Analytica Chimica Acta 696 (2011) 84–93
Contents lists available at ScienceDirect
Analytica Chimica Acta
journal homepage: www.elsevier.com/locate/aca
Comparison of near infrared and microwave resonance sensors for at-line
moisture determination in powders and tablets
Claudia C. Corredor ∗ , Dongsheng Bu, Douglas Both
Analytical and Bioanalytical Development, Bristol-Myers Squibb, New Brunswick, NJ, 08901, United States
a r t i c l e
i n f o
Article history:
Received 11 November 2010
Received in revised form 11 March 2011
Accepted 24 March 2011
Available online 15 April 2011
Keywords:
Near infrared (NIR)
Microwave resonance technology (MRT)
Process analytical technology (PAT)
At-line water determination
Powders
Tablets
a b s t r a c t
In this paper we demonstrate the feasibility of replacing KF for water content testing in bulk powders
and tablets with at-line near infrared (NIR) or microwave resonance (MR) methods. Accurate NIR and
MR prediction models were developed with a minimalistic approach to calibration. The NIR method
can accurately predict water content in bulk powders in the range of 0.5–5% w/w. Results from this
method were compared to a MR method. We demonstrated excellent agreement of both NIR and MR
methods for powders vs. the reference KF method. These methods are applicable to in-process control or
quality control environments. One of the aims of this study was to determine if a calibration developed
for a particular product could be used to predict the water content of another product (with related
composition) but containing a different active pharmaceutical ingredient (API). We demonstrated that,
contrary to the NIR method, a general MR method can be used to predict water content in two different
types of blends.
Finally, we demonstrated that a MR method can be developed for at-line moisture determination in
tablets.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Water can affect product quality, shelf life, chemical stability
and reactivity of pharmaceutical products [1–3]. The determination
of the water content in active pharmaceutical ingredients (APIs)
and drug products is important to demonstrating compliance with
the pharmacopeia and quality standards [4]. From a manufacturing viewpoint, moisture in APIs and excipients is a critical quality
attribute (CQA) which can impact drug product manufacturing unit
operations such as granulation, conveyance, compaction, drying,
etc. [5–7]. Karl Fischer (KF) titration is a universally acknowledged
method for measuring water in pharmaceutical products. Although
the technique is reliable under careful controlled conditions, it is
time consuming and destructive, requires the handling of organic
solvents, generates waste and in some cases can give erroneous
results due to side reactions with the KF reagent (such as aldol
condensation and redox side reactions) [8]. Although it has the
potential to be interfaced in production processes [9,10], it is generally not considered a high throughput assay owing to required
sample preparation. If water content is determined to be a CQA, it
would be desirable to be able to use an accurate predictive model
with a minimum set of calibration standards to facilitate determi-
∗ Corresponding author. Tel.: +1 732 227 5223.
E-mail address: claudia.corredor@bms.com (C.C. Corredor).
0003-2670/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.aca.2011.03.048
nations of moisture content as early as possible in the development
of the manufacturing process and for that application to be capable
of being deployed at-line in the process.
Since the publication of the Food and Drug Administration
(FDA) Process Analytical Technology (PAT) guideline: A Framework
for Innovative Pharmaceutical Development, Manufacturing, and
Quality Assurance [11], pharmaceutical companies have undertaken efforts to improve product quality through increased process
understanding and in-process controls rather than solely relying
on end-product testing. These controls are designed in a holistic
manner by embodying ICH Q8, 9 and 10 documents [12–14], which
incorporate risk and quality by design (QbD) into the development
program. An assessment of CQAs of materials in processes leads to
the correct attributes being measured.
Near-infrared (NIR) spectroscopy and microwave resonance
(MR) sensors are analytical approaches used for the timely monitoring of CQAs of materials and the implementation of PAT [15–24].
They are non-invasive techniques that do not require sample preparation and provide real-time data due to their fast acquisition and
processing times. NIR spectroscopy is well suited for measurement of moisture because water shows strong absorption bands
in NIR, most prominent the first overtone of OH stretching at
around 6800–7100 cm−1 (1470–1408 nm) and the combination
band of OH stretching and bending at around 5100–5300 cm−1
(1960–11887 nm). Luypaert et al. reviewed more than 40 applications of NIR for moisture reported until 2007 [16]. Since then,
more applications of NIR spectroscopy for the determination of
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
water in pharmaceutical products have been published [17–20].
Although the major pharmacopoeias have generally adopted NIR
techniques (the European [25] and United States Pharmacopoeia
[26] both contain a general chapter on near-infrared spectrometry and spectrophotometry, respectively), NIR has traditionally not
been considered an amenable technique for quality control (QC
release methods). This may be due to the fact that the NIR method
must be carefully calibrated vs. a reference method, and appropriate reference calibration standards of known moisture content
have to be generated. This calibration phase is time consuming and
requires the use of chemometrics. Due to the time and resources
that have to be invested during the calibration phase, implementation of NIR methods for batch release becomes practical when a
large number of batches of material are to be tested. Additionally, it
could be beneficial to use calibration sets developed with a minimal
number of standards. This would allow the timely implementation
of the NIR method and the release of a batch based on statistical
analysis of hundreds of units.
