جستجوی مقالات مرتبط با کلیدواژه « ann » در نشریات گروه « صنایع غذایی »

Determination of physical and mechanical properties of agricultural and horticultural crops is one of the most important parameters in design and manufacture of the operation machines. Determination of the effect of static and dynamic forces on Cherry Plum fruit during storage and transportation processes is very necessary for engineering. In this study, the physical properties of the Cherry Plum (including dimensional parameters and weight) and mechanical properties (including pressure test, punch test, and cyclic test) were measured at two levels of big and small sizes. The regression and ANN models based on physical properties data were defined and used to estimate the weight and volume of the fruits. The ANN model was the best model for estimating the fruit volume with 0.96 regression coefficient but it didn’t have a acceptable performance in estimating the fruits’ weight. The parameters of force and displacement, maximum stress at the breaking point, the elastic modulus in Hertz methods and Bosensky theory, the crust and mantle punch forces, the minimum stress based on Bosinskio theory, the distribution of vertical, horizontal and shear stresses in depth and surface in the failure point and etc were calculated based on the data of the mechanical properties. The results showed that due to the differences in the thickness of the mantle, the force absorption capacity in the big fruits was larger than the small ones. The results of comparison between the convex jaw and flat jaw in the compression tests showed that the fruits placed on the floor were more exposed to damage. The behavior of the stress distribution at the failure point showed that the horizontal stress would dictate the rupture in the fruits.

The aim of this research was to investigate the degradation kinetics of the major quality properties of medlar (Mespilus germanica) during cold storage. Medlar is a widely growth in northern Iran and its fruit is used as a nutritional component and as a medicinal remedy. In fruits, quality properties are used as a consumer-based criteria of acceptability. So it is important to evaluate parameters that affected the medlar quality. Measurement of these parameters is an expensive and time-consuming process. Therefore, parameter prediction due to affecting factors will be more useful. In the present research, mathematical models and artificial neural networks (ANN) were used for modelling the relationship between physicochemical properties and color attributes with cold storage time. Five kinetic models viz. zero order, first order, Second order, fractional conversion and Weibull models were used for modelling using MATLAB. Among the kinetics models, the Weibull model was found to be more suitable to predict the changes in all physicochemical ( , ) and color ( , ) parameters. In ANN, multi-layer perception (MLP) used with different number of neurons. The network’s inputs include storage time, medlar moisture content and ripening stage and the network’s output were the values of the physicochemical and color properties. The training rule was Momentum Levenberg-Marquardt. The transfer functions were Tansig, Purelin and Logsig. The results showed that MLP network with Levenberg-Marquardt training function, Purelin transfer function and 3-8-4-3 and 3-7-2 topologies had the best accuracy for prediction of for physicochemical and color properties. This network can predict physicochemical and color properties of the medlar with  coefficient of 0.9983 and 0.9992 and MSE of 0.021, 0.000008 and 0.000059 respectively.

