Mass
spectrometry in parallel with real-time machine learning techniques
were paired in a novel application to detect and identify chemically
specific, early indicators of fires and near-fire events involving
a set of selected materials: Mylar, Teflon, and poly(methyl methacrylate)
(PMMA). The volatile organic compounds emitted during the thermal
decomposition of each of the three materials were characterized using
a quadrupole mass spectrometer which scanned the 1–200 m/z range. CO2, CH3CHO, and C6H6 were the main volatiles detected
during Mylar thermal decomposition, while Teflon’s thermal
decomposition yielded CO2 and a set of fluorocarbon compounds
including CF4, C2F4, C2F6, C3F6, CF2O, and CF3O. PMMA produced CO2 and methyl methacrylate (MMA,
C5H8O2). The mass spectral peak patterns
observed during the thermal decomposition of each material were unique
to that material and were therefore useful as chemical signatures.
It was also observed that the chemical signatures remained consistent
and detectable when multiple materials were heated together. Mass
spectra data sets containing the chemical signatures for each material
and mixtures were collected and analyzed using a random forest panel
machine learning classification. The classification was tested and
demonstrated 100% accuracy for single material spectra and an average
of 92.3% accuracy for mixed material spectra. This investigation presents
a novel technique for the real-time, chemically specific detection
of fire related VOCs through mass spectrometry which shows promise
as a more rapid and accurate method for detecting fires or near-fire
events.