2011 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 detection wheat kernels infested with inset larvae was modified and enhanced. The method, based on monitoring electrical conductance through crushed grain, was modified to detect rice kernels infested by stored-grain insects and significantly improve detection ability for wheat. This method works well to detect live insects. Testing of this method is currently underway at a large rice processor and grain handler.
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, texture for real time sorting. Image methods were developed and tested for grass seed, flax seed, alfalfa, pulses, wheat, barley, oats, and popcorn.
Work was performed to develop sorting methods for soybeans so that beans with high oil content could be segregated and propagated, leading to crops that will produce more oil per unit of soybeans. Soybeans seeds were measure for oil and protein and planted in experimental plots. A more accurate calibration for NIRS soybean protein and oil measurement was developed.
A study with small pill-sized tracers embedded in bulk grain indicate they can be used to identify the source of origin for trace-back capability. Tracer physical properties were studied for different tracer compositions based on starch, cellulose and sucrose. These were used to determine what compositions were likely to withstand the rigors of grain handling operations. Statistical sampling procedures were studied during actual grain handling operations to verify embedded tracers could quantify the extent of grain mixing.
Stored-grain packing factors were measured in more than 40 bins in Kansas, Oklahoma, and Iowa covering a wide range of bin sizes. Bins of concrete construction ranged up to 140-ft deep and those of corrugated steel construction ranged up to 105-ft diameter with 90-ft eave height. The data is being used to calibrate a science-based computer model that is expected to predict packing factors with better accuracy than that achieved by the current method.
In pilot-scale tests, it was found that the amount of insects in bucket elevator leg boots affected the number of insects transferred through the elevator leg to other locations. These data are currently being combined with field tests and computer modeling to determine best management practices to minimize the spread of insect infestations through the bucket elevator leg boot of commercial elevator and feed and flour mill facilities.
Stored grain packing factors. Because grain is somewhat compressible when subjected to the cumulative weight exerted from the material above, accurate packing factors are required to determine the mass of grain in storage from bin dimensions and test weights. Accurate packing factors are critical for inventory control by stored grain managers and auditing by government agencies. ARS researchers at Manhattan, KS, are developing new packing factors for six grains using a nationwide field study supported by laboratory testing and evaluation of the physics of grain packing. The first beta version of user-friendly software containing early estimates of the new packing factors for use by farmers, elevator managers, and government officials has been released to a major partner/customer for evaluation. The new data should have better accuracy than the old methods because it accounts for many important variables in grain and bin properties that affect the final packing but were not taken into account by the old method, so it should improve our ability to determine the correct mass of grain in bins for inventory control and official auditing and provide better confidence in the results than was possible with the old methods.
Automated detection of fungus and its toxin in wheat kernels. An automated single kernel near-infrared (SKNIR) spectroscopic method was developed by ARS Researchers in Manhattan, Kansas, and evaluated for identification of wheat kernels damaged by Fusarium fungi and for estimating the toxin deoxynivalenol (DON). 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 scab 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 defect on grains, such as fungal infestation, cannot be detected and removed from food, feed or seed process streams. ARS researchers at Manhattan, KS, successfully built and demonstrated 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 demonstrated to and adopted by breeders and 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 and the technology will help deliver safer foods by reducing fungal contaminated products.
Wegulo, S.N., Bockus, W.W., Nopsa, J., De Wolf, E.D., Eskridge, K.M., Peiris, K., Dowell, F.E. 2011. Effects of integrating cultivar resistance and fungicide application on fusarium head blight and deoxynivalenol in winter wheat. Plant Disease. Vol. 95(5):554-560.
Boac, J.M., Casada, M., Maghirang, R.G., Harner, III, J.P. 2010. Material and Interaction Properties of Selected Grains and Oilseeds for Modeling Discrete Particles. Transactions of the ASABE. 53(4):1201-1216.
Lee, K., Armstrong, P.R., Thomasson, A., Sui, R., Casada, M., Herrman, T.J. 2011. Application of binomial and multinomial probability statistics to the sampling design process of a global grain tracing and recall system. Food Control. 22(7):1085-1094.
Brabec, D.L., Pearson, T.C., Flinn, P.W., Katzke, D. 2010. Detection of internal insects in wheat using a conductive roller mill and estimation of insect fragments in the resulting flour. Journal of Stored Products Research. 46(3):180-185.
Peiris, K., Pumphrey, M.O., Dong, Y., Maghirang, E.B., Berzonsky, W., Dowell, F.E. 2010. Near-infrared spectroscopic method for the identification of Fusarium head blight damage and prediction of deoxynivalenol in single wheat kernels. Cereal Chemistry. 87(6):511-517.
Pearson, T.C. 2010. High-Speed Sorting of Grains by Color and Surface Texture. Applied Engineering in Agriculture. 26(3):499-505.
Peiris, K.S., Dowell, F.E. 2011. Determining weight and moisture properties of sound and fusarium-damaged single wheat kernels by near infrared spectroscopy. Cereal Chemistry. 88(1):45-50.
Kalkan, H., Beriat, P., Pearson, T.C., Yardimci, Y. 2011. Detection of contaminated hazelnuts and ground red chili pepper flakes by multispectral imaging. Computers and Electronics in Agriculture. 77:28-34.
Sikulu, M., Killeen, G.F., Hugo, L.E., Ryan, P.A., Dowell, K.M., Wirtz, R.A., Moore, S.J., Dowell, F.E. 2010. Near-infrared spectroscopy as a complementary age grading and species identification tool for African malaria vectors. BioMed Central (BMC) Parasites and Vectors. 3:49. Online. Parasites and Vectors doi: 10.1186/1756-3305-3-49.
Lee, K., Armstrong, P.R., Thomasson, A., Sui, R., Casada, M., Herrman, T.J. 2010. Development and characterization of food-grade tracers for the global grain tracing and recall system. Journal of Agricultural and Food Chemistry. 58:10945-10957. doi:10.1021/jf101370k
Gonzales, H., Armstrong, P.R., Maghirang, R.G. 2009. Simultaneous Monitoring of Stored Grain With Relative Humidity, Temperature, and Carbon Dioxide Sensors. Applied Engineering in Agriculture. 25(4):595-604.
Klarica, J., Bittner, L., Pallua, J., Pezzei, C., Huck-Pezzei, V., Dowell, F.E., Schied, J., Bonn, G.K., Huck, C., Schlick-Steiner, B.C., Steiner, F.M. 2011. Near-infrared imaging spectroscopy as a tool to discriminate two cryptic Tetramorium ant species. Journal of Chemical Ecology. 37:549-522. Online Journal of Chemical Ecology DOI: 10.1007/s10886-011-9956-x.
Sikulu, M., Dowell, K.M., Hugo, L.E., Wirtz, R.A., Michel, K., Peiris, K.S., Moore, S., Killeen, G.F., Dowell, F.E. 2011. Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species. Malaria Journal. 10:186.
Tallada, J.G., Wicklow, D.T., Pearson, T.C., Armstrong, P.R. 2011. Detection of fungus-infected corn kernels using near-infrared reflectance spectroscopy and color imaging. Transactions of the ASABE. 54(3): 1151-1158.
Arthur, F.H., Casada, M. 2010. Directional Flow of Summer Aeration to Manage Insect Pests in Stored Wheat. Applied Engineering in Agriculture 26: 115-122.