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Analytica Chimica Acta 696 (2011) 84–93

Contents lists available at ScienceDirect

Analytica Chimica Acta

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omparison of near infrared and microwave resonance sensors for at-lineoisture determination in powders and tablets

laudia C. Corredor ∗, Dongsheng Bu, Douglas Bothnalytical and Bioanalytical Development, Bristol-Myers Squibb, New Brunswick, NJ, 08901, United States

r t i c l e i n f o

rticle history:eceived 11 November 2010eceived in revised form 11 March 2011ccepted 24 March 2011vailable online 15 April 2011

eywords:ear infrared (NIR)

a b s t r a c t

In this paper we demonstrate the feasibility of replacing KF for water content testing in bulk powdersand tablets with at-line near infrared (NIR) or microwave resonance (MR) methods. Accurate NIR andMR prediction models were developed with a minimalistic approach to calibration. The NIR methodcan accurately predict water content in bulk powders in the range of 0.5–5% w/w. Results from thismethod were compared to a MR method. We demonstrated excellent agreement of both NIR and MRmethods for powders vs. the reference KF method. These methods are applicable to in-process control orquality control environments. One of the aims of this study was to determine if a calibration developed

icrowave resonance technology (MRT)rocess analytical technology (PAT)t-line water determinationowdersablets

for a particular product could be used to predict the water content of another product (with relatedcomposition) 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 differenttypes of blends.

Finally, we demonstrated that a MR method can be developed for at-line moisture determination in

tablets.

. Introduction

Water can affect product quality, shelf life, chemical stabilitynd reactivity of pharmaceutical products [1–3]. The determinationf the water content in active pharmaceutical ingredients (APIs)nd drug products is important to demonstrating compliance withhe pharmacopeia and quality standards [4]. From a manufactur-ng viewpoint, moisture in APIs and excipients is a critical qualityttribute (CQA) which can impact drug product manufacturing unitperations such as granulation, conveyance, compaction, drying,tc. [5–7]. Karl Fischer (KF) titration is a universally acknowledgedethod for measuring water in pharmaceutical products. Although

he technique is reliable under careful controlled conditions, it isime consuming and destructive, requires the handling of organicolvents, generates waste and in some cases can give erroneousesults due to side reactions with the KF reagent (such as aldolondensation and redox side reactions) [8]. Although it has theotential to be interfaced in production processes [9,10], it is gen-rally not considered a high throughput assay owing to required

ample preparation. If water content is determined to be a CQA, itould be desirable to be able to use an accurate predictive modelith 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).

003-2670/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2011.03.048

© 2011 Elsevier B.V. All rights reserved.

nations of moisture content as early as possible in the developmentof the manufacturing process and for that application to be capableof being deployed at-line in the process.

Since the publication of the Food and Drug Administration(FDA) Process Analytical Technology (PAT) guideline: A Frameworkfor Innovative Pharmaceutical Development, Manufacturing, andQuality Assurance [11], pharmaceutical companies have under-taken efforts to improve product quality through increased processunderstanding and in-process controls rather than solely relyingon end-product testing. These controls are designed in a holisticmanner by embodying ICH Q8, 9 and 10 documents [12–14], whichincorporate risk and quality by design (QbD) into the developmentprogram. An assessment of CQAs of materials in processes leads tothe correct attributes being measured.

Near-infrared (NIR) spectroscopy and microwave resonance(MR) sensors are analytical approaches used for the timely moni-toring of CQAs of materials and the implementation of PAT [15–24].They are non-invasive techniques that do not require sample prepa-ration and provide real-time data due to their fast acquisition andprocessing times. NIR spectroscopy is well suited for measure-ment of moisture because water shows strong absorption bandsin NIR, most prominent the first overtone of OH stretching ataround 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 appli-cations of NIR for moisture reported until 2007 [16]. Since then,more applications of NIR spectroscopy for the determination of

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ater in pharmaceutical products have been published [17–20].lthough the major pharmacopoeias have generally adopted NIR

echniques (the European [25] and United States Pharmacopoeia26] both contain a general chapter on near-infrared spectrome-ry and spectrophotometry, respectively), NIR has traditionally noteen considered an amenable technique for quality control (QCelease methods). This may be due to the fact that the NIR methodust be carefully calibrated vs. a reference method, and appro-

riate reference calibration standards of known moisture contentave to be generated. This calibration phase is time consuming andequires the use of chemometrics. Due to the time and resourceshat have to be invested during the calibration phase, implemen-ation of NIR methods for batch release becomes practical when aarge number of batches of material are to be tested. Additionally, itould be beneficial to use calibration sets developed with a minimalumber of standards. This would allow the timely implementationf the NIR method and the release of a batch based on statisticalnalysis of hundreds of units.

