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

•Discrimination of geographical origin of oranges was performed by MS-based e-nose.•Three supervised statistical models were tested.•Classification by SELECT/LDA provided best prediction abilities in validation.•Twenty-eight VOCs had a different content in oranges from different geographical origins...

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Bibliographic Details
Published in:Food chemistry 2019-03, Vol.277, p.25-30
Main Authors: Centonze, Valentina, Lippolis, Vincenzo, Cervellieri, Salvatore, Damascelli, Anna, Casiello, Grazia, Pascale, Michelangelo, Logrieco, Antonio Francesco, Longobardi, Francesco
Format: Article
Language:English
Subjects:
Chemometrics
Citrus sinensis - chemistry
Citrus sinensis - metabolism
Discriminant Analysis
Electronic Nose
Gas Chromatography-Mass Spectrometry
Geographical origin
Italy
MS-based electronic nose
Oranges
Principal Component Analysis
Solid Phase Microextraction
South Africa
Spain
Volatile compounds
Volatile Organic Compounds - analysis
Volatile Organic Compounds - isolation & purification
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Summary:•Discrimination of geographical origin of oranges was performed by MS-based e-nose.•Three supervised statistical models were tested.•Classification by SELECT/LDA provided best prediction abilities in validation.•Twenty-eight VOCs had a different content in oranges from different geographical origins. 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.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2018.10.105