MR technology is also well suited for quality control and inprocess moisture analysis [27–33]. MR technology is a free-space
technique that allows reflection and transmission measurements in
the microwave frequency region without contact with the sample.
It enables continuous, density independent moisture monitoring of
solid products. MR sensors are based on the interaction of electromagnetic waves with granular or particulate materials. If a product
containing water is passed over a microwave resonance sensor, its
resonance frequency decreases and the half-width of the resonance
curve increases [27,30–33]. The magnitude of these changes can be
correlated to the water content of the samples. The heat increase
for the product to be analyzed is not relevant, as the output in the
measuring field at <10 mW is far below the transmission power of
modern cell phones (1–2 W) [27–30]. Despite the introduction of
MR sensors in the late 1960s as an effective tool for real-time, nondestructive sensing of moisture content in a variety of materials
[33 and literature herein], it was only recently demonstrated the
feasibility and advantages of its use as an on-line PAT tool for pharmaceutical processes [27,28,33]. This could have been the result of
the technical issues and high cost of the first generation of sensors
combined with the past general caution of pharmaceutical companies to the introduction of new probes, due in part to the past FDA
reviewers’ conservatism. Recent developments in solid-state and
planar-circuit technologies provide a variety of commercially available, inexpensive, reliable, and GMP compliant sensors. Contrary to
NIR, MR methods do not necessarily require the use of chemometrics (univariate calibration plots can be developed), making this
technique more amenable for application in cases in which sophisticated chemometric software and expert chemometricians are not
available.
Comparison of at-line NIR and microwave techniques for moisture determination is of great importance, since the basic principles
of their operations are different. For instance, the depth of penetration of NIR light in pharmaceutical powders and tablets measured
in reflectance mode ranges from 0.5 to 2.5 mm (the wide range of
reported depths of penetration can be attributed to several factors
such as wavelength, instrument settings, sample presentation and
physical and chemical properties) ([34,35], and literature therein).
If the depth of penetration is short and the water is not homogeneously distributed in the sample, the NIR determined water would
not be representative of the total water. For example, Dreassi et al.
determined a high percentage error in the water determination in
ranitidine HCl tablets for samples having a water content of less
than 2.5%, when determined by a reflectance NIR method [36].
Contrary to NIR, the stray fields generated in a microwave resonant cavity have penetration depths from 2 to 5 cm, and the water
determined is more representative of the tablet core or the bulk
powder. MR allows the determination of moisture of film coated
85
tablets unlike NIR, where water content must be determined on
uncoated tablets or only measures the coating water content. The
aim of this study is to investigate the use of a microwave sensor for
water determination in powders and tablets and its performance
compared to NIR.
2. Materials and methods
2.1. Materials
Avicel PH102 microcrystalline cellulose (MCC) was purchased
from FMC Biopolymer (Philadelphia, PA, USA). Magnesium stearate
(MgSt), Acetaminophen (APAP) and Hydroxypropyl methylcellulose NF (HPC) were purchased from Sigma–Aldrich (St. Louis, MO,
USA). A proprietary Bristol-Myers Squibb (BMS) active pharmaceutical ingredient was synthesized in house (BMS, New Brunswick, NJ,
USA), and used after purification. Hydranal composite 534805 from
Fluka Analytical. Hydranal Methanol-Dry from Sigma–Aldrich.
Potassium acetate pentahydrate, sodium chloride, magnesium
nitrate hexahydrate were purchased from Sigma–Aldrich.
2.2. FT-NIR instrument
Diffuse reflectance NIR spectra were acquired with an Antaris II®
Fourier-Transform Near Infrared (FT-NIR) analyzer from Thermo
Electron Corp. (Madison, WI), equipped with an InGaAs detector. The software package Result-Integration® accompanying the
FT-NIR instrument was used to acquire the spectra from the instrument. Each spectrum was the average of 64 scans over the range
of 10,000–4000 cm−1 (1000–2500 nm), with 8 cm−1 resolution. All
spectra were recorded through the bottoms of the sample vials
prior to KF titration. Triplicate measurements were made with the
FT-NIR spectrometer. The sample vial was rotated during measurements and inverted in between measurements. Calibration models
were built with partial least squares (PLS) regression, performed
with the Unscrambler® chemometrics software version 9.8 (Camo
Inc., Oslo, Norway). The data was centered prior to analysis.
