Chemistry / Kimya
Permanent URI for this collectionhttps://hdl.handle.net/11147/4072
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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: 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: 18Citation - Scopus: 22Prediction of Lignin and Extractive Content of Pinus Nigra Arnold. Var. Pallasiana Tree Using Near Infrared Spectroscopy and Multivariate Calibration(Taylor and Francis Ltd., 2009) 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 spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. Pinus nigra Arnold. Var. pallasiana is the second most growing pine species in Turkey. Even though its rotation period is very high, around 120 years, the forest products industry has widely accepted the use of Pinus nigra because of its ability to grow on a wide range of sites and its suitability to produce desirable products. In this study, 51 samples of Pinus nigra trees were collected and their lignin and extractive content were determined with standard reference (TAPPI) methods. Then, the same samples were scanned with near infrared spectrometer between 1000 and 2500 nm in diffuse reflectance mode. 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) were ranged between 0.35% (w/w) and 2.4% (w/w).Article Citation - WoS: 11Citation - Scopus: 13Near Infrared Spectroscopic Determination of Diesel Fuel Parameters Using Genetic Multivariate Calibration(Taylor and Francis Ltd., 2008) Özdemir, DurmuşThe use of full spectral region from near infrared spectroscopic analysis does not always end up with a good multivariate calibration model as many of the wavelengths do not contain necessary information. Due to the complexity of the spectra, some of the wavelengths or regions may, in fact, disturb the model-building step. Genetic algorithms are one of the useful tools for solving wavelength selection problems and may improve the predictive ability of conventional multivariate calibration methods. This study demonstrates application of genetic algorithm-based multivariate calibration to near infrared spectroscopic determination of several diesel fuel parameters. The parameters studied are cetane number, boiling and freezing point, total aromatic content, viscosity, and density. Multivariate calibration models were generated using genetic inverse least squares (GILS) method and used to predict the diesel fuel parameters based on their near infrared spectra. For each property, a different data set was used and in all cases the number of samples was around 250. Overall, percent standard error of prediction (%SEP) values ranged between 2.48 and 4.84% for boiling point, total aromatics, viscosity, and density. However, %SEP results for cetane number and freezing point were 11.00% and 14.86%, respectively.Article Citation - WoS: 63Citation - Scopus: 73Near Infrared Spectroscopic Determination of Olive Oil Adulteration With Sunflower and Corn Oil(Taiwan Food and Drug Administration, 2007) Özdemir, Durmuş; Öztürk, BetülDetermination of authenticity of extra virgin olive oils has become very important in recent years due to the increasing public concerns about possible adulterations with relatively cheap vegetable oils such as sunflower oil. This study was focused on the application of near infrared (NIR) spectroscopy in conjunction with multivariate calibration to identify the adulteration of olive oils. NIR transmittance measurements were made on pure olive oil and olive oil adulterated with varying concentrations (4-96%, v/v) of sunflower and corn oil in two sets of 26 binary and ternary mixtures. Multivariate calibration models were generated using genetic inverse least squares (GILS) method and used to predict the concentration of adulterants along with the concentration of olive oil in the samples. Over all, standard error of predictions ranged between 2.49 and 2.88% (v/v) for the binary mixtures of olive and sunflower oil and between 1.42 and 6.38% (v/v) for the ternary mixtures of olive, sunflower and corn oil.Article Citation - WoS: 8Citation - Scopus: 8Genetic Multivariate Calibration for Near Infrared Spectroscopic Determination of Protein, Moisture, Dry Mass, Hardness and Other Residues of Wheat(John Wiley and Sons Inc., 2006) Özdemir, DurmuşDetermination of wheat flour quality parameters, such as protein, moisture, dry mass by wet chemistry analyses takes long time. Near infrared spectroscopy (NIR) coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. In this study, two different wheat data sets are investigated with the aim of establishing successful calibration models using NIR spectra of wheat samples. The first data set (material 1) was obtained from the ftp address (ftp://ftp.clarkson.edu/pub/hopkepk/Chemdata/) and contained 100 NIR spectra of wheat of which wet chemical analysis of protein and moisture content were done with reference methods. The second data set (material 2) contained 176 spectra and was downloaded from http://www.spectroscopynow.com/Spy/basehtml/SpyH/1,1181, 2-1-2-0-0-newsdetail-0-74,00.html. This wheat data set was given with the quality parameters, such as protein content, moisture content, other residues, dry mass, protein content in dry mass and hardness that were determined previously. Multivariate calibration models generated with genetic inverse least squares method demonstrated very good prediction results for the parameter mentioned here. Overall, the average per cent recoveries (APR) ranged between 99.23% and 100.34% with a standard deviation (SD) ranging from 0.34 to 3.15 for all the parameters investigated, except hardness. The APR value of hardness was 103.32 with the SD of 14.97.Article Citation - WoS: 3Citation - Scopus: 11Determination of Benazepril Hcl and Hydrochlorothiazide in Pharmaceutical Preparations Using Uv-Visible Spectrophotometry and Genetic Multivariate Calibration Method(Taiwan Food and Drug Administration, 2005) Özdemir, Durmuş; Dinç, ErdalSimultaneous determination of binary mixtures of benazepril and hydrochlorothiazide in pharmaceutical tablets using UV-visible spectrophotometry, classical least squares (CLS) and three genetic algorithms (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV- spectra of 28 synthetic mixtures of benazepril (12∼36 μg/mL) and hydrochlorothiazide (10∼22 μg/mL) and 16 tablets containing both compounds. The spectra cover the range from 210 to 360 nm in 0.1 mn intervals. Several calibration models were built with the four methods. The root mean square error of calibration (RMSEC) and validation (RMSEV) for the synthetic data were in the range of 0.19 and 0.34 μg/mL for all the genetic algorithm based methods. The root mean square error of Prediction (RMSEP) values for the tablets were in the range of 0.04∼0.20 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component was also included.