Application of ANFIS approach for modeling of drying process of quince seed gum using infrared dryer

Authors
Navid Godini 1
Ashraf Gohari Ardabili 2
Fakhreddin Salehi 3
1 MSc Student, Department of Food Science and Technology, Bahar Faculty of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran.
2 Assistant Professor, Department of Food Science and Technology, Bahar Faculty of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran.
3 Associate Professor, Department of Food Science and Technology, Bahar Faculty of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran.
10.22034/FSCT.19.131.83
Abstract
ANFIS (Adaptive neuro-fuzzy inference system) is a combined neuro-fuzzy method for modeling transport phenomena (mass and heat) in the food processing. In this study, first, an infrared dryer was used to dry the extracted gum from quince seed. Then, ANFIS method was used to modeling and predicting the weight changes percentage of this gum when drying in infrared dryer. In the infrared dryer, the effect of samples distance from the radiation lamp and the effect of the gum thickness inside the container on the drying time and the weight loss percentage of quince seed gum during drying time were investigated. The results of drying of this gum by infrared method showed that by reducing the samples distance from the heat source from 10 to 5 cm, the average drying time of quince seed gum decreased from 58.0 minutes to 29.3 minutes (thickness 1.5 cm). Also, by reducing the gum thickness in the sample container from 1.5 to 0.5 cm, the average drying time of the extracted gum decreased from 45.7 minutes to 19.3 minutes (distance 7.5 cm). The ANFIS model was developed with 3 inputs of drying time, samples distance from heat source and gum thickness in the sample container to predict the weight changes percentage of this gum when drying in infrared dryer. The calculated coefficients of determination values for predicting the weight loss percentage of gum using the ANFIS-based subtractive clustering algorithm was 0.983. In general, it can be said that the high coefficients of determination between the experimental results and the outputs of the ANFIS model indicate the acceptable accuracy and usability of this method in modeling heat and mass transfer processes in the food industry.
Keywords
Adaptive Neuro-Fuzzy Inference System
mass transfer
Quince seed gum
Subtractive clustering
Weight loss percentage

Subjects

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