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The Use of Near-Infrared and Microwave Resonance Sensing to Monitor a Continuous Roller Compaction Process
The Use of Near-Infrared and Microwave Resonance Sensing to Monitor a Continuous Roller Compaction Process JOHN AUSTIN, ANSHU GUPTA, RYAN MCDONNELL, GINTARAS V. REKLAITIS, MICHAEL T. HARRIS School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907-2100 Received 7 February 2013; accepted 15 March 2013 Published online 9 April 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23536 ABSTRACT: Roller compaction is commonly used in the pharmaceutical and nutraceutical industries to increase and narrow the size distribution of a particulate material, making it easier to process. Both the moisture content of the material and the density of the roller compacted ribbon affect the uniformity and physical properties of the resultant granules. Without process analytical technologies, these parameters cannot be determined on-line or in real time. In this study, the more commonly used near-infrared (NIR) spectroscopy was compared and contrasted with microwave resonance for the determination of roller-compacted ribbons’ envelope density and moisture content. Results indicate that microwave resonance can offer improved accuracy, robustness, and ease-of-use compared with NIR spectroscopy for these property measurements. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:1895–1904, 2013 Keywords: granulation; physical characterization; multivariate analysis; near-infrared spectroscopy; powder technology; processing; automation; microwave sensing
INTRODUCTION The process analytical technology initiative by the US Food and Drug Administration has allowed for the increased use of various analytical techniques for process variable measurements.1 This in turn has created an interest in the pharmaceutical industry to monitor the effect of operating parameters and materials properties on final product quality. More efficient and well-controlled processes can be achieved using continuous manufacturing with on-line process analytics. Roller compaction is a dry granulation unit operation commonly used in the pharmaceutical and nutraceutical industries to increase the size of a particulate material in a controlled manner, making it easier to process. Compared with wet granulation, dry granulation offers several benefits. Most notably, these include the absence of an additional costly drying unit operation and allowing the processing of heat- and moisture-sensitive materials. Furthermore, roller compactors require less space, energy, and person-hours to operate compared with wet granulation processes. To increase the particle size of a Correspondence to: Michael T. Harris (Telephone: +765-4940963; Fax: +765-494-0805; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 102, 1895–1904 (2013) © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association
particulate material while also maintaining a narrow size distribution, the process parameters need to be precisely controlled. Both the moisture content and the envelope density of roller-compacted ribbons significantly affect the roller compaction process and thus the final quality of produced tablets. Over densification of the ribbon yields undesired tablets with both low hardness and high friability. In contrast, under densification usually yields granules with a wide size distribution, which results in tablets with a large variability in weight.2 The Johanson model has been used extensively to predict the density of roller-compacted ribbons, but it can only serve as a simplified basis and is thus not ideal for accurate process control or exceptional events management if one is interested in density or normal stress magnitude predictions.3–4 The moisture content of granules significantly affects their flowability, cohesivity, and compressibility.5 The presence of moisture can lead to the formation of both liquid and solid bridges between particles. In most cases, this leads to increased cohesion and friction, which reduce the flowability of the material.6–7 If the powder cannot flow predictably into the roller compaction region, then it will likely not compact uniformly. It is, therefore, desirable to monitor both the envelope density and the moisture content in real time as accurately as possible.
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Historically, near-infrared (NIR) spectroscopy has been the method of choice to monitor these parameters in roller-compacted ribbons.8–9 Instrumented rolls containing force transducers have also been used to monitor density distributions in roller-compacted ribbons.10 To the author’s knowledge, no studies thus far have investigated microwave resonance technology as a viable means of monitoring a roller compaction process. Microwave resonance technology has been used in the pharmaceutical industry to monitor fluidized bed operations11–12 as well as high shear processes.13 There are significant differences between NIR and microwave resonance sensing. NIR radiation interacts with a material by vibrating individual bonds within molecules, whereas microwaves rotate whole molecules in an alternating electromagnetic field. Furthermore, the large difference in operating frequency results in significantly different penetration depths. NIR sensors in reflectance mode can be used to probe the properties of only the top few millimeters of a material.14–15 In contrast, the fringing fields of planar microwave resonance sensors can penetrate several centimeters into a material. The frequency range of the microwave sensor determines the depth of penetration. In on-line sensing of particulate materials, it is very important to obtain representative samples.16 Significant differences between NIR and microwave resonance sensors in their interaction mechanism and penetration depth indicate a need to better understand their benefits and limitations for real-time sensing and process control.
METHODS AND MATERIALS Preconditioning of Materials Microcrystalline cellulose (MCC; Avicel PH200 obtained from FMC BioPolymer Corporation, Philadelphia, Pennsylvania), with an average particle size of 180 :m and loose bulk density ranging from 0.29 to 0.36 g/cc, was used for this study. Two days before testing, MCC was either humidified with steam or dried in an oven. This preconditioned the samples’ moisture content. Subsequently, the powders were tumbled in a drum to help ensure moisture content uniformity. Between conditioning and testing, the powders were stored in two layers of Ziploc bags for 2 days to ensure moisture content and temperature uniformity. Roller Compaction A model WP120×40 roller compactor on loan to Purdue University from Alexanderwerks in Remscheid, Germany, was used for this study. Flat rolls with a diameter of 12 cm and a width of 4 cm were used. The roll gap was not set by the operator but was controlled by the roller compactor to maintain a constant JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
Table 1. Operating Range and Normal Operating Conditions of Various Process Parameters Process Parameter Roller compactor (RC) roll gap (mm) RC hydraulic pressure (bar) RC roll speed (rpm) RC feed screw speed (rpm)
Operating Range
NOC
1–3 0–230 3–13 19–102
Variable Variable 6 26
compaction pressure. The operating range and normal operating conditions for each process parameter is shown in Table 1. Each preconditioned batch of MCC was tested separately. The hydraulic pressure was changed during each batch, and ranged from 20 to 80 bars. At least two ribbons were created for each permutation of moisture content and compaction pressure. In total, 119 ribbons were tested and analyzed. The ambient temperature was maintained at approximately 22◦ C, whereas the ambient humidity was maintained using a dehumidifier. Microwave Measurements A 167PA-0501-01000 forked microwave sensor, controlled by a PMD320PA, on loan from Sartorius Stedim in Bohemia, New York, was placed in-line with the produced roller-compacted ribbon, as shown in Figure 1. Although a prototype at the time of testing, this model is now commercially available. This model sensor was chosen because it allowed complete transmission of the microwave signal through the sample during testing. In addition, the sensor cavity operated as a resonator, and microwave resonant sensors have been shown to be more accurate compared with other types of microwave sensors.17 At resonance, a standing wave pattern forms in the cavity as the microwaves are reflected off the top and bottom walls of the cavity. When a material is placed inside of the sensor’s resonant cavity, the resonant frequency decreases. This phenomenon results as the speed of the electromagnetic waves temporarily reduces as they pass through the material. In addition, the half bandwidth of the resonant curve increases. This is largely because of an increase in the signal’s attenuation.18 By measuring the frequency and bandwidth shifts during resonance, it is possible to accurately measure both moisture content and density.19 Approximately 530 measurements were recorded over each 1-s interval and averaged together to yield one set of microwave measurements. This time interval was chosen to correspond with the length of sample tested using NIR analysis, discussed below.
