Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Zeng, Zhiheng; Chen, Ming; Wang, Xiaoming; Wu, Weibin; Zheng, Zefeng; Hu, Zhibiao; Ma, Baoqi

doi:10.3390/pr8111430

Open AccessArticle

Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

by

Zhiheng Zeng

,

Ming Chen

,

Xiaoming Wang

,

Weibin Wu

^*,

Zefeng Zheng

,

Zhibiao Hu

and

Baoqi Ma

College of Engineering, South China Agricultural University, Guangzhou 510642, China

^*

Author to whom correspondence should be addressed.

Processes 2020, 8(11), 1430; https://doi.org/10.3390/pr8111430

Submission received: 15 October 2020 / Revised: 2 November 2020 / Accepted: 3 November 2020 / Published: 9 November 2020

(This article belongs to the Special Issue Drying Kinetics and Quality Control in Food Processing)

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Abstract

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R² > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

Keywords: drying; konjac; vacuum; glucomannan; optimization

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MDPI and ACS Style

Zeng, Z.; Chen, M.; Wang, X.; Wu, W.; Zheng, Z.; Hu, Z.; Ma, B. Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Processes 2020, 8, 1430. https://doi.org/10.3390/pr8111430

AMA Style

Zeng Z, Chen M, Wang X, Wu W, Zheng Z, Hu Z, Ma B. Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Processes. 2020; 8(11):1430. https://doi.org/10.3390/pr8111430

Chicago/Turabian Style

Zeng, Zhiheng, Ming Chen, Xiaoming Wang, Weibin Wu, Zefeng Zheng, Zhibiao Hu, and Baoqi Ma. 2020. "Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)" Processes 8, no. 11: 1430. https://doi.org/10.3390/pr8111430

APA Style

Zeng, Z., Chen, M., Wang, X., Wu, W., Zheng, Z., Hu, Z., & Ma, B. (2020). Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Processes, 8(11), 1430. https://doi.org/10.3390/pr8111430

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Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

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