Confocal Raman microspectroscopy combined with chemometrics as a discrimination method of clostridia and serotypes of Clostridium botulinum strains

Abstract

Rapid and accurate identification of Clostridium botulinum is of great importance because it has been considered as an emerging food-borne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. This study demonstrates that confocal Raman microspectroscopy (CRM) combined with chemometrics can serve as a fast, reliable, and nondestructive method for detection and identification of C. botulinum at both species and serotypes level without any laborious pre-treatments. Three significant bacillus pathogens including C. botulinum, C. perfringens, and C. difficile were investigated with CRM. Additionally, two main C. botulinum strains causing botulism, C. botulinum type A, and C. botulinum type B were examined with CRM. Principal component analysis (PCA) was performed to differentiate the three species. PCA and linear discrimination analysis (LDA) were used for serotyping C. botulism strains. Four common and important preprocessing methods including Savitzky–Golay algorithm smoothing (SG), standard normal variate (SNV), multivariate scatter correction (MSC), and Savitzky–Golay algorithm 1st Derivative (SG 1st Der) were applied to improve the accuracy of identification and explore the impact of various single preprocessing methods on the model. The results proved that CRM coupled with chemometrics can be utilized for fast, reliable, and nondestructive identification of clostridia and serotypes of C. botulinum strains. This study proves for the first time that the CRM combined with chemometrics methods can be used as a potential means to detect and identify C. botulinum.