Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process
In this study, an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process. Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of Pharmaceutical Analysis |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177925000449 |
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| author | Orsolya Péterfi Nikolett Kállai-Szabó Kincső Renáta Demeter Ádám Tibor Barna István Antal Edina Szabó Emese Sipos Zsombor Kristóf Nagy Dorián László Galata |
| author_facet | Orsolya Péterfi Nikolett Kállai-Szabó Kincső Renáta Demeter Ádám Tibor Barna István Antal Edina Szabó Emese Sipos Zsombor Kristóf Nagy Dorián László Galata |
| author_sort | Orsolya Péterfi |
| collection | DOAJ |
| description | In this study, an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process. Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved. Drug content increases with pellet size; therefore, particle size monitoring can ensure product safety and quality. The direct imaging system, consisting of a rigid endoscope, a light source, and a high-speed camera, provides real-time information about pellet size and layer uniformity, enabling timely intervention in the case of out-of-spec products. A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus, ensuring that pellet size could be accurately determined despite the dense flow of the particles. After training the model, the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250–850 μm size range. The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods, demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring. |
| format | Article |
| id | doaj-art-ff9b46dc52eb45c2945d1f27cd4647fd |
| institution | Kabale University |
| issn | 2095-1779 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj-art-ff9b46dc52eb45c2945d1f27cd4647fd2025-08-22T04:56:06ZengElsevierJournal of Pharmaceutical Analysis2095-17792025-08-0115810122710.1016/j.jpha.2025.101227Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering processOrsolya Péterfi0Nikolett Kállai-Szabó1Kincső Renáta Demeter2Ádám Tibor Barna3István Antal4Edina Szabó5Emese Sipos6Zsombor Kristóf Nagy7Dorián László Galata8Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, HungaryDepartment of Pharmaceutics, Semmelweis University, Hőgyes E. str 7, 1092, Budapest, Hungary; Center for Pharmacology and Drug Research & Development, Semmelweis University, Budapest, Hungary; Corresponding author. Department of Pharmaceutics, Semmelweis University, Hőgyes E. str 7, 1092, Budapest, Hungary.Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, HungaryDepartment of Pharmaceutics, Semmelweis University, Hőgyes E. str 7, 1092, Budapest, HungaryDepartment of Pharmaceutics, Semmelweis University, Hőgyes E. str 7, 1092, Budapest, Hungary; Center for Pharmacology and Drug Research & Development, Semmelweis University, Budapest, HungaryDepartment of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, HungaryDepartment of Pharmaceutical Industry and Management, Faculty of Pharmacy, George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, Gheorghe Marinescu Street 38, 540142, Targu Mures, RomaniaDepartment of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, Hungary; Corresponding author.Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, HungaryIn this study, an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process. Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved. Drug content increases with pellet size; therefore, particle size monitoring can ensure product safety and quality. The direct imaging system, consisting of a rigid endoscope, a light source, and a high-speed camera, provides real-time information about pellet size and layer uniformity, enabling timely intervention in the case of out-of-spec products. A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus, ensuring that pellet size could be accurately determined despite the dense flow of the particles. After training the model, the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250–850 μm size range. The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods, demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring.http://www.sciencedirect.com/science/article/pii/S2095177925000449Machine visionConvolutional neural networksIn-line monitoringEndoscopeParticle size distributionPellet layering |
| spellingShingle | Orsolya Péterfi Nikolett Kállai-Szabó Kincső Renáta Demeter Ádám Tibor Barna István Antal Edina Szabó Emese Sipos Zsombor Kristóf Nagy Dorián László Galata Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process Journal of Pharmaceutical Analysis Machine vision Convolutional neural networks In-line monitoring Endoscope Particle size distribution Pellet layering |
| title | Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process |
| title_full | Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process |
| title_fullStr | Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process |
| title_full_unstemmed | Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process |
| title_short | Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process |
| title_sort | artificial intelligence aided endoscopic in line particle size analysis during the pellet layering process |
| topic | Machine vision Convolutional neural networks In-line monitoring Endoscope Particle size distribution Pellet layering |
| url | http://www.sciencedirect.com/science/article/pii/S2095177925000449 |
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