Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin)

Guo, Jiale; Wu, Jie; Zhang, Lixuan; Peng, Ziqin; Wei, Lixuan; Li, Wuxia; Shen, Jingzhi; Liu, Yanhong

doi:10.3390/foods15020245

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Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin)

by

Jiale Guo

¹,

Jie Wu

¹,

Lixuan Zhang

²,

Ziqin Peng

¹,

Lixuan Wei

¹,

Wuxia Li

¹,

Jingzhi Shen

¹ and

Yanhong Liu

^1,*

¹

College of Engineering, China Agricultural University, 17 Qinghua Donglu, Beijing 100083, China

²

YanTai Research Institute, China Agricultural University, Binhai Middle Road, Yantai 264670, China

^*

Author to whom correspondence should be addressed.

Foods 2026, 15(2), 245; https://doi.org/10.3390/foods15020245

Submission received: 17 December 2025 / Revised: 7 January 2026 / Accepted: 8 January 2026 / Published: 9 January 2026

(This article belongs to the Section Food Engineering and Technology)

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Abstract

Achieving comprehensive improvements in the drying rate (DR) and the quality after drying of agricultural products is a major goal in the field of drying. To further shorten the drying time while improving product quality, this study introduced a Convolutional Neural Network (CNN) and MultiHead Attention (MHA) to enhance the prediction accuracy of the Long Short-Term Memory (LSTM) network regarding the properties of dried samples. These properties included DR, shrinkage rate (SR), and total color difference (ΔE). The CNN-LSTM-MHA network was proposed, developing a novel hot-air drying (HAD) scenario utilizing an intelligent temperature control system based on the real dynamics of material properties. The results of drying experiments with temperature-sensitive yuba showed that the CNN-LSTM-MHA network’s predictive accuracy was better than that of other networks, as evidenced by its coefficient of determination (R²: 0.9855–0.9999), root mean square error (RMSE: 0.0001–0.0099), and mean absolute error (MAE: 0.0001–0.0120). Comparative analysis with fixed-temperature drying indicated that CNN-LSTM-MHA-controlled drying significantly reduced drying time and enhanced the SR, color, rehydration ratio (RR), texture, protein content, fat content, and microstructure of yuba. Overall, the findings highlight the potential of CNN-LSTM-MHA-based intelligent drying as a viable strategy for yuba stick processing, providing insights for other food drying applications.

Keywords: Long Short-Term Memory; Convolutional Neural Network; MultiHead Attention; hot-air drying; real-time control

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MDPI and ACS Style

Guo, J.; Wu, J.; Zhang, L.; Peng, Z.; Wei, L.; Li, W.; Shen, J.; Liu, Y. Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin). Foods 2026, 15, 245. https://doi.org/10.3390/foods15020245

AMA Style

Guo J, Wu J, Zhang L, Peng Z, Wei L, Li W, Shen J, Liu Y. Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin). Foods. 2026; 15(2):245. https://doi.org/10.3390/foods15020245

Chicago/Turabian Style

Guo, Jiale, Jie Wu, Lixuan Zhang, Ziqin Peng, Lixuan Wei, Wuxia Li, Jingzhi Shen, and Yanhong Liu. 2026. "Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin)" Foods 15, no. 2: 245. https://doi.org/10.3390/foods15020245

APA Style

Guo, J., Wu, J., Zhang, L., Peng, Z., Wei, L., Li, W., Shen, J., & Liu, Y. (2026). Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin). Foods, 15(2), 245. https://doi.org/10.3390/foods15020245

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Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin)

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