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Utilisation of an X-ray micro-computed tomography (µCT) density calibration to estimate whole maize kernel density for hardness classification A. GUELPA (1), A. Du Plessis (2), M. Kidd (3), M. Manley (1). (1) Stellenbosch University, Stellenbosch, South Africa; (2) CT Scanner, CAF, Stellenbosch University, Stellenbosch, South Africa; (3) Centre for Statistical Consultation, Stellenbosch University, Stellenbosch, South Africa
An X-ray micro-computed tomography (µCT) density calibration was constructed from polymers ranging in densities from 0.9 to 2.2 g.cm-3. The linear equation obtained, was used to estimate the whole kernel densities and two regions-of-interest (ROIs), of two maize hybrids (8 kernels each) differing in hardness. The estimated densities of the whole kernels of the hard hybrid were significantly higher than that of the soft hybrid kernels. The densities of the two ROIs, namely vitreous and floury endosperm were also estimated (vitreous endosperm: 16 kernels: 1.77±0.03 g.cm-3: 8 hard kernels: 1.74±0.01 g.cm-3: 8 soft kernels: 1.80±0.01 g.cm-3; floury endosperm: 16 kernels: 1.42±0.05 g.cm-3: 8 hard kernels: 1.43±0.07 g.cm-3: 8 soft kernels: 1.42±0.03 g.cm-3). As determined with ANOVA it was shown that floury endosperm densities did not significantly (<i>P</i> > 0.05) contribute to the density variation between the hybrid classes, whereas vitreous endosperm densities contributed significantly (<i>P</i> < 0.05). A threshold value of 1.78 g.cm-3 was determined by a receiver operating characteristic (ROC) curve for the vitreous endosperm densities, giving complete separation (100% sensitivity and specificity). Values higher than the threshold indicated soft kernels, and lower values hard kernels, confirming the proof of concept. In this study, it has been established that X-ray µCT has the potential to estimate whole kernel and ROIs densities. With the application of appropriate statistical methods, and after sufficient validation, maize kernels can likely be classed as belonging to their respective hybrid classes. View Presentation |
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