Five neat diesel samples were analyzed by gas chromatography-mass spectrometry and total ion chromatograms as well as extracted ion profiles of the alkane and aromatic compound classes were generated. A retention time alignment algorithm was employed to align chromatograms prior to peak area normalization. Pearson product moment correlation coefficients and principal components analysis were then employed to investigate association and discrimination among the diesel samples. The same procedures were also used to investigate the association of a diesel residue to its neat counterpart. Current limitations in the retention time alignment algorithm and the subsequent effect on the association and discrimination of the diesel samples are discussed. An understanding of these issues is crucial to ensure the accuracy of data interpretation based on such chemometric procedures.

No abstract

Forensic fire debris analysis focuses on the identification of a foreign ignitable liquid in debris collected from the scene of a suspected intentional fire. Chromatograms of the extracted debris are compared to a suitable reference collection containing chromatograms of unevaporated and evaporated ignitable liquids. However, there is no standardized method for the evaporation of ignitable liquids and the process itself can be time consuming, which limits the number of chromatograms of evaporated liquids included in the reference collection. This work describes the development and application of a variable-temperature kinetic model to predict evaporation rate constants and mathematically predict chromatograms corresponding to evaporated ignitable liquids. First-order evaporation rate constants were calculated for 78 selected compounds in diesel, which were used to develop predictive models of evaporation rates. Fixed-temperature models were developed to predict the rate constants at five temperatures (5, 10, 20, 30, 35 °C), yielding a mean absolute percent error (MAPE) of 10.0%. The variable-temperature model was then created from these data by multiple linear regression, yielding a MAPE of 16.4%. The model was applied to generate a reference collection of predicted chromatograms of diesel and kerosene corresponding to a range of evaporation levels. Using the modeled reference collection, successful identification of the liquid and level of evaporation in a test set of chromatograms was demonstrated.

No abstract