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Regions of interest multivariate curve resolution for liquid chromatography with ion mobility high-resolution tandem mass spectrometry: analysis of plastic additives
Summary
Scientists developed a smarter data-analysis method to identify hidden chemical additives—like phthalates and other compounds linked to health concerns—that leach out of plastics such as microplastics. By untangling complex, overlapping chemical signals more effectively, this tool could help researchers more accurately detect what harmful substances we're actually being exposed to through plastic pollution. This matters because better detection is the first step toward understanding and reducing our exposure to potentially toxic plastic chemicals in food, water, and the environment.
Ion mobility (IM) enhances liquid chromatography-mass spectrometry identification capabilities by providing an additional dimension that improves peak capacity, mass detection, and sensitivity, being particularly valuable for untargeted analysis. However, the high dimensionality and large amount of information in the datasets obtained in this type of analysis are a big challenge for data processing, slowing its broader application and potential. The regions of interest multivariate curve resolution (ROIMCR) is a chemometrics approach based on the bilinear model intrinsic structure of the data. It represents an effective strategy for efficient feature extraction, deconvolution, and resolution of overlapping signals in complex datasets without losing relevant information and maintaining instrumental mass accuracy. In this work, the application of ROIMCR is shown for the first time for liquid chromatography ion mobility mass spectrometry (LC-IM-MS) in data-dependent acquisition (DDA) mode, including MS1 and MS2 datasets for the analysis of plastic additives. An improvement in the discrimination of isomeric species and minimization of interferences was shown by working with mobility profiles. IM-MS data from five microsphere standards and three polyethylene (PE) plastic leachates, containing complex mixtures of unknown additives, were analyzed and resolved, and collision cross-section (CCS) values were calculated for each component. Using ROIMCR, 14 different plastic additives, including phthalates, siloxanes, and phosphates, present in the original PE polymers, were identified. Our findings highlight that ROIMCR provides an effective and scalable tool for resolving complex IM datasets and enhancing the identification of potential additives associated with microplastics.