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: 28Citation - Scopus: 33Rapid Detection of Green-Pea Adulteration in Pistachio Nuts Using Raman Spectroscopy and Chemometrics(John Wiley and Sons Inc., 2021) Taylan, Osman; Özdemir, Durmuş; Çebi, Nur; Yılmaz, Mustafa Tahsin; Sağdıç, Osman; Özdemir, Durmuş; Balubaid, Mohammed; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of TechnologyBACKGROUND 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 Determination of Aluminum Rolling Oil and Machinery Oil Residues on Finished Aluminum Sheet and Foil Using Elemental Analysis and Fourier Transform Infrared Spectroscopy Coupled With Multivariate Calibration(John Wiley and Sons Inc., 2014) İnanç Uçar, Özlem; Özdemir, Durmuş; Günyüz, Mert; Dündar, Mustafa Murat; Özdemir, Durmuş; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of TechnologyThe surface characteristics of rolled aluminum products such as sheets and foils are strongly affected by the particular rolling process and the type of aluminum rolling oil compositions. After the rolling process, coiled aluminum sheets and foils undergoes annealing to form desired crystal structure and remove the rolling oil residues. Depending on the time and the temperature that rolled aluminum exposed for annealing, rolling oil residues are mostly removed from the coiled aluminum products but if there is any contamination in rolling oil due to hydraulic and gearing parts of the rolling systems these heavier oils are not easily evaporates from the aluminum surfaces especially inner parts of the coiled aluminum sheets and foils. These rolling oil contaminants create serious problems for the some specific applications of these aluminum products in certain industries such as automotive and coating as remaining thin oil layer prevents proper painting and coating. Therefore, it is very crucial for the rolling industry to be able to monitor the heavy oil contamination on the rolled products and determine the source of these contaminants .In this study, it was aimed to develop a nondestructive infrared spectroscopic method combined with chemometric multivariate calibration techniques for the quantitative determination of rolling oil residues and contaminants on the rolled aluminum products. To be able to generate multivariate calibration methods, an industrial elemental analysis system was adopted for the quantitative determination of heavy oil contaminants on the rolled aluminum products and these were used as reference values for infrared analysis of the same samples. In addition, apart from conventional use of elemental analysis systems for the total organic analysis, the raw data (raw chromatogram) obtained from elemental analysis was used to directly generate multivariate calibration models for each contaminant by using synthetically contaminated surfaces as the calibration samples. The results promised that elemental analysis can be used not just for the total organic content but also specifically to determine amount of each contaminant on the aluminum surfaces, it is also, expected that infrared spectroscopy with grazing angle spectra collection accessories can be used for nondestructive analysis of these contaminants.s.Article 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; Özdemir, Durmuş; Özdemir, Durmuş; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of TechnologyIn 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: 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, Durmuş; Özdemir, Durmuş; 04.01. Department of Chemistry; 04. Faculty of Science; 01. Izmir Institute of TechnologyDetermination 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.