Dealing with interferences when sensing by SERS in a fluctuating matrix – a little help from design of experiments

Surface-Enhanced Raman Scattering (SERS) is a spectroscopic technique that has been highlighted as promising for the detection of trace analytes in complex biological, environmental or food samples for about thirty years, especially with regards to the fingerprint-like characteristics of its signal. However, like with any other spectroscopy, the quantification of a target can be interfered by analytes having close-by reactivity or impairing signal transduction. Moreover, these interfering species are likely to display fluctuating concentrations in complex samples, turning the robust calibration task into a gigantic workload. In this work, we present an original and cost-effective methodology to deal with numerous potential interferences when calibrating SERS sensors in complex matrices of fluctuating composition. Our step-wise procedure consists in first calibrating the sensor in a matrix of average composition, then pinpointing the significantly interfering species by testing the prediction ability of the sensor in matrices having non-averaged compositions and finally, building the robustness of the sensor against those problematic species only, using a limited set of calibration samples. We apply this methodology in the case of a SERS senor of Zn2+ ions in synthetic matrices having representative compositions of the Seine river.