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: 2A Novel Approach Utilizing Rapid Thin-Film Microextraction Method for Salivary Metabolomics Studies in Lung Cancer Diagnosis(Elsevier, 2024) Pelit, Fusun; Erbas, Ilknur; Ozupek, Nazli Mert; Gul, Merve; Sakrak, Esra; Ocakoglu, Kasim; Goksel, Ozlem; Özdemir, DurmuşThis study investigated the potential of targeted salivary metabolomics as a convenient diagnostic tool for lung cancer (LC), utilizing a rapid TFME-based method. It specifically examines TFME blades modified with SiO2 nanoparticles, which were produced using a custom-made coating system. Validation of the metabolite biomarker analysis was performed by these blades using liquid chromatography-tandem mass spectroscopy (LCMS/MS). The extraction efficiencies of SiO2 nanoparticle/polyacrylonitrile (PAN) composite-coated blades were compared for 18 metabolites. Response surface methodology (RSM) was used to optimize the analysis conditions. Linear calibration plots were obtained for all metabolites at concentrations between 0.025 to 4.0 mu g/mL in the presence of internal standard, with correlation coefficients (R-2) ranging from 0.9975 to 0.9841. The limit of detection (LOD) and limit of quantitation (LOQ) were in the range of 0.014 to 0.97 mu g mL(-1) and 0.046 to 3.20 mu gmL(-1), respectively. The %RSD values for all analytes were within the acceptable range (less than 20 %) for the proposed method. The method was applied to the saliva samples of 40 patients with LC and 38 healthy controls. The efficacy of metabolites for LC diagnosis was determined by in silico methods and the results reveal that phenylalanine and purine metabolism metabolites (e.g., hypoxanthine) are of great importance for LC diagnosis. Furthermore, potentially significant biomarker analysis results from the ROC curve data reveal that proline, hypoxanthine, and phenylalanine were identified as potential biomarkers for LC diagnosis.Article Citation - WoS: 1Citation - Scopus: 1Comparison of Palynological Method and Chromatographic Analysis Combined With Chemometrics To Identify Botanical Origin of Propolis(Springer, 2023) Güzelmeriç, Etil; Daştan, Tuğçe; Sen, Nisa Beril; Erdem, Özge; Özdemir, Durmuş; Yeşilada, ErdemThere has been a growing trend in consumer’s preferences for food supplements containing propolis due to having a wide range of phenolic compounds to promote health. Honeybees’ used main plant source will determine propolis chemical composition thus its biological activity. Thus, determination of the propolis botanical source is highly important for its standardization and prediction of its pharmacological activity. There are two commonly applied methods to seek propolis botanical sources: chromatographic techniques and palynological analysis. In this study, high-performance thin-layer chromatography (HPTLC) and ultra-high-performance liquid chromatography combined with mass spectrometer (LC-MS/MS) applied comparatively with pollen analysis to propolis samples. The results of the chromatographic analyses were evaluated with principal component analysis (PCA) and hierarchical clustering analysis (HCA). Consequently, chromatographic techniques applied in this study were found to be superior to pollen analysis to identify the main plant source of propolis. Besides, HPTLC images revealed not only main botanical sources but also minor sources of propolis. Therefore, HPTLC fingerprinting combined with PCA and HCA resulted in grouping propolis samples according to geographical regions. This study may lead to pharmaceutical industries for the quality assurance of propolis while preparing standardized propolis formulations in the market with desired pharmacological properties. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Article Citation - WoS: 9Citation - Scopus: 8Quantitative Determination of Phenolic Compounds in Propolis Samples From the Black Sea Region (türkiye) Based on Hptlc Images Using Partial Least Squares and Genetic Inverse Least Squares Methods(Elsevier, 2023) Güzelmeriç, Etil; Özdemir, Durmuş; Şen, Nisa Beril; Çelik, Cansel; Yeşilada, ErdemThe complex chemical composition of propolis is related to the plant source to be used by honeybees. Propolis type is defined based on the plant source with the highest proportion in its composition, which is determined by chromatographic techniques as high-performance thin-layer chromatography (HPTLC). In addition to