An algorithm for automated bacterial identification using matrix-assisted laser desorption/ionization mass spectrometry

An algorithm for bacterial identification using matrix, assisted laser deso rption/ionization (MALDI) mass spectrometry is being developed. This mass s pectral fingerprint comparison algorithm is fully automated and statistical ly based, providing objective analysis of samples to be identified. Based o n extraction of reference fingerprint ions from test spectra, this approach should lend itself well to real-world applications where samples are likel y to be impure. This algorithm is illustrated using a blind study. In the s tudy, MALDI-MS fingerprints for Bacillus atrophaeus ATCC 49337, Bacillus ce reus ATCC 14579(T), Escherichia coli ATCC 33694, Pantoea agglomerans ATCC 3 3243, and Pseudomonas putida F1 are collected and form a reference library. The identification of test samples containing one or more reference bacter ia, potentially mixed with one species not in the library (Shewanella alga BrY), is performed by com parison to the reference library with a calculate d degree of association. Out of 60 samples, no false positives are present, and the correct identification rate is 75%, Missed identifications are lar gely due to a weak B. cereus signal in the bacterial mixtures. Potential mo difications to the algorithm are presented and result in a higher than 90% correct identification rate for the blind study data, suggesting that this approach has the potential for reliable and accurate automated data analysi s of MALDI-MS.