Search

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. [ABSTRACT FROM AUTHOR]

In the current study, a simple image processing method based on a RGB images using machine vision system has been designed to evaluate the color and texture features of low -calorie doughnut crumb. The effect of maltodextrin substitution with margarine at the levels of 25, 50, 75 and 100% (w/w) was evaluated on vision properties. These features were crumb color (L*, a* and b*), pore area fraction, number of cells/cm2, pore shape, Euler number, fractal dimension of pore boundaries and crumb texture (contrast, entropy, correlation, energy and homogeneity). Results revealed that the addition of maltodextrin increased L* value and decrease a* value of crumb color significantly. The contrast, entropy and Euler number of doughnut crumb containing 75 and 100% maltodextrin were considerably higher than control, 25% and 50% samples. Correlation, energy, homogeneity, mean pore area and diameter, number of cells per square centimeter and pore area fraction decreased aftermaltodextrinaddition (75 or 100%) but no significant difference observed between control, 25% and 50% samples. The fractal dimension value of pore boundaries in doughnut containing 100% maltodextrin were the highest which indicates that the addition of maltodextrin caused more irregular and tortuosity porous boundaries. [ABSTRACT FROM AUTHOR]

The control pests and diseases is considered one of the most important operations of Citrus in the protection stage. Today, a lot of research has been done in various fields of agriculture, including the diagnosis of plant pests and diseases by using machine vision methods. One of the problems that reduce the accuracy of the machine for detecting pests in farm conditions is the presence of adverse factors such as shade and changes in light intensity at different times of the day. In this study, in order to find the appropriate light intensity at different times of the day and increase the brightness of the shady parts of the trees, lighting by a lamp at the imaging site has been used. For detect pest-infected trees (in this snail study) has been used to Deep learning method which has been studied and evaluated by various optimization algorithms such as RMSProp, Adam and SGDm. To evaluate and test the algorithm used, 8000 images were examined in 9 farm conditions and one laboratory state In farm conditions, the lowest detection accuracy of algorithms with 64.32% related to imaging in cloudy days with light intensity of 350 to 700 lux was obtained using RMSProp algorithm, which Detection accuracy was improved up to 95.25% using SGDm algorithm by creating a light intensity controlled by a lamp (approximately 9000 lux). In laboratory conditions where the images were prepared in a controlled environment with constant light intensity, the detection accuracy was Obtained 98.73% with SGDm algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Science & Technology (2008-8787) is the property of Tarbiat Modares University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the effects of Farsi gum (0, 1.5% and 3%) coating containing hemp seed oil (0, 0.075% and 0.15%) on mass and volume changes of grape were investigated during storage at 4°C for 28 days. Machine vision system with learning machine methods was used to detect coated grapes from an image and estimate their mass and volume based on the image features (length, width, height and area). Four machine learning models, including linear regression (LR), artificial neural networks (ANN), radial basis function support vector regression (RBF-SVR) and Linear basis function support vector regression (LBF-SVR) were developed to predict the mass and volume of the single grape. The estimated grape mass and volume by these methods was compared statistically with actual values. The mass and volume in all treatments showed a decreasing pattern during the cold storage. The results indicated that mass and volume change decrease with Farsi gum and hemp seed oil increasing. Furthermore, according to the model evaluation results, the prediction performance of RBF-SVR model had achieved better predictive accuracy compared with the results of LR, ANN and LBF-SVR models, with R2 of 0.998 and 0.989 for mass and volume estimation, respectively, which also showed a good agreement between actual and predicted values. These results revealed that SVR model was a promising tool for estimating the mass and volume of grape during storage. [ABSTRACT FROM AUTHOR]

Increasing information about color kinetic changes has made the improvement of color parameters possible. Analysis of the color kinetics data allows for undesirable changes to be minimized and optimizes the color retention in food products. Imaging and image processing operation were performed to extract color parameters in the color space L*a*b. Image J software was applied to analysis the images, and changing the color space was done using a two-stage method. The temperature sensitivity of rate constants was evaluated using Arrhenius equation. Results showed that the increase of ΔE during time at the beginning of frying was fast and then has been slower. The activation energy was determined 44.80 kJ/mol. Statistical analyses showed that frying time and frying temperature and also interaction of them had significant effects on total color changes of the meat cubes. Color measurement was carried out successfully using image processing as a non-destructive and simple method in evaluation of ostrich meat crust color and the results of machine vision system correlated with the visual observations. Kinetic of formation or deterioration of color during deep fat frying was well modeled using a first-order kinetic equation. [ABSTRACT FROM AUTHOR]

The mechanical properties of fruit are one of the most important determinants of standards for designing, transforming, processing, and packaging systems. One of the methods for changes demonstraction in the internal structure of fruits during storage is to perform stress relaxation tests in different predetermined strains. Therefore, the purpose of this study was to investigate the orange compression behavior in quasi-static mechanical loadings by performing stress-relaxation tests of samples at predetermined levels of strain and modeling the Maxwell and Peleg method and comparing it with image processing method. In addition, in this study, cross-sectional area changes were measured during loading by image processing. The modulus of elasticity in the image processing method, the Maxwell model and the Peleg model on days 0 to 9 increased from 3.91 to 4.5, 3.6 to 4.53 and 2.7 to 3.45, respectively. which in spite of increasing trend there was no significant difference between them. Since, no significant difference in the output of these two models (Maxwell and Peleg) was observed Peleg with the less number of elements (only two constants) was preferred compared to Maxwell method. [ABSTRACT FROM AUTHOR]