Rapid Assessment of Tablet Film Coating Quality by Multispectral UV Imaging
- 1.1k Downloads
Chemical imaging techniques are beneficial for control of tablet coating layer quality as they provide spectral and spatial information and allow characterization of various types of coating defects. The purpose of this study was to assess the applicability of multispectral UV imaging for assessment of the coating layer quality of tablets. UV images were used to detect, characterize, and localize coating layer defects such as chipped parts, inhomogeneities, and cracks, as well as to evaluate the coating surface texture. Acetylsalicylic acid tablets were prepared on a rotary tablet press and coated with a polyvinyl alcohol-polyethylene glycol graft copolymer using a pan coater. It was demonstrated that the coating intactness can be assessed accurately and fast by UV imaging. The different types of coating defects could be differentiated and localized based on multivariate image analysis and Soft Independent Modeling by Class Analogy applied to the UV images. Tablets with inhomogeneous texture of the coating could be identified and distinguished from those with a homogeneous surface texture. Consequently, UV imaging was shown to be well-suited for monitoring of the tablet coating layer quality. UV imaging is a promising technique for fast quality control of the tablet coating because of the high data acquisition speed and its nondestructive analytical nature.
KEY WORDSmultispectral UV imaging multivariate image analysis SIMCA tablet coating quality tablet quality attributes
Pharmaceutical tablets are commonly coated for reasons such as improvement of the visual appearance, facilitation of the tablet intake, taste masking, drug release control, and protection of the tablet core from light and moisture (1, 2, 3, 4, 5). Improper control of the coating quality can compromise active pharmaceutical ingredient (API) physical and chemical stability and may result in dose failure and dumping as well as an altered dissolution profile. Furthermore, inadequate appearance of the tablet coating reduces customer acceptance and patient compliance. In agreement with the standards specified by the regulatory authorities, film coating quality is routinely determined only with a small number of samples using, e.g., disintegration and dissolution tests as well as nonautomated inspection of tablet appearance in laboratories (6, 7, 8). Such quality control strategies are time-consuming and cost-intensive and follow the paradigm of measuring the quality from the product (7). Furthermore, these strategies do not allow to relate the obtained product characteristics to quality-relevant production parameters (7). Thus, inspired by the QbD and PAT initiatives of the regulatory authorities (9, 10, 11), pharmaceutical manufacturers are encouraged to build the quality into the product by understanding and mathematical relation of input variables to critical quality attributes (CQA) and control of the production process in real-time through the use of inline, fast, and nondestructive sensors. This strategy is beneficial because insufficient product quality is detected during the process, which can therefore be adjusted so that the product meets the defined specifications (9). In this context, vibrational spectroscopic techniques such as NIR (12, 13, 14, 15, 16) and Raman spectroscopy (17, 18, 19) have been demonstrated to be useful tools for evaluation of tablet coating quality as they are fast, nondestructive, and versatile and provide chemical as well as physical information on the samples. However, one drawback of such techniques is the lack of spatial information, as average spectra are obtained only from spots of a sample or from more than one tablet during a given integration time (20). Hence, for understanding and control of the film coating process, spatially resolved physicochemical information on the coated tablets can be beneficial (21,22). The detection and localization of coating defects such as chipping at either the center or the edge of the tablet may be caused by different phenomena, such as twinning of tablets during film coating or insufficient flexibility of the coating film and thus allowing systematic adjustment of the defect-causing process parameters (4). In this context, spectral imaging techniques have been shown to be attractive for evaluation of tablet coating quality, as they are nondestructive and provide chemical as well as spatial information on the samples. For instance, coating integrity, thickness, and uniformity have been evaluated by hyperspectral NIR imaging (23, 24, 25) and terahertz pulsed imaging (TPI) (24,26, 27, 28, 29). However, for real-time process monitoring, remaining challenges are the data acquisition speed (30) as well as the handling of the large data sets (20,31). To obtain spectra with sufficiently high signal to noise ratio, many scans of each sampling point need to be recorded having a negative impact on the sampling time. In this context, the application of imaging techniques that allow capturing spectra of wavelength regions in which the sample exhibits a high absorptivity is attractive because it potentially enables acquisition of high-quality spectra within a short sensor integration time leading to an increased image acquisition speed (32). From the abovementioned perspective, the main goal of this study was to assess the suitability of six-wavelength UV imaging for evaluation of the coating quality of tablets. Multispectral UV imaging has been previously demonstrated to be a well-suited technique for evaluation of the API solid-state form within uncoated tablets (32). In the present study, it was investigated if defects such as chipped parts, cracks, and inhomogeneities in the coating layer can be detected, identified, and localized by UV imaging combined with multivariate image and pattern recognition analysis. Moreover, the possibility to determine the surface texture of the tablet coatings from the UV images was examined.
