Discrimination of Sound and Granary-Weevil-Larva-Infested Wheat Kernels by Near-Infrared Diffuse Reflectance Spectroscopy

Abstract

Sound and infested wheat kernels containing late-instar granary weevil larvae, as identified by X-ray analysis, were used to evaluate the ability of near-infrared (NIR) spectroscopy to predict the presence of insect larvae in individual wheat kernels. Diffuse reflectance spectra at 1100-2500 nm were recorded from individual infested and sound kernels. Principal component analysis (PCA) of NIR spectra from sound kernels was used to construct calibration models by calculation of Mahalanobis distances. Calibration models were then applied to spectra obtained from both sound and infested kernels in a separate validation set. A 5-factor PCA model using data from a first-derivative spectral transformation was the best model for correctly classifying kernels in an expanded validation sample set, including 100% of sound, 93% of infested, 95% of sound air dried, 86% of infested air dried kernels, and 90% of sound kernels from 6 wheat varieties. Calibrations using the spectral region from 1100 to 1900 nm were least sensitive to kernel moisture differences. Similar results were obtained when discriminant analysis was applied to log 1/R data from selected discrete wavelengths of NIR spectra.