Hamid Bakhshabadi; Habibollah Mirzaee; Alireza Ghodsvali; Seyed Mahdi Jafari; Aman Mohammad Ziaiifar
Abstract
Introduction: Black Cumin seed (Nigella sativa L.) as one of the novel edible oil resources used commonly nowadays as seasoning in food product industries due to considerable medicinal properties and high nutritional impacts. Oil extraction by pressing method as an approach compared to other methods ...
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Introduction: Black Cumin seed (Nigella sativa L.) as one of the novel edible oil resources used commonly nowadays as seasoning in food product industries due to considerable medicinal properties and high nutritional impacts. Oil extraction by pressing method as an approach compared to other methods including solvent extraction is faster, safer and cheaper. In the oil extraction process, the preparation of the seeds is a substantial stage for obtaining oil with high quality and efficiency. Microwaves are electromagnetic waves that have a frequency ranged from 300 MHz to 300 GHz with corresponding wave lengths ranged from 1 mm to 1 m. On the other hand the artificial neural network as a powerful predictive tool in a wide scale of process parameters has been studied on an industrial scale in this research in order to achieve a simple, rapid, precise as well as effective model in the oil extraction of Nigella sativa L seed.
Materials and Methods: In the present study Black Cumin seeds after preparation including cleaning and passing resistance of the samples in front of air and moisture were stored in a plastic bag until the day of experiments. Then, they have been pre-treated with microwave within different processing times (90, 180 and 270 S) and powers (180, 540, and 900 W). Afterwards, seeds’ oil was extracted by screw rotational speed levels approach (11, 34 and 57 rpm). Different selected parameters including extraction efficiency, oil acidity value, color and oxidative stability were determined. To predict the alterations trend, the artificial neural network (ANN) design in MATLAB R2013a software was used.
Results and Discussion: According to MSE and R2 values obtained in this study, feed forward neural network with transfer function sigmoid hyperbolic tangent and Levenberg- Marquardt learning algorithm with topology of 3-10-5 (input layer with 3 neurons– a hidden layer with 10 neurons – output layer with 5 neurons) were selected as the optimal neural network with R2 more than 0.995 and MSE equal to 0.0005. Also, the results of the optimized and selected models were evaluated and these models with high correlation coefficients (over 0.949), were able to predict the changes' trend. According to the complexity and multiplicity of the effective factors in food industry processes and the results of this research, the neural network can be introduced as an acceptable model for modeling these processes. By determining the activation function in neural networks which was a function of sigmoid hyperbolic tangent in this study and also, with having the amounts of weight and bias, the connections created by the neuro-fuzzy model can be extracted. By defining this simple created mathematical equation, in computer software such as Excel, we can have a useful, simple and accurate program for predicting the desired parameters in the process of oil extraction by using microwave pre-treatment. Due to high accuracy of neural model we can trust the prediction of these models with high confidence, and this model can be used to optimize and control the process, which can lead to saving in energy and time, and on the other hand, can create a better final product.
Hamid Bakhshabadi; Mohammad Rostami; Masoumeh Moghimi; Abolfazl Bojmehrani; Anehbibi Bahelkeh; Negar Toorani
Abstract
Introduction: Using oilseeds in the human food stuffs, employing their meal for animal feed and also their usage in pharmaceuticals, soap making and fuel has prompted great interest for farmers to plant them and for the government to promote their cultivation. Among them, sunflower is one of the main ...
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Introduction: Using oilseeds in the human food stuffs, employing their meal for animal feed and also their usage in pharmaceuticals, soap making and fuel has prompted great interest for farmers to plant them and for the government to promote their cultivation. Among them, sunflower is one of the main oilseeds in the world which its cultivated area has expanded due to fair cultivation requirements, high yield of the oil, high nutritional value and also lack of anti nutritional factors. Sunflower (Helianthus annuus) is an annual plant belonging to Asteraceae family. This is a dicotyledonous, cross-pollinated monoecious plant that is fertilized by wind and insects. Sunflower seed oil has an excellent nutritional quality, as in recent years, cultivars with high oil (especially oleic acid) content have been substantially nurtured. The most different methods of extracting oil from oilseeds are the press and solvent methods. Similar to the other seeds with high oil content such as canola, the most effective way of extracting oil from sunflower is mechanical pressing followed by solvent extracting. In this method, the mechanical press extracts about 60 percent of the oil and the solvent method extracts the remaining oil. For the first time, the present study was aimed to improve temperature of cooker and moisture of output seeds for producing sunflower oil with lowest degree of insoluble fine partial in oil, moisture and acidity and meal with lowest levels of moisture and oil. Materials and Methods: Sunflower seeds used in this research were supplied from one of Iran's provinces and were transferred to the company of Khorasan cotton and oilseeds to produce oil and meal. After receiving the sunflower seeds in the factory, they were entered into silos in dark and ambient temperature; impurities such as dust, sands, stones, spoiled seeds, small weed seeds and other extraneous materials were separated by mechanical sieves. After cleaning, the seeds were entered into the cracker and they were broken into smaller particles and then were moved into the cooker; at this stage, the temperature of cooker and moisture content of the exiting seeds were set to 70, 80 and 900 C, and 7, 7.5 and 8%, respectively. Then, conditioned seeds were entered into the Buhler flicker device for flaking. Afterwards, the flakes were moved into the Desmet extractor (heating condition of 500C for 7 hours) to extract the oil from the seeds by hexane solvent. Then, the tests were performed on the oil and meal. Severalphysic-chemical properties of sunflower oil including insoluble fine partial, acidity values as well as moisture, protein and oil contents of the obtained meals were determined. Statistical analysis and process optimization were carried out using response surface methodology (RSM). Results and discussion: The achieved results expressed that with an increase in cooking temperature, insoluble fine partial and oil acidity values of the extracted oil were boosted while moisture content of oil and meal values alongside oil content of the obtained meal showed reduction. With increasing of the moisture content of cooker’s seeds, the insoluble fine partial value of the extracted oil was reduced while oil acidity value was increased. Increasing the moisture of cooker’sseeds led to the oil reduction in the meal. The highest oil content in the meal was achieved in the condition that the cooker temperature was 70oC and the moisture of output seeds from the cooker was 7%. The analysis of resulted data showed that two parameters of the cooker’s temperature and cooker’s seeds moisture content had significant effects on the moisture content of the meal. Increasing the cooker temperature from 70 to 90oC caused a decrease in the meal moisture. As result shown, increasing the moisture content of output seeds from the cooker increased the moisture content of the meal. Increasing the cooker temperature from 70 to 90oC reduced the protein amount of the meals. Results of different studies showed that increasing the temperature will decrease the protein amount of the meals. Increasing the moisture was also resulted in the decrease of residual protein in the meal. The obtained results of the optimization procedure revealed that the application of the cooking temperature of 70 °C and moisture content of the output seeds equal 7.73 and 7.65 % led to achieving products with the least values of acidity and insoluble fine partial in the obtained oil as well as meals with the minimum remaining oil.
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.
