The modelling of rupture force of white kidney beans (Phaseolus vulgaris L.) using the multiple linear regression (MLP) and artificial neural networks (ANN)

Year 2020, , 129 - 136, 31.03.2020

Sefa Altıkat

https://doi.org/10.20289/zfdergi.554929

Cited By: 1

Abstract

Objective: The
objective of this study modelling the rupture force of white kidney beans with the
multiple linear regression (MLR) and artificial neural networks (ANN).

Material and Methods: It
was used four different white kidney bean varieties  (Akman, Topçu, Göynük and Karacaşehir) at the
five different moisture contents (14.28%, 24.32%, 33.45%, 42.54% and 53.48%).
In the MLR and ANN models the moisture contents,  length, width, thickness, arithmetic mean
diameters, geometric mean diameters, surface area and  sphericity of the beans were used as input
parameters while the rupture force as output parameter. In addition, 24
different ANN architectures were used in the ANN.

Results: The
highest R² values for the Akman (0.979) and Karacaşehir (0.986) varieties
were obtained in the ANN11 architecture used by the Levenberg-Marquard learning
function and the logarithmic sigmoid - linear transfer function pairs with 12
neurons. However, the best prediction values for Topçu (0.963) and Göynük
(0.944) were obtained in ANN 7 and ANN 2 architectures, respectively. In
addition, the best pair of learning functions for Topçu and Göynük were
observed in Logarithmic sigmoid - Symmetric sigmoid and Logarithmic sigmoid-
linear transfer functions, respectively.

Conclusion:
The results of the study clearly showed that the ANN successfully modeled
rupture force in all the white kidney bean varieties.

Keywords

Physical property, mechanical property, simulink, moisture

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