Adutwum Lawrence A, Abel Robin J, Harynuk James
Department of Chemistry, Univeristy of Alberta, Edmonton, Alberta, Canada.
J Forensic Sci. 2018 Jul;63(4):1059-1068. doi: 10.1111/1556-4029.13657. Epub 2017 Oct 10.
Alignment of fire debris data from GC-MS for chemometric analysis is challenged by highly variable, uncontrolled sample and matrix composition. The total ion spectrum (TIS) obviates the need for alignment but loses all separation information. We introduce the segmented total ion spectrum (STIS), which retains the advantages of TIS while retaining some retention information. We compare the performance of STIS with TIS for the classification of casework fire debris samples. TIS and STIS achieve good model prediction accuracies of 96% and 98%, respectively. Baseline removal improved model prediction accuracies for both TIS and STIS to 97% and 99%, respectively. The importance of maintaining some chromatographic information to aid in deciphering the underlying chemistry of the results and reasons for false positive/negative results was also examined.
气相色谱-质谱联用仪(GC-MS)用于化学计量分析的火灾碎片数据比对,因样品和基质组成高度可变且不受控制而面临挑战。总离子流图(TIS)无需比对,但会丢失所有分离信息。我们引入了分段总离子流图(STIS),它保留了TIS的优点,同时保留了一些保留信息。我们比较了STIS和TIS在实际火灾碎片样品分类中的性能。TIS和STIS分别实现了96%和98%的良好模型预测准确率。基线扣除分别将TIS和STIS的模型预测准确率提高到了97%和99%。还研究了保留一些色谱信息对于帮助解读结果背后的化学原理以及假阳性/假阴性结果原因的重要性。