Food Engineering
Javad Safari; Jafar Hashemi; Azadeh Ranjbar Nedamani
Abstract
Introduction Due to the importance of product appearance quality in product grading and the impact of factors such as area, uniformity, and various defects on the product quality, and also, the ability to recognize these features at a very low cost, image processing techniques, is one of the methods ...
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Introduction Due to the importance of product appearance quality in product grading and the impact of factors such as area, uniformity, and various defects on the product quality, and also, the ability to recognize these features at a very low cost, image processing techniques, is one of the methods used to evaluate food quality. Therefore, in this study, a non-destructive image processing method was used to investigate the factors affecting the color and shrinkage of apple slices during drying. Materials and Methods Golden delicious apples were used in this research. The central part of the apple (including the rivet, seeds, and tail) was removed by a kernel separator and sliced into 3, 5, and 7mm thickness and approximately 7 mm diameter slices using a hand slicer without separating the skin. Three temperatures of 60, 70, and 80 °C were used to dry the samples. To determine the moisture content of a sliced apple, the samples were first weighed on a digital scale, then placed in a dryer, and the experiment was continued until the samples reached equilibrium mass. Due to the high importance of moisture ratio in controlling the drying process, moisture rate (MR) and moisture content (MC) were calculated, and samples were taken to investigate the amount of surface shrinkage, general color changes and browning index. After extracting L*, a*, and b* values, total color changes and browning index (to show the intensity of brown color in the product) for all samples before and after drying were calculated and evaluated to describe color changes after drying. Results and Discussion The drying kinetics results showed that the drying process significantly depends on the thickness of the samples. According to drying curves, at the early stages of drying, the decrease in humidity occurs more severely and the graph has a steeper slope, but as the process continues and the moisture content of the product decreases, the slope of the curve decreases. In the early stages of drying, due to the presence of water inside the fresh fruit cells, there is a pressure balance between the fruit and the surrounding environment, which causes the fruit to remain swollen. However, as the drying time progressed, contractile stresses are created, which cause superficial shrinkage. In this study, it was observed that increasing the thickness from 3mm to 7mm, reduced the final shrinkage on the surface of apple slices by 11% at 60 °C, 12% at 70 °C, and 13% at 80 °C. After moisture leaves the surface of the product and heat penetrates into the product, moisture begins to leave the product by conducting interstitial convection. When moisture moves to the surface, the mechanical balance and consequently the textural structure of the sample is disturbed due to the creation of different spaces in thickness. According to the results, increasing drying time and thus decreasing the moisture content, increases the percentage of apple shrinkage. On the other hand, at a certain thickness, with increasing temperature, the percentage of shrinkage changes in the thickness of the product decreases. Therefore, at thicknesses of 3, 5, and 7 mm, the increase in temperature from 60°C to 80°C, decreased the amount of shrinkage thickness by 16, 12, and 8%, respectively. It is in higher thicknesses that react with heat and change the color of the fruit due to the Maillard reaction. After complete drying of apple samples, the highest amount of color change was related to the thickness of 7 mm and a temperature of 80°C, which was equal to 1.254. Also, the lowest rate of discoloration of apple slices in a thickness of 3 mm and a temperature of 60 °C was 0.889. The browning index (Bi) in the high thickness of apple slices is less affected by the process temperature due to the increase in moisture level. For this reason, the rate of browning was very low among the experimental samples and the highest rate of browning was related to the thickness of 7 mm and the temperature of 80 °C was 585/2559. Also, the lowest rate of browning of apple slices was observed in the thickness of 3 mm and the temperature of 60 °C was 584.254. Conclusion Finally, it was found that the thickness and temperature factors can have an effect on the quality of product during drying process. The results of this study can provide a cheap and fast way to control the quality of fruits during drying and help producers of these products select the main process factors that affect the final quality.
Food Engineering
Mohsen Zandi; Ali Ganjloo; Mandana Bimakr; Abolfazl Gharebaghi
Abstract
Introduction: Grape is a non-climacteric fruit with a low rate of physiological activity but is subject to serious physiological and parasitic disorders after harvest and during long term storage (Ciccarese et al., 2013). Currently, Edible coatings have been studied as potential substitutes for conventional ...
