Shima Nasiri; Saman Abdanan; Mokhtar Heidari
Abstract
Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, ...
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Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, “juiciness”, “hardness”,“firmness” and “mealiness” are often key drivers of consumer preference. Many non-destructive methods, including image analysis, spectroscopy, ultrasound and sound techniques, have been developed to diagnose internal and external defects in fruits and vegetables. Cheng and Haugh (1994) used a frequency of 250-kHz, rather than 1-MHz, to detect hollow heart. They were not able to transmit successfully the ultrasound wave through the whole tuber using 1-MHz transducers but found the 250-kHz transducers to be practical for a transmission path length of up to 89.7 mm. In a research an acoustic setup was developed to simultaneously detect the resonant frequencies from equator and from calyx shoulder of pear. The researchers proposed index based on these two frequencies was used for firmness evaluation of non-spherical pear; Compared with two types of single frequency-based indices, the firmness sensitivity of the dual-frequency index is mostly close to that of MT penetration test. The firmness index can classify pears with a high total accuracy (93.4%), making it suitable for nondestructive detection of firmness of differently shaped pears (Zhang et al., 2018). The goal of this study was to develop a nondestructive method based on acoustic impulse response of quince fruit using genetic programming and artificial neural network during storage. Materials and Methods: In the experiment 120 quince fruits (Cydonia oblonga) were harvested from a field near Isfahan 181 days after full flowering of the trees. For each cultivar, only samples of similar size and without visible external damage were chosen. The samples were packed in sterile nylon bags and stored at 4°C. Non-destructive test (acoustic response) as well as destructive test (chemical measurement and penetration test) were performed every 15 days for 4 months (Akbari Bisheh et al., 2014). Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan) and expressed as °Brix. Ascorbic acid of the juice was measured by titration with copper sulfate and potassium iodide based on the Barakat et al. (1973) procedure. Titratable acidity was measured according to the AOAC method. To determine the total phenol content of juice, the Waterhouse method (2000) was used. Determination of the pH of the fruit extract using a pH meter (Portable Model P-755, Japan). Physical attributes of the samples including volume as well as major, minor, intermittent diameters and mass were calculated using the relations proposed by Stroshine and Hammand (1994). Penetration test was conducted by the material test machine (SANTAM, STM-20 model, Iran).In order to analyze the response sound signal of quince in time and frequency domain, a system equipped with a sample holder with foam rubber covered surface, an impact mechanism, a microphone and an electronic circuit was utilized. To record impact sound features a microphone was positioned next to the fruit and was hit at three speed level (0.3, 0.9 and 1.5 m/s). After recoding sound, five features (acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency) were extracted and used as inputs for models. In order to predict the stiffness, four methods of genetic programming, neural network and existing mathematical models (FI and SIQ-FT) were used. In order to carry out statistical analysis, analysis of variance (ANOVA) and Duncan's multiple range test at 5% probability level were performed according to the completely randomized design (CRD). Results and discussion: In this study, Duncan's multiple range comparison test was used to investigate the significant difference between destructive and non-destructive parameters at 5% probability level. According to the results, acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency were decreased by increasing storage time. Statistical analysis of the destructive tests also showed a decreasing trend at the 5% level. In several papers, two mathematical equations have been used to obtain the relationship between the mass resonance frequency and the sound of impact. In this study, genetic programming and neural network modeling were used to compare the results of these relationships. The regression coefficients between the actual and the predicted values for the resonance-mass relation and the effect of the sound from the collision were R2= 0.601 and R2= 0.754, respectively. Also, the regression values obtained from genetic programming and neural network modeling were R2= 0.9567 and R2 = 0.933, respectively. In a research, the overall R2 value amounts for stiffness prediction was reported to be 0.79 (Schotte et al., 1999). Abbaszadeh et al. (2013) evaluated watermelons texture using their vibration responses. They declared their proposed method could predict textural acceptability of watermelons with determination coefficients 0.99. According to the obtained values, the best methods for stiffness prediction were genetic programming and f neural network methods, respectively.
Mohsen Azadbakht; Mohammad Vahedi Torshizi; Mohammad Javad Mahmoodi
Abstract
This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. In this experiment, the pears were subjected to quasi-static loading (wide-edge and thin-edge) and different storage periods (5, 10 and 15 days). The amounts of the fruits’ ...
