The automation of spectral classification tasks has made machine learning models essential analytical tools. However, the complexity of hyperparameter tuning limits the practical use, particularly for novices. This study applies these classifiers to identify bacteria using surface-enhanced Raman spectroscopy (SERS), offering a rapid and non-invasive alternative to the gold standard, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). An evolutionary algorithm was employed to optimize the hyperparameters of 10 machine learning models. We found the topperforming model for the classification of the SERS spectra of E. coli and S. pneumoniae water suspensions. This approach yielded a test accuracy of 95.8%, 100%, 100% when using the Bernoulli Naïve Bayes, Support Vector Machine, and Multilayer Perceptron models, respectively. This demonstrates the potential of self-optimizing machine learning models as accessible analytical tools for diverse classification tasks in biophotonics. This automated approach extends to identify various samples and data structures, not just pathogens’ spectra.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.