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International Journal of Pharmaceutics

journal homepage: www.elsevier.com/locate/ijpharm

Shedding light on coatings: Real-time monitoring of coating quality atindustrial scale

Stephan Sachera, Patrick Wahla, Michael Weißensteinera, Matthias Wolfganga,Yekaterina Pokhilchuka, Bernd Looserb, Jochen Thiesb, Andrea Raffac, Johannes G. Khinasta,d,⁎

a Research Center Pharmaceutical Engineering GmbH, 8010 Graz, AustriabGlatt Maschinen- und Apparatebau AG, 4133 Pratteln, Switzerlandc Phyllon GmbH, 8010 Graz, Austriad Institute for Process and Particle Engineering, Graz University of Technology, 8010 Graz, Austria

A R T I C L E I N F O

Keywords:Optical coherence tomographyIn-line monitoringReal-time releaseCoating thicknessCoating qualityQuality assurance

A B S T R A C T

Optical Coherence Tomography (OCT) is increasingly being used for studies of pharmaceutical film coating. OCTallows fast and non-destructive analysis of the coating thickness and quality via high-resolution cross-sectionalimages. Information about both the coating thickness and the coating quality can be extracted. Most studies andOCT applications performed to date have been limited to off-line measurements and off-line computations ofcoating features based on data acquired in-line. This study examines OCT’s applicability to an industrial-scalepan coating process. Automated layer detection, classification and thickness calculation were executed in realtime. To evaluate the system’s performance, runs with various tablet size, coating solution concentration andoperating parameters were carried out and monitored. Our results indicate that, in addition to correct end-pointdetermination, OCT enables real-time monitoring of the coating processes (thickness, homogeneity androughness) and can support active process control by supplying information about the coating thickness andquality.

1. Introduction

In the pharmaceutical industry, continuous processes are increas-ingly used to manufacture a range of new and traditional products,mainly solid oral dosage forms via wet and dry granulation or directcompaction. Turn-key lines offered by various suppliers facilitate theshift to the continuous approach, and even pan coaters can now beintegrated into a fully continuous process. Tablet coating is a crucialstep since it defines the appearance of the final drug product. Moreover,the coating thickness and homogeneity are critical quality attributes,especially for functional coating. In such cases, a uniform thickness isrequired to guarantee a desired drug release rate to the patient.

Automated production in batch mode, as well as in continuousoperation, requires a control strategy that is based on real-time processdata, enabling a process that is robust yet flexible enough to react toany variations in the raw or intermediate materials and other changes.In many unit operations, in-line monitoring tools have successfully beenimplemented (Laske et al., 2017), and PAT-based control strategies forcontinuous manufacturing lines have been reported (Vargas et al.,2018; Singh et al., 2014).

While much progress has been made in real-time monitoring ofvarious properties, such as the API content of a powder mixture via NIR,most industrial coating processes still rely on off-line measurements ofthe critical quality attributes. The coating thickness and the process’send-point are determined by measuring the weight increase or thediameter gain using a small amount of dosage units, representing batchsizes up to hundreds of thousands of tablets. The measurement itself isexecuted manually and provides only the mean value without tablet-specific information. Another challenge is the variability of the tabletcores in terms of weight and dimension. These parameters stronglyinfluence the accuracy of both measurement methods.

In addition to the traditional methods, some new technologies havebeen applied. The coating thickness can for example be measured viaacoustic microscopy (Bikiaris et al., 2012) and the internal coatingstructure can be examined via X-ray computed tomography (Ariyasuet al., 2017). Moreover, a few in-line approaches have been proposed,including near-infrared spectroscopy (NIRS) (Maurer and Leuenberger,2009; Gendre et al., 2011; Hattori et al., 2018), Raman spectroscopy(Wirges et al., 2013; Barimani and Kleinebudde, 2017) and Terahertzpulsed imaging (TPI) (Maurer and Leuenberger, 2009; May et al., 2011;

https://doi.org/10.1016/j.ijpharm.2019.05.048Received 15 January 2019; Received in revised form 17 May 2019; Accepted 17 May 2019

⁎ Corresponding author at: Institute for Process and Particle Engineering, Graz University of Technology, 8010 Graz, Austria.E-mail address: khinast@tugraz.at (J.G. Khinast).

