Location: Food Quality Laboratory
2020 Annual Report
Objectives
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.
Progress Report
OSQR certification has been received for new project plan entitled “New Sensors and Methods for Phenotypic Analysis of Small Grains,” project number 8042-44000-003-00D. This is a final report on 8042-44000-001-00D.
Significant progress was made in the objectives of this project, which falls under National Program 306, Component 1, Problem Statement 1.A: Define, Measure, and Preserve/Enhance/Reduce Attributes that Impact Quality and Marketability. At the request of USDA Federal Grain Inspection Service (FGIS), a study was concluded on identifying candidates for use as a standard material for the ‘falling number’ procedure, a method that is practiced in government inspection and private industry operations to ensure high quality in U.S. wheat. (In short, falling number is the name given to a test and instrument that measures viscosity of a heated mixture of ground wheat and water. Viscosity is strongly influenced by alpha-amylase, the enzyme that breaks down starch into smaller chain molecules and, eventually, into glucose. Excessive enzyme activity translates into poor product quality.) After developing and executing a protocol to evaluate four candidates, the results of a several-month study indicated that of four native, unmodified starches tested, corn starch was the most precise, followed by, in order of declining precision, starches from rice, wheat, and potato. Further, falling numbers of all starches were invariant with time, indicating that the starches possess a suitably long shelf-life for use as standard reference materials.
Following the encouraging findings of starch candidates for falling number standard reference material, we selected the best material, corn starch, for a more extensive evaluation involving instruments in several USDA FGIS laboratories in the continental U.S. The newer study has sought to further characterize falling number performance using corn starch under the conditions of a well-regulated network of eight laboratories at elevations varying from 0 to 330 m. We found that when corrected to a common barometric pressure, such as that experienced at sea level, falling numbers within the network are in close agreement [range (highest minus lowest) is less than 10 percent of their average]. Two testing scenarios for an eventual monitoring program were envisioned: 1) categorically performing five runs of the standard material in succession without judging whether any run is an outlier, and 2) performing runs in succession until three qualifying (absolute difference between twin concurrent ‘tests’ being ten percent or less of their average) runs (where a ‘run’ consists of two simultaneous tests of a left column and a right column) are completed is acceptable. Control chart-type limits were determined for the acceptable dispersion of values from a single test event within a laboratory for each scenario, using a common statistical distribution model. The results indicated that all laboratories and biweekly evaluation periods fell within the modeled 99th percentile estimates of 6.0% (scenario 1) and 8.1% (scenario 2). Similarly, when control chart-type limits were calculated for across laboratory dispersion using another common statistical model, the 1st and 99th percentile estimates for instrument bias, defined as the difference between a laboratory’s testing event average and the corresponding mean for the other laboratories in the network, were approximately -10% and 10% for both scenarios. As of early summer 2020, the study results have been furnished to USDA FGIS and the draft of a manuscript for submission to a scientific journal is complete.
Also addressing the Component 1, Problem Statement 1a of NP306, as described above, a study was conceived and performed on whole wheat noodle quality. Whole wheat products have become increasingly popular in the human diet for reasons of improved nutrition and health. However, the presence of the components other than the starchy endosperm (i.e., white flour) typically results in flour having a shortened shelf life. A possible solution for lengthening shelf life is thermal processing of the bran whereby compounds prone to degradation are stabilized, and enzymes responsible for promoting degradation reactions are deactivated. Although the properties of many whole wheat products such as bread have been extensively studied, much less is known about how such formulations affect salted noodles, a popular product in Asian countries. Even less is known about the consumer's perception of appearance, mouthfeel, and flavor of the whole wheat version of this product. Through research known as sensory property analysis, we performed a study using a trained panel of ten individuals to assess the properties of five thermal processing treatments (autoclaving, roasting, jet-cooking, puffing and extruding) of isolated wheat bran, which, when combined with refined flour, produced whole wheat flours that were the primary ingredients in salted noodles. Using refined flour milled from one soft red winter wheat standard mixed with thermally processed commercial wheat bran (85:15, flour:bran, dry basis), noodles from the five bran treatments plus control (untreated bran) were presented to the panel, which scored various properties associated with appearance, mouthfeel, and flavor. The properties, uniformity, smoothness, glossiness, opacity, color, firmness, wheatiness, roastedness, bitterness and aftertaste showed statistically significant differences. Additionally, we found treatment differences for texture (tensile and compressive strength of noodles, which are instrument measurements related to mouthfeel), color, cooking by-products, noodle hydration (water uptake) during cooking, solids loss to cook water, and color of the cooked noodles. The puffed treatment often had the most extreme values in the direction of desirability. The jet-cooked treatment, on the other hand, was often extreme in the other direction. All bran treatments produced acceptable noodles, including the 'treatment' that involved no thermal processing of wheat bran, as judged by panelists' scores and by the separate chemical and physical analyses.
