Machine learning to forecast cell growth in bioreactor using Raman spectroscopy

Khatri, Vikas (2024) Machine learning to forecast cell growth in bioreactor using Raman spectroscopy. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

Practical challenge within a biopharmaceutical organization is addressed in this research that specializes in manufacturing a product, crucial for treating life-threatening diseases. The research draws data from their research laboratory and aims to predict cell growth in bioreactors by leveraging information from both the bioreactors and Raman spectroscopy. Unlike many existing studies that solely rely on data from bioreactors or Raman spectroscopy, our approach involves combining datasets. This comprehensive dataset will offer the organization a real-time overview and enhanced understanding of bioreactor process parameters. Traditional model such as partial linear regression (PLS) is built using SIMCA (Soft independent modelling by class analogy) software as part of this research. RMSEE and RMSECV performance indicators are used to evaluate the performance of the models to predict the cell growth. Predicting cell growth will contribute to achieving a batch on the initial attempt, thereby reducing production costs, maintaining supply chain demand, minimizing waste, decreasing labour hours, and lowering utility expenses.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Palaniswamy, Sasirekha UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > Biomedical engineering Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	05 Jun 2025 11:51
Last Modified:	05 Jun 2025 11:51
URI:	https://norma.ncirl.ie/id/eprint/7755

Actions (login required)

View Item