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Updated: Aug 29, 2025

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants
Published on: February 19, 2016
Molecularly imprinted core-shell Au nanoparticles for 2,4-dichlorophenoxyacetic acid detection in milk using
Shaolong Feng1, Yaxi Hu1, Lei Chen2
1Food, Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.
Abstract:
Surface-enhanced Raman spectroscopy (SERS) has been extensively investigated for rapid and sensitive detection of trace level chemical contaminants in foods. Lack of selectivity to the targeted molecules in food matrices and fairly poor spectral reproducibility remain the main challenges for practical SERS applications. Herein, an ingenious strategy was proposed to hybridize molecularly imprinted polymers (MIPs) with gold nanoparticles as the functional SERS substrate for selective separation and detection of 2,4-dichlorophenoxyacetic acid (2,4-D), a systemic herbicide that has acute toxicity and potential cancer risk. The core-shell AuNPs@MIPs nanoparticles were finely tailored by wrapping an ultrathin layer of MIPs shell on the surface of AuNPs, which allowed selectively separating and enriching 2,4-D to the near surface of AuNPs and ensured the enhancement of Raman scattering signal of the analyte. Embedding an internal standard (i.e., 4-aminothiophenol) inside AuNPs@MIPs for SERS spectral calibration improved the quantification accuracy for 2,4-D. Three-dimensional finite difference time domain (3D-FDTD) simulation demonstrated the maximal electric field enhancement presented in the gap between adjacent AuNPs@MIPs with the theoretical enhancement factor (EF) as high as 5.85 × 106. Chemometric models established using SERS spectra showed accurate differentiation and quantification results for 2,4-D in milk at various contamination levels with a limit of detection (LOD) of 0.011 μg/mL. Our approach to integrate MIPs with noble metallic nanoparticles has great potential for selective and quantitative detection of analytes using SERS for practical agri-food analysis.

