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Journal of the American Oil Chemists' Society

, Volume 89, Issue 10, pp 1823–1830 | Cite as

Determining Frying Oil Degradation by Near Infrared Spectroscopy Using Chemometric Techniques

  • Mustafa Öğütcü
  • Buket Aydeniz
  • Mehmet Burak Büyükcan
  • Emin YılmazEmail author
Original Paper

Abstract

A rapid method for the determination of some important physicochemical properties in frying oils has been developed. Partial least square regression (PLS) calibration models were applied to the physicochemical parameters and near infrared spectroscopy (NIR) spectral data. PLS regression was used to find the NIR region and the data pre-processing method that give the best prediction of the chemical parameters. Calibration and validation were appropriated by leave one out cross validation and test set validation techniques for predicting free fatty acids (FFA), total polar materials (cTPM; measured by chromatographic method and iTPM measured by an instrumental method), viscosity and smoke point of the frying oil samples. For PLS models using the cross validation techniques, the best correlations (r) between NIR predicted data and the standard method data for iTPM in oils were 93.79 and root mean square error of prediction (RMSEP) values were 5.53. For PLS models using the test set validation techniques, the best correlations (r) between NIR predicted data and standard method data for FFA, cTPM, viscosity and smoke point in oils were 92.58, 94.61, 81.95 and 84.07 and RMSEP values were 0.121, 3.96, 22.30 and 8.74, respectively. In conclusion, NIR technique with chemometric analysis was found very effective in predicting frying oil quality changes.

Keywords

Frying oils Near infrared spectra Quality control Oil degradation 

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Copyright information

© AOCS 2012

Authors and Affiliations

  • Mustafa Öğütcü
    • 1
  • Buket Aydeniz
    • 1
  • Mehmet Burak Büyükcan
    • 1
  • Emin Yılmaz
    • 1
    Email author
  1. 1.Department of Food Engineering, Faculty of EngineeringÇanakkale Onsekiz Mart UniversityÇanakkaleTurkey

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