Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Department: search.filters.department.Thesis (Master)--İzmir Institute of Technology, Food EngineeringSubject: search.filters.subject.Chemometrics

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Now showing 1 - 3 of 3
  • Master Thesis
    Year-To Differentiation of Black Tea Through Spectroscopic and Chemometric Analysis
    (01. Izmir Institute of Technology, 2024) Özen, Fatma Banu; Özen, Fatma Banu; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Bu çalışmada, hasat yılının siyah çayın çeşitli spektral profilleri üzerindeki etkisi incelenerek, iki hasat yılından siyah çay örneklerinin ayırt edilmesi amaçlanmıştır. Her metodolojinin avantajlarından yararlanılarak, orta kızılötesi, UV-görünür ve floresan spektroskopisinin yetenekleri, çok değişkenli istatistiksel yöntemlerle birlikte kapsamlı bir şekilde incelenmiştir. Değişen iklim koşulları ve mevsimsel döngüler nedeniyle, çay gibi gıda ürünlerinin bileşimi bir hasat yılından diğerine daha değişken hale gelmiş ve genel ürün kalitesini etkilemiştir. Bu nedenle, 2021 ve 2022'de hasat edilen 205 çay örneğini incelemek için orta kızılötesi, UV-görünür ve floresan spektroskopisi kullanılmıştır. Orta kızılötesi spektrumlar hem demlenmiş hem de toz örnek formları için toplanırken, yalnızca demlenmiş örnekler diğer spektroskopik yöntemler kullanılarak analiz edilmiştir. Örnekleri hasat yılına göre sınıflandırmak için PLS-DA, OPLS-DA ve SIMCA modelleri geliştirilmiştir. Orta kızılötesi verilere dayanan modeller toz örnekler için %93,33 ve aşılanmış örnekler için %90,33 doğru sınıflandırma oranlarına ulaşmıştır. Ek olarak, floresan ve UV-görünür spektral veriler sırasıyla %98,3 ve %100 başarı oranlarıyla oldukça doğru sonuçlar vermiştir. SIMCA diğer çok değişkenli yöntemlerle karşılaştırıldığında daha düşük performans gösterse de bulgular spektroskopik tekniklerin kemometrik yaklaşımlarla entegre edilmesinin siyah çayı farklı yıllarda etkili bir şekilde izleyebileceğini böylece, çay üretiminde kalite kontrol ve sınıflandırma süreçlerinin iyileştirilmesine katkıda bulunabilir.
  • Master Thesis
    Chemical Characterization of Olive Oils From Karaburun Peninsula
    (Izmir Institute of Technology, 2014) Uncu, Oğuz; Özen, Fatma Banu; Uncu, Oğuz; Özen, Fatma Banu; 03.08. Department of Food Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    Chemical characteristics of olive oils produced from Erkence olive variety that is mainly grown around Karaburun Peninsula of İzmir have not been investigated thoroughly although this variety has high oil content and ripens earlier compared to other olive types. Identifying the chemical characteristics of olive oils could be useful to obtain geographical indication labelling for olive oils produced from this variety. Aim of this study is to determine some important chemical characteristics of olive oils from Erkence olive variety produced in Karaburun region and to investigate the differences in olive oils that come from various parts of the Peninsula using chemometric techniques as principal component analysis (PCA) and partial least square (PLS) regression. For this purpose, total phenolic content, fatty acid profile, phenolic profile, total carotene and chlorophyll contents and oxidative stability of 64 olive oils were determined. FTIR spectra for these oils were also evaluated. According to PCA results, classification with respect to geographical origin was relatively more successful with FTIR analysis while phenolic and fatty acid profiles did not result very satisfactory separation between regions. Moreover, FTIR spectra and various chemical parameters were used to predict oxidative stability of all olive oil samples. Oxidative stability was predicted successfully from IR spectra whereas prediction from chemical parameters was not that successful. IR spectra were also used to predict various chemical parameters. As a result of PLS regression; chlorophyll and carotenoid, some individual phenolic components (pcoumaric, hydroxtyrosol) and some major fatty acids (oleic, linoleic and palmitic) were predicted.
  • Master Thesis
    Chemometric Studies for Classification of Olive Oils and Detection of Adulteration
    (Izmir Institute of Technology, 2008) Gürdeniz, Gözde; Özen, Fatma Banu; Özen, Banu; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    The aim of this study is to classify extra-virgin olive oils according to variety, geographical origin and harvest year and also to detect and quantify olive oil adulteration. In order to classify extra virgin olive oils, principal component analysis was applied on both fatty acid composition and middle infrared spectra. Spectral data was manipulated with a wavelet function prior to principal component analysis. Results revealed more successful classification of oils according geographical origin and variety using fatty acid composition than spectral data. However, each method has quite good ability to differentiate olive oil samples with respect to harvest year.Middle infrared spectra of all olive oil samples were related with fatty acid profile and free fatty acidity using partial least square analysis. Orthogonal signal correction and wavelet compression were applied before partial least square analysis.Correlation coefficient and relative error of prediction for oleic acid (highest amount fatty acid) were determined as 0.93 and 1.38, respectively. Also, partial least square regression resulted in 0.85 as R2 value and 0.085 as standard error of prediction value for free fatty acidity quantification.In adulteration part, spectral data manipulated with principal component and partial least square analysis, to distinguish adulterated and pure olive oil samples, and to quantify level of adulteration, respectively. The detection limit of monovarietal adulteration varied between 5 and 10% and R2 value of partial least square was determined as higher than 0.95. Hazelnut, corn-sunflower binary mixture, cottonseed and rapeseed oils can be detected in olive oil at levels higher than 10%, 5%, 5% and 5%, respectively.