Shima Nasiri; Saman Abdanan; Mokhtar Heidari
Abstract
Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, ...
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Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, “juiciness”, “hardness”,“firmness” and “mealiness” are often key drivers of consumer preference. Many non-destructive methods, including image analysis, spectroscopy, ultrasound and sound techniques, have been developed to diagnose internal and external defects in fruits and vegetables. Cheng and Haugh (1994) used a frequency of 250-kHz, rather than 1-MHz, to detect hollow heart. They were not able to transmit successfully the ultrasound wave through the whole tuber using 1-MHz transducers but found the 250-kHz transducers to be practical for a transmission path length of up to 89.7 mm. In a research an acoustic setup was developed to simultaneously detect the resonant frequencies from equator and from calyx shoulder of pear. The researchers proposed index based on these two frequencies was used for firmness evaluation of non-spherical pear; Compared with two types of single frequency-based indices, the firmness sensitivity of the dual-frequency index is mostly close to that of MT penetration test. The firmness index can classify pears with a high total accuracy (93.4%), making it suitable for nondestructive detection of firmness of differently shaped pears (Zhang et al., 2018). The goal of this study was to develop a nondestructive method based on acoustic impulse response of quince fruit using genetic programming and artificial neural network during storage. Materials and Methods: In the experiment 120 quince fruits (Cydonia oblonga) were harvested from a field near Isfahan 181 days after full flowering of the trees. For each cultivar, only samples of similar size and without visible external damage were chosen. The samples were packed in sterile nylon bags and stored at 4°C. Non-destructive test (acoustic response) as well as destructive test (chemical measurement and penetration test) were performed every 15 days for 4 months (Akbari Bisheh et al., 2014). Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan) and expressed as °Brix. Ascorbic acid of the juice was measured by titration with copper sulfate and potassium iodide based on the Barakat et al. (1973) procedure. Titratable acidity was measured according to the AOAC method. To determine the total phenol content of juice, the Waterhouse method (2000) was used. Determination of the pH of the fruit extract using a pH meter (Portable Model P-755, Japan). Physical attributes of the samples including volume as well as major, minor, intermittent diameters and mass were calculated using the relations proposed by Stroshine and Hammand (1994). Penetration test was conducted by the material test machine (SANTAM, STM-20 model, Iran).In order to analyze the response sound signal of quince in time and frequency domain, a system equipped with a sample holder with foam rubber covered surface, an impact mechanism, a microphone and an electronic circuit was utilized. To record impact sound features a microphone was positioned next to the fruit and was hit at three speed level (0.3, 0.9 and 1.5 m/s). After recoding sound, five features (acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency) were extracted and used as inputs for models. In order to predict the stiffness, four methods of genetic programming, neural network and existing mathematical models (FI and SIQ-FT) were used. In order to carry out statistical analysis, analysis of variance (ANOVA) and Duncan's multiple range test at 5% probability level were performed according to the completely randomized design (CRD). Results and discussion: In this study, Duncan's multiple range comparison test was used to investigate the significant difference between destructive and non-destructive parameters at 5% probability level. According to the results, acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency were decreased by increasing storage time. Statistical analysis of the destructive tests also showed a decreasing trend at the 5% level. In several papers, two mathematical equations have been used to obtain the relationship between the mass resonance frequency and the sound of impact. In this study, genetic programming and neural network modeling were used to compare the results of these relationships. The regression coefficients between the actual and the predicted values for the resonance-mass relation and the effect of the sound from the collision were R2= 0.601 and R2= 0.754, respectively. Also, the regression values obtained from genetic programming and neural network modeling were R2= 0.9567 and R2 = 0.933, respectively. In a research, the overall R2 value amounts for stiffness prediction was reported to be 0.79 (Schotte et al., 1999). Abbaszadeh et al. (2013) evaluated watermelons texture using their vibration responses. They declared their proposed method could predict textural acceptability of watermelons with determination coefficients 0.99. According to the obtained values, the best methods for stiffness prediction were genetic programming and f neural network methods, respectively.
Shima Nasiri; Saman Abdanan; Maryam Nadafzadeh
Abstract
Introduction: The development of brown spots on banana peel has a notable effect on the texture, color and taste of this fruit. So that the appearance of these spots reduces the quality of the fruit and affect its sale market. In recent years, in order to evaluate the quality and classification of agricultural ...
