Predictive approach for chemical risk assessment for food contact materials

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Predictive approach for chemical risk assessment for food contact materials

Project acronym: Predict Pack Modeling
Duration: Apr 2005 - Dec 2008
Funded by: Projet ACTIA RA05.22

Project name: Predictive approach for chemical risk assessment for food contact materials

Contacts: Régis Lebossé and Catherine Sauvageaot-Loriot (Senior project manager); Laboratoire Nationale de métrologie et d’Essais (LNE)

1. Context and problematic

Plastic packagings are formulated with substances which can migrate from materials into foodstuffs. The risks associated with this contamination are still poorly known and the exposure of consumers is increasing due to changing consumption patterns (nomadism, pods, etc..).

The European Commission is currently working to update and consolidate the regulation based on the principles of inertness and a list of authorised materials (positive lists). The European Commission now lists 937 substances (340 monomers and 597 additives) allowed for food contact. In this list, 502 substances (230 monomers and 272 additives) are subject to specific migration limits (SML) because of their toxicity.

Plastic materials in contact with foodstuff must absolutely be checked before being put on the market. According to European directive 2002/72/EC, the ability of plastic materials to be used in contact with food can also be demonstrated using modelling tools. Their use is however limited due to availability of some data, like the formulation of materials and partition coefficients of substances between plastics and food.

2. Results and Applications

Better knowledge of additives included in packaging materials

The ability of laboratories to identify and quantify the main substances (additives) in plastic materials (HDPE) was developed with a new deconvolution process applied on infrared spectra of extracts.

Four formulations of both HDPE and PS were chosen and used during the work. Standard extraction methods and quantification methods using HPLC-UV-ELSD and GC-FID were compared. A new deconvolution process applied on infrared spectra of extracts was developed to identify and quantify additives contained in HDPE.

These approaches could be extended for other unknown plastic packaging materials for which composition information are difficult to get for industrial conception secrets raison.

New method to predict partition coefficients between polymers and food simulants

Activity coefficients in both phases were approximated through a generalized off-lattice Flory-Huggins formulation applied to plastic materials and to liquids simulating food products. Potential contact energies were calculated with an atomistic semi-empirical forcefield. The simulations demonstrated that plastic additives have a significant chemical affinity, related to the significant contribution of the positional entropy, for liquids consisting in small molecules.

Comprehensive approach to compliance materials for food contact

In a logic of decision trees, a predictive and a experimentally approaches were combine to demonstrate the compliance of plastic materials. Different scenarios are considered depending on the degree of knowledge and the material parameters needed for modeling (additives, known quantities, availability of robust values of diffusion and/or partition coefficients).

3. Novelties, benefits and added value

This project was the first project targeting food packaging materials industry. This project has identified two main problems in the reuse of results obtained in major European projects : - the lack of knowledge of formulated food packaging materials related to their application - the lack of appropriate tools for trades and industrial products.

The way is now open to disseminate knowledge and methods developed (physico-chemical methods of calculation and simulation) to the industry, to the technical centers and to the control and health authorities.

4. Successful applications, success stories

- Contribution in “Safe Food Packaging” database,


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