فهرست مطالب ایمان گلپور

Nowadays, citrus fruits, especially oranges, is very important in the human nutrition regime, and its quality characteristics assessment is very important. This study aimed to predict some biochemical characteristics of blood orange, using machine vision and artificial neural networks. In this experiment, the amount of vitamin C content, sugar content, and acidity (pH) were obtained using destructive laboratory methods. Images of blood orange samples were captured and 108 texture features and 57 color features were extracted on CIElab, RGB, HSV, and HIS color spaces and finally, the artificial neural networks method has been used to estimate the desired properties. To evaluate and select the most optimal artificial neural network, a feed-forward neural networks with Levenberg-Marquardt learning algorithm, the different number of neurons, and different transfer functions in the hidden and output layers was used. Finally, using the best neural network and 165 textural-color features, the amount of vitamin C content, sugar content, and pH were estimated with a correlation coefficient of 0.950, 0.968, and 0.884, respectively. Therefore, considering the appropriate correlation coefficient, machine vision and image processing technology can estimate some biochemical characteristics of blood oranges accurately.

Steam generation system is a crucial and essential part of food industries which generates and distributes steam for consumption in domestic production units. Energy analysis based on the first law of thermodynamics was employed as the basic approach to assess energy systems. However, the energy approach does not provide information on the degradation of the energy quality occurring within energy systems and is, therefore, insufficient for sustainable design or optimization goals. Nevertheless, exergy analysis based on both the first and second laws of thermodynamics can overcome shortcomings of energy analysis. In the present study, the performance of equipment of the steam generation system in Pakdis’s juice production Company located in Urmia is investigated. Owing to the energy and exergy analyses, the sites with the highest loss of exergy are identified as the critical points of the process.

In this study, the steam generation unit of a juice production company located in Urmia, West Azarbaijan province in Iran was exergetically analyzed. Using mass, energy, and exergy balances for each component of the unit, the thermodynamic objective functions including the exergy efficiency, exergy destruction rate, exergy loss rate, and the potential improvement rate were assessed. After data acquisition, energy and exergy analysis of this unit was achieved by solving the related equations with the help of thermodynamic properties along with programming in EES software package.

The results showed that the highest exergy efficiency of 98.44% was assigned to the steam distributor (O) of the unit with a potential improvement rate of 1.51 kW and an exergy loss rate of 68.80 kW, as well as the pump (M) before the fourth boiler with an exergy efficiency of 19.69%, had the lowest value of exergy efficiency. The values of 12.55 and 11.93 kW were obtained for the exergy destruction rate and its potential improvement rate, respectively. The highest exergy destruction rate of the unit was for the first boiler with a value of 12391.80 kW, with an efficiency of 19.55% and a potential improvement rate of 10295.26 kW.

With regard to the energy and exergy analyses of the steam production system, more than 98% of the exergy destruction rate of the entire steam generation system was assigned to boilers, which had a major contribution to the exergetic efficiency of the system. The highest percentage of potential improvement was related to the first boiler and also the third boiler had the highest exergy loss rate, although the lowest exergy loss rate was the expansion tank of the system. In general, this study demonstrated the importance of exergy analysis for detecting the system components with the highest exergy destruction, which can be a breakthrough to identify these components and provides suitable solutions to improve the overall exergy efficiency of the steam-generating system.

Camel meat can be a suitable alternative for other red meat types in human nutrition, due to its low cholesterol and low-fat content and the appropriate protein content. This research aims to investigate and evaluate the fat content and freshness of camel meat using machine vision technology as a non-destructive method. Therefore, using image processing as a non-destructive method and Soxhlet device as a destructive method, the amount of fat content was predicted, and also the freshness was classified for camel meat. In the image processing section, 108 textual features and 39 color features were extracted in the RGB, HSV, HIS, and CIElab color spaces. Moreover, to predict the freshness and quality of meat, feed-forward back propagation artificial neural networks with one and two hidden layers, a various number of neurons, and threshold functions were used. Also, according to the regression diagram of fat content obtained from the destructive method (fat content obtained from Soxhlet device) with fat content obtained from non-destructive method (machine vision), the coefficient of determination and accuracy between them achieved 0.841. The results of the evaluation of the neural networks showed that the best desirable network for classification based on freshness is a one-hidden layer network with topology 147-3-1, tangent-sigmoid transfer function at hidden layer and purelin transfer function at output layer (R2= 0.996), and also to prete of fat content the best network is two-hidden layer network with linear, log-sigmoid, log-sigmoid transfer function at first hidden layer, second hidden layer and output layer respectively (R2= 0.99). Therefore, the results of this study show that the proposed system with the help of machine vision technology can predict the freshness and fat content of camel meat with acceptable accuracy.

The aim of this study was to determine the kinetics, effective moisture diffusivity, activation energy, specific energy consumption, and also predict the moisture content of white mulberry during the drying process with microwave-hot air dryer using mathematical models, Artificial Neural Networks (ANN) and Neuro-Fuzzy Inference System (ANFIS). Drying process was accomplished in three temperature levels (40, 55, and 70°C), three inlet air velocity levels (0.5, 1 and 1.5 m/s) and three microwave power levels (270, 450 and 630 W). To estimate the moisture ratio of white mulberry, 10 mathematical models, ANN and ANFIS were used to fit the experimental data of thin-layer drying. The results showed, the maximum and minimum effective moisture diffusivity during drying was calculated 3.56×10-9 and 3.86×10-10 m2/s, respectively. Also, the minimum and maximum effective moisture diffusivity during drying was achieved 48.54 and 1380.88 Mj/kg, respectively. Among the mathematical models under study, the Page model was the best model for describing the behavior of the thin layer of white mulberry drying. The mean square error (MSE) values for the mathematical models, ANN, and ANFIS were 0.00059, 0.0052 and 0.0044, respectively. Therefore, the ANFIS model with the highest Correlation Coefficient (R2=0.99995), the least percentage of mean relative error (ε=1.84) and mean square error (MSE=0.0044) were used to evaluate the moisture ratio in comparison with other methods implemented in this research Selected as the best model

Persian cumin (Bunium persicum) is one of the medicinal plants with high economic and expert value which its identification and classification is very important. Manual or visual inspection and classification of Persian cumin is very time-consuming and associated with errors. Therefore machine vision technology as a novel and non-destructive method can be a very good technique for identification and classification of the product. The aim of this study was to identify and to classify Persian cumin landraces based on color and texture features using image processing and artificial neural network. In this research, seven samples of Persian cumin landraces were collected from natural habitats of Kerman province and after image acquisition of samples, 36 color features and 108 textural features were extracted from the images. Identification of landraces was carried out using back propagation ANNs. Based on the results of the study, the mean classification accuracy using a one layer ANN, for the color, texture and color-texture features was equal to 93.55%, 93.50% and 96.40%, respectively. Also, the minimum value of mean square error for color, texture and color-texture features were obtained 0.172, 0.182 and 0.148, respectively.

Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.