MATERIALS AND METHODS
Acetylsalicylic acid (ASA), highly dispersed silicon dioxide (Aerosil® 200), and potato starch all of Ph. Eur. grade were obtained from Fagron (Barsbüttel, Germany). Microcrystalline cellulose (MCC, Avicel® PH-102) of Ph. Eur. grade was supplied by FMC Biopolymer (Dublin, Ireland), Kollicoat® IR (polyvinyl alcohol-polyethylene glycol graft copolymer (PVA/PEG)) of two colors (blue and yellow based on aluminum lakes) was donated by BASF (Ludwigshafen, Germany), and magnesium stearate (MgSt) by Baerlocher (Unterschleissheim, Germany).
Tablet compaction was performed on a rotary tablet press (Fette 102i, Fette Compacting, Schwarzenbek, Germany) equipped with faceted punches of 8 mm diameter. ASA tablets (50.0% (w/w) ASA, 40.5% (w/w) MCC, 8.0% (w/w) starch, 0.5% (w/w) silicon dioxide, and 1.0% (w/w) MgSt) with a weight of approximately 250 mg were obtained at main compaction pressures of about 200 MPa and a rotor speed of 20 rpm. All tableting experiments were performed in an air-conditioned room at 21°C and a RH of 45%.
ASA tablets were film coated with a mixture of Kollicoat® IR (blue and yellow, resulting in a green color) using a pan coater (Solidlab 1, Bosch, Schopfheim, Germany) equipped with a nozzle of 1.2 mm diameter. During the coating process, the product temperature range was 32–46°C, the air flow rate was varied between 53 and 61 m3 h−1, and the atomizing air pressure and fluid spray rate were set to 0.67 bar and 1.0 g min−1, respectively. Under these intentionally nonideal process conditions, tablets with intact coating layer that show an either homogeneous or inhomogeneous surface texture as well as tablets with a defect coating layer (chipped parts or inhomogeneous appearance) were obtained. A small fraction of coated ASA tablets were manually processed with a scalpel to obtain tablets with cracks in the coating layer. Uncoated tablets were prepared by gently scraping of the coating layer.
Photographs of all ASA tablets were taken with a 18-megapixel Canon EOS 600D-SLR camera attached to a Canon EF-S 18–55 mm 1:3.5–5.6 IS II objective (both from Canon, Tokyo, Japan) and processed using Adobe® Photoshop® CS2 (ver. 9.0, Adobe Systems, San Jose, USA).
Images of coated ASA tablets with either visually homogeneous or inhomogeneous coating were acquired using a Videometer MultiRay imager (Videometer, Hørsholm, Denmark) combined with the VideometerLab software (ver. 2.8, Videometer, Hørsholm, Denmark) to verify the surface texture analyzed by UV imaging. The instrument consists of a combined darkfield and coaxial brightfield illumination source which illuminates the sample at different angles. As wavelength 465 nm was selected, a silicon range imaging detector was used to collect the reflected light. The obtained images were of size 1280 × 960 pixels with a pixel size of 7.7 μm.
UV imaging of all tablets was performed using a VideometerLabUV multispectral imager (Videometer, Hørsholm, Denmark) and the VideometerLab software (ver. 1.6, Videometer, Hørsholm, Denmark). The imager was equipped with a Mercury-Xenon UV light source and bandpass filters to illuminate the samples at six wavelengths (254, 280, 300, 313, 334, and 365 nm) as well as a CCD camera to collect the light that is diffusely reflected by the samples. The field of view of the instrument with a size of 11.7 cm × 8.8 cm was imaged within about 18 s and resulted in a raw data array of dimension 960 × 1280 × 6, where the wavelengths are arranged along the third dimension. The spatial resolution was 77.9 μm.
