Matrix-First Benchmarking of Biosensors for Multi-Hazard Monitoring: Bacteria, Viruses, and Pesticides in Food and Water

Abstract

Biosensors are pivotal for detecting foodborne and waterborne hazards due to their portability, low cost, and rapid response. However, performance often degrades in real samples, where complex matrices reduce sensitivity and specificity and increase false positives/negatives. This systematic review synthesizes recent advances in biosensor platforms for monitoring contaminants in food and water, emphasizing how matrix properties govern analytical reliability and field usability. We present a matrix-first benchmarking perspective that compares biosensor performance across low-biomass waters, high-organic wastewater, and complex food extracts (high fat/protein, high particulate load, acidic, or high-salt), and summarizes dominant interference modes (fouling, nonspecific binding, ionic-strength shifts, and optical turbidity) alongside practical mitigation workflows (dilution/filtration, cleanup extraction, antifouling coatings, and microfluidic preconcentration). Beyond bacteria and viruses, this revision integrates pesticides as a third hazard class, covering enzyme-inhibition, aptamer, immuno-, and molecularly imprinted polymer sensing strategies, with representative case studies including glyphosate/AMPA, paraquat/diquat, chlorpyrifos, and atrazine. Overall, the matrix-first framework highlights design and workflow choices most likely to translate biosensors from proof-of-concept to deployable, multi-hazard monitoring tools for food and water safety.