PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7645
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Article Citation - WoS: 13Citation - Scopus: 13Authentication of Turkish Olive Oils by Using Detailed Pigment Profile and Spectroscopic Techniques(John Wiley and Sons Inc., 2020) Uncu, Oğuz; Uncu, Oğuz; Özen, Banu; Özen, Fatma Banu; Tokatlı, Figen; Tokatlı, FigenBACKGROUND Minor compounds of olive oil could have discriminatory characteristics in the authentication of this product. It was aimed to determine the detailed pigment profiles of Turkish olive oils and use them in differentiation of the samples in comparison to fast, reliable, and environmentally friendly Fourier-transform infrared (FTIR) and ultraviolet (UV)-visible spectroscopic techniques. Pigment contents of 91 olive oils obtained from different locations for two consecutive harvesting years were determined with chromatographic analysis and FTIR and UV-visible spectra of these samples were also obtained. All data were analyzed with orthogonal partial least-squares discriminant analysis to investigate the differentiation ability of these methods with regard to their detailed pigment and spectroscopic profiles. RESULTS Pheophytin a (2.78-8.98 mg kg(-1)) and lutein (1.19-4.07 mg kg(-1)) were the major pigments in all samples. Pigment profiles provided successful classification of olive oils with respect to their designated origins and harvesting year with average correct classification rates of 97%. UV-visible spectroscopy has quite similar results with pigment profiles in terms of its discriminatory power. In addition, FTIR and fused data were slightly better in discrimination of the samples, and the fused dataset has the highest correct classification rate of 100%. CONCLUSION Use of detailed pigment profiles is quite promising in authentication of olive oils. However, UV-visible and FTIR spectroscopic techniques could be reliable alternatives for the same purposes. All of the techniques studied have great potential in 'protected designation of origin' certification studies. (c) 2020 Society of Chemical IndustryArticle Citation - WoS: 14Citation - Scopus: 17Phenolics Profile of a Naturally Debittering Olive in Comparison To Regular Olive Varieties(John Wiley and Sons Inc., 2014) Aktaş, Ayşe Burcu; Özen, Banu; Tokatlı, Figen; Şen, İlknurBACKGROUND: Hurma, an olive variety that grows in a specific area in Turkey, loses its bitterness before harvesting, and therefore does not need further processing steps for the production of table olives. The total phenol content and phenolic profiles of (1) this naturally debittered olive type, Hurma; (2) the same olive variety, but not a naturally debittered type, Erkence; and (3) another variety, Gemlik, which is commonly consumed as table olive, were determined during their maturation period for two harvest years. RESULTS: The total phenol content of Hurma is the lowest compared to the other types regardless of harvest year, which has a significant effect on the phenolic content and composition of individual components for all olive types. All three olive types can be differentiated from each other especially during the late phase of maturation using the phenolics profile in combination with principal component analysis. CONCLUSION: The natural debittering phenomenon of Hurma olive on the tree involves a decrease in phenol content and a change in phenol composition. The differentiation in phenol composition especially becomes very significant in the late of period of maturation.Article Citation - WoS: 8Citation - Scopus: 9Modeling of Polygalacturonase Enzyme Activity and Biomass Production by Aspergillus Sojae Atcc 20235(Springer Verlag, 2009) Tokatlı, Figen; Tarı, Canan; Ünlütürk, Mehmet; Göğüş, NihanAspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R 2 of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 value of 0.83, whereas the regression models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 of 0.69.