2.3. Microwave resonance instrument
For the at-line determination of water content in powders and
tablets a Sartorius LMA 320PA microwave moisture analyzer (Sartorius Mechatronics, CO, USA), equipped with a LMA 330RE-026
sensor operating at 2.5 GHz was used. This microwave resonance
device works with a high precision resonance method. The resonance frequency of the sensor system is analyzed by continuous
scanning of the microwave frequency. When the field changes its
polarity rapidly, only the water molecules can follow this change
as they are small and have a strong dipole. This movement requires
energy, which is drawn from the electromagnetic field. This loss
of energy, which depends on the number of water molecules, is
detected. When the product containing water is passed over the
sensor, the resonance frequency decreases (due to a decrease of the
wavelength inside the material, f ) and the half-width of the resonance curve increases (due to losses of microwave energy inside
the material B), as shown in Fig. 1.
The mass-independent microwave moisture value (MW) is
given by:
MW = arctan
B
Wm − W0
B
,
= = arctan
A
f
f0 − fm
(1)
where B (also known as B parameter) is the increase of the halfwidth in Hz, f (also known as A parameter) is the shift in the
resonance frequency in Hz, f0 is the resonance frequency of the
empty resonator in Hz, fm is the resonance frequency of the filled
resonator in Hz, w0 is the half-width of the resonance of the empty
86
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
Table 1
Composition of calibration blends in the range of 1–5% w/w.
Fig. 1. Frequency (GHz) vs. scattering transmission coefficient S21 .
resonator in Hz, and Wm is the half-width of the resonance of
the filled resonator in Hz. The ratio of both quantities is virtually
independent of the mass and thus only a function of the moisture
content.
Samples that were used for calibration were available in the
same condition as samples used for validation purposes. The moisture range and ambient conditions used for calibration matched
later measuring conditions. Temperature compensation was not
applied since the temperature of the samples during calibration and
later measurements remained within ±5 ◦ C. Calibration measurements in the NIR and MR sensors were carried out with the same
samples that were used for determining the reference moisture by
KF method. Samples were kept in a closed vial during measurements. After KF and NIR measurements, the microwave resonance
was measured. The MW value used for calibration and measurement was the mean value of three independent measurements. A
compression test was executed prior to the calibration, following
the manufacture recommended procedure [37]. This compression
test should always be used to find out if there are A parameter (frequency difference) or B parameter (width difference) offsets that
have to be taken into account (when A parameter and B parameter differ from the standard A,B = 0 settings) to acquire density
independent MW values. Typically the compression test was run
with samples containing different moistures in the range expected
for the real samples. The compression test provides one regression
line per moisture value. If all regression lines intersect in the 0,0
point, no AB offset has to be manually input in the calibration. If
the regression lines intersect at a different point, the actual A and
B offset parameters should be manually input in the calibration
screen. Temperature was constantly recorded. Data was collected
and processed using TMV-TEWS® software (version 2.0.0.40). The
TMV software controls the MW sensor via a network PC or the unit
touch screen. The software allows the selection of different sensor
modes, measurement of reference standards for daily check calibration, performance of the compression test, and sample temperature
corrections. The corresponding product settings are recorded and
saved and the configuration is used for calibration and measurement of samples.
APAP (%)
MCC (%)
HPC (%)
Water (%)
KFa
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
32.0
56.0
55.0
15.0
55.0
68.0
20.0
35.0
35.0
28.0
73.0
38.0
18.0
58.0
50.0
28.0
68.0
44.0
10.0
60.0
15.0
22.0
65.0
53.0
25.0
57.0
12.5
55.0
75.0
35.0
36.0
70.0
−
−
33.0
24.0
27.0
10.0
15.0
12.0
40.0
12.5
10.0
6.0
8.0
7.0
11.0
3.0
−
−
2.0
1.0
3.0
−
−
−
−
2.5
4.5
1.0
−
−
3.0
2.73
1.78
2.52
3.48
3.70
0.93
2.65
2.17
1.13
4.83
5.05
3.23
3.03
1.44
4.49
2.82
a
KF values correspond to an average of three measurements.
reagent, Hydranal Composite 5 from Fluka Analytical. The solvent
was changed after each triplicate measurement. The performance
of the titration method was checked by determining the water
content of deionized water. Titration conditions were a minimum
extraction time of 1990s, a start and stop drift of 10 ␮L min−1 , a
polarisation current of 50 ␮A and an endpoint detection voltage of
250 mV. Sample masses ranged between 0.15 and 0.30 g.
The reference KF method for blends with water content from
0.2 to 1% w/w was coulometric KF. Coulometric KF measurements
were carried out at a constant room temperature of 16 ◦ C using a
Methrom 756 KF Coulometer controlled with Tiamo 1.2 software
and Hydranal solution (Sigma–Aldrich) for coulometric titrations.
2.5. Samples
For the calibration plot in the range of 1 to 5% w/w, a total of 16
calibration standards consisting of blends of APAP-MCC-HPC were
prepared. The amount of APAP ranged from 15 to 73% w/w, MCC
from 10 to 75% w/w, HPC from 0 to 40% w/w and water from 0 to
4.5% (Table 1). Blends were prepared in a L.B. Bohle minigranulator. Different amounts of APAP, MCC and HPC were added on the
high shear mixer/granulator and mixed for 5 min (Impeller speed:
1800 rpm. Chopper speed: 800 rpm). In some blends (Table 1),
water was added gravimetrically to the dry blend of APAP and
excipients and blended for an additional 5 min.
