Abstract
Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive spectroscopic technique that generates signal-enhanced fingerprint vibrational spectra of small molecules. However, without rigorous control of SERS substrate active sites, geometry, surface area, or surface functionality, SERS is notoriously irreproducible, complicating the consistent quantitative analysis of small molecules. While evaporatively prepared samples yield significant SERS enhancement resulting in lower detection limits, the distribution of these enhancements along the SERS surface is inherently stochastic. Acquiring spatially resolved SERS spectra of these dried surfaces, we have shown that this enhancement is governed by a power law as a function of analyte concentration. Consequently, by definition, there is no true mean of SERS enhancement, requiring an alternative approach to achieve reproducible quantitative results. In this study, we introduce a new method of analysis of SERS data using a cumulative distribution function (CDF). The antiviral drug tenofovir (TFV) in an aqueous matrix was quantified down to a clinically relevant concentration of 25 ng/mL using hydroxylamine-reduced silver colloids evaporated to dryness. The data presented in this study provide a rationale for the benefits of combining a novel statistical approach using CDFs with simple and inexpensive experimental techniques to increase the precision, accuracy, and analytical sensitivity of aqueous TFV quantification by SERS. TFV calibration curves generated using CDF analysis showed higher analytical sensitivity (in the form of a normalized calibration curve average slope increase of 0.25) compared to traditional SERS intensity calculations. A second aliquot of nanoparticles and analyte dried on the SERS surface followed by CDF analysis showed further analytical sensitivity with a normalized calibration curve slope increase of 0.23 and decreased variation among replicates represented by an average standard deviation decrease of 0.02 with a second aliquot. The quantitative analysis of SERS data using CDFs presented here shows promise to be a reproducible method for quantitative analysis of SERS data, a significant step toward implementing SERS as an analytical method in clinical and industrial settings.