Zahra Karimivaloujaei; Mohammad Hossein Abaspour fard; Mohammad Hosein Aghkhani; Saeid Tarighi
Abstract
Introduction: Packaging is one of the effective ways to increase the storage life and quality of the food products. Nowadays, most of the materials used in packaging are fossil origin and usually non-degradable and hardly dissoluble. Also biodegradable films, due to their fragility and poor resistant ...
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Introduction: Packaging is one of the effective ways to increase the storage life and quality of the food products. Nowadays, most of the materials used in packaging are fossil origin and usually non-degradable and hardly dissoluble. Also biodegradable films, due to their fragility and poor resistant to gas exchange are in limited use. It is possible that by employing nanotechnology, some particles on nano scale may be added to these polymer composites to improve the mechanical properties and permeability of the biodegradable packing films. Silver nanoparticles and zinc oxide have been incorporated in polymers individually by researchers. The objective of this study is to compare the effect of incorporating the mixture of silver and zinc oxide nanoparticles with the case of adding them separately into poly vinyl alcohol matrix, on some relevant mechanical and physical properties of the outcome Nono-composite films. Materials and methods: To make polymer films polyvinyl alcohol, solvent (deionized water) and glycerol (as softener) were used. Then, zinc oxide and silver nanoparticles at 3% by weight, were added to the polymer solution in two different ways, separately and in combination. To specify the pattern of nano-particles size distribution, transmission electron microscope (TEM) test was performed. To determine the characteristics of the films’ surface scanning electron microscope (SEM) was employed. To investigate the bondings between the components of nono-composite films, FTIR was employed.For identification of matrix structure and formation of nano-composite, the XRD was performed. To measure the infiltration of water vapor, the approved E96 ASTM method was used. Also, for measuring the color and transparency of films, the HunterLab test and for mechanical properties of the films, Instron Universal Testing Machine (H5 KS, England) were used (considering the ASTM standard for tensile tests - D88201). For statistical analysis and comparison of means, variance analysis and Dunkan test were performed, using SPSS software. Results & discussion: SEM, XRD and FTIR tests showed that nanoparticles were distributed uniformly within the polymer matrix, and react well with the polymer chains. Besides, the effect of adding silver and zinc oxide nanoparticles on the relevant properties of the films was significant. By individual adding of these nanoparticles on polyvinyl alcohol matrix, the tensile strength and the elongation of films increased. On the other hand, their transparency and water vapor permeability decreased. The results also showed that the combined incorporation of silver and zinc oxide nanoparticles into the packing films can significantly affect their mechanical properties and permeability. Hence, due to the high prices of silver nanoparticles than zinc oxide nanoparticle, the combined incorporation of these two nano-particles is recommended, while maintaining the properties of the nano-composite films in a reasonable level. It can be implied that the combined use of silver and zinc oxide nanoparticles in the polymer provides a more affordable Nano-film with good enough quality. It may also reduce their mutual side effects.
Hesam Omrani Fard; Mohammad Hossein Abaspour fard; Mehdi Khojastehpour; Ali Dashti
Abstract
Introduction: One of the new methods for improving the mechanical properties of bioplastics is the production of blending based bioplastics. Recent studies show that proteins, in combination with starch, form a strong network of hydrogen bonds and intermolecular interactions that resulted stable 3-D ...
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Introduction: One of the new methods for improving the mechanical properties of bioplastics is the production of blending based bioplastics. Recent studies show that proteins, in combination with starch, form a strong network of hydrogen bonds and intermolecular interactions that resulted stable 3-D materials. The big problem in the commercialization of blending based bioplastics is the lack of industrial machinery for the continuous production of bioplastics with the direct use of biopolymers. Industrial production of bioplastics is accompanied by increasing heat along with applying the pressure. It is necessary to know the kinetics of thermal degradation of bioplastics to study thermal behavior at different temperatures in order to design bioplastics processing devices and molding machines, software modeling of processes, mass and energy equilibrium, and optimizing energy consumption in the production process along with improving the thermal properties of the bioplastics.
