The Experts below are selected from a list of 5292 Experts worldwide ranked by ideXlab platform
A Tabatabaeefar - One of the best experts on this subject based on the ideXlab platform.
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estimating volume and mass of citrus fruits by image processing technique
Journal of Food Engineering, 2010Co-Authors: Mahmoud Omid, Mostafa Khojastehnazhand, A TabatabaeefarAbstract:Abstract An image processing based technique was developed to measure volume and mass of citrus fruits such as lemons, limes, oranges, and Tangerines. The technique uses two cameras to give perpendicular views of the fruit. An efficient algorithm was designed and implemented in Visual Basic (VB) language. The product volume was calculated by dividing the fruit image into a number of elementary elliptical frustums. The volume is calculated as the sum of the volumes of individual frustums using VB. The volumes computed showed good agreement with the actual volumes determined by water displacement method. The coefficient of determination ( R 2 ) for lemon, lime, orange, and tangerine were 0.962, 0.970, 0.985, and 0.959, respectively. The Bland–Altman 95% limits of agreement for comparison of volumes with the two methods were (−1.62; 1.74), (−7.20; 7.57), (−6.54; 6.84), and (−4.83; 6.15), respectively. The results indicated citrus fruit’s size has no effect on the accuracy of computed volume. The characterization results for various citrus fruits showed that the volume and mass are highly correlated. Hence, a simple procedure based on computed volume of assumed ellipsoidal shape was also proposed for estimating mass of citrus fruits. This information can be used to design and develop sizing systems.
Jinyin Chen - One of the best experts on this subject based on the ideXlab platform.
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preservation of xinyu Tangerines with an edible coating using ficus hirta vahl fruits extract incorporated chitosan
Biomolecules, 2019Co-Authors: Chuying Chen, Jinyin ChenAbstract:Xinyu tangerine is a citrus fruit that has enjoyed great popularity in China for its fewer dregs and abundant nutrients. However, it is considered an easily perishable fruit that is vulnerable to various pathogenic fungal infections, especially by Penicillium italicum, which reduces its storage life and commercial value. Normally, to reduce the losses caused by fungal deterioration of harvested fruit, polysaccharide-based edible coating, containing natural antimicrobial agents (e.g., plant extracts), have been applied. In current study, we evaluated the effects of Ficus hirta Vahl. fruits extract (FFE)–incorporated chitosan (CS) edible coating on Xinyu Tangerines during cold storage at 5 °C. The results showed FFE has efficacy as an antifungal against P. italicum in a dose-dependent manner in vivo, with an EC50 value of 12.543 mg·mL−1. It was found that the edible coating of FFE–CS exhibited a higher reduction of total soluble solid (TSS), titrable acid (TA), and ascorbic acid (AsA) content by reducing the fruit decay rate, weight loss, respiration rate, and malondialdehyde (MDA) content during cold storage at 5 °C. Moreover, the activities of protective enzyme such as superoxide dismutase (SOD), peroxidase (POD), and phenylalanine ammonia-lyase (PAL), which have been linked with reactive oxygen species (ROS) and the phenylpropanoid pathway, were higher in the FFE–CS-coated fruits. On the basis of these study results, the FFE–CS edible coating could reduce postharvest loss and enhance the storability of Xinyu Tangerines due to the in vivo antifungal activity of FFE.
