Location: Food Quality Laboratory
Project Number: 8042-44000-001-000-D
Project Type: In-House Appropriated
Start Date: May 6, 2015
End Date: May 5, 2020
Objective:
1: Enable new or refine commercial optical, viscometry and physical technologies that integrate indicators of starch soundness in wheat and barley.
A. Identify sources of variation in viscometry-based methodologies (e.g., falling number) that are used to indicate starch soundness, then develop and test alternative procedures to reduce precision error.
B. Develop a near-infrared spectroscopy model, or alternatively a Raman spectroscopy model, for ascertaining mixture levels of conventional and waxy hexaploid wheat.
2: Enable new, real-time rapid optical methods to measure defects and vitreousness in commercial wheat kernels.
A. Develop hyperspectral imaging procedures for identification of wheat kernels damaged by scab (Fusarium head blight), black point, heat, and frost, as defined by official inspection criteria.
B. Develop imaging system for assessing the percentage of vitreous kernels in durum and hard red spring wheat.
Approach:
Determine the underlying precision of the falling number procedure under ideal conditions, that is, the test when run with samples for which sampling error has been minimized. Samples of soft white wheat and three samples of club wheat that are representative pre-harvest sprouting will be evaluated. Within each subclass, the three samples are designed to be representative of low (< 300 s), moderate (300-375 s), and high (> 375 s) falling number. All thermal conditions of the falling number instrument within a test day will be held constant as practicable, including the starting temperature of the meal-water mixture, the temperature of the instrument’s bath, the volume of water within the bath, and the mass and volume of the meal-water test sample. In addition to the runs that are based on the conventional amount of meal and water (treatment A: 7 g + 25 mL), three alternate preparations will be examined. Treatment (A, B, C, D) and wheat class or subclass (SWW, Club) will be specified as fixed factors. Samples within wheat class, grind, and portions will be specified as random factors. Minimum-width 95% confidence intervals for the variance estimates will be calculated as wil best linear unbiased predictors (BLUPs) obtained from the mixed model provide estimates of FN averages for each treatment and wheat class sample. Once the precision of the FN method under standard and modified conditions has been established, the research will proceed in two tacks. First, the issue of sampling error will be addressed. Sampling will occur at typical transfer points, such as the truck dump bin at the country elevator, railcar fill sites, and the entry point to the mill. Second, if under standard operation the procedure produces coefficients of variation greater than 3%, modifications to the mixing and heating stage will be explored in an attempt to reduce air bubble entrainment, with rheological measurements (conducted in parallel) performed using a rheometer under constant shear and heating rate conditions.
NIR calibrations for mixture levels of conventional and waxy wheat will be developed with accuracies < 5%. Both Linear and non-linear quantitative modeling will be performed on both whole kernel and ground meal binary mixtures.
In hyperspectral imaging research of wheat kernels for damage or vitreousness, PCA will be examined to identify the wavelengths at local minima and maxima which inherently possess the greatest contrast. The 10 most sensitive wavelengths will be examined in paired combinations exhaustively (45 trials) using reflectance ratios. Linear discriminant analysis will be used to identify the wavelength pair whose image band ratio produces the greatest percentage of correctly classified kernels, and so on for the next pair. Three regions of interest (ROI) on the kernel, namely the endosperm, germ, and entire kernel, will be separately examined. With each ROI, image processing will be done at the pixel level, whereby subregions of damage in the ROI are first identified; then, depending on the size of the subregion, a decision will be made on whether to categorize the ROI and/or the kernel as normal or damaged.
