Investigation of the drying kinetics of a residual nut mixture using mathematical models and artificialneural networks

Tran Huu Duy, Nguyen Doan Kim Dang, Dang Nguyen Gia Han, Pham Tran Thanh Vy, Tran Ngoc Giau, Hong Van Hao, Nguyen Minh Thuy, Vo Quang Minh and Ngo Van Tai
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Tran Huu Duy: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Nguyen Doan Kim Dang: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Dang Nguyen Gia Han: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Pham Tran Thanh Vy: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Tran Ngoc Giau: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Hong Van Hao: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Nguyen Minh Thuy: Institute of Food and Biotechnology, Can Tho University, Can Tho city, Vietnam
Vo Quang Minh: College of Environment and Natural Resources, Can Tho University, Can Tho city, Vietnam
Ngo Van Tai: School of Food Industry, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

Research in Agricultural Engineering, vol. preprint

Abstract: The objective of this study was to evaluate the potential for the sustainable reuse and the value of the residual nut mixture (RNM) by-products (cashew nut, peanut, and soybean) after extraction. To investigate the drying kinetics, the RNM was dried at various temperatures (50 to 80 °C). The Balbay and Şahin model, which had a high coefficient of determination (R2) of 99.62-99.96%, a low root mean square error of 0.007-0.021, and χ2 of 0.001-0.005, was the one that best fit the experimental data out of the eight mathematical models that were used. Artificial neural networks showed higher and faster prediction capacity than the mathematical models. The effective moisture diffusion coefficient (Deff) increased gradually with the temperature, with an activation energy (Ea) of 13.67 kJ∙mol-1. The RNM powder produced by the optimal drying process (60 °C for 3.75 h) has a bright colour, high polyphenol content (2.68 mg gallic acid equivalent (GAE)∙g-1) and antioxidant activity, low moisture content (4.9%) and relatively high nutritional value, especially protein (27.27%), lipid (40.19%), and fibre (4.2%). Under these conditions, not only is efficient drying and preservation achieved, but the quality of the by-product powder is also maintained.

Keywords: nuts by-products; drying temperatures; moisture diffusion; activation energy; statistical metrics (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlrae:v:preprint:id:218-2025-rae

DOI: 10.17221/218/2025-RAE

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