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Lin et al., 2015). The benefits and drawbacks of the existing coatingprocess monitoring methods have been described (Knop andKleinebudde, 2013; Korasa and Vrečer, 2018) and compared experi-mentally (Cahyadi et al., 2010). The main challenges are the calibrationmodels required for spectroscopic methods, the limited resolution andthe low acquisition rates. Information about the coating quality ofsingle dosage units can only be obtained via NIRS and TPI.

It is crucial that the coating of each tablet is within the specification.However, the above methods can only provide mean values for anumber of tablets. Since the coating process is complex and influencedby many parameters (Suzzi et al., 2010), real-time information on thecoating quality is essential for automated production. Moreover, forprocess optimization accurate determination of the coating thickness,homogeneity and surface properties is essential. Coating roughness iscommonly determined off-line using tablet samples via a range ofmethods, such as scanning electron microscopy, surface profilometry(Niblett et al., 2017; Seitavuopio et al., 2006), laser scanning micro-scopy (Dohi et al., 2016) and TPI. TPI utilizes either the terahertzelectric field peak strength (Haaser et al., 2013) or the film surfacereflectance (Dohi et al., 2016) as an indirect measure for surfaceroughness.

Optical coherence tomography (OCT) is a high-resolution imagingtechnology that may help to tackle the above challenges. It does notrequire (significant) calibration and yields high-resolution images forassessing both inter- (Lin et al., 2017a) and intra- (Dong et al., 2017)tablet coating homogeneity. Additionally, surface roughness can bemeasured directly (Markl et al., 2018). However, OCT’S penetrationdepth is lower (maximum of a few hundred microns depending on thecoating material) than that of TPI, and scattering particles can drasti-cally limit the penetration. An overview on the applicability of OCT fordifferent common coating materials can be found in (Lin et al., 2017b).Due to its high acquisition rate, OCT enables the utilization of real-timedata for process control.

OCT has been implemented and tested in lab-scale settings for pancoating (Markl et al., 2015a) and fluid bed coating (Markl et al.,2015b). In both cases, the coating features were computed off-line,based on the in-line acquired images. This study accomplished one ofthe first in-line implementations of OCT in an industrial-scale pancoater with real-time data analysis. Several parameters, including (1)coating thickness, (2) homogeneity/variability and (3) roughness weremonitored in real time. The feasibility of OCT for end-point determi-nation and acquisition of data for feedback process control was in-vestigated.

2. Materials and methods

2.1. Tablet core and coating material

Oblong tablets with dimensions of 11.6×6.1×4.6mm were usedin the majority of the test runs. One run was executed with tablets of thesame shape but with dimensions of 18.5× 9×6.5mm. The tabletcores consisted of a placebo formulation. PVA-PEG copolymer

(Kollicoat IR from BASF, Germany) was applied as coating. An aqueoussolution of 15% w/w was prepared. One test was run with a con-centration of 10% w/w.

2.2. Coating process

The coating process was performed in a Glatt GCC 150 (Glatt,Pratteln, Switzerland) pan coater. The drum had a load volume rangebetween 15 l and 150 l. The load of cores in the beginning of the processwas 50 kg in all experiments, which equals 62.5 l with a tablet bulkdensity of 0.8 kg/l. The coater was equipped with 4 GCSD 5.1 spraynozzles with a 1.2 mm liquid insert. The standard gun-to-bed distancewas 200mm, and in one experiment 300mm. All of the followingprocess parameters were kept constant in all runs:

The drum speed was set to 8 rpm, the spray rate was 150ml/min,the atomizing air pressure was 1.4 bar and the pattern air pressure was0.7 bar. The outlet air temperature was monitored and controlled at45 °C. In one experiment, the spray rate was increased stepwise:100ml/min for 51min, 150ml/min for 35min and 175ml/min for therest of the run. Table 1 provides an overview of the experimental set-tings. The process was stopped when the coating thickness reached50 µm based on the reading of the OCT in-line device. These OCTmeasures at the process end are also listed in Table 1 together with theweight gain measurements and the total amount of sprayed coatingsolution. Samples of several hundred tablets were drawn at an intervalof 2.5 L of spray suspension for off-line reference measurements.