Analysis of the UV images was based on principal component analysis (PCA), Soft Independent Modeling by Class Analogy (SIMCA) analysis, as well as related statistics and was performed with Matlab (ver. 8.1, Mathworks, Natick, USA) combined with the Image Processing Toolbox (ver. 8.0, Mathworks, Natick, USA) and the PLS_Toolbox (ver. 7.3, Eigenvector Research, Wenatchee, USA). In-house written Matlab scripts were applied to the images to evaluate intactness as well as the surface texture of coated ASA tablets.
Subsequently, tablet pixels were separated from the background by detection of the circular tablet structures in the 365-nm UV images by Hough transformation (33). After erosion with a circle morphological structuring element with either three pixels (reference tablets) or one pixel (test tablets) in the radius, all remaining pixels within the circles were assigned to the respective tablet (34).
PCA Models Applied to the UV Images for Detection, Differentiation, and Localization of Coating Defects
All pixels of five uncoated and five intactly coated tablets
Uncoated ASA tablets reference dataset: all pixels of five uncoated tablets
Coating layer reference dataset: all pixels of five intactly coated tablets
Visualization of the spectral variance between uncoated and intactly coated tablets as well as between the respective pixels of tablets with different coating defects
Model for PCA-based SIMCA classification
Model for PCA-based SIMCA classification
Fig. 2b, c
Fig. 3, column III and IV
Fig. 3, column V
Fig. 3, column V
Used for SIMCA
Number of PCs included in the model
To distinguish tablets according to their coating texture by UV imaging, intact tablets that showed an either homogeneous (n = 8) or an inhomogeneous (n = 8) coating in the photographic images were analyzed as follows: in the first step, PCA-based background segmentation was applied (38). In the second step, pixels belonging to the facet of the tablets were assigned to the background by circular erosion of the tablet pixels using a circle morphological structuring element with 20 pixels in the radius (34).
The mean Xa values of the five homogeneously coated reference tablets were calculated and set as 100%. The Xa values of the remaining 11 tablets (test set) with a visually either homogeneous (n = 3) or inhomogeneous (n = 8) coating were calculated as percentage of the mean Xa value of the reference set. The data analysis procedures are explained in detail in the “Results and Discussion” section.
RESULTS AND DISCUSSION
Detection, Differentiation, and Localization of Coating Defects
In a next step, the coating integrity of ASA tablets that are chipped at either the center or the edge of the tablet surface (Fig. 3, row C and D), of a tablet with an inhomogeneous coating (row E), as well as of a tablet with a crack in the coating layer (row F) was analyzed based on the respective UV images (Fig. 3, column II). Therefore, the spectral features of each pixel of the test tablets were extracted from the UV images and projected onto the PCA model which was previously built based on all pixels of the reference dataset composed of five uncoated and five intactly coated ASA tablets (PCA model 1). As shown in the resulting PCA score plot (Fig. 3, column III, row A), all pixels of the uncoated ASA test tablet are plotted next to the cluster or are even superimposed on the cluster related to the uncoated ASA reference tablets, which confirms complete absence of the coating layer. Furthermore, all pixels of the ASA tablet with an intact coating serving as test (Fig. 3, row B) form a cluster in direct vicinity of coating pixels (column III), which verifies intactness of the coating layer of the tablet. In contrast, the score plots of tablets with a partially chipped coating (column III, rows C and D) reveal that the majority of the tablet pixels are superimposed on the reference cluster of coating pixels, but there are also a number of nonclustered pixels with scores in between the two reference clusters and even pixels that are superimposed onto the uncoated ASA tablets reference cluster. These observations show that a considerable number of the pixels of the test tablet show spectral characteristics of both the coating layer as well as the uncoated ASA tablet or even spectra similar to the uncoated tablet. Thus, the PCA results correctly indicate that regions of the coating layer are chipped, which is accompanied by the appearance of the uncoated ASA tablet and thus leads to a decreased intensity of the PC-1 scores of the enclosing pixels. However, not all coating defects are accompanied by the appearance of the subjacent ASA tablet as shown by photographic images of tablets with an inhomogeneous coating caused by twinning of tablets during the coating process (Fig. 3, row E) or with a crack in the coating (Fig. 3, row F). The score plots resulting from subjecting the pixels of these tablets to the PCA model reveal that most of the pixels are superimposed on the coating pixel cluster with a small number of pixels showing lower PC-1 and higher PC-2 score intensities compared to the pixels of the intactly coated test tablet (Fig. 3, row B). It is hypothesized that these pixels surround the defect regions of the coating and show a slightly reduced absorbance compared to the smooth regions of the coat because of a partial discoloration of the coating layer in the regions of the defects.
