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Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

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Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns.

Iyll-Joon Doh¹, Diana Vanessa Sarria Zuniga², Sungho Shin³

¹Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

A new hyperspectral elastic light-scatter phenotyping instrument (HESPI) accurately identifies microbial organisms. This advanced technique improves pathogen screening by analyzing multiple light-scatter patterns, achieving up to 95.9% classification accuracy.

Keywords:

bacterial colony phenotyping bacterial identification elastic light scattering hyperspectral imaging light diffraction optical sensing supercontinuum laser

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Area of Science:

Microbiology
Spectroscopy
Instrumentation

Background:

Elastic light-scatter (ELS) is effective for microbial identification based on colony light-scatter patterns.
Multispectral approaches enhance ELS accuracy, necessitating advanced instrumentation.
Pathogen screening relies on accurate and efficient microbial detection methods.

Purpose of the Study:

To design and validate a hyperspectral elastic light-scatter phenotyping instrument (HESPI).
To leverage broad-spectrum excitation and rapid wavelength selection for enhanced microbial analysis.
To improve microbial classification accuracy using comprehensive spectral data.

Main Methods:

Developed a HESPI utilizing a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF).
Collected hyperspectral ELS patterns from bacterial colonies of green-leafed vegetables.
Applied feature reduction and selection algorithms to manage large hyperspectral datasets.

Main Results:

Single-wavelength ELS classification accuracy ranged from 88.7% to 93.2% (473-709 nm).
Hyperspectral ELS analysis, with feature reduction, achieved an overall classification rate of 95.9%.
The HESPI demonstrated robust performance in classifying microflora.

Conclusions:

Hyperspectral ELS analysis significantly enhances microbial classification accuracy compared to single-wavelength methods.
The HESPI provides a powerful tool for microbial identification and pathogen screening.
Feature reduction is crucial for effectively utilizing complex hyperspectral data in microbial analysis.