ICOMS (Inline Characterisation of Multiphase Systems)


This commercialisation project (2022-24) seeks to further develop patented (US20210354096A1) emulsion analysis technology and establish a new spin-out company to bring it to market, novel offline (and potentially inline) soft sensor systems for characterising emulsion product quality. The technology developed to date, entitled ICOMS conducts automated feature extraction on micrograph samples in respect to emulsion droplet size/shape characteristics and predicts the product quality using Machine Learning.


  • Conventional quality analysis of pharmaceutical emulsions proved subjective and time-consuming.
  • Machine learning approach is far superior to manual classification
  • Machine learning approach proved 180 times faster and 10% to 40% more accurate.
  • The automated approach demonstrated promising potential in reducing over processing

ICOMS technology validation to date

  • 11th Oct 2018: Invention Declaration Form (IDF) was submitted to the IT Sligo Innovation Centre
  • 21st Feb 2019: Analysed a problematic emulsion product using the ICOMS software and proved extremely useful at GSK, Sligo
  • 12th Aug 2019: A pilot trial of the soft sensor prototype was successfully completed at GSK, Sligo
  • 10th Oct 2019: Patent application filed.
  • 14th Jan 2020: A second pilot trial was successfully completed at GSK, Barnard Castle, UK
  • 14th Jan 2020: License Transfer of the developed technology to GSK

Published Papers

1.Feb 2021: In-process analysis of pharmaceutical emulsions using computer vision and artificial intelligence, Chemical Engineering Research and Design (Elsevier).


2.Sep 2020: An integrated histogram-based vision and machine learning classification model for industrial emulsion processing, IEEE Transactions on Industrial Informatics.


3.April 2019: Machine learning for automated quality evaluation in pharmaceutical manufacturing of emulsions, Journal of Pharmaceutical Innovation (Springer Nature).


4.Jan 2021: EI Commercial Feasibility study completed.

Funding and Acknowledgement

This commercialisation project is funded by Enterprise Ireland.