Electron Impact-Mass Spectrometry Fingerprinting and Chemometrics for Rapid Assessment of Authenticity of Edible Oils Based on Fatty Acid Profiling

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KENAR A., Cicek B., ARSLAN F. N., Akin G., Elmas S. N. K., YILMAZ İ.

FOOD ANALYTICAL METHODS, cilt.12, sa.6, ss.1369-1381, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 6
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s12161-019-01472-0
  • Dergi Adı: FOOD ANALYTICAL METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1369-1381
  • Anahtar Kelimeler: Electron impact ionization-mass spectroscopy, Fingerprinting, Chemometrics, Authenticity, Edible oil, Fatty acid methyl ester, VIRGIN OLIVE OILS, GAS-CHROMATOGRAPHY, VEGETABLE-OILS, SOYBEAN OIL, ADULTERATION, SPECTROSCOPY, CLASSIFICATION, QUANTIFICATION, DIFFERENTIATION, DISCRIMINATION
  • Ankara Üniversitesi Adresli: Evet

Özet

A new methodology is described herein that provides an experimentally simple, rapid, and cost-effective mass fingerprinting method for the assessment of edible oil authentication based on their fatty acid methyl ester (FAMEs). This analytical approach is based on the application of electron impact (EI) ionization-mass spectrometry (MS) without chromatographic separation, followed by the treatment of the spectral data via chemometrics analysis, linear discriminant analysis (LDA), principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), and hierarchical cluster analysis (HCA). This fingerprinting analysis was applied by using a gas chromatography-mass spectroscopy instrument, without chromatographic column usage and ion identification; therefore, each measurement lasted about 1 min. All multivariate analyses provided excellent discriminations between the edible oil clusters with low classification error. LDA models constructed with six predictors and a total of 100% of edible oil samples from different brands were correctly classified. Furthermore, no misclassification was reported for the discriminant analysis in supervised SIMCA models with an accuracy of 95%. Thus, the present results pointed to the successful application of such methodology to detect, for the first time, authentication of the edible oils.