components F1, F2 and F3 (e).
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3.3. Multivariate calibration for quantitative analysis
3.4. Multivariate classification for qualitative analysis
Before a NIR spectrometer can do any quantitative
analysis, it has to be trained, i.e. calibrated using
multivariate methods. The calibration process basically involves the following steps:
In qualitative analysis, sample properties that have
to be related to spectral variations have discrete values
that represent a product identity or a product quality,
for example bgoodQ or bbadQ. To solve the selectivity
and interference problems of NIR spectra, multivariate
classification methods are used for grouping samples
with similar characteristics. Multivariate classification
methods, also known as pattern-recognition methods,
are subdivided in bsupervisedQ and bnon-supervisedQ
learning algorithms, depending on whether or not the
class to which the samples belong is known.
bNon-supervisedQ methods, also known as cluster
analysis, do not require any a priori knowledge
about the group structure in the data, but instead
produces the grouping, i.e. clustering, itself. This
type of analysis is often very useful at an early stage
of an investigation to explore subpopulations in a
data set, for instance different physical grades of a
material. Cluster analysis can be performed with
simple visual techniques, such as PCA (see Section
3.2) or some hierarchical methods leading to so-called
dendrograms.
bSupervised classificationQ methods, also known as
discriminant analysis, are used to build classification
rules for a number of pre-specified subgroups, i.e. the
group structure of the training set is known. The
classification rules are later used for allocating new or
unknown samples to the most probable subgroup.
Identity or good/bad quality are, thus, defined as
belonging to a group with known properties. Algorithms of this type such as LDA (= linear discriminant
analysis), QDA (= quadratic discriminant analysis),
SIMCA (= Soft Independent Modelling of Class
Analogies) or KNN (= K nearest neighbours) are
typically used for constructing spectral libraries.
Most of the classification methods can operate
either in wavelength space or in a dimension-reduced
factor space. In any case, their ultimate goal is to
establish mathematical criteria for parametrizing
spectral similarity, thus, allowing similarity between
samples or a sample and a class to be expressed
quantitatively. For this purpose, comprehensive libraries of spectra that represent the natural variation of
each product have to be constructed in a bcalibrationQ
process, with similarity being expressed by either a
correlation coefficient, such as the spectral match
1. Selection of a representative calibration sample set.
2. Spectra acquisition and determination of reference
values.
3. Multivariate modeling to relate the bspectral variationsQ to the breference valuesQ of the analytical
target property.
4. Validation of the model by cross validation, set
validation or external validation.
The multivariate regression methods most frequently used in quantitative NIR analysis are principal
component regression (PCR) and partial least-squares
(PLS) regression [15]. PCR uses the principal components provided by PCA (see Section 3.2) to perform
regression on the sample property to be predicted.
PLS finds the directions of greatest variability by
comparing both spectral and target property information with the new axes, called PLS components or
PLS factors. Thus, the main difference between the
two methods is that the first principal component or
factor in PCR represents the largest variations in the
spectrum, whereas in PLS it represents the most
relevant variations showing the best correlation with
the target property values. In both cases, the optimum
number of factors used to build the calibration model
depends on the sample properties and the analytical
target. Too many factors may lead to an boverfittedQ
model with a high regression coefficient and a low
standard error of calibration (SEC), but a large
standard error of prediction (SEP). Such a model is
not very robust and may fail when tested with an
independent validation set.
In some cases, the spectral data and the target
property may not be linearly related as a result of
physical sample properties or instrumental effects.
These cases can only be addressed by non-linear
calibration methods, such as PLS-2, locally weighted
regression (LWR) or artificial neural networks
(ANNs). For details on these methods interested
readers are referred to the corresponding chapters in
a recent textbook on multivariate calibration [16].
1116
value (SMV) [17], or a distance measure, such as
Euclidian or Mahalanobis distance.
A detailed description of the different classification
procedures is certainly beyond the scope of this paper.
Interested readers are, therefore, referred to a recent
textbook on the topic [18]. Worth mentioning here are
the following practical aspects:
! The correlation coefficient, being defined as the
cosine of the angle between vectors for the sample
spectrum and the average spectrum for each
product in the library, is a rather robust parameter
that can be favorably used for chemical identity
testing (see Section 5.1), since it relies on second
derivative spectra and is, thus, not influenced by
spectral offsets and globalintensity variations
resulting from physical differences or concentration changes.
! Distance-based methods, on the other hand, also
allow for product qualification. The conformity
index (CI), based on the wavelength distance
method, is one such parameter that has been used
successfully to pinpoint quality differences in raw
materials and products by using a so-called C-plot,
i.e. a plot of the absolute distance at each wavelength as a function of the wavelength [19] (see
also Section 5.1).
4. Regulatory aspects
4.1. Actual status of pharmaceutical NIR analysis
NIR spectroscopy has a large number of advantages over other analytical techniques, and, thus,
offers many interesting perspectives in pharmaceutical
analysis. The scientific rationale of this technology
has been established for many different applications
and justified by a huge number of publications from
academia and industry (see Section 5). However, in
the highly regulated pharmaceutical world, an analytical method is only valuable for routine implementation if it is approved by regulatory authorities.
Actually, the major pharmacopoeias have generally
adopted NIR techniques. The European [20] and
United States Pharmacopoeia [21] both contain a
general chapter on near-infrared spectrometry and
spectrophotometry, respectively. These chapters ad-
dress the suitability of NIR instrumentation for use in
pharmaceutical analysis focussing mainly on operational qualification and performance verification comprising wavelength scale and repeatibility, response
repeatibility, photometric linearity, and photometric
noise. Only some limited guidance is provided in terms
of developing and validating an application.
The general legal requirements for instrumentation
qualification procedures, namely design qualification
(DQ), installation qualification (IQ), operational qualification (OQ), and performance qualification (PQ),
are described in the cGMP guideline title 21 CFR part
211. For practical realization of these requirements,
the American Society for Testing and Materials
(ASTM) has provided NIR specific directions regarding appropriate methodology for establishing spectrophotometer performance tests including suitable
standards and multivariate calibration [22]. Further
guidance for evaluation of a NIR spectrophotometer
has been provided in a special report of the Analytical
Methods Committee of the British Royal Society of
Chemistry [23].
Many pharmaceutical companies have successfully implemented NIR spectrometers in their
quality control laboratories for routine use in raw
material identification and qualification. This is
based on the fact that major pharmacopoeias allow
manufacturers to use analytical methods other than
compendial ones for compliance testing, provided
they are validated according to parameters, such as
specificity, linearity, range, accuracy, precision,
repeatibility, reproducibility, detection limit, quantification limit, and robustness, as is detailed in the
U.S.P. Chapter 1225 on Validation of Compendial
Methods [24] and the general ICH Guidelines Q2A
and Q2B on Validation of Analytical Procedures
[25].
Interestingly, only few quantitative NIR methods
have gained regulatory approval as yet. The main
reason for this is that bnon-separativeQ multivariate
NIR methods differ markedly from bseparativeQ univariate chromatographic methods for which U.S.P.
Chapter 1225 and the general ICH Guidelines Q2A
and Q2B were written. Moffat et al. [26] discussed
these aspects extensively in an excellent paper
published in 2000. Based on the example of a
quantitative NIR method for the analysis of paracetamol in tablets, the authors made suggestions on
how NIR assays can best meet the ICH Guidelines on
Validation. The recently published Guidelines for the
Development and Validation of Near-Infrared Spectroscopic Methods in the Pharmaceutical Industry [27],
established by the NIR sub-group of the UK Pharmaceutical Analytical Sciences Group (PASG), cover the
unique and specific NIR requirements whilst remaining complementary to ICH Q2A and Q2B, which
address traditional method validation requirements. It
might be expected that the PASG guidelines, comprising hardware as well as software aspects, can help both
pharmaceutical industry and regulatory agencies in
evaluating future submissions of qualitative and
quantitative NIR methods. For details of the PASG
guidelines see www.pasg.org.uk/NIRmay01.pdf.
4.2. NIR spectroscopy in view of the U.S.F.D.A.
initiative on PAT
The production of pharmaceutical dosage forms is
usually a multistage operation, consisting of several
validated processes managed by standard operating
procedures (SOPs). Quality assurance, including
decisions concerning the satisfactory completion of
each unit operation, is actually based on off-line
testing to document quality of a small, nominally
random product sample. This approach is often very
time consuming and adds significantly to the manufacturing cycle time, since it requires the process to be
stopped during sample removal, data generation and
documentation. In addition, it does not assure zero
defect product quality, since risk assessment and risk
management are not included, e.g. critical process
parameters and material performance attributes may
not be identified.
In view of this undesirable situation for industry
and public health, it has been recognized that new
testing paradigms are required to succeed in both, an
increase in manufacturing efficiency and product
safety. The Process Analytical Technology (PAT)
initiative, driven by the United States Food and
Drug Administration (U.S.F.D.A.) and major pharmaceutical companies, is a challenging approach
intended to assist the progression of real-time or
parametric release and quality-by-design concepts
by providing an opportunity to move from the
laboratory-based btesting to document quality paradigmQ to a bcontinuous quality assurance paradigmQ.
1117
According to a recently published U.S.F.D.A.
Guidance for Industry [28], PATs are defined as
systems for real-time monitoring and control of
critical process parameters and material performance
attributes, thus, helping to improve process understanding, manufacturing cycle time, and final product quality. NIR spectroscopy and imaging may be
one of the major PAT tools, since these techniques
are well-suited for at-line, in-line and on-line
measurements. They can provide a wealth of
chemical and physical information important for
measuring process performance and open up opportunities to move forward from traditional quality
control concepts to process qualification and product
conformity testing. Although a number of challenges
concerning hardware design and regulatory approval
must be overcome to realize the full potential of NIR
spectroscopy and imaging as PAT tools, it may be
expected that parametric or even real-time release
concepts may be well assisted by the use of NIR
techniques (see Sections 5.3 and 6.3).
