UTILIZATION OF FLAVOR NETWORK ANALYSIS FOR A NEW RECIPE IN MARMARA REGION

Yıl 2019, , 396 - 408, 15.05.2019

Bengu Ozturk , Burcak Zeyrekce

https://doi.org/10.15237/gida.GD19012

Öz

Natural flavor compounds give
natural taste and odor characteristics to the food ingredients. According to
food pairing theory, ingredients that contain higher number of shared flavor
compounds go well together in a dish. In this study, flavor network analysis
was used to create a new food in Marmara Region by evaluating the ingredient
pairs based on number of shared compounds. A new dessert with four main
ingredients, rice, milk, bean and figs that shared higher number of flavor
compounds was formulated. Among the flavour compounds, eight of them were
common in all four ingredients and they interestingly had similar taste and
odor characteristics which showed how flavor pairing worked well in design of
new dish. According to 9-point hedonic scale of consumer preference test, 80%
of 20 panelists extremely liked the dish. Knowledge on flavour science and food
pairing theory will pave the way to create highly preferable food formulations.

Anahtar Kelimeler

Network analysis, flavor compounds, new recipe, sensory characteristics, Marmara region

MARMARA BÖLGESİNDE YENİ BİR TATLI TARİFİ İÇİN LEZZET BİLEŞİKLERİ AĞ ANALİZİNİN KULLANIMI

Öz

Doğal lezzet bileşikleri gıda malzemelerine karakteristik
tat ve koku özelliklerini vermektedir. Gıda eşleştirme teorisine göre, yüksek sayıda
ortak lezzet bileşiği içeren gıda malzemeleri birbiriyle uyum içerisinde güzel
tat veren bir yemek oluşturabilir. Bu çalışmada, Marmara Bölgesi’nde yeni bir
tarif oluşturmak için, malzemeler içerdikleri ortak bileşik sayısına göre
değerlendirilip tat bileşikleri ağ analizi metodu kullanıldı. Yüksek sayıda
ortak bileşik içeren eşleştirmelerden pirinç, süt, kuru fasulye ve incir
malzemelerini ihtiva eden yeni bir tatlı geliştirildi. Ortak bileşiklerden
sekiz tanesinin dört malzemede de bulunduğu ve şaşırtıcı bir şekilde birbiriyle
benzer tat ve koku maddeleri içerdiği görüldü ki bu da lezzet eşleştirme
teorisinin bu bölgedeki yeni bir tarif için kullanılabildiğini göstermektedir.
9-noktalı hedonik skala testine göre, 20 panelistin %80’i ‘Fevkalade beğendim’
seçeneğini işaretlemiştir. Lezzet bilimi ve gıda eşleştirme teorisinin
bilinmesi, tüketimi çok tercih edilen yeni gıda formülasyonlarının
geliştirilmesi imkânını bize verebilecektir.

