An untargeted method using headspace solid-phase microextraction coupled to electronic nose based on mass spectrometry (HS-SPME/MS-eNose) in combination with chemometrics was developed for the discrimination of oranges of three geographical origins (Italy, South Africa and Spain). Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant performances were compared. Among the tested models, SELECT/LDA provided the highest prediction abilities in cross-validation and external validation with mean values of 97.8% and 95.7%, respectively. Moreover, HS-SPME/GC-MS analysis was used to identify potential markers to distinguish the geographical origin of oranges. Although 28 out of 65 identified VOCs showed a different content in samples belonging to different classes, a pattern of analytes able to discriminate simultaneously samples of three origins was not found. These results indicate that the proposed MS-eNose method in combination with multivariate statistical analysis provided an effective and rapid tool for authentication of the orange's geographical origin.

Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds

Centonze, Valentina;Casiello, Grazia;Longobardi, Francesco
2019-01-01

Abstract

An untargeted method using headspace solid-phase microextraction coupled to electronic nose based on mass spectrometry (HS-SPME/MS-eNose) in combination with chemometrics was developed for the discrimination of oranges of three geographical origins (Italy, South Africa and Spain). Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant performances were compared. Among the tested models, SELECT/LDA provided the highest prediction abilities in cross-validation and external validation with mean values of 97.8% and 95.7%, respectively. Moreover, HS-SPME/GC-MS analysis was used to identify potential markers to distinguish the geographical origin of oranges. Although 28 out of 65 identified VOCs showed a different content in samples belonging to different classes, a pattern of analytes able to discriminate simultaneously samples of three origins was not found. These results indicate that the proposed MS-eNose method in combination with multivariate statistical analysis provided an effective and rapid tool for authentication of the orange's geographical origin.
File in questo prodotto:
File Dimensione Formato  
Centonze et al_2019.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 194.52 kB
Formato Adobe PDF
194.52 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
FOODCHEM-S-18-05549.pdf

accesso aperto

Tipologia: Documento in Pre-print
Licenza: Creative commons
Dimensione 454.69 kB
Formato Adobe PDF
454.69 kB Adobe PDF Visualizza/Apri

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/11586/228453
Citazioni
  • ???jsp.display-item.citation.pmc??? 5
  • Scopus 56
  • ???jsp.display-item.citation.isi??? 47
social impact