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The Scientific World Journal
Volume 2012, Article ID 250795, 6 pages
http://dx.doi.org/10.1100/2012/250795
Research Article

Quantification and Classification of Corn and Sunflower Oils as Adulterants in Olive Oil Using Chemometrics and FTIR Spectra

1Laboratory of Analytical Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Gadjah Mada University, Yogyakarta 55281, Indonesia
2Research Center of Halal Products, Gadjah Mada University, Yogyakarta 55281, Indonesia
3Laboratory of Analysis and Authentication, Halal Products Research Institute, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia

Received 10 October 2011; Accepted 16 November 2011

Academic Editor: Abdelhameed M. Othman

Copyright © 2012 Abdul Rohman and Y. B. Che Man. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Commercially, extra virgin olive oil (EVOO) is subjected to be adulterated with low-price oils having similar color to EVOO. Fourier transform infrared (FTIR) spectroscopy combined with chemometrics has been successfully used for classification and quantification of corn (CO) and sunflower oils (SFOs) in EVOO sets. The combined frequency regions of 3027–3000, 1076–860, and 790–698 cm-1 were used for classification and quantification of CO in EVOO; meanwhile, SFO was analyzed using frequency regions of 3025–3000 and 1400–985 cm-1. Discriminant analysis can make classification of pure EVOO and EVOO adulterated with CO and SFO with no misclassification reported. The presence of CO in EVOO was determined with the aid of partial least square calibration using FTIR normal spectra. The calibration and validation errors obtained in CO's quantification are 0.404 and 1.13%, respectively. Meanwhile, the first derivative FTIR spectra and PLS calibration model were preferred for quantification of SFO in EVOO with high coefficient of determination (R2) and low errors, either in calibration or in validation sample sets.