Abstract
Maize (Zea mays L.) meal, which is industrially produced using dry-milling, is an important staple food in many developing countries. Kernel hardness is often the characteristic that is measured to select hybrids desirable for milling. Conventional hardness methods present challenges and limitations. Therefore, high-throughput methodology was developed, using X-ray micro-computed tomography (μCT), to determine whole maize kernel volumes and densities as a means to discriminate between good and poor milling quality. Volume and density measurements of 150 kernels were obtained simultaneously from low-resolution (80 μm) μCT scans, reducing acquisition time and cost. Volume measurements were obtained for the individual kernels, as well as regions-of-interest (ROIs), i.e. vitreous and floury endosperm. Densities were also calculated for each maize kernel, as well as the ROIs, using a pre-developed density calibration. Classification results (77–93% correct classification), as obtained using descriptive statistics, i.e. receiver operating characteristic (ROC) curves, demonstrated X-ray μCT derived volume and density measurements of individual maize kernels as potential indicators of milling quality.
Published Version
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