Anahtar Kelimeler

Ağ analizi, lezzet bileşikleri, yeni tarif, duyusal özellikler, Marmara Bölgesi

Kaynakça

• Ahn, Y., Ahnert, S. E., Bagrow, J. P., Barabâsi, A. (2011). Flavor network and the principles of food pairing. Sci Rep, 1(196): 1-7.
• Ahnert, S.E. (2013). Network analysis and data mining in food science: the emergence of computational gastronomy. Flavour, 2(4): 1-3.
• Akkor, M. Ö. (2009). Bursa Mutfagi, Türkiye İş Bankasi Yayinlari, Istanbul.
• Barabási, A., Gulbahce, N., Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nat Rev Genet, 12(1): 56–68.
• Bayrak, M. F. (2015). Soframda Anadolu Marmara Yemekleri. Alfa Basim Yayim Dağitim San. ve Tic. Ltd. Sti., Istanbul.
• Blumenthal, H. (2009). The Fat Duck Cookbook, UK Ed. Bloomsbury Publishing PLC, London, United Kingdom.
• Bogojeska A., Kalajdziski S., Kocarev L. (2016). Processing and analysis of Macedonian cuisine and its flavours by using online recipes. In: Loshkovska S., Koceski S. (Eds), ICT Innovations 2015. Advances in Intelligent Systems and Computing, vol 399, (pp 143-152). Springer, Cham, Switzerland.
• Borgatti, S.P, Mehra, A., Brass, D.J., Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916): 892-895.
• Bozis, S. (2002). Istanbul lezzeti. (2nd Ed.). Tarih Vakfi Yurt Yayinlari, Istanbul.
• Burdock, G.A. (2009). Fenaroli’s handbook of flavor ingredients. (6th Ed.). CRC Press, Bosa Roca, United States.
• Dornenburg, A. and Page, K. (2008). The Flavor Bible. (2nd Ed.). Litte, Brown and Company, Hachette Book Group, New York.
• Dunne, J. A., Williams, R. J., Martinez, N. D. (2002). Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol Lett, 5(4): 558–567.
• Ergün Öztürk, Ö. and Öztürk, B. (2018). An ontology based Semantic Representation for Turkish Cuisine.26th IEEE Signal Processing and Communications Applications Conference, 1-5 May, Cesme- Izmir, Turkey.
• Geissbauer, R., Vedso, J., Schrauf, S. (2016). Industry 4.0: Building the digital enterprise. PWC. 2016 Global Industry 4.0 Survey.
• Ghaswala, D., Kundalia, H., Shah, N. (2018). Bon vivant: an artificial intelligence cooking app. Int J of Sci Eng, 3(1): 170-174.
• Habibi, I., Emamian, E. S., Abdi, A. (2014). Quantitative analysis of intracellular communication and signaling errors in signaling networks. BMC Syst Biol, 8(89): 1-15. Halici, N. (1990). Türk Mutfagi, Güven Matbaasi, Ankara.
• Higgins, K.T. (2017). Artificial intelligence and other advances in industrial baking. Food Processing, https://www.foodprocessing.com/articles/2017/artificial-intelligence-industrial-baking/Accessed 20 July 2018.
• Jain, A., Rakhi N. K., Bagler, G. (2015). Analysis of food pairing in regional cuisines of India. PLoS ONE, 10(10): 1-17.
• Kort, M., Nijssen, B., van Ingen-Visscher, K., Donders, J. (2010). Food pairing from the perspective of the ‘volatile compounds in food’ database. I.Blank, M.Wüst, C.Yeretzian (Eds.), Expression of Multidisciplinary Flavour Science: Proceedings of the 12th Weurman Symposium, Interlaken, Switzerland, Institut of Chemistry and Biological Chemistry,Winterthur, pp.589-592.
• Kutup, N. (2017). Network science, flavor ingredient compounds network and the birth of digital gastronomy. Apelasyon, 43: 1-14.
• McNamara, C. (2017). Digitalization: The future of food and beverage. Food Processing. https://www.foodprocessing.com/articles/2017/digitalization-the-future-of-food-and-beverage/Accessed 20 July 2018.
• Mizrahi, M., Gruber, R., Golan, A., Lachnish, A.Z., Mizrahi, A.B., Zoran, A. (2016, October). Digital gastronomy: methods & recipes for hybrid cooking. 29th Annual Symposium on User Interface Software and Technology, 16-19 October, Tokyo, Japan, pp. 541-552.
• Mouritsen, O. G., Edwards-Stuart, R., Ahn, Y-Y., Ahnert, S. E. (2017). Data-driven methods for the study of food perception, preparation, consumption, and culture. Frontiers ICT 4:15.
• Pimentel, T.C., Gomes da Cruz, A., Deliza, R. (2016). Sensory evaluation: sensory rating and scoring methods. The Encyclopedia of Food and Health, 4: 744-749.
• Pinel F., Varshney L.R.,Bhattacharjya D. (2015). A Culinary Computational Creativity System. In: Besold T., Schorlemmer M., Smaill A. (Eds), Computational Creativity Research: Towards Creative Machines. Atlantis Thinking Machines, (pp 327-346). Atlantis Press, Paris.
• Prescott, J. (2015). Multisensory processes in flavour perception and their influence on food choice. Curr Opin Food Sci, 3: 47-52.
• Reineccius, G. (2006). Flavor chemistry and technology, (2nd Ed.). Taylor & Francis Group, LLC, Boca Raton, FL, USA.
• Shrinivas, S.G., Vetrivel, S., Elango, N.M. (2010). Applications of graph theory in computer science an overview. Int J Eng Sci Technol, 2(9): 4610-4621.
• Şengül, S., Çakir, A., Çakir, G. (2015). Yöresel Mutfaklar. Beta Basim Yayim Dağitim A.Ş. Ankara.
• Taylor, A. and Hort, J. (2007). Modifying flavor in food. CRC Press LLC, Boca Raton, FL, USA.
• Toroa, C., Barandiarana, I., Posada, J. (2015). A perspective on knowledge based and intelligent systems implementation in Industrie 4.0. Procedia Comput Sci, 60: 362-370.