利用hs-spme-gc-ms监测干酪成熟过程中挥发性化合物的变化
Headspace solid-phase microextraction–gas chromatography-mass spectrometry (HS-SPME–GC-MS) and multivariate statistical analysis (MVA) are widely employed in the analyses/comparison of cheese volatiles. One shortcoming of this approach is that the SPME fiber-coat polarity has a significant effect on...
Gespeichert in:
Veröffentlicht in: | Dairy science & technology 2012, Vol.92 (4), p.321-333 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Headspace solid-phase microextraction–gas chromatography-mass spectrometry (HS-SPME–GC-MS) and multivariate statistical analysis (MVA) are widely employed in the analyses/comparison of cheese volatiles. One shortcoming of this approach is that the SPME fiber-coat polarity has a significant effect on the adsorption of volatiles and thus, on the results of MVA. The aim of this study was to test the applicability of different sets of MVA variables, obtained from HS-SPME–GC-MS experiments using the non-polar polydimethylsiloxane (PDMS) and the polar polyacrylate (PA) fibers, in the profiling of cheese volatile compounds and tracking ripening-induced changes. The volatile profiles and ripening-induced changes of two different types of Pasta-Filata “Pirotski kačkavalj” cheese (prepared from raw ewe’s or cow’s milk) were assessed at four different stages of ripening (1, 5, 20, and 30 days). The HS-SPME–GC-MS–MVA results showed that the distribution of volatile compounds into classes gives valuable information concerning cheese type and ripening phase. The extraction with a PA fiber gives a more realistic cheese volatile profile when the relative ratio of the constituent classes is in question, whereas the PDMS fiber is better in recognizing the contribution/concentration of single dominant volatile compounds. For a better and more complete description/profiling of cheese volatiles, we propose the use of combined (transformed) variables that include the information obtained from both PA and PDMS extraction experiments. |
---|---|
ISSN: | 1958-5586 |