The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. The first stage in the classification of Raman spectra is commonly some form of preprocessing. This pre-processing greatly affects the accuracy of the results and introduces user bias and over-fitting effects. In this paper, we propose the use of Support Vector Machines with a novel correlation kernel. Results, obtained from the analysis of Raman spectra of bacteria, illustrate that the correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing, even on highly-noisy data obtained using inexpensive equipment. In addition, the performance does not degrade when applied to distinct test sets, a key feature of a clinically viable diagnostic application of Raman Spectroscopy.

Date Published: 21 February 2011
PDF: 7 pages
Proc. SPIE 7890, Advanced Biomedical and Clinical Diagnostic Systems IX, 78901B (21 February 2011); doi: 10.1117/12.873308

Published in SPIE Proceedings Vol. 7890:
Advanced Biomedical and Clinical Diagnostic Systems IX
Anita Mahadevan-Jansen; Tuan Vo-Dinh; Warren S. Grundfest M.D., Editor(s)