Identification of putative volatile biomarkers of canine leishmaniasis in dog's breath and hair employing a novel algorithm for automated chromatographic peak detection and matching.

The analysis of the volatile compounds released by biological samples represents a promising approach for the non-invasive diagnosis of a disease. The present study, focused on a population of dogs infected with canine leishmaniasis, aimed to decipher the volatolomic profile associated with this disease in dogs, which represent the main animal reservoir for Leishmania pathogen transmission to humans. The volatiles emitted by the breath and hair of dogs were analysed employing the gas chromatography-mass spectrometry (GC-MS) technique.

An automated approach for analysis of Fourier Transform Infrared (FTIR) spectra of edible oils.

This paper reports a computational approach for analysis of FTIR spectra where peaks are detected, assigned and matched across samples to produce a peak table with rows corresponding to samples and columns to variables. The algorithm is applied on a dataset of 103 spectra of a broad range of edible oils for exploratory analysis and variable selection using Self Organising Maps (SOMs) and t-statistics, respectively. Analysis on the resultant peak table allows the underlying patterns and the discriminatory variables to be revealed.