marker component identification to specify the propolis type, quantification of its proportion is also significant for prediction and reproducible pharmacological activity. One drawback for propolis marker component quantita-tion is that during the chromatographical analysis, not the main but the other plant sources with less proportion may cause interferences during the chemical analysis. In this study, the amounts of marker components were compared with the reference analysis data obtained by high-performance liquid chromatography (HPLC) and from HPTLC images using Partial Least Squares (PLS) and Genetic Inverse Least Squares (GILS) regression methods. Firstly, HPTLC images of propolis samples were processed by an image algorithm (developed in MATLAB) where the bands of each standard and the samples were cut same dimensional pieces as 351 x 26 pixels in height and width, respectively. Simultaneously, reference analysis of the marker components in propolis samples was performed with a validated HPLC method. Consequently, the reference values obtained from HPLC versus PLS, and GILS predicted values of the eight compounds based on the digitized HPTLC images of the chromatograms were found to be matched successfully. The results of the multivariate calibration models demonstrated that HPTLC images could be used quantitatively for quality control of propolis used as a food supplement.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: 28Citation - Scopus: 33Rapid Detection of Green-Pea Adulteration in Pistachio Nuts Using Raman Spectroscopy and Chemometrics(John Wiley and Sons Inc., 2021) Taylan, Osman; Çebi, Nur; Yılmaz, Mustafa Tahsin; Sağdıç, Osman; Özdemir, Durmuş; Balubaid, MohammedBACKGROUND Ground pistachio nut is prone to adulteration because of its high economic value and wide usage. Green pea is known as the main adulterant in frauds involving pistachio nuts. The present study developed a new, rapid, reliable and low-cost methodology by using a portable Raman spectrometer in combination with chemometrics for the detection of green pea in pistachio nuts. RESULTS Three different methods of Raman spectroscopy-based chemometrics analysis were developed for the determination of green-pea adulteration in pistachio nuts. The first method involved the development of hierarchical cluster analysis (HCA) and principal component analysis (PCA), which differentiated authentic pistachio nuts from green pea and green pea-adulterated samples. The best classification pattern was observed in the adulteration range of 20-80% (w/w). In addition to classification methods, partial least squares regression (PLSR) and genetic algorithm-based inverse least squares (GILS) were also used to develop multivariate calibration models to determine quantitatively the degree of green-pea adulteration in grounded pistachio nuts. The spectral range of 1790-283 cm(-1)was used in the case of multivariate data analysis. A green-pea adulteration level of 5-80% (w/w) was successfully identified by PLSR and GILS. The correlation coefficient of determination (R-2) was determined as 0.91 and 0.94 for the PLSR and GILS analyses, respectively. CONCLUSION A Raman spectrometer combined with chemometrics has a high capability with regard to the detection of adulteration in pistachio nuts, combined with low cost, strong reliability, a high level of accuracy, rapidity of analysis, and minimum sample preparation.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 IndustryArticle Citation - WoS: 23Citation - Scopus: 28Classification of Turkish Monocultivar (ayvalık and Memecik Cv.) Virgin Olive Oils From North and South Zones of Aegean Region Based on Their Triacyglycerol Profiles(John Wiley and Sons Inc., 2013) Gökçebag, Mümtaz; Dıraman, Harun; Özdemir, DurmuşIn this study, a total of 22 domestic monocultivar (AyvalIk and Memecik cv.) virgin olive oil samples taken from various locations of the Aegean region, the main olive growing zone of Turkey, during two (2001-2002) crop years were classified and characterized by well-known chemometric methods (principal component analysis [PCA] and hierarchical cluster analysis [HCA]) on the basis of their triacylglycerol (TAG) components. The analyses of TAG components (LLL and major fractions LOO, OOO, POO, PLO, SOO, and ECN 42-ECN 50) in the oil samples were carried out according to the HPLC method described in a European Union Commission (EUC) regulation. In