MR technology is also well suited for quality control and in-rocess moisture analysis [27–33]. MR technology is a free-spaceechnique that allows reflection and transmission measurements inhe microwave frequency region without contact with the sample.t enables continuous, density independent moisture monitoring ofolid products. MR sensors are based on the interaction of electro-agnetic waves with granular or particulate materials. If a product

ontaining water is passed over a microwave resonance sensor, itsesonance frequency decreases and the half-width of the resonanceurve increases [27,30–33]. The magnitude of these changes can beorrelated to the water content of the samples. The heat increaseor the product to be analyzed is not relevant, as the output in the

easuring field at <10 mW is far below the transmission power ofodern cell phones (1–2 W) [27–30]. Despite the introduction ofR sensors in the late 1960s as an effective tool for real-time, non-

estructive sensing of moisture content in a variety of materials33 and literature herein], it was only recently demonstrated theeasibility and advantages of its use as an on-line PAT tool for phar-

aceutical processes [27,28,33]. This could have been the result ofhe technical issues and high cost of the first generation of sensorsombined with the past general caution of pharmaceutical compa-ies to the introduction of new probes, due in part to the past FDAeviewers’ conservatism. Recent developments in solid-state andlanar-circuit technologies provide a variety of commercially avail-ble, inexpensive, reliable, and GMP compliant sensors. Contrary toIR, MR methods do not necessarily require the use of chemomet-

ics (univariate calibration plots can be developed), making thisechnique more amenable for application in cases in which sophis-icated chemometric software and expert chemometricians are notvailable.

Comparison of at-line NIR and microwave techniques for mois-ure determination is of great importance, since the basic principlesf their operations are different. For instance, the depth of penetra-ion of NIR light in pharmaceutical powders and tablets measuredn reflectance mode ranges from 0.5 to 2.5 mm (the wide range ofeported depths of penetration can be attributed to several factorsuch as wavelength, instrument settings, sample presentation andhysical and chemical properties) ([34,35], and literature therein).

f the depth of penetration is short and the water is not homoge-eously distributed in the sample, the NIR determined water wouldot be representative of the total water. For example, Dreassi et al.etermined a high percentage error in the water determination inanitidine HCl tablets for samples having a water content of lesshan 2.5%, when determined by a reflectance NIR method [36].

ontrary to NIR, the stray fields generated in a microwave reso-ant cavity have penetration depths from 2 to 5 cm, and the wateretermined is more representative of the tablet core or the bulkowder. MR allows the determination of moisture of film coated

ica Acta 696 (2011) 84–93 85

tablets unlike NIR, where water content must be determined onuncoated tablets or only measures the coating water content. Theaim of this study is to investigate the use of a microwave sensor forwater determination in powders and tablets and its performancecompared to NIR.

2. Materials and methods

2.1. Materials

Avicel PH102 microcrystalline cellulose (MCC) was purchasedfrom FMC Biopolymer (Philadelphia, PA, USA). Magnesium stearate(MgSt), Acetaminophen (APAP) and Hydroxypropyl methylcellu-lose NF (HPC) were purchased from Sigma–Aldrich (St. Louis, MO,USA). A proprietary Bristol-Myers Squibb (BMS) active pharmaceu-tical ingredient was synthesized in house (BMS, New Brunswick, NJ,USA), and used after purification. Hydranal composite 534805 fromFluka Analytical. Hydranal Methanol-Dry from Sigma–Aldrich.Potassium acetate pentahydrate, sodium chloride, magnesiumnitrate 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 ThermoElectron Corp. (Madison, WI), equipped with an InGaAs detec-tor. The software package Result-Integration® accompanying theFT-NIR instrument was used to acquire the spectra from the instru-ment. Each spectrum was the average of 64 scans over the rangeof 10,000–4000 cm−1 (1000–2500 nm), with 8 cm−1 resolution. Allspectra were recorded through the bottoms of the sample vialsprior to KF titration. Triplicate measurements were made with theFT-NIR spectrometer. The sample vial was rotated during measure-ments and inverted in between measurements. Calibration modelswere built with partial least squares (PLS) regression, performedwith the Unscrambler® chemometrics software version 9.8 (CamoInc., Oslo, Norway). The data was centered prior to analysis.