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Introduction: Grape is a non-climacteric fruit with a low rate of physiological activity but is subject to serious physiological and parasitic disorders after harvest and during long term storage (Ciccarese et al., 2013). Currently, Edible coatings have been studied as potential substitutes for conventional plastics in food packaging. Edible coating is a thin layer of edible material formed as a coating on a food product. Edible coating can offer several advantages to the fresh fruit and vegetable industry such as improvement in the retention of color, acids, sugar and flavor components, the maintenance of quality during shipping and storage, the reduction of storage disorders and improved consumer appeal (Antoniou et al., 2015; Cazon et al., 2017; Fakhouri et al., 2015; Galus & Kadzińska, 2015). Farsi gum as a novel source of polysaccharides has drawn much attention in a wide range of various fields such as pharmaceutics, food and cosmetics industries. Functional properties of Farsi gum are influenced by its structure and molecular weight (Hadian et al., 2016; Joukar et al., 2017). By inclusion of bioactive compounds in the Farsi gum network the aforementioned impairments could be overcome and moreover, new protective and functional valences could be added. The inclusion of lipid-based component in Farsi gum gives it excellent light and moisture barrier properties. The benefic impact on human health of hemp seed oil is worldwide recognized. A recent study demonstrated the antimicrobial properties of hemp seed oil. Due to their abundance in biologically active compounds, hemp seed oil is promising natural alternatives that may extend the shelf-life, microbiological safety and nutritional values of food (Cozmuta et al., 2015; Leizer et al., 2000; Salarnia et al., 2018). Growing awareness of the quality of fruit has necessitated increasing effort to develop rapid and non-destructive methods for evaluating fruit quality (Bhargava & Bansal, 2020; Rachmawati et al., 2017; Tao & Zhou, 2017; Wu & Sun, 2013). The aim of this study was the consideration of image processing application for grape sorting based on visual surface characterize. Materials and Methods: Coating emulsion was prepared using (Farsi gum (0%, 1.5% and 3%), hemp seed oil (0%, 0.075% and 0.15%) and glyceride (0.3%)). grape fruit were coated by immersion in coating dispersion for 5 min. Samples were then allowed to loss the excess coating dispersion. Coatings were developed at room temperature during an hour. Samples were refrigerated at 4± 1°C for 28 days and analyses were performed at days 0 and 28. Defect identification and maturity detection of grape fruits are challenging task for the computer vision to achieve near human levels of recognition. The image acquisition was performed in a homogenously controlled lighting condition. Considering the camera lens’s focal length, the samples were placed 25 cm under the camera’s lens to be under camera’s field of view. The images of grape were segmented from the background using thresholding of the high contrast images via MATLAB software (R2019a, image processing toolbox). The optimum threshold value was obtained to be 0.35, 0.45 and 0.30 for R, G and B channel, respectively. Results and Discussion: The proposed techniques can separate between the defected and the healthy grape fruits, and then detect and classify the actual defected area. Classification is performed in two manners which in the first one, an input grape is classified with two different algorithms (RGB and binary). The Result showed that the accuracies for detecting the surface defects on grape were 97.73% and 96.30% using RGB and binary algorithms, respectively. Proposed method can be used to detect the visible defects of coated grape, and to grade the grape in high speed and precision. Conclusions: The results of this research and similar ones can provide helpful recommendations in grading fruits for fresh consumption. The simplicity and the efficiency of the proposed techniques make them appropriate for designing a low-cost hardware kit that can be used for real applications.
Food Engineering
Mojtaba Afsharipour; Hadi Samimi Akhijahani; Kazem Jafari Naeimi
Abstract
Introduction: The presence of various impurities leads to the problems in storage time, transmission, selling and consumption process of any product. Thus it is necessary to separate the impurities from the product for industrial processing. Descurainia Sophia is a tiny grain seed in light brown color ...
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Introduction: The presence of various impurities leads to the problems in storage time, transmission, selling and consumption process of any product. Thus it is necessary to separate the impurities from the product for industrial processing. Descurainia Sophia is a tiny grain seed in light brown color with elliptical shape and it grown up in humid climate. Electrostatic method is a proper way for separation and purification of materials which is based on the absorption and diffusion of charged particles in an electrical field with high voltage. An experimental research was carried out for recycling the plastic waste with tribo-electric system. The result of the research on plastic separation of waste materials showed that by increasing the voltage of the system the purification fold increases. The results of the effect of the applied voltage for recycling the plastic particles showed that by increasing the applied voltage in the electrodes the mass and purity of the polycarbonate (PC) increased and the mass and purity of polyamide (PA) decreased. There is a little information about separation and purification of fine grain seeds and this study is about separating of Descurainia Sophia seed and the effect of the parameters using regression analysis. Material and Methods: Descurainiaseed samples were collected from the farms located in Mahan city of Kerman province, Iran. Tribo-airo-electrostatic system contains of funnel and feeding container, the charger unit, the separating unit, air transmission channels and gathering unit. Charging unit consists of two aluminum electrode connected to a high voltage DC power supply adjustable between 0-100 kV. A blower was used to suspend material and increase the exposure time of particles in the electric field. The gathering is a box with different partitions divided by wooden sheets. Separated particles fall in the gaps based on the amounts of charges, weight and shape. The separating process takes place by considering physical properties. There are two important forces that acts on the falling of the object in electric field; the electric force acts in the horizontal direction, gravitational force acts in the vertical direction. Considering the purity of the separated seeds in the box, only four sections of the box were selected for size and frequency analysis. For obtaining gathered seed impurity, the digital pictures of the gathered samples imported in Matlab6.5 software and were analyzed using Image processing toolbar based on the differences between seed and impurity color. For regression analysis of the parameters voltage in the electrodes, the distance of the electrodes, the angle of the electrode and the mass of the boxes was considered. Laser diffraction method used for determination the size of separated particles and for this purpose FRITSCH Laser Particle Size Analyser -ANALYSETTE 22 NanoTec system was used. Results and discussion: The results illustrated that the Descurainiaseed takes negative charge and moves to the positive electrode and impure particles takes positive charge and moves to the negative electrode. The purity calculations of the experiments showed that the average percentage of box No.1 is more than 98%, box No.2 is between 65%-75%, box No.3 is between 30% to 50% and box No.4 is less than 50%. The values of correlation coefficient of the effective parameters for box No.1 was 90% and this means that 90% of the parameters of equation affected on the weight of the box No.1 for about 90%. By increasing the applied voltage and the angle of the electrodes of the separating unit and decreasing the distance of the electrodes, the purity of box increases. The size analysis of Descurainiaseed showed that the particles with larger dimensions take more negative charge and moves to negative electrode. The results of the study showed that tribo-airo-electrostatic system separated Descurainiaseed from waste particles properly. By considering the optimum value of separating parameters the purification increased by 98%. According to the results it can be stated that this system can be used for separation and purification of small grains such as alfalfa and clover.
