Identification and absolute quantification of NIAS: towards a return to food contact for recycled plastics

Recycling plastics is one of the main ways of combating the impact of human activity on the environment. To improve the circularity of these materials, IFPEN is working on industrial solutions involving the design and development of new plastic transformation processes for recycling. The return of recycled plastics to food contact represents one of the most profitable economic sectors. However, regulations governing materials intended to come into contact with foodstuffs are very strict. To assess potential health risks, particular attention is paid to unexpected and potentially harmful substances that can migrate from packaging materials to food, known as NIAS (Non intentionally added Substances). The central idea of the thesis project is to develop analytical methods based on the coupling between liquid chromatography (LC) and high-resolution mass spectrometry (HRMS). These methods will lead to the identification and quantification of NIAS, drawing on high-volume data processing inspired by omics science methods, and more specifically on the construction of molecular networks. This approach is the first objective of this thesis and will make it possible to obtain previously unpublished data on chemically and/or mechanically recycled packaging in terms of NIAS, and to compare them with those previously identified in the literature for packaging made from virgin raw materials. To meet the challenge of NIAS quantification, this thesis topic aims to explore the potential of structural fingerprints calculated from MS2 (tandem mass spectrometry) data, in combination with other descriptors, in the implementation of a response factor prediction model for NIAS detected by LC-HRMS using machine learning methods.
 

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. 
 

Academic requirements    University Master degree in analytical sciences    
Language requirements    English level B2 (CEFR) 
Other requirements    Complex data processing, modelling, molecular reconstruction, chemometrics/chemoinformatics, machine learning