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1.
Forensic Sci Int ; 140(1): 43-59, 2004 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-15013165

RESUMO

Analysis of the C(0)- to C(2)-naphthalene compounds present in automotive gasoline using gas chromatography-mass spectrometry with selected ion monitoring (GC-MS (SIM)) and principal component analysis (PCA) was used to discriminate between different samples of gasoline. Phase one of this study explored the ability of this method to differentiate gasoline samples at different levels of evaporation. A total of 35 random samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and lead replacement), were collected in Sydney, Australia and examined. The high-boiling C(0)- to C(2)-naphthalene compounds present in the gasoline were used to chemically fingerprint each sample at different levels of evaporation. Samples of 25, 50, 75 and 90% evaporated gasoline (by weight) were generated from the 35 samples of unevaporated gasoline. Analysis of the data by PCA followed by linear discriminant analysis (LDA) showed that the 35 samples formed 18 unique groups, irrespective of the level of evaporation. Good discrimination between gasoline samples that were collected on the same day was obtained. Phase two of this study examined the change in gasoline samples over time. The C(0)- to C(2)-naphthalene composition in 96 samples of gasoline collected from three service stations over a 16-week period was examined using the method described. In most cases, it was found that the C(0)- to C(2)-naphthalene profile changed from week to week, and from station to station. In a comparison of all 96 samples together it was found that the majority could be differentiated from one another. The application of the method to forensic casework is discussed.

2.
Forensic Sci Int ; 140(1): 71-7, 2004 Feb 10.
Artigo em Inglês | MEDLINE | ID: mdl-15013167

RESUMO

A gas chromatography-mass spectrometry with selected ion monitoring (GC-MS (SIM)) method was used to discriminate samples of unevaporated gasoline collected from Auckland, New Zealand and Sydney, Australia. This method was applied to 28 samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and premium plus unleaded), that were collected from service stations in Auckland, New Zealand in summer (February) and winter (August). The 14 samples of summer gasoline collected in New Zealand could be divided into seven unique groups. The 14 samples of winter gasoline from New Zealand could be divided into 14 unique groups. The 14 samples collected in New Zealand during February 2002 were then compared to 24 samples of unevaporated gasoline collected from service stations in Sydney, Australia during the same month. Most of the samples could be differentiated based on their country of origin.

3.
Forensic Sci Int ; 134(1): 1-10, 2003 Jun 24.
Artigo em Inglês | MEDLINE | ID: mdl-12842350

RESUMO

The comparison of two or more samples of liquid gasoline (petrol) to establish a common origin is a difficult problem in the forensic investigation of arsons and suspicious fires. A total of 35 randomly collected samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and lead replacement), were examined. The high-boiling fraction of the gasoline was targeted with a view to apply the techniques described herein to evaporated gasoline samples in the future.A novel micro solid phase extraction (SPE) technique using activated alumina was developed to isolate the polar compounds and the polycyclic aromatic hydrocarbons (PAHs) from a 200microl sample of gasoline. Samples were analysed using full-scan gas chromatography-mass spectrometry (GC-MS) and potential target compounds identified. Samples were then re-analysed directly, without prior treatment, using GC-MS in selected ion monitoring (SIM) mode for target compounds that exhibited variation between gasoline samples. Principal component analysis (PCA) was applied to the chromatographic data. The first two principal components (PCs) accounted for 91.5% of the variation in the data. Linear discriminant analysis (LDA) performed on the PCA results showed that the 35 samples tested could be classified into 32 different groups.

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