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Multivariate model to characterise relations between maize mutant starches and hydrolysis kinetics

Abstract : The many studies about amylolysis have collected considerable information regarding the contribution of the starch physico-chemical properties. But the inherent elaborate and variable structure of granular starch and, consequently, the multifactorial condition of the system hinders the interpretation of the experimental results. The immediate benefit of multivariate statistical analysis approaches with that regard is twofold: considering the factors, possibly interrelated, all together and not independently, providing a first estimation of the magnitude and confidence level of the relations between factors and amylolysis kinetic parameters. Based on data of amylolysis of 13 starch samples from wild type, single and double mutants of maize by porcine pancreatic a-amylase (PPA), a multivariate analysis is proposed. Amylolysis progress-curves were fitted by a Weibull function, as proposed in a previous work, to extract three kinetic parameters: the reaction rate coefficient during the first time-unit, k, the reaction rate retardation over time, h, and the final hydrolysis extent, X-infinity. Multivariate models relate the macromolecular composition and the fractions of crystalline polymorphic types to the kinetic parameters. h and X-infinity are found to be highly related to the measured properties. Thus the amylose content appears to be significantly correlated to the hydrolysis rate retardation, which sheds some light on the probable contribution of the amylose molecules contained in the granules. The multivariate models give correct prediction performances except for k whose a part of variability remains unexplained. A further analysis points out the extent of the characterisation effort of the granule structure needed to extend the fraction of explained variability
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Kamal Kansou, Alain Buleon, Catherine Gérard, Agnès Rolland-Sabaté. Multivariate model to characterise relations between maize mutant starches and hydrolysis kinetics. Carbohydrate Polymers, Elsevier, 2015, 133, pp.497 - 506. ⟨10.1016/j.carbpol.2015.07.042⟩. ⟨hal-02640249⟩



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