Consequently, pixels enclosing chipped coating regions are clearly separated from pixels of an intact coating by PCA, while pixels surrounding defects that are not accompanied by appearance of the subjacent ASA tablet can be hardly differentiated from pixels of an intact coating by analysis of the PCA score plots.
To achieve automatized detection, differentiation, and localization of the coating defects based on the UV images, a two-step image analysis routine was applied to the tablets shown in Fig. 3. First, to visualize all tablet coating defects, an edge detection algorithm (43) was applied to the score images (data not shown) of each tablet obtained from subjecting the test tablet pixels to the PCA model 1. In agreement with the UV images (Fig. 3, column II) as well as the previously obtained results from score plots of the tablets, pixels corresponding to the coating defects show a more or less altered UV spectrum compared to pixels enclosing an intact coating. Thus, the regions of such defects are being highlighted in the score images. The algorithm detects such structures caused by the difference in intensity of the PC-1 scores between adjacent pixels at the edges of the defects. The resulting binary images with the detected edges being highlighted are shown in Fig. 3, column IV. It is obvious that the detected edges which neither correspond to the entire tablet nor to the tablet facet can either be assigned to minor inhomogeneities of the coatings (rows E and F) or to the respective defects of the tablet coatings (rows C–F).
In a second step, to automatically distinguish between chipped coatings and coating defects that are not accompanied by appearance of the subjacent ASA tablet, a PCA-based SIMCA classification model was built with the purpose to assign each pixel of the test tablets that are shown in Fig. 3 to either the class of uncoated ASA tablet pixels (class 1) or the class of intact coating pixels (class 2). The SIMCA model was based on two PCA models (PCA model 2 and 3) calculated from the reference pixel datasets of either class 1 or 2. Pixels that were assigned to the class of uncoated ASA tablet pixels (class 1) were automatically highlighted in binary images (Fig. 3, column V). It is obvious that, in agreement with the photographs and the UV images, no single pixel of the tablet with a uniform coating (row B) and of the tablet with cracked coating (row F) were assigned to class 1. In contrast, all pixels of the uncoated ASA tablet (row A) as well as the pixels surrounding the pronounced coating defects at the center and edge of the tablets in rows C and D are correctly assigned to the class of uncoated ASA tablet pixels (class 1) and are thus highlighted in the binary images. As shown in the binary image of the tablet with an inhomogeneous coating (row E), all pixels except one, which could be removed by setting a threshold in the binary images based on the pixel quantity of highlighted pixel clusters, were correctly assigned to class 2. Specificity and sensitivity (Eqs. 2 and 3) of the classification model which were calculated based on all pixels of the homogeneously coated and uncoated test tablets (rows A and B) are both 1.0 for both class 1 as well as class 2 indicating an adequate model quality. Thus, the defects of the investigated coated tablets that are accompanied by appearance of the subjacent ASA formulation could be clearly detected by a SIMCA classification model based on their multispectral UV images. Subsequently, localization of these defects was achieved by calculation of the distance from the center of the tablet to the center of the defects, which gives valuable information for readjustment of the coating process parameters. By comparing the defects found by application of either the edge detection algorithm or of the SIMCA model, a differentiation of coating defects that are not accompanied by appearance of the subjacent ASA tablet and defects resulting from chipped coatings parts could be achieved. It has to be mentioned that the detection of coating defects which are accompanied by the appearance of the subjacent tablet formulation are routinely performed by machine vision systems working in the VIS range. However, for a reliable detection of coating defects with VIS instruments, usually a high contrast in coloration between the coating layer and the tablet formulation is required. In contrast to VIS imaging, UV imaging is able to distinguish between different compounds based on their chemical nature irrespective of the contrast in coloration between them (32). As different chemical compounds show distinct UV profiles, this fact is considered to be a major benefit of UV imaging compared to systems working in the VIS range and it indicates the potential of UV imaging for the detection of defects even if the applied coating is of similar color (such as a moisture barrier coating) as the tablet formulation.
