Abstract
Electrochemically deposited bimetallic copper-gold nanoparticles on indium tin oxide (Cu-AuNPs on ITO) glass are demonstrated to be a sensitive and reproducible surface-enhanced Raman scattering (SERS) platform. An optimal signal enhancement with reasonably good degree of homogeneity was obtained by tuning the deposition parameters of the electrochemical setup. For Raman active analytes such as malachite green (MG) and rhodamine 6G (R6G), the developed SERS platform yields a limit of detection (LOD) of 0.75 nM. The usability of the proposed SERS platform has been realized through detection of two important antibiotics namely sulfamethoxazole (SFZ) and tetracycline hydrochloride (TCH) commonly used in egg farms. Furthermore, a machine learning (ML)-based model coupled with a dimensionality reduction technique-principal component analysis (PCA)-has been implemented to classify the targeted analytes in egg samples.
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