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Optimization of Vacuum Frying Process for Sweet Potato Chip Manufacturing Using Response Surface Methodology and Artificial Neural Network Model

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Abstract

The purpose of this study was to optimize product yield and quality of the sweet potato chip manufacturing process in a pilot-scale industrial fryer using vacuum frying (VF) technology, response surface methodology (RSM), and artificial neural network (ANN) model. The variables, osmotic dehydration (OD) concentration, OD temperature, and VF temperature were designed to optimize the yield, oil content, and browning index (BI) of vacuum-fried sweet potato chips. Yield, oil content, and BI achieved optimal conditions for 52.46%, 10.65%, and 61.14 in RSM, and 53.52%, 11.58%, and 60.40 in ANN, respectively. Based on the statistical evaluation performance, the ANN model had a higher predictive performance than the RSM model. These findings highlight the high-quality pilot-scale manufacturing process along with a better statistical approach. Moreover, the optimized process can be used for the commercial production of vacuum-fried sweet potato chips.

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Acknowledgements

This study was financially supported by Chosun University (2022).

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Authors and Affiliations

  1. Department of Chemical Engineering, Graduate School of Chosun University, Gwangju, 61452, Korea

    Da-Song Kim, Jung Heon Lee & Hyun-Jae Shin

  2. Department of Biochemical Engineering, Chosun University, Gwangju, 61452, Korea

    Jung Heon Lee & Hyun-Jae Shin

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Kim, DS., Lee, J.H. & Shin, HJ. Optimization of Vacuum Frying Process for Sweet Potato Chip Manufacturing Using Response Surface Methodology and Artificial Neural Network Model. Biotechnol Bioproc E 28, 554–567 (2023). https://doi.org/10.1007/s12257-023-0061-0

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