Mohammad Aghaee
Research Subjects
Research Interests
Mohammad Aghaee has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Hector Budman
Department of Chemical Engineering, University of Waterloo, Waterloo, Canada
2 shared publications - Abhishek Mishra
Viral Vectors and Vaccines Bioprocessing Group, Department of Bioengineering, McGill University, Montreal, QC H3A 0G4, Canada
1 shared publication Alexis Francois
Cooper Standard Automotive
1 shared publicationKannan Murugesan Paulthangam
Cooper Standard Automotive
1 shared publicationKaushik Shah
Cooper Standard Automotive
1 shared publication- Luis Alberto Ricardez-Sandoval
University of Waterloo
1 shared publication
Latest Publications
- 12 December 2025
- Published by American Chemical Society (ACS) in Industrial & Engineering Chemistry Research
- Vol. 64 (51) , 24689-24705
Multimodal LSTM with Data Augmentation for Predicting Rubber Compound Cure Properties in Industrial Batch Processes
- 12 December 2025
- Published by American Chemical Society (ACS) in Industrial & Engineering Chemistry Research
- Vol. 64 (51) , 24689-24705
Multimodal LSTM with Data Augmentation for Predicting Rubber Compound Cure Properties in Industrial Batch Processes
- Open Access
- 1 April 2025
- Published by MDPI AG in Spectroscopy Journal
- Vol. 3 (2) , 12
Spectroscopic Advances in Real Time Monitoring of Pharmaceutical Bioprocesses: A Review of Vibrational and Fluorescence Techniques
The pharmaceutical industry has witnessed exponential growth in production volumes, driven by factors such as an aging global population and the COVID-19 pandemic. To meet the demand for high product quality alongside increased productivity, there is a growing emphasis on developing innovative Fermentation Analytical Technology (FAT) and Process Analytical Technology (PAT) tools for real-time performance monitoring, modeling, measurement, and control. Building on our earlier work involving in-line monitoring of Bordetella pertussis fermentations using fluorescence spectroscopy, this review explores and compares the applications of vibrational and fluorescence spectroscopy for real-time bioprocess monitoring. We examine recent technological advancements and ongoing challenges in the field. Various spectroscopic techniques are evaluated in terms of cost-effectiveness and practical applicability, with a particular focus on in-line spectroscopy as a promising, low-cost solution for effective bioprocess monitoring.
- 1 April 2025
- Published by MDPI AG in Spectroscopy Journal
- Vol. 3 (2) , 12
Open AccessSpectroscopic Advances in Real Time Monitoring of Pharmaceutical Bioprocesses: A Review of Vibrational and Fluorescence Techniques
The pharmaceutical industry has witnessed exponential growth in production volumes, driven by factors such as an aging global population and the COVID-19 pandemic. To meet the demand for high product quality alongside increased productivity, there is a growing emphasis on developing innovative Fermentation Analytical Technology (FAT) and Process Analytical Technology (PAT) tools for real-time performance monitoring, modeling, measurement, and control. Building on our earlier work involving in-line monitoring of Bordetella pertussis fermentations using fluorescence spectroscopy, this review explores and compares the applications of vibrational and fluorescence spectroscopy for real-time bioprocess monitoring. We examine recent technological advancements and ongoing challenges in the field. Various spectroscopic techniques are evaluated in terms of cost-effectiveness and practical applicability, with a particular focus on in-line spectroscopy as a promising, low-cost solution for effective bioprocess monitoring.
- 20 June 2023
- Published by American Chemical Society (ACS) in Industrial & Engineering Chemistry Research
- Vol. 62 (25) , 9773-9786
Unsupervised Fault Detection of Pharmaceutical Processes Using Long Short-Term Memory Autoencoders
- 20 June 2023
- Published by American Chemical Society (ACS) in Industrial & Engineering Chemistry Research
- Vol. 62 (25) , 9773-9786
Unsupervised Fault Detection of Pharmaceutical Processes Using Long Short-Term Memory Autoencoders