Differentiation of Unevaporated Gasoline Samples According to Their Brands, by SPME–GC–MS and Multivariate Statistical Analysis

Abstract

One of the aims of fire investigations is to identify associations among accelerants according to their source. In this study, 50 gasoline samples--representing five brands--were analyzed using solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). Chemometric procedures, such as principal component analysis (PCA) and discriminant analysis (DA), were applied to a data matrix obtained by the target compound chromatogram method, to discriminate samples according to their brand. PCA was successful in finding a natural grouping of samples according to their brand, suggesting that aromatic compounds were more useful than aliphatics for the purpose of this study. DA, if applied to aromatic compounds, gave both a classification ability and a prediction ability of 100%. The outstanding results obtained by this work provide the basis of a data matrix that could be used in real cases of arson to link a sample of unevaporated gasoline to its brand or refinery.

Ignitable liquid identification using gas chromatography/mass spectrometry data by projected difference resolution mapping and fuzzy rule-building expert system classification

“…A variety of methods such as covariance mapping [5,6], trace organic compound analysis by principal component analysis (PCA) and linear discriminant analysis (LDA) [7][8][9][10][11], and artificial neural networks [12,13] have been applied, which demonstrates the utility of chemometrics to forensic investigations. Data treatment by chemometric techniques can be performed more efficiently and objectively than manual operations by an expert, because the predictions are performed automatically based on statistical models and are not subject to inherent bias associated with manual interpretation.…”

Principal Component Analysis: A Powerful Interpretative Tool at the Service of Analytical Methodology

“…Therefore differences existing between the raw materials had been transferred to the final products, determining very clustered samples with consistent chemical properties (for A brand) and samples with a greater variability within the class (for D brand). The score plot of PC2 versus PC1, shown in figure 3, was obtained by Monfreda and Gregori (2011). In the study presented here, 25 diesel samples belonging to the same 5 brands studied by Monfreda and Gregori were analysed using the same analytical procedure, SPME-GC-MS. As in the previous work, chromatograms were examined using the TCC approach (Keto & Wineman, 1991, 1994Lennard at al., 1995).…”

Achieving a Near-Theoretical Maximum in Peak Capacity Gain for the Forensic Analysis of Ignitable Liquids Using GC×GC-TOFMS

“…The development and application of chemometric tools to assist in the interpretation of data collected from fire debris analysis has increased rapidly in recent years [10,13,25,26,[39][40][41][42], driven significantly by the demand to simplify data interpretation and ultimately reduce the bottleneck associated with fire debris analysis. Computerized pattern recognition techniques are acceptable according to ASTM E1618 [8], provided that the results are verified visually by a human analyst.…”