Development of Microwave Moisture Model Many empirical and semiempirical models to predict moisture content using microwave sensors have been proposed.20–23 Most have been developed for planar DOI 10.1002/jps
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Figure 1. On-line sensing configuration to monitor roller-compacted ribbons. (Blue arrow, roller compactor; red arrow, microwave sensor; and green arrow, NIR probe.)
sensors, such as ring resonators, or transmission measurements using horn antennas; these models could not predict the moisture content of ribbon samples as they passed through the open cavity resonator with the desired accuracy. For this study, a model of the form of Eq. 1 was used to predict moisture content. Although the constants were determined by fitting the semiempirical Eq. 1 to experimental data, the choice of the functional form of the equation was based on theory. The derivation of Eq. 1 is shown below. The quality factor (Q) of a resonant cavity is a dimensionless parameter that indicates the relative magnitude of the energy stored to the average power lost at resonance. Mathematically, this can be expressed as the ratio of the resonant frequency (fr ) to the bandwidth (B) of the resonant curve. The measured quality factor (Qmeas ) can be broken into three components, as shown in Eq. 2,24 where the contribution from the material’s dielectric losses (Qdiel ) is inversely proportional to its loss tangent (Eq. 3). MC = 1 Q meas
=
c1 + c2 B + c4 c3 − f r
B 1 1 1 = + + fr Q diel Q ext Q walls Q diel
1 = tan δ
(1) (2)
B = tan δ + const fr B tan δ = + const fr
(4) (5)
The loss tangent of a material quantifies its ability to dissipate electromagnetic energy. In most particulate–water systems, the majority of these losses are due to heating in the presence of water. It has been shown that in some materials, the loss tangent is nearly a linear function of moisture content (Eq. 6)25 . As the microwave device used in this work outputs the measured values of the changes in resonant frequency and bandwidth instead of absolute values, Eqs. 7 and 8 were combined with Eqs. 5 and 6 to yield the final microwave moisture model upon rearrangement (Eq. 1). The constants were fit empirically using JMP 10 (SAS, Cary, North Carolina) from microwave measurements and moisture reference values. MC = c1 tan δ + c2
(6)
f r = const − f r
(7)
B = const + B
(8)
(3)
As the resonant frequency of the cavity did not change drastically when the material was inserted, the contributions to the measured quality factor from radiation (Qext ) and losses due to the finite conductivity of the walls (Qwalls ) were assumed constant. With this assumption, and after insertion of Eq. 3 into Eq. 2, Eq. 4 was established. Rearrangement yielded DOI 10.1002/jps
Eq. 5.
Development of Microwave Density Model As has been stated previously, the resonant frequency of a resonator decreases as material is loaded into the electric field. The shift in the resonant frequency is directly related to the amount of mass present in the electric field. If the sample is contained in a known volume, the density of the sample can be monitored. Alternatively, if the sample volume is significantly JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
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larger than the electric field, the material can be assumed to be infinite and once again, the density can be determined. In this study, roller-compacted ribbons with similar volumes were monitored; this allowed microwave measurements to be correlated with density. Although the magnitude of the resonant frequency shift is the primary indicator of the amount of mass in the field, if multiple components are present with significantly different dielectric properties, an extra factor must be included, as shown in Eq. 9. The second term shown below corrects for the presence of water, which has a large dielectric constant and loss factor compared with most particulates. ρ = c1 f r + c2
f r + c3 B
(9)
The form of Eq. 9, especially the choice of the second term, was chosen because it minimized the error and bias when it was used to predict the envelope density of roller-compacted ribbons. Eq. 9 could likely be used to accurately monitor the bulk density of loose particulate materials as well. NIR Measurements A commercially available model Turbido OFS-12S120H NIR sensor obtained from Solvias AG in Basel, Switzerland, was placed directly downstream of the microwave sensor, as also shown in Figure 1. An NIR256L-1.7T1 spectrometer from Control Development Inc. in South Bend, Indiana was used to collect the spectra and was then recorded using Spec32 (version 1.5.4.8). The integration time was found to be 0.067 s; a 16-sample average was used. The length of the ribbons scanned during each test was thus 4 cm. The tip of the NIR probe was measured to be 0.5 cm in width. Analyses were carried out over the wavelength range of 904–1687 nm.
Development of Partial Least Squares Models The spectrum from each ribbon was exported to Unscrambler X, distributed by CAMO Software in Woodbridge, New Jersey, for pretreatment. The spectra were analyzed in either JMP 10, SAS, or Unscrambler X (CAMO Software) using principal component analysis (PCA) and partial least squares (PLS) regression with cross-validation. PCA is an algorithm that transforms a set of correlated variables into a set of linearly uncorrelated ones. This type of analysis is best used as a qualitative means of better understanding patterns in complex data. PLS regression is similar to PCA analysis; however, this quantitative type of analysis seeks to find the multidimensional transformation of JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
the data set that minimizes the variance in the response variable. Example spectra taken at similar moisture contents and varying envelope densities can be seen in Figure 2. When moisture content remains fixed and the density is allowed to vary, a baseline shift is observed for the NIR spectra. As the density decreases, the curves shift downward; a vertical shift was also reported by Acevedo et al.8 while monitoring roller-compacted ribbons of MCC. Thus, a good model to predict density should rely on this vertical shift while minimizing effects due to other process conditions. There is a peak present at the 1685-nm region strongly correlated to the presence of water. This region was therefore truncated while modeling the effects of density variations. As has been reported previously, standard normal variate (SNV) pretreatment helps to remove physical information from spectra such as particle size and the multiplicative interferences of scatter.26–27 This usually allows for clearer observation of differences in chemical composition. Example spectra pretreated with SNV taken at similar envelope densities and varying moisture contents can be seen in Figure 3. The most significant region for moisture content determination was found to be between 1440 and 1630 nm; this region has been highlighted in Figure 4. This region is important for moisture content determination because of the presence of a strong absorbance band for water present at 1450 nm.28 Although the peak found near 1685 nm was strongly correlated to the presence of water, it was not used to aid in the determination of moisture content. For some of the spectra, the peak maximum was shifted beyond the upper detection range of 1687 nm. Ribbon Density Reference Envelope density reference measurements were carried out using a GeoPyc 1360 Envelope Density Analyzer obtained from Micromeritics in Norcross, Georgia. Samples were weighed before testing using an XS104 model microbalance, obtained from Mettler Toledo in Columbus, Ohio. Because the NIR probe could only monitor a 0.5 cm wide area at the center of the ribbon, the centerline was cut out of the ribbon before testing. The center and edge pieces were tested separately. The center piece was used to calibrate the NIR probe, whereas the entire density used to calibrate the microwave sensor was found using a weighted average of the center and edge densities. An array of NIR probes could be used to sample the density of the entire ribbon. Ribbon Moisture Content Reference The reference moisture content of the ribbons was determined on a wet basis from Eq. 10, where mw is DOI 10.1002/jps
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Figure 2. A comparison of NIR spectra of microcrystalline cellulose taken at 4.65% moisture content and at varying densities (blue dash, 1.09 g/cc; red solid, 0.977 g/cc; green dot, 0.776 g/cc).
the mass of water and mp is the mass of dry powder. An HG63 Halogen Moisture Analyzer obtained from Mettler Toledo was used to determine the moisture content from weight loss on drying. An approximately 4-g section of each ribbon was used for analysis.
