Open AccessModeling of drying kiwi slices and its sensory evaluation
Author(s) -
Mahjoorian Abbas,
Mokhtarian Mohsen,
Fayyaz Nasrin,
Rahmati Fatemeh,
Sayyadi Shabnam,
Ariaii Peiman
Publication year - 2017
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.414
Subject(s) - kiwi , moisture , flavor , aroma , water content , mathematics , artificial neural network , biological system , food science , chemistry , materials science , artificial intelligence , computer science , composite material , geology , geotechnical engineering , biology
Abstract In this study, monolayer drying of kiwi slices was simulated by a laboratory‐scale hot‐air dryer. The drying process was carried out at three different temperatures of 50, 60, and 70°C. After the end of drying process, initially, the experimental drying data were fitted to the 11 well‐known drying models. The results indicated that Two‐term model gave better performance compared with other models to monitor the moisture ratio (with average R 2 value equal .998). Also, this study used artificial neural network ( ANN ) in order to feasibly predict dried kiwi slices moisture ratio ( y ), based on the time and temperature drying inputs ( x 1 , x 2 ). In order to do this research, two main activation functions called logsig and tanh , widely used in engineering calculations, were applied. The results revealed that, logsig activation function base on 13 neurons in first and second hidden layers were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R 2 value .997. Furthermore, kiwi slice favorite is evaluated by sensory evaluation. In this test, sense qualities as color, aroma, flavor, appearance, and chew ability (tissue brittleness) are considered.