2013 Annual Report
1a.Objectives (from AD-416):
(1) Develop spectroscopic imaging procedures (hyperspectral or multispectral) that can be used to assess quality of small grains (emphasizing wheat, rice, and barley) and oilseeds in bulk for grade, class, foreign material, and damage (mold, black point, heat, frost, and insect). (1a). Develop hyperspectral image analysis algorithms for determination of the level of mold damage from Fusarium Head Blight on wheat kernels. This will involve the development of image processing routines that identify the infected kernels in representative samples of intact wheat kernels and determine the regions of Fusarium damage within each infected kernel. (1b) Develop hyperspectral image analysis algorithms for identification of wheat kernels damaged by heat, frost, black point, and insects, as defined by official inspection criteria. (1c) Develop hyperspectral image analysis algorithms for the prediction of flour yield and break flour yield in soft red winter wheat. (2) Develop optical and mechanical methods and instrumentation for grain quality measurement that are applicable at points of sale, such as elevators, terminals, and mills. (2a) Develop rapid and objective optical methods for prediction of starch quality indicators in wheat, such as the ratio of amylose-to-amylopectin, and the identification of wheat into three states of waxiness: waxy, partial waxy, and wild type. (2b) Develop a near-infrared (NIR) spectroscopy procedure for wheat gluten quality determination for use in commerce.
1b.Approach (from AD-416):
Fusarium-inoculated hard red spring and hard red winter wheat samples will be imaged using an in-house near-IR hyperspectral system. Image analysis will be a multistep process. First, for each kernel a mask will be created from one of images whose wavelength creates a strong contrast between kernel and background. The mask will be applied to the images at all other wavelengths in order to remove the background. Principal component analysis (PCA) loadings from images of damaged and normal regions will be examined to identify the wavelengths at local minima and maxima, which inherently possess the greatest contrast between Fusarium damage and healthy endosperm.
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.
At the suggestion of the wheat milling industry, a study was implemented to develop a rapid and reliable near-infrared (NIR) spectroscopy-based method for measuring the amount of mixing of conventional wheat in low-amylose, or waxy, wheat. Waxy wheat varieties, developed during the past decade by conventional plant breeding practices, are now reaching the point of commercial interest. Anticipating the need for authenticating the impurity level of nonwaxy in waxy lots, a study was devised that will produce an analytical method, based on NIR spectroscopy (a common methodology used by the industry) to measure impurity. Samples of 2011- and 2012-harvested waxy wheat from two geographical locations (Nebraska and Arizona) and two color classes (red and white) were individually paired with conventional wheat from the same year, location, and color. Controlling factors included overall contents of moisture, protein, and lipid levels. Mixtures ranging from 0% (pure waxy) to 100% (pure conventional) of waxy and nonwaxy kernels, in steps of 5% (w/w) or less (near either end of pure state), were prepared and spectroscopically measured in both bulk whole kernel and ground meal bases. Multiple linear regression and partial least squares regression models were developed and compared to determine the best physical format of the sample (meal or bulk), wavelength region (subintervals within 950-1,650 nm for bulk, and 1,100-2,500 nm for meal), and spectral preprocessing (normalization, smoothing, and derivatization). The advantage of the near-infrared wavelength region for model development is the ease of sample preparation (virtually none) and widespread availability of highly precise equipment; however, the disadvantage of this region lies with a general lack of specificity in distinguishing closely related oligosaccharide molecules, exemplified in this study as the difficulty in measuring the abundance of branches in the amylopectin molecule (from C1 to C6 bonding of the glucose rings, characteristic of amylopectin) compared to the linear form (from C1 to C4 bonding) that is characteristic of amylose. Consequently, NIR spectral sensitivity to the waxy condition appears to also include physical (particle size distribution) and starch-lipid interaction.
Other work in the past year included the development of algorithms utilizing NIR spectroscopy for the evaluation of freshness of white rice. This research was an integral component of a visiting (from National Taiwan University) graduate student’s research and stems from the desire of the rice industry to have a reliable method to ensure that fresh rice from the newest crop season is not co-mingled with old season rice that is inherently of lower market value. Using a statistical technique known as independent components analysis (ICA), rice freshness was distinguished by reduction of the spectral data to independent components 2, 3 and 4.
Measuring mixture levels of conventional and waxy wheat at point of sale. In the past decade USDA scientists have developed varieties of hard wheat that are amylose-free, otherwise known as ‘waxy’ wheat. These new varieties hold promise to offer unique processing and cooking properties for both food and industrial uses. An incentive to the grower in adopting waxy varieties is the anticipated premium to the market price; however, for this to happen, a rapid and reliable system must be put in place that can authenticate the purity state of wheat that is brought to market. This is potentially a nettlesome problem because of the general similarity in appearance between conventional and waxy wheat. Building on their earlier fundamental work that examined the distinguishing features in the near-infrared (NIR) spectra of the eight genotypes of the waxy condition, scientists at Beltsville have successfully developed an NIR method that can measure binary mixture levels of waxy and conventional hard white and red wheat down to a level of a few percent of the minority component. Given that near-infrared spectroscopy instruments are commonly used at grain country elevators and mills, this method can be readily implemented by the industry. This means that testing for quality assurance of the waxy condition of a wheat lot can be performed in real time at point of sale and then at later points (e.g., mills, processors) to ensure stated levels of purity.
Delwiche, S.R., Souza, E.J., Kim, M.S. 2013. Near-infrared hyperspectral imaging for milling quality of soft wheat. Biosystems Engineering. 115:260-273.
Kim, M.S., Delwiche, S.R., Chao, K., Lefcourt, A.M., Chan, D.E. 2012. Visible to SWIR hyperspectral imaging for produce safety and quality evaluation. Sensing and Instrumentation for Food Quality and Safety. 5(5):155-164.