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Introduction: The development of brown spots on banana peel has a notable effect on the texture, color and taste of this fruit. So that the appearance of these spots reduces the quality of the fruit and affect its sale market. In recent years, in order to evaluate the quality and classification of agricultural products, the various systems based on computer vision technology have been widely considered. These systems as the computer image analysis methods have been successful in measuring the visual quality of different products (Riyadi et al., 2007; Roseleena et al., 2011; Rodriguez-pulido et al. 2012). According to research by Probha and Kumar (2015), the extracted color properties from the banana image were more effective than other features in identifying the different stages of the banana ripening. Also, Mendoza and Aguilera (2004) detected the different stages of banana ripening based on the color, texture parameters and the distribution of brown spots on banana peel using image processing technique with a precision of 98%. Nadafzadeh et al. (2018) designed a non-linear mathematical model using the Genetic Programming (GP) to predicting and evaluating the activity of polyphenol oxidase enzymes (PPO) and peroxides (POD) during the browning process of the banana peel; using the extracted parameters from image as inputs of proposed model, the correlation coefficients to predicting of PPO and POD enzymes were obtained 0.98 and 0.97, respectively.The aim of this study was to investigate the changes of color, dimensions and chemical parameters of several banana fruit groups (different in terms of appearance) as well as their marketability (the total acceptance of fruit) by Gaussian regression model (GPR) during the storage period. Therefore, using the proposed method in this research, the required product can be available according to the consumer demand. Materials and Methods: In this study, one hundred banana samples were prepared from a market on the first day of the experiments. Samples were different in terms of shape and size, and were classified into 5 different groups. Group A had small size and curvature; B group compared to Group A had more curvature; the curvature of the samples in the group C was high, and in terms of size were medium. While the size of the bananas in group D was large, they had a small curvature. Also, the features of the group E were similar to the group D, but the curvature was greater in this group (group E). All of the samples were kept at the ambient temperature (25° C) away from the direct light for 7 days. During the days of experiments (days 0, 2, 4 and 6), five samples were examined from each group: after taking images of samples under the constant light conditions, and performing of manual measurements, they were subjected to destructive tests (laboratory tests) and sensory tests. After the images acquisition of samples, the preprocessing operations such as image enhancement, noise removal by the area opening, and the implementation of the image segmentation process using the method of Otsu adaptive thresholding were conducted (Gonzalez et al., 2004). Finally, 11 color parameters (R, G, B, L, a, b, h, s, v, C, H) and 4 dimensional characteristics (diameter, curvature radius, long and small length) were extracted from each image. In the laboratory method, the TSS value was measured by a digital refractometer, and amount of pH and acidity were also measured by a fruit juice analysis titrator. Eventually, in order to investigate the changes of measured parameters, statistical analysis was performed in a randomized complete block design by SAS 9.3 software at a significance level of 5% using Duncan's multiple comparison test. Results and discussion: Gradually along with the appearance of dark spots on the banana peel, many of the qualitative parameters such as the color, dimensions and chemical features were changed during the storage period. According to results of the Duncan's multiple range test, the values of color coordinates R, G, B, L, b, h, v, C, and H gradually reduced, and the values of these parameters were significant in all the experiments days (p<0.05). The parameter S also had a decreasing trend during the storage period, and the changes of this parameter was significant in the first days of the experiments compared to the ending days; during this period, the color parameter a increased significantly. Due to the changes of the banana fruit texture, the amount of the curvature radius, the small and large lengths, total soluble solids, pH and total titration acidity gradually decreased. Based on the results of the statistical analysis, there were no significant differences between dimensional parameters measured by non-destructive method and manual measurement (p>0.05). It is worth noting that in this study, the spent time to conduct the manual measurements of the dimensional parameters of a banana sample was 510 seconds, while all of these measurements were performed using a digital image processing method at 1.015 seconds. Therefore, it can be said that when the number of samples is high, using of the proposed method is also very cost-effective in terms of time, and it has high accuracy during the measurement. In the sensory evaluation, the results show that the best and most acceptable group of bananas were groups C, D and E, which had long size and low curvature; these groups of bananas had delicious texture, desirable flavor and low levels of brown spots on their peel. In the following, the non-destructive parameters were used to the development of Gaussian regression model (GPR), and finally, it was shown that the quality of banana fruit as well as its marketability (the total acceptance of fruit) are predictable during the storage period by GPR with a correlation coefficient of 0.91, MAPE (20.47), RMSE (0.43), SRE (0.71) and RAV (0.20).The appearance quality of the banana fruit is very effective in its acceptability for customer. In this research, the image processing technique as a non-destructive method was used to extract a set of color (R, G, B, L, a, b, h, s, v, C and H) and morphological properties (diameter, curvature radius, long length and small length) from banana image in order to evaluate its quality during storage. According to the results of Duncan's statistical analysis at the probability level of 5% and Pearson correlation results, the most suitable parameters were chosen to apply in Gaussian regression model. The results showed that the image processing technique is capable to evaluating the changes of color and dimensional parameters of banana fruit, and also the proposed model have a satisfactory performance (R2=0.91) in predicting the overall acceptance parameter of the banana.
