Grain Quality Measurement and Preservation
M. R. Paulsen,* J. B. Litchfield, B. Ni, W. Xie
University of Illinois; U.S. Department of Agriculture

Corn that has been heated in the presence of moisture has difficulty during the starch-gluten separation phase in wet milling. Problems result from protein denaturation or starch gelatinization. Our tests using tetrazolium dye have been successful in visually showing heat damage. Dehydrogenase enzymes involved in respiration react with the tetrazolium, resulting in an insoluble, red formazan color in living cells. Non-living cells retain their natural color. Machine vision images of kernels that were heat treated at 60°C for 3 and 9 hours were compared to check samples that were not heated. The unheated kernels had a bright red stain. Kernels heated for 3 hours had a purplish color indicating onset of damage; while the kernels heated for 9 hours did not stain, indicating a totally dead germ.

Corn quality factors are important for corn wet milling and dry milling. The primary factor needed by wet millers is a test to assure that corn has not been dried at temperatures high enough to cause protein denaturation or starch gelatinization. Stress crack percentages are used as an indirect test for these conditions. Stress cracks below 20% will enable corn for wet milling to achieve high starch recovery. The primary factor needed by dry millers is hard endosperm corn to produce large flaking grits. Many overseas dry millers prefer true densities in the range of 1.25 to 1.28 g/cm³. The secondary factor needed by dry millers is low stress cracks, preferably below 20%.

Machine vision sensing has been used to develop methods for detecting and quantifying physical properties of corn and soybeans. The goal of this research is to develop the basis for on-line grain quality evaluation as a tool for assessing grain quality and end-use of the grain. Machine vision has been developed for hardness detection, breakage detection, seed viability, and stress crack analysis in corn. Machine vision has also been used to identify symptoms of surface diseases in soybeans using attributes of color, size, and texture.