2.3. Microwave resonance instrument

For the at-line determination of water content in powders andtablets a Sartorius LMA 320PA microwave moisture analyzer (Sar-torius Mechatronics, CO, USA), equipped with a LMA 330RE-026sensor operating at 2.5 GHz was used. This microwave resonancedevice works with a high precision resonance method. The reso-nance frequency of the sensor system is analyzed by continuousscanning of the microwave frequency. When the field changes itspolarity rapidly, only the water molecules can follow this changeas they are small and have a strong dipole. This movement requiresenergy, which is drawn from the electromagnetic field. This lossof energy, which depends on the number of water molecules, isdetected. When the product containing water is passed over thesensor, the resonance frequency decreases (due to a decrease of thewavelength inside the material, �f ) and the half-width of the res-onance curve increases (due to losses of microwave energy insidethe material �B), as shown in Fig. 1.

The mass-independent microwave moisture value (MW) isgiven by:

MW = arctan�B

�f= B

A= arctan

Wm − W0

f0 − fm, (1)

where �B (also known as B parameter) is the increase of the half-

width in Hz, �f (also known as A parameter) is the shift in theresonance frequency in Hz, f0 is the resonance frequency of theempty resonator in Hz, fm is the resonance frequency of the filledresonator 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

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Table 1Composition of calibration blends in the range of 1–5% w/w.

Sample APAP (%) MCC (%) HPC (%) Water (%) KFa

1 32.0 68.0 − − 2.732 56.0 44.0 − − 1.783 55.0 10.0 33.0 2.0 2.524 15.0 60.0 24.0 1.0 3.485 55.0 15.0 27.0 3.0 3.706 68.0 22.0 10.0 − 0.937 20.0 65.0 15.0 − 2.658 35.0 53.0 12.0 − 2.179 35.0 25.0 40.0 − 1.13

10 28.0 57.0 12.5 2.5 4.8311 73.0 12.5 10.0 4.5 5.0512 38.0 55.0 6.0 1.0 3.2313 18.0 75.0 8.0 − 3.0314 58.0 35.0 7.0 − 1.4415 50.0 36.0 11.0 3.0 4.49

as previously described for the samples with water content in the1–5% w/w range (Table 2). In these blends the amount of APAPranged from 57 to 95%, MCC from 3 to 25%, HPC from 2 to 33% andwater was not added.

Table 2Composition of BMS API calibration blends in the range of 1–5% w/w.

APAP (%) MCC (%) HPC (%) KFa

1 80.0 17.0 3.0 0.782 70.0 10.0 20.0 0.433 57.0 10.0 33.0 0.544 59.0 14.0 27.0 0.665 66.0 19.0 15.0 0.846 63.0 25.0 12.0 1.037 92.0 5.0 3.0 0.248 90.0 8.0 2.0 0.34

Fig. 1. Frequency (GHz) vs. scattering transmission coefficient S21.

esonator in Hz, and Wm is the half-width of the resonance ofhe filled resonator in Hz. The ratio of both quantities is virtuallyndependent of the mass and thus only a function of the moistureontent.

Samples that were used for calibration were available in theame condition as samples used for validation purposes. The mois-ure range and ambient conditions used for calibration matchedater measuring conditions. Temperature compensation was notpplied since the temperature of the samples during calibration andater measurements remained within ±5 ◦C. Calibration measure-

ents in the NIR and MR sensors were carried out with the sameamples that were used for determining the reference moisture byF method. Samples were kept in a closed vial during measure-ents. After KF and NIR measurements, the microwave resonanceas measured. The MW value used for calibration and measure-ent was the mean value of three independent measurements. A

ompression test was executed prior to the calibration, followinghe manufacture recommended procedure [37]. This compressionest should always be used to find out if there are A parameter (fre-uency difference) or B parameter (width difference) offsets thatave to be taken into account (when A parameter and B param-ter differ from the standard A,B = 0 settings) to acquire densityndependent MW values. Typically the compression test was run

ith samples containing different moistures in the range expectedor the real samples. The compression test provides one regressionine per moisture value. If all regression lines intersect in the 0,0oint, no AB offset has to be manually input in the calibration. Ifhe regression lines intersect at a different point, the actual A and

offset parameters should be manually input in the calibrationcreen. Temperature was constantly recorded. Data was collectednd processed using TMV-TEWS® software (version 2.0.0.40). TheMV software controls the MW sensor via a network PC or the unitouch screen. The software allows the selection of different sensor

odes, measurement of reference standards for daily check calibra-ion, performance of the compression test, and sample temperatureorrections. The corresponding product settings are recorded andaved and the configuration is used for calibration and measure-ent of samples.