Farhad Fatehi; Hadi Samimi Akhijahani
Abstract
Nowadays, in modern agriculture, the combination of image processing techniques and intelligent methods has been used to replace smart machine instead of humans. In this study, an artificial image processing and artificial neural network (ANN) method was used to classify strawberry fruit of Parus variety. ...
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Nowadays, in modern agriculture, the combination of image processing techniques and intelligent methods has been used to replace smart machine instead of humans. In this study, an artificial image processing and artificial neural network (ANN) method was used to classify strawberry fruit of Parus variety. In the first step, the fruit was divided into 6 classes (ANN outputs) by the expert, and 100 samples were randomly collected from each class. In the next step, the images of the samples were captured and three geometric properties with twelve color properties (as ANN inputs) were extracted. Optimum artificial neural network structures considering root mean squared error (RMSE) and correlation coefficient (R2) were investigated to classification process of the strawberry samples. Finally, the perceptron neural network with a structure of 6-18-15 was selected with an average accuracy of 83.83%.
Simin Ghasemizadeh; Behzad Nasehi; Mohammad Noshad
Abstract
In the study, the effect of compositional parameters (Xanthan, Corn flour and quinoa flour content) on sensory characteristics and image features of gluten free bread were evaluated. Results showed, addition of quinoa and corn flour significantly decreased L* value and increased a* value of crust and ...
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In the study, the effect of compositional parameters (Xanthan, Corn flour and quinoa flour content) on sensory characteristics and image features of gluten free bread were evaluated. Results showed, addition of quinoa and corn flour significantly decreased L* value and increased a* value of crust and crumb of gluten free bread. Also, increased percentage of corn flour has led to decreased amount of FDL* that indicates the area appears less nonhomogeneous on surface of gluten- free bread. The results also showed that using complete flour of quinoa causes softness in bread due to the presence of bran and networking, therefore, resulting in increased contrast, homogeneity and entropy, and decreased energy and correlation of produced breads. The results of sensory analysis showed that all samples containing quinoa flour have higher overall acceptance score than that of the control treatment. Correlation analysis showed a good linear relationship between image features and overall acceptance of gluten- free bread. Results showed that the optimized Adaptive Neuro-Fuzzy Inference System (ANFIS model) provide best accurate prediction method for overall acceptance of gluten-free bread (R2= 0.994 and MSE= 0.0015) and it could be a useful tool in the food industry to design and develop novel products.
Hossein Javadi kia; Mahdi Ghasemi-Varnamkhasti; Sajad Sabzi
Abstract
Introduction: Nowadays, with the development of imaging systems and image processing algorithms, a new branch of agriculture and food industry quality control has emerged. Meat and related products have high commercial value and they are one of the most important items of household food basket (Jackman ...
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Introduction: Nowadays, with the development of imaging systems and image processing algorithms, a new branch of agriculture and food industry quality control has emerged. Meat and related products have high commercial value and they are one of the most important items of household food basket (Jackman et al., 2011). The apparent color of the meat is one of the most important ranking factors which determines the quality and marketing value (Ramirez and Cava, 2007; Shiranita et al., 2000). There is a relationship between color, appearance etc. and the shelf-life of meat, since the passing time causes the color to be darkened in meat due to chemical reactions and shrinkage occures Therefore, determining the storage time is important in terms of quality and marketing value (Jackman et al., 2011; Tan, 2004). In recent years, virtual image on a computer as a helpful suggestion for meat grading has been emerged. Various studies have been conducted in the field and results in a number of applications suggests that the color image processing method for assessing the quality of meat is important (Girolami et al., 2013; Mancini and Hunt, 2005; Lu et al., 2000). Due to the importance of detection of freshness veal in order to preserving the quality and post ponding the meat spoilage and disease accordingly, designing a device to detect the storage time of slaughter and in other words the freshness of veal using image processing and response surface method was studied. For this purpose, two common environment and standard maintenance of fresh meat: first in the refrigerator with an average temperature of 3°C and second in cool place with a temperature of 8°C were considered and then the effects of storage time on the meat quality was observed using a digital camera Some common models were developed for image processing and the response surface method was applied.
Materials and methods: First, some meat from three sections of veal meat: hands, feet and neck, were prepared from Kermanshah slaughterhouse and the slaughtered time was recorded as an initial time. From each of the six states in total, 18 samples were taken appropriate to the thickness of one centimeter. Samples were randomly selected for inclusion in the standard conditions (ISIRI 692).
