2012 Annual Report
1a.Objectives (from AD-416):
Our overall objective is to maintain and improve grain quality and international competitiveness through the application of engineering principles. Most of our work concentrates on measuring properties of single kernels so that they can be segregated or the distribution of properties in a sample can be ascertained, versus measuring the average quality of a bulk sample. In general, the project will include the development of instruments to improve rapid (up to 500 kernels/s) measurement of grain quality at receiving stations, the development of non-destructive and rapid (up to 500 kernels/s) single-kernel sorting systems to improve post-harvest grain quality and similar systems to help grain breeders rapidly select specific traits for improved lines, and improve grain-storage and handling systems. Specific objectives are:
1. Develop sensors and methods to rapidly and accurately quantify attributes such as protein composition, starch composition, end-use quality, toxins, and micronutrients.
1.A. Develop or apply spectroscopic technologies that offer significant improvements over current technologies used to measure traits of single kernels and other biological materials.
1.B. Adapt a previously developed conductance mill for detection of live-insect infestations in wheat in other grains such as corn, rice, barley, and oats, and to dry beans, such as pinto and navy beans.
1.C. Develop procedures for rapid estimation of insect fragments in milled flour from minimally processed whole wheat.
2. Develop rapid single-kernel sorting systems to accurately remove defects such as fungal or insect-damaged grain and to isolate beneficial traits such as white wheat.
2.A. Develop high-speed, low-cost, image-based sorting systems for a variety of grains and legumes.
2.B. Develop Near-infrared spectroscopy (NIRS) -based, single-seed sorting systems for large grains and legumes.
2.C. Investigate applications of single-kernel NIRS technology to select kernels with specific traits.
3. Develop instrumentation, sensors, and systems to improve monitoring, and management of grain storage.
3.A. Develop traceability and identity-preservation techniques for grain handling.
3.B. Develop new stored-grain packing factors for the common grains in trade with known confidence intervals for a complete range of field conditions.
3.C. Investigate insect-pest densities and commingling of insects in grain that causes the spread of an infestation from bucket elevator boots.
3.D. Develop monitoring systems for grain-storage quality management.
3.E. Develop improved, environmentally-friendly insect-pest control methods for stored grain.
While the objectives of this project include pre-harvest (seeds) and post-harvest handling of grain, the work is interconnected in that the overall goal is to improve the quality of grain produced in the United States. Instruments developed to assess grain quality will be used to study how grain quality might change during storage and handling. An instrument previously developed by this group to detect insect-infested grain will be used to study the spread of insect infestations and the storability of grain based on insect population levels.
1b.Approach (from AD-416):
U.S. farmers grow over 15 billion bushels of corn, wheat, soybeans, and other grains annually to supply the nation and world with food, animal feed, and biofuels. A significant amount of this grain may be stored for a year or more before it is used or processed. Our goal is to improve U.S. grain quality and international competitiveness through the application of engineering principles, specifically to the areas of rapid (inspection results from at least 60 individual kernels per minute) quality measurement and in maintaining quality during storage. Much of our research concentrates on measuring the distribution of quality within samples, and on detecting traits that are present at very low levels. We propose to develop instruments to rapidly (up to 500 kernels/s) measure quality traits for inspection at the first point of grain delivery, for breeders in selecting traits for new lines, and for processors prior to grain buying or processing. We also propose to develop chemical-free technology to control insects and maintain quality during handing and storage. This research will lead to higher profits for the agriculture sector, higher-quality foods reaching consumers, and more food available for a growing world population.
Improved spectroscopic techniques were studied that will lead to an increase in the measurement accuracy of attributes of biological materials and allow additional attributes to be measured with respect to current near-infrared (NIR) techniques. This year, methods were developed to enhance detection of wheat kernels infested with toxin-producing fungi.
A method that was previously developed in this project for detecting wheat kernels infested with insect larvae was modified and enhanced. Testing of this method is currently underway at a large rice processor and grain handler. Tests were also performed for other grains such as corn, sorghum, and barley.
To detect insects that infest grain that were killed during fumigation and encapsulated inside grains, tests were performed to crack or slightly mill grains such that the dead insect would be removed from the kernels and could be recovered by sieving or imaging. The counts of dead insects are highly correlated with insect fragments found in flour, which is regulated by the Food and Drug Administration. This method could provide a rapid and inexpensive method for estimating insect fragments that would occur when grain is milled.
A low-cost digital data processor was linked to an image sensor and programmed to process images and make classifications of grains based on shape, color, and texture for real time sorting. The method is gaining widespread use by seed breeders and major seed hybrid companies. Additional programming and testing was performed to detect and sort grains on the basis of small blemishes and combining shape, color, texture, and blemish features so all types of defects can be removed in one pass through the instrument.
Work was performed to develop sorting methods for soybeans so that beans with high oil content could be segregated, leading to crops that will produce more oil. Soybeans seeds sorted on the basis of oil and protein were planted in experimental plots, at harvest we will determine if high oil or protein traits were inherited.
Methods were developed to reduce the work required to develop equilibrium moisture models for corn, resulting in a procedure that requires about half the effort in the preparation of samples and in testing time.
