Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network

Mohebbi, Mohebbat; Shahidi, Fakhri; Fathi, Milad; Ehtiati, Ahmad; Noshad, Mohammad

doi:10.1016/j.fbp.2010.08.001

Cited by 47 publications

(23 citation statements)

References 18 publications

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“…where y j is the net input of each neuron in the hidden and output layers, x i is input, n is number of inputs to the neuron, w ij is the weight of the connection between neuron I, and neuron j and b j is the bias associated with jth neuron (Mohebbi, Shahidi, Fathi, Ehtiati, & Noshad, 2011). Each neuron consists of a transfer function expressing its internal activation level.…”

Section: Mathematicallymentioning

confidence: 99%

Modeling of drying kiwi slices and its sensory evaluation

Mahjoorian

Mokhtarian

Fayyaz

et al. 2016

Food Science & Nutrition

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In this study, monolayer drying of kiwi slices was simulated by a laboratory-scale hot-air dryer. The drying process was carried out at three different temperatures of 50, 60, and 70°C. After the end of drying process, initially, the experimental drying data were fitted to the 11 well-known drying models. The results indicated that Two-term model gave better performance compared with other models to monitor the moisture ratio (with average R 2 value equal .998). Also, this study used artificial neural network (ANN) in order to feasibly predict dried kiwi slices moisture ratio (y), based on the time and temperature drying inputs (x 1 , x 2 ). In order to do this research, two main activation functions called logsig and tanh, widely used in engineering calculations, were applied.The results revealed that, logsig activation function base on 13 neurons in first and second hidden layers were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R 2 value .997. Furthermore, kiwi slice favorite is evaluated by sensory evaluation. In this test, sense qualities as color, aroma, flavor, appearance, and chew ability (tissue brittleness) are considered. K E Y W O R D SArtificial neural network, hot-air drying, sensory evaluation 1

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Section: Mathematicallymentioning

confidence: 99%

Modeling of drying kiwi slices and its sensory evaluation

Mahjoorian

Mokhtarian

Fayyaz

et al. 2016

Food Science & Nutrition

View full text Add to dashboard Cite

show abstract

“…In this method, the calculation is performed from the network input to the network output. Then, the error values were detected and considered to the previous layers and the weight vectors are gradually modified from the last layer to the first layer in order to get the minimum value of the prediction error (Mohebbi et al, 2011). The results of the optimization of perceptron neural networks with logarithm sigmoid activation function (Log-sig -Log-sig) and hyperbolic tangent (tanh) together with the obtained layout in different modes are shown in Figs 2-5.…”

Section: Resultsmentioning

confidence: 99%

“…In another research, the moisture content of bananas was studied in the process of osmotic dehydration through two methods of networks and a genetic algorithm. The results indicated that the application of neural networks with 7 and 10 nods in the first and second hidden layers, respectively, predicts the moisture content with a higher correlation coefficient value (R 2 =0.94) (Mohebbi et al, 2011). The main aim of this investigation is to predict the quantity of water loss and solid gain by application of different functions of the artificial neural network, and determination of the most appropriate arrangement of neurons through kiwifruit osmotic dehydration process.…”

Section: Introductionmentioning

confidence: 99%

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Jabrayili¹,

Farzaneh²,

Zare³

et al. 2016

International Agrophysics

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A b s t r a c t. Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y 1 ), osmotic solution temperature (y 2 ), and immersion time (y 3 ) were considered as the input parameters and solid gain value (x 1 ) and water loss value (x 2 ) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively.

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“…To increase the efficiency of the osmotic dehydration, these processes are increasingly aided by power ultrasound with the intensity above 1 W/cm 2 and frequency around 21-25 kHz (18,19). Higher frequencies are not recommended due to the increase of energy absorption by the solution and, as a consequence, reduction of penetration depth into the processed material (20).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Drying of Carrot Preliminary Processed with Ultrasonically Assisted Osmotic Dehydration

Mierzwa¹,

Kowalski²,

Kroehnke³

2017

Food Technol. Biotechnol.

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SummaryIn this paper the kinetics of osmotic dehydration of carrot and the influence of this pretreatment on the post-drying processes and the quality of obtained products are analysed. Osmotic dehydration was carried out in the aqueous fructose solution in two different ways: with and without ultrasound assistance. In the first part of the research, the kinetics of osmotic dehydration was analysed on the basis of osmotic dewatering rate, water loss and solid gain. Next, the effective time of dehydration was determined and in the second part of research samples were initially dehydrated for 30 min and dried. Five different procedures of drying were established on the grounds of convective method enhanced with microwave and infrared radiation. The influence of osmotic dehydration on the drying kinetics and final product quality was analysed. It was found that it did not influence the drying kinetics significantly but positively affected the final product quality. Negligible influence on the drying kinetics was attributed to solid uptake, which may block the pores, hindering heat and mass transfer. It was also concluded that the application of microwave and/or infrared radiation during convective drying significantly influenced the kinetics of the final stage of drying. A proper combination of aforementioned techniques of hybrid drying allows reducing the drying time. Differences between the particular dehydration methods and drying schedules were discussed.

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Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network

Cited by 47 publications

References 18 publications

Modeling of drying kiwi slices and its sensory evaluation

Modeling of drying kiwi slices and its sensory evaluation

Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit

Hybrid Drying of Carrot Preliminary Processed with Ultrasonically Assisted Osmotic Dehydration

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