.4. Karl Fischer instrument

The reference KF method for blends with water content fromto 5% w/w was volumetric KF. Volumetric KF measurementsere carried out at a constant room temperature of 16 ◦C using758 KFD Titrino titrator (Metrohm, Switzerland) controlled with

iamo 1.2 software and equipped with a titration stand 703 andthermostatic titration vessel. The blends were directly added

o the vessel containing approximately 40 mL of dry methanolMerck, Darmstadt, Germany) and titrated with a one-component

16 28.0 70.0 3.0 2.82

a KF values correspond to an average of three measurements.

reagent, Hydranal Composite 5 from Fluka Analytical. The solventwas changed after each triplicate measurement. The performanceof the titration method was checked by determining the watercontent of deionized water. Titration conditions were a minimumextraction time of 1990s, a start and stop drift of 10 �L min−1, apolarisation current of 50 �A and an endpoint detection voltage of250 mV. Sample masses ranged between 0.15 and 0.30 g.

The reference KF method for blends with water content from0.2 to 1% w/w was coulometric KF. Coulometric KF measurementswere carried out at a constant room temperature of 16 ◦C using aMethrom 756 KF Coulometer controlled with Tiamo 1.2 softwareand 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 16calibration standards consisting of blends of APAP-MCC-HPC wereprepared. The amount of APAP ranged from 15 to 73% w/w, MCCfrom 10 to 75% w/w, HPC from 0 to 40% w/w and water from 0 to4.5% (Table 1). Blends were prepared in a L.B. Bohle minigranula-tor. Different amounts of APAP, MCC and HPC were added on thehigh 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 andexcipients and blended for an additional 5 min.

For the calibration plot in the range of 0.2 to 1% w/w, a total of 11calibration standards were prepared, following the same procedure

9 86.0 5.0 9.0 0.2010 95.0 3.0 2.0 0.1611 74.0 23.0 3.0 0.93

aKF values correspond to an average of three measurements.

C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93 87

Table 3Composition of calibration blends with BMS API in the range of 1–5% w/w.

Sample BMS API (%) MCC (%) HPC (%) Water (%) KFa

1 32.0 68.0 2.902 15.0 60.0 24.0 1.0 3.643 55.0 15.0 27.0 3.0 3.954 68.0 22.0 10.0 1.135 35.0 53.0 12.0 2.166 32.0 28.0 40.0 1.437 70.0 15.5 10.0 4.5 5.458 18.0 75.0 7.0 3.109 58.0 35.0 7.0 1.73

10 50.0 36.0 11.0 3.0 4.6411 55.0 10.0 33.0 2.0 2.7112 56.0 44.0 1.9313 20.0 65.0 15.0 2.7614 38.0 55.0 6.0 1.0 3.4515 28.0 70.0 2.0 0.0 2.87

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Table 5Prediction results for a validation set, using calibration models in Table 1.

Pre-processing NIR spectra Slope Offset R2 RMSEP SEP

SNV 0.94 0.19 0.98 0.20 0.19First derivative 0.95 0.14 0.99 0.19 0.19SNV/first derivative 0.96 0.16 0.98 0.19 0.18

TP

16 48.5 38.0 11.0 2.5 4.32

a KF values correspond to an average of three measurements.

The 16 calibration standards consisting of blends of BMSPI-MCC-HPC were prepared in the high shear mixer/granulator

ollowing the same procedure described for the APAP blendsTable 3). All the blends were stored on tight containers. Mois-ure was determined by KF method (volumetric KF for blends with–5% w/w water content and coulometric KF for blends with 0.2–1%/w). KF values correspond to an average of three measurements.IR and MW data were collected in a period of no more that 30 minfter the moisture determination.

Tablets of approximately 100 mg weight and 5 mm diameterere prepared from a blend of APAP (10%), Avicel® 200 (89%) andagnesium stearate (1%) using a Piccola rotary tablet press (Riva

. A, Buenos Aires, Argentina) fitted with 8 punch sets. A set ofablets were exposed to humid air during different time intervalsn order to acquire different levels of moisture content. The rela-ive humidity of the air was either uncontrolled, i.e. ambient air, orontrolled by a saturated salt solutions in sealed desiccators equi-ibrated at 25 ◦C. The solutes used in the saturated solutions wereotassium acetate (∼23% RH), magnesium nitrate (∼53% RH) andodium chloride (∼75% RH). A set of tablets was placed on an oven.

. Results and discussion

.1. At-line moisture determination by near-infrared method

The composition of the blends used for calibration is shown inables 1 and 2. As shown in the Tables, blends with water contentetween 1 and 5% w/w contain higher levels of MCC and HPC and

ower level of APAP compared to the blends with water contentetween 0.2 and 1% w/w. Due to the quantitative differences ofhe two sets, and in order to understand the impact of composi-ion on the NIR spectra and PLS model performance, two differentalibration models were initially developed for each set of blends.

