Bergmann Alan J, Arturi Katarzyna, Schönborn Andreas, Hollender Juliane, Vermeirssen Etiënne L M
Swiss Centre for Applied Ecotoxicology, Überlandstrasse 133, 8600, Dübendorf, Switzerland.
Eawag Department of Environmental Chemistry, Überlandstrasse 133, 8600, Dübendorf, Switzerland.
Anal Bioanal Chem. 2025 Jan;417(1):131-142. doi: 10.1007/s00216-024-05632-y. Epub 2024 Nov 23.
Many chemicals in food packaging can leach as complex mixtures to food, potentially including substances hazardous to consumer health. Detecting and identifying all of the leachable chemicals are impractical with current analytical instrumentation and data processing methods. Therefore, our work aims to expand the analytical toolset for prioritizing and identifying chemical hazards in food packaging. We used a high-performance thin-layer chromatography (HPTLC)-based bioassay to detect genotoxic fractions in paperboard packaging. These fractions were then processed with non-targeted liquid chromatography high-resolution mass spectrometry (LC-HRMS/MS) and machine learning-based toxicity prediction (MLinvitroTox). The HPTLC bioassay detected four genotoxic zones in extracts of the paperboard. One-dimensional HPTLC separation and targeted fraction collection reduced the number of chemical features extracted from paperboard and detected with LC-HRMS by at least 98% (from 1695-2693 to 14-50). The entire process was successful for spiked genotoxic chemicals, which were correctly prioritized in the fractionation and non-target analysis workflow. The native chemical with the strongest genotoxicity signal was identified with a suspect list as 5-chloro-2-methyl-4-isothiazolin-3-one and confirmed with LC-HRMS/MS and HPTLC bioassay. Toward identification of the remaining unknown genotoxicants, two-dimensional HPTLC further reduced the number of chemical features. Genotoxicity predictions with MLinvitroTox based on molecular fingerprints of the unknown signals derived from their MS2 fragmentation spectra helped prioritize two chemical features and suggested candidate structures. This work demonstrates strategies for using HPTLC, HRMS, and toxicity prediction to help identify toxicants in food packaging.
食品包装中的许多化学物质会以复杂混合物的形式渗入食品中，其中可能包括对消费者健康有害的物质。使用当前的分析仪器和数据处理方法来检测和识别所有可渗出的化学物质是不切实际的。因此，我们的工作旨在扩展分析工具集，以便对食品包装中的化学危害进行优先级排序和识别。我们使用基于高效薄层色谱（HPTLC）的生物测定法来检测纸板包装中的遗传毒性成分。然后，这些成分通过非靶向液相色谱高分辨率质谱（LC-HRMS/MS）和基于机器学习的毒性预测（MLinvitroTox）进行处理。HPTLC生物测定法在纸板提取物中检测到四个遗传毒性区域。一维HPTLC分离和靶向馏分收集使从纸板中提取并通过LC-HRMS检测到的化学特征数量减少了至少98%（从1695 - 2693个减少到14 - 50个）。对于添加的遗传毒性化学物质，整个过程是成功的，这些化学物质在分馏和非靶向分析工作流程中被正确地进行了优先级排序。通过嫌疑列表鉴定出具有最强遗传毒性信号的天然化学物质为5-氯-2-甲基-4-异噻唑啉-3-酮，并通过LC-HRMS/MS和HPTLC生物测定法进行了确认。为了鉴定其余未知的遗传毒性物质，二维HPTLC进一步减少了化学特征的数量。基于从其MS2碎裂谱导出的未知信号的分子指纹，使用MLinvitroTox进行遗传毒性预测有助于对两个化学特征进行优先级排序并提出候选结构。这项工作展示了使用HPTLC、HRMS和毒性预测来帮助识别食品包装中有毒物质的策略。
