Multispectral Infrared Colony Phenotyping for High-Throughput Microbiological Control of Waters

Microbiological water quality assessment relies on culture-based methods that are time-consuming, resource-intensive, and often lack specificity. To address these limitations, we developed a prototype for automated, label-free, and nondestructive microbial identification based on discrete frequency infrared (DFIR) multispectral imaging. By combining monochromatic quantum cascade lasers (QCLs) with an uncooled bolometer array, this prototype captures spectral and morphological fingerprints of colonies directly on filtration membranes. A demonstration database of 3230 colonies from 11 strains across 7 genera was acquired. In average, deep-learning based classification achieved a 96.5% ± 1.3% correct identification rate. Overall, this prototype brings DFIR imaging one step closer to an industry-ready microbial identification tool.