Assessing the intrapore volume and surface area of single corn kernel using CT scan imaging and machine vision

The intragranular porosity of granular materials is crucial across various fields and influences mechanical strength, permeability, moisture retention, and overall reactivity. However, the existing pore structure assessment techniques are complicated, labor-intensive, and time-consuming, while the use of digital imaging and analysis is promising. Therefore, a machine vision ImageJ plugin was developed and tested that used digital computed tomography (CT) scans of single corn for the assessment of intrapore (within grain) volume and surface area as a proof-of-concept. Among the exhibited two distinct intrapore groups, the top has a higher number of intrapores (Image 1 ), while the sum of areas and perimeters in the bottom is greater. Compared to the whole kernel, the sum of areas of intrapores is minimal (27X smaller), yet their sum of perimeters is significantly larger (2.77X greater). The intrapore sizes are much smaller (Image 2–Image 3) compared to the whole kernel (Image 4). Shape descriptors indicate that the intrapores are generally elongated (aspect ratio = Image 5; circularity = Image 6). The total volume of intrapores (Image 7) is only Image 8 of the total volume of the whole kernel (Image 9); however, the total intrapores' surface area (Image 10) is Image 11 of the whole kernel's surface area (Image 12) due to the phenomenon of new surface area generation. The trapezoidal method of integration over Simpson's is recommended because of its simplicity for volume, and the conical frustum method for surface area determination. The developed plugin produced rapid analysis (CPU time Image 13 for Image 14 image slices), and the methodology can be easily extended to other grains and agricultural products to inspect the overall internal quality, damage, or structure.