RT journal article T1 Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics A1 Falatová, Barbara A1 Ferreiro González, Marta A1 Pérez Calle, José Luis A1 Álvarez Saura, José Ángel A1 Palma Lovillo, Miguel A2 Química Analítica A2 Química Física K1 hierarchical cluster analysi K1 linear discriminant analysis K1 headspace– K1 mass spectroscopy electronic nose K1 fire debris K1 total ion mass spectrum K1 ignitable liquid residues K1 interferences AB Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials' guidelines (ASTM E1618) based on gas chromatography-mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace-mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates-four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate. PB MDPI SN 1424-8220 YR 2021 FD 2021-02 LK http://hdl.handle.net/10498/24605 UL http://hdl.handle.net/10498/24605 LA eng NO Consejería de Economía, Conocimiento, Empresas y Universidad. Junta de Andalucía; 2014-2020 ERDF Operational Programme DS Repositorio Institucional de la Universidad de Cádiz RD 11-nov-2026