Explanatory multivariate analysis of ToF-SIMS spectra for the discrimination of bacterial isolates.
Multivariate analysis (PC-CVA and GA-CVA) was carried out on time-of-flight secondary ion mass spectra (ToF-SIMS) derived from 16 bacterial isolates associated with urinary tract infections, with an objective of extracting the spectral information relevant to their species-level discrimination. The use of spectral pre-processing, such as removal of the dominant peaks prior to analysis and analysis of the dominant peaks alone, enabled the identification of 37 peaks contributing to the principal components-canonical variates analysis (PC-CVA) discrimination of the bacterial isolates in the mass range of m/z 1-1000. These included signals at m/z 70, 84, 120, 134, 140, 150, 175 and 200. A univariate statistical analysis (Kruskal-Wallis) of the signal intensities at the identified m/z enabled an understanding of the discriminatory basis, which can be used in the development of robust parsimonious models for predictive purposes. The utility of genetic algorithm (GA)-based feature selection in identifying the discriminatory variables is also demonstrated. A database search of the identified signals enabled the biochemical origins of some these signals to be postulated.
School of Chemical Engineering and Analytical Science, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, UK M1 7ND. S.Vaidyanathan@sheffield.ac.uk
Seetharaman
S
Vaidyanathan
John S
JS
Fletcher
Roger M
RM
Jarvis
Alex
A
Henderson
Nicholas P
NP
Lockyer
Royston
R
Goodacre
John C
JC
Vickerman
D000465:Algorithms; D001419:Bacteria / Q000737:chemistry* / Q000145:classification* / Q000302:isolation & purification; D015373:Bacterial Typing Techniques / Q000379:methods*; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D013045:Species Specificity; D018629:Spectrometry, Mass, Secondary Ion