Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 2Citation - Scopus: 4Identification of Turkish Extra Virgin Olive Oils Produced in Different Regions by Using Nmr (h-1 and C-13) and Irms (c-13/C-12)(Wiley, 2023) Sevim, Didar; Köseoğlu, Oya; Ertaş, Hasan; Özdemir, Durmuş; Ulaş, Mehmet; Günnaz, Salih; Çelenk, Veysel UmutIsotope ratio mass spectroscopy (IRMS) and nuclear magnetic resonance (NMR) spectroscopy techniques are two of the analytical methods that are used to characterize food products. The aim of this study is to classify extra virgin olive oil (EVOO) samples collected from different regions of Turkey based on H-1 and C-13 NMR spectra along with IRMS d(13)C carbon isotope ratio data by using chemometrics multivariate data analysis methods. A total of 175 EVOO samples were analyzed in 2014/15 and 2015/16 harvest seasons. Multivariate classification and clustering models were used to identify geographical and botanical origins of the EVOOs. IRMS results showed that there was no significant difference in terms of d(13)C values between the years in terms of harvest year (p > 0.05), only extraction phase and variety were statistically significant factors (p < 0.05). The interactions of the factors showed that the harvest year x variety interaction is important. The outcomes of this research clearly indicated that considering the partial least squares discriminant analysis result with NMR spectra, the percent success of the model in the South Marmara, North Aegean, and South Aegean region samples were 95%, 95.7%, and 96.4% in the model set, respectively. The results showed that by using classification and clustering models, geographic marking and labeling of these oils can be carried out regardless of differences in year and production systems (2 and 3 phase extraction system) according the NMR analysis.Article Citation - WoS: 10Citation - Scopus: 12Determination of the Quality and Purity Characteristics of Olive Oils Obtained From Different Regions of Turkey, Depending on Climatic Changes(Wiley, 2022) Sevim, Didar; Köseoğlu, Oya; Özdemir, Durmuş; Hakan, Mehmet; Büyükgök, Elif B.; Uslu, Hatice; Dursun, ÖzgürVirgin olive oils (VOOs) obtained from olives grown in different regions of Turkey under changing climatic conditions sometimes show different sensory and chemical properties. This study was planned to determine whether these deviations are due to climatic changes or not. For this purpose, five different olive varieties (Ayvalik, Memecik, Gemlik, Nizip Yaglik, Kilis Yaglik) of commercial importance were harvested from the provinces/districts (four different region) where cultivation is intense during the 2017/2018-2020/2021 harvest years. Every year, olive samples were collected from 3 orchards from 13 provinces/districts. One hundred and fifty-six samples were subjected to the purity, quality and sensory analysis. Basic climatic values (average, minimum and maximum temperature, humidity and precipitation) were examined for four consecutive years. All of the examined olive oil samples were determined within the legal limits in terms of fatty acid composition and fatty acid ethyl ester values. However, delta-7-stigmastenol value from the sterol composition was found to be above 0.5% in some samples in all the years studied (total 21 samples). Delta-7-stigmastenol values of olive oil samples varied between 0.16% and 1.14%. Multiple linear regression analysis was applied using a genetic algorithm-based inverse least squares method to determine whether there is a relationship between climate data and delta-7-stigmastenol values. According to this result, it has been determined that the delta-7-stigmastenol value is high when the annual average relative humidity is low and the annual average temperature is high. There is an urgent need to make forward-looking plans due to climate change.Article Citation - WoS: 43Citation - Scopus: 48Determination of Honey Adulteration With Beet Sugar and Corn Syrup Using Infrared Spectroscopy and Genetic-Algorithm Multivariate Calibration(Wiley, 2018) Başar, Başak; Özdemir, DurmuşBACKGROUND Fourier transform infrared spectroscopy (FTIR) equipped with attenuated total reflectance accessory was used to determine honey adulteration. Adulterated honey samples were prepared by adding corn syrup, beet sugar and water as adulterants to the pure honey samples in various amounts. The spectra of adulterated and pure honey samples (n = 209) were recorded between 4000 and 600 cm(-1) wavenumber range. RESULTS CONCLUSION Genetic-algorithm-based inverse least squares (GILS) and partial least squares (PLS) methods were used to determine honey content and amount of adulterants. Results indicated that the multivariate calibration generated with GILS could produce successful models with standard error of cross-validation in the range 0.97-2.52%, and standard error of prediction between 0.90 and 2.19% (% w/w) for all the components contained in the adulterated samples. Similar results were obtained with PLS, generating slightly larger standard error of cross-validation and standard error of prediction values. The fact that the models were generated with several honey samples coming from various different botanical and geographical origins, quite successful results were obtained for the detection of adulterated honey samples with a simple Fourier transform infrared spectroscopy technique. Having a genetic algorithm for variable selection helped to build somewhat better models with GILS compared with PLS. (c) 2018 Society of Chemical Industry