2010 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.
Private and public seed breeding organizations ordered 10 sorters previously developed in our lab and refined through a Cooperative Research and Development Agreement (CRADA). These sorting machines separate grains having slight color differences and/or small blemishes. A second CRADA adapted sorters for fungal damaged popcorn. Another version was developed to select “hairless” canary seeds and used to develop “hairless” seed because silica hairs are an irritant shown to cause cancer.
Another instrument, that rapidly detects insect infested wheat and insect fragments in flour, was commercialized under a CRADA. A protocol for testing every rail car delivered to a mill was developed with industry partners. The instruments were demonstrated to numerous grain handling companies and has generated considerable domestic and international interest. We finished work that related counts of insect infested kernels to insect fragments found in milled flour. Insect fragments in flour are regulated by the FDA and is a very costly and time consuming measurement that millers perform daily.
An automated single kernel near-infrared (SKNIR) spectroscopic method was evaluated for identification of wheat kernels damaged by Fusarium fungi and for estimating the toxin deoxynivalenol (DON). The SKNIR system classified damaged and Fusarium damaged kernels (FDKs) with high accuracy. 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 will provide breeders with valuable information for evaluating resistant lines.
We investigated how the Fusarium head blight (FHB) disease and associated mycotoxins spreads in grain heads of resistant and susceptible varieties. A moderately resistant variety was shown to have comparatively lower mycotoxin levels in infected kernels than kernels from a susceptible variety. Moreover, in the susceptible variety, mycotoxins were detected in kernels in spikelets above and below the inoculated spikelet, while in the moderately resistance variety, mycotoxins were found mostly in the kernels in spikelets below the inoculated spikelet. This study also revealed the presence of both symptomatic kernels with non-detectable levels of mycotoxins and asymptomatic kernels with significant mycotoxin levels in FHB infected wheat spikes, providing breeders more information about FHB resistance and consequently enhance the efficiency of FHB resistance breeding programs.
A method that simplifies and reduces cost of developing calibrations to automatically measure grain and soybean composition was developed. Results show that accurate seed measurement is possible. This work was used to measure soybean breeder samples at a university. Seeds are currently planted in field trials to develop varieties to maximize oil and protein content. Another university has also adopted the instrumentation for single seed measurement.
This bridging project was created when the 5-year project 5430-44000-015-00D expired. The replacement project was certified September 2010 and will be implemented early in FY 2011.
Automated detection of scab-damaged 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 U.S. 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 U.S. Wheat and Barley Scab Initiative.
Rapid and automatic measurement of single seed composition. Rapid methods to measure the composition of single seeds of corn and soybeans can significantly enhance the ability of breeders to improve hybrids and varieties by selecting seeds that target breeder composition goals. Near-infrared reflectance spectroscopy (NIRS) is a method that has previously been used to measure single seed composition, such as protein, oil, and starch. A method to calibrate for single seeds using bulk sample measurement was investigated as an alternative approach by ARS researchers in Manhattan, Kansas. NIRS calibrations using bulk sample methods were developed for corn protein, oil, starch, and kernel density; soybean protein, oil, and fiber. Results indicated that accurate seed measurement is possible although soybean fiber measurement is less accurate. This work was used to measure soybean breeder samples from the University of Kentucky, and these seeds are currently under field trials to evaluate varieties being developed to maximize oil and protein content. Iowa State University has also adopted the instrumentation for single seed measurement.
Adapting ARS NIRS grain technology to detecting traits of disease vectors. ARS researchers in Manhattan, Kansas, developed technology to measure traits of single wheat kernels using near-infrared spectroscopy (NIRS). This technique can also determine traits of single insects, such as species and age. In cooperative work with the Centers for Disease Control, Atlanta, Georgia, and the Ifakara Health Institute, Ifakara, Tanzania, we showed that our NIRS determine mosquito species and age with about 90% accuracy. These findings have importance for monitoring control programs where species identification and reduction in the proportion of older mosquitoes that have the ability to transmit malaria is an important outcome. The technique has been adopted by the CDC and researchers in England, Austria, Australia, and Tanzania.
