## 6 Data Processing and Analysis  
### Compound Identification Workflows  
The non-targeted analysis of NIAS from plastic FCMs generates vast and complex datasets, requiring robust data processing workflows and advanced identification strategies. These workflows typically involve peak detection, feature alignment, spectral deconvolution, database matching, and confirmation or quantification with analytical standards, if available. However, the high dimensionality of the data often results in challenges such as feature redundancy, false positives, and difficulties distinguishing relevant signals from background noise. Additionally, reproducibility can be compromised by differences in data processing software, particularly when using proprietary algorithms, which may affect feature detection and compound annotation outcomes. The most common data processing steps for compound identification [70] are summarized in Figure 4.  
Software platforms such as MassLynx and UNIFI (Waters Corporation, Milford, MA, USA), Compound Discoverer (Thermo Fisher Scientific, Waltham, MA, USA), and MSDIAL [71] enable automated or semi-automated data processing and tentative compound identification. For instance, [PERSON] et al. [6] manually processed UHPLC-QTOF-MS data using the MassLynx software and the ChemSpider [72] and SciFinder (CAS, Columbus, OH, USA) databases, identifying 37 plasticizers and oligomers via exact mass and fragment ion matching, while [PERSON] et al. [20] employed the SciexOS software (SCIEX, Framingham, MA, USA) for automated peak grouping and deconvolution, cross-referencing spectra against an in-house library of 237 IAS/NIAS. In that work, the identification criteria were adopted from the SANTE 11813/2017 guidelines [73], including a mass accuracy of \(\pm\)5 ppm, a retention time shift of \(\pm\)0.1 min, two diagnostic ions (precursor and fragment ions) with an ion ratio tolerance of \(\pm\)30%, and a <20% difference between the observed and the theoretical isotope distribution. Similarly, [PERSON] et al. [30] combined automatic feature processing and elemental composition assignment with the SciexOS software for the manual tentative identification of detected compounds using the ChemSpider and MS/MS spectral databases, such as METLIN [74] or MassBank [75], in addition to in silico fragmentation tools and searches in the scientific literature. In that case, a mass tolerance of \(\pm\)10 ppm, an isotopic pattern distribution, factors for ring and double bonds, and MS/MS spectrum were considered. Although these identification approaches focus on spectral quality and database annotation, recent workflows increasingly incorporate prioritization strategies based on toxicological relevance, such as risk-based filtering or Threshold of Toxicological Concern (TTC) concepts. While these aspects are addressed in detail in Section 7, they are crucial to reduce the analytical burden and focus follow-up efforts on compounds with higher safety concerns. Spectral libraries, such as MassBank, the NIST/EPA/NIH Mass Spectral Library, and the NIST GC Retention Index (RI) Database (National Institute of Standards and Technology), as well as in silico fragmentation tools, such as CFM-ID [76], are critical for structural elucidation and tentative identification. For example, [PERSON] et al. [18] pre-processed GC-MS data (including peak detection, alignment, blank subtraction, and identification) with MSDIAL using RI calculations based on n-alkane (C9-C36) as well as NIST library matching  
Figure 4: Common steps in data processing workflows for compound identification. (score \(\geq\)75%, RI tolerance \(\pm\)20 when available). In their work, a total of 80 compounds were detected in PE and rPE, and 70 of them were tentatively identified, including hydrocarbons, esters, aldehydes, alcohols, ethers, acids, benzene derivatives, ketones, amides, and piperazine derivatives. Nevertheless, library gaps for the identification of NIAS remain a persistent challenge. For instance, [PERSON] et al.