Improving Protein Quantification with SERS Superspectra and Machine Learning.

Quantitative protein analysis by surface-enhanced Raman spectroscopy (SERS) remains challenging due to weak and heterogeneous protein adsorption on plasmonic surfaces. Here, we introduce a superspectra-guided SERS framework that leverages chemically distinct interaction environments to enhance quantitative performance. Silver nanorod (AgNR) substrates were functionalized with cysteamine (CM), cysteine (CN), and 6-mercapto-1-hexanol (MCH), together with unmodified (B) AgNRs, to create surfaces that probe complementary aspects of protein-surface interactions through charge- and chemistry-dependent binding. Using bovine serum albumin (BSA) as a model protein, we systematically constructed superspectra by concatenating SERS signals from all single-, pairwise-, triple-, and four-surface combinations and evaluated their performance using support vector regression (SVR) and random forest regression (RFR). Our results reveal that superspectra must be constructed selectively: single-substrate spectra lack sufficient chemical diversity, and superspectra incorporating all four surfaces often degrade accuracy due to noninformative or conflicting features, particularly those introduced by MCH. In contrast, superspectra derived from complementary surface chemistries, especially the CM&CN pair or the B&CM&CN triplet, yield markedly improved quantitative predictions. RFR consistently outperformed SVR, demonstrating superior robustness for integrating chemically heterogeneous spectral inputs. This work establishes, for the first time, design principles for constructing effective superspectra for protein SERS and highlights the importance of analyte-surface interaction complementarity in enabling accurate, scalable protein quantification.