Marwan El Chazli

From 26/07/2024 to 26/10/2024

Paired Teams

Digitalizing quality control in the food industry: AI and spectroscopy in the service of human health and sustainability

Johns Hopkins University

Marwan El Chazli graduated with a bachelor and masters in microengineering and biomedical technologies from Switzerland’s EPFL in 2023, after which he has pursued various scientific interests related to biosensing. During his masters thesis, he worked on the use of Raman spectroscopy for the detection and identification of bacterial pathogens in the food, pharmaceutical and medical industries. He is a researcher at EPFL’s Laboratory of Quantum and Nano-Optics and co-founder of Myriad Optics, a startup aimed at commercializing bacterial contamination tools. He is currently a visiting scholar in the Barman laboratory of Johns Hopkins University.

Bacterial contamination is a huge issue in the food industry, causing serious harm to consumers, resulting in massive produce waste and costing food companies hundreds of millions of dollars every year. A major obstacle in efficient bacterial detection methods is the reliance of today’s methods on bacterial cell cultures for the growth of bacteria collected from industrial samples, which can take up to 5 days before yielding any conclusive results. In this project, we explore the use of Surface-Enhanced Raman Spectroscopy, a type of laser-based spectroscopy in which bacterial cells are put in close contact with nanometer-sized noble metal features (like gold or silver) and scanned with a laser. The proximity of the cells to the metal produces very strong optical signals characteristic to the specific bacteria under study. We then use artificial intelligence to recognize those signals and identify the bacteria as dangerous or harmless.

Own picture, taken by my colleague Fahradin Mujovi

During the fellowship, we have tested several kinds of surface-enhanced Raman spectroscopy approaches to detect and identify bacteria and we are currently in the process of building a machine learning model that is able to recognize those. We have also explored the use of Quantitative Phase Imaging for the detection and identification of bacterial pathogens.

The fellowship has allowed us to explore more thoroughly the use of Surface-Enhanced Raman Spectroscopy for the detection and identification of bacteria, including the tests of various substrates in both labelled and label-free approaches. We’ve also created connections with labs and institutions across the US, including Stanford University, Cornell University and Johns Hopkins Hospital. These connections will be useful for creating collaboration and exchange of information relating to this project. We’re almost made progress in talking with food innovation centres in the US, which will be key in setting-up testing of our technology once it reaches maturity.