# Multispectral Infrared Colony Phenotyping for High‐Throughput Microbiological Control of Waters

## TL;DR

A new infrared imaging tool can quickly and accurately identify microbes in water samples, improving water quality monitoring.

## Contribution

A prototype using DFIR multispectral imaging for rapid microbial identification on filtration membranes is developed and tested.

## Key findings

- The prototype achieved 96.5% accuracy in identifying microbial colonies from a database of 3,230 colonies.
- The system captures both spectral and morphological fingerprints of colonies without the need for labels or destructive methods.

## Abstract

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.

Microbiological water quality monitoring still relies on slow, culture‐based methods with limited specificity. We present an automated, label‐free DFIR multispectral imaging prototype that captures both spectral and morphological fingerprints of microbial colonies directly on filtration membranes. Applied to a 3,230‐colony database, it achieved 96.5% accuracy, highlighting its potential as an industry‐ready tool for rapid microbial identification.