Abstract
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.
Highlights
Foodborne pathogens are among the chief culprits of foodborne diseases, which can seriously threaten the life of human beings [1,2,3]
To establish a more adaptable model and predict the bacterial species without restriction of any medium, this paper proposes the classification of bacterial colonies on different agar media based on hyperspectral imaging
In the range of 400–580 nm, the spectral reflectance of the same bacterial colonies cultured on tryptone soy agar (TSA) medium is the smallest compared to the other two media, and the spectral reflectance of the same bacterial colonies cultured on Luria–Bertani agar (LA) medium is the largest compared to the other two media
Summary
Foodborne pathogens are among the chief culprits of foodborne diseases, which can seriously threaten the life of human beings [1,2,3]. The existing food-borne pathogen detection methods mainly include traditional plate culture detection methods, immunological-based detection methods (including fluorescent antibody detection method [5] and enzyme-linked immunosorbent assays [6], etc.), molecular biology-based detection methods Molecules 2020, 25, 1797 polymerase chain reaction [7] and biological gene chip methods [8], etc.). It is of great significance to develop a rapid, non-destructive and efficient method for classifying foodborne pathogens. As a fast and non-destructive detection technology, hyperspectral technology has been widely used to analyze internal physical structure and biochemical composition information of biological samples [9]. Giovanni Turra et al [11] achieved the discrimination of five urinary tract infection pathogens cultured on blood agar plates based on hyperspectral techniques.
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