5. Pharmaceutical applications
NIR spectroscopy combined with multivariate
data analysis opens many interesting perspectives
in pharmaceutical analysis, both qualitatively and
quantitatively. Fast and nondestructive NIR measurements without any sample pre-treatments may
increase the analytical throughput tremendously.
The use of fiber optic probes offers the opportunity
for in-line and on-line process monitoring. The
special feature of combined chemical and physical
information allows for the assessment of a bspectral
signatureQ of raw materials, intermediates and final
dosage forms, which in turn offers the possibility of
a simultaneous determination of several sample
characteristics.
Notwithstanding these advantages, pharmaceutical
industry and regulatory bodies have been slow to
adopt the NIR technique, most probably since it
lacks the ability of mid-IR to identify samples by
mere inspection of spectra and involves calibration
by sophisticated mathematical techniques (see Section 3). Although the earliest publications on pharmaceutical NIR applications date back to the late
1960s, it was not until the last 20 years that NIR
1118
spectroscopy has gained increasing interest in the
pharmaceutical industry with the real breakthrough in
the 1990s as a result of hardware and software
improvements. Within the last 10 years a growing
number of research and review articles have reported
on the great potential of NIR spectroscopy in
pharmaceutical research, production, and quality
control focussing on various banalytical targetsQ, such
as identity, content uniformity, moisture content,
particle size, polymorphic and pseudopolymorphic
forms, hardness, thermal and biopharmaceutical properties. These different aspects, resulting from the dual
dependence of the NIR signal on chemical and
physical sample characteristics, will be discussed in
the context of raw material and intermediate identification and qualification (Section 5.1), analysis of
intact dosage forms (Section 5.2), and process
monitoring (Section 5.3), with a main focus on solid
dosage forms.
5.1. Identification and qualification of raw materials
and intermediates
Raw materials intended for use in pharmaceutical
products, i.e. active ingredients and excipients, are
subject to pharmaceutical quality requirements as
prescribed by Good Manufacturing Practice (GMP)
Guidelines for Medicinal Products, and pharmacopoeial monographs. To guarantee maximal product
safety, the GMP guidelines require special testing
procedures within the material supply chain (Directive
91/355/EEC, Chapter 5.30). In addition to the routine
release testing of the substance, single container
identification has to be performed for any lot of raw
material at any time of dispensal.
Since modern pharmaceutical processes rely heavily on a reproducible source and grade of raw
materials to ensure consistent finished product quality,
material qualification is another analytical requirement in the supply chain that has to be fulfilled.
Qualification is supposed to confirm the grade and/or
source of materials including physical properties, such
as particle size, density, morphology etc., which may
in turn indicate its suitability for the intended use.
Traditionally, pharmaceutical raw material identification and qualification, known as compliance testing,
has been based on compendial methods and/or
alternative validated in-house testing procedures.
The methods are time-consuming, as they are usually
performed in an off-line laboratory, are often wetchemical in nature, and are, therefore, not appropriate
to handle the enormous number of analyses of modern
industrial material identification and qualification
economically.
With the pharmacopoeial-based authorization to
use methods other than the compendial ones for
compliance testing and the GMP-based opportunity
of using bany appropriate procedure or measure to
assure the identity of the contents of each container
of starting materialsQ, it has been possible to take
advantage of multi-sensing NIR techniques based on
fiber optic probes for fast and nondestructive
pharmaceutical raw material identification and qualification. Many papers have reported on the feasibility of NIR identification and qualification of both
active ingredients and excipients [29–38], and most
companies have adopted some form of NIR material
testing in their supply chain, either in the warehouse
only and/or elsewhere in a manufacturing operation,
i.e. wherever rapid assessment of identity and quality
is needed. In combination with bar-code readers,
weighing stations, and electronic batch documentation a bsmartQ system can be developed that
guarantees successful manufacturing operations by
ensuring that the correct materials of the appropriate
quality are used in the manufacturing process (see
also Sections 4.2 and 5.3).
Using NIR techniques, the chemical identity of a
particular material is usually confirmed with a spectral
library approach. If an appropriate library has been
constructed, the combined chemical and physical
information in the spectra can also be used for material
qualification. Moreover, with an appropriate calibration setup, simultaneous quantitative measurements,
such as moisture content and particle size determinations, can be performed or bconformityQ approaches
can be used to predict material performance in
manufacturing processes. The different approaches
will be discussed in the following paragraphs.
5.1.1. Library approach
Chemical identification usually does not involve
any conceptual problems with respect to spectral
library development [30,31,39,40]. However, extension of the identification concept to material qualification is usually more complex. The key parameters
for constructing a robust spectral library may, therefore, be defined as follows:
1. Definition of library scope and purpose.
2. Selection of authentic sample spectra for calibration, internal and external validation.
3. Rationale of data pretreatments.
4. Selection of classification algorithm(s).
5. Determination of thresholds.
6. Maintenance and updating.
The library structure may depend on the software
limitations and the user’s requirements. In the
simplest case, all materials are incorporated into
one library [39]. Alternatively, they may be split into
sub-libraries to ensure the required level of specificity, as for discrimination of chemically similar
substances, such as close members of a homologous
series or different grades of microcrystalline cellulose
or lactose.
The selection of samples is critical to the success of
the application. Two sets of samples are required: one
for the construction of the library and an independent
one for external validation purposes to verify the
performance of the data base. The number of batches
required to train the system depends on the intended
scope, i.e. the required discriminatory power of the
method. The training set must collectively describe
the typical variation of the substance being analyzed.
As a rule of thumb, identification normally requires a
much smaller number of different batches (usually 3)
than qualification (usually 20 or more).
Data pretreatments (see also Section 3.1) strongly
depend on the application. For identification purposes,
second derivative and scatter correction are often used
to reduce offsets, due to variable physical material
characteristics. The rationale of transforms in qualification methods strongly depends on the parameter
of interest and is a case by case decision. The effect of
NIR data pre-processing on the pattern recognition of
pharmaceutical excipients has been discussed by
Candolfi et al. [41].
The classification model (see also Section 3.4) is
the heart of the library. The proper choice of the
algorithm depends on the scope of the library. For
identification purposes, where physical parameters are
not determined, it is usually sufficient to use a match
by wavelength correlation method based on second
1119
derivative data. For qualification of different grades of
excipients, more sophisticated algorithms, such as
SIMCA are recommended (see Section 3.4). Only
recently, Kemper and Luchetta have published a
comprehensive paper giving practical guidelines for
construction, validation and maintenance of spectral
libraries for raw material identification and qualification [42].
5.1.2. Conformity approach
In the early 1990s, van der Vlies and co-workers
[17,19] developed a discriminating method, which
they called the bconformityQ approach, and introduced
a new quality parameter, the Conformity Index (CI),
to replace compendial methods for identification,
assay, and moisture content determination of ampicillin trihydrate. It is worth mentioning that this was
the first NIR method for release testing of a bulk
pharmaceutical product for human consumption
approved by the U.S.F.D.A.
The CI is the largest value obtained by dividing the
absolute difference in absorption between sample and
reference spectrum (first or second derivative) for
each data point by the standard deviation of the
absorbance of the reference spectrum at that particular
data point. The authors defined the bstandard qualityQ,
i.e. the specification of their material at CI of 5 or
lower, and achieved a high sensitivity of CI for
chemical and physical deviations. With the so-called
Conformity Plot (C-Plot: CI versus wavelength plot) it
was possible to pinpoint the sources of even very
slight variations in chemical and physical properties,
including crystallinity. The conformity approach is
well suited for industrial raw material and intermediate qualification, since it gives qualitative answers to
quantitative questions without the need of exhaustive
calibration work.
5.1.3. Quantitative calibration models
Quantitative calibration models in raw material
qualification have been described for analytical
targets, such as moisture content [43–46], particle
size [37,46–51], specific surface area [52], polymorphic and pseudopolymorphic forms [53–56], amorphous/crystalline ratios [57–63], viscosity [34], and
gel strength [34]. Moisture content, particle size and
polymorphism, also relevant to pharmaceutical intermediates, will be discussed in more detail.
1120
Since chemical, physical, technological and biopharmaceutical properties of active ingredients and
excipients may be largely affected by their water
content and the type of water present, evaluation of
batch-to-batch variability or storage effects on water
content and water binding is usually an integral part of
material qualification. NIRS is an effective alternative
to traditional methods, such as thermogravimetry and
Karl Fischer titration for both water content and water
binding determinations. This is due to the fact that
O–H bands of water are very intensive in the NIR
region, exhibiting five absorption maxima (at 760,
970, 1190, 1450, 1940 nm), the positioning of which
depends on the hydrogen bonding intensity. The
specific band to be used for water determinations
depends on the desired sensitivity and selectivity level.
NIR quantification of moisture content is usually an
easy task with respect to data processing, i.e. MLR and
PLSR models have been reported. Moreover, reference
data provided by Karl Fischer titration are reliable. It
is, therefore, not surprising that NIR moisture content
determinations in both transmittance and reflectance
mode have been described extensively in the literature.
Most of the early work has been summarized and
discussed by Blanco [12]. Two papers are worth
mentioning here, since they demonstrate the potential
of NIRS to distinguish different states of water in raw
materials and intermediates. Ciurczak and coworkers
[46] were among the first who demonstrated the
opportunity of NIRS to differentiate between total,
bound, and surface bulk water in pharmaceutical raw
materials, thus, demonstrating the advantage of NIRS
over traditional methods, such as KFT and LOD. Dziki
et al. [45] detected differences in the location or
orientation of the water molecules within the crystal
lattice of sarafloxacin with NIRS and used this
approach to distinguish between acceptable and
unacceptable batches for formulation purposes.
