Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks

Doble, Philip; Sandercock, P. Mark L.; Pasquier, E. Du; Petocz, Peter; Roux, Claude; Dawson, Michael J.

doi:10.1016/s0379-0738(03)00002-1

Cited by 105 publications

(83 citation statements)

References 5 publications

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“…The software used to perform PCA was XLSTAT (AddinSoft), an add-in for Microsoft Excel. Principal Components Analysis (PCA) can be used to elucidate trends in the data while taking into account any correlations between the variables [8,[26][27][28][29]. Prior to conducting PCA, the data was normalized and autoscaled to correct for variation in peak areas due to compound concentration as well as variability in peak areas due to differences in the variance of the ions that were used to construct EIPs.…”

Section: Discussionmentioning

confidence: 99%

Microbial degradation of gasoline in soil: Effect of season of sampling

Turner

Pichtel

Rodenas

et al. 2015

Forensic Science International

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In cases where fire debris contains soil, microorganisms can rapidly and irreversibly alter the chemical composition of any ignitable liquid residue that may be present. In this study, differences in microbial degradation due to the season in which the sample is collected was examined. Soil samples were collected from the same site during Fall, Winter, Spring and Summer and the degradation of gasoline was monitored over 30 days. Predominant viable bacterial populations enumerated using real-time PCR and reverse transcriptase polymerase chain reaction (RT-PCR) enumeration revealed the predominant viable bacterial genera to be Alcaligenes, Bacillus, and Flavobacterium. Overall, the compounds most vulnerable to microbial degradation are the n-alkanes, followed by the mono-substituted alkylbenzenes (e.g., toluene, ethylbenzene, propylbenzene and isopropylbenzene). Benzaldehyde (a degradation product of toluene) was also identified as a marker for the extent of biodegradation.Ultimately, it was determined that soil collected during an unusually hot and dry summer exhibited the least degradation with little to no change in gasoline for up to 4 days, readily detectable n-alkanes for up to 7 days and relatively high levels of resilient compounds such as

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Section: Discussionmentioning

confidence: 99%

Microbial degradation of gasoline in soil: Effect of season of sampling

Turner

Pichtel

Rodenas

et al. 2015

Forensic Science International

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“…The experimental data was originally taken from a Canadian Petroleum Products Institute Report of the composition of unleaded summer and winter gasoline in 1993 [2] and reported by [3]. In this report, 44 samples of regular gasoline (22 winter, 22 summer), and 44 samples of premium gasoline (22 winter, 22 summer) were analyzed by GC-MS.…”

Section: Experimental Datamentioning

confidence: 99%

“…Also, visual analysis of gas chromatograms is subjective and is not always persuasive in a court of law. Pattern recognition methods offer a better approach to the problem of matching gas chromatograms of weathered fuels [2]. Pattern recognition methods involve less subjectivity in the interpretation of the data and are capable of identifying the samples correctly.…”

Section: Introductionmentioning

confidence: 99%

Data Mining in Identifying Premium and Regular Gasoline Using Support Vector Machines as Novel Approach for Arson and Fuel Spill Investigation

Olatunji¹,

Adeleke²

2010

Int. Jour. Info. Sci. Comp.

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In this work, a novel data mining model based on Support Vector Machines (SVM) for the identification of gasoline types has been investigated and developed. Detection and correct identification of gasoline types during Arson and Fuel Spill Investigation are very important in forensic science. As the number of arson and spillage becomes a common place, it becomes more important to have an accurate means of detecting and classifying gasoline found at such sites of incidence. However, currently only a very few number of classification models have been explored in this germane field of forensic science, particularly as relates to gasoline identification. Thus, we have developed Support Vector Machines (SVM) based identification model for identifying gasoline types. The model was constructed using gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Prediction accuracy of the model was evaluated and compared with earlier used methods on the same datasets. Empirical results from simulation showed that SVM based model produced accurate and promising results better than the best among the other earlier implemented Artificial Neural Network and Principal Component Analysis methods on the same datasets.

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“…40 In this paper, principal component analysis followed by linear discriminant analysis (PCA-LDA) and quadratic discriminant analysis (PCA-QDA) were compared for discrimination between healthy controls and cancer (ovarian and prostate) samples. In addition, a further classification between benign subtypes of prostate cancer (serum PSA (prostate-specific antigen) 4-10 ng mL -1 and serum PSA > 10 ng mL -1 ) was performed.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

Morais

Lima

2017

J. Braz. Chem. Soc.

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Mass spectrometry (MS) is a powerful technique that can provide the biochemical signature of a wide range of biological materials such as cells and biofluids. However, MS data usually has a large range of variables which may lead to difficulties in discriminatory analysis and may require high computational cost. In this paper, principal component analysis with linear discriminant analysis (PCA-LDA) and quadratic discriminant analysis (PCA-QDA) were applied for discrimination between healthy control and cancer samples (ovarian and prostate cancer) based on MS data sets. In addition, an identification of prostate cancer subtypes was performed. The results obtained herein were very satisfactory, especially for PCA-QDA. Selectivity and specificity were found in a range of 90-100%, being equal or superior to support vector machines (SVM)-based algorithms. These techniques provided reliable identification of cancer samples which may lead to fast and less-invasive clinical procedures.Keywords: mass spectrometry, classification, ovarian cancer, prostate cancer, QDA IntroductionMass spectrometry (MS) is an analytical technique that is used for determining the chemical composition of a given sample, to quantify compounds, 1 and to help elucidate molecular structures. 2,3 This technique has been increasingly utilized in biomedical and clinical research, 4 since it can overcome many limitations of classical immunoassays 5,6 and supports the development of fast and less-invasive clinical procedures. [7][8][9] MS is usually coupled with chromatography such as liquid chromatography (LC-MS) and gas chromatography (GC-MS). Other techniques such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) are often used in MS applications, including disease screening and diagnosis.5 Some examples of MS applications includes toxicology screening and toxic drug quantification using quadrupole MS/MS; 10 identification of inborn errors in metabolism or genetic defects in newborns for prenatal screening programs using electrospray tandem MS; 11 detection of drug-induced hepatotoxicity using MS-based metabolomics; 12 and identification and quantification of bleomycin in serum and tumor tissue by high resolution LC-MS. 13 MS-based techniques have been largely employed for cancer identification, such as for breast cancer, 14 prostate cancer, 15,16 ovarian cancer, 17 lung cancer, 18 and pancreatic cancer; 19 as well as for identifying many biomarkers. 18,[20][21][22][23][24] One of the main fields using MS data is metabolomics, which aims to identify and quantify small molecules involved in metabolic reactions. 25 Metabolomics studies have been applied in several areas, especially cancer. 26These analyses are typically performed in either targeted or untargeted approaches. 25 The target approach aims to identify and quantify specific metabolites or metabolite class; whereas in the untargeted analysis a new hypothesis for further tests is generated by ...

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Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks

Cited by 105 publications

References 5 publications

Microbial degradation of gasoline in soil: Effect of season of sampling

Microbial degradation of gasoline in soil: Effect of season of sampling

Data Mining in Identifying Premium and Regular Gasoline Using Support Vector Machines as Novel Approach for Arson and Fuel Spill Investigation

Principal Component Analysis with Linear and Quadratic Discriminant Analysis for Identification of Cancer Samples Based on Mass Spectrometry

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