Surface-enhanced Raman spectroscopy with partial least squares regression for rapid and accurate detection of malachite green in aquaculture water using large-size gold nanoparticles

Abstract

Malachite green is not used in aquaculture owing to its severe toxic effects on humans and other living organisms. The presence of this compound in fishery water should be quantified and reported in all water quality reports. In this paper, a method based on surface-enhanced Raman scattering combined with partial least squares regression for rapid, accurate, reliable, and sensitive detection of trace malachite green in fishery water is described. A prepared sol of 100-nm-size gold particles was used as an active substrate for Raman spectral data acquisition. Three parameters that affect detection, namely, the volume of gold nanoparticles (400 µL), the volume of sodium chloride (300 µL), and the adsorption time (8 min) were optimized in single-factor experiments. Under the optimal condition, the proposed method demonstrated a wide linear range of detectable concentrations (0.01–5.0 µmol/L) and the limit of detection was 2.7 × 10−9 M. The proposed method successfully detected spiked malachite green in fishery water samples, with satisfactory recoveries and relative standard deviations. Thus, this study suggests a rapid, simple, accurate, and sensitive approach for detecting malachite green residues in fishery water samples, which is promising for wide applications in environmental monitoring and food safety.