For the calibration plot in the range of 0.2 to 1% w/w, a total of 11
calibration standards were prepared, following the same procedure
as previously described for the samples with water content in the
1–5% w/w range (Table 2). In these blends the amount of APAP
ranged from 57 to 95%, MCC from 3 to 25%, HPC from 2 to 33% and
water was not added.
Table 2
Composition of BMS API calibration blends in the range of 1–5% w/w.
1
2
3
4
5
6
7
8
9
10
11
2.4. Karl Fischer instrument
The reference KF method for blends with water content from
1 to 5% w/w was volumetric KF. Volumetric KF measurements
were carried out at a constant room temperature of 16 ◦ C using
a 758 KFD Titrino titrator (Metrohm, Switzerland) controlled with
Tiamo 1.2 software and equipped with a titration stand 703 and
a thermostatic titration vessel. The blends were directly added
to the vessel containing approximately 40 mL of dry methanol
(Merck, Darmstadt, Germany) and titrated with a one-component
Sample
a
APAP (%)
MCC (%)
HPC (%)
KFa
80.0
70.0
57.0
59.0
66.0
63.0
92.0
90.0
86.0
95.0
74.0
17.0
10.0
10.0
14.0
19.0
25.0
5.0
8.0
5.0
3.0
23.0
3.0
20.0
33.0
27.0
15.0
12.0
3.0
2.0
9.0
2.0
3.0
0.78
0.43
0.54
0.66
0.84
1.03
0.24
0.34
0.20
0.16
0.93
KF values correspond to an average of three measurements.
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
Table 5
Prediction results for a validation set, using calibration models in Table 1.
Table 3
Composition of calibration blends with BMS API in the range of 1–5% w/w.
Sample
BMS API (%)
MCC (%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
32.0
15.0
55.0
68.0
35.0
32.0
70.0
18.0
58.0
50.0
55.0
56.0
20.0
38.0
28.0
48.5
68.0
60.0
15.0
22.0
53.0
28.0
15.5
75.0
35.0
36.0
10.0
44.0
65.0
55.0
70.0
38.0
a
HPC (%)
24.0
27.0
10.0
12.0
40.0
10.0
7.0
7.0
11.0
33.0
15.0
6.0
2.0
11.0
Water (%)
1.0
3.0
4.5
3.0
2.0
1.0
0.0
2.5
87
KFa
Pre-processing NIR spectra
Slope
Offset
R2
RMSEP
SEP
2.90
3.64
3.95
1.13
2.16
1.43
5.45
3.10
1.73
4.64
2.71
1.93
2.76
3.45
2.87
4.32
SNV
First derivative
SNV/first derivative
Second derivative
SNV/second derivative
0.94
0.95
0.96
0.95
0.96
0.19
0.14
0.16
0.12
0.20
0.98
0.99
0.98
0.97
0.97
0.20
0.19
0.19
0.22
0.22
0.19
0.19
0.18
0.22
0.21
KF values correspond to an average of three measurements.
The 16 calibration standards consisting of blends of BMS
API-MCC-HPC were prepared in the high shear mixer/granulator
following the same procedure described for the APAP blends
(Table 3). All the blends were stored on tight containers. Moisture was determined by KF method (volumetric KF for blends with
1–5% w/w water content and coulometric KF for blends with 0.2–1%
w/w). KF values correspond to an average of three measurements.
NIR and MW data were collected in a period of no more that 30 min
after the moisture determination.
Tablets of approximately 100 mg weight and 5 mm diameter
were prepared from a blend of APAP (10%), Avicel® 200 (89%) and
magnesium stearate (1%) using a Piccola rotary tablet press (Riva
S. A, Buenos Aires, Argentina) fitted with 8 punch sets. A set of
tablets were exposed to humid air during different time intervals
in order to acquire different levels of moisture content. The relative humidity of the air was either uncontrolled, i.e. ambient air, or
controlled by a saturated salt solutions in sealed desiccators equilibrated at 25 ◦ C. The solutes used in the saturated solutions were
potassium acetate (∼23% RH), magnesium nitrate (∼53% RH) and
sodium chloride (∼75% RH). A set of tablets was placed on an oven.
3. Results and discussion
3.1. At-line moisture determination by near-infrared method
The composition of the blends used for calibration is shown in
Tables 1 and 2. As shown in the Tables, blends with water content
between 1 and 5% w/w contain higher levels of MCC and HPC and
lower level of APAP compared to the blends with water content
between 0.2 and 1% w/w. Due to the quantitative differences of
the two sets, and in order to understand the impact of composition on the NIR spectra and PLS model performance, two different
calibration models were initially developed for each set of blends.
