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: 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: 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.