MC = 100% ×
mw mw + mp
(10)
RESULTS AND DISCUSSION Roller-compacted ribbons of MCC in the density range of 0.675–1.216 g/cc and the moisture content range of 2.1%–5.5% were tested. Although this moisture content range is relatively narrow, especially compared with wet granulation processes, it covers the expected range of values during roller compaction for MCC when exposed to different relative humidity levels. Over this moisture content range, significant changes
Figure 3. A comparison of NIR spectra of microcrystalline cellulose taken at approximately 0.948 g/cc envelope density (green dash: 5.37% MC, 0.949 g/cc; purple long dash: 4.81% MC, 0.951 g/cc; blue solid: 3.39% MC, 0.947 g/cc; and red dot: 2.15% MC, 0.944 g/cc). DOI 10.1002/jps
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Figure 4. An expanded view of 1440–1630 nm region in Figure 3 to highlight most significant region for moisture content determination (green short dash: 5.37% MC, 0.949 g/cc; purple long dash: 4.81% MC, 0.951 g/cc; blue solid: 3.39% MC, 0.947 g/cc; and red dot: 2.15% MC, 0.944 g/cc).
to MCC’s flowability and yield strength can still be observed,5 emphasizing the need to monitor its moisture content during the roller compaction process. Density Monitoring
NIR Spectroscopy Pretreatments, including first and second derivatives, SNV, and baseline shift were evaluated, but as the largest observable change in the spectra of different density ribbons was a shift in their baselines (Fig. 2), no spectral pretreatment was used. A two-factor model using mean centering and uniform weighting was developed using JMP 10 (SAS) from 119 raw spectra. The model was developed over the truncated frequency range of 904–1675 nm. The first principal component accounted for 98.3% of the variation in the spectral data; it quantified changes most closely correlated to density such as compaction pressure and roll thickness. The second principle component was most strongly correlated to moisture content variations and accounted for approximately 1.6% of the variance in the spectral data. Using “leave one out” cross-validation, a root mean squared error (RMSE) of calibration of 0.073 g/cc was found. This result is similar to that found by both Gupta et al.5 and Acevedo et al.8 while monitoring the density of MCC compacts and helps to validate the model. The magnitude and scattering of the density residuals can be seen in Figure 5 as the red triangles. As is shown, they are randomly scattered and do not indicate a discernible pattern or bias in the data not accounted for by this two-factor model. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
By combining NIR spectroscopic data with the pressure setpoint of the roller compactor, a more accurate PLS model was developed. As predicted by the Johanson equation, under normal operation, the pressure of the roller compactor is directly proportional to the envelope density.4 To weight the pressure information equally with the NIR spectral data, scaling in JMP 10 (SAS) was used. This resulted in a two-factor PLS model with a RMSE of calibration of 0.068 g/cc.
Microwave Resonance JMP 10 (SAS) was used to find the calibration coefficients in Eq. 9 for the microwave sensor with the same 119 ribbons used for the NIR analysis. As the microwave sensor monitored the density of the entire ribbon, compared with the NIR probe that monitored only the center density, the whole ribbon densities reference values were used. A RMSE of 0.044 g/cc was found. This error is approximately half of the error encountered using NIR analysis. A plot of the residuals from Eq. 9, shown in Figure 5 as blue squares, indicates that they are randomly distributed. Furthermore, the magnitude of the residuals was significantly reduced when employing the microwave model as compared with the NIR PLS model. This increased accuracy is likely because of the direct relationship between the magnitudes of microwave sensor measurements and the amount of mass present in the field. This differs from NIR analysis, wherein physical properties, such as density, need to be filtered from chemical effects using a technique such as PCA. DOI 10.1002/jps
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Figure 5. Density residuals of NIR two-factor PLS model based on raw spectral data and density residuals of microwave model.
As mentioned previously, a known or semi-infinite volume is needed to monitor very precisely the density of a material in the microwave field. Although the ribbon volume did not change with pressure significantly, by including the pressure setpoint (P), a more accurate microwave calibration can be obtained (Eq. 11). (11) ρ = c1 f r + c2 P + c3 The pressure setpoint takes the place of the compensation term for moisture content in Eq. 9. This substitution occurs because the majority of the density variation with pressure is due to changes in moisture content. As noted previously, moisture significantly affects the compressibility of most particulate materials. The inclusion of compaction pressure information in Eq. 11 resulted in a RMSE of 0.034 g/cc. The RMSE after inclusion of roller compaction pressure using a microwave sensor was once again about half of that found when using NIR sensing. It is important to note that the inclusion of resonance bandwidth into Eq. 11 did not significantly improve the results. This suggests that a less-expensive scalar network analyzer could be used to monitor the density of roller-compacted ribbons with similar accuracy to vector network analyzers. Moisture Content Monitoring
NIR Spectroscopy As was shown previously, significant changes in the spectra can be observed in the 1440–1630 nm region that correspond to moisture content differences once DOI 10.1002/jps
physical effects have been removed by pretreatment. Thus, this region was chosen to construct the PLS model for moisture content. Although spectral differences were most readily observed using SNV pretreatment, as shown in Figure 4, principal components found using first-derivative pretreatment were more strongly correlated with moisture content differences. Therefore, a three-factor model was constructed using Unscrambler X (CAMO Software) after first-derivative pretreatment utilizing a 15-point Savitzky–Golay algorithm. This PLS model employed mean centering and uniform weighting; this resulted in a RMSE of calibration of 0.115% moisture content using “leave one out” cross-validation. There was no significant improvement in the RMSE of calibration using a four-factor model (0.105% moisture content). Furthermore, the residuals, as shown in Figure 6, are randomly distributed and do not indicate a pattern that could be explained by including further factors. Finally, there was a strong linear correlation (greater than 0.8) between the T and U scores of the first three factors. This correlation dropped significantly for factors greater than three. On the basis of these observations, it was concluded that only three significant principle components were present in the firstderivative pretreated spectra. The first principal component, which accounted for 88% of the variance in the spectra and 68% of the variance in the model’s response, quantified the moisture content variations in the ribbons. The second principle component, which accounted for 10% of the variance in the data, quantified variations in density JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
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Figure 6. Moisture content residuals of two-factor model based on SNV pretreated data.
and accounted for 26% of the variance in the model’s response. The third principle component accounted for 1% of the variance in the spectral data and 5% of the variance in the model’s response. Not enough parameters were measured to properly identify what property of the ribbons the third principle component quantified. However, the strong linear correla-
tion (0.90) between the T and U scores of factor three, as well as the significant decrease in the RMSE when employing a three-factor model compared with a twofactor model (0.115% vs. 0.249%), both indicated that the third principle component was significant and not due to noise and should therefore have been included. As moisture content is not a function of compaction
Figure 7. Moisture content residuals of microwave model. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
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pressure, the addition of the pressure setpoint to the NIR model did not significantly improve the accuracy of the model.
Microwave Resonance JMP 10 (SAS) was used to find the constants in Eq. 1 by fitting microwave sensor measurements to the same 119 ribbon moisture content values used for NIR analysis. With four fitted constants, a RMSE of 0.065% moisture content was found. The microwave model’s residuals are randomly distributed, as shown in Figure 7. The RMSE found using microwave sensing was again approximately half of the error seen using NIR analysis. Microwave sensors are very sensitive to even tiny amounts of moisture, capable of detecting water concentrations in the ppm range.29 This is because of the large contrast in dielectric properties of water and most solid materials. For instance, the dielectric constant, which characterizes a material’s ability to store electrical energy, of water is approximately 80 in the frequency range of interest, whereas it is approximately two for MCC. In contrast, NIR detection schemes must use complex chemometric software and spectral pretreatments to remove physical effects from spectra before they can accurately monitor chemical compositions.
