Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics

In food production, reliable analytical methods for confirmation of purity or degree of spoilage are required by growers, food quality assessors, processors, and consumers. Seven parameters of physico-chemical properties, such as acid number, colority, density, refractive index, moisture and volatility, saponification value and peroxide value, were measured for quality and adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods such exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA. In general, the soybean and rapeseed oils were discriminated by PCA, and the two spoilt oils behaved differently with the rancid rapeseed samples exhibiting more object scatter on the PC-scores plot, than the adulterated soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models were devised, which were able to predict satisfactorily the category of the four different oil samples in the verification set. Rank ordering with the use of MCDM models indicated that the oil types can be discriminated on the PROMETHEE II scale. For the first time, it was demonstrated how ranking of oil objects with the use of PROMETHEE and GAIA could be utilized as a versatile indicator of quality performance of products on the basis of a standard selected by the stakeholder. In principle, this approach provides a very flexible method for assessment of product quality directly from the measured data.