Microwave drying of mango ginger (Curcuma amada Roxb)

Prediction of drying kinetics by mathematical modelling and artificial neural network

Thrupathihalli Pandurangapp Krishna Murthy, Balaraman Manohar

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Curcuma amada (Mango ginger) was dried at four different power levels ranging 315-800W to determine the effect of microwave power on moisture content, moisture ratio, drying rate, drying time and effective diffusivity. Among the fifteen thin layer drying models considered for evaluating the drying behaviour, the semi-empirical Midilli et al., model described the drying kinetics very well with R 2>0.999. Drying rate and effective diffusivity increased as the microwave power output increased. Activation energy was estimated by a modified Arrhenius type equation and found to be 21.6kWkg -1. A feed-forward artificial neural network using back-propagation algorithm was also employed to predict the moisture content during MW drying and found adequate to predict the drying kinetics with R 2 of 0.985.

Original languageEnglish
Pages (from-to)1229-1236
Number of pages8
JournalInternational Journal of Food Science and Technology
Volume47
Issue number6
DOIs
Publication statusPublished - 01-06-2012
Externally publishedYes

Fingerprint

Curcuma amada
Ginger
Mangifera
Curcuma
Microwaves
neural networks
Drying
mathematical models
drying
Neural networks
kinetics
Kinetics
prediction
Moisture
diffusivity
water content
thin-layer drying
Backpropagation algorithms
activation energy
Activation energy

All Science Journal Classification (ASJC) codes

  • Food Science
  • Industrial and Manufacturing Engineering

Cite this

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Microwave drying of mango ginger (Curcuma amada Roxb) : Prediction of drying kinetics by mathematical modelling and artificial neural network. / Krishna Murthy, Thrupathihalli Pandurangapp; Manohar, Balaraman.

In: International Journal of Food Science and Technology, Vol. 47, No. 6, 01.06.2012, p. 1229-1236.

Research output: Contribution to journalArticle

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