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This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. In this experiment, the pears were subjected to quasi-static loading (wide-edge and thin-edge) and different storage periods (5, 10 and 15 days). The amounts of the fruits’ total phenol, antioxidant and vitamin C contents were evaluated after each storage period. In the present study, multilayer perceptron (MLP) artificial neural network featuring a hidden layer and two activating functions (hyperbolic tangent-sigmoid) and a total number of 5 and 10 neurons in each layer were selected for the loading force and storage period so that the amounts of the total phenol, antioxidants and vitamin C contents of the fruits could be forecasted. According to the obtained results, the highest R2 rates for thin-edge and wide-edge loading in a network with 10 neurons in the hidden layer and a sigmoid activation function were obtained for total phenol content( =0.9539- =0.9865), antioxidant ( =0.9839- =0.9649) and vitamin C ( =0.9758); as for wide-edge loading in a network with 5 neurons in the hidden layer and hyperbolic tangent activation function, the highest R2 rate of vitamin C content was obtained equal to =0.9865. According to the obtained results, the neural network with these two activation functions possesses an appropriate ability in overlapping and predicting the simulated data based on real data.
Mohammad Ebrahim Mohammadpour Mir; Sara Nanvakenari; Kamyar Movagharnejad
Abstract
Introduction: Rice is one of the most important cereals and is the second-highest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial ...
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Introduction: Rice is one of the most important cereals and is the second-highest worldwide production after wheat and also is a good source of nutrients for humans. It plays an important role in the feeding of the many parts of the world including Iran. The harvested paddy rice has the high initial moisture content of nearly 25-28% (wet basis) that caused corruption. Therefore, in order to prevent corruption and safe storage, it must be dried to 10-13% moisture content. Drying is one of the oldest methods of preserving food and agricultural products that used to increase the food’s storage time. There are several methods for drying paddy rice that none of them are ideal and have several advantages and disadvantages that one of them that recently the use of it has been increased is microwave drying. Microwave drying uses electromagnetic radiations with the frequency range of 300 MHz to 300 GHz and the wavelengths of 1-0.01m. In microwave drying due to better energy concentration, moisture is removed more quickly so the drying time decreases. Due to the complex relationship between input and output variables in the drying process, the selection of the model that can estimate the drying behavior of the products is difficult. Hence, the use of intelligent modeling methods such as neural networks is the best choice. Materials and methods: In this research, in order to investigate the effect of microwave power on kinetics of rice drying, head rice yield and effective diffusivity coefficient of moisture, a continuous type of domestic microwave dryer ( DEM-281 QOT-PW) were used. This dryer has a microwave radiation chamber where the samples are put on it on the tray that was placed on a digital balance. The experiments were performed at three microwave power levels designated as 270, 360 and 450 W. Also, Shirudi paddy rice was used as the raw material and the drying rice process from the initial moisture content of 21% to the final moisture content of 11% is examined. In this study, the neural network toolbox of MATLAB 2017R was used to model the kinetics of rice drying in the microwave dryers. RBF and MLP have 3 layers including input, hidden and output layers. The input layer has two neurons that show the number of input variables that were time and microwave power and the output layer has one neuron that shows the number of output variables that was MR in this study. 70% and 30% of the data was used for training and testing the network, respectively. To estimate the ANN performance, mean square error (MSE) and the coefficient of determination (R2) was used. Results and discussion: The maximum and minimum drying time was 42 and 20 minutes in 270 and 450 watts, respectively. Also, the maximum and minimum effective diffusivity coefficient of moisture were 4.17 * 10^-9 and 1.82* 10^-9 in 450 and 270 watts, respectively. RBF network with Guassian transfer function and high neurons number and MLP network with Levenberg-Marquardt ( LM) learning algorithm and tan-sigmoid (tansig) transfer function with low neurons number were able to model the kinetics of drying as well as. In general, the drying time and head rice yield decreased but the effective diffusivity coefficient of moisture increased by increasing the microwave power. Also drying at different microwave power did not affect rice color and quality. The results of the modeling of rice drying by using two different neural networks including MLP and RBF demonstrated that the MLP network with Levenberg-Mrrqurdt (LM) learning algorithm and tan-sigmoid (tansig) transfer function has the better performance than the RBF network with Gussian transfer function and the error and the correlation coefficient in MLP are less and higher than the RBF, respectively.
Alireza Yousefi; Naser Ghasemian
Abstract
In this work, a hybrid GMDH–neural network model was developed in order to predict the moisture content of papaya slices during hot air drying in a cabinet dryer. For this purpose, parameters including drying time, slices thickness and drying temperature were considered as the inputs and the amount ...
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In this work, a hybrid GMDH–neural network model was developed in order to predict the moisture content of papaya slices during hot air drying in a cabinet dryer. For this purpose, parameters including drying time, slices thickness and drying temperature were considered as the inputs and the amount of moisture ratio (MR) was estimated as the output. Exactly 50% of the data points were used for training and 50% for testing. In addition, four different mathematical models were fitted to the experimental data and compared with the GMDH model. The determination coefficient (R2) and root mean square error (RMSE) computed for the GMDH model were 0.9960 and 0.0220,and for the best mathematical model (Newton model) were 0.9954 and 0.0230, respectively. Thus, it was deduced that the estimation of moisture content of thin layer papaya fruit slices could be better modeled by a GMDH model than by the mathematical models.
