, Volume 40, Issue 8, pp 855-867

A rapid method for the quantification of fatty acids in fats and oils with emphasis on trans fatty acids using fourier transform near infrared spectroscopy (FT-NIR)

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Abstract

A rapid method was developed for classifying and quantifying the FA composition of edible oils and fats using Fourier Transform near infrared spectroscopy (FT-NIR). The FT-NIR spectra showed unique fingerprints for saturated FA, cis and trans monounsaturated FA, and all n−6 and n−3 PUFA within TAG to permit qualitative and quantitative comparisons of fats and oils. The quantitative models were based on incorporating accurate GC data of the different fats and oils and FT-NIR spectral information into the calibration model using chemometric analysis. FT-NIR classification models were developed based on chemometric analyses of 55 fats, oils, and fat/oil mixtures that were used in the identification of similar materials. This database was used to prepare three calibration models—one suitable for the analysis of common fats and oils with low levels of trans FA, and the other two for fats and oils with intermediate and high levels of trans FA. The FT-NIR method showed great potential to provide the complete FA composition of unknown fats and oils in minutes. Compared with the official GC method, the FT-NIR method analyzed fats and oils directly in their neat form and required no derivatization of the fats to volatile FAME, followed by time-consuming GC separations and analyses. The FT-NIR method also compared well with the official FTIR method using an attenuated total reflectance (ATR) cell; the latter provided only quantification of specific functional groups, such as the total trans FA content, whereas FT-NIR provided the complete FA profile. The FT-NIR method has the potential to be used for rapid screening and/or monitoring of fat products, trans FA determinations for regulatory labeling purposes, and detection of contaminants. The quantitative FT-NIR results for various edible oils and fats and their mixtures are presented based on the FT-NIR model developed.