PhD position -SIGNALS QUANTIFICATION OF RAMAN PROBE FOR ALLERGEN DETECTION

Health Canada requires the food industry to declare 10 allergens and gluten sources on food packaging. Given the growing number of tests required, and the high cost of allergen detection methods such as ELISA (Enzyme-Linked Immuno-Sorbent Assay), the famous precautionary statement "May contain ..." has become overspread. In the absence of a more effective and less costly detection method, allergy sufferers will continue to impose either a restrictive diet or a risk to their health.

A team of researchers from three Quebec universities (Université de Sherbrooke, UdeS, Université de Montréal, UdeM and Université Laval) has teamed up with two government agencies (Canadian Food Inspection Agency, Health Canada) and two non-profit consumer organizations (Food Allergy Canada and Cœliaque Québec) to develop a new allergen detection method that is more robust, more sensitive, and less expensive than ELISA. The method is based on a Raman probe, which consists of a carbon nanohorn (CNH) in which a dye is encapsulated and onto which an antibody is grafted. The Raman signal will be more precise and detailed than the fluorescence signal from ELISA and will enable parallel acquisition of signals from more than one allergen at a time. The aim is to offer the agri-food industry an effective, robust and less expensive device. Ultimately, this technological leverage will enable stakeholders to improve the use of precautionary allergen labelling and better protect allergic consumers.

The aim of this proposed thesis project is to develop an algorithm to process the Raman signals by considering possible spectral overlap, fluorescence, and low signal/noise ratio. The candidate will (i) perform multivariate analysis and statistical analysis to separate relevant Raman signals (ii) identify Raman signal of each dye based on the distinctive aspects of their Raman spectra (iv) establish the calibration curves and the detection limits of the R-ELISA test. This work will improve the R-ELISA prototype that will determine the level of 5 different allergens from a controlled food matrix.

This thesis will be supervised by Prof. R. Gosselin and Prof. N. Braidy from Université de Sherbrooke. Most of the work will be carried out at the UdeS's Institut Interdisciplinaire d'innovation technologique (3IT), notably in collaboration with Prof. Paul Charette's team, which will develop the allergen detection prototypes. The project will be carried out in close collaboration with the Université de Montréal (teams of Professors R. Martel and S. Giasson, Chemistry Department), Université Laval and several project partners. The candidate will thus benefit from an exceptional research environment where students, engineers, professors, and organizations work hand in hand to develop the technologies of the future to improve public health and the food industry in Canada.

Researched Profile

• University degree and master's degree in engineering, data science, mathematics, or physics.
• Experience in development of data analysis algorithms (Python, MATLAB, or R).
• Ability to communicate both orally and in writing in English or French.
• Strong capacity for adaptation, autonomy, teamwork and problem-solving.
• Strong taste for design, experimental work, interdisciplinary R&D and entrepreneurship.

Contacts: emplois-materiaux@usherbrooke.ca

Documents to Provide: CV, transcripts of the past two years and references.

Type d'emploi : Temps plein

Rémunération: à partir de 23,00$ par heure

Heures prévues: 37 par semaine

Avantages:

• Bourses d'Études
• Formation linguistique offerte
• Heures d’arrivée et de départ flexibles

Exigences linguistiques souples:

• Français non requis

Horaire:

• Du Lundi au Vendredi

Formation:

• Maîtrise (Obligatoire)

Langue:

• Français (Souhaité)

Lieu du poste : En présentiel