Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Magnetic resonance spectroscopy during migraine attacks: A systematic review.

Cephalalgia : an international journal of headache·2026
Same author

Challenges in the Oral Administration of Gastro-Resistant Formulations: The Role of Vehicles and Bottled Waters.

Pharmaceutics·2026
Same author

PACAP and its receptors in the retina of various species.

Neuropeptides·2026
Same author

Evaluation of the Applicability of a 3D-Printed Inert Minitablet Core as a Carrier for Modified-Release Drug Delivery System.

Pharmaceutics·2026
Same author

Advancing bioprocess monitoring: data fusion and ANN-based prediction of arginine concentration in monoclonal antibody-producing CHO cell cultures.

New biotechnology·2026
Same author

ANN-assisted UV imaging for non-destructive dissolution prediction of HPMC matrix tablets.

International journal of pharmaceutics·2026

Related Experiment Video

Updated: Jan 16, 2026

Intra-lymph Node Injection of Biodegradable Polymer Particles
09:06

Intra-lymph Node Injection of Biodegradable Polymer Particles

Published on: January 2, 2014

15.1K

Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process.

Orsolya Péterfi1, Nikolett Kállai-Szabó2,3, Kincső Renáta Demeter1

  • 1Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111, Budapest, Hungary.

Journal of Pharmaceutical Analysis
|October 6, 2025
PubMed
Summary
This summary is machine-generated.

An AI-powered machine vision system enables real-time particle size analysis during pellet layering. This ensures drug product safety and quality by monitoring pellet size during manufacturing.

Keywords:
Convolutional neural networksEndoscopeIn-line monitoringMachine visionParticle size distributionPellet layeringProcess analytical technology

More Related Videos

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles
09:19

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles

Published on: March 28, 2021

9.6K
A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps
07:34

A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps

Published on: August 5, 2015

9.8K

Related Experiment Videos

Last Updated: Jan 16, 2026

Intra-lymph Node Injection of Biodegradable Polymer Particles
09:06

Intra-lymph Node Injection of Biodegradable Polymer Particles

Published on: January 2, 2014

15.1K
Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles
09:19

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles

Published on: March 28, 2021

9.6K
A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps
07:34

A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps

Published on: August 5, 2015

9.8K

Area of Science:

  • Pharmaceutical Technology
  • Process Analytical Technology
  • Artificial Intelligence in Manufacturing

Background:

  • Pellet layering is crucial for drug content uniformity.
  • Accurate particle size monitoring is essential for ensuring drug product safety and quality.
  • Existing methods for particle size analysis may not be suitable for real-time in-line monitoring.

Purpose of the Study:

  • To develop an artificial intelligence-based machine vision system for in-line particle size analysis during pellet layering.
  • To enable real-time monitoring of pellet size and layer uniformity.
  • To ensure timely intervention for out-of-spec products.

Main Methods:

  • Developed a machine vision system using a rigid endoscope, light source, and high-speed camera.
  • Employed a convolutional neural network-based instance segmentation algorithm for accurate particle detection in dense flow.
  • Trained and validated the system using pellet cores of varying sizes (250-850 μm) and tested in-line during large-scale drug layering.

Main Results:

  • The AI system accurately determined pellet size in real time, even with dense particle flow.
  • Real-time particle size data correlated well with reference methods.
  • Demonstrated the system's capability for in-line process monitoring during drug layering.

Conclusions:

  • The developed machine vision system is a feasible Process Analytical Technology (PAT) tool for in-line monitoring of pellet size.
  • This AI-based approach enhances control over the pellet layering process, improving product safety and quality.
  • Real-time particle size analysis facilitates timely interventions, optimizing pharmaceutical manufacturing.