Majid Rashidi - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Tangerine Mass Based on Geometrical Properties
2020Co-Authors: Majid Rashidi, Fereydoun KeshavarzpourAbstract:Tangerines are frequently graded on the basis of size, but it may be more suitable and economical to develop a system which grades by mass. In this study, nine linear regression models for predicting tangerine mass from some geometrical properties of tangerine such as length (L), diameter (D), geometrical mean diameter (GMD), first projected area (PA ), second projected area (PA ), criteria area (CAE) and estimated volume based 12 on an oblate spheroid assumed shape (V ) were suggested. Models were divided into three main Sp classifications, i.e. first classification (outer dimensions), second classification (projected areas) and third classification (estimated volume). The statistical results of the study indicated that in order to predict tangerine mass based on outer dimensions, the mass model based on GMD as M = - 150.5 +43.94 GMD with R = 0.89 can 2
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Classification of Tangerine Size and Shape Based on Mass and Outer Dimensions
2020Co-Authors: Fereydoun Keshavarzpour, Majid RashidiAbstract:Fruit size and shape are the most important quality parameters for evaluation by customer performance. In addition, misshapen fruits are generally rejected according to sorting standards. This study was conducted to determine quantitative classification algorithm for tangerine size and shape. To reach objective and reproducible results, mass and outer dimensions (height and diameter) of tangerine were measured and an assessment based on mass and outer dimensions was proposed. Results of the study indicated that mass and aspect ratio (height to diameter ratio) of tangerine can be used effectively to classify tangerine size and shape.
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Prediction of Tangerine Mass Based on Geometrical Properties Using Linear Regression Models
American-Eurasian Journal of Agricultural and Environmental Science, 2020Co-Authors: Majid Rashidi, Mahmood FayyaziAbstract:In this study, nine linear regression models for predicting tangerine mass from some geometrical properties of tangerine such as length (L), diameter (D), geometrical mean diameter (GMD), first projected area (PA ), second projected area (PA ), criteria area (CAE) and estimated volume based on an oblate spheroid 12 assumed shape (V ) was suggested. Models were divided into three main classes, i.e. first class (outer Sp dimensions), second class (projected areas) and third class (estimated volume). The statistical results of the study indicated that in order to predict tangerine mass based on outer dimensions, the mass model based on GMD as M = - 150.5 +43.94 GMD with R = 0.89 can be recommended. In addition, to predict tangerine mass 2 based on projected areas, the mass model based on CAE as M = - 26.08 + 4.842 CAE with R = 0.90 can be 2 suggested. Moreover, to predict tangerine mass based on estimated volume, the mass model based on V as Sp M = 16.00 + 0.828 V with R = 0.88 can be used. These models can also be utilized to design tangerine sizing Sp 2 machines equipped with an image processing system.
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Modeling of Tangerine Mass Based on Geometrical Properties
2020Co-Authors: Majid Rashidi, Fereydoun KeshavarzpourAbstract:2 Abstract: Nine linear regression models for predicting tangerine mass based on some geometrical properties of tangerine such as length (L), diameter (D), geometrical mean diameter (GMD), first projected area (PA ), 1 second projected area (PA ), criteria area (CAE) and estimated volume based on an oblate spheroid assumed 2 shape (V ) were suggested. The statistical results of the study indicated that in order to predict tangerine mass Sp based on outer dimensions, the mass model based on GMD as M = - 150.5 +43.94 GMD with R = 0.89 can be 2 recommended. In addition, to predict tangerine mass based on projected areas, the mass model based on CAE as M = - 26.08 + 4.842 CAE with R = 0.90 can be suggested. Moreover, to predict tangerine mass based on 2 estimated volume, the mass model based on V as M = 16.00 + 0.828 V with R = 0.88 can be used. Sp Sp 2
Fereydoun Keshavarzpour - One of the best experts on this subject based on the ideXlab platform.
