Application of microwave pretreatment to increase mass transfer rate during carrot slices drying process

Authors
Fakhreddin Salehi 1
Kimia Goharpour 2
Helia Razavi Kamran 2
1 Associate Professor, Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran
2 MSc Student, Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran
10.22034/FSCT.20.145.139
Abstract
The drying process is one of the methods of processing vegetables and fruits, helping to reduce the volume of the product, facilitate transportation, increase preservation ability, and reduce microbial activities. As a fast and effective heat source with thermal and non-thermal effects, microwaves can directly affect food and thus accelerate physicochemical reactions and drying rates, and produce high quality dried products. The purpose of this research is to use microwave pretreatment to increase the mass transfer rate in the drying process of carrot slices and to model the process using the genetic algorithm-artificial neural network method. In this study, the effects of microwave treatment time at five levels of 0, 15, 30, 45, and 60 seconds on the drying time and moisture content of carrot slices were investigated in three replications. This process was modeled using the genetic algorithm-artificial neural network method with 2 inputs (microwave processing time and drying process duration) and 1 output (moisture percentage). The results showed that by increasing the microwave treatment time, the rate of moisture removal from the samples increased and thus the drying time decreased. Different training algorithms were evaluated and the Levenberg–Marquardt algorithm was chosen as the best algorithm. Based on modeling data analysis, the Perceptron artificial neural network with 2-5-1 structure is the most suitable network to predict the moisture content of microwaves-treated carrot slices. In this study, the values of mean squared error (MSE), normalized mean squared error (NMSE), mean absolute error (MAE), and correlation coefficient (r) for predicting the moisture content of carrot slices during drying process were equal to 5.298, 0.006, 1.650, and 0.997, respectively. The results of the optimal neural network sensitivity analysis showed that the drying process duration was the most effective factor in predicting the moisture content of carrot slices.
Keywords
Genetic algorithm-artificial neural network
Levenberg–Marquardt
Microwave
Perceptron
Sensitivity Analysis

Subjects

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