Image-based artificial intelligence methods for product control of tablet coating quality

Research output: Contribution to journalJournal articleResearchpeer-review


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.

Original languageEnglish
Article number877
Issue number9
Number of pages9
Publication statusPublished - 2020

Bibliographical note

Funding Information:
Funding: This study was funded by Innovation Fund Denmark; Project: High Quality Dry Products with Superior Functionality and Stability-Q-Dry; File No: 5150-00024B.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

    Research areas

  • Artificial intelligence, Image analysis, In silico modelling, Multivariate analysis, Neural networks

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