Development of analytical methods for the identification and quantification of NIAS - THESIS

European regulations impose strict rules to ensure the safety of materials in contact with food (in particular EU Regulation No. 10/2011 on plastic materials and articles). It includes a positive list of substances permitted for use in packaging (intentionally added substances, IAS), which defines their migration limits. Non-intentionally added substances (NIAS) may also be present and must be assessed. Their migration limit must not exceed 0.01 mg/kg of food for non-CMR NIAS.

The sources of NIAS are multiple. They can be:

• degradation products of polymers (including oligomers) and their additives;
• products of secondary reactions during the manufacture of packaging;
• contaminants in raw materials and contaminants linked to the manufacturing and packaging process.

As a result, molecules can have very diverse structures and physicochemical properties. In the case of degradation products, reaction patterns may be known, which can help to identify and even anticipate the structures present. Numerous case studies are already available in the scientific literature. In the case of contaminants, on the other hand, the molecules can be not only diverse but also unexpected.

This situation is a major challenge for analytical chemistry techniques, especially as the evaluation of substances requires not only their identification, but also their quantification.

The central idea of the thesis project is to develop multi-technique analytical chemistry methods for the identification and quantification of NIAS based on high-volume data processing inspired by metabolomics methods in biological research (chemometric methods).

This project proposes to build on several previous thesis and in-house works, i.e. existing data concerning the deformation of commercial materials as part of Phuong-Mai Nguyen's thesis (IR, NMR), the construction of spectral fingerprint databases (MS and NMR) of IAS and NIAS by LNE (LNE project 2014/377 and approach used in Morandise Rubini's M2 internship), the methodological development and deformation of bio-based materials as part of Audrey Gratia's thesis (NMR, GC-MS), the methodological development of Amine Kassouf's data processing (Independent Component Analysis on GC-MS, IR and frontal fluorescence), the methods developed for the analysis of Baninia Habchi's HRMS spectra (ICA on Flow-Injection-HRMS) and the multi-block array analysis methods (ComDim) developed for different applications (eg. Monakhova et al., 2014).

The main results of the work will be:

• enrichment of spectral databases of current NIAS (GC-MS, LC-MS, NMR);
• exploring the potential and, where appropriate, developing methods for NIAS analysis by coupled methods (GCxGCxMS, HRMS - direct introduction, LC-MS (TOF), and coupled to existing routines (GC-MS, NMR) for NIAS analysis;
• the development of NIAS identification and quantification methods assisted by large data array processing methods;
• the development of rapid comparison methods for materials quality analysis;
• the implementation of expert tools in the UMT ACTIA SafeMat teams.