SynthesisMycotoxin research2026
Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.
Synthesis in Mycotoxin research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review aims to critically evaluate sustainable smart sensing technologies and AI-driven platforms for real-time mycotoxin detection, highlighting innovations, integration across the food supply chain, current limitations, and future directions for safer, data-driven food safety management. This systematic review followed PRISMA guidelines and covered studies published between 2015 and 2025. Literature searches were conducted in Scopus, Web of Science, PubMed, IEEE Xplore, and Google Scholar, yielding a total sample of 620 identified records. Peer-reviewed articles on smart sensors, biosensors, and AI-driven mycotoxin monitoring were included. After title, abstract, and full-text screening based on predefined eligibility criteria, approximately 160 studies were retained and formed the final sample for qualitative synthesis across the food supply chain. Sustainable smart sensing and AI-driven platforms are transforming real-time mycotoxin detection across the food supply chain by enabling rapid, sensitive, and decentralized monitoring from farm to fork. Emerging biosensors, optical sensors, and IoT-enabled devices integrated with machine learning improve early warning, traceability, and decision-making. However, key gaps remain, including limited sensor robustness under variable field conditions, high costs of advanced materials, energy demands, and scarcity of large, standardized datasets for AI training. Interoperability between sensing platforms and regulatory frameworks is also underdeveloped. Sustainability challenges involve balancing analytical performance with low-energy operation, sensor recyclability, and accessibility for low-resource settings. Future directions should prioritize biodegradable and reusable sensor materials, edge-AI and low-power electronics, federated data-sharing models, and climate-resilient deployment strategies. Integrating predictive analytics with risk assessment and policy alignment will be essential for scalable, sustainable mycotoxin management systems.
Indexed as
Identifiers
42192001What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.