Sensing element design in optical pesticide-detecting arrays

Abstract

The escalating global reliance on synthetic pesticides to secure agricultural productivity has intensified concerns over their adverse impacts on human health and ecosystems. With pesticide exposure linked to severe pathologies—including neurotoxicity, endocrine disruption, and carcinogenesis—there is an urgent need for rapid, sensitive, and field-deployable monitoring tools. Conventional analytical methods such as gas or liquid chromatography coupled with mass spectrometry offer high accuracy but are impractical for on-site, real-time screening due to their cost, complexity, and infrastructure demands. In response, optical (bio)sensor arrays have emerged as powerful alternatives that mimic biological sensory systems by generating multidimensional response patterns (analyte-specific fingerprints) from ensembles of cross-reactive sensing elements. This review provides a comprehensive and mechanism-driven analysis of the key sensing element classes used in these arrays for pesticide detection, including label-free plasmonic nanoparticles, chemosensors, host–guest systems, enzymes, antibodies, and aptamers. This review critically evaluates the operational principles, recent advances, practical limitations, and real-world applicability of each platform. By unifying diverse sensing paradigms under a common conceptual framework, this review distills key design principles from reported optical sensor arrays and provides actionable guidance for designing practical platforms to detect and discriminate pesticide residues—balancing robustness, simplicity, and scalability for real-world environmental and food safety applications.

Graphical abstract: Sensing element design in optical pesticide-detecting arrays

Article information

Article type
Critical Review
Submitted
30 Nov 2025
Accepted
02 Feb 2026
First published
23 Feb 2026

Anal. Methods, 2026, Advance Article

Sensing element design in optical pesticide-detecting arrays

M. R. Nassajan and F. Ghasemi, Anal. Methods, 2026, Advance Article , DOI: 10.1039/D5AY01971K

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