Mean particle size and particle size distribution of
solid raw materials and intermediates are key issues in
the formulation of many pharmaceutical products,
since they have a profound effect on bulk physical
properties, which in turn influence blending and flow
characteristics, density, compressibility, and dissolution rate. Particle size measurements with NIRS in
diffuse reflectance mode rely on the particle sizedependent scatter effect of powders resulting in nonlinearly sloping baselines [47,49]. Although the
potential of NIR spectroscopy for particle size
determination has been alluded to in many review
articles, only a few research papers have been
dedicated to this subject. Mean particle size [46–50]
or particle size distribution [37,51] measurements
with NIR spectroscopy have been reported, using
lactose monohydrate [37,49,50], microcrystalline cellulose [37,49,51], NaCl, and sorbitol [47], aspirin,
caffeine and paracetamol [49], and piracetam [48], as
model excipients and active ingredients, respectively.
Various chemometric approaches have been suggested for correlating particle size with NIR spectral
information and the literature data clearly reveal that
there is more than one way to model mean particle
size data with NIR spectra, depending on the particle
size range, shape of the particle size distribution,
materials refractive index, and absorption properties.
Ciurczak et al. [46] found an inverse relationship
between absorbance at each wavelength and mean
particle size, with two distinct segments below and
above 85 Am, indicating the complicating effect of
small particles for quantitative NIR mean particle size
measurements. Burger and coworkers have investigated this aspect in detail and the interested reader is
referred to some excellent papers of the group dealing
with radiative transfer investigations to quantify
absorption and scattering coefficients of pharmaceutical powders [4,64,65]. From a more practical point of
view, Blanco et al. [48] revealed that spectral
reproducibility was affected by sample compactness
and varied in an exponential manner with particle size
(in the range 175–325 Am), thus, pointing to the
importance of sample presentation for quantitative
particle size measurements.
Pharmaceutical raw materials may exist in amorphous or crystalline form, with polymorphism and
pseudopolymorphism being widely observed in crystalline compounds. The impact of a certain polymorphic or pseudopolymorphic form or the degree of
crystallinity on the physicochemical and biopharmaceutical material characteristics is well known. NIR
spectroscopy has been reported to be an alternative to
traditional techniques, such as DSC and X-ray powder
diffraction, for qualification and quantification of the
crystallinity [57–63] of miokamycin, lactose monohydrate, mannitol, sucrose and raffinose; of polymorphic or pseudopolymorphic forms of sulfathiazol,
caffeine and theophylline in bulk [53,54]; and of
crystallinity upon hydration during granulation processes [55,56]. The rationale behind this approach is
the sensitivity of NIR spectra to intermolecular
bondings. The magnitude of spectral differences
between the different forms is, therefore, the key
issue for quantitative determinations. Patel et al. [54]
demonstrated in a recent paper that NIRS can be used
to determine polymorphs of sulfathiazol in binary
mixtures in the range of 0.3% w/w. For amorphous/
crystalline mixtures of lactose monohydrate, the
amorphous content was accurately determined to
within 1% w/w. The literature data clearly reveal that
NIR results are comparable with other techniques,
thus, reflecting the potential of the method for the
assessment of different physical forms in bulk
materials and intermediates.
5.2. Analysis of intact dosage forms
The nondestructive and multivariate nature of NIR
techniques opens new perspectives in the pharmaceutical analysis of intact dosage forms, including
chemical, physical and related biopharmaceutical
aspects. This section will discuss NIR applications
for the characterization of solid dosage forms, namely
tablets, capsules, lyophilized products and implants.
5.2.1. Tablets
Most of the literature data available on NIR
applications for intact dosage forms focus on tablets,
ranging from identification and assay to physical and
biopharmaceutical parameters, such as hardness, coating thickness and dissolution rate. It is certainly
beyond the scope of this paper to review all the
published data in these fields. This section is rather
intended to provide an update of and comment on
some specific aspects that have not been reviewed in
detail yet. Special attention will be paid to the
importance of sample selection, sample presentation
and collection of reliable reference data for developing robust calibration models. Readers interested in a
more comprehensive coverage of the topics including
earlier data are referred to selected review articles
[12,66] and a recent book chapter [67].
Fast and nondestructive identification of active
ingredients and exipients in whole tablets, even
through the blister packaging, is certainly a domain
of NIR spectroscopy [68–70]. Generally, the measur-
1121
ing mode is not as critical as with quantitative
applications, except for very thick, highly absorbing
tablets and sugar-coated tablets, for which the
reflectance mode is recommended to overcome
problems of low analyte signal intensity or even total
absorption in transmittance. Challenges associated
with the identification of placebo and verum tablets
of different dosage levels (2, 5, 10 and 20% w/w)
within the blister packaging have been reported by
Dempster et al. [68]. The results of this study clearly
revealed a higher discriminating ability of direct
measurements compared to measurements through
the blister packaging, thus, emphasizing that the effect
of the packaging material on the accuracy of NIR
identification approaches may not be neglected.
Quantitative NIR analysis of active ingredients in
tablets has been widely reported and reviewed in the
literature. However, in the earliest NIR assays, tablets
were not analysed intact. The active was extracted
from the matrix or the tablets were at least pulverized
prior to NIR measurements. The opportunity to
accurately measure active contents in whole tablets
started in the late 1980s with the development and
subsequent commercialization of appropriate sample
holders that allow for a proper fit of even curved
tablets, thereby reducing variable positioning [10] and
stray light effects. Within the last 10 years, the number
of publications describing quantitative NIR measurements of active ingredients in intact tablets has
increased tremendously [26,71–84]. Various aspects
have been addressed, two of which will be discussed
in more detail, namely the rationale for selecting the
appropriate measuring mode, and the practical and
regulatory aspects to be considered in choosing the
appropriate chemometric approach, including calibration sample selection and data pretreatments.
Selecting the measuring mode for NIR tablet
analysis strongly depends on tablet thickness, composition and target parameter. Considering quantitative
analysis of active ingredients in tablets, the reflectance
mode, mainly used in early work, may have some
limitations, since it covers only a certain part of the
tablet [76]. This, in turn, can cause false results, if
homogeneity within the tablet cannot be assured or is
part of the delivery concept, such as in multilayer
tablets. Moreover, the assay of coated tablets may be
complicated in cases where the majority of spectral
information is coming from the coating polymer. In
1122
view of this, regulators have expressed their concerns
regarding reflectance measurements for content uniformity testing. Transmittance spectra, representing a
larger volume of the scanned tablet, certainly provide
a better description of a tablet matrix in bulk.
Improved accuracy, precision, and sensitivity of
transmittance measurements in various tablet assays
have been demonstrated in the literature [71,72].
However, it should not be neglected that a significantly narrower wavelength range is available in
bdiffuseQ transmittance mode, and limitations are
observed with very thick tablets [73]. Recent papers
dealing with NIR tablet assays for content uniformity
testing, therefore, clearly reveal that selection of the
appropriate measuring mode is a case by case decision
[71–73,75,78–84].
As a non-separative method, quantitative NIR
measurements on tablets rely heavily on chemometric
procedures for data modelling, with sample selection
and data pretreatments being the most critical issues
regarding calibration development. Since processrelated natural variations in tablet mass and hardness
affect the optical properties and, thus, the baseline of
the recorded spectra, derivative transformation and/or
normalization are usually required for accurate NIR
content uniformity measurements. Sample selection
for calibration modelling strongly depends on the
chemometric approach. For bconformityQ testing, the
calibration samples should bsimplyQ cover the normal
range of tablet variability, including intra-batch and
batch-to-batch variability. Out-of-specification samples should be considered in the validation step. For
quantitative modelling, additional requirements have
to be fulfilled, namely the use of tablets with an
extended range of active concentrations in the
calibration step. This is not an easy task in industrial
practice [77], since normal tablet production batches
are manufactured with tight tolerances. In an excellent
and comprehensive paper, Moffat and co-workers
have discussed this issue and given various options
for proper calibration sample selection [26]. In the
same paper, the authors provided suggestions on how
to meet the ICH Guidelines on Validation for NIR
quantitative analysis of active ingredients in tablets
(also see Section 4.1). Validation of quantitative NIR
methods has also been addressed by Blanco [74,75].
Considering the huge amount of literature data on
NIR assays for active qualification and quantification,
it is surprising that stability issues, i.e. identification
and quantification of degradation products in tablets,
have only rarely been addressed. There is merely one
early paper by Drennen and Lodder [85] that reports
the use of NIR diffuse reflectance spectroscopy for
monitoring the hydrolysis of acetylsalicylic acid to
salicylic acid in tablets upon water absorption. Due to
the combined spectral information on water and
salicylic acid, the authors were able to predict both
parameters from one single measurement, thus,
emphasizing the great potential of NIRS for tablet
stability testing. In addition to chemical stability,
polymorphic transitions might be another target
parameter that could be addressed in tablets [86].
The mechanical performance of tablets is of
importance for bulk handling, coating, packaging,
removal from blister, and disintegration. Current
methods of hardness testing are destructive in nature
and often subject to operator error. NIR spectroscopy,
on the other hand, offers the opportunity for fast and
nondestructive hardness measurements, and provides
additional information on structural features of the
tablet matrix. Several groups have described the
application of NIRS as an alternative method for
tablet hardness testing [87–92]. Since the approaches
are different with respect to the measuring mode, the
range of hardness levels included in the model, and
the chemometric data processing, they will be
discussed in more detail.
Drennen and co-workers [87,89] were among the
first who applied NIR spectroscopy to tablet hardness
testing. The authors used diffuse reflectance spectroscopy and realized that an increase in tablet hardness
causes a bprimaryQ effect of wavelength-dependent
nonlinear baseline shifting to higher absorbance
values, which can be attributed to a decrease in
multiplicative light scattering. Various tablet formulations, including coated tablets, were investigated at
hardness levels ranging from 1 to 7 kp [89] and from 6
to 12 kp [87], respectively. A pressure-dependent
bsecondaryQ spectral effect, namely a peak shifting at
higher hardness levels arising from changes in
intermolecular bonding, could be observed for some
materials. In view of these observations, the authors
used different approaches for different hardness
levels to correlate spectral data with hardness values.
