Abstract
This study predicts the moisture ratio history data of dehydrating yam slices from partial data using Artificial Neural Network (ANN) techniques. The moisture ratio history data at 65 oC, 75 oC, 85 oC, and 95 oC were recorded for the dehydration of 1.5 mm, 3.0 mm, and 4.5 mm thick yam slices in a Refractance Window Dryer. The Artificial Neural Network within MATLAB software (v. 8.5), using the Levenberg-Marquardt back-propagation algorithm, was trained with some of the data. After training, the Neural Network software predicted the moisture ratio of the primary variables not used in training. The predicted and experimental values were compared. The results showed that, the Artificial Neural Network (ANN) model using the Levenberg-Marquardt back-propagation training algorithm could accurately predict the experimental results not used in training., the predicted and observed data values fitted each other with correlation coefficient (R2) values of 0.97, 0.99 and 0.99, respectively, for the three-process condition considered. The high R2 establishes a strong correlation between the experimental and predicted values. This work is essential as it establishes that Artificial Neural Network (ANN) techniques, using the Levenberg-Marquardt back-propagation training algorithm, can predict food samples moisture ratios of in a drying process when data is incomplete.
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