Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns

Author:

Doh Iyll-Joon1ORCID,Zuniga Diana Vanessa Sarria2,Shin Sungho3,Pruitt Robert E.2,Rajwa Bartek4ORCID,Robinson J. Paul35,Bae Euiwon1ORCID

Affiliation:

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

2. Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USA

3. Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA

4. Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA

5. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA

Abstract

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473–709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Funder

U.S. Department of Agriculture, Agricultural Research Service

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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