2.3. Optical coherence tomography system

In all experiments, the same spectral-domain (SD) OCT spectro-meter was used (Fig. 1). The light source is a super luminescent diode(SLD, BLMS mini, Superlum Diodes Ltd., Ireland) with a central wa-velength of 832 nm and spectral full-width at half-maximum (FWHM)bandwidth of 75 nm, resulting in 4 µm axial resolution in air. Thespectrometer consists of a fiber collimator, diffraction grating(1500 lines/mm) and a focusing lens (all Thorlabs, USA), which projectthe spectrum onto a high-speed CMOS-sensor (Teledyne DALSA, Ca-nada). The system acquires up to 100,000 frames (=A-scans) persecond. In this work the exposure time for A-scans was set to 4 µs, thedead time to 16 µs compared to 15 µs and 1.9 µs, respectively, in pre-vious work (Markl et al., 2015a). The lower exposure time leads toreduced motion blurring caused by the movement of the tablet duringacquisition. In the sensor head, the beam is focused via a fiber focuser(OZ Optics Ltd., Canada) with a working distance of 35mm in the 1Dsensor. A non-polarizing beam splitter (Thorlabs, USA) divides the lightinto reference and measuring beams. This optical setup had a lateralresolution of 14 µm in focus.

A 1D probe was placed inside the coater beneath the perforated panclose to the exhaust air duct (Fig. 2). With this approach, measurementsare accomplished using the light beam that passes through the per-forations of the drum, which are passing the probe. Several studiesbased on advanced DEM simulations showed a good mixing behaviour

Table 1Experimental settings of coating runs; total spray quantity, tablet weight gain and coating thickness measured via OCT at the process end.

Test run Tablet dimensions [mm] PVA-PEG concentration [%w/w]

Spray rate [ml/min]

Distance gun to bed[mm]

Spray quantity[l]

Tablet weight gain[% w/w]

Coating thickness[µm]

A 11.6×6.1× 4.6 15 150 200 13.9 4.30 50.5B 11.6×6.1× 4.6 15 150 200 14.6 4.37 51.4C 11.6×6.1× 4.6 15 150 200 13.9 4.31 50.5D 11.6×6.1× 4.6 15 150 200 14.3 4.51 50.9E 11.6×6.1× 4.6 10 150 200 22.7 4.48 51F 18.5×9×6.5 15 150 200 11.4 3.61 51.2G 11.6×6.1× 4.6 15 100–150-175 200 14.7 4.44 50.5H 11.6×6.1× 4.6 15 150 300 15.7 4.51 51.6

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of tablets in the drum (Boehling et al., 2016a). Thus, there is sufficientexchange between tablets in front of the OCT probe (close to the per-forated pan) and tablets from regions close to the center of the drum.The optical fibre is contained in a rubber hose and connected to theOCT base unit outside of the coater. A purge gas for probe cleaning isrun inside the hose. Potential eccentricity and non-circularity of thedrum has to be considered for industrial application. During the testruns in the Glatt GCC 150 coater the tablets stayed perfectly in the field

of view for the entire operation. However, a higher amount of eccen-tricity and non-circularity could lead to a decrease of the number ofmeasured tablets. In a separate campaign with another type of pancoater from a different supplier it has been found that the number ofuseful measurements is reduced, but the coating thickness readings arenot influenced by a drum with a higher amount of eccentricity and non-circularity. As far as the drum speed is concerned, processes with cir-cumferential speeds of up to (at least) 1.5m/s can be monitored.

2.4. Coating thickness evaluation

As described by Markl et al. (2015a), SD-OCT uses light interferenceinformation obtained by combining the reference beam and the lightreflected from the scanned pharmaceutical dosage form. The resultinginterference pattern encodes the depth information based on its fre-quencies. For obtaining high-resolution depth profiles, a non-uniformFast Fourier Transformation (NUFFT) is applied to minimize the sen-sitivity fall-off and compensate for the spectrometer’s non-linearities inthe signal. However, in contrast to Markl et al. (2015a), in this work weused software to perform signal apodization before applying theNUFFT, cutting off the first and last parts of the interference patternthat contain high frequency noise and filtering the remaining signal viaa Hann window. As a result, the Fourier frequency side-lobes weresignificantly reduced and the reflecting interfaces were sharpened,delivering a deeper profile quality. Fig. 3 gives an overview of the dataprocessing workflow from the raw A-scan to the computed coatingproperties. In Fig. 4 exemplary images of different steps in the dataprocessing procedure are depicted. Image a shows the intensity of theinterference pattern of a single A-scan. Image b shows the intensity inthe OCT image over the image height and the grey value image (B-scan)generated from 1024 A-scans. The green line indicates the position,

Fig. 1. Schematic of the OCT system consisting of base unit and sensor heads. SLD=Super Luminescent Diode, DC=Directional Coupler, FF= Fiber Focuser,BS=Beam Splitter, I= Iris, M=Mirror, A=Absorber, FC=Fiber Collimator, DG=Diffraction Grating, L= Lens, CMOS=Sensor. The insert shows an evaluatedOCT image of a tablet coating layer. The purple line indicates the coating thickness.

