Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Book Part Citation - Scopus: 6Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue(Wiley, 2010) Korel, Figen; Balaban, Murat ÖmerThe increase in demand for seafood products has catalyzed the desire for higher standards regarding safety and quality issues. Since seafoods are perishable, freshness is a major quality parameter to be considered [1,2]. There is no unique freshness or spoilage indicator for seafood, therefore combinations of selected indicators need to be used to evaluate freshness [3,4]. An important and widely used method to determine freshness is sensory evaluation [5]. The Quality Index Method (QIM) uses a demerit point scoring system [6] based on the evaluation of the important sensory attributes (odour, texture, and appearance) of fish and other aquatic foods. The sensory quality is expressed by the sum of the demerit points, and a linear correlation between these points and the storage time is used to predict the freshness of the target seafood [5,7,8]. The QIM has been developed for various seafood species and products, such as Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus), European sardine (Sardina pilchardus) [9], gilthead seabream (Sparus aurata) [10], farmed Atlantic salmon (Salmo salar) [11,12], and cod (Gadus morhua) [13], etc. Even though QIM is fast and reliable in determining the freshness of seafood, it still requires experts to evaluate the quality attributes. Alternatively, appearance, odour, and taste can be measured by machine vision system (MVS), electronic nose (e-nose), and electronic tongue (e-tongue), respectively.Book Part Citation - Scopus: 10Electronic Nose Technology in Food Analysis(CRC Press, 2016) Korel, Figen; Balaban, Murat Ömer[No abstract available]Article Citation - WoS: 6Citation - Scopus: 10Determination of Volume of Alaska Pollock (theragra Chalcogramma) by Image Analysis(Taylor and Francis Ltd., 2011) Balaban, Murat Ömer; Chombeau, Melanie; Gümüş, Bahar; Cırban, DilşatThe objective of this study was to develop two methods to predict the volume of whole Alaska pollock and to compare the results with the experimentally measured volumes. One hundred fifty-five whole pollock, obtained from a Kodiak processor, were individually immersed in a graduated cylinder equipped with an outflow tube to catch the displaced water as a result of immersion. The weight of the water was recorded. Then the fish were placed in a light box equipped with a digital video camera, and the side view and top view recorded (2 images for each fish). A reference square of known surface area was placed by the fish. A cubic spline method to predict volume by integration of cross-sectional area slices based on the top and side views and an empirical equation using dimensional (length L, width W, depth D) measurements at three locations of the fish image were developed. The R 2 value for the correlation between the L × W × D versus measured volume was 0.987. The best R 2 for the correlation of the predicted volume by the cubic spline method versus the measured volume was 0.99. Image analysis can be used reliably to predict the volume of whole Alaska pollock. © Taylor & Francis Group, LLC.Article Citation - WoS: 6Citation - Scopus: 7Quality Evaluation of Alaska Pollock (theragra Chalcogramma) Roe by Image Analysis. Part Ii: Color Defects and Length Evaluation(Taylor and Francis Ltd., 2012) Balaban, Murat Ömer; Chombeau, Melanie; Gümüş, Bahar; Cırban, DilşatIn the second part of the study of the quality evaluation of pollock roe by image analysis, methods to quantify the color defects (green spots, dark strips, dark color, and uneven coloring due to freezer burn) were developed. Dark roes can be detected by their average L* value. Dark strips can be detected by quantifying the percentage of pixels that have an L* value below an L * threshold. Since there is wide variation among the average colors of the roes, this L * threshold value must be auto-adjusting to the color of the individual roe. Green spots can be detected by their darker color and by ignoring red blood vessels by setting an upper a * threshold. In this study, identifying pixels with L* values less than the L * threshold = 66% of the L * average of the roe, and a* values less than an a * threshold = 20 successfully detected dark strips and green spots. Detection and quantification of uneven color and freezer burn required a smoothing of the roe colors to reduce details. The color primitives method was used, with a setting of a color threshold (CT) = 75. The resulting images were analyzed by setting L * threshold values of 60, 65, 70, 75, 80, and 85% of L * average of individual roes. More surface area of the roe was judged as defective with increasing L * threshold. With proper selection of L * threshold, a * threshold, and CT value, image analysis can accurately quantify the color defects of pollock roe. Practical Application Abstract: Automation of pollock roe sorting by color would streamline the operation, reduce error rates, and help with standardization of quality. Combined with other capabilities of machine vision such as sorting by weight, this technology can be used for multiple purposes simultaneously. © 2012 Copyright Taylor and Francis Group, LLC.Article Citation - WoS: 12Citation - Scopus: 