Differentiation of edible fats from selected sources after heating treatments using fourier transform infrared spectroscopy (FTIR) and multivariate analysis

Abstract

Lard consumption is forbidden for Muslim people and researches have been done to differentiate lard from other fats for Halal authentication. Therefore, this study was conducted to differentiate lard from other edible fats after heating treatments using Fourier transformed infrared spectroscopy (FTIR) and multivariate data analysis techniques. Five samples of fats from different sources; lard, chicken, mutton, tallow and palm based shortening were heated at different temperature (120, 180 and 240 °C) and time (30, 60, 120 and 180 min). The spectra in the form of multivariate data were acquired using FTIR spectroscopy. Principal components analysis (PCA), k-mean cluster analysis (k-mean CA) and linear discriminant analysis (LDA) were used to compare the ability of each technique to differentiate the fats after the heating treatments. It was found that the combination of PCA with k-mean CA was able to differentiate heated fats according to its origin. LDA method was successfully used to classify 80.5 % of samples in its group. Thus, PCA, CA and LDA can be used as multivariate data analysis to differentiate the heated edible fats.