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Volatile Aroma Components And Ms-based Electronic Nose Profiles Of Dogfruit (Pithecellobium Jiringa) And Stink Bean (Parkia Speciosa)

Volatile Aroma Components And Ms-based Electronic Nose Profiles Of Dogfruit (Pithecellobium Jiringa) And Stink Bean (Parkia Speciosa)
Yonathan Asikin, Kusumiyati, Takeshi Shikanai, Koji Wada
Universitas Padjadjaran, Journal of Advanced Research 9 (2018) 79–85, Elsevier B.V. on behalf of Cairo University, https://doi.org/10.1016/j.jare.2017.11.003
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Universitas Padjadjaran, Journal of Advanced Research 9 (2018) 79–85, Elsevier B.V. on behalf of Cairo University, https://doi.org/10.1016/j.jare.2017.11.003
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Dogfruit (Pithecellobium jiringa) and stink bean (Parkia speciosa) are two typical smelly legumes from Southeast Asia that are widely used in the cuisines of this region. Headspace/gas chromatography/frame ionization detection analysis and mass spectrometry (MS)-based electronic nose techniques were applied to monitor ripening changes in the volatile ?avor pro?les of dogfruit and stink bean. Compositional analysis showed that the ripening process greatly in?uenced the composition and content of the volatile aroma pro?les of these two smelly food materials, particularly their alcohol, aldehyde, and sulfur components. The quantity of predominant hexanal in stink bean signi?cantly declined (P < 0.05) during the ripening process, whereas the major volatile components of dogfruit changed from 3-methylbutanal and methanol in the unripe state to acetaldehyde and ethanol in the ripe bean. Moreover, the amount of the typical volatile ?avor compound 1,2,4-trithiolane signi?cantly increased (P < 0.05) in both ripened dogfruit and stink bean from 1.70 and 0.93%, to relative amounts of 19.97 and 13.66%, respectively. MSbased nose pro?ling gave further detailed differentiation of the volatile pro?les of dogfruit and stink bean of various ripening stages through multivariate statistical analysis, and provided discriminant ion masses, such as m/z 41, 43, 58, 78, and 124, as valuable ‘‘digital ?ngerprint” dataset that can be used for fast flavor monitoring of smelly food resources.

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