M. Asafi; R. Meamar Dastjerdi; M. Noshad
Abstract
Introduction In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all ...
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Introduction In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all over the world. Sesame has been considered as an oil plant for cultivation in Iran's climatic conditions recently. Sesame contains about 58-44% oil, 18-25% protein and 13.5% carbohydrate. Sesame is grown mainly in the developing tropical and subtropical areas of Asia, Africa. The three countries of China, India and Myanmar are accounted as the largest producers of this product in the world. Screw pressing is the most reliable method for extracting oil from oilseed grains. This method is simpler than others and is more efficient in terms of cost and food security. The general objective of this research was to investigate the effects of rotational speed, temperature, type of screwing and die diameter on the amount of oil extraction from sesame oil and prediction of oil extraction using artificial neural network and compare to regression models. Materials and Methods In this research, a sesame oil extractor machine was designed and manufactured. Various experiments were carried out to determine the amount of oil extracted based on variable parameters such as the shape of the press screw, the rotational speed, the temperature and the diameter of the die. The experiment was performed at three levels of press screw type (constant pitch, variable pitch and conical), temperature (30, 60, 90), three levels of rotational speed (20, 50, 80 rpm) and three level of die diameter (6, 8, 10mm). The experimental design was factorial based on completely randomized design with three replications. The mathematical software (Matlab, 2012b) was used to determine the optimal neural network. The type of network was Multi-Layer Perceptron (MLP). In order to design this network, there were 3 neurons in the first layer (input), which was equal to the number of studied variable parameters (type of screw, rotational speed and temperature), the second layer was hidden layer, and the last layer (the output) had a neuron for the extracted oil) was equal to the number of outputs examined in this network. The Levenberg-Marquardt algorithm (LM) was used to train it, which is one of the fastest neural network training methods. The Second-order polynomial regressions were performed based on the step-by-step method and non-meaningful sentences were eliminated from the model. The accuracy of the models was determined by calculating the correlation coefficient and root mean square error (RMSE) indices. Results and Discussion The results of the experiments showed that the effect of type of press screw, rotational speed, extraction temperature and die diameter on the amount of oil extraction was significant (p≤0.01). The highest amount of extracted oil was obtained at conical press screw , rotational speed of 50 rpm, temperature of 60 °C and die diamter of 6 mm. An artificial neural network of three-layer perceptron and regression models were used to predict the amount of sesame oil extracted. The results showed that the artificial neural network model (1-8-3) with a correlation coefficient of 97.47% and a RMSE of 0.65 compared to linear regression and quadratic regression models had the higher efficiency in predicting the amount of extracted oil. Conclusion In this study, the effect of temperature, rotational speed, press screw type and die diameter on the amount of extracted oil were investigated. The results of this study showed that the change in the type of screw, rotational speed, diameter of die and temperature on the amount of extracted oil was significant at 1% level. Results also showed that the artificial neural network method was more efficient than linear and second order regression methods.
R. Meamar Dastjerdi; S. Minaei; M. H. Khoshtaghaza
Abstract
Development of ultrasound technique has not been progressing for evaluating the internal quality of fruits as fast as that of processed foods. In this research for quality assessment of pear fruit (Shah Miveh variety) an ultrasonic measurement system was constructed to transmit and receive the ultrasonic ...
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Development of ultrasound technique has not been progressing for evaluating the internal quality of fruits as fast as that of processed foods. In this research for quality assessment of pear fruit (Shah Miveh variety) an ultrasonic measurement system was constructed to transmit and receive the ultrasonic waves. The apparatus included a pulser-receiver, a pair of 75 kHz ultrasonic transducers with exponential horn, and a computer system for data acquisition and analysis. Several mechanical and chemical properties, including firmness, TSS, acidity, elastic modulus, pH and total dry matter for destructive quality assessment were measured. Velocity and attenuation of ultrasonic waves for nondestructive tests were also measured. The fruit quality levels for the experiment were: unripe, ripe and overripe. The results of tests showed that firmness was the best parameter for measuring fruit quality, as it decreased significantly with ripeness. The effect of ripeness on the velocity and attenuation of ultrasonic waves was also significant. Investigation showed a positive linear relationship between fruit firmness and wave velocity (R2=0.81). Furthermore, the relationship between fruit firmness and attenuation was exponential and wave attenuation decreased with increasing fruit firmness (R2=0.895). The Relationship between ultrasonic properties and fruit modulus of elasticity showed that the wave velocity increased and attenuation decreased with increasing elasticity. It can be concluded that the ultrasonic instrument equipped with exponential horns can effectively be utilized for pear quality assessment based on measurement of wave velocity and attenuation.
Design and Construction
R. Meamar Dastjerdi; S. Minaei; M. H. Khoshtaghaza
Abstract
Non-destructive ultrasonic testing is one of the methods utilized to evaluate quality of agricultural produce. Transducers used in this method are made for basically industrial applications. Since ultrasonic attenuation of waves in agricultural produce is very high, industrial transducers cannot be used ...
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Non-destructive ultrasonic testing is one of the methods utilized to evaluate quality of agricultural produce. Transducers used in this method are made for basically industrial applications. Since ultrasonic attenuation of waves in agricultural produce is very high, industrial transducers cannot be used in agriculture and needs to be modified. This is done with horns that concentrate energy on a small area at a certain distance from the transducer. In this paper, an exponential horn was designed, fabricated and tested using theoretical and computer-aided methods. Results showed that highly sophisticated horns can be designed using computer-aided method with a high accuracy. Analysis of the number of elements on the natural frequency of horn proved that the analysis was not precise at the low number of elements. Therefore, the number of elements should be increased when natural frequency of horn is almost fixed. The minimum number of elements was obtained to be 300. A comparison between theoretical and computer-aided methods showed a desirable performance of the computer-aided method with an error less than 1% without solving very complicated equations. Based on statistical analysis of the data, the effect of produce thickness (potato and carrot) on the velocity of ultrasonic waves in the horned probe was not significant. However, for the un-horned probe, velocity changed significantly with the sample thickness which is not desirable. Therefore, horned probe is more suitable for non-destructive ultrasonic tests than the un-horned probe.
