Construction of classification models for pathogenic bacteria based on LIBS combined with different machine learning algorithms

• Published in: Applied optics (2004) 2022-07, Vol.61 (21), p.6177
• Main Authors: Sun, Haorui, Yang, Canran, Chen, Youyuan, Duan, Yixiang, Fan, Qingwen, Lin, Qingyu
• Format: Article
• Language: English
• Subjects: Algorithms; Back propagation; Back propagation networks; Bacteria; Classification; Laser induced breakdown spectroscopy; Machine learning; Neural networks; Pathogens; Signal to noise ratio; Support vector machines
• ISSN: 1559-128X; 2155-3165
• DOI: 10.1364/AO.463278

Bacteria, especially foodborne pathogens, seriously threaten human life and health. Rapid discrimination techniques for foodborne pathogens are still urgently needed. At present, laser-induced breakdown spectroscopy (LIBS), combined with machine learning algorithms, is seen as fast recognition technology for pathogenic bacteria. However, there is still a lack of research on evaluating the differences between different bacterial classification models. In this work, five species of foodborne pathogens were analyzed via LIBS; then, the preprocessing effect of five filtering methods was compared to improve accuracy. The preprocessed spectral data were further analyzed with a support vector machine (SVM), a backpropagation neural network (BP), and k -nearest neighbor (KNN). Upon comparing the capacity of the three algorithms to classify pathogenic bacteria, the most suitable one was selected. The signal-to-noise ratio and mean square error of the spectral data after applying a Savitzky–Golay filter reached 17.4540 and 0.0020, respectively. The SVM algorithm, BP algorithm, and KNN algorithm attained the highest classification accuracy for pathogenic bacteria, reaching 98%, 97%, and 96%, respectively. The results indicate that, with the support of a machine learning algorithm, LIBS technology demonstrates superior performance, and the combination of the two is expected to be a powerful tool for pathogen classification.