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Extraction kinetics, modelling and optimization of phenolic antioxidants from sweet potato peel vis-a-vis RSM, ANN-GA and application in functional noodles

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Abstract

The kinetic, modeling and optimised processing parameters for the extraction of phenolic antioxidant from an orange flesh sweet potato cultivar using an aqueous medium was studied. For the process to be effective; reaction time (t), temperature (T) and solid-to-solvent ratio (E) were optimised using the response surface methodology (RSM) and the artificial neural network (ANN) algorithms. Linear interaction between solid to solvent ratio was established to be most significant. Processing variables were established to make 30.44% (T), 38.75% (E) and 30.81% (t) roles to the efficiency of the system. Optimal parameters of 90 °C (T), 6.79% (E) and 60.5 min (t) were established as optimum processing variables using the RSM while ANN algorithm predicts optimal extraction conditions points of 98.64 °C (T), 11.68% (E) and 60.5 min (t). ANN algorithm was the best tool for optimum points prediction due to its low values of mean relative per cent deviation modulus and its absolute average deviation. Antioxidant properties of noodles improved with fortification with 1% peel extract. Optimization conditions and predictive models described in this studies offers an opportunity for the formulation of food products with functional properties by food processor.

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Acknowledgements

The authors wish to express their appreciation to the Department of Food Science and Technology for proving facilities and equipment’s used in the conduct of the study.

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  1. Department of Food Science and Technology, Obafemi Awolowo University, Ile-Ife, Nigeria

    Oseni Kadiri, Saka O. Gbadamosi & Charles T. Akanbi

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  1. Oseni Kadiri
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Correspondence to Oseni Kadiri.

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Kadiri, O., Gbadamosi, S.O. & Akanbi, C.T. Extraction kinetics, modelling and optimization of phenolic antioxidants from sweet potato peel vis-a-vis RSM, ANN-GA and application in functional noodles. Food Measure 13, 3267–3284 (2019). https://doi.org/10.1007/s11694-019-00249-7

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