Publications by authors named "Rozalia Bicsar"
Article Synopsis
- The paper demonstrates the use of UV/VIS imaging to assess key properties of tablet formulations made of white components, including crushing strength, friability, disintegration time, and dissolution profiles.
- Images were taken using UV light for predicting API content and VIS light for other parameters, achieving a 5.6% error in API prediction and under 10% error in crushing strength assessments.
- The study highlights the effectiveness of machine learning and artificial neural networks for classifying tablet samples and predicting dissolution profiles, showcasing the potential of machine vision for real-time quality control in pharmaceuticals.
The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle size of meloxicam, a yellow model active pharmaceutical ingredient, in intact tablets by a digital UV/VIS imaging-based machine vision system. Two image processing algorithms were developed and coupled with pattern recognition neural networks for UV and VIS images for particle size-based classification of the prepared tablets.
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