Detection of Sitotroga cerealella (Olivier) infestation of Wheat Kernels Using Hyperspectral Reflectance

Abstract

Hyperspectral reflectance data were used to detect internal infestations of Angoumois grain moth, Sitotroga ceralella (Olivier), in wheat kernels. Kernel reflectance was measured with a spectroradiometer over a wavelength range of 350–2500 nm. Kernel samples were selected randomly and scanned every 7 d after infestation to determine the ability of the hyperspectral reflectance data to discriminate between infested and uninfested kernels. Immature stages of S. ceralella inside wheat kernels can be detected through changes in moisture, starch, and chitin content of the kernel. By using the spectrally-derived moisture variable (Log[1/R972nm]-Log[1/R1032nm]) and starch variable (Log[1/R982nm]-Log[1/R1014nm]), it was possible to discriminate between infested and uninfested wheat kernels with 100% classification accuracy based on 90% confidence intervals. Significant differences in the spectral reflectance between the infested and uninfested kernels were due to changes in moisture and starch content in wheat kernels. Three of the four chitin variables showed slight discrimination between the infested and uninfested wheat kernels based on 90% confidence intervals with 63.9%, 68.8%, 66.7%, and 41.6% classification accuracy of the three variables (Log[1/R1130nm]-Log[1/R1670nm]), (Log[1/R1139nm ]-Log[1/R1320nm]), (Log[1/R1202nm]-Log[1/R1300nm]), and (Log[1/R2046nm]-Log[1/R2302nm]), respectively. Spectral reflectance changes as a function of wheat kernel position relative to the spectroradiometer sensor did not differ significantly (P > 0.10).