Maryam Soltani Kazemi; Saman Abdanan; Mokhtar Heidari; Seyed Mojtaba Faregh
Abstract
Introduction: 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 ...
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Introduction: 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.
Materials and methods: 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.
Results and discussion: 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
Mehran Nouri; Behzad Nasehi; Vahid Samavati; Saman Abdanan
Abstract
Introduction: Fried foods such as donuts enjoyed worldwide for their taste, distinctive flavor, aroma and crunchy texture. There is, however, grave health concern over large fat content of fried foods (Melito and Farkas, 2013). There are several ways to lower fat content in deep-fried foods. One method ...
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Introduction: Fried foods such as donuts enjoyed worldwide for their taste, distinctive flavor, aroma and crunchy texture. There is, however, grave health concern over large fat content of fried foods (Melito and Farkas, 2013). There are several ways to lower fat content in deep-fried foods. One method is to reformulate the product by adding hydrophilic ingredients such as dietary fibers to reduce oil uptake during frying. Another method to reduce fat content is to partially cook the food using another heating method (Melito and Farkas, 2012). There is an increasing interest in microwaving foods for several reasons: it is faster than conventional methods, the energy consumption is often lower and foods cooked by microwaving maintain nutritional integrity.vIn foods, the appearance is a main criterion in making purchasing decisions. Appearance is used throughout the production –storage-marketing-utilization chain as the key means of judging the quality of individual units of product. The appearance of unities of products could be assessed by considering their color and surface texture. The use of computer-vision technology has quickly increased in the fields of quality inspection, classification and evaluation in processing a large number of food products (Brosnan and Sun, 2004). Therefore the aim of this study was to study the effects of microwave pre-treatment on sensory and appearance properties of donut.
Materials and methods: Response surface methodology and Box- Behnken design were applied to evaluate the effects of independent variable include microwave power (300-900 W), microwave time (30-90 s) and frying time (70-130 s) on sensory and appearance properties of donuts. Donuts were prepared according to the formulation by Melito and Farkas (2012) with some modifications. Ingredients used in donuts formulation were consisted of 100 g of wheat flour (9 g/100g), 52 g of water, 9.75 g of Shortening, 14 g of Egg, 14 g of water for yeast, 6.80 g of sugar, 6.80 g of nonfat dried milk powder, 3.25 g of active dried yeast, 1.70 g of Vanilla extract, 1.7 g of baking powder, 1.70 g of Salt, 1.3 g of Persian gum and 7.00 g of carrot pomace powder. The dough was cut into squares approximately 50 mm on each side. Then, the dough pieces were allowed to proof for 30 min at 27 ºC. The proofed samples were pre-treated using a microwave oven at different levels of microwave power and microwave time in accordance with the experimental design. Formerly, the per-treated donuts were deep-fat fried in a Moulinex deep-fat fryer (model F18-RA, France) filled with 1.5 L of vegetable frying oil (A mixture of Sunflower, palm, and soybean oil; Behshahr CO., Tehran, Iran) at different levels of frying time in accordance with the experimental design. The oil was preheated for 30 min prior to frying and replaced with fresh oil after every frying process. After frying, donuts were removed from the fryer and allowed to cool for 30 min on paper towels. They were then stored in coded sealed polyethylene bags.The evaluation of the crumb grain and crust color of donuts was performed using an image analysis system consisted of a Canon digital camera (model SX60 HS, Japan) and a personal computer with a Pentium(R) Dual-Core processor and Windows 7 Ultimate. The samples were photographed at a fixed distance of 30 cm from the crumb of samples, which were sitting inside a black box. The captured images were analyzed using the MATLAB R2014a software (The MathWorks Inc., Natick, Mass, USA).The CIE L*a*b* (or CIELAB) color model was used for determination of the crust color of donuts. Crumb grain features of the donut samples were obtained with described digital image analysis system. After imaging, each image was converted from RGB format to 8 bits (grey level) using the MATLAB software. In this format, an area of 3 × 3 cm2 was selected at the center of the captured image. After contrast enhancement of image, the image segmented using the Otsu algorithm, which produces highly uniform binary images (Otsu, 1979). Finally, crumb grain properties of donuts were studied by determination of cells densities and area of cells. Sensory evaluation of donut samples was carried out by assigning scores for crust appearance, crumb appearance, crust color, aroma, texture, taste and overall acceptance parameters based on a nine-point hedonic scale. (Stone et al., 2012).
