Detailed ESR Project Descriptions
Below you will find detailed descriptions of each individual ESR project that will provide training. You can also find them in our Call for Applicants document. This information will help you choose the one best aligned to your skills and interests before applying.
ESR SupervisorsWe have a world-class team to support you through your PhD. See our dedicated page here to read about our highly experienced supervisors assigned to each ESR project.
ESR 1: Project Title: Markers for pasta and rice authentication - WP1, WP2
Host: Fondazione Edmund Mach (FEM), Centre for Research and Innovation, San Michele All'adige, Italy
Supervisor: Dr. Federica Camin, Dr Luana Bontempo, FEM
Co-supervisor: Prof. Nives Ogrinc, JSI
Enrolment in Doctoral degree: University of Trento, Trento, Italy
Globally, cereals are the most organically grown crop. In general, a product, to be recognized as organic, must be produced respecting precise rules as established in EC Regulation No 834/2007 that describes the farming practices allowed in organic production and the control systems to be carried out in order to guarantee them. Despite this and that a certification center evaluates the complete traceability of organic products, in the last few years many cases of frauds were reported worldwide in the organic foods sector. Therefore, there is the need of analytical methods that can objectively guarantee the authenticity of these products.
The potential to determine the geographical origin and the authenticity of plant derived material using stable isotope ratios is well established in food authentication studies. Furthermore, in the last years the stable isotope ratios determined in bulk products or in specific compounds, in particular the nitrogen isotope ratio, have been largely investigated as a promising marker of organic – conventional production systems. As a first approximation, natural abundance measurements by Isotope Ratio Mass Spectrometry (IRMS) provide information on plant species, geographical origin as well as on the agricultural treatments the plant underwent. The main aim of this study is to develop methods for bulk and compound specific analysis of stable isotope ratios for the characterization of organic and conventional pasta and rice as well as for their geographical traceability.
To identify biomarkers for discriminating organic vs conventional pasta and rice using IRMS. In details:
(1) identification of markers for characterising organic and conventional production using stable isotope ratios of C and N in amino acids by GC-IRMS, mycotoxin analysis, residual analysis (LC/GC-MS);
(2) evaluation of the data through multivariate statistical analysis;
(3) identification of markers for tracing the geographical origin of pasta and rice using IRMS, ICP-MS;
(4) creation of traceability model through multivariate statistical analysis.
Identified biomarkers for discriminating organic vs conventional pasta and rice (D1.1); A model for tracing the geographical origin of pasta and rice (D1.2.); Database of authentic samples of pasta and rice (M1); Development of MS methods for determining bioactive compounds in pasta residue (M3).
S1: Barilla (M. Suman), 3 months, M7-9, technological preparation of pasta and mycotoxin analysis; S2: CSIC (R.A. Pérez/J.L Tadeo) 1 months, M17, residual analysis; S2S3: JSI (N. Ogrinc) 5 months, M25-29, ICP-MS and MC-ICP-MS analysis; S3S4: OrgSer (B. Hermann), 1 month, M32, Database management.
ESR Profile Requirements:
• A Master’s degree recognised by the EU in one of the following disciplines: analytical chemistry, food chemistry, food science, biotechnology, pharmacy or related discipline.
• Not have resided or carried out his/her main activity (work, studies, etc.) in Italy for more than 12 months in the 3 years immediately prior to his/her recruitment.
• Preference will be given to candidates with the best academic study performances according to the ECST grading scale and recognised by the EU.
• Preference will be given to candidates with experience or competencies in analytical chemistry methods, particularly in IRMS, MS, GC and LC techniques.
• Preference will be given to candidates with experience or competencies in data treatment, data analysis and interpretation.
• The successful candidate will be highly motivated, organised, creative, and enjoys working independently and as part of a research team.