Modeling of tomato drying using artificial neural network

Movagharnejad, Kamyar; Nikzad, Maryam

doi:10.1016/j.compag.2007.05.003

Cited by 195 publications

(112 citation statements)

References 36 publications

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“…Movagharnejad and Nikzad [45] performed experiments on drying of tomatoes in a tray dryer covering different variables like power of heater and air flow velocity. They modeled the data using ANN and empirical mathematical equations.…”

Section: Ff Tomatoesmentioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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Abstract-This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

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Section: Ff Tomatoesmentioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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“…ANN is capable of handling complex systems with nonlinearities and interactions between decision variables (Lertworasirikul 2008). Many authors successfully used ANN to describe the drying characteristics of many agricultural products; carrots (Erenturk and Erenturk 2007), ginseng (Martynenko and Yang 2006), tomato (Movagharnejad and Nikzad 2007), cassava and mango (Hernàndez-Pèrez et al 2004), grain (Liu et al 2007), Echinacea angustifolia (Erenturk et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Murthy

Manohar

2013

J Food Sci Technol

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Mango ginger (Curcuma amada) was dried in a through-flow dryer system at different temperatures (40-70°C) and air velocities (0.84 -2.25 m/s) to determine the effect of drying on drying rate and effective diffusivity. As the temperature and air velocity increased, drying time significantly decreased. Among the ten different thin layer drying models considered to determine the kinetic drying parameters, semi empirical Midilli et al., model gave the best fit for all drying conditions. Effective moisture diffusivity varied from 3.7 × 10 −10 m 2 /s to 12.5 × 10 −10 m 2 /s over the temperature and air velocity range of study. Effective moisture diffusivity regressed well with Arrhenius model and activation energy of the model was found to be 32.6 kJ/mol. Artificial neural network modeling was also employed to predict the drying behaviour and found suitable to describe the drying kinetics with very high correlation coefficient of 0.998.

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“…A well-trained ANN can be used as a predictive model for a specific application, which is a data-processing system inspired by biological neural system (Hertz et al 1991). Neural networks as an approximation approach has been also used for the prediction of microwave-vacuum for Tomato Slices (Poonnoy et al 2007) and hot air dryer pomegranate arils (Motevali et al 2010) tomato drying (Movagharnejad and Nikzad 2007). Therefore, the objectives of this study were as follows: 1) Investigation of the drying behavior of sour cherries 2) Comparison of artificial neural network and mathematical model for describing the drying behavior of sour cherries 3) Determination of energy consumption and specific energy consumption for drying sour cherries…”

Section: Introductionmentioning

confidence: 99%

Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks

et al. 2011

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Drying characteristics of sour cherries were determined using microwave vacuum drier at various microwave powers (360, 600, 840, 1200 W) and absolute pressures (200, 400, 600, 800 mbars). In addition, using the artificial neural networks (ANN), trained by standard BackPropagation algorithm, the effects of microwave power, pressure and drying time on moisture ratio (MR) and drying rate (DR) were investigated Based on the evaluation of experimental data fitting with semi-theoretical and empirical models, the Midilli et al. model was selected as the most appropriate one. Furthermore, the ANN model was able to predict the moisture ratio and drying rate quite well with determination coefficients (R 2 ) of 0.9996, 0.9961 and 0.9958 for training, validation and testing, respectively. The prediction Mean Square Error of ANN was about 0.0003, 0.0071 and 0.0053 for training, validation and testing, respectively. This parameter signifies the difference between the desired outputs (as measured values) and the simulated values by the model. The good agreement between the experimental data and ANN model leads to the conclusion that the model adequately describes the drying behavior of sour cherries, in the range of operating conditions tested.

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Modeling of tomato drying using artificial neural network

Cited by 195 publications

References 36 publications

Artificial Neural Networks in Fruits: A Comprehensive Review

Artificial Neural Networks in Fruits: A Comprehensive Review

Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks

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