Open AccessModeling thermal properties of exotic fruits pulps: an artificial neural networks approach
Author(s) -
Leandro Soares Santos,
Moysés N. Moraes,
Júlia Dos Santos Lopes,
Luciana Carolina Bauer,
Paulo Bonomo,
Renata Cristina Ferreira Bonomo
Publication year - 2020
Publication title -
research, society and development
Language(s) - English
Resource type - Journals
ISSN - 2525-3409
DOI - 10.33448/rsd-v9i11.9806
Subject(s) - artificial neural network , mean squared error , correlation coefficient , perceptron , coefficient of determination , polynomial , thermal diffusivity , mathematics , multilayer perceptron , artificial intelligence , backpropagation , empirical modelling , data set , predictive modelling , set (abstract data type) , biological system , computer science , pattern recognition (psychology) , statistics , simulation , thermodynamics , mathematical analysis , physics , programming language , biology
Thermophysical properties are important in design, simulation, optimization, and control of food processing. Its prediction is very important but theoretical basis is difficult and empirical models were commonly used. In this work, the modeling of neural networks was applied as an alternative to predict density, thermal conductivity and thermal diffusivity from the temperature and moisture content of jackfruit, genipap and umbu. Data sets from literature were used, combined and individually, to obtain four networks. Supervised multilayer perceptron networks were developed, using the back-propagation algorithm. Several configurations of artificial neural networks (ANNs) were evaluated with one or two hidden layers and a maximum of 21 and 12 neurons in each one, respectively. Data sets were divided to learning (60%) and verification (40%) steps. Best ANNs were chosen based on correlation coefficient and root mean square errors (RMSE), and compared with polynomial models using average absolute deviations (AADs). From total disposable data set, the best ANN developed presents one hidden layer with 15 neurons and shows the same predictive ability of ANNs created from individual fruits data sets, presenting close RMSE and correlation coefficient. The ANNs developed presents AADs near to polynomial models and appers as alternative to conventional modeling. Results indicate that the ANN created from total data set can replace nine polynomial models to predict the thermophysical properties of jackfruit, genipap and umbu pulps.