For hardness values in the range of 6 to 12 kp, they
used PCA/PCR based models, considering mainly
bsecondaryQ spectral effects, while removing baseline
shifts also resulting from tablet positioning variability
[87]. The SEP values obtained were as precise as the
laboratory hardness test. For hardness values in the
range of 1 to 7 kp, where the bprimaryQ spectral effect
was mainly observed, the authors developed a spectral
best-fit algorithm based on traditional statistical
methods [89]. The proposed approach exploits the
baseline shift and involves the determination of a bestfit line through each spectrum, thereby reducing the
spectrum to slope and intercept values, e.g. deweighting individual absorbance peaks and valleys.
The method was found to be insensitive to slight
formulation changes (1–10% w/w cimetidine) and
compared favorably to the multivariate PCA/PCR
method with SEP values of around 0.5 kp.
Morisseau and Rhodes [88] revealed SEP values in
the same range (0.3–0.6 kg) for different tablet
formulations, namely hydrochlorothiazide (15 and
20% w/w) and chlorpheniramine (2 and 6% w/w) in
a matrix of microcrystalline cellulose and magnesium
stearate, at six hardness levels ranging between 2 and
12 kg. The authors used MLR and PLS to model the
diffuse reflectance spectra. Obviously, due to the wide
range of hardness levels included in the calibration
model, it was not possible to develop acceptable
bmixedQ calibrations by combining data from two
concentrations of the same drug. In a recent paper,
Chen et al. [92] described the favorable use of
artificial neural networks (ANN) to predict tablet
hardness from diffuse reflectance NIR spectral data.
Interestingly, there is only one paper that describes
the use of NIR transmittance measurements for tablet
hardness determinations [91]. Based on the fact that
compaction of pharmaceutical powders results in
density variations in different directions and regions
of the tablet [93], the author suggests a better
predictability of whole tablet hardness values from
transmittance than from reflectance measurements
[91]. Indeed, the data revealed a strong correlation
between tablet hardness and transmission spectra over
a wide range of hardness levels (10–180 N). In
addition, material specific bprimaryQ and bsecondaryQ
spectral effects were used to study the consolidation
characteristics of different pharmaceutical excipients
and active ingredients [94], indicating the potential of
NIR transmittance applications in tablet formulation
development.
1123
Prediction of drug dissolution rates from whole
tablet NIR spectra is another application that has been
alluded to in many review articles. However, only a
few research papers are really concerned with this
topic, probably due to the challenge of providing
tablet samples that cover the appropriate range of
variability required to develop robust calibration
models. The first papers, dating back to the early
1990s [95,96], deal with the prediction of the
dissolution rate of carbamazepine tablets following
exposure to high humidity. NIR diffuse reflectance
spectra were collected periodically from whole tablets
stored in a hydrator. Dissolution rates were correlated
with the spectral data using PCR and the bootstrap
(BEST) algorithm for modelling. Although this
example clearly indicates the potential of NIRS for
nondestructive dissolution testing, its citation in
review articles is somewhat misleading, since in this
special example the most prominent parameter affecting dissolution rate was the moisture content. Quantitative modelling of drug dissolution rates of
commercialized tablets stored under normal conditions is certainly a greater challenge and requires
exhaustive calibration work based on a priori knowledge of the formulation- and process-dependent tablet
variables, as well as their effect on both the drug
dissolution profile and the spectra. A qualitative
bconformityQ approach (see Section 3.4) might be a
more practical option for modelling drug dissolution
from fast dissolving tablets.
Some authors [87,97–101] have examined the
opportunity of predicting the drug dissolution profile
of tablets with a rate-controlling film coat from whole
tablet NIR spectra. Kirsch and Drennen [87] used
theophylline tablets coated with various amounts of
ethylcellulose and collected the spectra in diffuse
reflectance mode. Reich and co-workers [97–101]
used a transmittance configuration to collect spectra
from Eudragit RL-coated theophylline tablets. In both
cases, reliable quantitative calibration models could
be developed to predict the time required for 50% of
the theophylline to be released. The rationale behind
these approaches is the effect of film coat thickness
and film coat uniformity on both drug dissolution rate
and NIR spectra. It is, therefore, not surprising that the
same authors used NIR diffuse reflectance and transmission spectroscopy to predict film coat thickness
[87,102] and even film coat uniformity [97–99] on
1124
tablets. SEP values for the determination of film coat
thickness [102] were comparable for transmission and
diffuse reflectance mode. However, reliable reference
data were difficult to achieve and were, thus, the
major source of error in the quantitative models.
Prediction of film coat uniformity and related gastroresistance with a conformity approach provided much
better results and required less calibration work [98].
This indeed emphasizes again that bnon-calibratingQ
qualitative chemometric techniques combined with
NIRS are valuable tools to answer quantitative
questions.
5.2.2. Capsules
Besides tablets, capsules are among the most
prominent solid dosage forms. Since hard and soft
capsules differ with respect to manufacturing technology and formulation, i.e. shell and fill composition,
which in turn may affect analytical target parameters
and NIR measurements, they will be discussed
separately.
Hard capsules are a rather versatile dosage form
that can be filled with a variety of formulations, such
as powders, granules, pellets, microtablets, and even
liquids or semi-solids. The empty shell, usually
composed of gelatin and 12–16% residual moisture
acting as a plasticizer, is purchased from a contract
manufacturer and filled on automatic high speed
filling machines. Identity, assay, moisture content
and drug dissolution are the key parameters in hard
capsule quality control. At first glance, NIR spectroscopy is actually an ideal method to simultaneously
determine these parameters from one single measurement, thus, replacing time-consuming compendial
methods. Moreover, stability testing, aiming at the
effect of storage conditions and shell/fill interactions,
might be facilitated. The reality is, however, somewhat more difficult, as will be discussed below.
In 1987, Lodder and co-workers [103] published a
paper describing the use of NIR spectroscopy and a
quantile-BEAST bootstrap algorithm for discriminating adulterated and unadulterated capsules. It is worth
mentioning that this was the first report of NIRS
applied to the analysis of intact dosage forms
following the deaths caused by cyanide-laced capsules
in the early and mid-1980s. The authors reported the
significance of shell color, which induced light
scattering, and sample positioning, which affected fill
monitoring, for NIR measurements on intact hard
capsules. The sources of variance in NIR measurements on hard capsules, being more pronounced than
with tablets, has been stressed in detail by Candolfi et
al. [10]. Positioning and time of measurement were
found to be the most important sources of variance.
Positioning effects were attributed to the loose and
movable filling and the round, smooth, and brilliant
shell, which affected the reflection angles. The time
factor expresses the effect of surrounding conditions,
such as temperature and relative humidity, on the
sample properties, by inducing small changes in the
water content of the gelatin shell.
Taking these aspects into consideration, it is not
surprising that only a few papers mainly focussing on
empty capsule shell properties have been published.
Buice et al. [104] and Berntsson et al. [105] described
NIR moisture determinations of empty capsule shells
using reflectance measurements with a filter and a
grating-based instrument, respectively. Buice et al.
used the time-dependent weight gain upon water
uptake of the transparent capsule shells in a hydrator
at 100% relative humidity as reference data for the
PCR model, and observed an inaccuracy of the NIR
method at high humidities. Several possible explanations were given. However, the most obvious one,
namely structural changes of the gelatin shell induced
at high moisture levels [106], was not considered and
certainly omitted in the PCR model simply based on
the first PC. Berntsson et al. used loss on drying
reference data in the moisture range of 5.6–18% w/w
and obtained best results using MLR based on three
wavelength regions for water and the gelatin backbone, respectively.
Since gelatin is susceptible to cross-linking when
traces of aldehydes are present in the fill, nondestructive monitoring of this reaction is highly
valuable, since it affects the in vitro dissolution rate
of the capsules. Gold et al. [107] published a paper
on NIR reflectance monitoring of formaldehydeinduced crosslinking of hard gelatin capsules.
Although the measurements were performed with
empty capsules, the target parameter for the calibration model was the dissolution rate of amoxicillin
used as a model drug in the fill. The NIR spectra of
stressed versus unstressed capsule shells revealed
changes reflecting new chemical bonds and water
loss upon cross-linking.
Within the last few years, Reich and co-workers
[108–112] have presented a large body of data
demonstrating the potential of NIR transmittance
and reflectance spectroscopy in hard capsule shell
qualification focussing on identification of the gelatin
type, manufacturing and storage-induced structural or
moisture changes, and related performance problems,
such as brittleness. The studies revealed that the
spectral range between 1800 and 2500 nm is favorable
for hard gelatin capsule shell identification and
qualification purposes. Different batches of chemically identical transparent and opaque capsules with
different mechanical performance upon filling, resulting from manufacturing-induced structural changes,
could be distinguished by characteristic band shifts in
this region (Fig. 4). Moisture content evaluation was
found to depend strongly on the type of colorant
present in the shell. Strong correlations of NIR
spectral data with DSC and DMTA test parameters,
e.g. differences in gelatin physical state (Tg), structural order (enthalpy), and viscoelastic properties (EV,
EW) were feasible [108]. In summary, these data
clearly reveal that NIR spectroscopy is a powerful tool
for predicting hard capsule shell performance upon
filling, thus allowing for at-line or even on-line
control of these parameters at capsule filling machines
(see Section 5.3.5).
Soft capsules consist of a lipophilic, hydrophilic or
amphiphilic liquid or semi-solid fill enveloped by a
one-piece, hermetically sealed outer shell. Contrary to
hard capsules, they are formed, filled, and sealed in
one continuous operation. Their shell, having a
thickness in the range of about 500 Am, is usually
Empty Hard Gelatin Capsules -3D- Loading Plot
B1 / elastic
B1 / brittle
Fig. 4. NIR discrimination of elastic and brittle hard gelatin capsule
shells.
