Refractive Index Spectral Fingerprints of Pathogenic Bacteria Revealed by Monte Carlo‐Optimized Opto‐Microfluidic Extinction Spectroscopy

Rapid optical differentiation of bacterial species remains challenging in turbid biological media due to strong scattering and absorption. Here, we present a physics-informed, label-free biophotonic framework for bacterial identification based on refractive index (RI) spectral signatures. An opto-microfluidic PDMS chip integrating air mirrors, microlenses, and fiber-optic probes was developed to measure transmission spectra from eight clinically relevant ESKAPEE pathogens. To address the inverse problem of optical parameter retrieval, a hybrid modeling approach combining Monte Carlo photon transport, Mie scattering theory, and a genetic algorithm was implemented to reconstruct wavelength-dependent RI spectra. The reconstructed spectra reveal clear interspecies differences, with higher RI values for Escherichia coli and Klebsiella pneumoniae and lower values for Staphylococcus aureus. These physics-derived RI features were subsequently used for classification using a one-dimensional convolutional neural network, achieving 97.75% accuracy. This approach demonstrates the potential of RI spectral fingerprints for label-free microbial identification and on-chip biophotonic diagnostics.