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SYMBIOSIS-EU

Scientific synergism of nano-bio-info-cogni science for an integrated system to monitor meat quality and safety during production, storage, and distribution in EU (Food Quality and Safety)

Project acronym: SYMBIOSIS-EU

Title of project: Scientific synergism of nano-bio-info-cogni science for an integrated system to monitor meat quality and safety during production, storage, and distribution in EU

Research area: Food Quality and Safety

Contract No: 211638

EU contribution: €2 280 098

Start date: October 2008

Duration: 42 months

Status: finalised

The SYMBIOSIS-EU was an European project that broght together 14 beneficiaries from 6 EU countries (plus one each from NZ and US) to study tools that will allow a rapid evaluation of meat quality and safety in comparison with the time consuming microbiological analyses or the unattractive and expensive sensory analysis, of which both are most often used to evaluate the freshness, spoilage and/or safety of meat and meat products (EU 2005).

The type and extent of microbial contamination on muscle foods immediately after harvest is depended on handling procedures and various treatments that may be applied during the dressing process. In this project it was evident that spoilage was caused by only a fraction of the microbial association and in particular from their succession of which another even smaller fraction so called Ephemeral Spoilage Organisms (ESO) become dominant through selection depending on conditions prevailing during storage.

The overall aim was to identify and quantitatively evaluate practical and easy to use chemical, biochemical and molecular indices and establish their applicability as quality monitors for inspection of meat safety and quality. The project has applied a multidisciplinary system wide approach relying on converging technologies (bioinformatics, nanotechnology, modelling) to obtain knowledge for meat quality that has been translated into simple devices and practical indicators of quality and safety.

The main objectives met in this project were (i) to develop and/or validate easy to use chemical/biochemical methods (e.g. biosensors, fluorescence, FT-IR), molecular methods (DNA, microarrays), (ii) to develop a suitable software platform for data sharing and integration, (iii) to apply multivariate statistical methods and machine learning (neural networks, fuzzy logic, genetic algorithms) to identify robust multiple compound quality indices, (iv) integration of the sensors and information platform and development of a system to automatically transform data acquired from a sample into a “diagnosis” of meat safety and quality. Within SYMBIOSIS-EU project, a heterogeneous datasets e.g. microbiological, metabolomics, and spoilage fingerprints taken from a wide range of handling, storage and distribution conditions of meat, was developed and all these datasets were integrated within a multi-level and multi disciplinary platform (Symbiosis Online Research Framework-SORF) and used to build robust models of product quality.

The Metabolomic data derived from HPLC, GC-MS and e-nose, the spectra taken from non-invasiveinstruments e.g. FTIR, RAMAN, fluorescent spectroscopy, and surface chemistry as obtained from image analysis, are analysed and correlated with the microbiological data and the sensory scores in order to build qualitative and quantitative models that predict the spoilage status of an unknown meat sample. The results have been initially investigated using multivariate analysis. Datasets were then used to build pattern recognition systems using algorithms such as partial least squares regression (PLS-R) models and support vector Machines (SVMs) in order to develop models acting as quality assessment classifiers.

A number of published articles have demonstrated the potential use of the above-mentioned tools/techniques.

Website of project: www.symbiosis-eu.net

Coordinator: George-John Nychas, gjn@aua.gr

Organisation: Agricultural University of Athens, Greece, www.aua.gr

Partners: