Succinylation improves the slowly digestible starch fraction of cardaba banana starch. A process parameter optimization study
ARTIFICIAL INTELLIGENCE IN AGRICULTURE(2020)
摘要
The study investigated the improvement of slowly digestible starch fraction of cardaba banana via octenyl succinic anhydride (OSA) modification process. A nonlinear (Response surface methodology [RSM] and artificial neural network [ANN]) and linear (partial least square [PLS]) models were employed and their predictability was compared. The result revealed that all the modelling techniques were accurate in predicting the experimental process. The optimized RSM values for the production of slowly digestible starch (SDS) fraction were OSA con-centration of 4%, reaction time of 47.49 min, and pH of 10 with a predicted SDS value of 44.64%. Among the modelling techniques, ANN was adjudged as the predictive model for improving the SDS yield. The regression co-efficient coupled with the variable important in the projection (VIP) values of the PLS model indicated that the OSA concentration was the most important factors responsible for high SDS yield. Finally, a structural comparison of the optimized starch against native starch revealed the formation of high ordered crystalline structure of the starch due to the impregnation of the modifying agent to the hydroxyl group of the cardaba banana starch.& COPY; 2020 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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关键词
Slowly digestible starch,Low glucose response,Artificial neural network,Partial least square
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