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Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging.

Jun-Li Xu¹, Ana Herrero-Langreo¹, Sakshi Lamba^2,3

¹School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.

Molecules (Basel, Switzerland)

|October 23, 2021

Summary

This summary is machine-generated.

Reflectance Fourier transform infrared (FTIR) imaging rapidly distinguishes between Bacillus subtilis and Escherichia coli bacteria on metal surfaces. Support vector machine models achieved high accuracy, demonstrating potential for food processing applications.

Keywords:

FTIR classification foodborne bacteria machine learning stainless steel

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Area of Science:

Analytical Chemistry
Microbiology
Spectroscopy

Background:

Rapid and non-invasive bacterial detection is crucial for food safety.
Fourier transform infrared (FTIR) spectroscopy offers potential for microbial analysis.

Purpose of the Study:

To investigate reflectance FTIR microscopic imaging for differentiating Bacillus subtilis and Escherichia coli.
To evaluate classification performance on stainless steel and aluminum substrates across various concentrations.

Main Methods:

Dried bacterial cell suspensions (Bacillus subtilis, Escherichia coli) on metallic substrates (stainless steel, aluminum).
Reflectance FTIR microscopic imaging with optical density (OD) range of 0.001 to 10.
Support vector machine (SVM) classification modeling with two distinct strategies.

Main Results:

Accurate classification (96% accuracy on STS, 91% on Al) achieved for OD ≥ 0.1.
Substrate transferability demonstrated (STS model to Al: 82% accuracy), but not vice versa.
Models trained on moderate concentrations (1 OD) generalized well to other concentrations.

Conclusions:

Reflectance FTIR is a feasible method for rapid, non-invasive classification of dried bacterial cells on relevant food processing substrates.
SVM modeling provides robust classification, with concentration and substrate influencing model performance and transferability.