## 6 Data Processing and Analysis
### Compound Identification Workflows
(2018) processed GC-Q-Orbitrap HRMS data using the Compound Discoverer software coupled to the NIST library, detecting 374 features in rLDPE extracts. However, only 83 substances were tentatively identified (score \(\geq\) 90%, RI tolerance \(\pm\) 50 when available), including plasticizers, surfactants, stabilizers, emulsifiers, LDPE oligomers, and other NIAS with industrial applications. That means that the other 291 detected features could not be tentatively identified with a sufficient level of confidence using automated procedures, and they would require significant efforts in manual annotation via fragment interpretation and literature cross-referencing. In such cases, RI prediction models can help narrow down plausible candidates, while in silico fragmentation tools provide ranked fragment matches against proposed structures, increasing confidence in annotation even when spectral libraries lack reference spectra. In addition to in silico fragmentation, molecular networking (MN) tools, such as GNPS ([PERSON] et al., 2017) and Cytoscape ([PERSON] et al., 2017), are bridging library gaps. MN is a computational approach designed to facilitate chemical identification in non-targeted studies using HRMS and MS/MS data by clustering MS/MS spectra based on spectral similarities ([PERSON] et al., 2017). Beyond enhancing data visualization, MN offers the advantage of being constructed directly from the same UHPLC-HRMS datasets used in conventional analyses, integrating chromatographic parameters (e.g., retention time), detection features (e.g., exact mass and adducts), and quantitative data. Feature annotation within MN is achieved by comparing measured spectra against spectral databases, generating tentative identifications weighted by similarity scores. However, the reliability of tentative identifications may be limited by variability in ionization efficiencies and unknown response factors, particularly for polar, reactive, or thermally labile NIAS. In the work conducted by [PERSON] et al. (2017), UHPLC-Q-Orbitrap HRMS and MN were used to investigate and classify oligomers in bioplastic packaging materials. The acquired data were manually processed for peak integration, molecular mass determination, and elemental formula generation, followed by spectral comparison with databases. To refine feature detection, the Progenesis QI software v. 2.4.6911.27652 (Nonlinear Dynamics, Durham, UK) was used for alignment, peak picking, adduct deconvolution, feature filtration based on strict acceptance criteria, and the extraction of MS/MS spectra. Then, MN was implemented in Python 3.7 and visualized in Cytoscape v.3.7.1, obtaining clusters of structurally related compounds for tentative identification. The global MN contained 596 nodes, with 491 spectral library hits and 176 putative proposals matching the exact masses measured by HRMS. The manual processing revealed 152 features, out of which 96 compounds were identified and grouped into three classes of oligomers: PLA, polybutylene adipate terephthalate (PBAT), and polybutylene succinate (PBS). However, MN revealed a new series of azelaic acid (AZA) oligomers, which remained unidentified using the manual method. Therefore, the study carried out by [PERSON] et al. (2017) demonstrates the potential of MN as an emerging technology that can be readily implemented in the packaging field, improving data visualization and enabling the classification of unknown migrating substances. The integration of multi-platform data is also becoming essential for comprehensive NIAS profiling. For example, [PERSON] et al. (2018) combined UHPLC-QTOF-MS data processing (MS-DIAL and others) with quantitative non-target analysis (qNTA) by quantitative structure-property relationship (QSPR) modeling to predict the response factor (peak intensity divided by concentration) for each compound using molecular descriptors.