Mycotoxins in Food: Green Detoxification Technologies, Mechanisms, Matrix Challenges and Analytical Advances

Abstract

More than 25% of the world's grain production is contaminated with mycotoxins. Conventional chemical, thermal, and biological detoxification techniques suffer from limitations such as incomplete toxins removal, nutritional deterioration, residue formation and restricted scalability in intricate food matrices. As a result, innovative solutions are probed for efficient and long-term mycotoxin removal. This review examines green detoxification technologies from a chemistry-centered perspective, starting with the intrinsic reactivity of major mycotoxins and their biological behavior. Classification, bioactivation through CYP450 pathways, oxidative stress, and direct molecular damage are discussed not as isolated toxicology topics, but as guiding principles that help explain why certain detoxification strategies succeed while others fail. Under mild, environmentally benign conditions, photocatalytic platforms based on heterojunction semiconductors, metal organic frameworks, carbon nitride, and magnetically recoverable nanocomposites enable light-driven production of reactive oxygen species that specifically degrade major toxic metabolites including aflatoxin variants, fumonisin compounds, as well as zearalenone. The selectivity, stability, and detoxifying effectiveness in actual food matrices are enhanced via adsorption-biocatalysis hybrid systems by combining high-affinity sorbents with immobilized enzymes or nanozymes. This review emphasizes the use of advanced analytical tools and biosensor technologies, particularly focusing on eco-friendly detoxification methods that minimize the formation of secondary toxins while facilitating the identification of degradation products. Moreover, unlike previous reviews that primarily focus on individual detoxification technologies, this review offers a novel interdisciplinary framework that integrates mycotoxin intrinsic chemistry, green detoxification technologies, and AI-assisted predictive strategies for selective and sustainable mycotoxin management. It also aligns these approaches with important Sustainable Development Goals (SDGs), specifically SDG 2 (zero hunger), SDG 3 (good health and well-being), SDG 9 (industry, innovation and infrastructure), and SDG 12 (responsible consumption and production). Furthermore, it critically evaluates the emerging applications of machine learning and artificial intelligence in the realms of material design, process optimization, and mycotoxin risk assessment, highlighting their potential as innovative solutions to the challenges addressed. This report is the first to offer an interdisciplinary framework for effective and sustainable mycotoxin management for ensuring food safety, summarizing recent advancements, addressing key challenges, and outlining future directions.