Determination of vegetable oils and fats adulterants in diesel oil by high performance liquid chromatography and multivariate methods

Abstract: The current legislation requires the mandatory addition of biodiesel to all Brazilian road diesel oil A (pure diesel) marketed in the country and bans the addition of vegetable oils for this type of diesel. However, cases of irregular addition of vegetable oils directly to the diesel oil may occur, mainly due to the lower cost of these raw materials compared to the final product, biodiesel. In Brazil, the situation is even more critical once the country is one of the largest producers of oleaginous products in the world, especially soybean, and also it has an extensive road network dependent on diesel. Therefore, alternatives to control the quality of diesel have become increasingly necessary. This study proposes an analytical methodology for quality control of diesel with intention to identify and determine adulterations of oils and even fats of vegetable origin. This methodology is based on detection, identification and quantification of triacylglycerols on diesel (main constituents of vegetable oils and fats) by high performance liquid chromatography in reversed phase with UV detection at 205nm associated with multivariate methods. Six different types of oils and fats were studied (soybean, frying oil, corn, cotton, palm oil and babassu) and two methods were developed for data analysis. The first one, based on principal component analysis (PCA), nearest neighbor classification (KNN) and univariate regression, was used for samples adulterated with a single type of oil or fat. In the second method, partial least square regression (PLS) was used for the cases where the adulterants were mixtures of up to three types of oils or fats. In the first method, the techniques of PCA and KNN were correctly classified as 17 out of 18 validation samples on the type of oil or fat present. The concentrations estimated for adulterants showed good agreement with the reference values, with mean errors of prediction (RMSEP) ranging between 0.10 and 0.22% (v/v). The PLS method was efficient in the quantification of mixtures of up to three types of oils and fats, with RMSEP being obtained between 0.08 and 0.27% (v/v), mean precision between 0.07 and 0.32% (v/v) and minimum detectable concentration between 0.23 and 0.81% (v/v) depending on the type of oil or fat in the mixture determined. [Copyright &y& Elsevier]