Surface-enhanced raman spectroscopy (SERS) for sensitive analysis of organophosphate pesticides in food samples

Abstract

With a growing global population, intensifying climate change, and raising demand for agricultural production, the use of pesticides is already substantial and expected to increase further. Pesticides remain indispensable in agriculture, playing a vital role by minimizing crop losses and maintaining consistent yields. Among them, organophosphorus pesticides (OPPs) are widely used owing to their high efficacy. However, their extensive application has raised serious concerns, particularly regarding health risks, environmental persistence, and bioaccumulation. These issues underscore an urgent need for sensitive and reliable detection methods to monitor the pesticide residues and safeguard both human health and environmental sustainability.

To address the concerns, this thesis aims to develop sensitive and reliable surface-enhanced Raman spectroscopy (SERS)-based approaches for the detection of two widely used OPPs: (a) chlorpyrifos and (b) fenthion. The work comprises four original studies, presented across chapters 1 to 4 (a critical review of current SERS-based pesticide detection methods and three experimental chapters), and a concluding chapter summarising key findings and proposing future research directions. Collectively, these studies contribute to the advancement of SERS-based pesticide detection through innovations in nanostructure fabrication, detection system optimisation, and demonstration of practical applicability in real food matrices.

Chapter 1 provides the conceptual foundation for this research by critically examining the role of pesticides in agriculture and their detection technologies. It reviews the classification, toxicity and regulation of pesticides, highlighting the limitations of current detection techniques and positioning SERS as a promising alternative. Particular attention is given to the design and functionalization of nanomaterials, whose optical, catalytic, and spectroscopic properties are pivotal to enhancing SERS substrate performance. Key detection strategies such as aggregation-induced enhancement, surface charge-dependent mechanisms, and aptamer based molecular recognition techniques are discussed in the context of improving both sensitivity and selectivity. The chapter also reviews the applicability of SERS in detecting pesticide residues in food samples. With the advancement of chemometrics and machine learning, the integration of these tools into SERS workflows is highlighted as essential for interpreting complex spectral data and improving analytical robustness. The chapter concludes by outlining future directions, including device miniaturization for on-site detection, the development of reusable and universal substrates, and the broader implications for regulatory compliance and global food safety.

Chapter 2 presents the development of a rapid and robust SERS-based sensing platform for chlorpyrifos detection, utilizing agglomeration and aggregation of gold nanoparticles (AuNPs) induced by sodium chloride (NaCl) to enhance Raman signal output. In the presence of NaCl, the interaction between AuNPs and chlorpyrifos induces a concentration-dependent transition from agglomeration to aggregation of AuNPs, resulting in a distinct nonlinear SERS response. By integrating colorimetric analysis, absorbance spectra, and principal component analysis (PCA) further improve signal discrimination and analytical reliability. As a result, the approach achieves a linear detection range from 1 ppb to 1 ppm with a limit of detection (LOD) of 9 ppb, and an inverse response from 1 to 1000 ppm with a LOD of 1 ppm, effectively covering a wide detection range. Furthermore, a simple acetonitrile swabbing technique enables reproducible detection of chlorpyrifos on cucumber samples, even at concentrations as low as 0.11 ppm. The use of chemometric tools enhances selectivity, enabling accurate differentiation of chlorpyrifos from structurally similar organophosphorus pesticides, including profenofos, fenthion, and diazinon, on cucumber surface. Overall, this work demonstrates the feasibility of a low-cost, rapid, and field-deployable SERS platform for pesticide residue detection, contributing to more effective and accessible food safety monitoring systems.

Chapter 3 presents a highly sensitive dual-mode detection platform for another organophosphorus pesticide residue, i.e. fenthion, based on gamma-cyclodextrin (γ-CD)-functionalized catalytic nanomaterials (so-called nanozymes), specifically (i) γ-CD coated gold nanoparticles (γ-CD@AuNPs) and (ii) γ-CD coated silver nanoparticles (γ-CD@AgNPs). These engineered nanoparticles exhibit both strong peroxidase-like catalytic behaviour and SERS capability. The γ-CD modification allows host–guest recognition, thereby enabling selective interaction with fenthion. Upon binding, fenthion induces aggregation of the γ-CD-capped nanoparticles, which in turn suppresses their catalytic activity (i.e. for the oxidation of the colorimetric substrate TMB (3,3′,5,5′-tetramethylbenzidine)) and SERS enhancement. This results in a concentration-dependent decrease in both the absorbance at 370 nm (colorimetric signal) and the Raman intensity at 1609 cm-1 (SERS signal). The platform achieved detection limits as low as 7.8 ppb for SERS and 0.618 ppm for colorimetric analysis. Moreover, the SERS sensor achieved a LOD of 0.18 ppm using the swab method in tomato sample. This integrated nanozyme-SERS platform provides a rapid, label-free, and cost-effective approach for fenthion detection, with validated applicability to real food samples such as tomatoes, highlighting its potential for routine pesticide monitoring.

Chapter 4 investigated the synthesis of bimetallic nanoparticles using γ-CD and systematically characterized their structures, specifically Aucore-Agshell and hollow Ag/Au alloy nanostructures. In particular, the hollow Ag/Au alloy nanoparticles demonstrated superior performance in both peroxidase-like catalysis and SERS, representing enhancements of 2.43, 4.6, 18, and 2.74 times compared with γ‑CD@AuNPs, Aucore–Agshell nanoparticles, γ‑CD@AgNPs, and Cit‑AuNPs, respectively. These features render them as a promising platform for future dual-mode sensing applications, with broad analyte detection capabilities and enhanced signal tunability.

Chapter 5 summarizes the key findings of this thesis, critically evaluates the limitations of the developed SERS platforms, and outlines future research directions. This work systematically investigated the plasmonic and catalytic behaviours of metal nanostructures, for the sensitive detection of chlorpyrifos and fenthion. Sodium citrate-synthesized spherical AuNPs (~14 nm) were used for SERS-based detection of CPF, revealing a unique bell-shaped response curve that illustrated the relationship between CPF concentration and SERS signal intensity, offering mechanistic insight into analyte–nanoparticle interactions. Subsequently, γ-CD-capped nanozyme was used for the detection of fenthion. Notably, γ-CD@AgNPs exhibited aggregation-dependent POD inhibition upon exposure to fenthion, providing a dual-mode SERS–colorimetric platform. The development of bimetallic nanostructures with tunable optical and catalytic properties was found to be a promising direction for pesticides detection, and the integration of machine learning and device miniaturization is critical in translating these laboratory findings into reliable, on-site detection tools for broader applications in food safety. Nonetheless, more optimisation work is needed to ensure the sensors are selective, robust, and reliable under practical conditions, especially in real food systems. Overall, this thesis provides a solid foundation for the future development of smart, adaptable SERS sensors, contributing to global efforts in toward food safety and sustainable agriculture.

Thesis is embargoed until 31 July 2028.

Date of Award	Jul 2026
Original language	English
Awarding Institution	Queen's University Belfast
Supervisor	Cuong Cao (Supervisor) & Christopher Elliott (Supervisor)