2009 Annual Report
1a.Objectives (from AD-416)
Develop instrumentation and procedures for objective grading, on-line quality measurement, sorting, and correlation of grade and quality measurements of single kernels and bulk samples to end-use properties of cereal grains and their products.
1b.Approach (from AD-416)
Develop rapid sensing and sorting technology for single kernel 'micro' traits such as GMO's, protein quality, starch quality, toxins, potential biosecurity issues, and traits important for evaluating grain for non-food uses (fuel, plastics, etc). Specific projects will include: development of high-speed (>100 kernels/s) sorting techniques for separating corn kernels infested with various molds, including mycotoxin producing molds; and develop methods to detect mutant corn kernels for enhancement of breeding programs.
Develop rapid (~1 kernel/s) sensing and sorting technology for single kernel 'macro' traits such as hardness, moisture content, oil, starch, class, internal insects, and protein, especially for grains other than wheat (sorghum, corn, oats, etc). Specific projects will include: development of a single kernel characterization system to help predict milling yield in corn and wheat; and use of advanced signal processing from various sensors to rapidly and automatically detect moldy and insect damaged kernels.
Develop techniques to predict end-use characteristics, and to determine the accuracy and impact of these predictions. This includes studying the synergy of combining multiple measurements, evaluating sampling plans, assessing risk of various methods, conducting epidemiological studies, etc.
Color image based sorter for separating red and white wheat: Simple imaging system was developed to inspect and sort wheat samples and other grains at moderate feed-rates. The sorter is able to separate hard red kernels from hard white kernels (95% to 99%) and is an economical and useful instrument for sorting wheat and other grains with high accuracy. These devices are now used by over ten wheat breeders across the country.
Hardware-based image processing for high-speed inspection of grains: A high-speed, low-cost, image-based sorting device was developed to detect and separate grains with slight color differences and small defects on grains. Throughput of the sorter is approximately five times that of previously developed sorting systems that use imaging technology. Testing of the system resulted in accuracies well above what can be accomplished with currently available sorting systems. The sorter should find uses for removing other defects found in grain, such as insect-damaged grain, scab-damaged wheat, and bunted wheat.
Selecting and Sorting Waxy Wheat Kernels Using Near-Infrared Spectroscopy: An automated single kernel near-infrared (NIR) sorting system was used to separate single wheat kernels with amylose-free (waxy) starch from reduced-amylose (partial waxy) or wild-type wheat kernels. Our results demonstrate that automated single kernel NIR technology can be used to select waxy kernels from segregating breeding lines or to purify advance breeding lines for the low-amylose kernel trait. Calibrations based on either amylose content or the waxy trait performed similarly.
Measuring Grain and Insect Characteristics using NIR Laser Cluster Technology:
The potential of using an eight-wavelength near-infrared (NIR) laser cluster spectrometer for measuring wheat quality (hardness index, protein content, moisture content, and waxy character) and determining tsetse fly pupae sex was investigated and compared to a commercial single kernel near infrared (SKNIR) system. This research showed that a NIR laser cluster system can be used to predict some grain and insect traits with acceptable accuracy, and some predictions can likely be improved if other wavelengths are used in the laser cluster system.
NIR absorbance characteristics of deoxynivalenol and of sound and Fusarium-damaged wheat kernels: Near infrared (NIR) absorption spectra of deoxynivalenol (DON) and single wheat kernels with or without DON were examined. Shifts in absorption peak positions between the two types of kernels were observed at 1425-1440 nm and 1915-1930 nm.
An automated single kernel near-infrared system was used to select kernels to enhance end-use quality of hard red wheat breeder samples. Samples were sorted, re-planted, and the progeny evaluated. Average hardness index of the harvested wheat samples for segregating populations improved significantly.
Used a recently developed near-infrared reflectance spectroscopy instrument to collect spectra of individual maize kernels from diverse maize germplasm. The NIR was found to be a valuable and practical tool for high throughput quantification of the major chemical constituents in single maize kernels.
Commercialization of hidden stored grain insect detection system. Detection of Wheat Kernels with hidden insect infestations using an electrically conductive roller mill: Grain kernels infested by insects may show no indication on their exterior, but often contain hidden larvae. Although grain is always inspected for insect infestations upon shipping and receiving, many infested samples go undetected. Many methods for detecting infested wheat have been developed but none has seen widespread use due to expense or inadequate accuracy, or both. In this study, a simple laboratory roller mill system was modified to measure and analyze the electrical conductance of wheat as it was crushed. This facilitated detection of wheat kernels with live insects hidden inside of them. Furthermore, the apparatus is low cost (~$1500 for parts) and can inspect a one kg sample in less than one minute. A CRADA was formed to produce and market commercial versions of the roller mill and the first salable version is complete. The technology is currently being adopted by General Mills, Inc. More widespread adoption of this technology is expected in the next few years.
Automated detection of scab damaged wheat kernels. Fusarium head blight (FHB), or scab, is a destructive disease of wheat. FHB causes yield reductions of up to 50% and crop losses in the US have exceeded $1 billion in some years. In addition, FHB can produce the toxin deoxynivalenol which must be below FDA guidelines. Visible detection of FHB is laborious and subjective and ARS scientists in Manhattan, KS evaluated the use of automated near-infrared technology to detect FHB. Results showed that visual detection was strongly correlated to NIR detection and that the NIR method was more repeatable. This technology should help the grain industry more consistently detect FHB and thus improve the safety of the US food supply. The technology can also be used to rapidly screen new wheat lines for FHB resistance.
