Sensory characteristics and volatile components in aromas of boiled prawns prepared according to experimental designs |
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| Affiliation: | 1. Department of Nutrition and Food Science, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan;2. Research and Development Division, Kikkoman Corporation, 399 Noda, Noda, Chiba 278-0037, Japan;1. Department of Chemistry and Pharmacy, Emil Fischer Center, Friedrich-Alexander-Universität Erlangen-Nürnberg, Henkestrasse 9, 91054 Erlangen, Germany;2. Department Sensory Analytics, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354 Freising, Germany;3. Agricultural Biochemistry Department, Faculty of Agriculture, Ain Shams University, P.O. Box 68, Hadayek Shobra, 11241 Cairo, Egypt;1. Van Yüzüncü Yıl University, Faculty of Engineering, Department of Food Engineering, Van, Turkey;2. Yıldız Technical University, Chemical and Metallurgical Engineering Faculty, Department of Food Engineering, Istanbul, Turkey;3. Bayburt University, Faculty of Engineering, Department of Food Engineering, Bayburt, Turkey;4. King Abdulaziz University, Faculty of Engineering, Department of Industrial Engineering, Jeddah, Saudi Arabia;5. Van Yüzüncü Yıl University, Faculty of Fisheries, Department of Seafood Processing Technology, Van, Turkey;1. Department of Food Technology, Faculty of Veterinary Medicine, Universidade Federal Fluminense, Niterói, Rio de Janeiro, Brazil;2. Nutrition Institute, Universidade Federal do Rio de Janeiro, Macaé, Rio de Janeiro, Brazil |
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| Abstract: | Comparing samples prepared according to a two-level fractional-factorial design, parts of prawns and pH were selected as essential factors for generating boiled prawn aroma. Aroma characteristics in samples prepared based on a three-level full-factorial design for pH (2, 7, and 12) and parts (shell: S, meat: M, and meat with shell: W) were quantitatively described using 10 attributes. “Sweet”, “cooked fish”, “roasted shrimp” and “boiled prawn” scores were higher in M and W samples at pH 7, but “sewage” scores were higher in all S samples. Response surfaces in statistically significant models obtained for seven attributes clearly visualized how parts and pH influenced them. Partial least squares regression models composed of selected influential peaks for seven attributes were highly predictable. |
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