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ing between microwave and NIR methods can be concisely summarized by noting that NIR trades precision for versatility, whereas microwave sensing offers the opposite. It is important to know what properties are the most critical control variables and to utilize the most appropriate sensing methods available.
ACKNOWLEDGMENTS This work was completed with support from the National Science Foundation’s Engineering Research Center for Structured Organic Particulate Systems grant number 104728. We would like to thank and acknowledge Dan Kopec and Sartorius Stedim for the loan of the microwave sensor used to execute this study. In addition, we would like to thank Professor James Litster at Purdue University for the use of the Particle Design and Formulation Laboratory, without which we would not have been able to complete this study.
REFERENCES
Both microwave resonance and NIR sensing have been shown to be viable process analytical tools for monitoring the moisture content and envelope density of roller-compacted ribbon. In this study, microwave resonance technology was shown to be markedly more accurate than NIR, with approximately half of the RMSE for both density and moisture determination. Furthermore, as was also noted by Corredor et al.,19 microwave resonance benefits from working without sophisticated chemometric software, requiring fewer calibration standards, and having the ability to transfer between formulations more easily. Microwave resonance sensors of the type used in this study operate at a very low power level and do not pose a health risk to the operator. Furthermore, they do not heat the material under test. Although microwave sensors were shown to be more accurate than NIR sensors for the determination of the moisture content and envelope density in roller-compacted ribbons, NIR sensors still do offer some important benefits. Most notably, NIR sensing can be used to monitor the chemical composition of a particulate blend of many components more accurately than microwave sensing if correctly calibrated. The difference in moisture content and density sens-
1. FDA. 2004. Guidance for industry: PAT—A framework for innovative pharmaceutical development, manufacturing, and quality assurance. 2. Akseli I, Iyer S, Lee HP, Cuitino AM. 2011. A quantitative correlation of the effect of density distributions in rollercompacted ribbons on the mechanical properties of tablets using ultrasonics and X-ray tomography. AAPS PharmSciTech 12(3):834–853. 3. Muliadi AR, Litster JD, Wassgren CR. 2012. Modeling the powder roll compaction process: Comparison of 2-D finite element method and the rolling theory for granular solids (Johanson’s model).. Powder Technol 221:90–100. 4. Johanson J. 1965. A rolling theory for granular solids. Journal of Applied Mechanics 32:842–848. 5. Gupta A, Peck GE, Miller RW, Morris KR. 2005. Influence of ambient moisture on the compaction behavior of microcrystalline cellulose powder undergoing uni-axial compression and roller-compaction: a comparative study using near-infrared spectroscopy. J Pharm Sci 94(10):2301–2313. 6. Emery E. 2008. Flow properties of selected pharmaceutical powders. University of Saskatchewan. 7. Nokhodchi A. 2005. Effect of Moisture on compaction and compression. Pharm Tech 6:46–66. 8. Acevedo D, Muliadi A, Giridhar A, Litster JD, Romanach RJ. 2012. Evaluation of three approaches for real-time monitoring of roller compaction with near-infrared spectroscopy. AAPS PharmSciTech 13 (3):1005–1012. 9. Feng T, Wang F, Pinal R, Wassgren C, Carvajal MT. 2008. Investigation of the variability of NIR in-line monitoring of roller compaction process by using Fast Fourier Transform (FFT) analysis. AAPS PharmSciTech 9(2):419–424. 10. Nesarikar VV, Vatsaraj N, Patel C, Early W, Pandey P, Sprockel O, Gao Z, Jerzewski R, Miller R, Levin M. 2012. Instrumented roll technology for the design space development of roller compaction process. Int J Pharm 426(1–2):116–131. ¨ 11. Buschmuller C, Wiedey W, Doscher C, Dressler J, Breitkreutz J. 2008. In-line monitoring of granule moisture in fluidized-bed dryers using microwave resonance technology. Eur J Pharm Biopharm 69(1):380–387. 12. Lourenco V, Herdling T, Reich G, Menezes JC, Lochmann D. 2011. Combining microwave resonance technology to
DOI 10.1002/jps
JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
CONCLUSIONS
1904
13.
14.
15.
16.
17. 18.
19.
20.
AUSTIN ET AL.
multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation. Eur J Pharm Biopharm 78(3):513–521. Gradinarsky L, Brage H, Lagerholm B, Bjorn IN, Folestad S. 2006. In situ monitoring and control of moisture content in pharmaceutical powder processes using an open-ended coaxial probe. Meas Sci Technol 17(7):1847–1853. Shi Z, Anderson CA. 2010. Application of Monte Carlo simulation-based photon migration for enhanced understanding of near-infrared (NIR) diffuse reflectance. Part I: Depth of penetration in pharmaceutical materials. J Pharm Sci 99(5):2399–2412. Saeed M, Probst L, Betz G. 2011. Assessment of diffuse transmission and reflection modes in near-infrared quantification, part 2: Diffuse reflection information depth. J Pharm Sci 100(3):1130–1141. Muzzio FJ, Goodridge CL, Alexander A, Arratia P, Yang H, Sudah O, Mergen G. 2003. Sampling and characterization of pharmaceutical powders and granular blends. Int J Pharm 250(1):51-64. Chen L. 2004. Microwave electronics: Measurement and materials characterization. Chichester: John Wiley, pp 37–42. Knochel R, Taute W, Doscher C. 2007. Stray field ring resonators and a novel trough guide resonator for precise microwave moisture and density measurements. Meas Sci Technol 18(4):1061–1068. Corredor CC, Bu DS, Both D. 2011. Comparison of near infrared and microwave resonance sensors for at-line moisture determination in powders and tablets. Anal Chim Acta 696(1–2):84–93. Austin J, Rodriguez S, Sung P-F, Harris M. 2013. Utilizing microwaves for the determination of moisture content independent of density. Powder Technol 236:17–23.
JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
21. Meyer W, Schilz W. 1980. A microwave method for density independent determination of the moisture content of solids. J Phys D-Appl Phys 13(10):1823–1830. 22. Trabelsi S, Kraszewski AW, Nelson SO. 1998. A microwave method for on-line determination of bulk density and moisture content of particulate materials. IEEE Trans Instrum Meas 47(1):127–132. 23. Sung P-F, Hsieh Y-L, Angonese K, Dunn D, King RJ, Machbitz R, Christianson A, Chappell WJ, Taylor LS, Harris MT. 2011. Complex dielectric properties of microcrystalline cellulose, anhydrous lactose, and alpha-lactose monohydrate powders using a microwave-based open-reflection resonator sensor. J Pharm Sci 100(7):2920–2934. 24. Sarabandi K, Li ES. 1997. Microstrip ring resonator for soil moisture measurements. IEEE Trans Geosci Remote Sensing 35(5):1223–1231. 25. King RJ, King KV, Woo K. 1992. Microwave moisture measurement of grains. IEEE Trans Instrum Meas 41(1):111– 115. 26. Barnes R, Dhanoa M, Lister S. 1989. Standard normal variance transformation and de-treading of nearinfrared diffuse reflectance spectra. Appl Spectrosc 43:772– 777. 27. Candolfi A, De Maesschalck R, Jouan-Rimbaud D, Hailey PA, Massart DL. 1999. The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. J Pharm Biomed Anal 21(1):115–132. 28. Carter GA, McCain DC. 1993. Relationship of leaf spectral reflectance to chloroplast water content determined using NMR microscopy. Remote Sens Environ 46(3):305– 310. 29. King RJ. 1994. Microwave reflection resonator sensors.United States: KDC Technology Corporation.