Image processing:
More than 600 images were acquired at various storage times and they were then evaluated to find the appropriate separation methods for meat from image background. The best way to separating the meat image from the background in the image was using the RGB color and the B space values with 150 value as the threshold. In other words, the exact coordinates of meat pixels were obtained. Then background isolated by edge detection with Cany filter with coefficient of 0.7. Finally meat image was isolated from background. Then various parameters of meat image were extracted. The number of parameters were more than 50 parameters. Then sensitivity analysis were selected as three parameters: Contrast, Roughness, and Texture that had more influence on time change from the moment of slaughter and were selected as appropriate inputs of models.
Modeling by Response Surface Method:
In this method, selected parameters were used as inputs and the time of slaughter in minutes, was used as output of the model. Because of the more difference of the values of various parameters from each other, all data were normalized. Generally due to the three organs of veal and two different environments to maintain, six models in the Software Design Expert 7.0 were designed and optimized using response surface methods. In the next step, data samples at ambient temperature as well as refrigerated samples were modeled.
Results and discussion: The results of the models by the response surface methods were good and acceptable. In the final step the general models were good; these models were about all of data in environment and refrigerator.
Conclusion: In this study, considering the importance of using fresh meat calves by people as well as processing plants, some algorithms were designed and developed to estimate the pasted time of the calf slaughtered. For this purpose Samples were prepared from three parts of slaughtered calves: ham, shoulder and neck. The samples were stored in the environment and common standards place the first in refrigerator with a temperature of 3 ° C and another in cool environment with an average temperature of 8 ° C. Then some images were taken from samples at specified times. Then some parameters were extracted from images produced by the image processing in MATLAB. Then by response surface method was designed and optimized. Suitable models and finaly suggested device has ability to estimate the time of slaughter by taking image.
Saman Abdanan; Mehran Nouri; Maryam Soltani Kazemi; Somaye Amraei
Abstract
Introduction: Nutritional quality of food during storage has become increasingly an important problem. The loss of some nutrients such as ascorbic acid (vitamin C) might be a critical factor for the shelf life of some products as citrus juice concentrates, since vitamin C content of citrus juices undergoes ...
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Introduction: Nutritional quality of food during storage has become increasingly an important problem. The loss of some nutrients such as ascorbic acid (vitamin C) might be a critical factor for the shelf life of some products as citrus juice concentrates, since vitamin C content of citrus juices undergoes destruction during storage (Plaza et al., 2011a). Ascorbic acid is an important component of our nutrition and used as additive in many foods because of its antioxidant capacity. Thus, it increases quality and technological properties of food as well as nutritional value (Larisch et al., 1998). However, ascorbic acid is an unstable compound and even under minor desirable conditions it decomposes easily. Degradation of ascorbic acid proceeds both aerobic and anaerobic pathways and depends upon many factors such as oxygen, heat, light, storage temperature and storage time. Oxidation of ascorbic acid occurs mainly during the processing of citrus juices, whereas, anaerobic degradation of ascorbic acid mainly appears during storage which is especially observed in thermally preserved citrus juices (Lee & Coates, 1999). It was reported that several decomposition reactive products occur via the degradation of vitamin C and these compounds may combine with amino acids, thus result in formation of brown pigments (Wibowo et al., 2015). In recent years, several nondestructive methods such as computer vision, spectroscopy, ultrasonic have been developed to objectively evaluate different agricultural materials (Abdanan Mehdizadeh et al., 2014; Wang and Paliwal, 2007). However, due to the physical properties of fruit, machine vision has not been discussed much in the literature (Fernanzed-Vazquez et al., 2011). One disadvantage of using spectroscopic methods is that these methods require expensive equipment and also carrying these instruments are difficult. On the contrary, the combining of a digital camera and its image processing software that replaces the traditional measuring instruments have been widely used to provide a cheaper and versatile form to measure some internal quality of many foods. Therefore, the goal of this research is to determine the best features of surface texture (entropy, homogeneity, contrast, correlation and prominence) in order to predict quality factors (pH, acidity, soluble solids and ascorbic acid) of citrus juice.
Materials and methods: Orange, sour lemon, sour orange and tangerine fruit were obtained from one of local marker in Ahvaz, Iran. All samples were washed and the juice was extracted using a Pars-Khazar rotary extractor. The citrus juice, (sour orange, orange, lemon and tangerine) immediately after pasteurization process, were kept at a temperature of refrigerators (4º C) for 60 days in darkness. After taking images of the citrus juice, pH, acidity, ascorbic acid and soluble solids were measured on days 0, 20, 40 and 60.
Physicochemical analysis:
The pH of samples was determined with a pH meter (Methrohm, 827 pH lab, Switzerland). The soluble solids content of concentrates was determined as o Bx using a refractometer (Atago Co, Ltd. Carnation, WA). Total titrable acidity was assessed by titration with sodium hydroxide (0.1 N) and expressed as % citric acid (Kimball, 1999). Ascorbic acid was determined using 2,6-dichlorophenolindophenol by visual titration (Kabasakalis, 2000).
Imaging and color analysis:
Samples were placed under the camera (Canon PowerShot SX60 HS, Japan) of a computer vision system at the distance of 300 mm inside a black box with the size of 100 ×100 ×100 cm3. The samples were illuminated using four fluorescent lamps at the angle of 45o in relation with the sample.