.1.1. Samples with moisture ranging from 1 to 5% w/wFig. 2a shows the NIR spectra of blends of APAP-MCC-HPC

ith 1–5% w/w water content after SNV pre-processing. Significant

able 4LS calibration models built with different data pretreatments for 1–5% w/w range.

Pre-processing Slope Offset R2 RMSEC

SNV 0.98 0.05 0.98 0.16First derivative 0.99 0.04 0.99 0.15SNV/first derivative 0.99 0.03 0.99 0.14Second derivative 0.98 0.05 0.98 0.17SNV/second derivative 0.98 0.04 0.98 0.16

Second derivative 0.95 0.12 0.97 0.22 0.22SNV/second derivative 0.96 0.20 0.97 0.22 0.21

changes in spectral features of the samples studied were observedat varying moisture and composition levels. As observed in Fig. 2a,the NIR spectra of the blends show the two major absorption bandsof water at around 5155 cm−1 and 6895 cm−1. The absorbance atthese regions increases when the water level increases. These vari-ations in the absorbance spectra are retained in the correspondingfirst derivative (Fig. 2b).

Calibration models were developed by correlating the NIRspectra at the two main spectral regions of water absorption(7362–6919 cm−1 and 5453–5176 cm−1) with water content usingpartial least-squares (PLS) regression (Table 4 and Fig. 1a in supple-mentary material). A randomized design, was employed where thetwo major excipients and the water added were varied randomlywith respect to APAP (Table 1). High correlation between majorcomponents was avoided, since component effect on spectral base-line and slope variations due to physical properties were unknown.Several data pre-processing methods were used and compared,including Standard Normal Variate (SNV), Multiplicative ScatterCorrection (MSC), first and second derivatives. In the smoothing ofthe derivatives (Savitzky-Golay), 21 points window and third orderpolynomial were used.

Table 4 summarizes the parameters of the models, including theslope, offset, correlation coefficient (R2), root mean square error ofcalibration (RMSEC), and root mean square error of cross valida-tion (RMSECV). PLS figures of merit (selectivity SEL, sensitivity SEN,detection limit DL and quantification limit QL) were also calculatedfor each model [38–40]. In this application, spectra pretreatmentby 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 andcoefficients of determination (R2) are close to one, and the optimalnumber of factors is 2. The joint test of significance of intercept andquadratic term showed that there is no significance of intercept orcurvature at the 95% confidence level for all regression models. Thecalibration model constructed using SNV followed by first deriva-tive 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 greatlydiffer and the sensitivity calculated based on Net Analyte Signal(NAS) is better for models using second derivative, all of them wereused to predict the water content of the samples in an independentvalidation set (Table 5). Fig. 1b in supplementary material showsthe NIR predicted water content vs. the reference method (KF) for

independent validation samples in the range of 1–5% w/w. For thisprediction, the best chemometric model in Table 4 was used (SNVfollowed by first derivative).

RMSECV SEN SEL DL QL

0.17 3.16 0.46 0.11 0.330.16 0.013 0.59 0.13 0.400.15 0.075 0.35 0.18 0.540.18 0.001 0.67 0.10 0.300.17 0.004 0.36 0.17 0.51

88 C.C. Corredor et al. / Analytica Chimica Acta 696 (2011) 84–93

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ig. 2. (a) NIR spectra of blends of APAP-MCC-HPC with different water content (ravitzky-Golay 21 smoothing points of the spectra in (a).

SNV followed by first derivative provided the best predictionodel, with the lowest Standard Error of Prediction (SEP 0.18%/w), as shown in Table 5. The RMSEC (0.14% from Table 4) and the

EP (0.18% from Table 5) are similar, showing that the correlationsre similar for the validation set compared with the calibration set.ccurate prediction models were obtained with a minimum set ofalibration samples. As previously reported [42], since the magni-ude of noise and variability of off-line static samples are greatlyeduced when compared to on-line samples, lower number of cali-ration samples was required to account for the spectral variability.he RMSEP and SEP of NIR methods in Table 5 are comparable tor lower than values for PLS methods reported in the literature16,19,20,41,42].

.1.2. Samples with moisture ranging from 0.2 to 1% w/wFig. 3a shows the NIR spectra of blends of APAP-MCC-HPC

ith 0.2–1% w/w water content. The absorbance at the 5155 cm−1

nd 6895 cm−1 regions increases when the water level increases.

from 1 to 5% w/w) after SNV pre-processing. (b) SNV followed by first derivative

Fig. 3b shows the corresponding first derivative. Calibration mod-els built with different data pretreatment and two spectral regions(7362–6919 cm−1 and 5453–5176 cm−1) for this moisture rangeare shown in Table 6. Fig. 2a in supplementary material shows thecalibration model by using SNV followed by first derivative.

