A high-throughput X-ray micro-computed tomography (μCT) approach for measuring single kernel maize (Zea mays L.) volumes and densities

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 (mu CT), 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 mu m) mu CT 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 mu CT derived volume and density measurements of individual maize kernels as potential indicators of milling quality. (C) 2016 Elsevier Ltd. All rights reserved.