Rapid characterization and identification of fatty acids in margarines using horizontal attenuate total reflectance Fourier transform infrared spectroscopy (HATR-FTIR)

Abstract

Fourier transform infrared spectroscopy (FTIR) with horizontal attenuated total reflectance (HATR) coupled to multivariate analysis was used to predict chemical composition, fatty acid profile, nutritional relationships between fatty acids, and to identify trans fatty acids (TFA) of margarines. For model building and validation, a set of 42 margarines samples were analyzed in terms of fatty acid profile, total fat, moisture, and salt content. The quantitative models were based on incorporating the above chemical parameters of the different margarines and HATR-FTIR spectral information into the calibration model using chemometric analysis. Chemical parameters for total fat, moisture, and salt content ranged 39–84.5%, 14.5–59%, and 0.3–2.6%, respectively. Regarding fatty acids, the concentration of TFA, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA) ranged 0–34.06%, 17.17–54.20%, 15.26–34.49%, and 4.02–53.89% (g/100 g margarine), respectively. Principal components regression (PCR) and partial least square analysis (PLS) were used to inspect the variation within the sample set. The best model to predict the chemical composition was obtained using the algorithm partial least squares (PLS) with a R2 greater than 0.933 and SEC and SEP less than 1.294 and 1.406, respectively. The optimized SIMCA model used to identify high or low TFA content showed 100% correct classification rate of both margarines with less than 2.0 g TFA/100 g fat as more than 2.0 g TFA/100 g fat. Compared with traditional chemical analysis, the FTIR-HATR models analyzed margarines in minutes and directly in their neat form.