Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/174881
Share/Export:
logo share SHARE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Title

Relating sensory analysis with SPME-GC-MS data for Spanish-style green table olive aroma profiling

AuthorsLópez-López, Antonio CSIC ORCID ; Sánchez Gómez, Antonio Higinio CSIC ORCID CVN ; Cortés Delgado, Amparo CSIC ORCID ; Castro Gómez-Millán, Antonio de CSIC ORCID ; Montaño, Alfredo CSIC ORCID
KeywordsGreen table olives
Aroma
Sensory profile
SPME-GC-MS
Chemometrics
Issue DateMar-2018
PublisherElsevier
CitationLWT - Food Science and Technology 89: 725-734 (2018)
AbstractThe sensory profile and volatile composition of 24 samples of Spanish-style green table olives were studied by Quantitative Descriptive Analysis and solid phase micro-extraction gas chromatography coupled to mass spectrometry (SPME-GC-MS), respectively, with the aim to characterize this type of table olive. The aroma of samples was described by the sensory panel using nine descriptors (lactic, green fruit, ripe fruit, grass, hay, musty, lupin, wine, and alcohol). A total of 133 volatile compounds were identified in the headspace of samples. Principal component analysis (PCA) applied to both datasets showed a poor separation of samples according to cultivars, but a trend to separate according to sampling time. Reliable partial least squares (PLS) regression models were developed for four sensory descriptors (lactic, lupin, wine, and alcohol) and allowed identifying the compounds both positively and negatively correlated to such odor sensations. Such models could be used to predict the intensity of the above-mentioned descriptors as a function of SPME-GC-MS data.
Description46 Páginas; 5 Tablas; 3 Figuras; Material suplementario: 2 tablas y 5 figuras
Publisher version (URL)http://dx.doi.org/10.1016/j.lwt.2017.11.058
URIhttp://hdl.handle.net/10261/174881
ISSN0023-6438
Appears in Collections:(IG) Artículos




Files in This Item:
File Description SizeFormat
Postprint_2018_LWT_V89_P725.pdfArtículo principal2,35 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work

Page view(s)

310
checked on Jul 6, 2022

Download(s)

368
checked on Jul 6, 2022

Google ScholarTM

Check


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.