Artificial neural network and multiple linear regression modelling for prediction of moisture content of red beetroots during ultrasound assisted vacuum drying

Authors : Nuray İnan Çınkır

Pages : 430-444

View : 32 | Download : 89

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :The present work aimed to evaluate the possibility of artificial neural networks (ANN) and multiple linear regression (MLR) to characterize the drying kinetics of red beetroot slices during ultrasound assisted vacuum drying. The ANN model was chosen due to impact of the hidden layer\\\'s neuron number and size. The best ANN model was obtained by three layers (5 inputs, 15 neurons in one hidden layer and 1 output) with RMSE of 0.0117, MAPE of 2.293 and R2 of 0.9996 for all data. The results showed that the ANN is a more effective predictive tool since it can yield better outcomes than MLR.
Keywords : kurutma, modelleme, kırmızı pancar, ultrases, yapay sinir ağları, çoklu doğrusal regresyon

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