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INTRODUCTION The process analytical technology initiative by the US Food and Drug Administration has allowed for the increased use of various analytical techniques for process variable measurements.1 This in turn has created an interest in the pharmaceutical industry to monitor the effect of operating parameters and materials properties on final product quality. More efficient and well-controlled processes can be achieved using continuous manufacturing with on-line process analytics. Roller compaction is a dry granulation unit operation commonly used in the pharmaceutical and nutraceutical industries to increase the size of a particulate material in a controlled manner, making it easier to process. Compared with wet granulation, dry granulation offers several benefits. Most notably, these include the absence of an additional costly drying unit operation and allowing the processing of heat- and moisture-sensitive materials. Furthermore, roller compactors require less space, energy, and person-hours to operate compared with wet granulation processes. To increase the particle size of a Correspondence to: Michael T. Harris (Telephone: +765-4940963; Fax: +765-494-0805; E-mail: [email protected]) Journal of Pharmaceutical Sciences, Vol. 102, 1895–1904 (2013) © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association
particulate material while also maintaining a narrow size distribution, the process parameters need to be precisely controlled. Both the moisture content and the envelope density of roller-compacted ribbons significantly affect the roller compaction process and thus the final quality of produced tablets. Over densification of the ribbon yields undesired tablets with both low hardness and high friability. In contrast, under densification usually yields granules with a wide size distribution, which results in tablets with a large variability in weight.2 The Johanson model has been used extensively to predict the density of roller-compacted ribbons, but it can only serve as a simplified basis and is thus not ideal for accurate process control or exceptional events management if one is interested in density or normal stress magnitude predictions.3–4 The moisture content of granules significantly affects their flowability, cohesivity, and compressibility.5 The presence of moisture can lead to the formation of both liquid and solid bridges between particles. In most cases, this leads to increased cohesion and friction, which reduce the flowability of the material.6–7 If the powder cannot flow predictably into the roller compaction region, then it will likely not compact uniformly. It is, therefore, desirable to monitor both the envelope density and the moisture content in real time as accurately as possible.
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Historically, near-infrared (NIR) spectroscopy has been the method of choice to monitor these parameters in roller-compacted ribbons.8–9 Instrumented rolls containing force transducers have also been used to monitor density distributions in roller-compacted ribbons.10 To the author’s knowledge, no studies thus far have investigated microwave resonance technology as a viable means of monitoring a roller compaction process. Microwave resonance technology has been used in the pharmaceutical industry to monitor fluidized bed operations11–12 as well as high shear processes.13 There are significant differences between NIR and microwave resonance sensing. NIR radiation interacts with a material by vibrating individual bonds within molecules, whereas microwaves rotate whole molecules in an alternating electromagnetic field. Furthermore, the large difference in operating frequency results in significantly different penetration depths. NIR sensors in reflectance mode can be used to probe the properties of only the top few millimeters of a material.14–15 In contrast, the fringing fields of planar microwave resonance sensors can penetrate several centimeters into a material. The frequency range of the microwave sensor determines the depth of penetration. In on-line sensing of particulate materials, it is very important to obtain representative samples.16 Significant differences between NIR and microwave resonance sensors in their interaction mechanism and penetration depth indicate a need to better understand their benefits and limitations for real-time sensing and process control.
METHODS AND MATERIALS Preconditioning of Materials Microcrystalline cellulose (MCC; Avicel PH200 obtained from FMC BioPolymer Corporation, Philadelphia, Pennsylvania), with an average particle size of 180 :m and loose bulk density ranging from 0.29 to 0.36 g/cc, was used for this study. Two days before testing, MCC was either humidified with steam or dried in an oven. This preconditioned the samples’ moisture content. Subsequently, the powders were tumbled in a drum to help ensure moisture content uniformity. Between conditioning and testing, the powders were stored in two layers of Ziploc bags for 2 days to ensure moisture content and temperature uniformity. Roller Compaction A model WP120×40 roller compactor on loan to Purdue University from Alexanderwerks in Remscheid, Germany, was used for this study. Flat rolls with a diameter of 12 cm and a width of 4 cm were used. The roll gap was not set by the operator but was controlled by the roller compactor to maintain a constant JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
Table 1. Operating Range and Normal Operating Conditions of Various Process Parameters Process Parameter Roller compactor (RC) roll gap (mm) RC hydraulic pressure (bar) RC roll speed (rpm) RC feed screw speed (rpm)
Operating Range
NOC
1–3 0–230 3–13 19–102
Variable Variable 6 26
compaction pressure. The operating range and normal operating conditions for each process parameter is shown in Table 1. Each preconditioned batch of MCC was tested separately. The hydraulic pressure was changed during each batch, and ranged from 20 to 80 bars. At least two ribbons were created for each permutation of moisture content and compaction pressure. In total, 119 ribbons were tested and analyzed. The ambient temperature was maintained at approximately 22◦ C, whereas the ambient humidity was maintained using a dehumidifier. Microwave Measurements A 167PA-0501-01000 forked microwave sensor, controlled by a PMD320PA, on loan from Sartorius Stedim in Bohemia, New York, was placed in-line with the produced roller-compacted ribbon, as shown in Figure 1. Although a prototype at the time of testing, this model is now commercially available. This model sensor was chosen because it allowed complete transmission of the microwave signal through the sample during testing. In addition, the sensor cavity operated as a resonator, and microwave resonant sensors have been shown to be more accurate compared with other types of microwave sensors.17 At resonance, a standing wave pattern forms in the cavity as the microwaves are reflected off the top and bottom walls of the cavity. When a material is placed inside of the sensor’s resonant cavity, the resonant frequency decreases. This phenomenon results as the speed of the electromagnetic waves temporarily reduces as they pass through the material. In addition, the half bandwidth of the resonant curve increases. This is largely because of an increase in the signal’s attenuation.18 By measuring the frequency and bandwidth shifts during resonance, it is possible to accurately measure both moisture content and density.19 Approximately 530 measurements were recorded over each 1-s interval and averaged together to yield one set of microwave measurements. This time interval was chosen to correspond with the length of sample tested using NIR analysis, discussed below.