3.1.1. Samples with moisture ranging from 1 to 5% w/w
Fig. 2a shows the NIR spectra of blends of APAP-MCC-HPC
with 1–5% w/w water content after SNV pre-processing. Significant
changes in spectral features of the samples studied were observed
at varying moisture and composition levels. As observed in Fig. 2a,
the NIR spectra of the blends show the two major absorption bands
of water at around 5155 cm−1 and 6895 cm−1 . The absorbance at
these regions increases when the water level increases. These variations in the absorbance spectra are retained in the corresponding
first derivative (Fig. 2b).
Calibration models were developed by correlating the NIR
spectra at the two main spectral regions of water absorption
(7362–6919 cm−1 and 5453–5176 cm−1 ) with water content using
partial least-squares (PLS) regression (Table 4 and Fig. 1a in supplementary material). A randomized design, was employed where the
two major excipients and the water added were varied randomly
with respect to APAP (Table 1). High correlation between major
components was avoided, since component effect on spectral baseline and slope variations due to physical properties were unknown.
Several data pre-processing methods were used and compared,
including Standard Normal Variate (SNV), Multiplicative Scatter
Correction (MSC), first and second derivatives. In the smoothing of
the derivatives (Savitzky-Golay), 21 points window and third order
polynomial were used.
Table 4 summarizes the parameters of the models, including the
slope, offset, correlation coefficient (R2 ), root mean square error of
calibration (RMSEC), and root mean square error of cross validation (RMSECV). PLS figures of merit (selectivity SEL, sensitivity SEN,
detection limit DL and quantification limit QL) were also calculated
for each model [38–40]. In this application, spectra pretreatment
by MSC did not improve the performance of the calibration model,
as previously reported [41] (data not shown).
For all the calibration models shown in Table 4, the slopes and
coefficients of determination (R2 ) are close to one, and the optimal
number of factors is 2. The joint test of significance of intercept and
quadratic term showed that there is no significance of intercept or
curvature at the 95% confidence level for all regression models. The
calibration model constructed using SNV followed by first derivative shows the lowest root mean square error of calibration (RMSEC,
0.14%), lowest root mean square error of cross validation (RMSECV,
0.15%) and best sensitivity (Fig. 1a in supplementary material).
However, since the different calibration models did not greatly
differ and the sensitivity calculated based on Net Analyte Signal
(NAS) is better for models using second derivative, all of them were
used to predict the water content of the samples in an independent
validation set (Table 5). Fig. 1b in supplementary material shows
the NIR predicted water content vs. the reference method (KF) for
independent validation samples in the range of 1–5% w/w. For this
prediction, the best chemometric model in Table 4 was used (SNV
followed by first derivative).
Table 4
PLS calibration models built with different data pretreatments for 1–5% w/w range.
Pre-processing
Slope
Offset
R2
RMSEC
RMSECV
SEN
SEL
DL
QL
SNV
First derivative
SNV/first derivative
Second derivative
SNV/second derivative
0.98
0.99
0.99
0.98
0.98
0.05
0.04
0.03
0.05
0.04
0.98
0.99
0.99
0.98
0.98
0.16
0.15
0.14
0.17
0.16
0.17
0.16
0.15
0.18
0.17
3.16
0.013
0.075
0.001
0.004
0.46
0.59
0.35
0.67
0.36
0.11
0.13
0.18
0.10
0.17
0.33
0.40
0.54
0.30
0.51
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Fig. 2. (a) NIR spectra of blends of APAP-MCC-HPC with different water content (ranging from 1 to 5% w/w) after SNV pre-processing. (b) SNV followed by first derivative
Savitzky-Golay 21 smoothing points of the spectra in (a).
SNV followed by first derivative provided the best prediction
model, with the lowest Standard Error of Prediction (SEP 0.18%
w/w), as shown in Table 5. The RMSEC (0.14% from Table 4) and the
SEP (0.18% from Table 5) are similar, showing that the correlations
are similar for the validation set compared with the calibration set.
Accurate prediction models were obtained with a minimum set of
calibration samples. As previously reported [42], since the magnitude of noise and variability of off-line static samples are greatly
reduced when compared to on-line samples, lower number of calibration samples was required to account for the spectral variability.
The RMSEP and SEP of NIR methods in Table 5 are comparable to
or lower than values for PLS methods reported in the literature
[16,19,20,41,42].
3.1.2. Samples with moisture ranging from 0.2 to 1% w/w
Fig. 3a shows the NIR spectra of blends of APAP-MCC-HPC
with 0.2–1% w/w water content. The absorbance at the 5155 cm−1
and 6895 cm−1 regions increases when the water level increases.
Fig. 3b shows the corresponding first derivative. Calibration models built with different data pretreatment and two spectral regions
(7362–6919 cm−1 and 5453–5176 cm−1 ) for this moisture range
are shown in Table 6. Fig. 2a in supplementary material shows the
calibration model by using SNV followed by first derivative.
