Near infrared (NIR) hyperspectral imaging and X-ray computed tomography combined with statistical and multivariate data analysis to study Fusarium infection in maize
| dc.contributor.advisor | Manley, Marena | en_ZA |
| dc.contributor.advisor | Britz, T. J. | en_ZA |
| dc.contributor.advisor | Geladi, Paul | en_ZA |
| dc.contributor.author | Williams, Paul James | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of AgriSciences. Dept. of Food Science. | en_ZA |
| dc.date.accessioned | 2013-02-05T12:24:17Z | en_ZA |
| dc.date.accessioned | 2013-03-15T07:25:39Z | |
| dc.date.available | 2013-02-05T12:24:17Z | en_ZA |
| dc.date.available | 2013-03-15T07:25:39Z | |
| dc.date.issued | 2013-03 | en_ZA |
| dc.description | Thesis (PhD)--Stellenbosch University, 2013. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT: Maize (Zea mays L.) is used for human and animal consumption in diverse forms, from specialised foods in developed countries, to staple food in developing countries. Unfortunately, maize is prone to infection by different Fusarium species that can produce harmful mycotoxins. Fusarium verticillioides is capable of asymptomatic infection, where infected kernels show no sign of fungal growth, but are contaminated with mycotoxins. If fungal contamination is not detected early on, mycotoxins can enter the food chain. Rapid and accurate methods are required to detect, identify and distinguish between pathogens to enable swift decisions regarding the fate of a batch or consignment of cereal. Near infrared (NIR) hyperspectral imaging and multivariate image analysis (MIA) were evaluated to investigate the fungal development in maize kernels over time. When plotting principal component (PC) 4 against PC5, with percentages sum of squares (%SS) 0.49% and 0.34%, three distinct clusters were apparent in the score plot and this was associated with degree of infection. Prominent peaks at 1900 nm and 2136 nm confirmed that the source of variation was due to changes in starch and protein. Variable importance plots (VIP) confirmed the peaks observed in the PCA loading line plots. Early detection of fungal contamination and activity (20 h after inoculation) was possible before visual symptoms of infection appeared. Using NIR hyperspectral imaging and MIA it was possible to differentiate between species of Fusarium associated with maize. It was additionally applied to examine the fungal growth kinetics on culture media. Partial least squares discriminant analysis (PLS-DA) prediction results showed that it was possible to discriminate between species, with F. verticillioides the least correctly predicted (between 16-47% pixels correctly predicted). For F. subglutinans 78-100% and for F. proliferatum 60-80% pixels were correctly predicted. Three prominent bands at 1166, 1380 and 1918 nm were considered to be responsible for the differences between the growth zones. Variations in the bands at 1166 and 1380 nm were correlated with the depletion of carbohydrates as the fungus grew while the band at 1918 nm was a possible indication of spore and new mycelial formation. By plotting the pixels from the individual growth zones as a function of time, it was possible to visualise the emergence and interaction of the growth zones as separate growth profiles. The microstructure of fungal infected maize kernels was studied over time using high resolution X-ray micro-computed tomography (μCT). The presence of voids and airspaces could be seen in two dimensional (2D) X-ray transmission images and in the three dimensional (3D) tomograms. Clear differences were detected between kernels imaged after 20 and 596 h of inoculation. This difference in voids as the fungus progressed showed the effect of fungal damage on the microstructure of the maize kernels. Imaging techniques are important for rapid, accurate and objective evaluation of products for quality and safety. NIR hyperspectral imaging offers rapid chemical evaluation of samples in 2D images while μCT offers 3D microstructural information. By combining these image techniques more value was added and this led to a comprehensive evaluation of Fusarium infection in maize. