Ion beam-induced nanoripples patterns for SERS based saliva analysis to detect oral cavity cancer

Abstract

Saliva is a non-invasive sample for oral cancer detection due to the presence of cancer biomarkers. In this reported study, we investigated the potential use of SERS (Surface-Enhanced Raman Spectroscopy) for the non-invasive identification of oral cavity cancer by analysing the SERS spectra of saliva. We developed a SERS substrate by depositing Ag nanoparticles on glass ripples. SERS analysis was conducted on saliva samples collected from 10 oral cancer patients and 8 healthy volunteers having tobacco habits. The average spectra of cancer patient saliva and healthy volunteers’ saliva show differences. Principal component analysis (PCA) and Linear discriminant analysis (LDA)-based multivariate statistical analysis were used for classifying the collected spectra. The PCA-LDA-based classification shows 70% sensitivity and 62.5% specificity. This study suggests that SERS analysis of saliva incorporated with PCA-LDA-based multivariate analysis is a potential tool for the non-invasive detection of cancer.