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Ethylene vinyl alcohol copolymer (EVOH) films containing green tea extract were successfully produced by extrusion. The films were brown and translucent, and the addition of the extract increased the water and oxygen barrier at low relative humidity but increased the water sensitivity, the glass transition temperature, and the crystallinity of the films and improved their thermal resistance. An analysis by HPLC revealed that the antioxidant components of the extract suffered partial degradation during extrusion, reducing the content of catechin gallates and increasing the concentration of free gallic acid. Exposure of the films to various food simulants showed that the liquid simulants increased their capacity to reduce DPPH(•) and ABTS(•+) radicals. The release of green tea extract components into the simulant monitored by HPLC showed that all compounds present in the green tea extract were partially released, although the extent and kinetics of release were dependent on the type of food. In aqueous food simulants, gallic acid was the main antioxidant component released with partition coefficient values ca. 200. In 95% ethanol (fatty food simulant) the K value for gallic acid decreased to 8 and there was a substantial contribution of catechins (K in the 1000 range) to a greatly increased antioxidant efficiency. Kinetically, gallic acid was released more quickly than catechins, owing to its faster diffusivity in the polymer matrix as a consequence of its smaller molecular size, although the most relevant effect is the plasticization of the matrix by alcohol, increasing the diffusion coefficient >10-fold. Therefore, the materials here developed with the combination of antioxidant substances that constitute the green tea extract could be used in the design of antioxidant active packaging for all type of foods, from aqueous to fatty products, the compounds responsible for the protection being those with the higher compatibility with the packaged product.

Food packaging is of high societal value because it conserves and protects food, makes food transportable and conveys information to consumers. It is also relevant for marketing, which is of economic significance. Other types of food contact articles, such as storage containers, processing equipment and filling lines, are also important for food production and food supply. Food contact articles are made up of one or multiple different food contact materials and consist of food contact chemicals. However, food contact chemicals transfer from all types of food contact materials and articles into food and, consequently, are taken up by humans. Here we highlight topics of concern based on scientific findings showing that food contact materials and articles are a relevant exposure pathway for known hazardous substances as well as for a plethora of toxicologically uncharacterized chemicals, both intentionally and non-intentionally added. We describe areas of certainty, like the fact that chemicals migrate from food contact articles into food, and uncertainty, for example unidentified chemicals migrating into food. Current safety assessment of food contact chemicals is ineffective at protecting human health. In addition, society is striving for waste reduction with a focus on food packaging. As a result, solutions are being developed toward reuse, recycling or alternative (non-plastic) materials. However, the critical aspect of chemical safety is often ignored. Developing solutions for improving the safety of food contact chemicals and for tackling the circular economy must include current scientific knowledge. This cannot be done in isolation but must include all relevant experts and stakeholders. Therefore, we provide an overview of areas of concern and related activities that will improve the safety of food contact articles and support a circular economy. Our aim is to initiate a broader discussion involving scientists with relevant expertise but not currently working on food contact materials, and decision makers and influencers addressing single-use food packaging due to environmental concerns. Ultimately, we aim to support science-based decision making in the interest of improving public health. Notably, reducing exposure to hazardous food contact chemicals contributes to the prevention of associated chronic diseases in the human population.

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The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H] + adducts and 95.0% of CCS values for [M + Na] + adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H] + and [M + Na] + adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.