The joint test of significance of intercept and quadratic terms
showed that there is no significance of intercept or curvature at the
95% confidence level for all of the regression models. The optimal
number of factors is 3. The calibration model constructed using SNV
followed by first derivative shows the lowest RMSEC (0.036%) and
RMSECV (0.043%). All the calibration models were used to predict
the water content of an independent validation set (Table 7). All
the models were used for prediction, since as previously observed
(Section 3.1.1), they do not greatly differ and the sensitivity is better
for models based on second derivative.
Fig. 2b in supplementary material shows the NIR predicted
water content vs. the reference method (KF) for independent validation samples in the range of 0.2–1% w/w. For the independent
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
89
Fig. 3. (a) NIR spectra of blends of APAP-MCC-HPC with different water content (ranging from 0.2 to 1% w/w) after SNV pre-processing. (b) SNV followed by first derivative
Savitzky-Golay 21 smoothing points of the spectra in (a).
validation set, SNV followed by first derivative provided the best
prediction model, with the lowest SEP (0.052% w/w), as shown in
Table 7. The RMSEP for the reference KF coulometric method was
determined to be 9 × 10–4%.
Since the selected spectral regions and the best signal preprocessing (SNV followed by first derivative) was the same for
both models, and the error of the KF reference methods was determined to be equivalent for both set of samples (no matrix effects
90
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
Table 6
PLS calibration models built with different data pretreatment for 0.2–1% w/w range.
Pre-processing
Slope
Offset
R2
RMSEC
RMSECV
SEN
SEL
DL
QL
SNV
First derivative
SNV/first derivative
Second derivative
SNV/second derivative
0.97
0.97
0.98
0.97
0.98
0.015
0.016
0.010
0.015
0.014
0.97
0.97
0.98
0.97
0.98
0.045
0.042
0.036
0.042
0.040
0.056
0.049
0.043
0.049
0.048
3.75
0.0241
0.021
0.0015
0.012
0.235
0.298
0.289
0.378
0.312
0.045
0.032
0.033
0.03
0.03
0.137
0.096
0.101
0.09
0.08
Table 7
Prediction results for an independent validation set, using calibration models in
Table 6.
Pre-processing
Slope
Offset
R2
RMSEP
SEP
SNV
First derivative
SNV/first derivative
Second derivative
SNV/second derivative
0.91
0.90
0.93
0.85
0.90
0.018
0.033
0.033
0.059
0.049
0.91
0.92
0.96
0.91
0.96
0.078
0.074
0.052
0.079
0.055
0.066
0.067
0.052
0.070
0.053
observed for the volumetric and coulometric KF methods), calibration data from 0.2 to 5% w/w was combined in a single calibration
plot (Fig. 3a in supplementary material, figures of merit are provided in the plot). All independent samples from 0.2 to 5% w/w
were predicted using the general calibration model (Fig. 3b in supplementary material, figures of merit are provided in the plot). This
combined calibration plot can accurately predict water content in
the range of 1–5% w/w. However, the calibration plot in the range of
0.2–1% presents higher sensitivity and selectivity and lower DL and
QL and more accurately predicts samples in this range, compared
to the combined calibration plot.
3.2. At -line moisture determination by microwave resonance
technology
3.2.1. Samples with moisture ranging from 1 to 5% w/w
Similar to NIR, the microwave sensor has to be calibrated against
standard reference methods. Fig. 4a in supplementary material
shows the MR sensor calibration plot using APAP-MCC-HPC blends
with water content between 1 and 5% w/w.
The MW value (given in terms of attenuation and phase shift of
the microwave resonance curve as shown in equation 1 in Section
2.3) was plotted against the water content determined by volumetric KF.
The linear regression revealed the following regression line:
MW = 0.04812 (KF) + 0.01057
(2)
The correlation coefficient for the shown regression line was
found to be 0.992. The joint test of significance of the intercept and
quadratic terms showed that there was no significance of intercept
or curvature at the 95% confidence level. At the measurement conditions specified, the calibration plot is not linear below 1.5% or
above 6.0%.
Fig. 4b in supplementary material shows the comparison of the
reference vs. the predicted moisture values from MR sensor and
NIR, showing the corresponding regression lines. Table 8 shows the
comparison of the prediction results for an independent validation
set by KF, NIR and microwave.
A comparison of the NIR and MR methods is very important
because the principle of water detection is different in both cases.
