The use of forensic polycyclic aromatic hydrocarbon signatures and Compound Ratio Analysis Techniques (CORAT) for the source characterisation of petrogenic/pyrogenic environmental releases.

This study utilised recent developments in forensic aromatic hydrocarbon fingerprint analysis to characterise and identify specific biogenic, pyrogenic and petrogenic contamination. The fingerprinting and data interpretation techniques discussed include the recognition of: The distribution patterns of hydrocarbons (alkylated naphthalene, phenanthrene, dibenzothiophene, fluorene, chrysene and phenol isomers), • Analysis of “source-specific marker” compounds (individual saturated hydrocarbons, including n-alkanes (n-C5 through 0-C40) • Selected benzene, toluene, ethylbenzene and xylene isomers (BTEX), • The recalcitrant isoprenoids; pristane and phytane and • The determination of diagnostic ratios of specific petroleum / non-petroleum constituents, and the application of various statistical and numerical analysis tools. An unknown sample from the Irish Environmental Protection Agency (EPA) for origin characterisation was subjected to analysis by gas chromatography utilising both flame ionisation and mass spectral detection techniques in comparison to known reference materials. The percentage of the individual Polycyclic Aromatic Hydrocarbons (PAIIs) and biomarker concentrations in the unknown sample were normalised to the sum of the analytes and the results were compared with the corresponding results with a range of reference materials. In addition, to the determination of conventional diagnostic PAH and biomarker ratios, a number of “source-specific markers” isomeric PAHs within the same alkylation levels were determined, and their relative abundance ratios were computed in order to definitively identify and differentiate the various sources. Statistical logarithmic star plots were generated from both sets of data to give a pictorial representation of the comparison between the unknown sample and reference products. The study successfully characterised the unknown sample as being contaminated with a “coal tar” and clearly demonstrates the future role of compound ratio analysis (CORAT) in the identification of possible source contaminants.

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.