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Thermal and Functional Characterization of Starch from Argentinean Corn
Author(s) -
Seetharaman K.,
Tziotis A.,
Borras F.,
White P. J.,
Ferrer M.,
Robutti J.
Publication year - 2001
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem.2001.78.4.379
Subject(s) - germplasm , amylose , amylopectin , starch , differential scanning calorimetry , corn starch , food science , chemistry , agronomy , biology , physics , thermodynamics
ABSTRACT Screening accessions in a germplasm bank aids in the identification of plants with unusual properties ranging from agronomic traits to functional and compositional traits of the seed itself. Results from this study confirm the presence of a wide variation in the thermal and functional properties of starch from several landraces of corn in the Argentinean germplasm. Thermal properties of starch measured using differential scanning calorimetry (DSC) identified several corn landraces with properties of potential commercial interest. The pasting and textural properties of gels obtained from starch of different corn landraces also exhibited considerable variability. The degree of variation in thermal and functional properties of corn reported in this study is comparable to the thermal properties of starch from several other crop species. These corn races show promise for further regeneration to create inbred lines with unusual traits. The potential for further improvement of corn races exists not only based on thermal properties, but directly for specific functional attributes as well. Correlation analyses suggest that the variability in thermal and functional attributes are a function of amylose content, granule size distribution and, possibly, differences in the structural makeup of amylose and amylopectin. The strong correlation observed between the thermal properties and pasting and textural properties will allow for the estimation of starch properties from small sample sizes.