Commercialization of sorting technology and adoption by seed breeders/producers. ARS researchers at Manhattan, Kansas, commercialized and transferred a low cost color image based sorting device for grains to through a CRADA partnership. These instruments have been sold to various seed breeders and seed foundations in the US and internationally. The new sorting system has unprecedented accuracy, throughput, and low cost for inspection/sorting systems. A CRADA is also in place to adapt the machine for sorting popcorn, and the camera design has been transferred to an electronics manufacturer. These instruments have been extensively used for separation of large bulks of popcorn, yellow and brown flax, red and white wheat, scab-damaged wheat, and removing weed seeds from seed stocks to improve quality of breeding lines and end-use products. North Dakota State University seed foundation states that the machines shortened production time for yellow flax by one year, increased production by 20%, and reduced contaminates by 90% over past practices. Other users report similar impact.
Ince, F., Onaran, I., Pearson, T.C., Tewfik, A., Cetin, A., Kalkan, H., Yardimci, Y. 2008. Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns. Transactions of the ASABE. 51(4):1461-1469.
Ince, N.F., Onaran, I., Pearson, T.C., Tewfik, A., Cetin, A. 2008. Discrimination Between Closed and Open Shell (Turkish) Pistachio Nuts Using Undecimated Wavelet Packet Transform. Biological Engineering (ASABE). 1(2):159-172.
Dowell, F.E., Maghirang, E.B., Jayaraman, V. 2010. Technical Note: Measuring Grain and Insect Characteristics using NIR Laser Array Technology. Applied Engineering in Agriculture. 26(1):165-169.
Peiris, K., Pumphrey, M.O., Dowell, F.E. 2009. NIR absorbance characteristics of deoxynivalenol and of sound and Fusarium-damaged wheat kernels. Journal of Near Infrared Spectroscopy. 17:213-221. doi: 10.1255/jnirs.846.
Mayagaya, V.S., Michel, K., Benedict, M.Q., Killeen, G.F., Wirtz, R.A., Ferguson, H.M., Dowell, F.E. 2009. Non-destructive Determination of Age and Species of Anopheles gambiae s.l. Using Near-Infrared Spectroscopy. American Journal of Tropical Medicine and Hygiene. 81(4):622-630.
Dowell, F.E., Maghirang, E.B., Baenziger, P. 2009. Automated Single-Kernel Sorting to Select for Quality Traits in Wheat Breeding Lines. Cereal Chemistry. 86(5):527-533. doi: 10-1094/CCHEM-86-5-0527.
Spielbauer, G., Armstrong, P.R., Baier, J., Allen, W.B., Richardson, K., Shen, B., Settles, M. 2009. High-Throughput Near-Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels. Cereal Chemistry. 86(5):556-564.
Pearson, T.C. 2009. Hardware-based image processing for high-speed inspection of grains. Computers and Electronics in Agriculture. 69(1):12-18.
Grace, T., Wisely, S.M., Brown, S.J., Dowell, F.E., Joern, A. 2010. Divergent Host Plant Adaptation Drives the Evolution of Sexual Isolation in the Grasshopper Hesperotettix viridis (Orthoptera: Acrididae) in the Absence of Reinforcement. Biological Journal of the Linnean Society, London. 100:866-878.
Webster, T.C., Dowell, F.E., Maghirang, E.B., Thacker, E.M. 2009. Visible and near-infrared spectroscopy detects queen honey bee insemination. Apidologie. 40(5):565-569.
Peshlov, B.N., Dowell, F.E., Drummond, F.A., Donahue, D.W. 2009. Comparison of Three Near Infrared Spectrophotometers for Infestation Detection in Wild Blueberries Using Multivariate Calibration Models. Journal of Near Infrared Spectroscopy. 17:203-212.