After taking images, color images were transformed to L*a*b* color space. The L* parameter (luminosity) is an attribute by which a surface emits more or less light and can take values between 0 (absolute black) to 100 (absolute white). The parameters a* and b* represent the chromaticity, where a* defines the red-green component (red for positive values and green for negative values) and the b* parameter defines the yellow-blue component (yellow for positive values and blue for negative values) (Quevedo et al., 2009a). Following color transformation, the well-known textural parameter called the Gray-Level Co-Occurrence Matrix (GLCM function) was applied to the images and six features through Eq. 1-6 were extracted (Table 1).
Results and discussion: Color changes during storage in three color channels L*,a*,b* showed that the variation of channel L* could illustrate deterioration of citrus juice better than other channels. In the Figure 1, a gallery of four selected images (taken at different times in the experiment) corresponding to one sour orange sample and their corresponding surface intensity (based on L* value) are showed.
The results of statistical analysis depicted that acidity and ascorbic acid, in four citrus juices, significantly (P
Sima Shamsaei; Seyed Mohammad Ali Razavi; Bahareh Emadzadeh; Esmaeil Atayesalehi
Abstract
Introduction: An emulsion is made of dispersed particles through the continuous phase, while not dissolving happens between two phases. Mayonnaise is oil-in- water emulsion (James and Dakin, 1962), as one of the most sauces used in the world. It has a mild odor and taste, creamy to pale yellow color ...
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Introduction: An emulsion is made of dispersed particles through the continuous phase, while not dissolving happens between two phases. Mayonnaise is oil-in- water emulsion (James and Dakin, 1962), as one of the most sauces used in the world. It has a mild odor and taste, creamy to pale yellow color and a pH in the range of 3.6- 4.0, which does not exceed 4.1 (Iranian National Standard, No 2454). Emulsion products are naturally instable. Different factors such as temperature, particles size, stirring, mechanical movements, constituents’ concentration, presence or absence of stabilizers and thickeners may affect the emulsion stability (David, 1999). Ocimum basilicum L., known as basil (or ‘‘Reyhan” in Iran), is a common herb plant grown in Iran. Soaking in water, basil seeds become gelatinous. The high mucilage content of basil seeds can make it a novel source of edible gum (Razavi et al., 2008). The objective of this paper was to investigate the effect of basil seed gum as well as xanthan as fat replacers on some physical and rheological properties of low fat mayonnaise. Materials and methods: Emulsion stability determination: Mayonnaise samples were centrifuged at 5000 rpm for 30 minutes. Emulsion stability (ES) was then determined using the following relation (Worrasinchai S et al., 2006): Stability index= (Total volume/ Volume of emulsion remaining)100. Particle size measurement: Particle size distribution of low-fat mayonnaise samples was determined using laser light diffraction technique (Fritsch Analysette 22, Germany). Rheological measurements: A rotational viscometer (Visco 88, Malvern, UK) equipped with a thermal circulator was used to measure the steady shear rheological properties of samples at the shear range of 14-300 s-1 and constant temperature of 25 oC. Power law, Bingham, Casson, and Herschel-Bulkley models were fitted on the experimental data and the rheological parameters of these models were determined using Slidewrite plus-bar Graph software (1983, Advanced Graphics Software, Inc, USA). Image processing: A scanner was used to capture the samples’ image and the scanner resolution was set to 300 dpi. 7g sample was placed onto a plate and then 152×210 Pixel parts was cut from the obtained image. All images were stored in JPEG format for further analysis. The Clemex image processing software (Clemex Vision Professional, PE4, Canada) was used to determine the color parameters (L*, a* and b*). Results and Discussion: Steady shear flow behavior: The results showed that all samples are classified rheologically as non-Newtonian shear thinning fluids. According to R2 values, Power law was considered as the best rheological model to describe the flow behavior of samples. The maximum and the minimum consistency coefficients of Power law model were observed for the formulation containing 0.75% xanthan gum and 0.45% basil seed gum, respectively. In this study, the apparent viscosity of mayonnaise (in shear rate 42 s-1) raised with increasing gum concentration that this increase in samples 4 and 5 were not significant (P>0.01). The highest apparent viscosity was observed in sample 3 that was prepared with a concentration 0.75% of xanthan gum, while the lowest viscosity was related to sample 4 that was contained of 0.45% basil seed gum. With increasing gum concentration, Bingham viscosity of the samples increased, but this increase in the samples (1, 7, 8) and (2, 6, 9) was not significant (P> 0.01). Yield stress values of Herschel-Bulkley (τH), Bingham (τB) and Casson (τC) models raised with the increasing of gum concentration. Highest yield stress value was related to mayonnaise containing 0.45% xanthan gum and the lowest yield stress value related to mayonnaise prepared with 0.45% basil seed gum. Particle size distribution: Particle size distribution of mayonnaise at concentration of 0.6 % xanthan gum, basil seed gum and mixture of xanthan- basil seed gum had mono-modal particle size distribution. Emulsion stability: Among samples, formulations of 1, 2, 3, 8, and 9 were quite stable and there was no instability (two-phase state). Higher stability in emulsions containing xanthan gum was probably due to higher viscosity of this gum compared to basil seed gum. Color: The best color was observed in sample 6. In this study, with increasing concentration of gum in three samples (1, 2, 3), the amount of L* decreased, it means that the brightness of the product was reduced while in samples containing a mixture of gums (xanthan- basil seed gum), increasing the gum concentration resulted in an increase in L* parameter.
