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Open AccessArticle

Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality

1
BASF A/S, Malmparken 5, 2750 Ballerup, Denmark
2
Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark
3
Research Group for Nano-Bio Science, National Food Institute, Technical University of Denmark, Kemitorvet, 2800 Kgs. Lyngby, Denmark
*
Author to whom correspondence should be addressed.
Pharmaceutics 2020, 12(9), 877; https://doi.org/10.3390/pharmaceutics12090877
Received: 12 August 2020 / Revised: 8 September 2020 / Accepted: 11 September 2020 / Published: 15 September 2020
Mimicking the human decision-making process is challenging. Especially, many process control situations during the manufacturing of pharmaceuticals are based on visual observations and related experience-based actions. The aim of the present work was to investigate the use of image analysis to classify the quality of coated tablets. Tablets with an increasing amount of coating solution were imaged by fast scanning using a conventional office scanner. A segmentation routine was implemented to the images, allowing the extraction of numeric image-based information from individual tablets. The image preprocessing was performed prior to utilization of four different classification techniques for the individual tablet images. The support vector machine (SVM) technique performed superior compared to a convolutional neural network (CNN) in relation to computational time, and this approach was also slightly better at classifying the tablets correctly. The fastest multivariate method was partial least squares (PLS) regression, but this method was hampered by the inferior classification accuracy of the tablets. Finally, it was possible to create a numerical threshold classification model with an accuracy comparable to the SVM approach, so it is evident that there exist multiple valid options for classifying coated tablets. View Full-Text
Keywords: in silico modelling; neural networks; image analysis; artificial intelligence; multivariate analysis in silico modelling; neural networks; image analysis; artificial intelligence; multivariate analysis
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Hirschberg, C.; Edinger, M.; Holmfred, E.; Rantanen, J.; Boetker, J. Image-Based Artificial Intelligence Methods for Product Control of Tablet Coating Quality. Pharmaceutics 2020, 12, 877.

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