1125
composed of gelatin, water and one or two polyol
plasticizers [113,114]. Analysis of soft gelatin capsules, i.e. identity, assay, hardness, moisture content,
dissolution, and stability testing, is usually a very
time-consuming procedure, due to the more or less
complex composition of shell and fill. A nonseparative, multi-sensing method, such as NIR spectroscopy, providing combined chemical and physical
information of shell and fill, would certainly be
desirable. However, only a few papers have been
published dealing with the application of NIR to soft
gelatine capsule analysis [111,115–119]. Several
reasons might be responsible for this: (1) The thick,
often colored gelatin shell strongly absorbs in the NIR
region, thus, more or less complicating NIR measurements of target parameters in the fill. (2) Positioning
for spectra collection can be an important source of
variance, due to shape effects, e.g. variable shell
thickness within the capsule, seam effects, and bicoloring [10]. (3) Room conditioning is required
during NIRS measurements to reduce undesired
effects of moisture changes in the shell [10].
Considering these challenges, it is not surprising
that NIR feasibility studies focussing on shell crosslinking [115], shell moisture content [116], plasticizer
content [116–119] and related physical shell performance [111] have been performed with transparent,
emptied capsules and/or film formulations. Gold et al.
[115] used NIR reflectance measurements to study the
migration of formaldehyde from a polyethylene glycol
(PEG) fill into the shell and its reaction with gelatin.
The authors used clear capsules and extracted the fill
before data collection. The spectral changes clearly
revealed the formation of new chemical bonds and a
depletion of water in the shell with increasing
concentration of formaldehyde in the PEG fill. Only
recently, Reich and co-workers presented a series of
conference proceedings demonstrating the potential of
NIRS for assessing the chemical and physical properties of soft gelatine capsule shells immediately after
processing and upon storage [111,116–119]. To
reduce the variance associated with positioning and
interferences with the fill, the authors used transparent
film formulations instead of soft capsules in their
feasibility studies, which were performed in transflectance mode. The spectral data revealed that the
complex dynamic gelatin/water/plasticizer system of a
soft capsule shell that has been reported in the
1126
literature [113,114], requires careful selection of data
pretreatments and data processing for modelling
moisture and plasticizer content determinations
[116–119]. Moreover, the type of gelatin was found
to be an important issue that should not be neglected.
However, with the appropriate chemometric approach,
robust calibration models were able to reliably
quantify moisture (range: 6–12% w/w; SEP= 0.3%;
Karl Fischer reference data) and plasticizer content
(range: 0–50% w/w relative to gelatin; SEP= 1.3%) in
different formulations with respect to gelatin and
plasticizer type [116]. These results clearly indicate
that understanding the NIR spectral changes of soft
gelatin capsule shells associated with water and
plasticizer changes is a prerequisite for future applications of NIR spectroscopy in soft capsule quality
control and stability testing.
5.2.3. Lyophilized products
Lyophilization is usually performed to increase the
storage stability of hydrolytically unstable drugs that
are intended to be used as injectables or to achieve an
instantly soluble oral dosage form. High cake porosity, low residual moisture, and, in the case of proteins,
an amorphous, glassy state are the most prominent
quality criteria of lyophilized products.
Traditionally, the moisture content of lyophilized
products is determined by time-consuming methods,
such as Karl Fischer titration. In addition, the
procedure requires the vial to be opened for analysis.
Moisture determination with NIR diffuse reflectance
techniques can be performed in a fast and noninvasive manner through the glass vials. Due to these
advantages, the NIR technique has been welladopted in the pharmaceutical industry for efficient
moisture content determination of lyophilized products. Early and recent scientific papers in this field
[120–128] have focussed on the investigation of
parameters affecting measurement accuracy, such as
cake dimensions [120,125], particle size [123],
porosity [123,124], and formulation changes [124].
Derksen et al. [123] used the NIR approach for
stability testing and correlated moisture content data
with the concentration of the active ingredient to
calculate product shelf-lives. Only recently, Sukowski and Ulmschneider [125] described high speed
AOTF-based NIR measurements of lyophilized vials
for moisture compliance, i.e. release testing.
Interestingly, very little data is available on the use
of NIRS for quality control of lyophilized proteins
[124,126–128]. Lin and Hsu [124] used five different
proteins to evaluate the accuracy of NIR moisture
content determinations using different chemometric
approaches. The results revealed differences between
the proteins with respect to calibration modelling.
Reich and co-workers [126,127] reported the use of
NIR spectroscopy to evaluate stress-induced structural
changes of proteins and stabilization effects of sugars
upon lyophilization, storage, and rehydration. Spectra
of stressed and unstressed proteins revealed changes
associated with the primary, secondary, and tertiary
structure of the proteins. Sensitive amide I, II and III
bands and the water absorption band could be used for
the assessment of protein structural changes and
aggregation, moisture content changes, and even the
physical state (Tg) of the lyophilized product. Based
on MIR reference data, reliable calibration models for
the determination of changes in the a-helical structure
were achieved [126]. In addition, feasibility of NIR
qualification and quantification of amorphous to
crystalline transitions as a function of storage conditions were shown.
Although there are still a number of challenges to
overcome, it can be expected that in the near future
noninvasive NIR measurements will at least partly
replace mid-IR measurements for stability testing of
lyophilized proteins. Moreover, this approach is
interesting for on-line and in-line process monitoring
(see Section 5.3.2).
5.2.4. Polymeric implants and microspheres
Within the last 20 years, polymeric implants and
microspheres have gained increasing interest as
parenteral drug delivery systems to provide sustained
release profiles. The matrix of such systems usually
consists of a hydrophobic, non-degradable polymer
and optionally a water-soluble pore-forming additive,
or a biodegradable polymer, such as polylactide-coglycolide (PLGA). Quantitative analysis of active
ingredients and/or release-controlling excipients
within these dosage forms usually involves destructive extraction procedures. Moreover, release testing
is time-consuming and often requires huge amounts of
test samples, since these dosage forms are sometimes
formulated to release the active component over
weeks or months.
Lysozym in tablet after incubation [mg]
- NIR measurement
The application of NIRS as a fast and nondestructive alternative method for quantification of
excipients and actives within polymeric drug delivery
systems, such as implants, films and microspheres has
been reported in the literature by two different groups.
Brashear et al. [129,130] investigated the use of NIR
reflectance measurements for quantification of an
active compound, namely lomefloxacin HCl, and a
pore-forming excipient, namely polyethylene glycol
(PEG) 600, in poly(e-caprolactone) microspheres and
implants fabricated by a melt-mold technique. Analyte
specific wavelength selection and second derivative
transformation followed by PLS modelling allowed
for excellent correlations with UV results for the
active and weight-based theoretical values for PEG,
respectively. Reich and co-workers [131–135] used
NIR transmittance and reflectance spectroscopy
together with analyte specific wavelength selection,
second derivative transformation, and PLS data
processing to determine theophylline and quinine
content (0–20% w/w) within PLGA microparticles
and tablets [132], and lyophilized protein/sugar
mixtures (absolute protein content: 0–2.5% w/w) in
lipid matrices [134].
The same group described the application of NIR
transmittance and reflectance measurements for monitoring matrix hydration, matrix degradation, and drug
release (theophylline and lysozyme) from biodegradable PLGA tablets, films and microspheres [131–
1127
133,135]. The studies revealed that release monitoring
of drugs from PLGA matrices is a great challenge,
since upon incubation in buffer solution the polymer
hydrates and slowly hydrolyses, and the matrix
erodes. Spectral changes recorded from tablets, films
or microspheres, therefore, comprise not only the
information of the decreasing drug content, but also
the information of the changing structure of the
polymer matrix. Anyhow, reliable calibration models
could be obtained for both dried and hydrated
samples, thus, indicating the potential of NIRS even
for the analysis of complex matrix systems (Fig. 5).
5.3. Process monitoring and process control
Noninvasive monitoring of all relevant process
steps leading to a pharmaceutical drug product is an
integral part of the PAT paradigm of real-time or
parametric release and quality by design (see Section
4.2). Ideally, the pharmaceutical survey chain should
include raw material income (see Section 5.1), all unit
operations leading to intermediates and final products,
and packaging.
The noninvasive and multivariate character of NIR
techniques provides an interesting platform for
pharmaceutical process monitoring and control.
Although most of the reported applications of NIR
spectroscopy in the pharmaceutical industry are offline or at-line, there are also some on-line and in-line
Lysozym release from PLGA
Validation Spectra f(x)=0.9601x+0.2566 r=0.974755
8
Calibration Spectra f(x)=0.9777x+0.0964 r=0.988771
6
4
2
Transmittance
SEP = 0.42
2
4
6
8
Lysozym in tablet after incubation [mg] - Reference measurement
Fig. 5. Quantitative calibration model for NIR determination of in vitro lysozyme release from poly(d,l-lactide-co-glycolide) tablets (PBS pH
7.4/37 8C).
1128
applications. In this section, the current state and
future potential of NIR techniques in pharmaceutical
at-line, on-line, and in-line process monitoring and
process control will be reviewed and discussed, with
the main focus on technological unit operations that
are critical for the manufacture of solid dosage forms.
A discussion on chemical reactions, crystallization
and fermentation processes, or extraction and purification procedures, all relevant operations in the
production of pharmaceutical raw materials, is
beyond the scope of this paper and will not be
considered. For these topics, the interested reader is
referred to an excellent textbook chapter dealing with
chemical reaction monitoring [136] and some interesting papers containing a comprehensive discussion
of chemical reaction [137,138], polymorph conversion [139,140] and bioprocess [141–143] monitoring
with NIR spectroscopy.
5.3.1. Powder blending
Mixing is a fundamental and critical process in the
manufacturing process of solid and semisolid pharmaceutical dosage forms. The ultimate goal of any
mixing procedure is to achieve an bideal mixQ, i.e. a
situation where the components of a mixture are
homogeneously distributed. In practice, this cannot be
achieved in many cases, in particular when dealing
with powder blends, since the nature of an boptimalQ
powder blend may be rather diversified depending on
the material characteristics and the blender type [144].