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING: Mielies (Zea mays L.) word in verskeie vorms deur mens en dier verbruik, van gespesialiseerde voedsel in ontwikkelde lande, tot stapelvoedsel in ontwikkelende lande. Ongelukkig is mielies onderhewig aan besmetting deur verskeie Fusarium spesies wat skadelike mikotoksiene kan produseer. Fusarium verticilloioides is in staat tot asimptomatiese infeksie waar die besmette pit geen teken van fungusgroei toon nie, maar (reeds) met mikotoksiene besmet is. Indien fungusbesmetting nie vroegtydig opgespoor word nie, kan mikotoksiene die voedselketting betree. Vinnige en akkurate metodes word benodig om patogene op te spoor, te identifiseer en ook om onderskeid tussen patogene te tref om sodoende (effektiewe) besluite aangaande die gebruik van ‘n lot of besending graan te neem. Naby-infrarooi (NIR) hiperspektrale beelding en meerveranderlike beeld ontleding (MIA) is geëvalueer om fungusontwikkeling in mieliepitte oor tyd te ondersoek. Wanneer hoofkomponent (PC) 4 teenoor PC5 gestip word, met persentasies som van kwadrate (%SS) 0.49% en 0/34%, is drie afsonderlike groepein die telling grafiek waargeneem. Dit is geassosieer met die graad van besmetting. Prominente pieke by 1900 nm en 2136 nm het bevestig dat veranderinge in stysel en proteïene die bron van die variasie was. Veranderlike belangrikheidsgrafieke (VIP) het die pieke wat in die PCA beladingslyngrafieke waargeneem is, bevestig. Vroegtydige opsporing (bespeuring) van fungusbesmetting en aktiwiteit (20 h na inokulasie) was moontlik voor visuele besmettingsimptome verskyn het. Onderskeid tussen Fusarium spesies wat met mielies geassosieer word, was moontlik deur gebruik te maak van NIR hiperspektrale beelding en MIA. Dit is bykomend toegepas om fungusgroeikinetika op kwekingsmedia te bestudeer. Parsiële kleinste kwadrate diskriminantanalise (PLS-DA) voorspellingsresultate het getoon dat dit moontlik was om tussen spesies te onderskei, met F. verticillioides die minste korrek voorspel (tussen 19-47% beeldelemente korrek voorspel). Vir F. subglutinans is 78-100% en vir F. proliferatum is 60-80% beeldelemente korrek voorspel. Drie prominente bande by 1166, 1380 en 1918 nm is oorweeg as oorsaak vir die verskille tussen die groeisones. Variasies in die bande by 1166 en 1380 nm is gekorreleer met die vermindering van koolhidrate soos die fungus groei, terwyl die band by 1918 nm ‘n moontlike aanduiding van spoor en nuwe miseliale vorming is. Deur die beeldelemente van die individuele groeisones as ‘n funksie van tyd te stip, was dit moontlik om die verskyning en interaksie van die groeisones as aparte groeiprofiele te visualiseer. Hoë-resolusie X-straal mikro-berekende tomografie (μCT) is gebruik om die mikrostruktuur van fungusbesmette mieliepitte oor tyd te ondersoek. Die voorkoms van leemtes en lugruimtes kon in die twee-dimensionele (2D) X-straal transmissie beelde en in die drie-dimensionele (3D) tomogramme gesien word. Duidelike verskille is waargeneem tussen pitte wat na 20 en 596 h na inokulasie verbeeld is. Hierdie verskil in leemtes soos die fungus vorder, het die effek van fungusskade op die mikrostruktuur van mieliepitte getoon. Beeldingstegnieke is belangrik vir vinnige, akkurate en objektiewe evaluasie van produkte vir kwaliteit en veiligheid. NIR hiperspektrale beelding bied vinnige chemiese evaluering van monsters in 2D beelde, terwyl μCT 3D mikrostrukturele inligting gee. Meer waarde is toegevoeg deur hierdie beeldingstegnieke te kombineer en dit het gelei tot ‘n omvangryke evaluering van Fusarium besmetting in mielies. | af_ZA |
| dc.format.extent | xxi, 130 p. : ill. (some col.) | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/79904 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | |
| dc.subject | Near infrared hyperspectral imaging | en_ZA |
| dc.subject | X-ray computed tomography | en_ZA |
| dc.subject | Fusarium diseases of plants | en_ZA |
| dc.subject | Maize - Quality | en_ZA |
| dc.subject | Dissertations -- Food science | en_ZA |
| dc.subject | Theses -- Food science | en_ZA |
| dc.title | Near infrared (NIR) hyperspectral imaging and X-ray computed tomography combined with statistical and multivariate data analysis to study Fusarium infection in maize | en_ZA |
| dc.type | Thesis | en_ZA |