Fig. 2. 1D OCT probe integrated into a Glatt GCC 150 pan coater.

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which is linked to the depicted A-scan. Each A-scan describes a depthprofile with 1024 pixels over a spatial range of 2.76mm in air. Re-garding the real axial resolution, the correct refractive index of a ma-terial (coating) has to be known.

The detection procedure is executed for each single A-scan andconsists of the following steps: First the A-scan is classified into threecategories (air/tablet/coater). Then the air-coating interface is definedby the detection of the top interface via ellipse fitting. The second in-terface is detected based on the previously-established top interface.Finally the coating layer properties are determined. For classificationbetween air and solid material the overall A-scan intensity is comparedwith the background noise, while the distinction between tablet andcoater is based on the A-scan peak profile. Image c in Fig. 4 shows theclassification of all A-scans within one B-scan. In parallel, a cost mapbased on first and second order derivatives is calculated and used forthe interface detection steps. The key element is the ellipse fitting,which uses the cost map as input parameters. Criteria were defined toassure only real interfaces are processed. For the top interface betweenair and coating a minimum intensity of the pixels along the fitted ellipseis required. For the second interface (between coating and core)thresholds for the image gradient are set. Image d in Fig. 4 shows the

top ellipse fit in red and the ellipse fit for the second interface in blue,while image e shows the resulting coating layer. The parameters have tobe optimized for a specific coating depending on structures in thecoating and the coating-core interface. Therefore, OCT scans are pro-cessed with different settings and manually rated regarding detectedpositives and false-positives. The best set of parameters for each for-mulation is in our experience independent of process parameters, drumload and tablet shape. The operator is not required to set any adjust-ments for all subsequent, automated runs with the same coating/corecombination. The reported coating thickness of the scanned pharma-ceutical is computed based on the shortest distance between its fittedellipses perpendicular to the top ellipse fit. The ellipse fitting works forevery eccentricity. Therefore, even flat surfaces, such as the tablet band,can be fit with the chosen approach. The image processing algorithm isprimarily executed on GPUs (nVidia GeForce 1080Ti), making it pos-sible to evaluate up to 25 OCT images per second. The insert in Fig. 1shows an evaluated OCT image of the coating layer. The ellipse fits andthe computed coating thickness are indicated in green and purple, re-spectively.

For each image of the scanned pharmaceutical, the software de-termines such coating layer properties as the thickness, the surface

Fig. 3. Flow chart of the data processing.

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roughness and the layer homogeneity.

2.5. Reference analytics

Light microscopic (LM) imaging was applied in the off-line in-vestigation of tablet coating thickness. Between 10 and 20 tablets fromvarious process times and test runs were selected randomly. The tabletswere cut at one convex side between the center and the outer edge witha sharp knife. After reaching half of the tablet height, the tablets werebroken manually. This procedure allowed an optimal conservation ofthe coating layer. The broken convex sides of the tablets were analysedusing a light microscope (Leica DM 4000M from Leica, Germany). A 50-fold magnification was applied in the reflectance mode. Measurementsof coating thickness were performed using the software LeicaApplication Suite v4.9. Due to operators’ influence, an error of 10% wasestimated based on the range of subjective identification of the layerboundaries.

Weight gain was also measured off-line. 100 tablets were weighedbefore the process began and at each sampling interval using an AT261Delta Range (Mettler Toledo, Germany). The tablet moisture wasmeasured on a LOD scale HB 43-S (Mettler Toledo, Germany) andconsidered in the weight gain calculation.

