The use of pheromone-based or semiochemical lures and devices for the detection of insect pest population and monitoring in agriculture is a common practice. In many countries the use of these devices is exempt from registration requirements based on regulatory thresholds set by the relevant authorities, however, not in South Africa. The question arises whether the pheromones or semiochemicals dispensed through such devices, influence the naturally occurring compounds observed and whether a concern of toxicity and ecotoxicity is justified. A tomato field was selected in a commercial growing area of South Africa and a novel five-component lure, T.V. PheroLure®, was identified from a local manufacturer, Insect Science (Pty) Ltd. The T.V. PheroLure® consists of a Volatile Organic Compound (VOC) blend which is placed in a polyethylene bulb. Tomato VOCs were collected before, during and after the application of the T.V. PheroLure® which was used in combination with a yellow bucket funnel trap. The VOCs were collected at different heights (0 cm, 30 cm and 60 cm) of the tomato plants, from planting until harvest (22 weeks) and surrounding tomato fields without the T.V. PheroLure®. The results obtained indicated that: (i) the T.V. PheroLure® had no significant influence on the natural VOCs observed in the tomato field and (ii) that the height of sampling had no influence on VOCs observed. This study also indicated that apart from a slight increased contribution of limonene, there was no significant influence observed from the T.V. PheroLure® compounds on the natural background VOCs found in the tomato field. Therefore, it could be argued that the natural phenology of the plant has a greater influence on the VOCs observed than T.V. PheroLure® and that the concern of toxicity and ecotoxicity is unjustified when using these devices for monitoring purposes only. / Environmental Sciences / M. Sc. (Environmental Science)

Volatile Organic Compounds (VOCs) are commonly used as indicators of an organism's health, among other factors. Traditionally, gas chromatographs (GC) are used to classify these but are prohibitively expensive and impractical for field use. This thesis outlines the motivations, design, construction, and characterization of a portable GC. This proof-of-concept uses off-the-shelf components to show that the production of a device is feasible. It was able to successfully generate carrier gas from the surrounding air via filtration by activated carbon fiber filters. It was also able to reliably produce distinguishable peaks for acetone and hexane at retention times that were reasonable for a prototype system. With some modifications, this system has the strong potential for long-term implementation in the field.

In this research, a proton exchange membrane fuel cell (PEMFC) sensor was investigated for specific detection of volatile organic compounds (VOCs) for point-of-care (POC) diagnosis of the physiological conditions of humans. A PEMFC is an electrochemical transducer that converts chemical energy into electrical energy. A Redox reaction takes place at its electrodes whereas the volatile biomolecules (e.g. ethanol) are oxidized at the anode and ambient oxygen is reduced at the cathode. The compounds which were the focus of this investigation were ethanol (C2H5OH) and isoflurane (C3H2ClF5O), but theoretically, the sensor is not limited to only those VOCs given proper calibration. Detection in biosensing, which needs to be carried out in a controlled system, becomes complex in a multivariate environment. Major limitations of all types of biosensors would include poor selectivity, drifting, overlapping, and degradation of signals. Specific detection of VOCs in multi-dimensional environments is also a challenge in fuel cell sensing. Humidity, temperature, and the presence of other analytes interfere with the functionality of the fuel cell and provide false readings. Hence, accurate and precise quantification of VOC(s) and calibration are the major challenges when using PEMFC biosensor. To resolve this problem, a statistical model was derived for the calibration of PEMFC employing multivariate analysis, such as the “Principal Component Regression (PCR)” method for the sensing of VOC(s). PCR can correlate larger data sets and provides an accurate fitting between a known and an unknown data set. PCR improves calibration for multivariate conditions as compared to the overlapping signals obtained when using linear (univariate) regression models. Results show that this biosensor investigated has a 75% accuracy improvement over the commercial alcohol breathalyzer used in this study when detecting ethanol. When detecting isoflurane, this sensor has an average deviation in the steady-state response of ~14.29% from the gold-standard infrared spectroscopy system used in hospital operating theaters. The significance of this research lies in its versatility in dealing with the existing challenge of the accuracy and precision of the calibration of the PEMFC sensor. Also, this research may improve the diagnosis of several diseases through the detection of concerned biomarkers.