Affiliation:
1. Department of Food Engineering and Technology Central Institute of Technology, Deemed to be University Kokrajhar Assam India
Abstract
AbstractThe research delved into the ascendancy of thermosonication (TS) on elevating the alimentary components of yacon (Smallanthus sonchifolius) tuber juice, derived from the yacon plant. This investigation introduces an innovative approach utilizing artificial neural networks (ANNs) coupled with genetic algorithm (GA) for the multifaceted optimization of extraction procedures. It aims to pinpoint the most effective processing parameters for TS, including variations in amplitudes: X1 (40, 50, and 60%) and temperatures: X2 (30, 40, and 50°C) over sonication times: X3 (15, 30, 45, and 60 min). Further, the approach is focused to amplify quality of yacon juice (YJ) and microbial attributes by enhancing certain quality characteristics such as anti‐oxidant activity (AOA), ascorbic acid (AA), total phenolic content (TPC), total carotenoid content (TCC), total flavonoid content (TFC), yeast and mold count (YMC) and total viable count (TVC). The maximum levels of AOA (33.73 ± 2.21%), AA (25.82 ± 2.03 mg/100 ml), TPC (85.08 ± 3.67 mg GAE/ml), TCC (115.82 ± 2.46 βCE μg/ml), TFC (22.52 ± 2.11 mg QE/ml), TVC, and YMC (not detected) were achieved in thermosonicated YJs under optimal conditions using ANN coupled with GA.Practical ApplicationsYacon tuber juice (YJ) treated with thermosonication (TS) showed improved nutritional characteristics, making them a valuable source of functional compounds for nutraceuticals and food industries. Research emphasizes the effectiveness of artificial neural network in predicting the extraction efficiency of YJ and proposes TS as a potential replacement for traditional thermal pasteurization to maintain or enhance YJ quality and functionality. TS provides cost‐effective and sustainable solutions to the food industry by reducing processing time and energy consumption when compared to pasteurization.