IIARD International Institute of Academic Research and Development

International Journal of Agriculture and Earth Science (IJAES )

E- ISSN 2489-0081
P- ISSN 2695-1894
VOL. 11 NO. 3 2025
DOI: 10.56201/ijaes.vol.11.no3.2025.pg1.11

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Application of Artificial Neural Network Modelling on the Drying Kinetics of Yam Slices During Drying

P D I Ebienfa, BE Yabefa,

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Abstract

The drying kinetics of yam slices is a critical process in food preservation, influencing both the quality and shelf life of the product. This study explores the application of Artificial Neural Network (ANN) modeling to predict the drying behavior of yam slices under various drying conditions. A series of experiments were conducted to obtain moisture loss at a varying thicknesses (5-12mm), temperatures (50-90°C) and air velocities(1.5-5.5m/s) at a time interval of for 0 - 220 minutes. To observe a good representation of situation diversity, experimental data were divided into learning and testing databases. The network's inputs (In) were air temperature (T)/80, air velocity (V)/5.5, slice thickness (d)/12, and time (t)/220; the output (Out) was moisture content (db). The collected data were then used to train and validate an ANN model, which was designed to capture the complex nonlinear relationships inherent in the drying process. The ANN architecture was optimized through a systematic approach, including the selection of appropriate input parameters (temperature, time, and air velocity) as well as the determination of hidden layers and neurons. The model's performance was evaluated using statistical metrics such as relative mean square error (MAE), standard deviation of MAE (STDA), percentage of relative mean square error (% MRE), standard deviation of % MRE (STDR), and R2. From the findings all three drying kinetics achieved a minimum value of root mean square error (RMSE) in the range of 0.00052 to 0.00092. Results indicate that the ANN model effectively simulates the drying kinetics of yam slices. This research highlights the potential of ANN as a powerful tool for optimizing drying processes in the food industry. The findings contribute to the growing body of knowledge on the application of artificial intelligence in food technology, paving the way for future studies on other agricultural products.

keywords:

Neural network, yam slices, modelling, drying, temperature, air velocities, time

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