Location: Food Quality Laboratory
2011 Annual Report
Hyperspectral image analysis will also be used to examine three wheat milling properties: milling yield (% straight grade flour) defined as the percent by mass of all flour fractions recovered through a 94-mesh screen; solvent retention capacity in 50% (w/w) sucrose solution, a measure of the water affinity of the macro-polymers (starch, arabinoxylans, gluten, and gliadins); and solvent retention capacity in 5% (w/w) lactic acid, an indicator of gluten strength.
Near-IR spectroscopy will explored as a method for measuring the degree of waxiness in hexaploid wheat. Wild type, partial waxy (waxy null alleles in one or two genomes), and waxy samples (null alleles in all genomes), drawn from breeders' advanced lines of hexaploid wheat, will be used. Gel electrophoresis will be used to identify the waxy protein (granule bound starch synthase, GBSS) in each sample.
Lastly, a near-IR procedure for wheat gluten quality will be developed in conjunction with a rheological procedure. The wheat samples consist of approximately 50 lines grown in field replicated (3x) plots over three consecutive seasons. Half of these lines are transgenic, in which the gene construct modifies the length of the central repeat region within the high molecular weight (HMW) glutenin subunits. Different levels of gene expression, hence, level of glutenin protein, are represented as a function of the transgenic ancestor. Thus, this set will contain a much wider range in the ratio of glutenin-to-gliadin than naturally encountered. Flour from these samples will be evaluated for glutenin and gliadin contents by SE-HPLC another ARS laboratory. At Beltsville, the flour will be scanned in the NIR and FT-mid-IR regions. Rheological properties, such as the recovery response for a gluten specimen subjected to a controlled regiment of compressive force and hold time, will be measured at a third laboratory. Spectral calibrations for glutenin and gliadin concentrations, as well as calibrations for the rheological parameters (percent recovery and recovery time constant), will be developed using partial least squares regression. Additionally, classification algorithms (PLS discriminant analysis and SVM) algorithms will be developed that will identify the genetically modified lines based on their spectral response.
Image processing routines were written to transform raw spectral image data into usable characterizations of internal physical and chemical properties of the cereal grains (wheat, barley) for assessment of quality and potential safety (mold) concerns. Work is nearly complete on applying linear discriminant analysis (LDA) to categorize (poor to excellent) wheat according to the milling characteristics as measured on a pilot mill housed at the USDA Soft Wheat Quality Laboratory.
A study was completed on using NIR reflectance spectroscopy to distinguish waxy and partial waxy states in common (bread) wheat. Waxiness is a condition of endosperm starch that chemically describes the lack of branching in the molecule, with long linear chains of the repeating unit attributed to the waxy condition. The degree of branching in turn affects the processing characteristics of wheat starch, either for food or industrial purposes. From the genetic standpoint, waxiness is controlled by just one gene that encodes for synthesis of the enzyme, granule bound starch synthase, which in turn regulates the production of the linear form of the starch molecule. Because common wheat is hexaploid (three sets of chromosome pairs), eight genetic combinations exist. For the study, all combinations were available, these being the fully waxy condition, the six intermediate (‘partial’) waxy states, and the wild type condition. Using LDA (see above) on NIR spectra of three forms (ground, whole, and single kernel), the findings showed that the fully waxy wheat is recognizable at 90-100% accuracy. However, accuracy drops off with the other states, even when the intermediate waxy states are combined. The spectral sensitivity to the amylose-lipid complex is suggested as the major contributing factor to waxiness classification success.
Research began on developing a single kernel imaging system that captures images of wheat kernels cascading in air from a feeder. The system allows for the simultaneous viewing of two opposing sides of a kernel. Morphological (size and shape) and kernel surface (texture) properties are addressed. This real time imaging system is to be used in inspection operations and its principles can be applied to online operations in flour mills for removing damaged and diseased kernels from the mainstream. Design of hardware, optics, and software to capture images was completed, and work continues on reducing image processing time. The targeted conditions are fusarium damage, black point, and heat damaged kernels.
Delwiche, S.R., Graybosch, R.A., St Amand, P., Bai, G. 2011. Starch waxiness in hexaploid wheat (Triticum aestivum L.) by NIR reflectance spectroscopy. Journal of Agricultural and Food Chemistry. 59:4002-4008.
Delwiche, S.R., Kim, M.S., Dong, Y. 2011. Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging. Sensing and Instrumentation for Food Quality and Safety. 5:63-71.
Yang, I., Delwiche, S.R., Kim, M.S., Tsai, C., Lo, Y. 2009. Determination of wheat kernel black point damage using hyper-spectral imaging. Journal of Agricultural Machinery. 18:29-44.
Kim, M.S., Chao, K., Chan, D.E., Jun, W., Lefcourt, A.M., Delwiche, S.R., Lee, K. 2011. Line-scan hyperspectral imaging platform for agro-food safety and quality evaluation: System enhancement and characterization. Transactions of the ASABE. 54(2):703-711.
Kim, M.S., Chao, K., Chan, D.E., Yang, C., Lefcourt, A.M., Delwiche, S.R. 2011. Hyperspectral and multispectral imaging technique for food quality and safety evaluation. In: Cho, Y., Kang, S. editors. Emerging Technologies for Food Quality and Food Safety Inspection. New York, N.Y.: CRC Press. p. 207-234.