Materials and methods: In this study, the dynamics thermal decomposition of bioplastics prepared from a mixture of potato whole flour-gelatin and glycerol with a control sample consisting of potato whole flour and glycerol was investigated and compared. The gelatin was extracted from chicken feet using chemical methods. In this research, two isoconversional models including Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) models were considered. Using each of these models, thermal decomposition kinetic parameters were calculated for bioplastic samples.
Result and discussion: The results showed that the maximum activation energy of the mixed bioplastics determined 162 and 150 kJ/mol by FWO method at the conversion ratio of 0.9 and 0.5 respectively, while it was 217 kJ/mol at the ratio of 0.6 for control bioplastics. The amounts of kinetic parameters calculated in this study, were able to determine the thermal behavior at different temperatures and the thermal decomposition process. Also, it can help to redesign and optimize the methods of molding and shaping of potato-gelatin based bioplastics by the use of existing machinery in the industry.
Mahmood Reza Golzarian; Ali Mohammadzadeh; Mohammad Hossein Abaspour fard
Abstract
Introduction: Every year about 600 million tons of fruits and vegetables are produced in Asia and around 35% out of it is wasted during production, postharvest, processing, distribution and consumption (FAO, 2011). In most cases, the sale rate of agricultural products is affected by their internal quality. ...
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Introduction: Every year about 600 million tons of fruits and vegetables are produced in Asia and around 35% out of it is wasted during production, postharvest, processing, distribution and consumption (FAO, 2011). In most cases, the sale rate of agricultural products is affected by their internal quality. Although consumers are unable to detect product’s internal quality and freshness while buying, their negative perceptioncan be formed against their next buy if the internal quality of what they bought does not meet their satisfaction (Leemans et al., 2002). For assessing fruits quality factors some destructive and non-destructive tests are performed. The qualityfactors are categorized into external quality and internal quality factors. With the visual inspection methods, the external features of Bio-materials (e.g. shape, color and texture) can be evaluated (Shiranita et al., 1998) while the internal quality factors, including freshness, cannot be determined from these apparent visual characteristics (Jha et al., 2002). Therefore, the shelf life of agricultural products that are internally defective is less as they perish sooner and the infection expands quicker (Ohali, 2011). Among the common nondestructive methods for assessing internal quality parameters, MRI, X-RAY, Ultrasonic and NMR can be named (Du et al., 2004; Mery et al., 2011). In fruits, vegetables and fruits, the status of freshness is affected by the changes occurred in their physical, chemical and biological structures. These changes and, therefore, freshness, conventionally, is quantified by parameters such as product’s mechanical stress, moisture content, temperature and pH.Recently, some advanced technologies such as thermography have been used in quality assessment of agricultural products. Thermography is performed in two types: active and passive. In passive thermography, the heat emitted from the objects is recorded by the camera while in active thermography, which is more common in post-harvest applications, there is an external energy source to produce a thermal contrast between the sample product and the background. The objectives of this research areto use thermography in order to study the effects of time after harvest on the distribution of arils surface temperature and to relate the thermal properties to the freshness of arils.Materials and methods: Freshly harvested pomegranate fruits of Khazar variety were provided from Kashmar gardens. The arils were extracted from 35 randomly selected fruits. The arils of each fruit were kept for 15 days at 5°C. The arils were thermally and visibly imaged and their physical and mechanical properties were measured every 5 days: first day, fifth day, tenth day and fifteenth day after openingthe fruit to have variations in freshness. The size of thermals images was 320×240 pixels with the temperature resolution of 0.08°C. The images were taken with the emissivity set at 0.95, which was obtained from masking method (using a high-emissivitypatch). This