Mass spectrometry-based multimodal approaches for the identification and quantification analysis of microplastics in food matrix.
Front Nutr
; 10: 1163823, 2023.
Article
en En
| MEDLINE
| ID: mdl-37090781
Background: Microplastics (MPs) and nanoplastics (NPs) have become emerging contaminants worldwide in food matrices. However, analytical approaches for their determination have yet to be standardized. Therefore, a systematic study is urgently needed to highlight the merits of mass spectrometry (MS) based methods for these applications. Purpose: The aim of the study is to review the current status of MS-based multimodal analysis for the determination of MPs in food matrices. Methods: Web of Science and Google Scholar databases were searched and screened until Jan. 2023. Inclusion criteria: "publication years" was set to the last decades, "English" was selected as the "language," and "research area" was set to environmental chemistry, food analysis and polymer science. The keywords were "microplastics," "nanoplastics," "determination," "identification/quantification," and "mass spectrometry." Results: Traditional spectrometry techniques offer good abilities to conduct the multimodal analysis of MPs in terms of color, shape and other morphologies. However, such technologies have some limitations, in particular the relatively high limits of detection. In contrast, MS-based methods supply excellent supplements. In MS-based methods, gas chromatographic-mass spectrometry (GC-MS), and LC-MS/MS were selected as representative methods for determining MPs in the food matrices, while specialized MS methods (i.e., MALDI-ToF MS and ToF-SIMS) were considered to offer great potential in multimodal analysis of MPs especially when interfaced with the imaging systems. Significance: This study will contribute to gaining a deeper insight into the assessment of the exposure levels of MPs in human body, and may help build a bridge between the monitoring studies and the toxicology field.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Front Nutr
Año:
2023
Tipo del documento:
Article
País de afiliación:
China