Eggplant (Solanum melongenaL.) is being cultivated in North America, Asia and the Mediterranean area. Its limited shelf life is one of the important restrictions in the trade of eggplant as a fresh product. Drying is one of the most current methods used to maintenance agricultural products. This process improves the food stability, since it reduces significantly the water and microbiological activity of the material and minimizes physical and chemical changes during its storage. Dynamic modeling of drying characteristics for various agricultural products, including artificial intelligence techniques that include artificial neural networks (ANNs), particle swarm optimization (PSO) and grey wolf optimizer (GWO) Which has attracted a lot of attention recently, because the ability to learn from these systems to detect fruit and vegetable behaviors is a complex process in which mathematical models simply do not apply in recent decades. The main objective of this research was to determine the effective moisture diffusivity, and specific energy consumption of eggplant slices with a semi-industrial continuous band dryer. Moreover, some novel methods including ANN, PSO and GWO as an approximating tools were developed and evaluated for prediction of Deff and SEC of the process. Freshly harvested eggplant were purchased from a local market and stored in the refrigerator at about 4°C for experiments. The initial moisture content of eggplant was determined by oven method. About 40 g of eggplant slice (4 mm thickness) with three replicates were dried at 70°C for 24 h. Eggplant slice with average initial moisture content of 10.25% (d.b.) was chosen as the drying material.
The dryer consists of an adjustable centrifugal blower, hot air suction tube, heater, control panel, air channel to uniform distribution of hot air, drying chamber, Belt (20 cm, 200 cm), three inverters (LS, Korea), temperature and humidity sensors, electrical motor, removable upper part, base, shafts, three infrared lamps (Philips, Belgium) and belt guide. The experiments were performed at air temperatures of 45, 60, and 75C, air velocities of 1, 1.5, and 2 m/s, and belt linear speeds of 2.5, 6.5, and 10.5 mm/s. Feed and cascade forward neural networks were used in this study. There are two types of multilayer perceptron neural network. Two training algorithms including LevenbergMarquardt (LM) and Bayesian regulation (BR) back propagation algorithms were used for updating network weights. The PSO is a simple, powerful and metaheuristic technique that can be applied to solve optimization problems. In the PSO model, every solution is showed as a particle that is alike to a bird flying via the space of a potential solution. In order to mathematically model the social governance of wolves when designing Grey Wolf Optimizer (GWO), assume the fittest solution as the alpha ( ). Consequently, the second and third best solutions are named beta ( ) and delta ( ), respectively. In the present study, the application of Artificial Neural Network (ANN), particle swarm optimization (PSO) and grey wolf optimizer (GWO) for predicting the and was investigated. Based on several statistical operates [such as coefficient of correlation ( ) and mean-square error ( ), mean absolute error ( )], for predicting and was found that the GWO ( =0.9915, =0.9986, Respecively) performs better than the PSO (with =0.9927, =0.9890) and ANN (with = 0.9618, =0.9773) models. Drying behavior of eggplant slices at different air temperatures of 45, 60, and 75C, air velocities of 1, 1.5, and 2 m/s and belt linear speeds of 2.5, 6.5, and 10.5 mm/s was studied. The moisture ratio was reduced exponentially with drying time as expected. When the temperature was increased, the drying time eggplant fruit reduced. In other words, at high temperatures, the transfer of heat and mass was higher and the water loss was more excessive. Effective moisture diffusivity and specific energy consumption were calculated after drying of turnip fruit. Maximum values of for eggplant were 1.14×10-8 m2/s. The lowest amount specific energy consumption ( ) was calculated at the boundary of 130.62 MJ/kg.

In this research work, in order to estimate the drying properties of eggplant and turnip in a combined microwave-convection dryer it was used artificial neural network method in order to estimate the Drying process was accomplished in three temperature levels (40, 55, and 70 °C), three inlet air velocity levels (0.5, 1.1 and 1.7 m/s) and three microwave power levels (270, 450 and 630 W) in a combined microwave-convection dryer that the three parameters were utilized as input in predicting the effective moisture diffusion coefficient and specific energy consumption in artificial neural network. A feed forward and cascade forward back propagation neural network with training functions of Levenberg - Marquardt (LM) and Bayesian Regulation (BR) for training of patterns. According to results, the highest value of the effective moisture diffusion coefficient for eggplant and turnip was obtained 3.39×10-9 and 3.05×10-9 m2/s, respectively. Results of ANN investigations showed that the optimum feed forward back propagation network with 3-20-20-2 topology and training function of Levenberg – Marquardt could predict the effective moisture diffusion coefficient and specific energy consumption with determination coefficients of 0.9821 and 0.9952 and mean square error of 0.00014 in various drying conditions of eggplant and turnip. Also the highest determination coefficients for prediction of drying rate and moisture ration obtained 0.9698 and 0.9988 with the value of mean square error of 0.0045 in cascade back propagation neural network with training algorithm of Levenberg – Marquardt.

