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

© The Institution of Engineering and Technology
Received 06/12/2019, Accepted 12/11/2020, Revised 12/11/2020, Published 05/01/2021

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.

Inspec keywords: computerised instrumentation; emergency services; signal classification; support vector machines; correlation methods; Fourier transform infrared spectra

Other keywords: solid mixtures; liquid mixtures; reference substances; support vector machine; peak correlation classifier; PCC; FTIR spectrum; FTIR spectra; gaseous mixtures; detection capability; Fourier transform infrared spectrometry; decision confidence; FTIR spectrometry; toxic substances; emergency situations; correlation similarities; synthetic substances; SVM classifier

Subjects: Computerised instrumentation; Support vector machines; Digital signal processing; Signal processing and detection; Public administration; Computerised instrumentation

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