School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Dr, Atlanta, Georgia 30318, United States.
Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Dr, Atlanta, Georgia 30313, United States.
J Am Soc Mass Spectrom. 2023 May 3;34(5):826-835. doi: 10.1021/jasms.2c00304. Epub 2023 Apr 20.
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 / range. CO, CHCHO, and CH were the main volatiles detected during Mylar thermal decomposition, while Teflon's thermal decomposition yielded CO and a set of fluorocarbon compounds including CF CF CF, CF CFO, and CFO. PMMA produced CO and methyl methacrylate (MMA, CHO). 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.
质谱分析与实时机器学习技术相结合，应用于一种新的方法，以检测和识别涉及一组选定材料（聚酯薄膜、聚四氟乙烯和聚甲基丙烯酸甲酯）的火灾和火灾临近事件的化学特异性早期指标。使用四极杆质谱仪对三种材料的热分解过程中释放的挥发性有机化合物进行了特征描述，该质谱仪扫描范围为 1-200。在聚酯薄膜热分解过程中，主要检测到 CO、CHCHO 和 CH 等挥发性物质，而聚四氟乙烯的热分解则产生 CO 和一组氟碳化合物，包括 CF CF CF、CF CFO 和 CFO。聚甲基丙烯酸甲酯产生 CO 和甲基丙烯酸甲酯（MMA，CHO）。每种材料热分解过程中观察到的质谱峰图案都是该材料特有的，因此可用作化学特征。还观察到，当多种材料一起加热时，化学特征保持一致且可检测。收集并使用随机森林面板机器学习分类分析了包含每种材料和混合物化学特征的质谱数据集。该分类方法对单一材料光谱的测试和验证达到了 100%的准确性，对混合材料光谱的平均准确性达到了 92.3%。该研究提出了一种通过质谱实时、特异性检测与火灾相关 VOC 的新方法，有望成为一种更快速、更准确的火灾或火灾临近事件检测方法。
