The volatile fraction of a food plays a fundamental role in its characterization and appreciation by consumers, and thus can be used to authenticate and assess the quality of food products. Key odorants in foods are very often chiral molecules with an enantiomeric excess. Reliable quality control therefore entails fast, fully automated methods that can quantify key odorants, and determine their enantiomeric compositions. This study reports the development of a simple, fast, simultaneous, and fully automated total analysis system to quantify and measure the enantiomeric excess of γ- and δ- lactones, in natural and artificial peach flavored juices. Stir-bar sorptive extraction (SBSE) is combined with fast enantioselective GC–MS analysis and online statistical processing to quantify target quality components, including at trace levels, and effectively discriminate between samples .
“Truly Natural”: Fully Automated Stir-Bar Sorptive Extraction with Enantioselective GC–MS Quantitation of Chiral Markers of Peach Aroma
Cecilia Cagliero
First
;Alessandro Guglielmetti;Chiara Cordero;Erica Liberto;Arianna Marengo;Barbara Sgorbini;Patrizia Rubiolo;Carlo Bicchi
2019-01-01
Abstract
The volatile fraction of a food plays a fundamental role in its characterization and appreciation by consumers, and thus can be used to authenticate and assess the quality of food products. Key odorants in foods are very often chiral molecules with an enantiomeric excess. Reliable quality control therefore entails fast, fully automated methods that can quantify key odorants, and determine their enantiomeric compositions. This study reports the development of a simple, fast, simultaneous, and fully automated total analysis system to quantify and measure the enantiomeric excess of γ- and δ- lactones, in natural and artificial peach flavored juices. Stir-bar sorptive extraction (SBSE) is combined with fast enantioselective GC–MS analysis and online statistical processing to quantify target quality components, including at trace levels, and effectively discriminate between samples .File | Dimensione | Formato | |
---|---|---|---|
LCGC_NAmerical_April2019_fin.pdf
Accesso riservato
Tipo di file:
PDF EDITORIALE
Dimensione
422.74 kB
Formato
Adobe PDF
|
422.74 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.