Open AccessAPPLICATION OF NEURAL NETWORK FOR ESTIMATION OF PISTACHIO POWDER SORPTION ISOTHERMS
Author(s) -
Hamid Tavakolipour,
Mohsen Mokhtarian
Publication year - 2014
Publication title -
latin american applied research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 23
eISSN - 1851-8796
pISSN - 0327-0793
DOI - 10.52292/j.laar.2014.440
Subject(s) - sorption , gravimetric analysis , desorption , water content , equilibrium moisture content , adsorption , artificial neural network , sorption isotherm , moisture , chemistry , materials science , biological system , chromatography , thermodynamics , computer science , composite material , machine learning , geotechnical engineering , organic chemistry , geology , physics , biology
Moisture sorption isotherms for pistachio powder were determined by gravimetric method at temperatures of 15, 25, 35 and 40ºC. Some mathematical models were tested to measure the amount of fitness of experimental data. The mathematical analysis proved that Caurie model was the most appropriate one. As well, adsorptiondesorption moisture content of pistachio powder were predicted using artificial neural network (ANN) approach. The results showed that, MLP network was able to predict adsorption-desorption moisture content with R2 values of 0.998 and 0.992, respectively. Comparison of ANN results with classical sorption isotherm models revealed that ANN modeling had greater accuracy in predicting equilibrium moisture content of pistachio powder.