Development of Microwave Moisture Model Many empirical and semiempirical models to predict moisture content using microwave sensors have been proposed.20–23 Most have been developed for planar DOI 10.1002/jps
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Figure 1. On-line sensing configuration to monitor roller-compacted ribbons. (Blue arrow, roller compactor; red arrow, microwave sensor; and green arrow, NIR probe.)
sensors, such as ring resonators, or transmission measurements using horn antennas; these models could not predict the moisture content of ribbon samples as they passed through the open cavity resonator with the desired accuracy. For this study, a model of the form of Eq. 1 was used to predict moisture content. Although the constants were determined by fitting the semiempirical Eq. 1 to experimental data, the choice of the functional form of the equation was based on theory. The derivation of Eq. 1 is shown below. The quality factor (Q) of a resonant cavity is a dimensionless parameter that indicates the relative magnitude of the energy stored to the average power lost at resonance. Mathematically, this can be expressed as the ratio of the resonant frequency (fr ) to the bandwidth (B) of the resonant curve. The measured quality factor (Qmeas ) can be broken into three components, as shown in Eq. 2,24 where the contribution from the material’s dielectric losses (Qdiel ) is inversely proportional to its loss tangent (Eq. 3). MC = 1 Q meas
=
c1 + c2 B + c4 c3 − f r
B 1 1 1 = + + fr Q diel Q ext Q walls Q diel
1 = tan δ
(1) (2)
B = tan δ + const fr B tan δ = + const fr
(4) (5)
The loss tangent of a material quantifies its ability to dissipate electromagnetic energy. In most particulate–water systems, the majority of these losses are due to heating in the presence of water. It has been shown that in some materials, the loss tangent is nearly a linear function of moisture content (Eq. 6)25 . As the microwave device used in this work outputs the measured values of the changes in resonant frequency and bandwidth instead of absolute values, Eqs. 7 and 8 were combined with Eqs. 5 and 6 to yield the final microwave moisture model upon rearrangement (Eq. 1). The constants were fit empirically using JMP 10 (SAS, Cary, North Carolina) from microwave measurements and moisture reference values. MC = c1 tan δ + c2
(6)
f r = const − f r
(7)
B = const + B
(8)
(3)
As the resonant frequency of the cavity did not change drastically when the material was inserted, the contributions to the measured quality factor from radiation (Qext ) and losses due to the finite conductivity of the walls (Qwalls ) were assumed constant. With this assumption, and after insertion of Eq. 3 into Eq. 2, Eq. 4 was established. Rearrangement yielded DOI 10.1002/jps
Eq. 5.
Development of Microwave Density Model As has been stated previously, the resonant frequency of a resonator decreases as material is loaded into the electric field. The shift in the resonant frequency is directly related to the amount of mass present in the electric field. If the sample is contained in a known volume, the density of the sample can be monitored. Alternatively, if the sample volume is significantly JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
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larger than the electric field, the material can be assumed to be infinite and once again, the density can be determined. In this study, roller-compacted ribbons with similar volumes were monitored; this allowed microwave measurements to be correlated with density. Although the magnitude of the resonant frequency shift is the primary indicator of the amount of mass in the field, if multiple components are present with significantly different dielectric properties, an extra factor must be included, as shown in Eq. 9. The second term shown below corrects for the presence of water, which has a large dielectric constant and loss factor compared with most particulates. ρ = c1 f r + c2
f r + c3 B
(9)
The form of Eq. 9, especially the choice of the second term, was chosen because it minimized the error and bias when it was used to predict the envelope density of roller-compacted ribbons. Eq. 9 could likely be used to accurately monitor the bulk density of loose particulate materials as well. NIR Measurements A commercially available model Turbido OFS-12S120H NIR sensor obtained from Solvias AG in Basel, Switzerland, was placed directly downstream of the microwave sensor, as also shown in Figure 1. An NIR256L-1.7T1 spectrometer from Control Development Inc. in South Bend, Indiana was used to collect the spectra and was then recorded using Spec32 (version 1.5.4.8). The integration time was found to be 0.067 s; a 16-sample average was used. The length of the ribbons scanned during each test was thus 4 cm. The tip of the NIR probe was measured to be 0.5 cm in width. Analyses were carried out over the wavelength range of 904–1687 nm.
Development of Partial Least Squares Models The spectrum from each ribbon was exported to Unscrambler X, distributed by CAMO Software in Woodbridge, New Jersey, for pretreatment. The spectra were analyzed in either JMP 10, SAS, or Unscrambler X (CAMO Software) using principal component analysis (PCA) and partial least squares (PLS) regression with cross-validation. PCA is an algorithm that transforms a set of correlated variables into a set of linearly uncorrelated ones. This type of analysis is best used as a qualitative means of better understanding patterns in complex data. PLS regression is similar to PCA analysis; however, this quantitative type of analysis seeks to find the multidimensional transformation of JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
the data set that minimizes the variance in the response variable. Example spectra taken at similar moisture contents and varying envelope densities can be seen in Figure 2. When moisture content remains fixed and the density is allowed to vary, a baseline shift is observed for the NIR spectra. As the density decreases, the curves shift downward; a vertical shift was also reported by Acevedo et al.8 while monitoring roller-compacted ribbons of MCC. Thus, a good model to predict density should rely on this vertical shift while minimizing effects due to other process conditions. There is a peak present at the 1685-nm region strongly correlated to the presence of water. This region was therefore truncated while modeling the effects of density variations. As has been reported previously, standard normal variate (SNV) pretreatment helps to remove physical information from spectra such as particle size and the multiplicative interferences of scatter.26–27 This usually allows for clearer observation of differences in chemical composition. Example spectra pretreated with SNV taken at similar envelope densities and varying moisture contents can be seen in Figure 3. The most significant region for moisture content determination was found to be between 1440 and 1630 nm; this region has been highlighted in Figure 4. This region is important for moisture content determination because of the presence of a strong absorbance band for water present at 1450 nm.28 Although the peak found near 1685 nm was strongly correlated to the presence of water, it was not used to aid in the determination of moisture content. For some of the spectra, the peak maximum was shifted beyond the upper detection range of 1687 nm. Ribbon Density Reference Envelope density reference measurements were carried out using a GeoPyc 1360 Envelope Density Analyzer obtained from Micromeritics in Norcross, Georgia. Samples were weighed before testing using an XS104 model microbalance, obtained from Mettler Toledo in Columbus, Ohio. Because the NIR probe could only monitor a 0.5 cm wide area at the center of the ribbon, the centerline was cut out of the ribbon before testing. The center and edge pieces were tested separately. The center piece was used to calibrate the NIR probe, whereas the entire density used to calibrate the microwave sensor was found using a weighted average of the center and edge densities. An array of NIR probes could be used to sample the density of the entire ribbon. Ribbon Moisture Content Reference The reference moisture content of the ribbons was determined on a wet basis from Eq. 10, where mw is DOI 10.1002/jps
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Figure 2. A comparison of NIR spectra of microcrystalline cellulose taken at 4.65% moisture content and at varying densities (blue dash, 1.09 g/cc; red solid, 0.977 g/cc; green dot, 0.776 g/cc).
the mass of water and mp is the mass of dry powder. An HG63 Halogen Moisture Analyzer obtained from Mettler Toledo was used to determine the moisture content from weight loss on drying. An approximately 4-g section of each ribbon was used for analysis.
