Title: USING TWO-DIMENTIONAL VIBRATIONAL CORRELATION SPECTROSCOPY TO STUDY AGRICULTURAL PROBLEMS Authors
|Barton Ii, Franklin|
|DE Haseth, James - UNIVERSITY OF GEORGIA|
Submitted to: United States-Japan Cooperative Program in Natural Resources
Publication Type: Proceedings
Publication Acceptance Date: November 18, 2004
Publication Date: December 8, 2004
Citation: Himmelsbach, D.S., Barton II, F.E., De Haseth, J.A. 2004. Using two-dimentional vibrational correlation spectroscopy to study agricultural problems. In: Proceedings of the 33rd Annual United States-Japan Cooperative Program in Natural Resources, December 8-21, 2004, Honolulu, Hawaii. p. 258-262. Interpretive Summary: The existence of multiple two-dimensional vibrational spectroscopic methods in separate programs have made them difficult to use together. Two forms of two-dimensional correlations have now been combined into one program that is accessible in the programming language, MATLAB. This permits the use of methods that compare spectral data within a single region or between two regions to be used together. Thus, near-infrared, mid-infrared and Raman spectral data of samples can all be to examine often hidden features. This has been applied to investigate the responses in these three regions to the differences between amylose and amylopectin, the major components of starch. This has led to the further understanding of spectral predictions of the composition and physical properties of grains.
Technical Abstract: A MATLAB based version of two forms of two-dimensional vibrational correlation spectroscopy has been developed to permit access to both self or homospectral correlation and cross or heterospectral correlation of various types vibrational spectroscopy. These types of vibrational spectroscopy include: near-infrared, mid-infrared and Raman. Two-dimensional vibrational spectroscopy was used to detect the spectral differences between amylose and amylopectin in all three of these spectral regions. This has permitted the interpretation of near-infrared from mid-infrared and Raman regions given the ability to understand and predict both compositional and viscoelastic properties of rice flour.