Mahmood Reza Golzarian; Mansoureh Shamili; Omid Doosti Irani; Peyman Azarkish
Abstract
Introduction: Machine vision, which uses image processing techniques, is a branch of artificial intelligence that simulates human vision. These systems can be used for quality control, sorting and grading of agricultural products. Unlike engineering materials, agricultural fruits are living tissues that ...
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Introduction: Machine vision, which uses image processing techniques, is a branch of artificial intelligence that simulates human vision. These systems can be used for quality control, sorting and grading of agricultural products. Unlike engineering materials, agricultural fruits are living tissues that continue living even after they are harvested from trees or bushes. Therefore, the post-harvest process such as handling and packaging need to be done such that they make the least damage to these products (Barchi et al., 2002). Combination of suitable techniques and post-harvest management is required to bring down the waste loss in this supply chain. Fruits are susceptible to receive mechanical damages during harvesting (either manually or mechanized), or in transport or at the time of initial packaging. These damages may cause damage to the internal tissue of the fruit that subsequently causes the internal substances of the damaged cell leave spread out. While eradicating the fruit, the surrounding fruits are also affected negatively.Mangos are sensitive to mechanical and thermal sudden change (Xing & Baerdemaker, 2005). Today, surface defect detection and grading of many fruits including mangos are still performed in many cases with the help of trained workers which is time consuming and cost effective.. Image processing has been successfully used for measurement and calibration of products; it shows also a good potential to be used for assessing the quality of products (Mata et al, 2012). There has been no or very little research on the quality assessment of mangos based on the dark spots on the skin surface of mango fruits.The aim of this study was to detect and identify surface damage in mangoes of Kelk-e Sorkh cultivar using digital image processing as it has higher accuracy and processing speed as opposed to manual detection.Materials and methods:Mango fruits were picked from a garden in Minab in Hormozgan province, in Iran. Sixty samples were selected for imaging. These samples had black spots on the skin surface due to mechanical damages received during harvesting and handling. The imaging was performed in a homogenously controlled lighting condition(in an imaging box)against a blue sheet as background and at 24°C and 22% RH. The images were taken in visible range with a Nikon Coolpix P510 digital camera (Nikon Inc, Japan) of 4928 x 3264 dimensions (16.1 MP resolution). Considering the camera lens’s focal length, the samples were placed 20 cm under the camera’s lens to be in camera’s field of view. The taken images were read and analyzed in Matlab (Ver 2011a, Mathworks Inc, US). The quality of segmentation process, which is an important step in image processing project, affects the quality of information extracted from the objects or regions of interest (ROIs) in the next steps. The images of mangos were segmented from the background using thresholding of the high contrast images of red and blue difference. The optimum threshold value was obtained to be 0.3. Then, the affected and healthy regions of mangos were specified manually in each image. Then, the color features in two L*a*b* and RGB and HSI color models were extracted from each region on the sample surface.Results and discussion: The statistical analysis of these features showed that the accuracies for detecting the surface defects on mangos were 90% and 91.6% using the color factor of G and 0.16*G/0.5R in RGB color space, respectively. However, from the a* data, only 56 samples were correctly classified as damaged. This showed the classification accuracy of 93.33% using this color parameter. The accuracy reached to 100% when the two color parameters of a* and L* was used as an integrated color parameter of 0.16*L-a*. In L*a*b* color space, the influence of ambient light on the color of samples is trivial and much less than that on RGB. This can be the reason for higher classification error when R, G and B color components, which might be due to non-uniform lighting and the existence of highly bright or highly dark points on the surface of samples. According to USDA standard, the ratio of the size of defect region to the size of whole fruit can be used as an indicator for grading mangos (USDA, 2006). In this research, the k-means clustering was used to group the mangos based on their defect region size. The results showed that the mangos could be classified into three categories of grade 1, with the defect size of less than 5% of the total area, grade 2: when the defect region size was between 5 and 15% and grade 3: when the defect area size was more than 15% and less than 25%. By K-means clustering, the samples were grouped in two clusters. The cut-off point between two clusters was found from the ROC curve to be 3.11. The parameter of ROC area was equal to unity, which indicated the high discrimination capability of the clustering model. Conclusion: In this research, after assessing several color factors and their combinations, four color components of a*, green (G), 2G/R and L*-0.16a* were selected and used for classifying mechanically defected mangos and the results were promising. The results of this research and similar ones can provide helpful recommendations in grading mangos considering the higher capability of Hormozgan province in Iran for producing mangos for fresh consumption, being used in high-quality domestic market, being exported to global markets.
Behnam Fiyuzi; Mostafa Mazaheri Tehrani; Esmail Khazaei
Abstract
Introduction: Due to the lack of proper harvest, packaging, transport and storage, about 30% of country dates production cannot be directly absorbed into the consumer market and must be exchanged to valuable products in transformation industries.Hydrocolloids are used in fruit snack formulations to create ...
