UC Irvine

We demonstrate the advantage of using machine learning for surface enhanced Raman scattering (SERS) spectral analysis for quantitative detection of pyocyanin in Luria-Bertani media. Planar Au nanoparticle clusters were selfassembled on PS-b-PMMA diblock copolymer template using EDC crosslinking chemistry and electrohydrodynamic flow to fabricate SERS substrates. Resulting substrates produce uniform SERS response over large area with signal relative standard deviation of 10.8 % over 50 μm × 50 μm region. Taking advantage of the uniformity, 400 SERS spectra were collected at each pyocyanin concentration as training dataset. Tracking the intensity of pyocyanin 1350 cm-1 vibrational band shows linear regime beginning at 10 ppb. PLS analysis was also performed on the same training dataset. Without being explicitly "told" which spectrum to look for, PLS analysis recognizes the SERS spectrum of pyocyanin as its first loading vector even in the presence of other molecules in LB media. PLS regression enables quantitative detection at 1 ppb, 1 order of magnitude earlier than univariate regression. We hope this work will fuel a push toward wider adoption of more sophisticated machine learning algorithms for quantitative analysis of SERS spectra.