Development of a cost-effective dual-mode hyperspectral imaging system with machine learning for enhanced bacterial classification

This work presents the development of a cost-effective dual-mode hyperspectral imaging (HSI) system integrated with machine-learning models to detect and classify bacteria with enhanced accuracy. The HSI system was constructed using commercial off-the-shelf components and 3D-printed parts, with detailed optical simulations performed to aid in the design and validate the system’s performance. A compound prism-grating-prism was implemented in an on-axis spectrograph configuration to simplify the optical assembly and minimize field-dependent aberrations. The system supports wide-field HSI in both reflectance and fluorescence modes, illuminated by chip-on-board LED sources with a visible-to-near-infrared spectrum, and a narrow-band UV. The determined spectral resolution of the custom HSI system was 1.55 nm, while the spatial resolutions were approximately 0.81 mm and 0.49 mm for in-track and cross-track directions, sufficient for spatio-spectral imaging of bacterial colonies. Furthermore, a model-training framework leveraging spectral feature fusion from both modes was developed to classify bacterial species, S. aureus and P. aeruginosa. The classification accuracies achieved using reflectance, fluorescence, and dual modes were 92.55%, 93.48%, and 97.11%, respectively. This dual-mode optical-computational platform not only demonstrates enhanced classification accuracy but also represents a scalable and economical solution for high-throughput bacterial identification.