Paper-based colorimetric sensing for pesticide residue detection: Recognition mechanisms, signal transduction, and quantitative readout strategies.

Abstract

Paper-based colorimetric assays offer a low-cost and portable approach for pesticide residue screening, yet reliable quantification remains a major barrier to practical deployment. Unlike previous reviews centered mainly on sensing-platform classification or individual recognition strategies, this review establishes a continuous analytical framework linking target recognition, signal transduction, and quantitative readout engineering. Representative sensing approaches are examined to clarify how variability introduced at different stages propagates through the analytical workflow and ultimately affects quantitative performance. The analysis indicates that reliability is governed by the weakest link in the sample-to-result chain, while downstream signal amplification or data modeling cannot compensate for unstable upstream recognition, sample transport, or color generation. On this basis, this review proposes a design-oriented evaluation framework to guide the development of paper-based pesticide analytical devices with improved reproducibility, standardization, and field applicability.