Master Degree / Yüksek Lisans Tezleri

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Access type: Open accessInstitution Author: search.filters.institutionAuthor.Avcı, Başak

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  • Master Thesis
    Effect of Deep-Frying on the Quality Parameters of Vegetable Oils
    (Izmir Institute of Technology, 2015) Avcı, Başak; Özen, Fatma Banu; Tokatlı, E.Figen; Özen, Fatma Banu; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    It was aimed to determine the changes in several chemical parameters, fatty acid composition and phenolic profiles of different types of olive oils (extra virgin, virgin, blended, refined) and sunflower oil in the deep-frying process. In addition, UV and FTIR spectroscopic data were collected during frying and they (UV, FTIR and the combination of FTIR and UV) were analyzed with multivariate statistical techniques (PCA, OPLS-DA and OPLS) to classify the oil samples and predict the chemical parameters. A domestic deep-fat electrical fryer was used to fry french fries for 4 minutes at 180ºC ten times without oil replenishment and oil samples were removed after 1st, 2nd, 3rd, 4th, 5th, 7th and 10th frying. Free fatty acid, peroxide value, K232, K270, total chloropyll and carotenoid contents, color, oxidative stability, total phenol content, total polar compounds, and fatty acid profiles of all oil samples and phenolics of extra virgin and virgin olive oils were determined. At the end of the tenth frying, no significant changes were detected in acidity, stability, and fatty acid compositions. Phenolic contents of extra virgin and virgin olive oils decreased after the fourth frying cycle. According to PCA and OPLS-DA results, UV spectra was able to discriminate oil samples. Chemical parameters, except peroxide value, were predicted with high accuracy by spectroscopic data. The fatty acids were predicted successfully with FTIR data, while the total phenol content was predicted by UV data with a higher R2 value. The combination of FTIR and UV spectra predicted the rest of the chemical measures in OPLS regression with a high accuracy.