About This Webinar
Surface-Enhanced Raman Spectroscopy (SERS) and Raman spectroscopy yields large data sets with information-rich spectra. Classical linear methods have limitations, especially for SERS spectra of single molecules, where the spectra are highly dependent on the orientation of molecules on surfaces, and for large data sets. Methods from data sciences are increasingly used to classify spectra into categories and predict SERS spectra for new data based on trained algorithms. In one example, the classification of single molecule spectra will be shown for neurotransmitters, and other biological metabolites were identified with a barcoding data processing method, processed with TensorFlow using a convolutional neural network architecture.
This machine-learning-driven data processing significantly improved the positive assignment rates for a series of metabolites and allows for complex measurements of the cell’s biochemistry. In another example, efforts for the control and optimization of nanoparticle synthesis using a continuous flow chemistry approach that can be controlled with machine learning will be presented. Finally, efforts in the classification and concentration prediction will be presented with different sensing schemes.
*** This presentation premiered during the
2025 Photonics Spectra Spectroscopy Summit. For more information on Photonics Media conferences and summits, visit
events.photonics.com
About the presenter
Jean-François Masson, Ph.D., is the Department of Chemistry department chair at Université de Montréal and specializes in plasmonic techniques, nanomaterials, and biosensing.
He earned his Bachelor of Science in chemistry from Université de Sherbrooke in 2001, his doctorate from Arizona State University in 2005, and completed a postdoctoral fellowship at Georgia Tech from 2005 to 2007.
Masson has developed a series of instrumental and data processing tools based on plasmonic techniques, including a portable surface plasmon resonance instrument commercialized by Affinité Instruments, which he co-founded in 2015. His innovations also include a maple sap colorimetric test commercialized by the Centre ACER, as well as advancements in nanomaterials and machine learning for Raman spectroscopy.
He has published over 150 articles, and his research has led to the filing of more than 10 patents covering instruments, materials, and surface chemistry innovations for biosensing.
Masson currently serves as the executive editor of ACS Sensors and his work has been recognized with several awards, including the Innovation Award from the Quebec government (2022), the Fred Beamish Award (2013), and McBryde Medal (2019) from the Canadian Society for Chemistry. He also earned a spot on The Analytical Scientist’s 2019 list of the 100 Most Influential Analytical Scientists and was awarded an Alexander von Humboldt Fellowship (2013–2014).