Multielement Profiling by ICP-MS in Food Traceability

Fingerprinting techniques based on elemental composition and multivariate statistical analysis of compositional data can be used for identification and classification of a specific agricultural product according to their geographical provenance. The analytical approach assumes that the elemental composition of an agricultural product such as for example, wine, coffee, tea, olive oil, and fruit juice will reflect the composition of the soil on which they are cultivated thanks to biogeochemical cycling. The geographically/geologically sensitive parameters like element traces composition are of significant relevance in order to identify the origin of some food products. In recent years, many serious diseases appear related to foodstuffs, so providing the motivation for the scientific community to work more intensively in this area. The aim of this PhD thesis has been to characterize the trace and ultra-trace elements content in cow milk samples and evaluate the chemometric techniques in order to investigate the potentiality of multi-element composition as a marker of geographical origin of milk samples coming from different region of production based on ultra-trace triple quadrupole ICP-MS. In this thesis, a brand new ICP-MS facility for ultra-trace elemental analysis was tested and set into operational conditions, including the optimization of several analytical methods for the determination of trace and ultra-trace elements such as Rare Earth elements in raw cow milk. 50 samples from different geographical origin were analyzed and the results evaluated with chemometric method, for the classification of cow milk samples from different origin. The approach elaborated has been proved to be an effective way to characterize food products from different geographical origin, providing a fingerprint of the element patterns in the samples.