MC = 100% ×
mw mw + mp
(10)
RESULTS AND DISCUSSION Roller-compacted ribbons of MCC in the density range of 0.675–1.216 g/cc and the moisture content range of 2.1%–5.5% were tested. Although this moisture content range is relatively narrow, especially compared with wet granulation processes, it covers the expected range of values during roller compaction for MCC when exposed to different relative humidity levels. Over this moisture content range, significant changes
Figure 3. A comparison of NIR spectra of microcrystalline cellulose taken at approximately 0.948 g/cc envelope density (green dash: 5.37% MC, 0.949 g/cc; purple long dash: 4.81% MC, 0.951 g/cc; blue solid: 3.39% MC, 0.947 g/cc; and red dot: 2.15% MC, 0.944 g/cc). DOI 10.1002/jps
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Figure 4. An expanded view of 1440–1630 nm region in Figure 3 to highlight most significant region for moisture content determination (green short dash: 5.37% MC, 0.949 g/cc; purple long dash: 4.81% MC, 0.951 g/cc; blue solid: 3.39% MC, 0.947 g/cc; and red dot: 2.15% MC, 0.944 g/cc).
to MCC’s flowability and yield strength can still be observed,5 emphasizing the need to monitor its moisture content during the roller compaction process. Density Monitoring
NIR Spectroscopy Pretreatments, including first and second derivatives, SNV, and baseline shift were evaluated, but as the largest observable change in the spectra of different density ribbons was a shift in their baselines (Fig. 2), no spectral pretreatment was used. A two-factor model using mean centering and uniform weighting was developed using JMP 10 (SAS) from 119 raw spectra. The model was developed over the truncated frequency range of 904–1675 nm. The first principal component accounted for 98.3% of the variation in the spectral data; it quantified changes most closely correlated to density such as compaction pressure and roll thickness. The second principle component was most strongly correlated to moisture content variations and accounted for approximately 1.6% of the variance in the spectral data. Using “leave one out” cross-validation, a root mean squared error (RMSE) of calibration of 0.073 g/cc was found. This result is similar to that found by both Gupta et al.5 and Acevedo et al.8 while monitoring the density of MCC compacts and helps to validate the model. The magnitude and scattering of the density residuals can be seen in Figure 5 as the red triangles. As is shown, they are randomly scattered and do not indicate a discernible pattern or bias in the data not accounted for by this two-factor model. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
By combining NIR spectroscopic data with the pressure setpoint of the roller compactor, a more accurate PLS model was developed. As predicted by the Johanson equation, under normal operation, the pressure of the roller compactor is directly proportional to the envelope density.4 To weight the pressure information equally with the NIR spectral data, scaling in JMP 10 (SAS) was used. This resulted in a two-factor PLS model with a RMSE of calibration of 0.068 g/cc.
Microwave Resonance JMP 10 (SAS) was used to find the calibration coefficients in Eq. 9 for the microwave sensor with the same 119 ribbons used for the NIR analysis. As the microwave sensor monitored the density of the entire ribbon, compared with the NIR probe that monitored only the center density, the whole ribbon densities reference values were used. A RMSE of 0.044 g/cc was found. This error is approximately half of the error encountered using NIR analysis. A plot of the residuals from Eq. 9, shown in Figure 5 as blue squares, indicates that they are randomly distributed. Furthermore, the magnitude of the residuals was significantly reduced when employing the microwave model as compared with the NIR PLS model. This increased accuracy is likely because of the direct relationship between the magnitudes of microwave sensor measurements and the amount of mass present in the field. This differs from NIR analysis, wherein physical properties, such as density, need to be filtered from chemical effects using a technique such as PCA. DOI 10.1002/jps
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Figure 5. Density residuals of NIR two-factor PLS model based on raw spectral data and density residuals of microwave model.
As mentioned previously, a known or semi-infinite volume is needed to monitor very precisely the density of a material in the microwave field. Although the ribbon volume did not change with pressure significantly, by including the pressure setpoint (P), a more accurate microwave calibration can be obtained (Eq. 11). (11) ρ = c1 f r + c2 P + c3 The pressure setpoint takes the place of the compensation term for moisture content in Eq. 9. This substitution occurs because the majority of the density variation with pressure is due to changes in moisture content. As noted previously, moisture significantly affects the compressibility of most particulate materials. The inclusion of compaction pressure information in Eq. 11 resulted in a RMSE of 0.034 g/cc. The RMSE after inclusion of roller compaction pressure using a microwave sensor was once again about half of that found when using NIR sensing. It is important to note that the inclusion of resonance bandwidth into Eq. 11 did not significantly improve the results. This suggests that a less-expensive scalar network analyzer could be used to monitor the density of roller-compacted ribbons with similar accuracy to vector network analyzers. Moisture Content Monitoring
NIR Spectroscopy As was shown previously, significant changes in the spectra can be observed in the 1440–1630 nm region that correspond to moisture content differences once DOI 10.1002/jps
physical effects have been removed by pretreatment. Thus, this region was chosen to construct the PLS model for moisture content. Although spectral differences were most readily observed using SNV pretreatment, as shown in Figure 4, principal components found using first-derivative pretreatment were more strongly correlated with moisture content differences. Therefore, a three-factor model was constructed using Unscrambler X (CAMO Software) after first-derivative pretreatment utilizing a 15-point Savitzky–Golay algorithm. This PLS model employed mean centering and uniform weighting; this resulted in a RMSE of calibration of 0.115% moisture content using “leave one out” cross-validation. There was no significant improvement in the RMSE of calibration using a four-factor model (0.105% moisture content). Furthermore, the residuals, as shown in Figure 6, are randomly distributed and do not indicate a pattern that could be explained by including further factors. Finally, there was a strong linear correlation (greater than 0.8) between the T and U scores of the first three factors. This correlation dropped significantly for factors greater than three. On the basis of these observations, it was concluded that only three significant principle components were present in the firstderivative pretreated spectra. The first principal component, which accounted for 88% of the variance in the spectra and 68% of the variance in the model’s response, quantified the moisture content variations in the ribbons. The second principle component, which accounted for 10% of the variance in the data, quantified variations in density JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
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Figure 6. Moisture content residuals of two-factor model based on SNV pretreated data.
and accounted for 26% of the variance in the model’s response. The third principle component accounted for 1% of the variance in the spectral data and 5% of the variance in the model’s response. Not enough parameters were measured to properly identify what property of the ribbons the third principle component quantified. However, the strong linear correla-
tion (0.90) between the T and U scores of factor three, as well as the significant decrease in the RMSE when employing a three-factor model compared with a twofactor model (0.115% vs. 0.249%), both indicated that the third principle component was significant and not due to noise and should therefore have been included. As moisture content is not a function of compaction
Figure 7. Moisture content residuals of microwave model. JOURNAL OF PHARMACEUTICAL SCIENCES, VOL. 102, NO. 6, JUNE 2013
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pressure, the addition of the pressure setpoint to the NIR model did not significantly improve the accuracy of the model.
Microwave Resonance JMP 10 (SAS) was used to find the constants in Eq. 1 by fitting microwave sensor measurements to the same 119 ribbon moisture content values used for NIR analysis. With four fitted constants, a RMSE of 0.065% moisture content was found. The microwave model’s residuals are randomly distributed, as shown in Figure 7. The RMSE found using microwave sensing was again approximately half of the error seen using NIR analysis. Microwave sensors are very sensitive to even tiny amounts of moisture, capable of detecting water concentrations in the ppm range.29 This is because of the large contrast in dielectric properties of water and most solid materials. For instance, the dielectric constant, which characterizes a material’s ability to store electrical energy, of water is approximately 80 in the frequency range of interest, whereas it is approximately two for MCC. In contrast, NIR detection schemes must use complex chemometric software and spectral pretreatments to remove physical effects from spectra before they can accurately monitor chemical compositions.
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ing between microwave and NIR methods can be concisely summarized by noting that NIR trades precision for versatility, whereas microwave sensing offers the opposite. It is important to know what properties are the most critical control variables and to utilize the most appropriate sensing methods available.
