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Multielement profiling by ICP-MS in food traceability
Authors C. Telloli, A. Rizzo  Year 2018
Pubblication type Poster International Conference with referee
Abstract Fingerprinting techniques based on elemental composition and multivariate statistical analysis of compositional data can be used for the identification and classification of a specific agricultural product according to its 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, the practices to which they are subjected and to the local environmental conditions thanks to biogeochemical cycling.
The geographically/geologically sensitive parameters such as trace element composition are of significant relevance in order to characterize and subsequently identify the origin of a given food product.
Proof of provenance has become an important issue, in accordance with national legislation and international standards. based on EU has been supporting the potential of differentiating quality products on a regional basis.
Consequently, the determination and measurement of multi-element concentrations in selected regional products may provide unique compositional fingerprint for characterizing their geographical origin.
The aim of this work has been firstly to set up a high-level analytical facility for elemental analysis based on a triple quadrupole ICP-MS and subsequently to test and apply this approach to a selected set of food products. In this framework, the chosen food has been milk from different farms and regions. The samples analyzed were characterized in term of trace and ultra-trace elements.
Reference 2nd European ICP-QQQ Forum
Spetember 19-22, 2018
Munich, Germany
Repository reference DI138-014
Research unit TRAC
LastUpdate 04/03/2021
Related research topics
Code Topic Description
4.2 Isotopic Traceability of products Isotopic techniques for checking product's origin

impact factor Falso

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