2005 Annual Report
1.What major problem or issue is being resolved and how are you resolving it (summarize project aims and objectives)? How serious is the problem? What does it matter?
The U.S. is a major consumer and exporter of cereal grains. However, for the U.S. grain industry to remain competitive internationally and to meet domestic consumer demands for quality, it must continually improve the quality of grain and grain products. We propose to address three important problems that the U.S. grain industry faces in order to improve end-use quality of grain: .
1)Grain properties that have the most influence on final or end-use product quality are not well known. For example, we do not know how most wheat kernel properties affect bread quality. This makes selection of grain difficult for buyers, and it is difficult for breeders to know what traits should be propagated;.
2)Many instruments to detect quality of whole grains suffer from poor accuracy, high cost, being too slow, requiring toxic chemicals, or do not directly measure what customers need to know: end-use qualities;.
3)In many cases, economically viable instruments do not exist to measure and/or sort whole grain defects that occur in small fractions of grain but can have a deleterious effect on quality, such as insect-damaged wheat kernels or mold damaged grain.
The proposed research relates to the Quality Characterization, Preservation, and Enhancement Component of NP 306, and in particular, Problem Areas 1a and 1b: Definition and Basis for Quality, and Methods to Evaluate Quality, respectively.
This technology will enable grain handlers to detect high-quality specialty grains, GMO's and food safety concerns such as toxins, biosecurity issues, quarantine issues, etc. for subsequent segregation. The technology will also help handlers improve quality by sorting individual kernels to improve quality and food safety of the grain. Millers and bakers will gain insight into properties of the kernels that correlate to higher quality products, thus enabling them to better select grain for their specific needs. In some cases, technologies will enable sorting of grain to improve low-quality grain to a higher quality, such as by removing fungal-damaged or low-protein kernels from mixed lots. Given knowledge of grain properties that produce premium end-use qualities and non-destructive methods to measure these properties, grain customers will be able to purchase grain that more consistently meets their quality needs and producers will be able to segregate grain lots with higher quality. In addition, breeders will be able to use this technology to identify single kernels with traits that would be desirable to propagate.
New technology and information developed through this research will be of use throughout the entire grain industry where quality and/or safety are of a concern. This includes producers, breeders, growers, grain handlers, marketers, millers, bakers, and government agencies such as the Extension Service, FGIS/GIPSA, FSIS, APHIS, and OSHA.
2.List the milestones (indicators of progress) from your Project Plan.
The primary goal of our research is to improve the quality and safety of grain and grain products through the development of instrumentation and procedures for objective grading and on-line quality measurement and sorting, and correlation of single-kernel and bulk-sample grain properties to end-use quality measurements. Specific objectives, or milestones, are:.
1)Develop automated, rapid-sensing and sorting technology for single kernels. Systems will be developed to a) detect wheat kernel defects using acoustics, and b) detect characteristics of single corn kernels by automating the acquisition of a plurality of measurements including imaging, weight, and near-infrared spectra;.
2)Measure characteristics of single kernels and bulk samples that are critical to the success of the grain industry by utilizing experimental or commercial instrumentation. This includes a) detecting and removing kernels with mycotoxin-producing molds, b) detecting mutants for corn breeders, c) measuring oat milling parameters, and d) detecting insect fragments in flour; and.
3)Develop techniques, using single and multiple measurements, to predict end-use characteristics such as flour yield, bake absorption, farinograph stability, loaf volume, etc. from whole grain or minimally processed grain, and to determine the accuracy and impact of these predictions. This includes studying the synergy of combining multiple measurements.
4a.What was the single most significant accomplishment this past year?
Automated NIR Sorting Technology Commercialized. The single kernel sorting system developed to detect specific grain attributes was commercialized through a CRADA with a company in Stockholm, Sweden. The system was demonstrated at several international conferences in 2005 and is being publicly marketed. The system automatically scans individual wheat kernels, and then sorts kernels based on specific attributes such as protein content, hardness, amylose content, etc. The system is now being used by breeders to select specific traits from early generation breeder samples. This will significantly reduce the time and expense required to develop cultivars with specific end-use traits. The system is also being evaluated for use in detecting food safety attributes such as vomitoxin during routine grading. Although it was developed for wheat, it is also finding applications in sorghum and millet.
4b.List other significant accomplishments, if any.
Detecting Durum Wheat Quality. Durum wheat production accounts for approximately 8% of the wheat production worldwide, and is mainly used to make semolina for macaroni, spaghetti, and other pasta products. The best durum wheat for pasta products should appear hard, glassy and translucent, and have excellent amber color, good cooking quality, and high protein content. Nonvitreous (starchy) kernels are opaque and softer, and result in decreased yield of coarse semolina. Thus, vitreousness of durum wheat has been used as one of the major quality attributes in grading. Traditionally, grain grading has been primarily done by visual inspection by trained personnel. This method is subjective and tedious. It also produces great variations in inspection results between inspectors. We used digital imaging technology for determining durum vitreousness. Results showed that 100% of non-vitreous kernels and 92.6% of mottled kernels, which is one of the hardest defect categories to consistently detect visually, could be correctly classed.
Improving the Quality of White Wheat through Rapid Sorting. White wheat is gaining acceptance throughout the Midwest as a class that can improve our competitiveness in export markets. All breeding programs in the Midwest are developing white wheat cultivars. We are able to improve the quality of white wheat cultivars being used in breeding programs by removing wheat of other classes, such as red wheat, from samples using high speed sorting procedures developed through an agreement with Satake, Inc. There is no other technology available to remove these contaminating kernels. Almost all white wheat being developed in the Midwest and Pacific Northwest is now shipped to our research unit for purification through our sorter. Our sorting has reduced the development time for these new cultivars by several years, has saved the breeders hundreds of hours, and has salvaged some cultivars that would have been terminated if our technology was not available.
