Spéciation of chromium in a chromium enriched yeast.

The sample under investigation in this project is an experimental chromium enriched yeast used as a possible additive in animal foodstuff, which was produced by growing yeast in the presence of chromium (III) chloride. Chromium on its own in not biologically active but chromium in the form of chromium enriched yeast is biologically active. The objective of this project was to show the complete absence of chromium(VI) from the sample. A literature survey describing previous work carried out on the speciation of Cr(VI) has been carried out. The principal methods of detection of Cr(VI) used in this project are Polarography, G.F.A.A. Spectroscopy, U.V. Spectroscopy and H.P.L.C. For each of the above methods a calibration curve was obtained and each method was applied to the yeast extract. The H.P.L.C. and U.V. spectroscopic method are specific for Cr(VI) but polarography and G.F.A.A. spectroscopy measure total chromium. Tris-NaOH buffer has been investigated for the extraction of chromium(VT). Problems associated with air oxidation of Cr(III) in alkaline solution have identified and procedures described for the suppression of air oxidation. Procedures are described for the application of the extraction procedure to the yeast extract and for the determination of Cr(VI) in the extract. Procedures are also described for the preconcentration of Cr(VI) on a HPLC column and for the application to the yeast extract. The rate of reduction of Cr(VI) by ascorbic acid is investigated and found to be first order with respect to ascorbic acid concentration. The reduction capacity of the yeast is also investigated and it was found that in acid solution the yeast will reduce Cr(VI) but in neutral or basic solution the reduction capacity is diminished. Conclusions regarding the objectives of the project are drawn and suggestions for further work are given.

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.