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Introduction: Due to the lack of proper harvest, packaging, transport and storage, about 30% of country dates production cannot be directly absorbed into the consumer market and must be exchanged to valuable products in transformation industries.Hydrocolloids are used in fruit snack formulations to create novel texture, increase stability due to their water-holding capacity, improve texture and have an impact on flavor release and other structural and sensory properties in the respective products. Gelatin is a gel forming hydrocolloid. Xanthangum is a kind of long-chained polysaccharide with high molecular weight. LikeXanthan and guar gums can also interact with some polysaccharides such as gelatin, agar and carrageenan synergistically leading to increased viscosity or gel forming power. This type of synergistic behavior among polysaccharides is commercially valuable, because it creates a novel texture and a more desirable structure.This study on viscosity and textural changes caused by using a mixture of gums in food formulations is important and will affect the cost of various stages of the process.Material and methods: The initial materials containing date(Shahani variety), condensed whey(brix=35) were provided from Asali Mod compony, Xanthan gum from Sigma company. powdered glucose and date were bought from Mashhad bazar.In order to produce gel based on date puree, the date were fist washed up, peeled and cut into pieces. Then the pieces were grinded. The prepared puree was mixed with hydrocolloids and sweeteners at 70°C with specific ratios. The mixture was then poured into stainless steel mesh molds with cavity dimensions of 1.2 cm × 2 cm × 2 cm and the molds were kept at 4°C for 2 hours to form the gel. Then the obtained gel was taken out of the mold cavities and the samples were dried at Environmental drier with 1.5 m/s airflow rate. In this research, the produce of jelly viable products based on date puree by the different rates of gelatin hydrocolloids in two levels (6 and 8%) and Xanthan in two levels (0.25 and 0.75%) and condensed whey in three levels (5, 10 and 20%) was studied. Dependent variables were consisting of moisture content, water activity, PH, Brix, protein, ash, texture assessment and color parameters. In final has done the sensory evaluation.To measure pH, pH meter (Hana, Portugal) was used. The measurement of mixture Brix was performed by an optical refract meter (Carlze, Germany).Moisture, protein and ash were measured according to the Iran national standard.In order to determine the water activity of the samples, equal weights of the samples were grinded and the water activity was measured by a aw meter (Testo model 200, England) at 20°C. Texture profile analyzer (QTS25 CNS Farnell England) equipped with a software was used to determine the textural properties of the samples. Samples were compressed and decompressed in two reciprocating cycles by a round plate cylindrical probe with 3.5 cm diameter, 1 mm/s probe speed and 5 g force to 30% initial height. Histological properties obtained from force-deformation curve are as follows: Hardness, Cohesiveness, Elasticity, Adhesiveness and Chewiness.In order to measure color parameters of samples, three samples were choose randomized from each formulation, and pictures were taken with 90 angle and pictures were saved with IPG format. The other stages of picture processing were done by ImageJ 1.40g software.Sensory test was performed with the judgment of 10 trained panelists. In order to evaluate the samples. A 9-point Hedonic method (1: very undesirable - 9: very desirable) was used. 5 sensory attributes (color, texture, flavor, odor and overall acceptance) were evaluated.This study was triplicated through a completely randomized design. Gelatin hydrocolloids in two levels (6 and 8%) and Xanthan in two levels (0.25 and 0.75%) and condensed whey in three levels (5, 10 and 20%)were considered as the independent variables and a design composed of 12 formulations was created. SPSS software was used for the statistical analysis of the parameters. Mean of the replicates were compared via the multi-range Duncan`s test at 95% confidence level.Results and discussion: The obtained results showed that with increasing of condensed whey decrease the PH formulation but ash, moisture, water activity and the rate of protein in samples significantly have increased. Also effect of Xanthan and gelatin had an increase trend in water activity of samples. Also with increasing every three variables in formulation, brix had an increase trend. According to results with increasing hardness gelatin has increased the chewing and continuity feature of texture but adhesion had a decreasing trend. The increasing of Xanthan formulation led to increase of elasticity, context chewing of gum and decreasing of samples. Colorimetric results in method of Image processing were not significant on none of colorimetric variables. But in generally with increasing hydrocolloids in formulation had decreasing the light intensity and L*had a decreasing process. Sensory evaluate shows samples containing of 20% whey have less general acceptance. Also the samples containing of highest percentage of hydrocolloids allocated to themselves the less of rating flavors and aromas.
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.
Amir Jajarmi; Masoud Taghizadeh
Abstract
Introduction: Lime (Citrus aurantifolia L) is belonged to citrus family and has two varieties on the basis of sweet or sour taste; two well known varieties of sour lime are Persian and Key that are cultivated in the southern of Iran. Based on FAO statistics, Iran produced about 615,000 tons of lime in ...