emissivity value was within the range documented for biological products, i.e. 0.93-0.99 (Hellebrand et al., 2006). The thermal images were taken from the arils every 10 seconds for 180 seconds after imposing thermal shock by placing the arils in a freezer compartment at -2°C for 60 seconds. The distance from the thermal camera to the arils was 30cm and the room temperature was 22.5°C. The images were processed and analyzed in Matlab (MathworksInc, US) and the thermal features were extracted from the histogram of each thermal images, which included: mean temperature, variance, third moment, smoothness, homogeneity and entropy.Linear Discriminant Analysis (LAD) was employed for classification based on the mentioned features. The validity of input data was examined using Leave-one-out method. Statistical analysis was carried out using stepwise regression method in SPSS ver. 16.Results and discussion: The temperature extraction from the aril regions was done using the fusion of the segmented red/green ratio and the thermal image. The results showed that the temperature gradient with respect to time for one-day was the same as that for the five-day arils. This behavior was probably because the sound and fresh part of these arils was still large enough so that it causes less sensitivity with respect to the temperature change. However, the temperature gradient for ten-day and fifteen-day arils was relatively large. The analysis of temperature variations on arils surface showed that the less fresh the arils were,the more thermally sensitive they were with respect to their surroundings. The less fresh arils were cooler than the one-day and five-day arils. This might be due to the extended evaporation from the surface and the larger emissivity of older arils than fresher ones. The larger emissivity in less fresh tissues cause quicker heat penetration inwards or quicker heat loss from inside out, thus, the tissue become cool or hot quicker. Conversely, the fresh tissues have reduced heat transfer.They release heat in a cold environment or becomes warm it a warmer environment at a slow pace rate.The extracted temperature features were used in a Linear Discriminant Analysis (LDA) model for quality assessment and classification of pomegranate arils stored for three 60-second periods. The mean accuracy of classification of arils for three 60-second periods of imaging were obtained to be 62.1%, 72% and 79.8%. The optimum classification results were obtained from the third 60s. In this range, the accuracy of classification ofone-day, five-day, ten-day and fifteen-day arils were 98.7%, 69.23%, 65.4% and 89.8%, respectively. Conclusion: Twelve thermal features were extracted from thermal images of arils for classification in terms of freshness. The results confirm that thermography can be used as a non-destructive method for determining the freshness status of pomegranate arils during storage periods.
Ali Mohammadzadeh; Mohammad Hossein Abaspour fard; Mahmood Reza Golzarian
Abstract
Introduction: Pomegranate fruit as one of the most popular fruits native to Iran, belongs to Punica family (Punica granatum L). Iran with an annual production of about 700 tons is the largest producer of pomegranate fruits in the world. Colorfulness and healthiness are two important features of pomegranates, ...
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Introduction: Pomegranate fruit as one of the most popular fruits native to Iran, belongs to Punica family (Punica granatum L). Iran with an annual production of about 700 tons is the largest producer of pomegranate fruits in the world. Colorfulness and healthiness are two important features of pomegranates, which cannot easily be controlled. Some negative characteristics of this fruit such as sun burning, cracking and scratchingcan reduce its economic value. Moreover, separating the arils from membrane (flesh) and sorting them based on their color and size is a laborious task which still is a challenging concern (Blasco et al., 2003). Despite these challenges, the demand for “ready-to-eat” of arils is increasing. Up to now several devices have been proposed to remove the arils from membrane with different operation principles. However, these devices leave some membrane segments with arils and also makeit difficult to sort the arils from color and size points of view (Khazaei et al., 2008; Singh et al., 2007). With the visual inspection methods, the external features of Bio-materials (e.g. shape, color and texture) can be evaluated. While for assessing their internal parameters, nondestructive methods such as MRI, X-RAY and