FT-IR spectroscopy for identification of closely related lactobacilli

Abstract

Fourier Transform Infrared (FT-IR) spectroscopy was used to analyse 56 strains from four closely related species of Lactobacillus, L. sakei, L. plantarum, L. curvatus and L. paracasei. Hierarchical Cluster Analysis (HCA) was used to study the clusters in the data, but in the dendrogram, the spectra were not differentiated into four separate clusters corresponding to species. When the data were analysed with Partial Least Squares Regression (PLSR), the strains were differentiated into four clusters according to species. It was also possible to recognise strains that were incorrectly identified by conventional methods prior to the FT-IR analysis. PLSR was used to identify strains from three of the species, and the results were compared to two other multivariate methods, Soft Independent Modelling of Class Analogy (SIMCA) and K-Nearest Neighbour (KNN). The three methods gave equally good identification results. The results show that FT-IR spectroscopy in combination with PLSR, or other multivariate methods, is well suited for identification of Lactobacillus at the species level, even in quite large data sets.