AbstractMaize is the main staple food for much of the population in developing countries. It is however, highly susceptible to infection by fungi and subsequently to contamination with mycotoxins. Mycotoxins are toxic secondary metabolites produced by filamentous fungi, mainly Aspergillus, Fusarium, and Penicillium. Among these, aflatoxins, produced mainly by Aspergillus flavus and Aspergillus parasiticus, pose the greatest threat for agriculture, trade and human health in developing countries. Consumption of highly contaminated grains results in acute aflatoxicosis which can be fatal, while chronic aflatoxicosis as a result of long term low level exposure is more prevalent and is highly correlated with increased incidences of liver cancer, malnutrition, immunosuppression, and impaired growth in children. Contamination also results in economic losses from reduced grain quality, loss of animal productivity and reduced accessibility to international markets.
Management of aflatoxin remains problematic, particularly in developing countries where production and storage conditions favour contamination, coupled with a lack of well-established regulatory systems to frequently monitor food samples prior to trade or human consumption. The problem is further compounded by limited analytical capacity. Available screening and analytical techniques are fraught with numerous challenges, including high cost, laborious and time consuming procedures and lack of necessary infrastructure. There is therefore a need to develop a suitable system for detecting the presence of the toxigenic fungi and their toxins in order to provide real time monitoring data that would facilitate removal of contaminated lots. The use of volatile organic compounds (VOCs) produced by mycotoxigenic fungi upon plant infection has been identified as a potential novel diagnostic technique for detection of mycotoxins, to circumvent the drawbacks associated with current techniques.
The first objective of this thesis was to evaluate the potential for Gas chromatography - Mass spectroscopy (GC-MS) based analysis of VOCs for initial screening of maize to detect aflatoxin contamination using Australian and Kenyan maize varieties infected with A. flavus under laboratory and field conditions. The study also aimed to identify unique VOCs as markers for both A. flavus infection and aflatoxin contamination of maize. The results show the potential for GC-MS to discriminate between maize inoculated with 2 % Tween 20 as control, non-aflatoxigenic and aflatoxigenic A. flavus with accuracies that ranged from 81 % to 100 % (n = 15 to 30 samples per class). The classification accuracies achieved for maize varieties naturally infected with A. flavus were, however, much lower than for the artificially inoculated samples, ranging from only 48 % (n = 34 control vs 31 contaminated) to 80 % (n = 41 control vs 11 contaminated). The VOCs effective in discriminating maize infected and not infected with A. flavus were variety dependent. Tetramethyl pyrazine was significantly and consistently effective in discriminating controls from all maize infected with aflatoxigenic A. flavus for Australian maize variety DK703w. The compound was not identified as important in discriminating controls from maize infected with the non-aflatoxigenic A. flavus isolate, indicating it could be unique to aflatoxigenic A. flavus. A similar pattern was observed for p-xylene which was effective in discriminating between Kenyan maize varieties that were infected and not infected with A. flavus.
The second objective was to evaluate the potential for electronic nose to detect aflatoxin contamination in maize. The performances of three electronic nose instruments based on different sensor technologies were compared using an Australian maize variety artificially inoculated with A. flavus. Electronic noses with metal oxide semiconductor sensors were more effective than conducting polymer sensors based electronic nose in discriminating between maize infected and not infected with A. flavus. Based on the marginally higher classification accuracies achieved, field portability and lower capital cost, the electronic nose equipped with metal oxide semiconductor sensors and thermocycling (DiagNose) was selected for further evaluation, using Kenyan maize varieties artificially and naturally infected with A. flavus. The DiagNose was able to discriminate between controls and maize samples artificially inoculated with A. flavus for two Kenyan varieties, Duma 43 and Pioneer, with accuracies that ranged from 72 % to 88 % (n = 30 samples per class). Classification accuracies for maize varieties that were naturally infected with A. flavus ranged from 61 % (n = 34 control vs 31 contaminated) to 86 % (n = 41 control vs 11 contaminated).
This study demonstrates the potential use of GC-MS based analysis of VOCs and electronic noses for detection of A. flavus infection and aflatoxin contamination in artificially and naturally contaminated maize samples. The classification accuracies achieved for both techniques are however, at this stage too low to justify deployment for practical field use at this stage. There is therefore need for further research to improve on their performance before they can be deployed in the maize industry.
|Date of Award||2019|
|Supervisor||Michelle Gahan (Supervisor) & Dennis Mcnevin (Supervisor)|