The volatiles of three typical Italian Pecorino cheeses were analysed by headspace solid-phase microextraction (HS-SPME) coupled with gas-chromatography with mass spectrometry detection (GC–MS) evaluating its potentiality to discriminate Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF). A Design of Experiments (DOE) was employed to optimize the extraction conditions in terms of sample temperature and exposure time in the HS-SPME/GC–MS procedure. Linear Discriminant Analysis (LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the volatile composition data to classify samples. Both methods showed high calibration accuracy (100% of non-error rate in cross-validation) and good predictions (87.5% of correct classification, on the external validation set) providing a powerful tool for discriminating Pecorino cheeses, despite their production occurs in different production cycles, in nearby geographical areas and, in the case of PS/PR origins, the raw materials come from common sources.
HS-SPME/GC–MS volatile fraction determination and chemometrics for the discrimination of typical Italian Pecorino cheeses
Biancolillo A.
;Rossi L.;D'Archivio A. A.
2021-01-01
Abstract
The volatiles of three typical Italian Pecorino cheeses were analysed by headspace solid-phase microextraction (HS-SPME) coupled with gas-chromatography with mass spectrometry detection (GC–MS) evaluating its potentiality to discriminate Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF). A Design of Experiments (DOE) was employed to optimize the extraction conditions in terms of sample temperature and exposure time in the HS-SPME/GC–MS procedure. Linear Discriminant Analysis (LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the volatile composition data to classify samples. Both methods showed high calibration accuracy (100% of non-error rate in cross-validation) and good predictions (87.5% of correct classification, on the external validation set) providing a powerful tool for discriminating Pecorino cheeses, despite their production occurs in different production cycles, in nearby geographical areas and, in the case of PS/PR origins, the raw materials come from common sources.File | Dimensione | Formato | |
---|---|---|---|
PecorinoGC.pdf
non disponibili
Tipologia:
Documento in Versione Editoriale
Licenza:
Creative commons
Dimensione
3.85 MB
Formato
Adobe PDF
|
3.85 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.