For instance, the depth of penetration of NIR light in pharmaceutical
powders and tablets measured in diffuse reflectance mode (in the
wavelengths of interest) ranges from 0.25 to 0.5 mm [34,35] while
the stray fields generated in a microwave resonant cavity in the
frequency range of about 2–3 GHz have penetration depths from
2 to 5 cm. MR sensors on the other hand, measures total unbound
water. At frequencies between 2 and 3 GHz, only physically bound
water (including adsorbed water, trapped or liquid-inclusion water
and absorbed water) is determined. Chemically bound water
(water of crystallization) is not monitored as this requires different wavelengths and intensities of the applied microwaves. Careful
consideration of the technique of choice should be paid, particularly
in applications in which anhydrate to hydrate transformation can
take place (such as wet granulation) and the total water is used for
final point determination. The SEP of the KF method was 0.11%. The
relative standard deviation (RSD) of replicate KF determinations of
the same sample was in the range between 0.02 and 0.09% w/w.
These data suggest that a source of the calculated SEP of the NIR
and MR predictions is due to sample non-homogeneity and error
associated with the reference method.
The MR calibration model accurately predicted the water content of an independent validation set. The MR SEP was 0.17%. This
value was similar to the SEP of the NIR method (0.18%). The robustness of the MR calibration model was tested by removing samples
in the calibration set. The calibration model was recalculated after
the reduction, and the water content of the validation test was predicted using the new calibration model. The calibration set could
be reduced to 6 calibration standards with no impact on the prediction results of the independent validation set (Table 8). To ability
to develop calibration plots with low number of standards is beneficial, especially in cases in which the API is in short supply or
expensive. Also, since the MR tool is non-invasive, samples could
be reused for additional analysis. An advantage vs. the NIR method
is that multivariate data analysis is not required. Both NIR and
microwave methods can potentially replace the KF method for the
blends with 1–5 w/w% water content.
3.2.2. Samples with moisture ranging from 0.2 to 1% w/w
Fig. 5a in supplementary material shows the MR calibration plot
using APAP-MCC-HPC blends with water contents between 0.2 and
1% w/w.
The linear regression revealed the following regression line:
MW = 0.0558 (KF) + 0.0031.
Table 8
Comparison of prediction results for an independent validation set for standards
(stds) in Table 1 (1–5% w/w).
Prediction
Slope
Offset
R2
RMSEP
SEP
KF (volumetric)
NIR (14 stds)
NIR (7 stds)
MR (11 stds)
MR (6 stds)
0.97
0.96
0.97
0.93
0.93
0.086
0.16
0.12
0.05
0.07
0.99
0.98
0.97
0.99
0.99
0.10
0.19
0.24
0.17
0.16
0.11
0.18
0.23
0.17
0.15
(3)
The correlation coefficient for the shown regression line was
found to be 0.985. The joint test of significance of intercept and
quadratic term showed that there is no significance of intercept
or curvature at the 95% confidence level. Fig. 5b in supplementary material shows the comparison of the reference vs. the
predicted moisture values from MR and NIR at this moisture range.
Table 9 shows the comparison of the prediction results for an independent validation set by KF, NIR and MR.
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
Table 9
Comparison of prediction results for the three methods for an independent validation set.
Prediction
Slope
Offset
R2
RMSEP
SEP
KF 0.2–1%
(coulometric)
NIR
MR
1.029
−0.018
0.991
0.034
0.0009
0.932
0.980
0.034
0.017
0.963
0.976
0.052
0.037
0.051
0.038
The MR model accurately predicted the water content of an
independent validation set. The MR calibration model performed
better than the NIR method when predicting an independent validation set. The prediction model shows the slope closest to 1.0, the
lowest RMSEP (0.037%), and lowest SEP (0.038%), and is therefore
considered the optimal result.
3.3. Investigation of the feasibility of developing general NIR and
MR calibration plots
The possibility of developing general NIR or MR calibration plots
for different products of related composition but different API was
tested by preparing blends of a proprietary BMS API-MCC-HPC,
as described in Section 2.5. A general calibration plot for blends
with similar excipient composition but different API can potentially
reduce method development time and resources. Fig. 4 shows the
NIR spectra of the BMS API, APAP and blends of BMS API-MCC-HPC
and APAP-MCC-HPC. The spectrum of the BMS API shows characteristic peaks that overlap the absorption in the water regions.
Calibration models using the BMS API-MCC-HPC samples were
developed by correlating the NIR spectra with water content using
PLS regression. As in the case of the APAP blends, a randomized
design was employed to ensure that the component correlation was
minimized and that the PLS solution was specific to water. Similar
to the models developed in Section 3.1, several data pre-processing
methods were used and compared.
For the BMS API-MCC-HPC blends, the best calibration model
used first derivative and loadings in the 7478–6876 cm−1 and
5303–5056 cm−1 regions (slightly different regions that the APAP
method). The slope was 0.98, the correlation coefficient was 0.98,
91
the RMSEC was 0.144%, the RMSEC was 0.16%, and the optimal number of factors was 4. Fig. 6 in supplementary material shows the
prediction results for an independent validation set by using the
best calibration model obtained with the BMS API-MCC-HPC samples and the best APAP model developed in Section 2.5. The best
calibration model developed for the APAP-MCC-HPC blends could
not accurately predict the water content in blends of BMS API-MCCHPC (especially at 2–5 w/w%), although only the API was changed.
