To date several non-targeted screening approaches and methodologies have been developed to identify and quantify unknown compounds, including non-intentionally added substances (NIAS), migrating from food contact materials (FCMs). However, different procedures can lead to different results. Thus, in a review published on January 26, 2022, in the peer-reviewed journal Food Additives & Contaminants: Part A, Christina Nerín from the Universidad de Zaragoza, Spain, and co-authors give an overview of the methodologies for the non-targeted screening of NIAS migrating from FCMs, including the analytical techniques’ strengths and weaknesses. In addition, the paper recommends approaches for NIAS assessment, proposing sample preparation techniques as well as procedures to screen, identify, and quantified a wide range of NIAS. Guidance on understanding and interpreting the results, as well as reporting requirements, are also outlined. Nerín and co-authors concluded that “with today’s knowledge and available equipment, it is not possible to give an accurate picture of all migrants from FCMs, particularly those migrating below the level of detection or quantification.” Thus, the authors proposed to make compromises (e.g., semi-quantify concentrations when appropriate quantification is impossible), recognize strengths and weaknesses of each technique, and bear the goal of NIAS determination in mind that is to support risk assessment and management of FCMs. To reach those goals, the scientists proposed to also apply further techniques, besides analytical chemistry, such as bioassays. Bioassays allow researchers to analyze the toxicity of chemical mixtures migrating from plastics and other FCMs (FPF reported).
Methods to evaluate NIAS are continuously developed. Xue-Chao Song, also from the University of Zaragoza, Spain, and co-authors developed a machine-learning collision cross-section (CSS) model for NIAS identification in FCMs. The article was published on January 18, 2021, in the Journal of Agricultural and Food Chemistry. CSS can serve as a structural descriptor of compounds obtained from traveling wave ion mobility spectrometry (IMS), a technique to separate ions by collision with a gas buffer (e.g., nitrogen). The advantage of CCS values is that they are not instrument-dependent such that when using the same experimental conditions, values generated in different laboratories and with different instruments can be compared. The researchers used molecular descriptors, “i.e., numeric values that provide a fingerprint of a compound’s structural and physicochemical properties,” to predict CCS values of roughly 500 chemicals commonly used in FCMs. They reported that the model predicted 92% of protonated molecules with relative errors < 5%. The scientists applied the model to analyze oligomers migrating from polyamide FCMs adhesives and predicted CCS values were compared with experimental data derived by traveling wave IMS. Through suspect screening, 12 oligomers were tentatively identified and for 11 of them, the scientists improved the confidence of identification by comparing the predicted CCS values with the experimental data. The authors concluded that their developed CCS tool can reliably predict chemicals in FCMs and expressed the aim “to turn in-house prediction models into tools truly applicable in all laboratories.” The article further outlines the opportunities and challenges of CCS prediction tools.
Researchers of the same group have previously tested different techniques for oligomer detection and reported on the oligomers migrating from biopolymers (FPF reported) and from biodegradable teacups (FPF reported).
Dimitra Diamantidou from the Aristotle University of Thessaloniki, Greece, and co-authors also developed a method to analyze NIAS, but targeted to determine seven cyclic polyethylene terephthalate (PET) and polybutylene terephthalate (PBT) oligomers in post-mortem blood samples. The method presented in an article published on January 2022, in the journal Analytical and Bioanalytical Chemistry, is based on ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS). The researchers applied the method to 34 post-mortem whole blood samples and detected PET trimer in four of them. According to the authors, their analytical method is useful “to assess the exposure and thus the potential hazard and health risks associated with these NIAS from PET and PBT FCMs through food consumption.”
References
Diamantidou, D. et al. (2022). “Liquid chromatography‑mass spectrometry method for the determination of polyethylene terephthalate and polybutylene terephthalate cyclic oligomers in blood samples.” Analytical and Bioanalytical Chemistry. DOI: 10.1007/s00216-021-03741-6
Nerín, C. et al (2022). “Guidance in selecting analytical techniques for identification and quantification of non-intentionally added substances (NIAS) in food contact materials (FCMS).” Food Additives & Contaminants: Part A. DOI: 10.1080/19440049.2021.2012599
Song, X.-C. et al. (2022). “Prediction of Collision Cross Section Values: Application to Non Intentionally Added Substance Identification in Food Contact Materials.” Journal of Agricultural and Food Chemistry. DOI: 10.1021/acs.jafc.1c06989
