Digital Surface‐Enhanced Raman Scattering With Event Counting and Spectrum Learning for Label‐Free Protein Quantification

Accurate quantification of low‐abundance biomolecules remains a challenge for rapid or label‐free diagnostics, especially at clinically relevant concentrations. Here, we develop a statistical and machine learning‐assisted surface‐enhanced Raman scattering (SERS) framework for label‐free quantification of phosphorylated tau‐181 (p‐tau181), a blood biomarker of neurodegeneration. We integrate digital SERS event counting with full‐spectrum artificial neural network (ANN) regression to achieve sensitive and interpretable concentration estimates from simple plasmonic substrates in a ~3‐h workflow. In the digital layer, individual spectra are classified as “on” or “off” based on background‐adjusted signal thresholds, and the on‐fraction is modelled using a binomial generalized linear model to derive limits of detection (LoD) and quantification (LoQ). This probabilistic treatment suppresses hotspot‐driven intensity variance and enables reliable detection deep in the sparse‐occupancy regime. In parallel, a feed‐forward ANN trained on aggregated, minimally processed spectra learns multiband Raman signatures and outputs calibrated, continuous concentration predictions with isotonic alignment. Benchmarking with rhodamine 6G yielded LoD ≈ 30 pM and LoQ ≈ 55 pM; label‐free p‐tau181 achieved LoD ≈ 8 pM (460 pg/mL) and LoQ ≈ 20 pM (1140 pg/mL) from digital SERS, and R 2 ≈ 0.97 with LoD/LoQ ≈ 20 pM/70 pM via ANN regression. Both approaches identified characteristic protein bands at ~716 and ~1003 cm −1 as key predictive features, confirming chemically specific detection. Together, these complementary layers provide statistically grounded, reproducible quantification from raw spectra without hand‐engineered peak selection. By maintaining interpretability and scalability, the approach outlines a practical path toward quantitative, label‐free biomarker assays suitable for translational and longitudinal applications.