Location: Food Quality Laboratory2016 Annual Report
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.
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.
“Rapid Methods for Quality and Safety Inspection of Small Grain Cereals,” began in spring 2015. Progress during the first year of the project is described herein. In addressing the project’s first objective, a post harvest wheat quality procedure known as falling number (FN), used throughout the United States and elsewhere, was extensively evaluated in order to characterize its level of precision, and sources of error that negatively contribute to such. The procedure is an indirect measurement of the susceptibility of seeds (primarily wheat and barley) to break dormancy, germinate, and consequently compromise the value of these raw resources as ingredients in processed goods. Sales contracts, from first point of sale to export destination, often specify a minimum value for FN whereby grain failing to meet the value is either discounted in price or rejected altogether. One aspect of ensuring reliability and confidence in FN lies with the steps for obtaining a sample of grain, typically weighing 2 lbs, that is representative of the cargo lot from which it is drawn. When applied to the first point of sale, that is, wheat arriving by tandem wheel truck to the country elevator, sampling should be performed according to a USDA protocol that specifies probing at several locations throughout the truck bed. This study had two goals: first, to determine the degree of uncertainty associated with obtaining a representative sample of wheat from a truck bed; and second, to provide recommendations on whether retesting by re-probing is warranted in cases when FN is lower than the prescribed minimum. Both goals were aimed at facilitating the efficiency and fairness of U.S. wheat commerce, with details of the study as follows. Soft red and white commercial wheats from two seasons and two regions (states of Washington and Ohio) were sampled upon arrival at country elevators and a flour mill. By catch stream sampling or probe sampling, catches and probings were run in quadruplicate according to standard methodology for FN. Among-catches and among-probings variance estimates, inclusive of sampling error and method error, were quite small, typically with a standard deviation being less than 3 percent of the mean. Statistical modeling revealed that reanalysis of a truck’s cargo in the hope of obtaining a FN above a cutoff of, for example, 300 units is not advised when the initial value is more than 5 units from the cutoff. For the second component of the project’s first objective, we prepared mixtures of conventional and waxy wheat to precise mass concentrations ranging from 0% (pure waxy) to 100% (pure conventional) using breeders stock from three seasons and two locations as well as stock from commercial sources from one season. (As a background, “waxy” is a term used to describe cereals whose molecular makeup of endosperm starch consists almost exclusively of amylopectin at the expense of amylose. The unique processing characteristics of waxy wheat have been the impetus for developing new varieties through conventional plant breeding practices that possess this trait. A necessary component for the industry to accept the higher valued waxy wheat into the marketplace is a mechanism for verifying that waxy lots truly are waxy, a considerable challenge because visual differences between conventional and waxy are very slight or nil. Near-IR reflection spectroscopy becomes a logical candidate for consideration because of its reliability, ease of use, and general acceptance by the cereals industry.) Altogether, nine complete and independent mixture sets were prepared in a series of 1% and 5% portion by weight increments and scanned by near-IR reflectance in both bulk whole kernel and ground meal formats. The parent samples were controlled for protein content and moisture level between pair components in order to avoid spurious correlations to mixture level caused by these two macro constituents (easily measurable by near-IR). Our findings indicated that the procedure is capable of measuring the level of mixing to within five to ten percent of the weight fraction, which is considered to be sufficient for the wheat trade and processing industries. Thus, NIR spectroscopy may be used for estimating the level of “contamination” by conventional wheat waxy wheat lots when mixture levels are above 10% w/w. This technique can be immediately applied because of the longstanding acceptance, possession, and use of near-infrared spectroscopy in the cereals industries for the measurement of other quality determining properties such as protein, moisture, hardness, and ash. Wheat traders, millers, and processors stand to benefit from this research. Additional research is currently underway on the use of hyperspectral imaging (1000-1700 nm) on bulk intact seed samples to detect waxy mixtures, with the advantage being that identification of the waxy or non-waxy condition is made at the seed level. In satisfying the one active component of the project’s second objective, non-destructive cereals quality evaluation was directed toward mold-damaged wheat kernel detection by hyperspectral imaging, whereby hundreds of kernels arranged randomly in a single layer are examined and image processing is used to identify the damaged kernels. Based on our findings of several years ago, specific wavelengths are selected for differentiating sound and damaged categories. Work is currently underway on developing an imaging algorithm using two or three wavelength bands that will identify kernels damaged by Fusarium head blight, in which damaged kernel percentages are compared to the conventional method of analysis, this being human visual inspection.
1. Measuring mixture levels of waxy and conventional wheat by near-IR reflection. Starch makes up approximately three quarters by weight of the whole wheat kernel and is in even greater proportion upon the milling of wheat into flour. With starch consisting of two macromolecules, amylose and amylopectin, despite their similar molecular structure, the proportions of these affect the properties of the finished product. The milling and processing industries desire a rapid and workable procedure that will allow the verification of identity preserved higher valued low-amylose, ‘waxy’, wheat lots. USDA-ARS scientists at Beltsville, Maryland, developed a procedure based on near-infrared (NIR) spectroscopy to fill this need. This technique can be immediately applied because of the longstanding acceptance, possession, and use of NIR spectroscopy in the cereals industries.
2. Sensing food contamination by hyperspectral imaging. USDA-ARS scientists in Beltsville, Maryland, developed non-destructive spectral imaging methods to examine sources of contamination in food, be it by nature (e.g., mold in cereal grains) or by deliberate, nefarious adulteration (e.g., melamine in milk powder). Analytical laboratories traditionally rely on highly technical methods for detecting contaminant levels; however, these methods require levels of operator skill and analysis time that make them ill-suited for industrial food processing facilities. Near infrared hyperspectral imaging is a simpler technique that utilizes light wavelengths just beyond the visible light region to analyze food products. This work has the potential to benefit manufacturers of food powder by offering a method that is readily adaptable to industrial processing operations.
Delwiche, S.R., Graybosch, R.A. 2016. Binary mixtures of waxy wheat and conventional wheat as measured by nir reflectance. Talanta. 146:496-506.
Delwiche, S.R. 2015. Basics of spectroscopic analysis. In: Park, B., Lu, R., editors. Hyperspectral Imaging Technology in Food and Agriculture. New York, NY: Springer. p. 57-79.
Huang, M., Kim, M.S., Delwiche, S.R., Chao, K., Qin, J., Mo, C., Esquerre, C., Zhu, Q. 2016. Quantitative analysis of melamine in milk powders using near-infrared hyperspectral imaging and band ratio. Journal of Food Engineering. 181:10-19.
Huang, M., Kim, M.S., Chao, K., Qin, J., Mo, C., Esquerre, C., Delwiche, S.R., Zhu, Q. 2016. Penetration depth measurement of near-infrared hyperspectral imaging light for milk powder. Sensors. 16(4):441.