Vitreosity, or hardness, is an important grain quality factor for com.
An increasing amount of research is aimed at understanding the geneti
c and biochemical basis of vitreosity and at improving vitreosity of h
igh-lysine (opaque-2) com through breeding strategies. Previous method
s for quantifying vitreosity were destructive, subjective, or required
sophisticated equipment and expertise. This study evaluated a simple
video image analysis procedure for quantifying vitreosity and determin
ed how various processing steps affected the results. Kernels were sur
rounded with modeling clay and viewed on a light box with a monochrome
video camera. The video signal was captured to a personal computer an
d analyzed with commercially available hardware and software. A segreg
ating F2 population from a cross of vitreous Pool 29 QPM X nonvitreous
B73o2 was classified visually into 10% steps of vitreosity. High corr
elations were observed between visual classification and average grays
cale values of captured video images at all stages of processing. Gray
scale value was inversely proportional to kernel thickness. Removing i
mage background and eliminating a segment corresponding to the embryo
area increased the average grayscale range and resolution, but adjustm
ent for kernel thickness did not substantially improve the correlation
. This method allows researchers and breeders to quantify vitreosity o
f corn and other cereal grains on a continuous scale with readily avai
lable equipment and expertise and overcomes the problems of subjectivi
ty, destructiveness, and complexity associated with other approaches.