## 1 Introduction
Moreover, innovations in data acquisition modes, such as data-independent acquisition (DIA) and data-dependent acquisition (DDA), have allowed for the collection of comprehensive spectral data, which can then be processed using advanced computational tools and spectral databases. These developments have not only improved the detection capabilities but also enhanced the reliability of compound identification, even in the absence of reference standards [11; 12]. The analytical challenges associated with NIAS detection are multifaceted. Sample preparation, for example, is a critical step that can influence the overall sensitivity and accuracy of the analysis. Migration tests must be carefully designed to mimic the worst-caseconditions of FCM use, employing a range of food simulants that reflect different food types [13]. The extraction and concentration of migrants from these simulants require optimized procedures to ensure that NIAS are not lost or degraded during analysis [14]. In addition, the diverse chemical properties of NIAS, from volatile to non-volatile compounds, demand the use of complementary chromatographic techniques. While liquid chromatography (LC) is often preferred for non-volatile compounds due to its compatibility with soft ionization methods such as electrospray ionization (ESI), gas chromatography (GC) is typically employed for the analysis of volatile and semi-volatile substances using electron ionization (EI) [15; 16]. The shift toward NTA approaches represents a paradigm change in the analysis of FCMs. Unlike traditional targeted approaches, NTA does not require a predefined list of target compounds, thereby enabling the detection of a broader spectrum of substances. This is particularly important given the sheer number of potential NIAS, many of which may arise from unexpected sources or novel degradation pathways. The comprehensive nature of non-targeted screening has revealed a far greater chemical complexity in FCMs than previously recognized, highlighting the need for ongoing research and method development [12; 17]. In addition to technical advancements, recent studies have also focused on the integration of chemometric techniques to interpret complex datasets generated by non-targeted screening. Multivariate statistical methods, such as principal component analysis and partial least-squares discriminant analysis, have been applied to differentiate between various FCM formulations and to identify key markers that may be indicative of NIAS presence [11; 18]. These approaches enable a deeper understanding of the factors influencing NIAS migration, including the type of polymer, the nature of additives, and the conditions of plastic use and recycling. Furthermore, by highlighting patterns and clustering substances with similar occurrence profiles or migration behaviors, chemometric tools can support prioritization by flagging recurring or highly migratory unknowns for further toxicological evaluations. When combined with metadata such as polymer origin or known use categories, these statistical models can help focus risk assessment efforts on substances of potential concern. Understanding the challenges associated with the migration of NIAS from plastic FCMs and their implications for food safety is a current matter of concern. As research progresses, there is a growing recognition of the necessity for advanced analytical methodologies to address the complexities posed by NIAS [19]. The study of NIAS in plastic FCMs is relevant for several reasons: (a) Integrated analytical and toxicological approaches are required to comprehensively assess potential risks. (b) The growth of NTA techniques has enabled the detection and identification of a much wider range of compounds. (c) By detecting new unknown compounds, the understanding of chemical migration and potential human exposure risks is increased. (d) The integration of advanced analytical techniques with evolving regulatory frameworks highlights the essential role of scientific innovation in public health protection.