Nano-enabled fluorescent sensing platforms for on-site detection of pesticides in food
Abstract
Pesticide residues in food matrices pose significant threats to global food safety. Conventional chromatographic methods, though highly reliable, are constrained by high costs, lengthy analysis times, and limited field deployability, restricting their use for rapid on-site monitoring across food supply chains. Nanomaterial-based fluorescent sensing platforms have emerged as promising analytical alternatives, leveraging the unique optical properties of quantum dots (QDs), carbon dots (CDs), Graphene quantum dots (GQDs), metal-organic frameworks (MOFs), and metal nanoclusters (MNCs). This review systematically examines recent advances in these platforms for pesticide detection, with particular emphasis on analytical chemistry perspectives spanning recognition mechanism design, signal transduction, and practical performance in complex food matrices. The structure-property relationships of major pesticide classes and their implications for molecular recognition are first discussed, followed by a systematic analysis of fluorescence modulation mechanisms, including photoinduced electron transfer (PET), Förster resonance energy transfer (FRET), inner filter effect (IFE), and aggregation-induced emission (AIE). Nanomaterial design strategies are further evaluated with respect to synthesis reproducibility, surface functionalization, and batch-to-batch consistency. The integration of artificial intelligence (AI) algorithms for automated signal processing and multi-analyte…
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