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Introduction: Lime (Citrus aurantifolia L) is belonged to citrus family and has two varieties on the basis of sweet or sour taste; two well known varieties of sour lime are Persian and Key that are cultivated in the southern of Iran. Based on FAO statistics, Iran produced about 615,000 tons of lime in 2010, and is ranked among 10 lime producersinworld wide. Physical properties of fruits are essential information in designing equipments and processes being used in different manufacturing stages such as harvesting, cleaning, sorting and grading, transporting, packaging, as well as estimating of cooling and heating loads during heat transfer processes. Moreover, physical properties affect products acceptability since consumers usually prefer fruits and vegetables with brighter color, appropriate size and uniform shape. Among physical properties, weight, volume and projected area are important parameters for designing sorting equipment.Materials and methods: In this study, lime samples were selected from Key variety which is cultivated in southern of Iran. From the whole, physical properties of about 300 limes were investigated and regression model were developedto estimate weight and volume on the basis of length, width, thickness and projected area.In order to determine the initial moisture, tenlime samples were randomly selected and taken in to hot air oven at 80C for 24 h. The average values of three replicateswerereported.The major dimensions (Length (L), width (W), thickness (T)) were measured using a micrometer with an accuracy of 0.01 mm.Projected area of limes was calculated in three dimensions using image processing technique. Apparent color for the lime samples was also measured in terms of CIE ‘L*’ (lightness), “a*” (redness and greenness) and “b*” (yellowness and blueness), using image processing techniques. Step wise regression was used to develop multivariate models. In this method, the independent variables would enter the equation successively based upon their degree of dependency. In order to estimate weight and volume of lime samples,three category modelswere developed as follow. 1- Regression models based on length, width and thickness. 2-Regression models for predicting weight of lime based on calculated volume. 3- Regression models based on projected area. When there are a large number of variables in the database, it is very likely that subsets of variables are highly correlated with each other.In this study, principle component analysis (PCA)was applied in order to have an accurate and reliable evaluation from existed correlation between physical properties of lime.Result and discussion: The initialmoisture content of limeswas found to be 84.34%.The averaged values of length, width and thickness of limes were35.84, 32.92 and 32.56, respectively. The static coefficient of friction for limes was determined on fourdifferent surfaces namely plywood, galvanized iron sheet, rubber and glass. The glass and plywood showedmaximum and minimum static coefficient of friction respectively. In addition, the sphericity and aspect ratio of lime were found to be 94.32% and 92.18%, respectively. The obtained sphericityvalues were similar to values reported by Sharifi et al., (2007) for orange variety of Tamson and lower than orange varieties of Navel reported by Topuz et al., (2006). The obtained results showed high correlation between three major dimensions and lime’s weight. The predictive models for lime have lower coefficient in comparison with LorestaniandTabatabaeefar(2006) research report forheterogeneous shape of limes. Among regression models for weight prediction of limes, the best model was obtained on the basis of the third projected area with R2of 0.921. The regression models on the basis of calculated volume showed appropriate performance for prediction of lime’s weight. Among regression models on the basis of dimensions, the single parameter model based on lime’s width found to bethe highest coefficient for prediction of volume. Similar toweight prediction, single parameter model on the basis of the third projected area showed the best performance for volume prediction. Conclusion: The results obtained from principle component analysis confirmed the regression models and showed high correlation between physical properties such as projected area, dimension, weight and volume with each other as well as positive correlation with coefficient of friction on the rubber surface and negative correlation on theglass surface.
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.
Amir Pourfarzad; Mohammad Hossein Hadad Khodaparast; Mahdi Karimi; Seyed Ali Mortazavi
Abstract
The effect of adding sodium stearoyl-2-lactylate (SSL) and propylene glycol (PG) (0 - 0.5 g/100g) to emulsifier gel formulation on the crumb and crust characteristics of Barbari bread fortified with soy flour in order to optimize these characteristics were evaluated. The obtained results showed that ...
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The effect of adding sodium stearoyl-2-lactylate (SSL) and propylene glycol (PG) (0 - 0.5 g/100g) to emulsifier gel formulation on the crumb and crust characteristics of Barbari bread fortified with soy flour in order to optimize these characteristics were evaluated. The obtained results showed that addition of SSL caused an increase in the crumb and crust L* and cell density. The a*, b*, average cell size and porosity of bread crumb decreased by increasing SSL. PG had increasing effect on b* of crumb and decreasing effect on L* of crust. However, no significant difference (p ≥ 0.05) was observed in a* and b* of crust. The results for optimization using central composite design suggested that a mixture containing 0.5 g/100g of SSL and 0.5 g/100g of PG could be a proper improver gel to achieve the best characteristics.
Mohammad Reza Amiryousefi; Mohebbat Mohebbi; Faramarz Khodaiyan
Abstract
Analysis of food surfaces is of interest because many processes depend on their complexity. Food surfaces show several textural characteristics related to their nature, composition and processing. Food surface images and their microscopic details need to be translated into numerical data before engineering ...
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Analysis of food surfaces is of interest because many processes depend on their complexity. Food surfaces show several textural characteristics related to their nature, composition and processing. Food surface images and their microscopic details need to be translated into numerical data before engineering analysis. Fractal geometry is a novel concept to describe the complexity of natural shapes. In order to introduce a nondestructive method estimating the effect of process conditions on ostrich meat plates’ surface, in this research an image analysis technique was applied and the concept of fractal dimension was used to quantity the changes. Results show that fractal dimensions of the surfaces decreased with frying. Furthermore, with the increase in frying temperature, frying time and power of microwave pretreatment, a growing procedure in fractal dimension was observed. Fractal dimension as a quantity index could describe the shrinkage of deep-fried ostrich meat as a physical property.