The joint test of significance of intercept and quadratic termsshowed that there is no significance of intercept or curvature at the95% confidence level for all of the regression models. The optimalnumber of factors is 3. The calibration model constructed using SNVfollowed by first derivative shows the lowest RMSEC (0.036%) andRMSECV (0.043%). All the calibration models were used to predictthe water content of an independent validation set (Table 7). Allthe 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 predictedwater content vs. the reference method (KF) for independent val-idation 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

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ig. 3. (a) NIR spectra of blends of APAP-MCC-HPC with different water content (raavitzky-Golay 21 smoothing points of the spectra in (a).

alidation set, SNV followed by first derivative provided the bestrediction model, with the lowest SEP (0.052% w/w), as shown inable 7. The RMSEP for the reference KF coulometric method wasetermined to be 9 × 10–4%.

from 0.2 to 1% w/w) after SNV pre-processing. (b) SNV followed by first derivative

Since the selected spectral regions and the best signal pre-processing (SNV followed by first derivative) was the same forboth models, and the error of the KF reference methods was deter-mined 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 6PLS 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 0.97 0.015 0.97 0.045 0.056 3.75 0.235 0.045 0.137First derivative 0.97 0.016 0.97 0.042 0.049 0.0241 0.298 0.032 0.096SNV/first derivative 0.98 0.010 0.98 0.036Second derivative 0.97 0.015 0.97 0.042SNV/second derivative 0.98 0.014 0.98 0.040

Table 7Prediction results for an independent validation set, using calibration models inTable 6.

Pre-processing Slope Offset R2 RMSEP SEP

SNV 0.91 0.018 0.91 0.078 0.066First derivative 0.90 0.033 0.92 0.074 0.067SNV/first derivative 0.93 0.033 0.96 0.052 0.052

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Second derivative 0.85 0.059 0.91 0.079 0.070SNV/second derivative 0.90 0.049 0.96 0.055 0.053

bserved for the volumetric and coulometric KF methods), calibra-ion data from 0.2 to 5% w/w was combined in a single calibrationlot (Fig. 3a in supplementary material, figures of merit are pro-ided in the plot). All independent samples from 0.2 to 5% w/were predicted using the general calibration model (Fig. 3b in sup-lementary material, figures of merit are provided in the plot). Thisombined calibration plot can accurately predict water content inhe range of 1–5% w/w. However, the calibration plot in the range of.2–1% presents higher sensitivity and selectivity and lower DL andL and more accurately predicts samples in this range, compared

o the combined calibration plot.

.2. At -line moisture determination by microwave resonanceechnology

.2.1. Samples with moisture ranging from 1 to 5% w/wSimilar to NIR, the microwave sensor has to be calibrated against

tandard reference methods. Fig. 4a in supplementary materialhows the MR sensor calibration plot using APAP-MCC-HPC blendsith water content between 1 and 5% w/w.

The MW value (given in terms of attenuation and phase shift ofhe microwave resonance curve as shown in equation 1 in Section.3) was plotted against the water content determined by volumet-ic KF.

The linear regression revealed the following regression line:

W = 0.04812 (KF) + 0.01057 (2)

The correlation coefficient for the shown regression line wasound to be 0.992. The joint test of significance of the intercept anduadratic terms showed that there was no significance of interceptr curvature at the 95% confidence level. At the measurement con-itions specified, the calibration plot is not linear below 1.5% or

bove 6.0%.

Fig. 4b in supplementary material shows the comparison of theeference vs. the predicted moisture values from MR sensor andIR, showing the corresponding regression lines. Table 8 shows the

able 8omparison of prediction results for an independent validation set for standardsstds) in Table 1 (1–5% w/w).

Prediction Slope Offset R2 RMSEP SEP

KF (volumetric) 0.97 0.086 0.99 0.10 0.11NIR (14 stds) 0.96 0.16 0.98 0.19 0.18NIR (7 stds) 0.97 0.12 0.97 0.24 0.23MR (11 stds) 0.93 0.05 0.99 0.17 0.17MR (6 stds) 0.93 0.07 0.99 0.16 0.15

0.043 0.021 0.289 0.033 0.1010.049 0.0015 0.378 0.03 0.090.048 0.012 0.312 0.03 0.08

comparison of the prediction results for an independent validationset by KF, NIR and microwave.