Lastly for falling number, we continued work on distinguishing the two causes of low falling number: pre-harvest sprouting (PHS, brought on by rainy weather before harvest) and late-maturity amylase (LMA, brought on by which large daily temperature fluctuations during the period of grain fill). Because the former condition affects end-product quality more severely than the latter, a rapid method to distinguish between the two conditions (currently not available) is desired by the wheat processing industry. We have been developing heating and shear (paddle speed) protocols for use by the ‘rapid visco analyser’, a viscometer developed for the cereals industry that we hypothesize will elucidate the differences in viscosity of wheat meals brought on by the action of one, starch cleaving enzyme alone (LMA), versus that from when the starch-cleaving enzyme is active along with protein- and lipid-cleaving enzymes. Additionally, a simpler test was developed that examines the electrical conductivity of soak water from a fixed mass of seeds. A preliminary study using six samples from each cause (PHS, LMA) revealed conductivity differences consistent with cause; however, the limited availability of samples has necessitated a broadening of genetic and environmental variation in future work in order to confirm the findings.
In addressing the project’s second objective (but also related to falling number research), we obtained wheat lines of varying degrees of pre-harvest sprouting for analysis by hyperspectral imaging (HSI) from a cooperating ARS laboratory in Pullman, Washington. Likewise, lines afflicted with another condition, known as late maturity amylase, were also obtained. Together, these two conditions predispose wheat with heightened levels of the enzyme responsible for breaking down starch into smaller sugar-like molecules, a biological process that is necessary for the natural process of seed germination but usually deleterious for mankind’s use of the seed for processing into flour. Similar to our completed study on the use of HSI to examine wheat infected with Fusarium Head Blight (scab), this technology has promise for non-destructive rapid analysis of cereal seeds for food safety and quality inspection. Our goal in the most recent year has been to determine whether wheat kernels with high enzyme activity can be identified, either through a sensitivity to chemical changes in the outermost layer of the starchy endosperm (the aleurone layer) and the endosperm itself, or through physical changes to the seed as it begins the germination process. We are currently exploring the use of traditional (linear) models for quantitative (concentration-type) and qualitative (e.g., low, medium, high) analyses, as well as newer (non-linear) models, such as neural networks for the same purpose.
Accomplishments
1. Standard reference material for the Falling Number wheat quality indicator. The Falling Number is a method used the world over to measure the quality of harvested wheat. There is a need to identify a stable standard reference material for increased levels of precision and accuracy. USDA ARS scientists at Beltsville, Maryland, determined that corn starch is an excellent material because of its high precision in Falling Number tests, long shelf life, and low cost. Government regulatory agencies in the United States and Canada and private grain-handling and milling companies are developing the acceptable limits for a standard chart for the wheat Falling Number.
Review Publications
Delwiche, S.R., Tao, H., Breslauer, R., Vinyard, B.T., Rausch, S.R. 2020. Is it necessary to manage falling number in the field? Agrosystems, Geosciences & Environment. https://doi.org/10.1002/agg2.20014.
Delwiche, S.R., Morris, C.F., Kiszonas, A. 2019. Compressive strength of Super Soft wheat endosperm. Journal of Cereal Science. https://doi.org/10.1016/j.jcs.2019.102894.
Delwiche, S.R., Rausch, S.R., Vinyard, B.T. 2020. Evaluation of a standard reference material for falling number. Cereal Chemistry. https://doi.org/10.1002/cche.10259.
