We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Emerging analytical technologies for detecting microplastics and nanoplastics in food: bridging challenges and innovations
Summary
Tiny plastic particles are showing up in our food, but scientists still don't have great tools to reliably detect and measure them—making it hard to know exactly how much we're consuming or how worried we should be. This review rounds up promising new detection technologies, like advanced laser-based imaging and AI-powered analysis, that could make spotting these particles faster and more accurate. The bigger takeaway: before we can properly assess the health risks of microplastics in food, researchers need to agree on standardized testing methods—work that's still very much in progress.
The ubiquitous presence of microplastics (MPs, < 5 mm) and nanoplastics (NPs, < 1 μm) in food systems has raised serious concerns regarding food safety and human health. Accurate detection and quantification, however, remain formidable challenges due to the complexity of food matrices, particle heterogeneity, and the lack of harmonized analytical protocols. This review critically examines the emerging analytical technologies that are reshaping the detection landscape of MPs and NPs in food. Emphasis is placed on next-generation methods-including stimulated Raman scattering (SRS) microscopy, surface-enhanced Raman spectroscopy (SERS), hyperspectral imaging (HSI), and microfluidic-nanodroplet platforms-along with artificial intelligence (AI)-assisted analytical frameworks that enable automated identification and classification. The mechanisms by which lipids, proteins, and other food components interfere with nanoscale detection are systematically discussed, highlighting the need for improved pretreatment and matrix-deconvolution strategies. Moreover, we identify persistent gaps in reference materials, quantification standards, and interlaboratory comparability. Looking forward, future progress will depend on the co-development of operational definitions, fit-for-purpose analytical workflows, AI-assisted data interpretation, and reference-material-based interlaboratory validation systems. In this way, methodological innovation can be more effectively translated into reliable food safety assessment and regulatory implementation.