A comparison of the NIR and MR methods is very importantbecause the principle of water detection is different in both cases.For instance, the depth of penetration of NIR light in pharmaceuticalpowders and tablets measured in diffuse reflectance mode (in thewavelengths of interest) ranges from 0.25 to 0.5 mm [34,35] whilethe stray fields generated in a microwave resonant cavity in thefrequency range of about 2–3 GHz have penetration depths from2 to 5 cm. MR sensors on the other hand, measures total unboundwater. At frequencies between 2 and 3 GHz, only physically boundwater (including adsorbed water, trapped or liquid-inclusion waterand absorbed water) is determined. Chemically bound water(water of crystallization) is not monitored as this requires differ-ent wavelengths and intensities of the applied microwaves. Carefulconsideration of the technique of choice should be paid, particularlyin applications in which anhydrate to hydrate transformation cantake place (such as wet granulation) and the total water is used forfinal point determination. The SEP of the KF method was 0.11%. Therelative standard deviation (RSD) of replicate KF determinations ofthe 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 NIRand MR predictions is due to sample non-homogeneity and errorassociated with the reference method.

The MR calibration model accurately predicted the water con-tent of an independent validation set. The MR SEP was 0.17%. Thisvalue was similar to the SEP of the NIR method (0.18%). The robust-ness of the MR calibration model was tested by removing samplesin the calibration set. The calibration model was recalculated afterthe reduction, and the water content of the validation test was pre-dicted using the new calibration model. The calibration set couldbe reduced to 6 calibration standards with no impact on the predic-tion results of the independent validation set (Table 8). To abilityto develop calibration plots with low number of standards is ben-eficial, especially in cases in which the API is in short supply orexpensive. Also, since the MR tool is non-invasive, samples couldbe reused for additional analysis. An advantage vs. the NIR methodis that multivariate data analysis is not required. Both NIR andmicrowave methods can potentially replace the KF method for theblends with 1–5 w/w% water content.

3.2.2. Samples with moisture ranging from 0.2 to 1% w/wFig. 5a in supplementary material shows the MR calibration plot

using APAP-MCC-HPC blends with water contents between 0.2 and1% w/w.

The linear regression revealed the following regression line:

MW = 0.0558 (KF) + 0.0031. (3)

The correlation coefficient for the shown regression line wasfound to be 0.985. The joint test of significance of intercept andquadratic term showed that there is no significance of interceptor curvature at the 95% confidence level. Fig. 5b in supplemen-

tary material shows the comparison of the reference vs. thepredicted moisture values from MR and NIR at this moisture range.Table 9 shows the comparison of the prediction results for an inde-pendent validation set by KF, NIR and MR.

C.C. Corredor et al. / Analytica Chim

Table 9Comparison of prediction results for the three methods for an independent valida-tion set.

Prediction Slope Offset R2 RMSEP SEP

KF 0.2–1%(coulometric)

1.029 −0.018 0.991 0.034 0.0009

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NIR 0.932 0.034 0.963 0.052 0.051MR 0.980 0.017 0.976 0.037 0.038

The MR model accurately predicted the water content of anndependent validation set. The MR calibration model performedetter than the NIR method when predicting an independent vali-ation set. The prediction model shows the slope closest to 1.0, the

owest RMSEP (0.037%), and lowest SEP (0.038%), and is thereforeonsidered the optimal result.

.3. Investigation of the feasibility of developing general NIR andR calibration plots

The possibility of developing general NIR or MR calibration plotsor different products of related composition but different API wasested by preparing blends of a proprietary BMS API-MCC-HPC,s described in Section 2.5. A general calibration plot for blendsith similar excipient composition but different API can potentially

educe method development time and resources. Fig. 4 shows theIR spectra of the BMS API, APAP and blends of BMS API-MCC-HPCnd APAP-MCC-HPC. The spectrum of the BMS API shows char-cteristic peaks that overlap the absorption in the water regions.alibration models using the BMS API-MCC-HPC samples wereeveloped by correlating the NIR spectra with water content usingLS regression. As in the case of the APAP blends, a randomizedesign was employed to ensure that the component correlation wasinimized and that the PLS solution was specific to water. Similar

o the models developed in Section 3.1, several data pre-processingethods were used and compared.

For the BMS API-MCC-HPC blends, the best calibration model

sed first derivative and loadings in the 7478–6876 cm−1 and303–5056 cm−1 regions (slightly different regions that the APAPethod). The slope was 0.98, the correlation coefficient was 0.98,

ig. 4. NIR spectra of BMS API (blue), APAP (purple), blend of APAP-MCC-HPC (green) ando the on-line version of the article).