Blackberry is a perennial woody plant native to warm, temperate, and subtropical regions of Asia, Africa, North America, and southern Europe. Blackberry fruit (Morus Alba Varnigra L.) is a rich source of anthocyanins. Furthermore, it has great many medicinal properties such as an antidiabetic (Asano et al., 2001), antihyperglycemic (Andallu &Varadacharyulu, 2003), antiviral (Du et al., 2003), antioxidative (Kim et al., 1998), hypolipidemic (El-Beshbishy et al., 2006), and neuroprotective (Kang et al., 2006). However, measuring some qualitative and nutrient parameters in this fruit such as anthocyanins, vitamin C and phenol directly has become a major issue (Pace et al, 2013). Therefore, researchers try to predict aforementioned parameters by mathematical models. One of these models is the fractal model which is widely used to study the properties of the images/objects (Welstead, 1999; Zhang, 2007). Recently, many researchers try to develop different methods to classify or predict the agricultural products quality (Langner, 2001). In a research Seng and Mirisaee (2009) designed a machine vision algorithm for classification of fruits (apple, lemon, strawberry and banana) based on color, shape and size. Li and He investigated the application of visible/near infrared spectroscopy (Vis/NIRS) for measuring the acidity of Chinese bayberry. The model for prediction the acidity (r=0.963), standard error of prediction (SEP) 0.21 with a bias of 0.138 showed an excellent prediction performance. Therefore, the aim of this study was to predict biochemical parameters (TSS, anthocyanins, browning compounds, total phenols, Ascorbic Acid, pH) of blackberry juice, nondestructively, during maturity process using machine vision and fractal analysis. To develop predictive models and data classification, artificial neural networks (ANN) and k-nearest neighbor (k-NN) were used.
Eighty blackberry fruits from four maturity stages were selected. The fruit samples were placed in airtight polyethylene bags, stored in an ice-filled cooler and transported to the laboratory to keep at cold temperature (4±1◦C).
Fresh fruits were squeezed by a household juicer, and immediately transported to the laboratory. Then, juice images were taken with a digital camera CASIO (Model Exilim EX-ZR700; 16 megapixels, Japan) and stored to the computer.
There are several ways to measure the fractal dimension. In this study, the proposed method by Addison (2005) was used to calculate the fractal dimension.
Feature selection is one of the issues that have been raised in the context of machine learning. In this study, floating search method feature selection was used (Pudil et al., 1994).
k-Nearest Neighbor (k-NN) is one of the simplest methods for information classification. In this study, the Euclidean distance between two points was used to determine the distance between the input data with the training patterns (Mucherino et al., 2009).
To train the neural network, Levenberg–Marquardt training algorithm was used. In this regard, the data were divided randomly into two parts (two-thirds for training (60) and one-third (20) for testing the network). Input parameters were Xa, Xb, X, Y and S and output parameters were TSS, ascorbic acide, acidity, polyphenols, anthocyanins, brown-causing substances and pH. Moreover, in this study, the number of neurons in the hidden layer was selected by trial and error method.
After selecting the best features extracted from the image processing with the highest correlation with chemical parameters (TSS, anthocyanins, total phenols, ascorbic acid, and pH), a machine vision system was designed and built to be able to determine the internal properties of black mulberry juice.
Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan). The anthocyanin content was estimated following the procedure of Holecraft et al., (1998). 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 Eksi and Turkman, (2011) method. Waterhouse (2002) method was used for measuring the total phenol of juice.
Artificial neural network (ANN) and (k-NN) models were used to predict the changes of anthocyanin (AC), browning compounds, ascorbic acid (AA), total phenols (TP), acidity, TSS and pH in mulberry juice during ripening based on fractal analysis. Two features namely: maximum fractal and fractal curve area were selected from five extracted features and used for training neural network and k-NN classifier

.Color is the first quality attribute of food evaluated by consumers, and is therefore an important quality component of food which influences consumer’s choice and preferences (Maguire, 1994). Color measurement of food products has been used as an indirect measure of other quality attributes such as flavor and contents of pigments because it is simpler, faster and correlates well with other physicochemical properties. Therefore, rapid and objective measurement of food color is required in quality control for the commercial grading of products (Trusell et al., 2005). Among different color spaces, L*a*b* color space is the most practical system used for measuring of color in food due to the uniform distribution of colors in this system as well its high similarity to human perception of color. All of the commercial L*a*b* colorimeters generally measure small, non- representative areas (Pathare et al., 2013) while the RGB digital cameras obtain information in pixels. Therefore, this research establishes a computational solution which allows acquiring of digital images in L*a*b* color units for each pixel from the digital RGB image (Fernandez-Vazquez et al., 2011). In recent years, computer vision has been used to objectively measure the color of different foods since they provide some obvious advantages over a conventional colorimeter, namely, the possibility of analyzing of each pixel of the entire surface of the food, and quantifying surface characteristics and defects (Mendoza & Aguilera, 2004). The color of many foods has been measured using computer vision techniques (Pedreschi et al., 2011; Lang et al., 2012). A computational technique with a combination of a digital camera, image processing software has been used to provide a less expensive and more versatile way to measure the color of many foods than traditional color-measuring instruments. This study used four models to carry out the RGB to L*a*b* transformation: linear, quadratic, support vector regression and neural network. This article presents the details of each model, their performance, and their advantages and disadvantages. The purpose of this work was to find a model (and estimate its parameters) for obtaining L*a*b* color measurements from RGB measurements.
The images used in this work were taken with the following image acquisition system (Samsung, SM-N9005 color digital camera with 13 Mega pixels of resolution ,Fig.1). The camera was placed vertically at a distance of 60 cm from the samples and the angle between the axis of the lens and the sources of illumination was approximately °45. Illumination was achieved with 4 natural daylight 150 W lights.
Fig. 1. Schematic diagram of image acquisition system.
In order to calibrate the digital color system, the color values of 42 color charts were measured. Each color chart was divided into 24 regions. In each region, the L*a*b* color values were measured using a Minolta colorimeter. Additionally, a RGB digital image was taken of each chart, and the R, G and B color values of the corresponding regions were measured using a Matlab program which computes the mean values for each color value in each region according to the 24 masks. In this study four models for the RGB to L*a*b* transformation namely: linear, quadratic, artificial neural network (ANN), support vector regression (SVR) have been used.
In the evaluation of the performance of the models, the support vector regression and neural network model stands out with an error of only 0.88 and 2.37, respectively. Leon et al. (2004) investigated some models for the RGB to L*a*b* conversion. In the evaluation of the performance of the models, the neural network model showed an error of only 0.93%. In another research Yagzi et al. (2009) measured the L*a*b* values of atlantic salmon fillets subjected to different electron beam doses (0, 1, 1.5, 2 and 3 kGy) using a Minolta CR-200 Chroma Meter and a machine vision system. For both Minolta and machine vision the L* value increased and the a* and b* values decreased with increasing irradiation dose. However, the machine vision system showed significantly higher readings for L*, a*, b* values than the Minolta colorimeter. According to the construction of these models, the correlation between measured and predicted color is well established; therefore, based on the promising obtained results from Computer vision, it is possible to find a L*a*b* color measuring system that is appropriate for an accurate, exacting and detailed characterization of a food item based on a color digital camera. In order to show the capability of the proposed method, the color of an orange was measured using both a Minolta colorimeter and the studied approach. The colorimeter measurement was obtained by averaging 6 measurements in 6 different places of the surface of the orange, whereas the measurement using the digital color image was estimated by averaging all pixels of the surface image. The results are summarized in Fig. 2.
b* a* L* Measurement 35.49 28.32 58.98 Minolta colorimeter
37.35 27.30 61.20 Machine Vision (SVR)
30.60 30.19 60.18 Machine Vision (ANN)
Fig. 2. Estimate of L*a*b* values of an orange

