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. The algorithm is user-friendly; it involves a minimal number of tunable parameters and would be useful for analysis of a large and complicated FTIR dataset.