Exploratory SAXS and HPAEC-PAD studies of starches from diverse plant genotypes
A substantial amount of effort has been expended on developing a suitable model to describe the small angle X-ray scattering (SAXS) profiles of starch. The modelling work now offers good descriptions of the fine structure of the starch granule, with recent advances [Daniels, D.R., & Donald, A.M....
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Published in: | Carbohydrate polymers Vol. 64; no. 3; pp. 433 - 443 |
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Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Oxford
Elsevier Ltd
30-05-2006
Elsevier Science |
Subjects: | |
Online Access: | Get full text |
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Summary: | A substantial amount of effort has been expended on developing a suitable model to describe the small angle X-ray scattering (SAXS) profiles of starch. The modelling work now offers good descriptions of the fine structure of the starch granule, with recent advances [Daniels, D.R., & Donald, A.M. (2003). An improved model for analysing the SAXS of starch granules. Biopolymers, 69, 165–175] having substantially improved the modelling of B-type crystal-structure starches. However, up till now physical evidence obtained experimentally to support the modelling results has been difficult to obtain. The work presented here shows experimental evidence in support of the hypotheses deduced from the modelling process. This support comes from results of work combining modelling of the SAXS profiles of a diverse range of starches with the physical characterisation technique of high performance anion exchange chromatography using pulsed amperometric detection (HPAEC-PAD). HPAEC-PAD has been used to characterise the chain-length profile of amylopectin for degrees of polymerisation (DP) 6–60 glucose units. This provides, for the first time, physical information of the fine structure of starch that can be compared directly with the results obtained from modelling the SAXS profile. To assist the identification of trends between the variables determined from SAXS profile modelling and those measured by HPAEC-PAD, a data mining approach, principal component analysis (PCA), was employed. |
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Bibliography: | http://dx.doi.org/10.1016/j.carbpol.2005.12.026 |
ISSN: | 0144-8617 1879-1344 |
DOI: | 10.1016/j.carbpol.2005.12.026 |