Thin-layer drying of tea leaves: Mass transfer modeling using semi-empirical and intelligent models

Fathi M., . and Roshanak S., . and Rahimmalek M., . and Goli S. A. H., . Thin-layer drying of tea leaves: Mass transfer modeling using semi-empirical and intelligent models. pp. 40-46. ISSN 2231-7546

Moisture content is a critical factor in quality and shelf-life of foods and agricultural products. This research dealt with prediction of moisture ratio of tea leaves using intelligent genetic algorithm-artificial neural networks (multilayer perceptron MLP; and radial basis function RBF) and semi-empirical models during different thin-layer drying processes (i.e. sun air hot air and microwave drying). Effective diffusivities were found in the range of 7.510-7 to 9457.210-7m2/h which the highest Deff value was achieved for microwave drying. Moisture ratio data were modeled using fourteen semi-empirical equations among which Henderson and Pabis Henderson and Pabis- modified two-term-modified and Wang and Singh models received highest correlation coefficients. However the prediction efficiencies of MLP (MSE NMSE and MAE of 0.0084 0.0597 and 0.0722 respectively) and RBF (MSE NMSE and MAE of 0.0043 0.0973 and 0.0564 respectively) networks were found to be more competent than semi-empirical models and therefore could be applied successfully for predicting moisture ratio of tea leaves during different drying processes.

Full text available from: Official URL: http://www.ifrj.upm.edu.my/23%20(01)%202016/(7).pd...

Abstract

Moisture content is a critical factor in quality and shelf-life of foods and agricultural products. This research dealt with prediction of moisture ratio of tea leaves using intelligent genetic algorithm-artificial neural networks (multilayer perceptron MLP; and radial basis function RBF) and semi-empirical models during different thin-layer drying processes (i.e. sun air hot air and microwave drying). Effective diffusivities were found in the range of 7.510-7 to 9457.210-7m2/h which the highest Deff value was achieved for microwave drying. Moisture ratio data were modeled using fourteen semi-empirical equations among which Henderson and Pabis Henderson and Pabis- modified two-term-modified and Wang and Singh models received highest correlation coefficients. However the prediction efficiencies of MLP (MSE NMSE and MAE of 0.0084 0.0597 and 0.0722 respectively) and RBF (MSE NMSE and MAE of 0.0043 0.0973 and 0.0564 respectively) networks were found to be more competent than semi-empirical models and therefore could be applied successfully for predicting moisture ratio of tea leaves during different drying processes.

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Item Type:	Article
AGROVOC Term:	Tea
AGROVOC Term:	Leaves
AGROVOC Term:	Drying
AGROVOC Term:	Mass transfer
AGROVOC Term:	Moisture content
AGROVOC Term:	Microwave treatment
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	24 Apr 2025 06:27
URI:	http://webagris.upm.edu.my/id/eprint/22419

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