Mohammad Vahedi Torshizi; Mohsen Azadbakht
Abstract
This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading ...
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This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading and dynamic loading. All loaded pears were stored in accordance with three storage period designs: 5-day storage, 10-day storage, and 15-day storage. Following each period, the variations of pear texture were scanned by using the CT-Scan technique as a non-destructive test. Then, the firmness of pear texture was measured using a penetrometer. Data were simulated and evaluated using MLP and RBF artificial neural networks. The results showed that with increasing storage time and loading force , the firmness significantly decreased (1% level) in all three types of loading, In addition, pear texture was destructed under dynamic compression loading in order to compare with other two loadings. Best value artificial neural network for wide edge loading (12 neuron-RBF) was (R2 Wide edge= 0.9738– RMSE Wide edge=0.3419- MAE Wide edge =0.268) and for thin edge loading (4 neuron-RBF) was (R2Thin edge = 0.9946– RMSE Thin edge =0.170977- MAE Thin edge =0.133), also for dynamic loading (8 neuron-RBF) was (R2 Dynamic loading = 0.9933– RMSE Dynamic loading =0.230- MAE Dynamic loading= 0.187).
Mohsen Azadbakht; Mohammad Javad Mahmoodi; Mohammad Vahedi Torshizi
Abstract
Introduction: The study of relationship between physical properties such as mass and volume and other physical properties, such as geometric dimensions, has been the subject of numerous studies by researchers. The fruit size, shape and mass are important in sorting and measuring fruits, and it determines ...
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Introduction: The study of relationship between physical properties such as mass and volume and other physical properties, such as geometric dimensions, has been the subject of numerous studies by researchers. The fruit size, shape and mass are important in sorting and measuring fruits, and it determines that the fruits can be put in boxes of transport or plastic bags by a specific size. Damage to the fruit may be due to various causes, including Impact, pressure and vibration, all of which cause physical damage at moment or at storage time, the amount of damages depends on location of impact, the size and volume. Also, the volume and physical properties of agricultural products are very important for storage. On the other hand, cell damage and forces involved in fruits reason bruising in fruits, which can be controlled by physical properties. Quality assessment is usually carried out using a combination of destructive and non-destructive methods, generally done by the product manufacturers or the first purchasers, and includes the separation of materials based on specific size and weight. Among non-destructive methods used, the use of CT and X-rays, which allow a person to examine bruises at different times in the fruit, is increasing. Due to the fact that mass and volume of fruits for storage, transportation, packaging and etc are of great importance, in this research, the relationship between pear fruit volume and mass with bruise percentage during the storage period was studied using non-destructive CT scan tests due to dynamic loads.
Materials and methods: Firstly, the pendulum and the required masses were made in a workshop. The fruits were placed in the desired position and then the device arm was raised to the desired angle (90°), and in the controlled state of the arm impact the pear. The pendulum had a 200 g and three different attachment masses of 100, 150, and 200 g for knocking. It should be noted that air resistance and friction were neglected through this procedure. In this research, via CT scan, the relationship between mass and volume of pears (Before and after the impact) due impact loading and storage times with bruise was investigated. Before loading and storing, 50 pears were examined using Scan CT and 27 pears with zero bruise percentage were selected, the next chosen pears were subjected to impact loading with a pendulum with three weight of 300, 350 and 400 g and 5, 10 and 15 days storage was used to investigate the effect of impact on pears. Then, after impact and storing, with the use of CT scan in each period of storage, the rate of pear bruise was calculated.
Results and Discussion: The pears volume before impact with the bruise percentage for all three weights had a negative and non-significant correlation and the decrease pear mass percentage with the bruise percentage for all three weights has a positive correlation and pears mass before impact with the bruise percentage for all three weights had a positive and non-significant correlation. Any decrease in pear mass percentage had a positive correlation with caries percentage for all three weights. The correlation test showed that with the increase in pear volume, the bruise percentage was decreased and a direct correlation was found between the decreasing percentage volume and the bruise percentage. –also The effect of 5-day storage duration was found considerable on the bruise percentage subject to the exertion of 350 g and higher impact rates
Zeynab Raftani Amiri; Hengameh Darzi Arbabi
Abstract
Thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. An artificial neural network (ANN) was developed to predict thermal conductivity of pear juice. Temperature ...
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Thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. An artificial neural network (ANN) was developed to predict thermal conductivity of pear juice. Temperature and concentration were input variables. Thermal conductivity of juices was outputs. The optimal ANN model consisted 2 hidden layers with 5 neurons in first hidden layer and the second one has only one neuron. The ANN model was able to predict thermal conductivity values which closely matched the experimental values by providing lowest mean square error (R2=0.999) compared to conventional and multivariable regression models. However this method also improves the problem of determining the hidden structure of the neural network layer by trial and error. It can be incorporated in heat transfer calculations during juices processing where temperature and concentration dependent thermal conductivity values are required.
