A high-throughput X-ray micro-computed tomography (μCT) approach for measuring single kernel maize (Zea mays L.) volumes and densities. (May 2016)

• Record Type: Journal Article
• Authors: Guelpa, Anina; du Plessis, Anton; Manley, Marena
• Is Part Of: Journal of cereal science. Volume 69(2016)
• Page Start: 321
• Page End: 328
• Publication Date: 2016-05
• Subjects: Zea mays L. -- X-ray micro-computed tomography -- Maize milling quality -- Kernel volume -- Kernel density -- Low resolution scans; Grain -- Periodicals; Cereal products -- Periodicals; Céréales -- Périodiques; Produits céréaliers -- Périodiques; Cereal products; Grain; Periodicals; 664.705
• DOI: 10.1016/j.jcs.2016.04.009
• Languages: English
• ISSNs: 0733-5210

Abstract

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. Highlights: A high through-put X-ray micro-computed tomography procedure for maize is presented. Large number of maize kernels scanned simultaneously resulted in lower resolution. Sufficient segmentation of regions-of-interest was achieved with lower resolution. Acceptable maize milling quality classification accuracies were obtained with X-ray.