Pharmaceutical powder blending processes are, therefore, optimized during development in such a way as
to stop the process when the mixture homogeneity is
within a pre-defined bspecificationQ regarding active
content uniformity.
Current approaches to assess powder blend homogeneity are time consuming and hampered by
sampling errors [144], since they involve the removal
of unit-dose samples from defined mixer locations
using a sample thief, the extraction of the active drug
from the sample matrix, and the drug content analysis
by either HPLC or UV spectroscopy. The distribution
of individual excipients is typically assumed to be
homogeneous if the active ingredient is uniformly
distributed. In the traditional pharmaceutical sense,
blend homogeneity obviously addresses only the
distribution, i.e. the content uniformity of the active
drug substance while assuming that the excipients are
also evenly distributed. The role of the excipients,
which not only improves dosage form compliance, but
also affects the technological and biopharmaceutical
performance of the formulation, is simply neglected.
Considering these disadvantages of traditional
powder blend monitoring procedures, the potential
value of a noninvasive NIR on-line or in-line approach
is evident. NIR monitoring of powder blending can be
performed with fiber-optic reflectance probes, thus,
minimizing assay time and sampling error. Moreover,
since most pharmaceutical active ingredients and
excipients absorb NIR radiation, NIR measurements
can provide homogeneity information regarding all
mixture components. The multi-sensing property of
NIR diffuse reflectance spectra, resulting from absorption and scattering, provides a bmultivariate fingerprintQ of both chemical and physical sample properties.
The use of NIR spectroscopic techniques for
powder blend uniformity analysis has been reported
by several authors using off-line analysis of samples
taken from different blender locations at various
blending times [145–147], and on-line or in-line
monitoring of powder mixing [148–153]. For on-line
and in-line monitoring, two different approaches of
spectral data acquisition have been used, namely in a
bstop-startQ fashion, where the blender is kept stationary during NIR measurements, and in a bdynamicQ
fashion with moving samples.
Sekulic and co-workers [148] were among the first
who reported the use of a NIR fiber-optic probe
inserted in the axis of rotation of a tumble blender for
real on-line stop-start measurements at different times
of the blending process. Only recently, El-Hagrasy
[154] pointed out that multiple spectral sampling
points in the blender are essential for accurate and
precise estimation of mixing end points when using
the stop–start fashion. This result was further substantiated by the additional use of a NIR camera that
enabled large spectral images of the blend to be
obtained (see also Section 6.3).
To allow proper in situ analysis of moving powder
blends, the effect of sample movement on the spectral
response was addressed in detail by Berntsson et al.
[155,156]. The authors realized that sample movement can cause unwanted spectral artefacts when
heterogeneous samples are analyzed with a dispersive,
mechanically scanning grating spectrometer. The
performance of an FT spectrometer was found to be
suitable for the analysis of powders moving at
moderate speeds (up to 1 m s1).
Several data processing strategies for the assessment of blend homogeneity and/or optimal blending
times from NIR measurements have been evaluated in
the literature. Most of these reports were concerned
with qualitative assessments, such as dissimilarity
between the spectra of a mixture and the ideal
spectrum of the mixture [146,151] or a moving block
standard deviation of NIR spectra [146,148,150].
These approaches generally revealed acceptable
results, although Wargo and Drennen [147] suggested
that bootstrap techniques provided a greater sensitivity for blend homogeneity assessment than chi-square
calculations. Some recent papers [156,157] are also
concerned with quantitative analysis, pointing out that
quantitative analysis is a prerequisite for a complete
resolution of the chemical and physical properties of
the mixture. Non-linearity, which was found to be a
feature of powder blends containing coarse and fine
particles, was not a problem when using a cubic PLS
calibration.
To summarize, it can be concluded that on- and inline powder blend monitoring with NIR spectroscopy
is not an easy task, but feasible and in line with the
PAT paradigm of real-time release, focussing on
continuous process understanding and quality control
of all production steps, rather than a final product
control only.
5.3.2. Drying
The manufacturing process of a solid pharmaceutical dosage form usually involves several steps, often
including at least one blengthyQ drying process,
resulting from the time required to dry the material
plus the time to analytically verify the drying
endpoint. Fluid-bed drying and tray drying in a large
oven are the most frequently used methods for wet
granules. Microwave vacuum drying is another
option, although less popular. Freeze- and spraydrying are the methods of choice for temperature- and
moisture-sensitive drug substances. Current methods
to determine drying endpoints include indirect in-line
methods, such as temperature measurements, and
direct off-line moisture analysis of samples taken
from the dryer. Since O–H vibrations of water exhibit
a large absorption in the NIR region, on-line
monitoring of moisture levels using NIR fiber-optic
1129
probes is a feasible option to optimize drying times.
Several approaches, including microwave, vacuum,
fluid-bed and freeze drying processes, have been
described in the literature.
White [158] published a paper in 1994 reporting
the use of NIR for on-line moisture endpoint detection
in a microwave vacuum dryer. The calibration
equation used NIR absorbances of water and the
matrix measured at 1410, 1930 and 1630 nm,
respectively. For samples containing less than 6%
moisture, NIR values were within 1% of the Karl
Fischer reference data with a SEP of 0.6%. At
moisture levels above 6%, a bias was observed,
which was attributed to sampling limitations and the
broad range of moisture contents (0.7–25.7%) considered in the calibration. Changes in drug content of
the granules did not affect the prediction of moisture
content, thus, demonstrating the robustness of the
calibration model.
The work of Harris and Walker [159] involved
real-time quantification of organic solvents, water and
mixtures thereof, evaporating from a vacuum dryer. A
fiber-optic coupled AOTF-NIR spectrometer was used
for data collection from the vapor stream and a
balance was placed in the dryer to record the reference
data. PLS calibration models were built for on-line
prediction of optimal drying times. Morris et al. [160]
and Wildfong et al. [161] used NIR in-line monitoring
to visualize the different stages during a fluid-bed
drying process and to accurately determine the
endpoint of accelerated fluid-bed drying processes.
Only recently, Zhou et al. [162] described the
advantage of NIRS for in-line monitoring of a drying
process with concomitant distinction between bound
and free water of a drug substance forming different
hydrates. The study revealed that NIRS can serve as a
tool to ensure that the desired hydrate form is
achieved at the end of a drying process.
An interesting paper on the in situ monitoring of a
freeze-drying process has recently been published by
Brqlls et al. [163]. A NIR fiber-optic probe fitted to a
FT spectrometer was placed in the center of a vial 1
mm above the bottom. An aqueous PVP solution was
used as a model formulation. NIR monitoring of the
different stages of the process, namely freezing,
primary, and secondary drying, was able to detect
the freezing point, completion of ice formation, and
transition from the frozen solution to an ice-free
1130
material. Moreover, NIR spectra provided new information about the drying process, such as the
desorption rate and the steady-state value at which
drying was complete. These results clearly indicate
that the application of an in situ NIR configuration
offers the possibility of studying product characteristics during freeze-drying, thus, increasing our
understanding of important parameters in the formulation development of lyophilized products.
5.3.3. Granulation
The production of tablets often requires a granulation step to improve powder flow and compaction
characteristics, as well as to achieve content uniformity. Wet granulation is usually performed in a high
speed mixer or a fluid-bed granulator and comprises
the following critical steps: wetting, granule formation
and drying. At-line or in-line monitoring and endpoint
determination of wet granulation processes with NIR
spectroscopy offers the possibility of simultaneously
determining particle size and moisture content. Moreover, water/excipient interactions, hydrate formation,
and/or blend segregation may be assessed easily. The
following examples taken from the literature will
illustrate the potential and limitations of granulation
process monitoring with NIR spectroscopy in both
formulation development and in routine production.
In 1996, List and Steffens [164] published a paper
on NIR in-line monitoring of a wet granulation
process in a mixer granulator. The process was
stopped after certain time intervals and a NIR sensor
probe within the mixer recorded the spectra. A reliable
quantitative PLS calibration model for moisture
determination of a placebo mixture ranging between
6 and 15% w/w was developed and validated using
Karl Fischer reference data. Best results were obtained
with the following spectral pretreatments: wavelength
selection (5000–5500 cm1), normalization, and first
derivative. The authors discussed the limitations of
transferring placebo calibrations to active products
and demonstrated the feasibility of qualitative NIR
particle size monitoring during granulation.
Watano and co-workers [165,166] were among the
first who reported the use of a NIR sensor for moisture
monitoring and process automation of an agitation
fluid-bed granulation process. A fixed-wavelength
NIR filter instrument was used to study the effects
of operational variables on the NIR moisture measure-
ments. The authors observed a significant effect of the
liquid flow rate and the process air temperature [166].
Frake et al. [167] reported the use of in-line NIR to
investigate granule water uptake and particle size
changes during aqueous top-spray fluid-bed granulation. During the process, spectra were obtained every
2.5 min with a mounted fiber-optic probe fitted to a
grating-based spectrometer ranging from 1100 to
2500 nm. To determine moisture content quantitatively, and, thus, allowing for exact endpoint determination, the second derivative absorbance changes at
1932 nm were calibrated against LOD and Karl
Fischer reference data. A linear relationship was
obtained with SEC values in the order of 0.5% for
both models ranging from 1.5 to 11% w/w of
moisture. For particle growth monitoring, the authors
tried to develop another calibration model, again
based on one single wavelength only, namely 2282
nm. However, considering the complex full range
spectral effects of particle size changes (see also
Section 5.1), it is not surprising that the authors failed
to develop an acceptable quantitative calibration
model for particle size determination.
Goebel and Steffens [168] presented successful
data for a simultaneous on-line determination of
particle size and moisture content of samples in a
fluid-bed granulation process using a FT spectrometer.
The robustness of the PLS calibration models, based
on Karl Fischer and laser diffraction reference data,
was evaluated by applying them to development and
pilot-scale plants. The results clearly revealed that
particle size measurements are a greater challenge for
NIR on-line monitoring configurations than moisture
content determination, a fact that was attributed to
sample presentation, e.g. density effects and certain
variables of the fiber-optic probes.
