WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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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 Development of Chemometrics Method Based on Infrared Spectroscopy for the Determination of Cement Composition and Process Optimization [article](ACG Publications, 2022) Özdemir, Durmuş; Gümüş, Mehmet Gökhan; Tepeli, DilekIn combination with a multivariate calibration method, FTIR-ATR spectroscopy was presented as a rapid method for the determination of some major oxides (CaO, SiO2, Al2O3, Fe2O3) and minor oxides (MgO, SO4, Na2O, and K2O) in diverse materials (raw material, raw meal, additives, clinker, and types of cement) in cement manufacturing. The FTIR spectroscopy based multivariate models were generated by taking X-ray fluorescence (XRF) as a reference method. Among a number of spectral preprocessing methods, extended multiplicative scatter correction (EMSC) yielded the best PLS models. The standard error of prediction (SEP) for the optimal FTIR based PLS models ranged from 0.10 to 2.07 (w/w%), and the regression coefficient (R2) ranged from 0.95 to 0.99 for PLS predicted vs XRF reference plots. Statistical evaluation of the both methods was carried out by paired t-test at the 95% confidence level and the results showed that the FTIR-ATR combined with PLS model results are consistent with the XRF reference measurements for all the oxides studied. Compared to the XRF method, which can take anywhere from a few minutes to an hour for each measurement, the proposed method is faster, cheaper, and safer. The presented technology also allows rapid monitoring of a cement factory production line.Article Citation - WoS: 2Evaluation of Malassezia Species by Fourier Transform Infrared (ft-Ir) Spectroscopy(Ankara Microbiology Society, 2011) Ergin, Çağrı; Vuran, M. Emre; Gök, Yaşar; Özdemir, Durmuş; Karaarslan, Aydın; Kaleli, İlnur; Çon, Ahmet HilmiMalassezia species which are lipophilic exobasidiomycetes fungi, have been accepted as members of normal cutaneous flora as well as causative agent of certain skin diseases. In routine microbiology laboratory, species identification based on phenotypic characters may not yield identical results with taxonomic studies. Lipophilic and lipid-dependent Malassezia yeasts require lipid-enriched complex media. For this reason, Fourier transform infrared (FT-IR) spectroscopy analysis focused on lipid window may be useful for identification of Malassezia species. In this study, 10 different standard Malassezia species (M.dermatis CBS 9145, M.furfur CBS 7019, M.japonica CBS 9432, M.globosa CBS 7966, M.nana CBS 9561, M.obtusa CBS 7876, M.pachydermatis CBS 1879, M.slooffiae CBS 7956, M.sympodialis CBS 7222 and M.yamatoensis CBS 9725) which are human pathogens, have been analyzed by FT-IR spectroscopy following standard cultivation onto modified Dixon agar medium. Results showed that two main groups (M1; M.globosa, Robtusa, M.sympodialis, M.dermatis, M.pachydermatis vs, M2; M.furfur, M.japonica, M.nana, M.slooffiae, M.yamatoensis) were discriminated by whole spectra analysis. M.obtusa in M1 by 1686-1606 cm(-1) wavenumber ranges and M.japonicum in M2 by 2993-2812 cm(-1) wavenumber ranges were identified with low level discrimination power. Discriminatory areas for species differentiation of M1 members as M.sympodialis, M.globosa and M.pachydermatis and M2 members as M.furfur and M.yamatoensis could not be identified. Several spectral windows analysis results revealed that FT-IR spectroscopy was not sufficient for species identification of culture grown Malassezia species.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.Conference Object Labeling of Gly-Gly With Technetium-99m and the Assessment of It's Radiopharmaceutical Potential(Springer Verlag, 2001) Taner, M.S.; Özdemir, Durmuş; Köseoğlu, K.; Argon, M.; Dirlik, A.; Duman, Y.[No abstract available]Article Citation - WoS: 37Citation - Scopus: 41Determination of Olive Oil Adulteration With Vegetable Oils by Near Infrared Spectroscopy Coupled With Multivariate Calibration(SAGE Publications, 2010) Öztürk, Betül; Yalçın, Ayşegül; Özdemir, DurmuşThere has been growing public awareness about the health benefits of olive oil throughout the world in recent years, resulting in a significant increase in its consumption as part of the daily diet This demand has attracted fraudulent attempts to market olive oil which has been adulterated with cheaper oils. This study focuses on the near infrared (NIR) spectroscopic determination of adulteration of olive oil by vegetable oils using multivariate calibration. The binary, ternary and quaternary mixtures of olive, soybean, cotton, corn, canola and sunflower oils were prepared using a random design. The absorbance spectra of these synthetic samples were measured by a near infrared (NIR) spectrometer. A genetic algorithm-based variable selection algorithm, coupled with an inverse least squares multivariate calibration method (GILS) was used to build calibration models for possible adulterants and olive oil in the adulterated mixtures The correlation coefficients of actual versus predicted concentrations resulting from multivariate calibration models for the different oils were between 0 90 and 0.99 The results demonstrated that NIR spectroscopy in conjunction with the GILS method makes it possible to determine the adulteration of olive oils regardless of adulterant vegetable oils over a wide range of concentrations.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
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