Abstract
The optimum reaction conditions for the derivation of acetylated (esterified) starch using response surface methodology (RSM) and artificial neural network (ANN) were studied. All the independent variables (starch solids, acetic anhydride concentration, and reaction time) were of significant (p < .05) importance in achieving esterified starch of Amaranthus viridis. Optimum conditions of 152.46 g of starch, 11 ml of acetic anhydride and time of 2.92 min with corresponding acetyl content and degree of substitution (DS) of 1.74% and 0.06, respectively, were established for ANN. The RSM gave optimum conditions of 149.57 g (starch), 10.38 ml (acetic anhydride) and 3 min (time) with corresponding acetyl content and DS of 1.61% and 0.06, respectively. The order of priority of the process variables was established as acetic anhydride (42.59%), starch solids (33.90%), and reaction time (23.51%). The results provided useful information on development of economic and efficient acetylation process for modification of A. viridis starch.
Highlights
Genus Amaranthus contains over 60 species but only a few are cultivated, and many are considered weeds (Marin, Narcisa, & Popa, 2008)
Amaranthus viridis is cultivated with low labor cost and high grain yield compared to some other sources of starch such as cassava, corn, and breadfruit
The variables were optimized, using response surface methodology (RSM) and artificial neural network (ANN) coupled with genetic algorithm
Summary
Genus Amaranthus contains over 60 species but only a few are cultivated, and many are considered weeds (Marin, Narcisa, & Popa, 2008). Amaranthus viridis is cultivated with low labor cost and high grain yield compared to some other sources of starch such as cassava, corn, and breadfruit. Artificial Neural Network (ANN), which is a computational method that can mimics the neurological processing capability of the human brain, has been applied to modeling of many food processing studies These studies include gluconic acid (Osunkanmibi, Olowlabi & Betiku, 2015), ethanol (Betiku & Taiwo, 2015) and oxalic acid (Emeko et al, 2015) production processes as well as in enzymatic reaction catalyzed by amyloglucosidase (Bas & Boyaci, 2007). The variables were optimized, using RSM and ANN coupled with genetic algorithm
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