UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks

• Published in: International journal of pharmaceutics 2022-05, Vol.620, p.121773-121773, Article 121773
• Main Authors: Mészáros, Lilla Alexandra, Farkas, Attila, Madarász, Lajos, Bicsár, Rozália, Galata, Dorián László, Nagy, Brigitta, Nagy, Zsombor Kristóf
• Format: Article
• Language: English
• Subjects: Image analysis; Machine vision; Meloxicam; Multivariate Analysis; Neural Networks, Computer; Particle Size; Particle size analysis; Particle size distribution; Pattern recognition neural network; Tablet inspection; Tablets
• ISSN: 0378-5173; 1873-3476
• DOI: 10.1016/j.ijpharm.2022.121773

[Display omitted] •Machine vision recently gained interest in the pharmaceutical industry.•Meloxicam was a model active pharmaceutical ingredient for particle size analysis.•Only an image of a tablet can be applied to extract critical quality attributes.•Image processing and analysis methods were developed for quality monitoring.•Quality-based classification was executed using pattern recognition neural network. 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. The developed method can identify tablets containing finer or larger particles than the target with more than 97% accuracy. Two algorithms were developed for UV and VIS images for particle size analysis of the prepared tablets. According to the applied statistical tests, the obtained particle size distributions were similar to the results of the laser diffraction-based reference method. Digital UV/VIS imaging combined with multivariate data analysis can provide a new non-destructive, rapid, in-line tool for particle size analysis in tablets.