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54 pages, November 28, 2009

Abstract:

This proposed project is focused on improving the reliability, transferability and speed of NIR spectroscopic methods of analysis for amino acids in whole soybeans and soy proteins for food and feed precise matrix formulations. The results of this research have the potential to substantially improve agricultural efficiency, human and animal food functionality, and human health. Novel correlation approaches and techniques, such as NIR microspectroscopy, NIR-based Cross-correlation and Hyperspectral Imaging are being developed, and were also included. Finding soybean varieties that vary in specific amino acids, such as essential, or conditionally essential, amino acids, is critical for finding new uses of specific soybean lines from the National Soybean Germplasm Collection, with improved traits for food and feed applications. Soybean genetic selection and breeding experiments for developing high-protein, high-yield varieties, with improved amino acid and fatty acid content cultivars require rapid, economical and reliable composition analysis of protein, oil, moisture, fatty acid and amino acid levels of large sets of soybean samples. Conventional "wet chemistry" analytical methods for such soybean components are both time-consuming and costly. As a result, soybean researchers and breeders have an imperative need to utilize faster and less expensive analysis methods that the ones currently in use. Near Infrared (NIR) Spectroscopy is a rapid and inexpensive method for composition analysis for academia and industry professionals alike. Recent advancements in instrumentation design, such as the application of the Diode Array (DA) technique and the Fourier Transform Near-Infrared and Infrared (FT-NIR/FT-IR) techniques, have significantly improved the overall instrument performance and advancement in the field of grain analysis. In the following research proposal, the state-of-the-art FT-NIR instruments will be evaluated and utilized. I plan to make calibration plots of selected amino acids in soybean lines. Data analysis and calibration methodology will also be substantially improved, in order to allow rapid NIR analysis of the large number of soybean samples required for improving the genetics of soybean seed composition for agricultural cost savings and human health food applications.

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Open access Copyright@2009 by vip6

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AANIRSCorrelation3.doc

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