NIR optical characteristics of Deoxynivalenol determined. Developed rapid near infra red (NIR)techniques for nondestructive automatic sorting of Fusarium damaged wheat kernels and for estimation of deoxynivalenol (DON) levels in single wheat kernels. ARS scientists in Manhattan, KS studied NIR optical characteristics of DON to identify NIR absorption bands and to assess the applicability of NIR technique for direct measurement of DON in order to improve the calibrations. NIR transmission spectra of DON (0.5 - 2000 ppm) dissolved in acetonitrile and that of water (0 - 640 ppm) in acetonitrile were studied to identify NIR absorption bands of DON and water and to see how strong NIR absorption bands of water interact with DON NIR absorption bands. This information resulted in calibrations to measure DON in wheat to assist breeders in developing scab and DON resistant lines.
Prediction of maize seed attributes using a rapid single kernel near infrared instrument. Non-destructive measurements of seed attributes would significantly enhance breeder selection of seeds with specific traits and potentially improve hybrid development. A single kernel near infrared reflectance (NIR) instrument was developed for rapidly predicting maize grain attributes, which would enable plant breeders to quickly select promising individual seeds. With the overall goal to develop spectrometric calibrations, absorbance spectra from 904 to 1685 nm were collected from 87 maize samples, 30 kernels each, (2610 kernels total) representing a wide variability in the essential amino acids, tryptophan and lysine, and crude protein, oil, and soluble sugar contents. Average sample spectra were matched to bulk reference values. Partial least squares calibration models with cross-validation were developed for both relative (% dry matter) and absolute (mg per kernel) constituent contents. Similarly, models using bagging PLSR were developed. The best model obtained was for relative contents of crude protein with an R2p of 0.751 and a SEP of 0.472 mg/kernel. Kernel mass was also highly predictable (R2p = 0.761, SEC = 0.029 g). Tryptophan, lysine, and oil were less predictable but had good potential for segregating individual seeds using NIR. Soluble sugar contents had poor model statistics. Bagging PLSR yielded models with similar levels of prediction. Prediction models indicate that breeders could roughly sort crude protein but other minor constituents had poor predictions.
Adoption of color image sorting technology by several breeders. A low cost sorting device for wheat was built using a standard personal computer and color camera. Special programming techniques were used so that the throughput of the sorter would be high while keeping the sorter cost low. The sorting system was tested on its ability to separate red wheat from white wheat for wheat breeding programs. At a wheat throughput of 30 kernels per second, or 3.5 Kg per hour, the sorter is able to correctly separate 95% to 99% of the wheat. The accuracy is 15 to 20% higher than what can be achieved with traditional sorters. This sorter helps breeding programs isolate desirable kernels so that they can be propagated, which will result in faster releases of new and improved varieties of grain. Over ten wheat breeders in the United States have already adopted this system as their tool of choice for separating red and white wheat. The sorter is also being used to separate barley from durum, brown and gold flax seed, oat groats from un-hulled oats, remove scab damaged seeds, and separating vitreous from non-vitreous durum.
|Number of New CRADAS||1|
|Number of Active CRADAs||2|
|Number of Web Sites Managed||1|
Wegulo, S.N., Dowell, F.E. 2008. Near-infrared versus visual sorting of Fusarium-damaged kernels in winter wheat. Canadian Journal of Plant Science. 88: 1087-1089.
Coronado, M., Yuan, W., Wang, D., Dowell, F.E. 2009. Predicting the concentration and specific gravity of biodiesel-diesel blends using near-infrared spectroscopy. Applied Engineering in Agriculture. 25(2): 217-221.
Dowell, F.E., Maghirang, E.B., Graybosch, R.A., Berzonsky, W.A., Delwiche, S.R. 2009. Selecting and Sorting Waxy Wheat Kernels Using Near-Infrared Spectroscopy. Cereal Chemistry. 86(3):251-255.
Armstrong, P.R., Weiting, M. 2008. Design and Testing of an Instrument to Measure Equilibrium Moisture Content of Grain. Applied Engineering in Agriculture. 24(5):617-624.
Carver, B.F., Hunger, B.M., Edwards, J.T., Rayas-Buarte, P., Klatt, A.R., Porter, D.R., Seabourn, B.W., Bai, G., Dowell, F.E., Yan, L., Martin, B.C. 2008. Registration of 'Guymon' wheat. Journal of Plant Registrations. 2(1):33-35. Available: http://jpr.scijournals.org/cgi/reprint/2/1/33.
Dowell, F.E., Maghirang, E.B., Fernandez, F.M., Newton, P.N., Green, M.D. 2008. Detecting Counterfeit Antimalarial Tablets by Near-Infrared Spectroscopy. Journal of Pharmaceutical and Biomedical Analysis. 48:1011-1014. DOI: 10.1016/j.jpba.2008.06.024.
Pearson, T.C., Brabec, D.L., Dogan, H. 2008. Improved discrimination of soft and hard white wheat using the SKCS and imaging parameters. Sensing and Instrumentation for Food Quality and Safety. 3:89-99.
Pearson, T.C., Brabec, D.L., Haley, S. 2008. Color image based sorter for separating red and white wheat. Sensing and Instrumentation for Food Quality and Safety. 2:280-288.