3. Results and discussion

The OCT images were acquired in-line during the coating processand the coating thickness was calculated in real-time. A refractive indexof 1.49 for the coating material was estimated based on measurementsfor the polymer at dry coated tablets. As a reference, light microscopicimages were acquired. It was assumed that the refractive index is uni-form over the entire coating layer of the tablets. Mechanical attrition oftablets, as well as the residual moisture content in the coating layer, caninfluence the refractive index. Latter was assumed to be constant for thetablets at the outer region of the coater close to the pan. However, atthe investigated layer thicknesses the resulting uncertainty for themeasured coating thickness is below the resolution limit. The acquisi-tion of OCT data began with some delay after the coating process start.For clear identification of a layer a width of at least 4 pixels in an imageis needed. The interface on both sides of this layer has at least one pixeleach. This results in a total of 6 pixels, which for n=1.49 correspondsto 10.9 µm. The limit of detection was thus set to 12 µm. Therefore, nocoating layer was evaluated in the earliest stage of the coating process.Fig. 5 shows images at the various stages of run A. The coating thick-ness increases steadily with the process time. The images representtablets scanned by the sensor through the perforated pan. The visibility

Fig. 4. Steps in the data processing procedure. Image a: Intensity of the interference pattern of a single A-scan, Image b: Intensity in the OCT image over the imageheight and the grey value image (B-scan), Image c: Classification of all A-scans within one B-scan, Image d: Top ellipse fit (red) and second interface ellipse fit (blue),Image e: Resulting coating layer. The scale bars represent the image depth in air.

Fig. 5. OCT images acquired in-line at various stages of run A. Process times are given in minutes. The scale bar represents the coating material dimension.

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of tablets is limited by the time span a perforation passes the lightbeam. Note that the images show sharper interfaces and a lower imagenoise compared to previous studies (Markl et al., 2015a). This resultsfrom an improved design of the spectrometer and apodization. Thescale is given for the z-axes in the direction of light beam (A-scan). Thex-axes scale varies depending on the speed of tablets passing by thelight beam.

Only images of objects, which were classified as tablets by the al-gorithm, were used in the thickness calculation. The coating thicknesswas computed in real-time following the ellipse-fit approach describedabove. When an OCT measurement is performed, the current thicknessreadings and the trend are displayed by the OCT system. The data isstored for further analysis.

Fig. 6 shows an increase in the coating thickness over the processtime in test runs A, B, C and D. Each data point represents the meanvalue of coating thickness over one minute of process time. Appropriatethickness readings began at 12 µm due to the resolution of the system.After a process time of approximately 20min, the coating thicknessexceeded this level. To exclude misleading data, a threshold of 12 µmwas set in the software. This initial thickness level is represented by ahorizontal line in Fig. 6. Within each minute, 50 to 70 scans wereclassified as tablets with extractable thickness information. Due to theset detection criteria this rate was lower in the beginning and ap-proached a constant level, when the coating thickness exceeded 20 µm.

As can be seen from Fig. 6 the slope is lower in the first phase up to20min. This is due to the algorithm that discards any measurementsbelow 12 µm (there is a significant variance in the measurements). As aconsequence, the one-minute average is shifted to higher values,leading to a less pronounced increase of the mean coating thickness.Above approximately 20 µm this effect is not visible any more. For runsA, B, C and D with the same conditions the measurements are re-producible to a great extent. The OCT system was used for end-pointdetermination. In all runs, the coating process was terminated manuallywhen the linear fit of all OCT measurements of the current run reacheda thickness of 50 µm. The mean values of the last 150 measurementsduring the process are summarized in Table 1. They represent anaverage final coating thickness of the batch.

In runs with equal tablet sizes, slight differences in the coatingthickness clearly reflect the variations in the total suspended solution. Ahigher spray quantity leads to an increase in the coating thickness andthe weight gain. In Fig. 7, the weight gain and the increase of coatingthickness are compared based on the spray quantity, including datafrom the replication runs (A and B) and runs with a lower polymer

concentration (E) and bigger tablets (F). OCT data shows an almost-linear increase in the coating thickness expected over the process timeand an increasing spray quantity. However, the weight gain dataslightly deviate from the linear behaviour, possibly due to a limitedamount of measured samples relative to a very large number of tabletsmeasured via OCT. A variation in the tablet core weight could also be apotential source of the inaccuracy.

