Micro confocal Raman spectroscopy combined with chemometrics method for fast identification of bacteria

Due to the growing societal demand for public safety and environmental protection, there is an increasing focus on the technology of rapid on-site bacterial detection. The present study aims to establish an analytical method using microscopic confocal Raman spectroscopy combined with chemometrics to accurately identify different bacteria, thus enabling nondestructive testing. This paper examines the Raman spectra of Gram-negative bacteria Escherichia coli, Gram-positive bacteria Staphylococcus aureus, Bacillus cereus, and others. The spectra were obtained using a microscopic confocal Raman spectrometer and analyzed for their characteristics. The Matlab-based PCA method reduces the dimensionality of the data. Six pattern recognition algorithms in chemometrics, namely linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN), naive Bayesian (NB) model, classification decision tree (CT) and support vector machine (SVM), were used to classify the Raman spectral data. The results show that linear discriminant analysis and the k-nearest neighbor model achieved a recognition accuracy of 97.62%. The method has several advantages, including no damage to bacterial samples, simple operation, and ideal identification effect. It provides guidance and a basis for accurate identification of Gram-negative and Gram-positive bacteria.