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Open AccessArticle

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning

1
Department of Mechatronics Engineering, College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2
Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
3
Department of Preventive Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Molecules 2020, 25(8), 1797; https://doi.org/10.3390/molecules25081797
Received: 13 February 2020 / Revised: 14 March 2020 / Accepted: 17 March 2020 / Published: 14 April 2020
(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)
A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria–Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification. View Full-Text
Keywords: bacterial pathogens; Visible-Near-infrared hyperspectral imaging; grasshopper optimization algorithm; support vector machine; variable selection; optimization; bacterial contamination bacterial pathogens; Visible-Near-infrared hyperspectral imaging; grasshopper optimization algorithm; support vector machine; variable selection; optimization; bacterial contamination
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Gu, P.; Feng, Y.-Z.; Zhu, L.; Kong, L.-Q.; Zhang, X.-L.; Zhang, S.; Li, S.-W.; Jia, G.-F. Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning. Molecules 2020, 25, 1797.

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