Reducing mycotoxins in corn. A high-speed single-kernel sorter was used to remove mycotoxins from corn. It was found that using spectral absorbance at 750nm and 1200nm could distinguish kernels with aflatoxin-contamination greater than 100ppb from kernels with no detectable aflatoxin with over 98% accuracy. When these two spectral bands were applied to sorting corn at high speeds, reductions in aflatoxin averaged 82% for corn samples with an initial level of aflatoxin over 10 ppb. Most of the aflatoxin is removed by rejecting approximately 5% of the grain. Fumonisin is also removed along with aflatoxin during sorting. The sorter reduced fumonisin by an average of 88% for all samples. This technology will help insure the safety of the US food and feed supply.
Detecting insect fragments in flour. Primary pests of stored cereals that develop and feed inside grain kernels are the main source of insect fragments in wheat flour. The Food and Drug Administration (FDA) has set a defect action level of 75 or more insect fragments per 50 gram of flour. The current standard flotation method for detecting insect fragments in flour is very labor intensive and expensive. We investigated the potential of near-infrared spectroscopy (NIRS) to detect insect fragments in wheat flour at the FDA defect action level. Fragments counts with both the NIRS and the standard flotation methods correlated well with the actual number of fragments present in flour samples. However, the flotation method was more sensitive below the FDA defect action level than the NIRS method. Although the flotation method is very sensitive at the FDA action level, this technique is time consuming (almost 2 h/sample) and expensive. Although NIRS currently lacks the sensitivity of the flotation method, it is rapid, does not require sample preparation, and could be easily automated for a more sophisticated sampling protocol for large flour bulks. Therefore, this method should be reexamined in the future because NIRS technology is rapidly improving.
Applying NIR sorting technology to other disciplines. The NIR spectroscopy procedures developed for determining single kernel attributes were found to apply to determining characteristics of single insects and other commodities. Thus, we applied NIR spectroscopy to detecting insect parasitoids, insect species, insect age grading, and fig quality in cooperation with the Biological Research Unit, ARS USDA, Manhattan, KS; the Dept. Entomology at KSU, Manhattan, KS; the CDC, Atlanta, GA; and the Horticultural Crops Research Laboratory, Fresno, CA. Results showed we could detect parasitized weevils and flies, fly and mosquito age, stored grain insect species, and fig quality using NIR spectroscopy. This information can be used to develop control strategies for various pest insects and to automate fig grading.
4c.List any significant activities that support special target populations.
5.Describe the major accomplishments over the life of the project, including their predicted or actual impact.
Automated NIR Sorting Technology Commercialized. The single kernel sorting system developed to detect specific grain attributes was commercialized through a CRADA with a company in Stockholm, Sweden. The system was demonstrated at several international conferences in 2005 and is being publicly marketed. The system automatically scans individual wheat kernels, and then sorts kernels based on specific attributes such as protein content, hardness, amylose content, etc. The system is impacting breeding programs in several states by allowing breeders to select specific traits from early generation breeder samples. This significantly reduces the time and expense required to develop cultivars with specific end-use traits. The system is also being evaluated for use in detecting food safety attributes such as vomitoxin during routine grading. Although it was developed for wheat, it is also finding applications in sorghum and millet. This accomplishment relates to Milestone 3 “Predict end-use quality”; NP 306 Action Plan Component 1 “Quality Characterization, Preservation, and Enhancement”, Problem Area 1b “Methods to Evaluate and Predict Quality”; and Performance Measure 1.1.2: “Provide higher quality, healthy foods that satisfy consumer needs in the US and abroad” of Strategic Plan Goal 1, Objective 1.1.
7.List your most important publications in the popular press and presentations to organizations and articles written about your work. (NOTE: List your peer reviewed publications below).
Dowell, F.E., O.K. Chung, E.B. Maghirang, and R.O. Pierce. Predicting bread quality from multiple measurements. Presented at the International Cereal Chemists Meeting. Vienna, Austria. July, 2005.
Dowell, F.E., J.E. Throne, J.E. Baker, E.B. Maghirang, A. Parker, R. Wirtz, H. Bossin, A. Robinson, A. Broce, J. Perez-Mendoza, and M. Benedict. Measuring insect characteristics by near-infrared spectroscopy and applications to the SIT. Presented at the Sterile Insect Technique Conference, Vienna, Austria. May, 2005.
Dowell, F., T. Pearson, P. Armstrong. High throughput grain quality analysis. Presented at the AOAC annual meeting, October, 2004.
Wang, N., Zhang, N., Dowell, F.E., Pearson, T.C. 2005. Determining vitreousness of durum wheat using transmitted and reflected images. Transactions of the ASAE. Vol 48(1):219-222.
Dowell, F.E., Parker, A.G., Benedict, M.Q., Robinson, A.S., Broce, A.B., Wirtz, R.A. 2005. Sex separation of tsetse fly pupae using near-infrared spectroscopy. Bulletin of Entomological Research 95(3):249-257
Xie, F., Pearson, T.C., Dowell, F.E., Zhang, N. 2004. Detecting vitreous wheat kernels using reflectance and transmittance image analysis. Cereal Chemistry. 2004. 81(5):594-597.