Laboratory and field measurements of grain packing in storage bins continue. In laboratory tests, higher moisture wheat (13% wb) showed greater packing than the low moisture (10% wb) wheat, which was expected as moisture makes samples more compressible. A software package was developed using these results to provide preliminary estimates of packing for corn, hard red winter wheat, soft red winter wheat, soybeans, and grain sorghum.
Insect cross-contamination in elevators and mills. The bucket elevators used to move grain at commercial grain elevator and feed mill facilities cause mixing of insects with clean grain that moves through the elevator. This problem was investigated by ARS scientists at Manhattan, KS in pilot-scale bucket elevators and in elevator and feed mill facilities during a two year period. Insect densities in the boot and pit areas were impacted by seasonal temperatures and facility sanitation practices. Warm temperatures during the summer and fall and a decrease in facility cleaning practices throughout the year caused insect densities to increase. Best management practices were developed to minimize the spread of insect infestations from the boot areas of commercial elevator and feed and flour mill facilities. Facilities that follow these practices can minimize contamination of clean grain with infested grain and avoid costly grain discounts.
Automated detection of fungi and toxins in wheat kernels. The selection of wheat genetic lines resistant to Fusarium and deoxynivalenol (DON) is hampered by the inability to select seeds free of fungi and toxins. An automated single kernel near-infrared (SKNIR) spectroscopic method was developedd by ARS Researchers in Manhattan, Kansas, and evaluated for identification of wheat kernels damaged by Fusarium fungi and for estimating DON levels. Because the method is non-destructive, seeds may be saved for generation advancement. The automated method is rapid, and the sorting of grains into several fractions depending on DON levels provides breeders with valuable information for evaluating resistant lines. Breeders throughout the US are using this technology to study fungal infections, objectively score breeding lines, and to select resistant seed to speed development of lines resistant to Fusarium head blight as part of the US Wheat and Barley Scab Initiative. Additionally, this technique is being used to study the distribution of Fusarium and DON within kernels.
New capabilities for automated sorting of grains. Many types of defects found on grains, such as fungal infestation, cannot be detected and removed from food, feed or seed process streams. ARS Researchers at Manhattan KS successfully developed a new type of electronic sorting machine that can detect and separate many types of weed seeds, discolored seeds, and fungal infected seeds. This new capability has been adopted by producers of grass seed, flax seed, alfalfa seed, pulses, corn seed, soybean seed, and wheat seed. Additionally, a popcorn producer is evaluating the technology for sorting out fungal damaged popcorn kernels. This technology will improve quality of many crops by ensuring better seed that is free from weeds or other contamination, ensuring more uniform seeds, and the technology will help deliver safer foods by reducing fungal contaminated products.
Yorulmaz, O., Pearson, T.C., Cetin, A. 2012. Detection of fungal damaged popcorn using covariance features. Computers and Electronics in Agriculture. 84:47-52.
Brabec, D.L., Pearson, T.C., Flinn, P.W. 2012. Detection of lesser grain borer larvae in internally infested kernels of brown rice and wheat using an electrically conductive roller mill. Cereal Foods World. http://dx.doi.org/10.1094/CPLEX-2012-0316-01R.
Morris, C.F., Delwiche, S.R., Bettge, A.D., Mabille, F., Abecassis, J., Pitts, M.J., Dowell, F.E., Deroo, C., Pearson, T.C. 2011. Collaborative analysis of wheat endosperm compressive material properties. Cereal Chemistry. 88:391-396.
Pearson, T.C., Knievel, D., Hucl, P. 2011. Automated sorting of glabrous versus pubescent annual canarygrass seeds. Applied Engineering in Agriculture. 27(4):663-667.
Hernandez Nopsa, J.F., Baenziger, P., Eskridge, K.M., Peiris, K.S., Dowell, F.E., Harris, S.D., Wegulo, S.N. 2012. Differential accumulation of deoxynivalenol in two winter wheat cultivars varying in FHB phenotype response under field conditions. Canadian Journal of Plant Pathology. 34(3):380-389.
Dowell, F.E., Noutcha, A.M., Michel, K. 2011. Short Report: The effect of preservation methods on predicting mosquito age by near-infrared spectroscopy. American Society of Tropical Medicine and Hygiene. 85(6):1093-1096.
Peiris, K.S., Bockus, W.W., Dowell, F.E. 2012. Infrared spectral properties of germ, pericarp, and endosperm sections of sound wheat kernels and those damaged by Fusarium graminearum. Applied Spectroscopy. 66(9):1053-1060.
Aw, W.C., Dowell, F.E., Ballard, W.O. 2012. Using near-infrared spectroscopy to resolve the species, gender, age, and the presence of Wolbachia infection in laboratory-reared Drosophila. Genes, Genomes, Genetics. 2:1057-1065.
Jeannotte, R., Nicolaysen, K., Dowell, F.E., Johnson, T., West, D. 2012. Griddlestones from Adak Island, Alaska: Their provenance and the biological origins of organic residues from cooking. In: West,D., Hatfield, V., Wilmerding, E., Lefevre, C. and Gualtieri, L. The People Before: The geology, paleoecology and archaelogy of Adak Island, Alaska. Oxford: Archaeopress. 269-287.
Armstrong, P.R., Tallada, J.G. 2012. Prediction of kernel density of corn using single-kernel near infrared spectroscopy. Applied Engineering in Agriculture. 28(4):569-574.