NMR are preferred. To classify and identify bio-materials (e.g. fruits), several methods have been examined including Fuzzy technique (Hu et al., 1998), Multilayer (Luo et al., 1999) and Linear Discriminant Analysis (LAD) (Manickavasagan et al., (2010). The primary objective of this research wasto discriminate arils from membrane segments. Subsequently, the fruit components were classified into red, pink, white arils and membrane segments, using LAD method. Ultimately, the accuracy of classifications based on different images’ features was evaluated. Materials and methods:Pomegranate fruits of Khazar variety were provided from Kashmar gardens. Prior to imaging step the fruits were categorized in four groups each of 50 samples. The arils were ranked as red, pink and white using human sensory. The images of arils samples were prepared using a Nikon Coolpix digital camera (Nikon co, Japan), in a chamber having six LED lamps, from a distance of 15 cm. During image processing, the images were first converted into grayscale format and then transformed into binary images. Subsequently, several morphological (see table 2) and textural image (see Table 3) features were extracted for classification purpose. For color features three color spaces including RGB, HSI and L*a*bwere examined (see Fig 3). The arils were classified and discriminated from membrane using 12 morphological, 10 color and six textural features. Linear Discriminant Analysis (LAD) was employed for classification based on the mentioned features. The validity of input data was examined using theleave-one-out cross validation method. Statistical analysis was carried out using SPSS ver. 16.Results and discussion: The classification accuracy of arils based on morphological features was about 97.53% and the membrane segments were discriminated from arils with accuracy of 95.06% (Table 4). The classification with color features provided the accuracy of 45% when the “R” component of the images was considered (Table 5). This is mainly due to similar red band of the arils classes.The accuracy of classification improved whenHSI components were used andthe accuracy of 84% was achieved (Table 6). The best accuracy of classification with color features observed using L*a*b* color space. In this case the accuracy was 89.1% (Table 6). In the final stage of classification, six textural features obtained from statistical moments including mean grayscale, standard deviation, third moment, evenness, entropy and homogeneity were used. As shown in Table 7 with these components the accuracy of classification improved up 93.3%. Considering the classification with different features (morphological, color and textural) it can be said that, in general, the accuracy of discriminating membranes from arils is less accurate than the accuracy of discrimination between different arils (red, pink and white). This was observed in all methods of classifications with different image features. With regard to the specific functionality of each extracted feature, the combination of the features was used for classification. Due to the increasing number of input features, the stepwise method was used for rankingof input features.Out of 26 input features of classification model, 14 superior features were selected using stepwise method. The results of classificationwith the combination of different features are shown in Table 8. As it can be seen, the average accuracy of classification with the combination of features improved up to 99%. Fig. 4 shows the classification of the pomegranate components based on the combination of the features, using Linear Discriminant Analysis (LDA) method.Conclusion: A classification model was employed to classify pomegranate arils and membranes, using Linear Discriminant Analysis method. To improve the accuracy of classification, different image features were extracted and examined. In order to achieve a higher accuracy, the combination of features wasalso tested. This improved the accuracy of classification up to 99%. Since the combination of features is a costly and time-consuming process, the stepwise method was used to rank and select the superior features before their use in classification step.
Hamed Sigari; Mohammad Tabasizadeh; Mohammad Hossein Abaspour fard; Mahmood Reza Golzarian
Abstract
Introduction: Harvesting of Kiwifruit (Actinidiadeliciosa, family: Actinidiaceae) is usually performed in mid-October in Iran. The average weight of this fruit is about 70 g. Hayward is the most popular kiwifruit variety in the world mainly due to its large size, oval shape and high shelf life. Drying ...
