Chemistry / Kimya

Permanent URI for this collectionhttps://hdl.handle.net/11147/4072

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Institution Author: search.filters.institutionAuthor.Öztürk, BetülSubject: search.filters.subject.Multivariate calibration

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 37
    Citation - Scopus: 41
    Determination of Olive Oil Adulteration With Vegetable Oils by Near Infrared Spectroscopy Coupled With Multivariate Calibration
    (SAGE Publications, 2010) Öztürk, Betül; Yalçın, Ayşegül; Özdemir, Durmuş
    There 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: 63
    Citation - Scopus: 73
    Near Infrared Spectroscopic Determination of Olive Oil Adulteration With Sunflower and Corn Oil
    (Taiwan Food and Drug Administration, 2007) Özdemir, Durmuş; Öztürk, Betül
    Determination 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: 16
    Citation - Scopus: 19
    Genetic Multivariate Calibration Methods for Near Infrared (nir) Spectroscopic Determination of Complex Mixtures
    (TUBITAK, 2004) Özdemir, Durmuş; Öztürk, Betül
    The simultaneous determination of ternary mixtures of methylene chloride, ethyl acetate, and methanol using near infrared (NIR) spectroscopy and 4 different genetic algorithms based multivariate calibration methods was demonstrated. The 4 genetic multivariate calibration methods are genetic partial least squares (GPLS), genetic regression (GR), genetic classical least squares (GCLS) and genetic inverse least squares (GILS). The sample data set contains the NIR spectra of 63 ternary mixtures and covers the range from 900 to 2000 nm in 2 nm intervals. Of these 63 spectra, 42 were used as the calibration set, and 21 were reserved for the prediction purposes. Several calibration models were built with the 4 genetic algorithm based methods for each component that makes up the mixtures. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) were in the range of 0.22 to 2.5 (% by volume (v/v)) for all the 4 methods. A comparison of genetic algorithm selected wavelengths for each component and for each method was also included.