Electron impact-mass spectrometry fingerprinting and chemometrics for rapid assessment of authenticity of edible oils based on fatty acid profiling
| Autor: | Adnan Kenar, Burhanettin Çiçek, Gönül Akin, Ibrahim Yilmaz, Şükriye Nihan Karuk Elmas, Fatma Nur Arslan |
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| Přispěvatelé: | Arslan, Fatma Nur, Akın, Gönül, Elmas Karuk, Şükriye Nihan, Yılmaz, İbrahim |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
chemistry.chemical_classification
Chromatography Chemistry Edible Oil Fatty acid Fatty Acid Methyl Ester Mass spectrometry Linear discriminant analysis Applied Microbiology and Biotechnology Authenticity Analytical Chemistry Rapid assessment Chemometrics chemistry.chemical_compound Principal component analysis Electron impact mass spectrometry Fingerprinting Safety Risk Reliability and Quality Safety Research Electron Impact Ionization-Mass Spectroscopy Fatty acid methyl ester Food Science |
| ISSN: | 0004-6884 |
| Popis: | WOS:000468847900008 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. Scientific Research Project Center of Karamanoglu Mehmetbey University [18-M-18]; Scientific Research Project Center of Ankara University; Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [TUBITAK-116Z159]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) This study is supported financially by the Scientific Research Project Center of Karamanoglu Mehmetbey University (Project number 18-M-18). The authors would like to thank the Scientific Research Project Center of Ankara University and the Scientific and Technological Research Council of Turkey (TUBITAK) (Project number TUBITAK-116Z159) for providing the financial support to use the Unscrambler (R) X10.4 (CAMO software, Oslo, Norway) program. The authors would also like to thank TUBITAK under the 2219-Research Fellowship Program for International Postdoctoral for providing the financial support to carry out this research work. |
| Databáze: | OpenAIRE |
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