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Introduction: Harvesting of Kiwifruit (Actinidiadeliciosa, family: Actinidiaceae) is usually performed in mid-October in Iran. The average weight of this fruit is about 70 g. Hayward is the most popular kiwifruit variety in the world mainly due to its large size, oval shape and high shelf life. Drying fresh products is a long-standing method for conservation of food products. This method reduces water-borne and microbiological activities in fresh products while only minor physical and chemical changes occur in these products. Drying, therefore, is regarded as a common method used for food product conversation. There have been several researches on modeling drying of food products. Wang et al. (2007) worked on a mathematical modeling for drying apple slices in a hot air drying process and determining the effective thermal diffusivity. These researchers stated that Midili model was found to be the best for predicting the moisture content changes during drying. Torgul (2005) confirmed this finding in modeling the drying of apple slices in an infrared drying system. How ever not much research has been carned out on drying kiwifruit slices. Therefore, in this research, the drying process of kiwi slices in a vacuum dryer was examined in order to understand their behavior during the process and to determine a best predictive model for drying and also study the diffusivity coefficient for this product. Materials and Methods: In thes research Hayward variety of kiwifruit for was used Sinco this variety is commonly grown in Iran. The fruits were purchased from local market in mid-October and transferred to a cool storage (50 C) in a lab at the Department of Biosystems Engineering at the Ferdowsi University of Mashhad. The samples used in this study were of medium size and suitable for cutting in a cylinder-shape cutter.The initial moisture content was determined by so-called oven-drying method on wet basis according to the following equation (Mohesnin, 1986):〖%MC〗_wb=(Initial weight-Final weight (after drying in oven))/(Initial weight) 100 (1) The moisture content was determined as 80.23% on a wet basis. The kiwifruits were sliced at 3 mm thickness using a 35 mm-diameter cylinder and weighed with a digital scale. The slices were moved out of the dryer and weighed every 30 min to monitor their moisture content. Weighing continued until the sample’s moisture content reached to 15-20% on a wet basis. Moisture ratio of kiwifruit slices during drying process was determined according to the follow equation:……………… ……….(2)where MR is moisture ratio (dimensionless), Mt is moisture content at any desirable time, Me equilibrium moisture content, percent, dry basis, and M0 is the initial moisture content (kg H2O/kg of dry matter). The value of Me is very small compared with Mt and M0, hence, the error involved in the simplification of above equation by omitting Me is negligible. The experimental drying data were fitted in various drying models commonly used for monitoring the trend of being-dried products. A few of which models are as follows:MR=exp (-kt) : Newton modelMR = exp (_ktn): Page modelMR = 1 + a.t + bt2: Wang and Singh modelMR = a.exp (_ktn) + b.t: Midilli modelIn this research, two statistical proameters were used to evaluate the goodness of fit of the tested models to the experimental data: the coefficient of determination (R2) and root mean square error (RMSE) between the experimental and the predicted moisture ratio values. Diffusivity coefficient for each slice was determined from the following equation:MR=8/π^2 ∑_(n=0)^∞▒〖1/(2n+1)^2 exp[-(π^2 (2n+1)^2)/4 (D_eff t)/a^2 ] 〗 (3) where a is sample thickness (in meter), t drying time (in seconds), n is the number of observations and Deff is effective diffusivity coefficient (in m2.s-1).In long drying process, the following simplified equation is used:MR=8/π^2 exp[-(〖π^2 D〗_eff t)/(4a^2 )] (4) The diffusivity coefficient is the slope of the straight line when experimental drying data in terms of Ln (MR) is plotted versus drying time (t).Results and Discussion: The results of this research revealed that the best prediction curve of moisture content against time was drawn using of MTLAB software. In this regards the rational function with first degree in both numerator and denominator and the third degree polynomial function with maximum coefficient of determination (R2) of 0.9991 and 0.9977 and minimum root mean square error (RMSE) of 0.01267 and 0.02412 were the best prediction models, respectively (Table 2). Furthermore, the drying time becomes shorter as the thickness of kiwifruit slices becomes thinner. This is mainly due to the higher thermal gradient within the thinner slices and hence faster moisture removal due evaporation. The heat diffusivity coefficient was also determined from “Ln (MR) – Time” curves (Figure 3). It was observed that with increase of fruit’s thickness, the heat diffusivity coefficient increases. This phenomenon may be related to the molecular dynamics and the surface tension of materials being dried. In other words the minimum and maximum values of the diffusivity coefficient were observed as 2.0904E-6 and 7.1303E-6 m2.s-1 for fruit thicknesses of 3 and 9 mm, respectively (table 3).Conclusion: The trend of moisture content evolution against drying time during vacuum drying of kiwifruit was investigated using MTLAB software. Different prediction models were examined for the prediction of moisture removal during vacuum drying of kiwifruit. The rational and polynomial functions were determined as the most accurate prediction models with the coefficient of determination (R2) of higher than 0.99 and RMSE of about 0.02. Furthermore, the heat diffusivity coefficient of kiwifruit slices was investigated as a function of slice thickness. A general increasing trend observed for this coefficient as the thickness of the slices increased.
