Food Engineering / Gıda Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/12
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Article Citation - WoS: 11Citation - Scopus: 13Capsaicin Emulsions: Formulation and Characterization(Taylor and Francis Ltd., 2017) Akbaş, Elif; Söyler, Betül; Öztop, Mecit HalilOleoresin capsicum, the oil extract of chili pepper, is mainly composed of capsaicin. Capsaicin is a hydrophobic volatile compound exhibiting antimicrobial activity against various microorganisms. Capsaicin in the form of an emulsion-based carrier system could be a good alternative to enhance bioavailability and simultaneously to increase the shelf-life of food. In this study, capsaicin emulsions were formulated using three different surfactants (Tween 80, commercial soy lecithin, and sucrose monopalmitate/SMP). Effects of aqueous phase composition, pH, and heating the pre-homogenized dispersion were investigated. For characterization, NMR relaxometry, color, turbidity, and antioxidant activity experiments were conducted. Antimicrobial efficacies of the emulsions were also evaluated against Escherichia coli andStaphylococcus aureus. Mean particle sizes of emulsions with surfactants Tween 80, lecithin, and SMP were found to be 68.30, 582.63, and 50.10 nm, respectively. Lecithin-containing emulsions showed the highest antimicrobial activity against S. aureus with 4.60 log reduction, whereas the same effect was observed in Tween 80-containing emulsions against E. coli with 3.86 log reduction. Emulsions prepared with SMP showed the highest antioxidant activity with 0.482 mg DPPH/L emulsion. The formulated emulsions have the potential to be used in food industry as antimicrobial food grade solutions.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: 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.