The in-line OCT measurements also allow monitoring the influenceof process parameters on the coating process performance. Fig. 8summarizes all test runs with distinct process settings. In each run, oneraw material attribute or process parameter was varied compared to thereplication runs A to D. For an easier comparison, only run A is showntogether with runs E, F, G and H. With a load of bigger tablets (run F)comprising a reduced total surface area, the same final coating thick-ness is achieved after a shorter process time. An increase in the coatingthickness occurs faster at the same spray rate. In contrast, a lowerconcentration of polymer in the coating solution (run E) leads to slowerincrease in the coating thickness and 50% longer process time, as ex-pected. With a greater distance between the spray gun and the tabletbed (run H), more of the coating solution evaporates before hitting thetablets. In addition, polymer and solvent are lost in the coater exhaustat a higher rate. As such, the desired coating thickness is reached laterand significantly more coating solution is required. In run H, the sprayfailed after 41min and for 8min no coating solution was available. Thisevent was immediately recognized via the OCT monitoring. It is de-picted as plateau without further increase in the coating thickness inrun H (Fig. 8).

Run G was executed at various spray rates, starting at 100ml/min.After 51min the rate was increased to 150ml/min. OCT data clearlyshow an increase in the slope right after the spray rate was modified.After another 35min, the spray rate was increased to 175ml/min.Again, the change was reflected in the OCT data at a total process timeof approximately 86 to 90min. This change of the slope is not thatobvious since the rate change was smaller. The three phases were ap-proximated by a linear fit. The rate of coating increase during run G was0.20 µm/min in the first phase, 0.48 µm/min in the second phase and0.59 µm/min in the last phase. For comparison purposes, a linear fit forrun A was performed. Only data after 40min of process time were in-cluded. The linear fit of coating thickness had a slope of 0.51 µm/min.The second phase of run G is thus very similar to run A.

These results show that OCT allows the implementation of in-pro-cess control actions. Deviations from the nominal increase in thecoating thickness can be detected in real-time. Therefore, OCT data canbe used to trigger process control actions.

As an additional feature, homogeneity of the coating layers wascalculated based on the OCT images. The coating homogeneity is de-fined as a ratio between the pixels with brightness above a definedthreshold and the total number of pixels. The total number of pixelscomprises all pixels between the ellipse fits air-coating and coating-coreof the coating layer in question (green area in the insert in Fig. 1),except for a small margin. Dark pixels can stem from air inclusions,such as pores or dispersed solids. A coating with a small number of darkpixels indicates high homogeneity, and thus, good coating quality.

Fig. 9 shows the coating homogeneity as a function of the processtime for the replication runs A and B, and in run E with a lower polymerconcentration in the coating solution. Data points represent mean va-lues over one minute of process time. The homogeneity increases andreaches a plateau at a certain stage of the process. The coating layerbecomes more uniform and the dark areas diminish, meaning that thenumber of pores in the coating layer is mitigated. While the homo-geneity in run A and B is very similar, it differs significantly from that inrun E. Fig. 9 contains exemplary images of the final stage of run A, Band E. The images from run A and B contain many dark spots, and theimages from run E have fewer of them and look more homogenous.Actually, there was no variation in the process parameters in all 3 runs,except that the concentration of the coating solution was lower in run E.

Fig. 6. Coating thickness measured in-line over the process time in runs A-D.Mean values calculated for 1min process time are shown.

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There are two potential reasons for different coating homogeneity.First, inhomogeneities are smoothened more effectively due to slowercoating growth and longer process time at a lower concentration in E.Second, the amount and size of bubbles in the original coating solutioncan vary for different runs, resulting in different pores in the coatinglayer. These coating solutions are prepared before the coating run andcan differ in terms of gas (bubble) content, even for the same mixingequipment. Differences can for example occur due to different mixerspeeds or resting times. Operators are commonly selecting agitatorspeed, mixing and resting durations based on visual observation ofbubble formation. This can lead to a variable amount of bubbles andtheir sizes.

For the determination of coating homogeneity as a function of theprocess time, one has to consider also the computational procedure.Due to segmentation of the coating layer by the ellipse fit, parts of theinterface might be used for the computation of the homogeneity. Theseinterface areas increase the relative error, especially at thinner layers.With increasing thickness of the coating layer this effect vanishes and

the homogeneity readings become more precise. However, additionalwork has to be invested to strengthen the reliability of homogeneitymeasurements in the early stages of the coating process.

