Identification of Listeria Species Using a Low-Cost Surface-Enhanced Raman Scattering System With Wavelet-Based Signal Processing

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.