19Quality Evaluation of Alaska Pollock (theragra Chalcogramma) Roe by Image Analysis. Part I: Weight Prediction(Taylor and Francis Ltd., 2012) Balaban, Murat Ömer; Chombeau, Melanie; Gümüş, Bahar; Cırban, DilşatRoe is an important product of the Alaska pollock (Theragra chalcogramma) industry. About 31% of the value for all pollock products comes from roe, yet roe is 5% of the weight of the fish. Currently, the size (weight), color, and maturity of the roe are subjectively evaluated. The objective of this study was to develop methods to predict the weight of Alaska pollock roe based on its view area from a camera and to differentiate between single and double roes. One hundred and forty-two pollock roes were picked from a processing line in a Kodiak, AK plant. Each roe was weighed, placed in a light box equipped with a digital video camera, images were taken at two different angles from one side, then turned over and presented at two different angles again (four images for each roe). A reference square of known surface area was placed by the roe. The following equations were used to fit the view area (X) versus weight (Y) data: linear, power, and second-order polynomial. Error rates for the classification of roes by weight decreased significantly when weight prediction equations for single and double roes were developed separately. A turn angle method, a box method, and a modified box method were tested to differentiate single and double roes by image analysis. Machine vision can accurately determine the weight of pollock roe. Practical Application Abstract: An image analysis method to accurately determine if pollock roe is a single or a double was developed. Then view area versus weight correlations were found for single and double roes that reduced incorrect weight classification rates to half that of human graders. © 2012 Copyright Taylor and Francis Group, LLC.Article Citation - WoS: 57Citation - Scopus: 69Prediction of the Weight of Alaskan Pollock Using Image Analysis(John Wiley and Sons Inc., 2010) Balaban, Murat Ömer; Chombeau, Melanie; Cırban, Dilşat; Gümüş, BaharDetermining the size and quality attributes of fish by machine vision is gaining acceptance and increasing use in the seafood industry. Objectivity, speed, and record keeping are advantages in using this method. The objective of this work was to develop the mathematical correlations to predict the weight of whole Alaskan Pollock (Theragra chalcogramma) based on its view area from a camera. One hundred and sixty whole Pollock were obtained fresh, within 2 d after catch from a Kodiak, Alaska, processing plant. The fish were first weighed, then placed in a light box equipped with a Nikon D200 digital camera. A reference square of known surface area was placed by the fish. The obtained image was analyzed to calculate the view area of each fish. The following equations were used to fit the view area (X) compared with weight (Y) data: linear, power, and 2nd-order polynomial. The power fit (Y = A·XB) gave the highest R2 for the fit (0.99). The effect of fins and tail on the accuracy of the weight prediction using view area were evaluated. Removing fins and tails did not improve prediction accuracy. Machine vision can accurately predict the weight of whole Pollock. © 2010 Institute of Food Technologists®.Article Citation - WoS: 17Citation - Scopus: 20Composition, Color and Mechanical Characteristics of Pretreated Candied Chestnuts(Taylor and Francis Ltd., 2006) Korel, Figen; Balaban, Murat ÖmerRaw-peeled chestnuts were treated with citric acid or sodium metabisulphite, steamed, and dipped into sugar solutions containing dextrose and sucrose, or dextrose and fructose. Composition, mineral content, weight change, rheological properties, and color were measured at each step. Carbohydrate content increased during processing. Candied chestnuts were low in protein (1.31-1.35%) and lipids (0.29-0.78%) but high in carbohydrates (73.48-76.13%). Their mineral concentrations were: Ca 19.08-46.70, Cu 0.19-0.52, Fe 0.88-1.98, K 180.5-659.1, Mg 26.83-69.57, Mn 0.70-2.42, Zn 1.51-6.95 mg/100 g sample. Rheological properties were affected by processing steps. Dipping into sugar solutions did not affect rheological properties. Color changes were quantified, and average L*, a*, and b*values measured.Article Citation - WoS: 8Citation - Scopus: 12Odor Evaluation of Shrimp Treated With Different Chemicals Using an Electronic Nose and a Sensory Panel(Taylor and Francis Ltd., 2007) Luzuriaga, Diego A.; Korel, Figen; Balaban, Murat ÖmerAn electronic nose with 12 conducting polymer sensors was used to measure odors of raw shrimp treated with different chemicals. Headless shell-on pink shrimp (Pandalus jordani) were treated with bleach (0, 25, 50, 100 and 200 ppm), phosphates (0, 2, 4 and 6% w/v) and sulfites (0, 0.75, 1.25 and 2% w/v) and stored at 2°C for 48 hours. Odors were evaluated by sensory panels and an electronic nose. Aerobic plate counts were performed. Discriminant function analysis was used as the pattern recognition technique to differentiate samples based on odors. Results showed that the electronic nose could discriminate differences in odor due to chemicals present in shrimp. The correct classification rates for bleach, phosphate and sulfite treated shrimp were 92.7, 95.8, and 99.2%, respectively.