Quality assessment of foods and raw materials is fundamental for maintaining high quality standards. Fats, oils and other fat products are directly consumed by humans and are also used as ingredients in foods and feed, therefore their authenticity is of great importance regarding food safety aspects, their sensory and nutritional quality, legal compliance, economic reasons and guarantee of a well-defined quality. Furthermore, quality of fat products used in feeds is essential due to their influence in the food chain. To assess the authenticity of a fat product it is fundamental to know their biological and geographical origin, technologies applied, fat modification techniques used, and chemical composition of the authentic oils and potential adulterants.
The detection of adulteration of foods is possible through the identification of discriminative markers of the adulterant. Recently, the volatile profile might be used as a fingerprint of each fat product. By analysing results using multivariate statistical techniques (as PLS-DA) new statistical models might be created to assign fat products into identity classes. These models will be useful to determine rapidly the identity (and therefore the quality, including the origin, composition, safety and stability) of fat products. In this project, it will be selected a wide range of fat products used in foods and feeds (covering biological, geographical and technological variation, and novel fat products).
Markers for authenticity will be analyzed using state-of-the-art technology (GC, PTR-MS) and statistical models will be created (by PLS-DA) to define identity classes. The results that can be obtained from this project are of interest for industrial applications, the assessment of food safety, the detection of adulterations, and in the information and protection of consumers. Furthermore, the applicant will acquire experience in new analytical and statistical techniques useful for her future research.