Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron

The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spectral analysis of surface-enhanced Raman scattering (SERS). By performing radical SERS scanning through the coffee-ring area and sampling across the coffee ring, complicated chemical information was spatially gathered for further spectra analysis. The corresponding application in classification of serum samples from breast cancer patients was also proposed. A simulated serum environment was constructed by mixing phenylalanine, hypoxanthine, and bovine serum albumin (BSA), yielding the coffee-ring patterns along with gold nanoparticles. Distinct divergence in the distributions between hypoxanthine and phenylalanine within the rings were characterized, which is attributed to the inherent electrostatic properties of the noble metal colloid and the interactions among different solvents. Subsequently, this method was applied to serum samples from patients diagnosed with the four breast cancer subtypes. By preparing serum with SERS substrates and forming the coffee-ring patterns, radial SERS scanning was conducted across the rings. The acquired spectra were spatially segmented and processed by employing a multilayer perceptron for learning and prediction. The classification results demonstrated a predictive accuracy of 85.7% in distinguishing among the four breast cancer subtypes, highlighting the feasibility and effectiveness of the coffee-ring assisted radial SERS analysis.