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Article

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging

1
School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
2
School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland
3
Institute of Food and Health, University College Dublin, Belfield, Dublin 4, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Barry K. Lavine
Molecules 2021, 26(20), 6318; https://doi.org/10.3390/molecules26206318
Received: 9 September 2021 / Revised: 6 October 2021 / Accepted: 9 October 2021 / Published: 19 October 2021
(This article belongs to the Special Issue New Insights into Vibrational Spectroscopy and Imaging)
This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing. View Full-Text
Keywords: FTIR; foodborne bacteria; classification; machine learning; stainless steel FTIR; foodborne bacteria; classification; machine learning; stainless steel
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MDPI and ACS Style

Xu, J.-L.; Herrero-Langreo, A.; Lamba, S.; Ferone, M.; Scannell, A.G.M.; Caponigro, V.; Gowen, A.A. Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging. Molecules 2021, 26, 6318. https://doi.org/10.3390/molecules26206318

AMA Style

Xu J-L, Herrero-Langreo A, Lamba S, Ferone M, Scannell AGM, Caponigro V, Gowen AA. Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging. Molecules. 2021; 26(20):6318. https://doi.org/10.3390/molecules26206318

Chicago/Turabian Style

Xu, Jun-Li, Ana Herrero-Langreo, Sakshi Lamba, Mariateresa Ferone, Amalia G. M. Scannell, Vicky Caponigro, and Aoife A. Gowen. 2021. "Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging" Molecules 26, no. 20: 6318. https://doi.org/10.3390/molecules26206318

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