Evaluation of Tablet Coating Texture
It is worth to mention that in this study analysis of the coating intactness and texture by UV imaging is based on different PCA models and texture analysis has only been applied to tablets that have previously shown to be intactly coated. Thus, the PCA model for texture analysis is trained particularly on intactly coated tablets and consequently provides reliable results only with regard to the coating texture of these tablets. To avoid misleading conclusions, it is essential that the two PCA models for evaluation of tablet coating intactness and for texture analysis are applied to the test tablets one after another.
Multispectral UV imaging allowed accurate and fast characterization of tablet coating layer intactness. Coating defects such as chipped parts, cracks, and inhomogeneities could be detected and analyzed by image analysis routines applied to the UV images of the coated ASA tablets. Tablets with intact coating and either homogeneous or inhomogeneous coating texture were successfully differentiated based on the UV images. The amount and relevance of the obtained data combined with a high-speed image acquisition makes UV imaging an attractive technique for at-line quality control of the coating process. In this context, the implementation of multispectral UV imaging in the manufacturing line of coated tablets has to be further investigated. Furthermore, although neither warming of the samples nor any visual changes of the tablet coating were observed after exposure of the samples to UV radiation during the measurements, the potential influence of UV radiation on curing of the tablet coating should be investigated in a further study.
- 4.Levina M, Cunningham CR. The effect of core design and formulation on the quality of film coated tablets. Pharm Technol Eur. 2005;17(4):29–37.Google Scholar
- 6.EDQM. European pharmacopoeia. 8th ed. Strasbourg: Council of Europe; 2013.Google Scholar
- 8.FDA. Code of federal regulations title 21. part 211. Current good manufacturing practice for finished pharmaceuticals. 2014. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm?cfrpart=211&Showfr=1. Accessed 15 May 2015.
- 9.FDA. Guidance for industry pat—a framework for innovative pharmaceutical development, manufacturing, and quality assurance. 2004. http://www.fda.gov/downloads/drugs/guidances/ucm070305. Accessed 15 May 2015.
- 10.FDA. Pharmaceutical CGMPs for the 21st century: a risk-based approach. 2004. http://www.fda.gov/downloads/drugs/developmentapprovalprocess/manufacturing/questionsandanswersoncurrentgoodmanufacturingpracticescgmpfordrugs/ucm176374. Accessed 15 May 2015.
- 11.ICH. Guideline on pharmaceutical development Q8 (2r). 2009. http://www.ich.org/fileadmin/public_web_site/ich_products/guidelines/quality/q8_r1/step4/q8_r2_guideline. Accessed 15 May 2015.
- 13.Gendre C, Genty M, Boiret M, Julien M, Meunier L, Lecoq O, et al. Development of a process analytical technology (PAT) for in-line monitoring of film thickness and mass of coating materials during a pan coating operation. Eur J Pharm Sci. 2011;43(4):244–50. doi: 10.1016/J.Ejps.2011.04.017.CrossRefPubMedGoogle Scholar
- 34.Gonzalez RC, Woods RE, Eddins SL. Digital image processing using Matlab. 1st ed. Upper Saddle River: Prentice Hall; 2004.Google Scholar
- 40.Seitavuopio P. The roughness and imaging characterisation of different pharmaceutical surfaces. 2006. http://hdl.handle.net/10138/19122. Accessed 15 May 2015.
- 48.Eriksson L. Multi-and megavariate data analysis. 2nd ed. Umea: Umetrics AB; 2006.Google Scholar