all analyzed samples the value of trilinolein (LLL), the least abundant TAG, did not exceed the maximum limit of 0.5 % given by the EUC regulation for different olive oil grades. The ranges of abundant TAG, namely LOO, OOO, POO, PLO, and SOO, were 13.30-16.08, 37.27-46.36, 21.39-23.24, 4.93-7.03, and 4.72-6.00 %. The TAG data of Aegean virgin olive oils were similar to those of products from important olive-oil-producing Mediterranean countries was determined. Also, the estimation of major fatty acids (FA) was carried out by using a formula based on TAG data. The PCA results showed that some TAG components have an important role in the characterization and geographical classification of 22 monocultivar virgin olive oil. The Aegean virgin olive oil samples were successfully classified and discriminated into two main groups as the North and South (growing) subzones or AyvalIk and Memecik olives (cultivars) according to the HCA results based on experimental TAG data and calculated major FA profile.Article Citation - WoS: 24Citation - Scopus: 32Determination of Lignin and Extractive Content of Turkish Pine ( Pinus Brutia Ten.) Trees Using Near Infrared Spectroscopy and Multivariate Calibration(Springer Verlag, 2011) Üner, Birol; Karaman, İbrahim; Tanrıverdi, H.; Özdemir, DurmuşDetermination of quality parameters such as lignin and extractive content of wood samples by wet chemistry analyses takes a long time. Near-infrared (NIR) spectroscopy coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the NIR spectra, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus brutia Ten. is the most growing pine species in Turkey. Its rotation period is around 80 years; the forest products industry has widely accepted the use of Pinus brutia Ten. because of its ability to grow on a wide range of sites and its suitability to produce desirable products. Pinus brutia Ten. is widely used in construction, window door panel, floor covering, etc. Determination of lignin and extractive content of wood provides information to tree breeders on when to cut and how much chemicals are needed for the pulping and bleaching process. In this study, 58 samples of Pinus brutia Ten. trees were collected in Isparta region of Turkey, and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near-infrared spectrometer between 1,000 and 2,500 nm in diffuse reflectance mode, and multivariate calibration models were built with genetic inverse least squares method for both lignin and extractive content using the concentration information obtained from wet standard reference method. Overall, standard error of calibration (SEC) and standard error of prediction (SEP) ranged between 0.35% (w/w) and 2.40% (w/w).Article Citation - WoS: 4Citation - Scopus: 7Determination of Aluminum Rolling Oil Additives and Contaminants Using Infrared Spectroscopy Coupled With Genetic Algorithm Based Multivariate Calibration(Elsevier Ltd., 2010) Yalçın, Ayşegül; Ergün, Didem; İnanç Uçar, Özlem; Özdemir, DurmuşGenetic algorithm based multivariate calibration models were generated for infrared spectroscopic determination of aluminum rolling oil additives and contaminants such as gear and hydraulic oils. Two different additives and six different suspected contaminants were investigated in the base oil lubricant. Routine analysis samples from 9 different aluminum rolling systems were collected in a period of 2 months in an aluminum rolling plant and gas chromatography (GC) is used as the reference method. Infrared absorbance spectra of the samples were then collected and the reference values obtained with GC were used together with these spectra for model building. Inverse least squares method was optimized with a genetic algorithm by selecting the most contributing regions of the infrared spectra for each component. The R2 values between GC and multivariate spectroscopic determinations were around 0.99 indicating a good correlation between the two methods. Performance of genetic algorithm based multivariate calibration models were also compared with partial least squares (PLS) method. The study showed that infrared spectroscopy coupled with multivariate calibration can be used for continuous monitoring of additives and contaminants in aluminum rolling oil. By this way, analysis time is significantly reduced and simultaneous determination of all the components can be accomplished. © 2010 Elsevier B.V. All rights reserved.