Table grapes are food products of considerable commercial value for several countries (USA, Brazil, Italy, South Africa, China, Chile, India and Australia are the most important producers). In Europe, Italy ranks first place for table grape production with more than eight million tons per year (ISTAT, 2011). Recently, we developed an innovative analytical method for the characterization of various table grape cultivars. In our study, multivariate statistical analysis applied to 1H NMR data of table grapes, revealed that the inter-vineyard variability of the metabolic profile has a greater discriminating effect over the intra-vineyard one.1 This presentation deals with the effects of several agronomical practices on the metabolic profile of the table grapes during different production stages. The variation of the metabolic features of the grapes was followed by 1H NMR spectroscopy. Moreover, 1H NMR spectra of ripe table grapes were processed to be used as input for expert classification systems based on three different algorithms: J48, Random Forest and an Artificial Neural Network performed with the Error Back Propagation procedure. The performances of the three algorithms in the discrimination of grapes on the bases of some common features (variety, vintage, use of plant growth regulators, trunk girdling, vineyard location) will be shown. References: 1. V. Gallo, P. Mastrorilli, I. Cafagna, G. I. Nitti, M. Latronico, V. A. Romito, A. P. Minoja, C. Napoli, F. Longobardi, H. Schäfer, B. Schütz, M. Spraul, J. Agric. Food Chem. (2012), submitted.

Metabonomics of table grapes: from metabolic profiles monitoring to classification of grapes by expert informatic systems

MASTRORILLI, Pietro;GALLO, Vito;LATRONICO, Mario;Triggiani M;BEVILACQUA, Vitoantonio
2012

Abstract

Table grapes are food products of considerable commercial value for several countries (USA, Brazil, Italy, South Africa, China, Chile, India and Australia are the most important producers). In Europe, Italy ranks first place for table grape production with more than eight million tons per year (ISTAT, 2011). Recently, we developed an innovative analytical method for the characterization of various table grape cultivars. In our study, multivariate statistical analysis applied to 1H NMR data of table grapes, revealed that the inter-vineyard variability of the metabolic profile has a greater discriminating effect over the intra-vineyard one.1 This presentation deals with the effects of several agronomical practices on the metabolic profile of the table grapes during different production stages. The variation of the metabolic features of the grapes was followed by 1H NMR spectroscopy. Moreover, 1H NMR spectra of ripe table grapes were processed to be used as input for expert classification systems based on three different algorithms: J48, Random Forest and an Artificial Neural Network performed with the Error Back Propagation procedure. The performances of the three algorithms in the discrimination of grapes on the bases of some common features (variety, vintage, use of plant growth regulators, trunk girdling, vineyard location) will be shown. References: 1. V. Gallo, P. Mastrorilli, I. Cafagna, G. I. Nitti, M. Latronico, V. A. Romito, A. P. Minoja, C. Napoli, F. Longobardi, H. Schäfer, B. Schütz, M. Spraul, J. Agric. Food Chem. (2012), submitted.
4th EuCheMS Chemistry Congress (4th Congress of the European Association for Chemical and Molecular Sciences)
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/25318
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact