SHORT COMMUNICATION Identification and classification of respiratory syncytial virus (RSV) strains by surface-enhanced Raman spectroscopy and multivariate statistical techniques S. Shanmukh & L. Jones & Y.-P. Zhao & J. D. Driskell & R. A. Tripp & R. A. Dluhy Received: 14 November 2007 / Revised: 26 December 2007 / Accepted: 8 January 2008 / Published online: 31 January 2008 # Springer-Verlag 2008 Abstract There is a critical need for a rapid and sensitive means of detecting viruses. Recent reports from our laboratory have shown that surface-enhanced Raman spectroscopy (SERS) can meet these needs. In this study, SERS was used to obtain the Raman spectra of respiratory syncytial virus (RSV) strains A/Long, B1, and A2. SERS-active substrates composed of silver nanorods were fabricated using an oblique angle vapor deposition method. The SERS spectra obtained for each virus were shown to posses a high degree of reproducibility. Based on their intrinsic SERS spectra, the four virus strains were readily detected and classified using the multivariate statistical methods principal component analysis (PCA) and hierarchical cluster analysis (HCA). The chemo- metric results show that PCA is able to separate the three virus strains unambiguously, whereas the HCA method was able to readily distinguish an A2 strain-related G gene mutant virus (ΔG) from the A2 strain. The results described here demonstrate that SERS, in combination with multivariate statistical methods, can be utilized as a highly sensitive and rapid viral identification and classification method. Keywords Virus . SERS . Detection . RSV . Multivariate statistics . Nanorod Introduction With the development of new antiviral drugs for respiratory viruses, and the need to address emerging virus infections or bioterrorism, rapid and sensitive detection methods are critical for the control and prevention of disease. Current antibody-based detection methods generally lack the sensi- tivity that is required for low-level virus detection [1, 2]. To overcome this limitation, polymerase chain reaction (PCR)- based detection assays are often used but are cumbersome, require amplification of pathogen, and are costly [3]. More recently, analytical methods such as microcantilevers [4], evanescent wave biosensors [5], immunosorbant electron microscopy [6], and atomic force microscopy [7] have been investigated as methods to overcome limitations of sensitiv- ity and complexity involved in detection assays, but these techniques are unable to discriminate between virus species with reasonable sample throughput. Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful analytical tool that extends the possi- bilities of vibrational spectroscopy to solve a vast array of chemical and biochemical problems. SERS is an extension of standard Raman spectroscopy, a vibrational spectroscopic technique that provides high structural information content [8] and one that has been used in biochemistry and in the life sciences [9]. SERS differs from standard Raman scattering in that the incoming laser beam interacts with the oscil- lations of plasmonic electrons in metallic nanostructures to enhance, by up to 14 orders of magnitude, the vibrational spectra of molecules adsorbed to the surface [10, 11]. SERS provides ultrasensitive detection limits, even approaching Anal Bioanal Chem (2008) 390:1551–1555 DOI 10.1007/s00216-008-1851-0 S. Shanmukh : J. D. Driskell : R. A. Dluhy (*) Nanoscale Science and Engineering Center, Department of Chemistry, University of Georgia, Athens, GA 30602, USA e-mail: dluhy@uga.edu Y.-P. Zhao Nanoscale Science and Engineering Center, Department of Physics and Astronomy, University of Georgia, Athens, GA 30602, USA L. Jones : R. A. Tripp Nanoscale Science and Engineering Center, Center for Disease Intervention, Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA