Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 1 May 2024 (07:38:51 CEST)
How to cite:
Yankova, Y. Y.; Cirstea, S.; Cole, M.; Warren, J. Identification and Discrimination of Petrol Sources by Nuclear Magnetic Resonance Spectroscopy and Machine Learning in Fire Debris Analysis. Preprints2024, 2024050031. https://doi.org/10.20944/preprints202405.0031.v1
Yankova, Y. Y.; Cirstea, S.; Cole, M.; Warren, J. Identification and Discrimination of Petrol Sources by Nuclear Magnetic Resonance Spectroscopy and Machine Learning in Fire Debris Analysis. Preprints 2024, 2024050031. https://doi.org/10.20944/preprints202405.0031.v1
Yankova, Y. Y.; Cirstea, S.; Cole, M.; Warren, J. Identification and Discrimination of Petrol Sources by Nuclear Magnetic Resonance Spectroscopy and Machine Learning in Fire Debris Analysis. Preprints2024, 2024050031. https://doi.org/10.20944/preprints202405.0031.v1
APA Style
Yankova, Y. Y., Cirstea, S., Cole, M., & Warren, J. (2024). Identification and Discrimination of Petrol Sources by Nuclear Magnetic Resonance Spectroscopy and Machine Learning in Fire Debris Analysis. Preprints. https://doi.org/10.20944/preprints202405.0031.v1
Chicago/Turabian Style
Yankova, Y. Y., Michael Cole and John Warren. 2024 "Identification and Discrimination of Petrol Sources by Nuclear Magnetic Resonance Spectroscopy and Machine Learning in Fire Debris Analysis" Preprints. https://doi.org/10.20944/preprints202405.0031.v1
Abstract
Abstract: Petrol is considered the most common fire accelerant. However, the identification and classification of petrol sources through the years has been proven to be a challenging field in the investigation of fire debris analysis. This research explored the possibility of identifying petrol sources by high field NMR methods accompanied by ML (Machine Learning). The automated identification and classification of petrol brands were achieved for first time based on the ML clas-sification model developed in this research. A hierarchical classification model was constructed using local classifiers to categorize neat or weathered petrol into its sources.
Keywords
Machine Learning; petrol; fire investigation; NMR; MATLAB
Subject
Chemistry and Materials Science, Analytical Chemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
