<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The relationships between physico-chemical and rheological properties are considered complex nonlinear systems. Thus, the artificial neural network (ANN) and regression models were used for the rheological characterization of Brazilian honeys, based on low-cost measurements of water content and temperature. The steady shear viscosity (η) performed well when measured in the test phase in a 2-12-1 neuron multilayer perceptron (MLP) ANN (model 1) with a root mean square error (RMSE) and correlation coefficient (r) equal to 0.0430 and 0.9681, respectively. The parameter loss modulus (G″), storage modulus (G′) and complex viscosity (η*) were predicted in the temperature sweep test by small amplitude oscillatory shear (SAOS) measurements during heating and cooling, and the MLP ANNs with architectures of 2-9-3 (model 2) and 2-3-3 (model 3) showed RMSE values equal to 0.0261 and 0.0387 in the test phase, respectively. For all the determined parameters, non-linear exponential models showed similar results to models 1, 2 and 3. An ANN with 3-9-3 architecture (model 4) showed RMSE and r for G′ equal to 0.0158 and 0.7301, for G″ equal to 0.0176 and 0.9581, and for η* equal to 0.0407 and 0.9647, respectively, in the test phase for date of the frequency sweep test obtained by SAOS. These results were far superior to those obtained by second-order multiple linear models. The acquisition of all models is an important application for the processing of honey and honey-based products, since these properties are essential in engineering calculations and quality control of products.</p></abstract>

The relationships between physico-chemical and rheological properties are considered complex nonlinear systems. Thus, the artificial neural network (ANN) and regression models were used for the rheological characterization of Brazilian honeys, based on low-cost measurements of water content and temperature. The steady shear viscosity (η) performed well when measured in the test phase in a 2-12-1 neuron multilayer perceptron (MLP) ANN (model 1) with a root mean square error (RMSE) and correlation coefficient (r) equal to 0.0430 and 0.9681, respectively. The parameter loss modulus (G″), storage modulus (G′) and complex viscosity (η*) were predicted in the temperature sweep test by small amplitude oscillatory shear (SAOS) measurements during heating and cooling, and the MLP ANNs with architectures of 2-9-3 (model 2) and 2-3-3 (model 3) showed RMSE values equal to 0.0261 and 0.0387 in the test phase, respectively. For all the determined parameters, non-linear exponential models showed similar results to models 1, 2 and 3. An ANN with 3-9-3 architecture (model 4) showed RMSE and r for G′ equal to 0.0158 and 0.7301, for G″ equal to 0.0176 and 0.9581, and for η* equal to 0.0407 and 0.9647, respectively, in the test phase for date of the frequency sweep test obtained by SAOS. These results were far superior to those obtained by second-order multiple linear models. The acquisition of all models is an important application for the processing of honey and honey-based products, since these properties are essential in engineering calculations and quality control of products.

Artificial Neural Network and Regression Models to Evaluate Rheological Properties of Selected Brazilian Honeys

Journal of Apicultural Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The relationships between physico-chemical and rheological properties are considered complex nonlinear systems. Thus, the artificial neural network (ANN) and...

Artificial Neural Network and Regression Models to Evaluate Rheological Properties of Selected Brazilian Honeys

Article Category: Original Article

Publié en ligne: 07 nov. 2020

Pages: 219 - 228

Reçu: 12 juil. 2019

Accepté: 08 mai 2020

DOI: https://doi.org/10.2478/jas-2020-0017

Mots clés
artificial neural network models, Complex Viscosity, loss modulus, regression analysis, Viscosity

© 2020 Vanelle M. D. Silva et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Artificial Neural Network and Regression Models to Evaluate Rheological Properties of Selected Brazilian Honeys

Article Category: Original Article

Publié en ligne: 07 nov. 2020

Pages: 219 - 228

Reçu: 12 juil. 2019

Accepté: 08 mai 2020

DOI: https://doi.org/10.2478/jas-2020-0017

Mots clésartificial neural network models, Complex Viscosity, loss modulus, regression analysis, Viscosity

© 2020 Vanelle M. D. Silva et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
artificial neural network models, Complex Viscosity, loss modulus, regression analysis, Viscosity