ACKNOWLEDGMENTS This work was completed with support from the National Science Foundation’s Engineering Research Center for Structured Organic Particulate Systems grant number 104728. We would like to thank and acknowledge Dan Kopec and Sartorius Stedim for the loan of the microwave sensor used to execute this study. In addition, we would like to thank Professor James Litster at Purdue University for the use of the Particle Design and Formulation Laboratory, without which we would not have been able to complete this study.
REFERENCES
Both microwave resonance and NIR sensing have been shown to be viable process analytical tools for monitoring the moisture content and envelope density of roller-compacted ribbon. In this study, microwave resonance technology was shown to be markedly more accurate than NIR, with approximately half of the RMSE for both density and moisture determination. Furthermore, as was also noted by Corredor et al.,19 microwave resonance benefits from working without sophisticated chemometric software, requiring fewer calibration standards, and having the ability to transfer between formulations more easily. Microwave resonance sensors of the type used in this study operate at a very low power level and do not pose a health risk to the operator. Furthermore, they do not heat the material under test. Although microwave sensors were shown to be more accurate than NIR sensors for the determination of the moisture content and envelope density in roller-compacted ribbons, NIR sensors still do offer some important benefits. Most notably, NIR sensing can be used to monitor the chemical composition of a particulate blend of many components more accurately than microwave sensing if correctly calibrated. The difference in moisture content and density sens-
1. FDA. 2004. Guidance for industry: PAT—A framework for innovative pharmaceutical development, manufacturing, and quality assurance. 2. Akseli I, Iyer S, Lee HP, Cuitino AM. 2011. A quantitative correlation of the effect of density distributions in rollercompacted ribbons on the mechanical properties of tablets using ultrasonics and X-ray tomography. AAPS PharmSciTech 12(3):834–853. 3. Muliadi AR, Litster JD, Wassgren CR. 2012. Modeling the powder roll compaction process: Comparison of 2-D finite element method and the rolling theory for granular solids (Johanson’s model).. Powder Technol 221:90–100. 4. Johanson J. 1965. A rolling theory for granular solids. Journal of Applied Mechanics 32:842–848. 5. Gupta A, Peck GE, Miller RW, Morris KR. 2005. Influence of ambient moisture on the compaction behavior of microcrystalline cellulose powder undergoing uni-axial compression and roller-compaction: a comparative study using near-infrared spectroscopy. J Pharm Sci 94(10):2301–2313. 6. Emery E. 2008. Flow properties of selected pharmaceutical powders. University of Saskatchewan. 7. Nokhodchi A. 2005. Effect of Moisture on compaction and compression. Pharm Tech 6:46–66. 8. Acevedo D, Muliadi A, Giridhar A, Litster JD, Romanach RJ. 2012. Evaluation of three approaches for real-time monitoring of roller compaction with near-infrared spectroscopy. AAPS PharmSciTech 13 (3):1005–1012. 9. Feng T, Wang F, Pinal R, Wassgren C, Carvajal MT. 2008. Investigation of the variability of NIR in-line monitoring of roller compaction process by using Fast Fourier Transform (FFT) analysis. AAPS PharmSciTech 9(2):419–424. 10. Nesarikar VV, Vatsaraj N, Patel C, Early W, Pandey P, Sprockel O, Gao Z, Jerzewski R, Miller R, Levin M. 2012. Instrumented roll technology for the design space development of roller compaction process. Int J Pharm 426(1–2):116–131. ¨ 11. Buschmuller C, Wiedey W, Doscher C, Dressler J, Breitkreutz J. 2008. In-line monitoring of granule moisture in fluidized-bed dryers using microwave resonance technology. Eur J Pharm Biopharm 69(1):380–387. 12. Lourenco V, Herdling T, Reich G, Menezes JC, Lochmann D. 2011. Combining microwave resonance technology to
DOI 10.1002/jps
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13.
14.
15.
16.
17. 18.
19.
20.
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multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation. Eur J Pharm Biopharm 78(3):513–521. Gradinarsky L, Brage H, Lagerholm B, Bjorn IN, Folestad S. 2006. In situ monitoring and control of moisture content in pharmaceutical powder processes using an open-ended coaxial probe. Meas Sci Technol 17(7):1847–1853. Shi Z, Anderson CA. 2010. Application of Monte Carlo simulation-based photon migration for enhanced understanding of near-infrared (NIR) diffuse reflectance. Part I: Depth of penetration in pharmaceutical materials. J Pharm Sci 99(5):2399–2412. Saeed M, Probst L, Betz G. 2011. Assessment of diffuse transmission and reflection modes in near-infrared quantification, part 2: Diffuse reflection information depth. J Pharm Sci 100(3):1130–1141. Muzzio FJ, Goodridge CL, Alexander A, Arratia P, Yang H, Sudah O, Mergen G. 2003. Sampling and characterization of pharmaceutical powders and granular blends. Int J Pharm 250(1):51-64. Chen L. 2004. Microwave electronics: Measurement and materials characterization. Chichester: John Wiley, pp 37–42. Knochel R, Taute W, Doscher C. 2007. Stray field ring resonators and a novel trough guide resonator for precise microwave moisture and density measurements. Meas Sci Technol 18(4):1061–1068. Corredor CC, Bu DS, Both D. 2011. Comparison of near infrared and microwave resonance sensors for at-line moisture determination in powders and tablets. Anal Chim Acta 696(1–2):84–93. Austin J, Rodriguez S, Sung P-F, Harris M. 2013. Utilizing microwaves for the determination of moisture content independent of density. Powder Technol 236:17–23.
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21. Meyer W, Schilz W. 1980. A microwave method for density independent determination of the moisture content of solids. J Phys D-Appl Phys 13(10):1823–1830. 22. Trabelsi S, Kraszewski AW, Nelson SO. 1998. A microwave method for on-line determination of bulk density and moisture content of particulate materials. IEEE Trans Instrum Meas 47(1):127–132. 23. Sung P-F, Hsieh Y-L, Angonese K, Dunn D, King RJ, Machbitz R, Christianson A, Chappell WJ, Taylor LS, Harris MT. 2011. Complex dielectric properties of microcrystalline cellulose, anhydrous lactose, and alpha-lactose monohydrate powders using a microwave-based open-reflection resonator sensor. J Pharm Sci 100(7):2920–2934. 24. Sarabandi K, Li ES. 1997. Microstrip ring resonator for soil moisture measurements. IEEE Trans Geosci Remote Sensing 35(5):1223–1231. 25. King RJ, King KV, Woo K. 1992. Microwave moisture measurement of grains. IEEE Trans Instrum Meas 41(1):111– 115. 26. Barnes R, Dhanoa M, Lister S. 1989. Standard normal variance transformation and de-treading of nearinfrared diffuse reflectance spectra. Appl Spectrosc 43:772– 777. 27. Candolfi A, De Maesschalck R, Jouan-Rimbaud D, Hailey PA, Massart DL. 1999. The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. J Pharm Biomed Anal 21(1):115–132. 28. Carter GA, McCain DC. 1993. Relationship of leaf spectral reflectance to chloroplast water content determined using NMR microscopy. Remote Sens Environ 46(3):305– 310. 29. King RJ. 1994. Microwave reflection resonator sensors.United States: KDC Technology Corporation.
DOI 10.1002/jps