Rantanen and co-workers published a series of
papers [169–171] dealing with the evaluation of a
NIR sensor of only a few wavelengths for in-line
moisture monitoring of fluid-bed granulation. In one
of the papers [171], the authors investigated the effect
of particle size, particle composition and binder type
on NIR moisture monitoring using a full range off-line
FT spectrometer. The study revealed that wetting and
particle growth changes the reflection and refraction
properties of the granules in a complex manner,
depending not only on the wavelength, but also on the
absorption properties of the powder matrix and the
binder type. Calibration of in-line NIR moisture
measurements, even with a fixed-wavelength setup,
therefore, requires understanding and consideration of
these factors affecting NIR signals.
The use of spectral changes of solid powders and
granules associated with moisture uptake and/or
moisture loss is not limited to moisture content
determinations. They can help to understand the
chemical and physical performance of active compounds and excipients in wet granulation processes.
Buckton et al. [172] used NIR to study the effect of
granulation on the structure of microcrystalline
(MCC) and silicified (SMCC) microcrystalline cellulose and to explain the compressibility changes of
MCC after wet granulation. It was found that MCC,
SMCC and wet granulated SMCC had essentially
identical physical structures, while wet granulated
MCC exhibited structural changes in the NIR spectrum related to C–H bonding. With the NIR assessment of the altered physical structure, it was possible
to explain the change in compressibility of MCC after
wet granulation.
Derbyshire et al. [173] used NIR together with
other analytical techniques, namely DSC, NMR, and
TDS, to study the molecular properties of water in
hydrated mannitol. In accordance with the results
obtained from the other methods, NIR spectral data at
5172 cm1 (O–H bond of water) and 5930 cm1
(C–H stretching peak) clearly indicated two transition
points for the coordination between water molecules
and mannitol molecules, namely at 0.11 and 0.25 g/g,
respectively. The authors speculate that the transitions
are associated with different stages of microdissolution of the solid, thereby changing the hydrogenbonded network between water and mannitol, e.g. the
molecular response of water and mannitol in the
spectra. This result argues for the potential of NIR inline measurements in predicting the quantity of water
required for the successful formation of granules
[174].
With the opportunity to monitor solid/water interactions, i.e. to detect different states of water
molecules in a solid, it is not surprising that NIR
spectra may also provide information on pseudopolymorphic transitions during wet granulation. In two
subsequent papers, R7sanen et al. [55] and Jorgensen
et al. [56] demonstrated the efficiency of NIR
spectroscopy to study the state of water and, thus,
1131
the hydrate formation of anhydrous theophylline and
caffeine during wet granulation.
5.3.4. Pelletization
Interestingly, only little literature data is available
on NIR monitoring of pelletization. In 1996, Wargo
and Drennen [175] developed an at-line NIR method
to monitor the layering of non-pareil seeds with an
aqueous suspension containing diltiazem HCl, polyvinyl pyrrolidone, and micronized talcum. Three
independent calibration models were developed to
determine endpoint pellet potency of 15, 30 and 55%
w/w diltiazem beads. The models were successfully
transferred from a laboratory scale to pilot scale.
Radtke et al. [176] described in- and at-line NIR
configurations for moisture monitoring during matrix
pellet production in a rotary fluidized bed. The authors
found out that sample presentation is as critical in this
case as in granulation process monitoring.
5.3.5. Tabletting and capsule-filling
High speed automatic capsule filling and tabletting
machines require non-segregating powder blends or
granule mixtures with good flow characteristics to
work properly, and ensure content uniformity and
consistent dissolution profiles of the final product. In
practice, segregation of free-flowing particulate mixtures with differences in particle size and/or density is
likely to occur through inherent vibrations during
blender discharge, batch transfer to the filling or
compression area, and even within the equipment.
Since NIR techniques are able to recognize
chemical and physical changes of particulate blends
[177], whole tablets and filled capsules, noninvasive
NIR monitoring of tabletting and capsule filling, from
the very beginning to the very end of the process,
would be valuable to increase production speed and
improve product quality. A NIR sensor on the feed
hopper of a capsule-filling machine or a tablet press
could effectively identify the powder mixture and
detect segregation problems of particulate matter upon
feeding the equipment. The final product could be
further assessed for content uniformity, dissolution
properties, and, in the case of tablets, for hardness (see
also Section 5.2). Indeed, there are some industrial
approaches leaning in this direction, although they
have not yet been fully exploited, due to limitations in
spectra collection of tablets or capsules produced at
1132
high speed. However, it might be expected that
progress in process instrumentation and chemometric
data processing will speed up the development of NIR
process monitoring in tabletting and capsule filling in
the near future.
5.3.6. Film coating
Film coating is a process commonly employed in
the pharmaceutical industry to either improve the taste
or swallowing of tablets, or to control drug dissolution
rate from the solid dosage forms. Regardless of the
intended use, the functionality of a film coat is closely
related to its thickness and uniformity around the solid
core. In most production settings, the endpoint of a
coating process is determined by in-process sample
acquisition, the weighing of a known sample size and
the determination of the theoretical amount of applied
polymer. Correct film coat thickness and uniformity
are evaluated indirectly by disintegration and/or
dissolution testing. In the PAT sense, this analytical
procedure has two major disadvantages: first, determination of mass increase does not account for mass
loss of core material, thus, reducing the accuracy of
the method; and secondly, disintegration and/or
dissolution testing are only indirect, rather timeconsuming methods for the measurement of coating
levels and uniformity.
NIR techniques, on the other hand, allow for a
rapid, noninvasive at-line and in-line monitoring and
control of film coating processes prior to biopharmaceutical testing. Kirsch and Drennen [178] and Wargo
and Drennen [175] were among the first to describe
the use of NIR for at-line monitoring of film coating
processes on tablets and pellets. A Wurster column
was retrofitted with a sample thief, allowing withdrawal of 10-tablet samples during coating. Samples
were collected after different time intervals and
measured on a grating-based NIR spectrometer in
reflectance mode. In the case of pellets [175], coating
samples were classified by a bootstrap pattern
recognition technique. The bootstrap standard deviation plot made a qualitative identification of coating
endpoints possible. In the case of tablets [178],
quantitative calibration models for the determination
of applied polymer solids, namely ethylcellulose and
hydroxypropylmethyl cellulose formulations, were
developed based on mass increase reference data (0–
30% w/w) corrected for core attrition. The NIR
method provided predictions of applied polymer films
with SEP values of 1.07% or less, depending on the
coating formulation. For pigment-free coating formulations, the calibration model was based mainly on
distinct absorption peaks of the coating polymer. In
formulations containing high concentrations of waterinsoluble dyes and opacifying agents, such as titanium
dioxide, baseline shifts were the primary spectral
change caused by an increase in film thickness.
Subsequent papers on this topic were published by
Andersson et al. [179,180] who described an industrial in-line approach for film coat monitoring of
pharmaceutical pellets with fiber-optic probes. Calibration models for the determination of film coat
thickness were based on reference data obtained from
image analysis [181].
Despite these interesting and excellent papers
clearly reflecting the great value of NIR techniques
for at-line or in-line monitoring of a coating process,
the multivariate potential of NIR spectroscopic methods has not been fully exploited in this field. As
indicated by Reich and Frickel in a series of conference
proceedings [97–102], NIRS could be implemented as
a useful at-line or in-line tool to survey and determine
the effect of process conditions on film coat uniformity
(Fig. 6) and related biopharmaceutical properties (see
also Section 5.2.1). As will be discussed in section
6.3, imaging techniques might be an additional tool to
improve product quality and the production speed of
film-coated dosage forms [182].
Fig. 6. NIR discrimination of Eudragit L film coats on tablets; effect
of spraying temperature before ageing (20bT: 20 8C, 30bT: 30 8C)
and after ageing (20aT, 30aT).
5.3.7. Packaging
Packaging is the last step in the production line of
a pharmaceutical product. To ensure the product
safety of pharmaceuticals, a last identity check of the
product on the packaging line would be highly
desirable. Such an inspection system based on the
combination of a conventional high resolution camera
with an on-line diode NIR spectrometer ranging from
900 to 1700 nm at 6 nm resolution has been
developed recently. The system is supposed to
perform a 100% identity check at full line speed
(i.e. 12,000 tablets per minute) before closing the
blister. The potential of this type of equipment has
been evaluated in a feasibility study [183]. Using
hard gelatin capsules of different shell and fill
compositions, the authors could demonstrate that the
real-time algorithms used in this system work as
reliably and accurately as a PCA-based data evaluation of spectra collected on an off-line lab spectrometer to ensure the identification of flawed products.
It may, therefore, be expected that other configurations based on high speed NIR spectrometer or NIR
imaging techniques will be developed in the near
future for identity check on packaging lines.
FPA
1133
6. NIR imaging
6.1. Basic principles and instrumentation
NIR imaging is a combination of NIR spectroscopy
with digital image processing. A NIR imaging system
is basically composed of an illumination source, an
imaging optic, a spectral encoder selecting the wavelengths, and a focal plane array (FPA) as indicated in
Fig. 7. NIR light from an illumination system is
focussed upon the sample. The diffuse reflectance
image of the sample is collected by an imaging optic,
the configuration of which depends on the sample size
and type. For macroscopic or microscopic images a
focusing lens or a microscope objective are used,
respectively. Data collection proceeds by recording a
series of images on the near-infrared (i.e. InSb or
InGaAs) FPA at each wavelength position selected by
a spectral encoder, such as a liquid crystal tunable
filter element (LCTF) or an interferometer. The result
is a three-dimensional data set, known as a spectral
hypercube with the x and y axis representing spatial
information and the z axis representing the spectral
information.
Signal
Processing
Filter
False Colour Image
Imaging
Optic
Sample
Fig. 7. Basic configuration of a near-infrared imaging system.
