Towards a greener future: The role of sustainable methodologies in metabolomics research

Sustainability is a growing priority in scientific research, and metabolomics is no exception. Traditional metabolomics workflows rely on hazardous solvents, raising concerns regarding their environmental impact. Recent advancements in green analytical chemistry lay the ground for the integration of eco-friendly approaches in metabolomics from matrix collections and pre-treatment, through sample preparation till data analysis. This review explores the current state of sustainable metabolomic workflows, with a particular focus on green sample preparation methods, solvent-free, low-solvent extraction techniques, and energy-efficient instrumental analysis. Computational advancements, including AI-driven models, machine learning-based semi-quantification, and predictive algorithms for solvent selection, further enhance sustainability by reducing resource consumption. The applicability of these approaches in metabolomic studies, particularly in plant and food research is explored. By integrating innovative green methodologies across all stages of metabolomic workflows, researchers can significantly reduce environmental footprints while maintaining analytical rigor.