| Uric acid has previously been proposed as an indicator of insect contamination in grains and cereal products. However, little is known about the fate of uric acid during processing. To determine the fate of uric acid during wheat milling, samples of hard red winter wheat were inoculated with kernels containing late instar granary weevil (Sitophilus granarius) larvae. The samples were milled on a Buhler experimental mill to produce six flours and two millfeed fractions. Uric acid was quantified by reversed-phase HPLC, using ion-pairing with tetrabutylammonium phosphate and ultraviolet detection. Over 90% of the uric acid was in the flour with approximately 50% in the first break fraction. Only about 10% of the original uric acid was in the millfeed. The stability of uric acid during the extrusion of wheat flours was also investigated. Flours contaminated with uric acid were extruded on a Brabender single screw extruder at feed moisture contents of 28, 30 and 32% (db) and temperatures of 120, 140, and {dollar}160spcirc{dollar}C. It was found that 62% to 80% of uric acid in flour survived extrusion, depending on the extrusion conditions.; To evaluate the ability of near-infrared spectroscopy to detect internal insect infestation, sound and infested wheat kernels containing late instar granary weevil larvae were used. Identification was made based on qualitative analysis of the near-infrared (NIR) reflectance spectra of individual wheat kernels using discriminant analysis. Principal component analysis (PCA) of NIR spectra from sound kernels was used to construct calibration models by the calculation of Mahalanobis distances. The spectral region from 1100 to 1900 nm gave the best results. A five factor PCA model using spectral data from a first derivative transformation was the best model for correctly classifying kernels in an expanded sample set with an accuracy over 90%. Similar results were obtained when discriminant analysis was applied to log 1/R data from selected wavelengths of NIR spectra. Application of NIR spectroscopy and pattern recognition techniques for identifying internally infested wheat kernels is promising. |