1134
Regarding instrumentation there are basically two
different approaches. The first approach is the wavelength scanning method, also known as the bstaring
imager methodQ. Sample and camera are kept stationary and single images are recorded for each
wavelength. The spectral information is provided
either by a number of discreet filters, by tuneable
filters, or by combination with an imaging Fouriertransform spectrometer. The images recorded for the
different wavelengths are combined by the software
and the spectra calculated. The second approach, also
known as bpush-broom scanningQ method, requires a
relative movement between camera and sample to
scan over the surface. The imaging system records the
spatial information linewise and provides the spectral
information for each pixel along the line by projection
along the second axis of the two-dimensional camerachip. The spectral encoding is provided by either
linear variable filters, a digital micro-mirror array in
combination with a grating, or dispersive optics. The
computer software combines the slices, derives the
second axis and, thus, reconstructs the full image.
Experimental setups based on the staring imager
method are mainly used in research and quality
control laboratories with data acquisition times of
typically 2 min or less. The second approach is used
for conveyor belt survey with data acquisition times
depending on the spectral encoder. A detailed
description of the different principles can be found
in some recent textbooks [184,185].
6.2. Analytical targets and strengths
Conventional, i.e. non-imaging NIR spectroscopy,
analyzes the sample in bulk and determines an
average composition across the entire sample. NIR
imaging, on the other hand, provides information
about the spatial distribution of the components
comprising the sample. It is, therefore, a powerful
bline extensionb of conventional NIR analysis in a
number of different ways [186]:
! The opportunity to visualize the spatial distribution
of a chemical species throughout the sample enables
the degree of chemical and/or physical heterogeneity within a given sample to be determined.
! The array-based spectral sensing of a NIR imaging
system also allows for trace sample measurements,
because the spectral data are collected in parallel
and, thus, are not hampered by a dilution effect in
the same way as NIR bulk measurements are. This
is a great advantage over conventional NIRS when
analyzing low dose actives or excipients in a
pharmaceutical formulation.
! Moreover, NIR imaging enables quantitative information to be obtained without running separate
calibration samples, since pure component spectra
are directly available from the spectral imaging
data cube of heterogeneously mixed samples. This
approach can help to save time and money when
building a quantitative calibration model for
pharmaceutical applications, in particular for
expensive peptide or protein drug formulations.
NIR spectroscopic imaging has only a short
history when compared with MIR and Raman
imaging techniques. This is due to the fact that its
advantages over Raman and MIR imaging techniques, such as adaption to a wide variety of fieldsof-view (FOV) and extreme tolerance to variations
in sample geometry, have only recently been fully
exploited [186]. With the use of simple quartz–
tungsten halogen sources and an image filtering,
instead of a source filtering approach, NIR imaging
techniques enable wide-field illumination for a
variety of magnifications and imaging modes,
ranging from around 0.2 to 125 mm. In addition,
flatness of the sample is not a prerequisite as in
Raman and MIR imaging. On the contrary, NIR
imaging systems allow experiments to be performed
on very irregular samples, since NIR imaging
systems perform well in the reflectance mode with
large depths-of-field and an excellent signal-to-noise
ratio of the arrays.
6.3. Pharmaceutical applications
With the addition of spatial information and
parallel data collection, NIR imaging certainly meets
the challenging analytical needs of pharmaceutical
quality and process control, and may serve as a
versatile adjunct to conventional, non-imaging NIR
spectroscopy in many fields. Despite the obvious
strengths of NIR imaging techniques, the number of
scientific papers and technical notes describing their
practical use is limited and mainly in other fields,
e.g. plastic sorting [187], high-throughput screening
of biological material [186] on conveyor belts,
remnant analysis of works of art [188], and
identification of atherosclerotique plaques by means
of an intra-arterial catheter imaging system recently
developed at the University of Kentucky. Pharmaceutical papers, as discussed in more detail in the
following paragraphs, focus on three different
aspects, namely blend uniformity analysis in powders and tablets, composition and morphological
features of coated tablets and multi-layer granules,
and spatial changes in biodegradable PLGA matrix
systems upon matrix hydration, degradation and
active release.
El-Hagrasy et al. [154] used an InSb imaging
camera with discrete bandpass filters encompassing
absorption bands of the blend components, in
addition to a conventional NIR fiber-optic probe in
six sapphire windows mounted at different locations
in a V-blender, to monitor powder blend homogeneity of salicylic acid/lactose mixtures and compare
the potential of the two techniques. Data analysis
indicated the necessity of using multiple sampling
points for mixing endpoint determination by traditional NIRS and clearly revealed that coupling both
techniques might provide a very robust tool for
monitoring powder blending, since the volume of
powder captured by the imaging technique is much
larger.
Koehler et al. [186] demonstrated the use of NIR
imaging to visualize and quantify the spatial distribution of the active ingredient in a tablet. The
authors used an unsupervised PCA score plot to
qualitatively visualise the degree of chemical heterogeneity of the formulation showing the active in
unevenly distributed clumps. An alternate least
square regression method, based on pure component
spectra isolated from the spectral data cube of the
tablet, was used to build a quantitative concentration
distribution estimate of the active in the tablet.
Although in this special case, the active concentration was 20% by weight, the example clearly
demonstrates the strength of NIR imaging for the
analysis of low dose drugs.
Correlation of physical properties and technological functionality of powder blends with their chemical heterogeneity is the approach described by
Hammond and Clarke [189]. The group has used
1135
NIR imaging to identify mixing problems as being
responsible for bad and good flow characteristics of
powder blends, as well as tablet sticking and tablet
fracture. The results clearly reveal that NIR imaging is
a powerful tool for matrix characterization not only in
final product control, but also in research, development and scale-up of solid pharmaceutical dosage
forms, i.e. for process and formulation optimization
purposes.
The same group pointed out that matrix characterization of complex solid dosage forms requires an
understanding of the spatial relationship and interaction of drug formulation components. NIR imaging
was, therefore, used to examine the internal structure
of time-release granules [190]. The chemical image of
a bisected granule (0.9 mm2) was obtained at 10-nm
intervals from 1000 to 1700 nm through a 10
microscope objective with a total acquisition time of
approximately 2 min. In contrast to the visible image,
the NIR chemical image clearly revealed that the
distinct layers and boundaries were consistent with the
expected physical structure and composition of this
particular formulation.
Another interesting application of NIR imaging is
the chemical visualization of coating layers on
tablets. In a technical note published at the AAPS
Annual Meeting in 2001, Lewis and co-workers
[190] showed the chemical image of a sectioned
multilayer-coated tablet. The macroscopic chemical
image depicted the tablet core and two distinct
coating layers of different thickness. Due to the large
field-of-view (FOV), a detailed examination of the
film coat uniformity on the tablet core was not
feasible. Moreover, sectioning of the tablet was
necessary to achieve the multilayer chemical image
of the tablet. However, considering that formulationand/or process-induced microheterogeneities in film
coats on tablets or pellets might have rather
important implications on their biopharmaceutical
properties, the necessity of spectroscopic imaging
techniques for film coat uniformity analysis is
obvious. Interestingly, the application of microscopic
ATR-FTIR imaging rather than NIR imaging has
been reported for this purpose [182]. Nondestructive
chemical images (250 Am 250 Am) of Eudragit FS
30 D film coats were obtained from different areas
(i.e. at the center part and at the edges) of the tablets
to visualize and relate different coating levels,
1136
phase protein adsorption on the PLGA matrix
certainly occurs (Fig. 8).
In conclusion, the literature data discussed in this
section clearly reveal that spectroscopic imaging
approaches, with NIR imaging in particular, have a
huge potential for gaining rapid information about the
chemical structure and related physical or biopharmaceutical properties of all types of pharmaceutical
dosage forms, thus improving product quality and
enhancing production speed.
process and/or curing conditions to film coat
uniformity. The study revealed that, due to its low
penetration depth, ATR-FTIR imaging may provide
interesting new insights in the processes involved in
film coating and, thus, a better understanding and
control of manufacturing defects resulting in functionally important microheterogeneities. Although
using the mid-IR, this example again indicates the
overall great potential of spectroscopic imaging
techniques in research, development, scale-up and
production control of pharmaceutical dosage forms.
Structurally even more complex than film-coated
oral tablets or granules are biodegradable poly(d,llactide-co-glycolide) (PLGA) matrix systems for
parenteral use. As discussed in Section 5.2.4,
hydration, degradation and drug release kinetics can
be successfully monitored by classical NIR spectroscopy, however, without any information on the
spatial changes. In an attempt to fill this gap, NIR
imaging was used (1) to investigate the time-dependent spatial microenvironmental changes within biodegradable PLGA films upon in vitro hydration and
degradation in different media [191], and (2) to
chemically visualize the distribution and relative
abundance of a model protein, namely lysozyme, in
PLGA matrix tablets, immediately after processing
and during the release phase [182]. Within these
studies it could be demonstrated for the first time
without fluorescence-labeling that during the release
7. Concluding Remarks
This review has covered some of the recent
methods and pharmaceutical applications of NIR
spectroscopy and imaging. As a fast and noninvasive
multivariate technique, conventional NIR spectroscopy has already gained wide industrial acceptance
for raw material identification and/or qualification,
and nondestructive chemical analysis of intact dosage
forms. Considering the continuing improvements in
hardware and software design, and the analytical
requirements of the most recent concepts of quality by
design and real-time or parametric release, it is
anticipated that in the near future both NIR spectroscopy and imaging may progressively become routine
methods for pharmaceutical process monitoring and
process control.
50
15
A
B
10
25750
25750
40
5
25700
0
-5
25650
-10
30
Micron
Micron
25700
25650
20
25600
25600
10
25550
25550
0
53400
53450
53500
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53550
53600
53400
53450
53500
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53550
53600
Fig. 8. False-color near-infrared images of lysozyme distribution (10% initial loading) at the surface of a poly(d,l-lactide-co-glycolide) tablet
(A) after 4 days in PBS pH 7.4 and (B) after 14 days in PBS pH 7.4 (T = 37 8C).
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Near-infrared spectroscopy and imaging: Basic principles

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