Nestlé Institute of Packaging Sciences, Nestlé Research, Société des Produits Nestlé S.A., 1015 Lausanne, Switzerland.
Nestlé Institute of Health Sciences, Nestlé Research, Société des Produits Nestlé S.A., Vers-chez-les-Blanc, 1000 Lausanne 26, Switzerland.
Food Chem. 2025 Jan 1;462:140853. doi: 10.1016/j.foodchem.2024.140853. Epub 2024 Aug 12.
Chemicals from packaging materials might be transferred into food resulting in consumer exposure. Identifying these migrated chemicals is highly challenging and crucial to perform their safety assessment, usually starting by the understanding of the chemical composition of the packaging material itself. This study explores the use of the Molecular Networking (MN) approach to support identification of the extracted chemicals. Two formulations of bioplastics were analyzed using Liquid Chromatography hyphenated to High-Resolution Mass Spectrometry. Data processing and interpretation using a conventional manual method was performed as a point of comparison to understand the power of MN. Interestingly, only the MN approach facilitated the identification of unknown chemicals belonging to a novel oligomer series containing the azelaic acid monomer. The MN approach provided a faster visualization of chemical families in addition to the highlight of unrelated chemicals enabling to prioritize chemicals for further investigation improving the safety assessment of packaging materials.
包装材料中的化学物质可能会迁移到食品中,从而使消费者暴露在这些化学物质之下。识别这些迁移的化学物质极具挑战性,对于进行安全性评估至关重要,通常首先要了解包装材料本身的化学组成。本研究探讨了分子网络(MN)方法在支持提取化学物质识别方面的应用。使用液相色谱-高分辨质谱联用技术分析了两种生物塑料配方。数据处理和解释采用传统的手动方法进行,作为比较来了解 MN 的强大功能。有趣的是,只有 MN 方法才能促进对属于含有壬二酸单体的新型低聚物系列的未知化学物质的鉴定。MN 方法不仅可以更快地可视化化学族,还可以突出无关的化学物质,从而优先选择要进一步研究的化学物质,提高包装材料的安全性评估。