We investigated the ability to distinguish between six species within the Listeria genus (including the human pathogen Listeria monocytogenes) based on a bacteria sample's surface-enhanced Raman scattering (SERS) spectrum. Our measurement system consists of a portable low-cost Raman spectral acquisition unit and associated signal processing and classification modules. First, Listeria was cultured and then adsorbed onto silver colloidal nanoparticles for SERS measurements. A total of 483 SERS spectra were collected and preprocessed (using a stationary wavelet transform decomposition) to remove noise and baseline artifact. Distinguishing features were extracted by retaining detail wavelet coefficients of significant value across multiple scales. Using a linear classifier in association with "leave one out"cross-validation, the system achieved maximum classification accuracies of 96.1% (six-category) and 97.9% (two-category, L. monocytogenes versus all others). Dimensionality reduction was used to decrease the number of features from 74 to 5 while maintaining similar classification accuracy. In the future, it is envisioned that a measurement system such as this, which is a combination of low-cost hardware with sophisticated signal processing, could play a complementary role with existing methods in realizing a rapid inexpensive means of identifying food-borne bacterial pathogens.

Additional Metadata
Keywords Biomedical signal processing, Feature extraction, Listeria, Pattern classification, Raman spectroscopy, Surface-enhanced Raman scattering (SERS), Wavelet transforms
Persistent URL dx.doi.org/10.1109/TIM.2009.2019317
Journal IEEE Transactions on Instrumentation and Measurement
Citation
Green, G.C. (Geoffreu C.), Chan, A, Luo, B.S. (B. Steven), Dan, H. (Hanhong), & Lin, M. (Min). (2009). Identification of listeria species using a low-cost surface-enhanced raman scattering system with wavelet-based signal processing. IEEE Transactions on Instrumentation and Measurement, 58(10), 3713–3722. doi:10.1109/TIM.2009.2019317