Quantitative detection of acyclovir by surface enhanced Raman spectroscopy using a portable Raman spectrometer coupled with multivariate data analysis

Acyclovir (ACV) is a synthetic antiviral agent with serious side effect, particularly its nephrotoxicity, so this study was to explore the ultrasensitive detection of ACV by surface-enhanced Raman scattering (SERS). The enhancement capability of nanoparticles prepared by different chemical reduction were compared, and Ag nanoparticles reduced by citrate are the most propriate enhanced substrate for acyclovir. In addition, comparison between prominent SERS-enhanced bands and the precise mode descriptions predicted through density functional theory (DFT) simulations is used to understand the mechanisms between ACV and metallic surface. 130 different levels of ACV concentrations in a range from 10^-1∼10^-7 were used to build quantitative prediction models by two different modeling methods, partial least-squares (PLS) regression and artificial neural network (ANN). Under the optimal conditions, the performance of the PLS model was much better than ANN. The results demonstrated that SERS imaging with multivariate analysis holds great potential for the sensitive and cost effective clinic test of ACV and its metabolites in biological fluids.