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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Food Quality Laboratory » Research » Research Project #438413

Research Project: New Sensors and Methods for Phenotypic Analysis of Small Grains

Location: Food Quality Laboratory

Project Number: 8042-44000-003-00-D
Project Type: In-House Appropriated

Start Date: May 7, 2020
End Date: May 6, 2025

Objective 1: Enable new or refine commercial viscometry, spectroscopic imaging, and physical technologies that integrate indicators of wheat endosperm integrity. • Sub-objective 1.A. Develop a physicochemical procedure that distinguishes between pre-harvest sprouting and late maturity amylase. • Sub-objective 1.B. Develop a standard reference material for the falling number procedure, to be used in official inspection operations. Objective 2: Enable new, real-time, rapid optical methods to detect and measure sprouting, mold, and black point in harvested grain. • Sub-objective 2.A. Develop spectral imaging procedures for identification of wheat seeds damaged by black point, insect, and arrested development (immaturity). • Sub-objective 2.B. Develop imaging procedures for assessing dormancy in wheat lines when challenged with conditions favoring germination.

Wheat samples from annual breeders evaluation lines will be measured for falling number (FN). Samples with FN less than 300 s will be selected for further analysis. For these samples, a minimum of 30 seeds will be bisected transversely along the seed axis, and brush-end halves will be accumulated, as will the germ-end seeds. Amylase activity will be measured for each group. Because the late maturity amylase condition produces elevated activity more uniformly throughout a seed’s aleurone layer compared to the pre-harvest sprout condition (with activity concentrated near the germ), samples with equivalent amylase activities between the halves will be identified as LMA, and those with imbalanced amylase activities will be identified as PHS. Proteolytic enzyme activity will be measured using an azocasein substrate. The results of this assay will be confirmatory on whether PHS or LMA is the cause for elevated amylase activity as the latter condition should not produce elevated proteolytic activity. As with FN, the basis of the analytical procedure to be developed will be viscometry. Four native (unmodified) starches, wheat, corn, potato and rice, will be obtained from a laboratory chemical supplier. Rather than adding enzyme to a full fixed mass to bring FN down to a practical range, pure starch masses without added amylase will be adjusted to produce a target FN of 300 s at constant volume of added water (25 mL). Preliminary studies suggest masses of 6.25 g for potato, 5.0 g for wheat and rice, and 4.0 g for corn starches. Weekly runs will be collected on all four starches (5 twin-tube runs for each) on each of two FN instruments over a three-month period. FN stability across time will be examined by calculating the among-weeks and instrument x weeks variance components for each native starch. A repeated measures ANOVA will be performed to reveal effects of starch type, time, instrument, and their interactions. Starches will also be characterized experimentally for amylase activity, amylose/amylopectin ratio, and the presumably nil contents of nitrogen and ash. An imaging system will be developed that can be used as an inspector’s assistant to grade wheat and identify the various defect categories (e.g., mold, black point, frost, heat). Image analysis will be a multistep process. A first region of analysis (ROI) will encompass the section of the grain that is exclusively endosperm. A second ROI will be a section that includes endosperm and, for black point, the brush end; a third ROI will be the entire grain as identified by a masking procedure. With each ROI, image processing will be done at the pixel level, whereby subregions of defect 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 grain as normal or defective. Image analysis will also be used to develop a procedure for evaluating preharvest sprouting propensity in wheat breeding programs that can replace laborious and subjective methods of visually counting germinated seed and categorizing severity of sprouted seed within spikes.