2006 Annual Report
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.
Rapid Methods for Predicting Grain, Flour, and End-Use Quality. Eight quality variables that can be measured rapidly were chosen for predicting various breadmaking quality parameters (bake water absorption, bake mix time, proof height, loaf weight, crumb score, and loaf volume, and loaf volume regression) of hard red winter (HRW) and hard red spring (HRS) wheat samples. The variables used in the predictions were: test weight; average hardness, weight, length, and diameter as measured by the SKCS 4100; and protein content, mixograph water absorption, and average total wet gluten as measured by near-infrared spectroscopy. Based on the eight-variable model obtained separately for HRW and HRS wheat, loaf volume and loaf specific volume can be predicted with a model R2 greater than 0.80; bake water absorption, bake mix time, and proof height with R2 ranging from 0.58 to 0.78; loaf weight, crumb score and loaf volume regression can be predicted only with R2 ranging from 0.27 to 0.39. For loaf volume and loaf specific volume, which has the highest potential of being predicted using rapid measurement techniques, the single highest partial R2 that accounted for 98% of the model R2 of both HRW and HRS wheat was flour protein content. Additionally, flour protein content was a significant variable for predicting bake mix time, loaf weight, and loaf volume regression for HRW wheat and bake water absorption and bake mix time for HRS wheat.
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 white corn. It was found that using spectral absorbance at 500nm and 1200nm could distinguish kernels with aflatoxin-contamination. When these two spectral bands were applied to sorting corn at high speeds, reductions in fumonisin averaged 82% for corn samples with an initial level of aflatoxin over 10 ppb. Most of the fumonisin is removed by rejecting approximately 5% of the grain. 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.
Properties of Corn Kernels Infected by Fungi. Near infrared spectra, x-ray images, color images, near infrared images, and physical properties of single corn kernels were studied to determine if combinations of these measurements could distinguish fungal infected kernels from non-infested kernels. Kernels used in this study were inoculated in the field with eight different fungi: Acremonium zeae, Aspergillus flavus, Aspergillus niger, Diplodia maydis, Fusarium graminearum, Fusarium verticillioides, Penicillium spp.
Trichoderma viride. Results indicate that kernels infected with Acremonium zeae and Penicillium were difficult to distinguish from non-infested kernels while all of the other severely infected kernels could be distinguished with greater than 91% accuracy. A neural network was also trained to identify infecting mold species with good accuracy, based on the near infrared spectra. These results indicate that this technology can potentially be used to separate fungal infected corn using high speed sorter; and, automatically and rapidly identify the fungal species of infested corn kernels. This will be of assistance to breeders developing fungal resistant hybrids as well as mycologists studying fungal infected corn.
Relation of single wheat kernel particle size distribution to Perten SKCS 4100 hardness index. Material from single kernels crushed on the SKCS 4100 was collected and milled in a fabricated mill, which simulates the last two rolls of a Quadramat Jr. The PSD of each single kernel was then measured using a laser particle counter. It was found that the difference between the maximum and minimum slope of the PSD below 55 micrometers could distinguish most of the hard and soft kernels. These slopes correspond to a peak in the PSD between 20 to 30 micrometers. Particle size distributions from soft kernels normally have a peak in this particle size range while hard kernels have a small, or no, peak. SKCS low level data, as well as the raw crush profile, were analyzed to find a correlation with this slope. After stepwise selection, HI, and three normalized crush profile values were used to predict the PSD slope. The predicted slope correctly classified 95% of the hard and soft kernels. These results indicate that a calibration for the SKCS based on single kernel particle size is possible and this may give a better indication of end use quality of a wheat sample. Low-cost bi-chromatic image sorting device for grains. A low-cost linescan imaging system was developed to inspect and sort grains and other products at high speeds (40 kernels/s). The device captures bi-chromatic images from opposite sides of each kernel and processes the images in real time using high speed microcontrollers. Detection of scab-damaged wheat kernels was used in this study to establish system feasibility and limits. Simple image statistics and intensity histograms were used as features and were able to distinguish good kernels from scab-damaged kernels with 95% accuracy. For each kernel, image acquisition required approximately 15 ms, while 5 ms were required for image processing and classification. The controller can output a signal to divert (sort) kernels or save the images on a compact flash card for transfer to a personal computer for off-line analysis. All parts for the system cost less than $2000.
Camera attachment for automatic measurement of single-wheat kernel size on a Perten SKCS 4100. Wheat kernel size any shape is an important quality factor and characteristic for adjusting milling processes. Measuring kernel size is tedious and time consuming so it cannot be done as often as some wheat millers would like. Automated machines for measuring kernel size suffer from inaccuracies and/or high cost. The Perten Single Kernel Characterization System (SKCS 4100) is an automated instrument which measures several single kernel quality characteristics such as weight, moisture content, hardness, and diameter. Of all of these measurements, the diameter measurement is the least accurate. A low cost color camera was attached to an SKCS 4100 to enable more accurate kernel size determinations. Using image data combined with SKCS data, errors in estimating kernel length and diameter were reduced by 56% and 66%, respectively.
Detection of damaged wheat kernels by impact-acoustic emissions. A system was built that is able to distinguish good wheat kernels from a variety of damaged kernels by dropping kernels, one at a time, onto a steel plate and digitally analyzing the resulting sounds from the impact. The types of damage studied were insect damaged kernels with exit holes, hidden insect damaged kernels without exit holes, sprout damage, and scab damage. It was found that 98% of the good kernels and 87% of the insect damaged kernels with exit tunnels can be distinguished from each other. Accuracy for scab and sprout damaged kernels was 70% and 45% for hidden insect damaged kernels. The device should be capable of inspection rates exceeding 40 kernels/s, or ~70g/min. It is non-destructive and can be made to sort kernels into one of three different groups. This technology should help grain inspectors and millers better ascertain the quality of a wheat load under consideration.
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.
Pearson, T.C. and D.T. Wicklow. Optical sorting of whole corn kernels contaminated with aflatoxin or fumonisin. Multicrop aflatoxin and fumonisin elimination and fungal genomics workshop. October 2005.
Pearson, T.C. 2006. Low-cost bi-chromatic image sorting device for grains. ASABE Paper No. 063085. St. Joseph, Mich.: ASABE.
Delwiche, S.R., Graybosch, R.A., Hansen, L.E., Souza, E., Dowell, F. 2006. Single kernel near-infrared analysis of tetraploid (durum) wheat for classification of the waxy condition. Cereal Chemistry. 83(3):287-292.
Delwiche, S.R., Pearson, T.C., Brabec,, D.L. 2005. High-speed optical sorting of soft wheat for reduction of deoxynivalenol. Plant Disease Journal. 89(11):1214-1219.
Haff, R.P., Jackson, E.S., Pearson, T.C. 2005. Non-destructive detection of pits and pit fragments in dried plums. Applied Engineering in Agriculture.21(6):1021-1026.
Perez-Mendoza, J., Throne, J.E., Maghirang, E.B., Dowell, F.E., Baker, J.E. 2005. Insect fragments in flour: relationship to lesser grain borer (Coleoptera: Bostrichidae) infestation level in wheat and rapid detection using near-infrared spectroscopy. Journal of Economic Entomology 98: 2282-2291.
Toews, M.D., Pearson, T.C., Campbell, J.F. 2006. Imaging and automated detection of Sitophilus oryzae L. (Coleoptera: Curculionidae) pupae in hard red winter wheat. Journal of Economic Entomology 99: 583-592.