Introduction: In the last years the Italian imports of oranges, mainly from Spain and South-Africa, have significantly increased, therefore there is a real possibility for the Italian consumer to buy foreign products sold fraudulently as Italian. For this reason, in this work an HS-SPME/MS-eNose method for the discrimination of the geographical origins of oranges was developed and validated. Methods: Oranges samples coming from Italy, Spain and South Africa were analyzed by an HS-SPME/MS-eNose method. Subsequently, three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built for the geographical origin discrimination and the relevant performances were compared. Moreover, an HS-SPME/GC-MS method combined with ANOVA was used to identify discriminating compounds. Results: Although all tested statistical models gave acceptable performance, the SELECT/LDA model showed the highest percentages in terms of prediction ability in cross-validation and external validation, with average values of 97.8% and 95.7%, respectively. In particular, the external prediction ability of 95.7% was obtained with all South African and Spanish samples correctly recognized while only 2 samples out of 19 Italian samples were not correctly assigned, with a specific prediction rates of 89.5%. Moreover HS-SPME/GC-MS analysis showed that, although 28 out of 65 identified VOCs had a different content in samples belonging to different origin classes, no compound was able to discriminate at the same time the three geographical origins. Conclusions: In this study, a rapid and inexpensive method based on MS-eNose analysis in combination with chemometrics was successfully used to discriminate oranges coming from Italy, Spain and South Africa. Although HS-SPME/GC-MS analysis showed the absence of specific markers, differences in the pattern and content of VOCs of orange samples of the three different geographical origins were observed.
Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by Headspace Solid Phase Microextraction Mass spectrometry-based Electronic Nose (HS-SPME MS-eNose) and Chemometrics
Lippolis V;Cervellieri S;Damascelli A;Pascale M;Logrieco AF
2018
Abstract
Introduction: In the last years the Italian imports of oranges, mainly from Spain and South-Africa, have significantly increased, therefore there is a real possibility for the Italian consumer to buy foreign products sold fraudulently as Italian. For this reason, in this work an HS-SPME/MS-eNose method for the discrimination of the geographical origins of oranges was developed and validated. Methods: Oranges samples coming from Italy, Spain and South Africa were analyzed by an HS-SPME/MS-eNose method. Subsequently, three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built for the geographical origin discrimination and the relevant performances were compared. Moreover, an HS-SPME/GC-MS method combined with ANOVA was used to identify discriminating compounds. Results: Although all tested statistical models gave acceptable performance, the SELECT/LDA model showed the highest percentages in terms of prediction ability in cross-validation and external validation, with average values of 97.8% and 95.7%, respectively. In particular, the external prediction ability of 95.7% was obtained with all South African and Spanish samples correctly recognized while only 2 samples out of 19 Italian samples were not correctly assigned, with a specific prediction rates of 89.5%. Moreover HS-SPME/GC-MS analysis showed that, although 28 out of 65 identified VOCs had a different content in samples belonging to different origin classes, no compound was able to discriminate at the same time the three geographical origins. Conclusions: In this study, a rapid and inexpensive method based on MS-eNose analysis in combination with chemometrics was successfully used to discriminate oranges coming from Italy, Spain and South Africa. Although HS-SPME/GC-MS analysis showed the absence of specific markers, differences in the pattern and content of VOCs of orange samples of the three different geographical origins were observed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
