Recurrent Neural Network-based Classification and Optimization of Pathogen Raman Spectra with LASSO Dimensionality Reduction

Enhancing biosecurity measures through the advanced classification of pathogens via Raman spectroscopy is pivotal. Unlike traditional pathogen detection methods, Raman spectroscopy utilizes non-invasive sampling and offers high specificity. This study employs Long Short-Term Memory (LSTM) networks, an advanced form of Recurrent Neural Networks (RNNs), to analyze Raman spectra from various foodborne pathogens. Leveraging the LSTM’s ability to process sequential data, we have developed a novel approach that optimizes pathogen detection in food samples. This model integrates spectral data processing with sophisticated LSTM algorithms to precisely identify pathogenic signatures. Preliminary results demonstrate that our LSTM-based model significantly outperforms traditional classification methods. The improvement is attributed to the LSTM’s capability to effectively capture the spectral nuances and temporal dependencies within the data. Moreover, we introduce an optimization technique that enhances the model’s predictive accuracy, ensuring robust pathogen detection across diverse conditions. This research not only provides a more efficient method for pathogen classification using Raman spectroscopy but also opens new avenues for the application of neural networks in biosecurity and public health.