Elham Gharoyi; Mohammad Hossein Abaspour fard; Nasser Shahtahmassebi; Mehdi Khojastehpour
Abstract
In this study, ZnO nanoparicles and polymer nanocomposite were synthesized for film preparing of food packaging. The structural, physical and anti-microbial properties were then studied. ZnO nanoparticles were synthesized by sol- gel method. The Structural analysis by XRD verified the formation of zinc ...
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In this study, ZnO nanoparicles and polymer nanocomposite were synthesized for film preparing of food packaging. The structural, physical and anti-microbial properties were then studied. ZnO nanoparticles were synthesized by sol- gel method. The Structural analysis by XRD verified the formation of zinc oxide phase. TEM images showed that the nanoparticles are spherical shape and their average size between 20 to 25nm. To prepare nanofilm, Zno nanoparticles were added at three concentration levels of 1,3 and 5%wt to the PVA matrix which prepared by solution processing method. SEM images of the film showed that in all samples the nanoparticles were distributed well in the polymer matrix. With using the Fourier Transmission Infrared (FTIR) at a wave length of 558cm- 1 Zn-0 band at all of nanoparticle concentrations were establish. The effect of inserting ZnO on the mechanical and antibacterial properties and moisture content of the PVA were also investigated. The increase of ZnO concentration in PVA from one to three percents causes the increase of tensile strength by 11% .The moisture content reduced up to 20%, when nanoparticles concentration increases from zero to five percents. Furthermore, by adding ZnO on PVA the antibacterial activity of the composite film was further improved.
Saeideh Fayyazi; Mohammad Hossein Abaspour fard; Abbas Rohani; Hassan Sadrnia; Seyed Amir Hasan Monadjemi
Abstract
Due to variation in economic value of different varieties of rice, reports indicating the possibility of mixing different varieties on the market. Applying image processing and neural networks techniques to classify rice varieties is a method which can increase the accuracy of the classification process ...
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Due to variation in economic value of different varieties of rice, reports indicating the possibility of mixing different varieties on the market. Applying image processing and neural networks techniques to classify rice varieties is a method which can increase the accuracy of the classification process in real applications. In this study, several morphological features of rice seeds’ images were examined to evaluate their efficacy in identification of three Iranian rice varieties (Tarom (Mahali), Fajr, Shiroodi) in the mixed samples of these three varieties. On the whole, 666 images of rice seeds (222 images of each variety) were acquired at a stable illumination condition and totally, 17 morphological features were extracted from seed images. Fisher's coefficient (FC), Principal component analysis (PCA) methods and a combination of these two methods (FC-PCA) were employed to select and rank the most significant features for the classification. The so called LVQ4 (Learning Vector Quantization) neural network classifier was employed for classification using top selected features. The classification accuracy of 98.87, 100 and 100% for Fajr, Tarom and Shiroodi, 100 and 100% for Fajr and Shiroodi, 100 and 100% for Tarom and Shiroodi and 97.62 and 95.74% for Fajr and Tarom were obtained, respectively. These results indicate that image processing is a promising tool for identification and classification of different rice varieties.