Fig. 10 shows the coating roughness in the replication runs A, B, Cand D. The coating roughness is defined as the standard deviation of thetrue coating surface from the ellipse fit between air and coating. Notethe high resolution of the y-axis. All runs had reproducible results. Theroughness increases linearly with the process time. The roughness of thetablet surface can be caused by mechanical tablet to tablet interactions.The number of contacts between the tablets also increases linearly withthe process time. An increase of roughness induced by frictional con-tacts between tablets was reported by Rowe (1988). Seitavuopio et al.(2006) observed increasing roughness with increasing process timeespecially in the beginning of the process. After a certain amount ofsprayed coating solution the coating roughness remained at a constantlevel, while in the present study no asymptotic roughness value is

Fig. 7. Increase in the weight gain and coating thickness with the sprayed amount of coating solution in runs A, B, E and F.

Fig. 8. Coating thickness measured in-line over the process time for run A andruns E-H. Mean values calculated for 1min of process time and a linear fit forruns A and G are shown.

Fig. 9. Coating homogeneity over process time in runs A, B and E. Mean valuescalculated for 1min of process time are depicted. The inserts show the OCTimages acquired in-line at the final stage of runs A, B and E. Runs A and B (moredark spots) have a lower coating homogeneity compared to run E (fewer darkspots).

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reached. The coating roughness was in the range of 4.5 µm for all re-plication runs. In a study with Eudragit as coating polymer similarroughness values of approximately 4 µm were measured by means ofOCT and contact profilometry, when a mean coating thickness of 51 µmwas reached (Markl et al., 2018). Another study using a coating con-taining hypromellose and PEG revealed a final coating roughness in therange between 4 µm and 6 µm measured by a laser scanning microscope(Dohi et al., 2016). Although roughness values of different studies are inthe same range, studies are needed to clarify the phenomena influen-cing the final tablet coating roughness.

Due to the significant amount of data acquired during the OCT in-line measurement, the information about the coating quality variabilityamong the tablets (homogeneity) or even within individual tablets(roughness) is accessible in real-time. However, the OCT data have tobe filtered before they can be used for process control. To this end, acomputation of mean values, as suggested in this work, can be per-formed. Additionally, the inter-tablet coating homogeneity can be usedas a measure for the quality of the coating process. Displaying the fulldata of an in-line OCT measurement illustrates the full variation ofcoating thickness within one single batch. Fig. 11 shows all data ac-quired in-line in run D. Small grey dots represent all in-line measure-ments of single tablets performed throughout the process. Big bluecircles represent mean values for one minute of process time. The pre-defined detection limit of 12 µm is again applied, excluding any mea-surement below 12 µm. The relative standard deviation (RSD) corre-sponding to the inter tablet coating variability is depicted as yellowcircles. The RSD is higher at the beginning of the process and decreaseswith increasing process time, which is in agreement with DEM simu-lation studies (Boehling et al., 2016b). This means that the coatinguniformity becomes better. An even higher RSD is expected in the earlystage of the coating process, but these data are not accessible due to thelimit of detection of 12 µm coating thickness. The final RSD in a rangeof 10% is comparably higher as in previous studies (Markl et al., 2015a)with approximately 6% to 7% at the same final coating thickness of50 µm. This difference potentially comes from the scale variation inboth investigated coating processes. However, other studies in a lab-scale coating equipment found RSDs of coating thickness of 25% and20% at a coating thickness level of 50 µm measured with OCT and TPI,respectively (Lin et al., 2017a).

At a first glance, a high variability in the coating thickness at spe-cific process times appears to be spurious data. However, the OCTimages of single tablets indicate that these variations are indeed real.Fig. 12 shows some images of tablet coatings from the very last stage of

run D. The coating thickness varies between 39 and 69 µm, whichcorresponds to the readings in Fig. 11. Mean coating thickness andrelative standard deviation calculated for the last 150 measurements inruns D and E are summarized in Table 2. Additionally, the minimumand maximum coating thickness values in the last 150 images are listed.

Fig. 13 shows examples of light microscopic (LM) images of tabletsin run D, which were selected from different sides of the tablet (upperand lower convex side and band). All of these tablets originate from theend of the process and have a mean coating thickness of 50 µm ac-cording to OCT. The coating thickness significantly varies in a rangebetween 36 and 82 µm, depending on the position of the tablet.