Although general NIR water methods have been proposed [20,41],
the development of a universal NIR calibration plot for water in this
case is hindered by the overlap of the API signal in the water region.
Samples of BMS API-MCC-HPC were also used to build a calibration plot in the microwave sensor. Fig. 7a in supplementary material
shows the MR calibration plots using BMS API -MCC-HPC blends
and APAP-MCC-HPC blends. For both materials, MW increases linearly with moisture and the data are superimposed. A small offset
of 0.008% in the regression line of the BMS API was observed with
respect to the APAP regression. The MW values for both types of
blends were plotted against the water content determined by volumetric KF. The linear regression for the general model is:
MW = 0.0455 (KF) + 0.0246.
(4)
The correlation coefficient for the shown regression line was
found to be 0.973. From this equation moisture content in either
material can be determined from a single calibration equation without knowledge of bulk density. The effectiveness of equation 4
in predicting moisture content on an independent validation set
of BMS API-MCC-HPC samples was evaluated (Fig. 7b in supplementary material). As seen in the figure, there is a high degree of
agreement between the set two plots.
Trabelsi et al. demonstrated that moisture content variation in
wheat and soybeans increased linearly and calibration data for both
materials were superimposable, using a microwave sensor operating at 5.8 GHz [32]. As expected, calibration data corresponding
to blends of BMS API-HPC-MCC and APAP-MCC-HPC are superimposable. Excellent agreement of the moisture values determined
by KF and those predicted by the general microwave calibration plot
for BMS API-MCC-HPC blends, demonstrated the utility of a general
microwave calibration model.
Fig. 4. NIR spectra of BMS API (blue), APAP (purple), blend of APAP-MCC-HPC (green) and BMS API-MCC-HPC (for reference to the color information in the figure legend refer
to the on-line version of the article).
92
C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93
Acknowledgements
3.4. Microwave resonance method for moisture determination in
tablets
The feasibility of developing a MR calibration plot for tablet
cores was tested by preparing tablets of APAP-MCC-Mg stearate
as described in Section 2.5. Tablets to be measured were
loosely filled into a plastic holder fitted in the sensor. The
tablet weight was 100 mg, tablet thickness was 4 mm and tablet
diameter was 5 mm. The microwave sample holder was completely filled with approx. 150 tablets for a sample mass of
approximately 15 g per each moisture point. Temperature was
recorded during the entire measurements. However, temperature correction was not required, since the calibration and
prediction samples were measured at room temperature and the
temperature did not deviate more than ±5 ◦ C. Moisture range
and sample conditions used for calibration matched later measurement conditions. Before developing the calibration plot, a
sensor compression test was run as described by the manufacturer.
Fig. 8a in supplementary material shows the MR sensor calibration plot using APAP tablets with water content between 1.5 and
4.5% w/w. The MW value was plotted against the water content
determined by volumetric KF. The linear regression revealed the
following regression line:
MW = 0.0337 (KF) + 0.0503.
(5)
The correlation coefficient for the shown regression line was
found to be 0.995. At the measurement conditions specified, calibration plot is not linear below 1.5% or above 4.5%.
The repeatability of the method was determined as the relative
standard deviation (RDS) of 10 replicate measurements of the same
sample. An RSD of <1.0% of MW signal was found when the mass
in the sample holder changed no more that ±12%. A higher RSD
of MW signal (2.8%) was observed when the mass in the sample
holder changed in the range of ±12–25%. To maintain the highest
reproducibility, the mass in the sample holder was kept as constant
as possible, by filling the sample holder to the same level before
measurement.
Fig. 8b in supplementary material shows the comparison of the
reference vs. the predicted moisture values from MR. The slope of
the regression line was 1.015. The offset was 0.017. The correlation coefficient was 0.993. The RMSEP was 0.134% and the SEP was
0.117%. These values demonstrate that the MR calibration model
accurately predicted the water content of the independent validation set. The RMSEP and SEP for the tablets were lower than the
corresponding values for the powder blends at the same moisture
levels.
4. Conclusions
MR technology has been shown to be a viable means of moisture analysis for bulk powders and tablets. This study demonstrated
that the MR method for bulk powders in the order of 0.5–5%
w/w gave similar results to the NIR method, without the need of
sophisticated chemometric software and also provides the opportunity to utilize fewer calibration standards. The MR method
accurately predicted the water content of powder and tablets,
when compared to the standard KF method. We also demonstrated that a general microwave calibration plot developed for
a particular product can be used to predict the water content of
a different product with related composition but different APIs.
The feasibility of using a universal calibration model significantly
reduced microwave moisture method development for a second
product.
The authors would like to acknowledge Dan Kopec from Sartorius Mechatronics (Arvada, CO, USA) for the loan of the microwave
sensor and Kevin Macias, for the technical support during the tablet
preparation.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.aca.2011.03.048.
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