Results and discussion: Results showed that roughness of the donuts surface increased significantly (p
Mehran Nouri; Behzad Nasehi; Vahid Samavati; Saman Abdanan
Abstract
Introduction: Increased awareness of diet-health association has led to the growth of health food industry. Deep-fat fried foods such as donuts enjoy wide popularity owing to their taste, distinctive flavor, aroma and crunchy texture. There is, however, a great health concern over large fat content of ...
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Introduction: Increased awareness of diet-health association has led to the growth of health food industry. Deep-fat fried foods such as donuts enjoy wide popularity owing to their taste, distinctive flavor, aroma and crunchy texture. There is, however, a great health concern over large fat content of fried foods. Incorporating the dietary fiber such as hydrocolloids into the food substrate in the batter formulation is one of the most effective strategies to decrease fat uptake in fried foods. Dietary fibers act as water binders in a coating or batter formulation through which reduce fat uptake of fried foods. That is, an increase of water content of food could lead to a decrease of oil penetration during the frying process. Persian gum (PG), as a novel gum, is exudates of the wild or mountain almond trees (the main source is Amygdalus scoparia Spach). Carrot pomace is a fibre-rich by-product of carrot juice industries which contains approximately 80% of carrot carotenes. Carrot juice yield is reported to be only 60-70% and the remaining pomace is usually disposed of as feed or fertilizer. There is an increasing interest in microwaving foods for several reasons: it is faster than conventional methods, the energy consumption is often lower and foods cooked by microwaving maintain nutritional integrity. Therefore, the aim of this study was to examine the effect of microwave pre-treatment on physico-chemical properties of donut containing Persian gum and carrot pomace powder sources of dietary fiber.
Materials and methods: Donuts were prepared according to the formulation reported by Melito and Farkas (2012). Ingredients used in control donut formulation were consisted of 100 g of wheat flour (9 g/100g), 38 g of water, 9g of Shortening, 13g of Egg, 13g of water for yeast, 6.3g of sugar, 6.3g of nonfat dried milk powder, 3g of active dried yeast, 1.6g of Vanilla extract, 1.6g of baking powder, and 1.6g of Salt. For the making of donuts, the flour blends were prepared by replacing wheat flour with 1.2 g/100g PG and 645 g/100g CPP. As well, water was added at 48.16 g/100g based on flour weight. The exudate gums of mountain almond trees were collected in Lorestan province. In order to eliminate foreign matters such as dust and dirt, the PG was washed three times with its threefold weight of ethanol (96% w/v) for 15 min under constant stirring. After removing ethanol by drying in an oven (at 60º C for 6 h) the PG was ground using a coffee grinder (model 320, Spain), sieved (180 µm) and packaged in polyethylene packs and then stored in 4ºC. Fresh carrots were purchased from a local market. Carrots were washed and then pressed with a juice extractor and the resultant pomace was collected. The carrot pomace was blanched in water (80 ± 2°C for 3 min) and then cooled in cold water (4º C). The pomace water was drained with cheese-cloth prior to drying. Finally, the carrot pomace was dried in an oven (60º C for 12 h). The dried pomace was ground using a coffee grinder to fine powder. The carrot pomace powder was sieved (180 µm) and packed in polyethylene packs and then stored in 4ºC. Specific volume of donuts was determined using the rapeseed displacement AACC method. Moisture content of donuts crumb was measured using a oven at 105 ºC for 3. The fat content of dried donuts was determined by Soxhlet extraction with petroleum ether for 5 h. Firmness and springiness were measured in triplicate using a TA.XT2i Texture Analyzer equipped with a 5 kg load cell and a P/35 mm aluminum cylindrical probe. Crumb grain (total number of cells and porosity) and crumb color of donuts were evaluated using an image analysis system consisted of a digital camera, a personal computer and MATLAB R2014a software. The control and optimized donuts were evaluated for acceptance of their appearance, crust color, crumb color, aroma, texture, taste and overall acceptance based on a nine-point hedonic scale. Response Surface Methodology (RSM) and Box-Behnken design with 3 factors were applied to obtain optimal levels of independent variables including microwave power (300-900 W), microwave time (30-90 s) and frying time (70-130 s).
Results and discussion: The results indicatedthat moisture content significantly (p
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
Saman Abdanan; Somaye Amraei
Abstract
Introduction:.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 ...
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Introduction:.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. Materials and Methods: 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. Results and discussion: 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 Method 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