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Prediction of Tangerine Mass Based on Geometrical Properties
2020Co-Authors: Majid Rashidi, Fereydoun KeshavarzpourAbstract:Tangerines are frequently graded on the basis of size, but it may be more suitable and economical to develop a system which grades by mass. In this study, nine linear regression models for predicting tangerine mass from some geometrical properties of tangerine such as length (L), diameter (D), geometrical mean diameter (GMD), first projected area (PA ), second projected area (PA ), criteria area (CAE) and estimated volume based 12 on an oblate spheroid assumed shape (V ) were suggested. Models were divided into three main Sp classifications, i.e. first classification (outer dimensions), second classification (projected areas) and third classification (estimated volume). The statistical results of the study indicated that in order to predict tangerine mass based on outer dimensions, the mass model based on GMD as M = - 150.5 +43.94 GMD with R = 0.89 can 2
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Classification of Tangerine Size and Shape Based on Mass and Outer Dimensions
2020Co-Authors: Fereydoun Keshavarzpour, Majid RashidiAbstract:Fruit size and shape are the most important quality parameters for evaluation by customer performance. In addition, misshapen fruits are generally rejected according to sorting standards. This study was conducted to determine quantitative classification algorithm for tangerine size and shape. To reach objective and reproducible results, mass and outer dimensions (height and diameter) of tangerine were measured and an assessment based on mass and outer dimensions was proposed. Results of the study indicated that mass and aspect ratio (height to diameter ratio) of tangerine can be used effectively to classify tangerine size and shape.
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Modeling of Tangerine Mass Based on Geometrical Properties
2020Co-Authors: Majid Rashidi, Fereydoun KeshavarzpourAbstract:2 Abstract: Nine linear regression models for predicting tangerine mass based on some geometrical properties of tangerine such as length (L), diameter (D), geometrical mean diameter (GMD), first projected area (PA ), 1 second projected area (PA ), criteria area (CAE) and estimated volume based on an oblate spheroid assumed 2 shape (V ) were suggested. The statistical results of the study indicated that in order to predict tangerine mass Sp based on outer dimensions, the mass model based on GMD as M = - 150.5 +43.94 GMD with R = 0.89 can be 2 recommended. In addition, to predict tangerine mass based on projected areas, the mass model based on CAE as M = - 26.08 + 4.842 CAE with R = 0.90 can be suggested. Moreover, to predict tangerine mass based on 2 estimated volume, the mass model based on V as M = 16.00 + 0.828 V with R = 0.88 can be used. Sp Sp 2
Mahmoud Omid - One of the best experts on this subject based on the ideXlab platform.
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estimating volume and mass of citrus fruits by image processing technique
Journal of Food Engineering, 2010Co-Authors: Mahmoud Omid, Mostafa Khojastehnazhand, A TabatabaeefarAbstract:Abstract An image processing based technique was developed to measure volume and mass of citrus fruits such as lemons, limes, oranges, and Tangerines. The technique uses two cameras to give perpendicular views of the fruit. An efficient algorithm was designed and implemented in Visual Basic (VB) language. The product volume was calculated by dividing the fruit image into a number of elementary elliptical frustums. The volume is calculated as the sum of the volumes of individual frustums using VB. The volumes computed showed good agreement with the actual volumes determined by water displacement method. The coefficient of determination ( R 2 ) for lemon, lime, orange, and tangerine were 0.962, 0.970, 0.985, and 0.959, respectively. The Bland–Altman 95% limits of agreement for comparison of volumes with the two methods were (−1.62; 1.74), (−7.20; 7.57), (−6.54; 6.84), and (−4.83; 6.15), respectively. The results indicated citrus fruit’s size has no effect on the accuracy of computed volume. The characterization results for various citrus fruits showed that the volume and mass are highly correlated. Hence, a simple procedure based on computed volume of assumed ellipsoidal shape was also proposed for estimating mass of citrus fruits. This information can be used to design and develop sizing systems.
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Determination of Tangerine Volume Using Image Processing Methods
International Journal of Food Properties, 2010Co-Authors: Mostafa Khojastehnazhand, Mahmoud Omid, Ahmad TabatabaeefarAbstract:In this study, two image processing techniques; namely, segmentation and ellipsoid approximation, were used to estimate the volume of Tangerines. The proposed system consisted of two CCD cameras, capture cards, an appropriate lighting system and a computer. The paired t-test showed the estimated volume using segmentation method was not significantly different from the volume determined by water displacement method (p > 0.05). The difference between the volumes estimated by ellipsoid approximation and water displacement method was statistically significant (P < 0.05). The Bland–Altman approach showed the size of Tangerines has no effect on the accuracy of volume estimation by segmentation method.
