Peak correlation classifier (PCC) applied to FTIR spectra: a novel means of identifying toxic substances in mixtures

Abstract

Fourier transform infrared (FTIR) spectrometry is commonly used for the identification of reference substances (RSs) in solid, liquid, or gaseous mixtures. An expert is generally required to perform the analysis, which is a bottleneck in emergency situations. This study proposes a support vector machine (SVM)‐based algorithm, the peak correlation classifier (PCC), designed to rapidly detect the presence of a specific threat or reference substance in a sample. While SVM has been used in various spectrographic contexts, it has rarely been used on FTIR spectra. The proposed algorithm discovers correlation similarities between the FTIR spectrum of the RS and the test sample and then uses SVM to determine whether or not the RS is present in the sample. The study also shows how the additive nature of FTIR spectra can be used to create ‘synthetic’ substances that significantly improve the detection capability and decision confidence of the SVM classifier.

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“…For the analysis part, it does signal smoothing and identification through comparison to databases. Generally, in the literature, FTIR analysis is dominated by the need to detect a particular substance inside the sample, according to its known spectral signature [19, 20].…”