Classification of salivary based NS1 from Raman Spectroscopy with support vector machine

Non-Structural Protein 1 (NS1) antigen has been recognized as a biomarker for diagnosis of flavivirus viral infections at early stage. Surface Enhanced Raman Spectroscopy (SERS) is an optical technique capable of detecting up to a single molecule. Our previous work has established the Raman fingerprint of NS1 with gold as substrate. Our current study aims to classify NS1 infected saliva samples from healthy samples, a first ever attempt. Saliva samples from healthy subjects, NS1 protein and NS1-saliva mixture samples were analyzed using SERS. The SERS spectra were then pre-processed prior to classification with support vector machine (SVM). NS1-saliva mixture at concentration of 10ppm, 50ppm and 100ppm were examined. Performance of SVM classifier with linear, polynomial and radial basis function (RBF) kernels were compared, in term of accuracy, sensitivity, and specificity. From the results, it can be concluded that SVM classifier is able to classify the samples into NS1 infected samples and normal saliva samples. Of the three kernels, performance in using polynomial and RBF kernel is found surpassing the linear kernel. The best performance is attained with RBF kernel with accuracy of [97.1% 93.4% 81.5%] for 100ppm, 50ppm and 10ppm respectively.