Emad Aydani; Mahdi Kashani-Nejad; Mohsen Mokhtarian; Hamid Bakhshabadi
Abstract
In this study, Response Surface Methodology (RSM) was used to optimize osmo-dehydration of orange slice. Effect of osmotic solution temperature in the range of 30 to 60 °C, immersion time from 0 to 300 min and sucrose concentration from 35 to 65 brix degree on water loss, solid gain, moisture content, ...
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In this study, Response Surface Methodology (RSM) was used to optimize osmo-dehydration of orange slice. Effect of osmotic solution temperature in the range of 30 to 60 °C, immersion time from 0 to 300 min and sucrose concentration from 35 to 65 brix degree on water loss, solid gain, moisture content, water loss to solid gain ratio and brix change were investigated by Central Composite Design (CCD). Applying response surface and contour plots optimum for osmotic dehydration were found to be at temperature of 30 °C, immersion time of 229.2 minute and sucrose concentration of 65%. At this optimum point, water loss, solid gain, WL/SG ratio, moisture content (dry base) and brix difference were found to be 30.316 (g/100 g initial sample), 13.51 (g/100 g initial sample), 2.45, 2.77 % and 15.79, respectively. The result of artificial neural network indicated that the perceptron neural network with one hidden layer is able to anticipate the dehydration characteristics. This network predicted solid gain and moisture content with 5 neuron per hidden layers with R2 values of 0.937 and 0.959, respectively and brix difference and water loss with 30 neuron per hidden layer with R2 values of 0.961 and 0.942, respectively.
Alireza Ghodsvali; Mohsen Mokhtarian; Hamid Bakhshabadi; Fatemeh Arabamerian
Abstract
Malting is a complex biotechnological process that includes steeping; germination and drying of cereal grains
under controlled conditions of temperature and humidity. In this research malting process parameters were
predict by modular neural network with different activation function included, logsig-logsig, ...
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Malting is a complex biotechnological process that includes steeping; germination and drying of cereal grains
under controlled conditions of temperature and humidity. In this research malting process parameters were
predict by modular neural network with different activation function included, logsig-logsig, tanh-tanh, logsigtanh,
logsig-identity and tanh-identity. Steeping time (x1) and germination time (x2) were used as input
parameters and hot water extract (y1), malting yield (y2) and enzyme activity (β-Gluconase) (y3) were selected as
output parameters. The results showed that using perceptron neural network with tanh-identity activation
function had the best result among all of activation functions to predict effective parameters of malting process.
As well, this network was able to predict hot water extract, malting yield and enzyme activity (β - Gluconase)
with R2 value of 1, 0.984 and 0.995, respectively.
