Doctoral Degrees (Food Science)

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Browsing Doctoral Degrees (Food Science) by browse.metadata.advisor "Geladi, Paul"

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    Maize endosperm texture characterisation using the rapid visco analyser (RVA), X-ray micro-computed tomography (μCT) and micro-near infrared (microNIR) spectroscopy
    (Stellenbosch : Stellenbosch University, 2015-04) Guelpa, Anina; Manley, Marena; Geladi, Paul; Du Plessis, Anton; Stellenbosch University. Faculty of Agrisciences. Dept. of Food Science.
    ENGLISH ABSTRACT: Maize kernels consists of two types of endosperm, a harder vitreous endosperm and a softer floury endosperm, and the ratio of the vitreous and floury endosperm present mainly determines the hardness of the kernel. Maize (Zea mays L.) is a staple food in many countries, including South Africa, and is industrially processed into maize meal using dry-milling. For optimal yield and higher quality products, hard kernels are favoured by the milling industry. Despite many maize hardness methods available, a standardised method is still lacking, furthermore, no dedicated maize milling quality method exists. Using an industrial guideline (chop percentage), a sample set of different maize hybrids was ranked based on milling performance. Unsupervised inspection (using principal component analysis (PCA) and Spearman’s rank correlation coefficients) identified seven conventional methods (hectoliter mass (HLM), hundred kernel mass (HKM), protein content, particle size index (PSI c/f), percentage vitreous endosperm (%VE) as determined using near infrared (NIR) hyperspectral imaging (HSI) and NIR absorbance at 2230 nm (NIR @ 2230 nm)) as being important descriptors of maize milling quality. Additionally, Rapid Visco Analyser (RVA) viscograms were used for building prediction models, using locally weighted partial least squares (LW-PLS). Hardness properties were predicted in the same order or better than the laboratory error of the reference method, irrespective of RVA profile being used. Classification of hard and soft maize hybrids was achieved, based on density measurements as determined using an X-ray micro-computed tomography (µCT) density calibration constructed from polymers with known densities. Receiver operating classification (ROC) curve threshold values of 1.48 g.cm-3 , 1.67 g.cm-3 and 1.30 g.cm-3 were determined for the entire kernel (EKD), vitreous (VED) and floury endosperm densities (FED), respectively at a maximum of 100% sensitivity and specificity. Classification based on milling quality of maize hybrids, using X-ray µCT derived density and volume measurements obtained from low resolution (80 µm) µCT scans, were achieved with good classification accuracies. For EKD and vitreous-to-floury endosperm ratio (V:F) measurements, 93% and 92% accurate classifications were respectively obtained, using ROC curve. Furthermore, it was established that milling quality could not be described without the inclusion of density measurements (using PCA and Spearman’s rank correlation coefficients). X-ray µCT derived density measurements (EKD) were used as reference values to build NIR spectroscopy prediction models. NIR spectra were acquired using a miniature NIR spectrophotometer, i.e. a microNIR with a wavelength range of 908 – 1680 nm. Prediction statistics for EKD for the larger sample set (where each kernel was scanned both germ-up and germ-down) was: R2 V = 0.60, RMSEP = 0.03 g.cm-3 , RPD = 1.67 and for the smaller sample set (where each kernel was scanned only germ-down): R2 V = 0.32, RMSEP = 0.03 g.cm-3 , RPD = 1.67. The results from the larger sample set indicated that reasonable predictions can be made at the fast NIR scan rate that would be suitable for breeders as a rough screening method.
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    Near infrared (NIR) hyperspectral imaging and X-ray computed tomography combined with statistical and multivariate data analysis to study Fusarium infection in maize
    (Stellenbosch : Stellenbosch University, 2013-03) Williams, Paul James; Manley, Marena; Britz, T. J.; Geladi, Paul; Stellenbosch University. Faculty of AgriSciences. Dept. of Food Science.
    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.