ica Acta 696 (2011) 84–93 91

the RMSEC was 0.144%, the RMSEC was 0.16%, and the optimal num-ber of factors was 4. Fig. 6 in supplementary material shows theprediction results for an independent validation set by using thebest calibration model obtained with the BMS API-MCC-HPC sam-ples and the best APAP model developed in Section 2.5. The bestcalibration model developed for the APAP-MCC-HPC blends couldnot accurately predict the water content in blends of BMS API-MCC-HPC (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 thiscase 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 calibra-tion plot in the microwave sensor. Fig. 7a in supplementary materialshows the MR calibration plots using BMS API -MCC-HPC blendsand APAP-MCC-HPC blends. For both materials, MW increases lin-early with moisture and the data are superimposed. A small offsetof 0.008% in the regression line of the BMS API was observed withrespect to the APAP regression. The MW values for both types ofblends were plotted against the water content determined by vol-umetric KF. The linear regression for the general model is:

MW = 0.0455 (KF) + 0.0246. (4)

The correlation coefficient for the shown regression line wasfound to be 0.973. From this equation moisture content in eithermaterial can be determined from a single calibration equation with-out knowledge of bulk density. The effectiveness of equation 4in predicting moisture content on an independent validation setof BMS API-MCC-HPC samples was evaluated (Fig. 7b in supple-mentary material). As seen in the figure, there is a high degree ofagreement between the set two plots.

Trabelsi et al. demonstrated that moisture content variation inwheat and soybeans increased linearly and calibration data for bothmaterials were superimposable, using a microwave sensor oper-ating at 5.8 GHz [32]. As expected, calibration data correspondingto blends of BMS API-HPC-MCC and APAP-MCC-HPC are super-

imposable. Excellent agreement of the moisture values determinedby KF and those predicted by the general microwave calibration plotfor BMS API-MCC-HPC blends, demonstrated the utility of a generalmicrowave calibration model.

BMS API-MCC-HPC (for reference to the color information in the figure legend refer

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.4. Microwave resonance method for moisture determination inablets

The feasibility of developing a MR calibration plot for tabletores was tested by preparing tablets of APAP-MCC-Mg stearates described in Section 2.5. Tablets to be measured wereoosely filled into a plastic holder fitted in the sensor. Theablet weight was 100 mg, tablet thickness was 4 mm and tabletiameter was 5 mm. The microwave sample holder was com-letely filled with approx. 150 tablets for a sample mass ofpproximately 15 g per each moisture point. Temperature wasecorded during the entire measurements. However, temper-ture correction was not required, since the calibration andrediction samples were measured at room temperature and theemperature did not deviate more than ±5 ◦C. Moisture rangend sample conditions used for calibration matched later mea-urement conditions. Before developing the calibration plot, aensor compression test was run as described by the manufac-urer.

Fig. 8a in supplementary material shows the MR sensor calibra-ion plot using APAP tablets with water content between 1.5 and.5% w/w. The MW value was plotted against the water contentetermined by volumetric KF. The linear regression revealed theollowing regression line:

W = 0.0337 (KF) + 0.0503. (5)

The correlation coefficient for the shown regression line wasound to be 0.995. At the measurement conditions specified, cali-ration plot is not linear below 1.5% or above 4.5%.

The repeatability of the method was determined as the relativetandard deviation (RDS) of 10 replicate measurements of the sameample. An RSD of <1.0% of MW signal was found when the massn the sample holder changed no more that ±12%. A higher RSDf MW signal (2.8%) was observed when the mass in the sampleolder changed in the range of ±12–25%. To maintain the highesteproducibility, the mass in the sample holder was kept as constants possible, by filling the sample holder to the same level beforeeasurement.Fig. 8b in supplementary material shows the comparison of the

eference vs. the predicted moisture values from MR. The slope ofhe regression line was 1.015. The offset was 0.017. The correla-ion coefficient was 0.993. The RMSEP was 0.134% and the SEP was.117%. These values demonstrate that the MR calibration modelccurately predicted the water content of the independent valida-ion set. The RMSEP and SEP for the tablets were lower than theorresponding values for the powder blends at the same moistureevels.

. Conclusions

MR technology has been shown to be a viable means of mois-ure analysis for bulk powders and tablets. This study demonstratedhat the MR method for bulk powders in the order of 0.5–5%/w gave similar results to the NIR method, without the need of

ophisticated chemometric software and also provides the oppor-unity to utilize fewer calibration standards. The MR methodccurately predicted the water content of powder and tablets,hen compared to the standard KF method. We also demon-

trated that a general microwave calibration plot developed forparticular product can be used to predict the water content ofdifferent product with related composition but different APIs.

he feasibility of using a universal calibration model significantlyeduced microwave moisture method development for a secondroduct.

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ica Acta 696 (2011) 84–93

Acknowledgements

The authors would like to acknowledge Dan Kopec from Sarto-rius Mechatronics (Arvada, CO, USA) for the loan of the microwavesensor and Kevin Macias, for the technical support during the tabletpreparation.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.aca.2011.03.048.

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