Table 2 contains the analyses of light microscopic images for runs Dand E. Approximately 100 measurements per run were performed withLM, which is not a representative number for qualifying a true coatingthickness. In general, it is difficult to compare absolute values that wereobtained using different measurement approaches. However, the meancoating thicknesses values of 57.4 µm and 55.1 µm in run D and E, re-spectively, differ only slightly from the OCT in-line measurements. Therelative standard deviation (RSD) of LM measurements is greater thanthe RSD of OCT measurements. For the LM investigations, individualmeasurement locations on the tablets were selected manually. In con-trast, the thickness is calculated as a distance between the two ellipsefits in OCT. Therefore, OCT leads to one thickness value per image andsingle thickness deviations in the tablet coating are not considered. Assuch, the real variation in the tablet coating thickness is in the rangethat the LM images suggest. OCT can capture the thickness variationsusing the roughness value, which accounts for all deviations in eachmeasured tablet.

Significant variations in the coating thickness at a single processtime can be due to several phenomena. First, coating is a statisticalprocess, in which not every tablet is exposed to the same amount ofspray. As a result, the coating thickness varies between the tablets(inter-tablet coating homogeneity). Inappropriate process conditionsmay enhance such effects, leading to a polymer film that does notsmoothen due to interfacial tension effects and wetting, e.g., if theprocess temperature is too low or if the spray is too dry. Lin et al.(2017a) showed that the thickness distribution typically follows aGaussian-like distribution. Second, the coating thickness variation canbe caused by inhomogeneity on the core surface. Markl et al. (2018)reported that small valleys in the tablet core can be filled with thecoating material, resulting in a higher thickness, while the elevatedregions that can be observed on the final coated tablet may have a

Fig. 10. Coating roughness as a function of process time for runs A, B, C and D.Mean values calculated for 1min of process time are depicted.

Fig. 11. Coating thickness as a function of process time for run D. Small dotsrepresent the in-line measurements for single tablets performed throughout theprocess. Big blue circles represent mean values averaged over one minute. Bigyellow circles indicate the relative standard deviation (RSD) of the meancoating thickness corresponding to the inter tablet coating variability.

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thinner coating layer. In addition, the coating thickness is thinner onthe bends of the tablets (e.g., the area where convex side and tabletband come together). Fig. 13 shows a clearly thinner coating layer inthese regions. Sticking and picking can also transfer coating betweentablets and lead to spatial variations in coating thickness, especially,when the process conditions are not optimized and the coating spray istoo wet. At this process stage local forces caused by mechanics in thetablet bulk can result in dents in the tablet surface and a reducedcoating thickness.

4. Conclusion

This work investigated the applicability of Optical CoherenceTomography (OCT) for in-line monitoring of industrial coating pro-cesses. An OCT system was implemented in an industrial-scale pancoater. Various process parameters, concentrations of the coating so-lution and tablet sizes were selected in the test runs. The results in-dicated that automated layer detection, classification and thicknesscalculation can be executed in real-time. Not only the coating thickness,but also the coating quality features were computed. OCT shows anincrease in the coating roughness with an increase in the process time.The coating homogeneity provides information about the internalstructures, such as pores and solids agglomerations. Analyzing the OCTand light microscopic images reveals significant variations in thecoating thickness, both within the batch and within single tablets.

Compared to conventional weight gain methods, OCT is a reliabletechnology for end-point determination. More importantly, OCT canhelp to detect deviations from the prescribed coating process profilemuch faster and more accurately than any other existing technology.Monitoring the coating process via OCT allows to monitor the processbehaviour in real-time. By capturing a high amount of data on the

Fig. 12. OCT images of tablet coating acquired in-line at the last stage of run D. Computed coating thickness is given in µm. The scale bar represents the dimension ofthe coating material.

Table 2Coating thickness evaluation of tablets at the final process stage based on OCTand light microscopy (LM).

Mean thickness[µm]

Minimum/maximumthickness [µm]

Relative standarddeviation [µm]

OCT LM OCT LM OCT LM

Run D 50.9 57.4 39.2/68.9 30.2/83.2 5.3 10.1Run E 51.0 55.1 39.8/63.8 30.1/77.4 4.6 9.3

Fig. 13. Light microscopic images of tablet coating at the end stage of run D.

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coating thickness and quality, OCT can support active process controland thus may act as enabler for a real-time release strategy.

Declaration of Competing Interest

None.

Acknowledgements

This work was funded by the Austrian COMET Program under theauspices of the Austrian Federal Ministry of Transport, Innovation andTechnology (bmvit), the Austrian Federal Ministry of Economy, Familyand Youth (bmwfj) and by the State of Styria (Styrian Funding AgencySFG). COMET is managed by the Austrian Research Promotion AgencyFFG.

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