Sprouted wheat kernels adversely affect bread and pasta making quality, thus lowering the grade and value to millers, bakers and grain dealers. In this study, the potential of using soft X-ray system in detecting the sprouted wheat kernels was evaluated. Sprouted kernels were produced by germinating seeds. Both the sprouted and healthy samples were X-rayed using a soft X-ray system. White specks were observed in all the sprouted kernel X-ray images. Algorithms were written to extract 55 image features including gray level modeling and histogram from the scanned images. Identification of sprouted and healthy kernels was determined using statistical and neural network classifiers. A four-layer back propagation neural network model correctly classified 90% and 95% of the sprouted and healthy kernels, respectively. Statistical classifier correctly identified 87% and 92% of the sprouted and healthy kernels, respectively.
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Physical appearance and kernel morphology significantly affect the grade of a harvested crop in addition to other factors such as test weight, percentage of foreign matter and constituent components. Moisture content of grain can potentially affect the physical appearance and kernel morphology, which in turn affects the grade of the harvested crop. In this project, we evaluated the effect of moisture content on the classification capability of color, morphology and textural features of imaged grains. Color images of individual kernels and bulk samples of wheat and barley were acquired using a high resolution color camera with an IEEE 1394 interface machine vision system. Algorithms developed by Grain Storage Research Laboratory were implemented to extract color, textural and morphological features from grain images. Image segmentation was applied to remove the background from the single kernel images. The extracted features were analyzed and classified for the effect of moisture content using statistical classifiers and a devised back propagation neural network model.
The knowledge of distribution of pore space inside grain bulks is essential for determining the airflow resistance of grains. In this study, the internal pore structure and the 3D-distribution of air paths inside grain bulks were studied using X-ray computed tomography images. Image analysis methods were applied to the binary 3D X-ray CT images on the spatial distribution of voids to generate the connected, individualized pore objects of different size and shapes. Morphometric parameters such as 3D air path volume distribution, structure separation factor, Euler number, fragmentation index, structure model index were calculated based on hexahedral marching cubes volume model and marching cubes 3D surface construction algorithm. The quantified numerical measures of spatial integrity of air path networks were analyzed and compared with the air flow resistance of grain bulks. The results showed that the connectivity of airspace and the non uniform distribution of air path network inside grain bulks were responsible for the difference in airflow resistance between horizontal and vertical directions to the airflow of grain bulks.
Knowledge of the structure and properties of microscopic surfaces of durum wheat starch granules is essential for understanding the functional and physico-chemical properties. The nanoscale surface undulations on the starch granules inside durum wheat macroscopically influence the milling properties. The objective of this study was to visualize the size and dispersion of the starch grains in vitreous and non-vitreous durum wheat kernels using atomic force microscopy. The distribution of starch granules in the vitreous and non-vitreous durum wheat starch samples were examined and compared. The results of our study confirm the ‘blocklet’ model of the ultra structure of the starch granule surface. Image contrast enhancement using UV/Ozone treatment of microtomed starch samples improved the characterization of growth rings on the starch samples. The observation of growth rings in the non-vitreous starch granule surfaces indicates that amylopectin is more common than amylose compared to the composition of vitreous starch.
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Bionanotechnology LaboratorySuresh Neethirajan
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