Apple fruit is one of the most worthy garden Product with high nutritional Value and its production in Iran makes more job and Exchange technology. From different apple Non-destructive quality control methods, machine vision technology achieves the more speed, quality, greater productivity and higher valuation for the product. Usually, apple bruise overlaps with Peduncle and in these causes, serious problems of recognition for quality sorting occurs. In this research work it was tried to work out this problem and to increase the sorting systems performance precision. In order to accomplish this, two separate algorithms based on color to identify bruise and pedicle was designed in Matlab. It was achieved 97.14% accuracy for the bruise algorithm and 100% accuracy for the pedicle algorithm. Then with integration of these two algorithms, an algorithm was achieved with 94.29% accuracy. Further experiments to investigate the possibility of increasing the accuracy in detecting bruise with time maintenance was performed by the bruise algorithm. The results indicate that the bruise detection quality by this algorithm gradually increased and after two to three days it reaches the desired consistency. Another algorithm with special properties of bruise and pedicle pictures shape such as roundness value, ratio of area to Perimeter square and also coefficient of variation (cv) of distances of spaced points on the edge from center of gravity of picture was designed. Then bruise and pedicle were distinguished from each other with an accuracy of 100% with this algorithm along with the ANN which it proving the importance of using these techniques, combined with machine vision techniques to increase the accuracy of sorting machines performance.

Stress relaxation is one of the defined tests to characterize the viscoelasticproperties of agricultural materials. Stress relaxation data are very importantbecause they provide useful and valuable information such as fruit firmness andripening, food processing and predicting changes in the material during mechanicalloading. Viscoelastic behavior of pomegranate that is cultivated in Iran has beenstudied in current research. For this purpose, stress relaxation test was conductedwith Ardestani cultivar of pomegranate for three sizes (small, medium and large)and three different harvesting times. Two common models (the GeneralizedMaxwell model and the Peleg model) were fitted to the experimental data. ForMaxwell model coefficients, values for RMSE, R2 and MAPE less than 0.003,greater than 0.98 and less than 0.385 were obtained respectively. The value ofMAPE has been acquired for all harvesting times and pomegranate sizes except forpomegranate with medium size in first harvesting time. Results showed thatMaxwell model describes the tension relaxation behavior of pomegranate withlower error than Peleg model in a suitable way. The ability of artificial neuralnetworks technique as an alternative to predict the viscoelastic behavior ofpomegranate was assessed. This assessment showed that artificial neural networksand Maxwell model can predict the tension more reliably and more precisely thanPeleg model. According to analysis of tension data using models coefficients,pomegranate behaves like solid viscoelastic in every harvesting stage. It also showsthat elastic behavior of pomegranate is more in the third stage of harvesting thanfirst and second stages.