Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5869
Title: Chemometric studies on zNose™ and machine vision technologies for discrimination of commercial extra virgin olive oils
Authors: Kadiroğlu, Pınar
Korel, Figen
Keywords: Chemometrics
Electronic nose
Extra virgin olive oil
Machine vision system
Sensory analysis
Issue Date: Sep-2015
Publisher: John Wiley and Sons Inc.
Source: Kadiroğlu, P., and Korel, F. (2015). Chemometric studies on zNose™ and machine vision technologies for discrimination of commercial extra virgin olive oils. JAOCS, Journal of the American Oil Chemists' Society, 92(9), 1235-1242. doi:10.1007/s11746-015-2697-1
Abstract: The aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose™) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). Based on the zNose™ analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS-DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS-DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS-DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose™ in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.
URI: https://doi.org/10.1007/s11746-015-2697-1
http://hdl.handle.net/11147/5869
ISSN: 0003-021X
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
5869.pdfMakale855.7 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

3
checked on Nov 27, 2021

WEB OF SCIENCETM
Citations

4
checked on Nov 27, 2021

Page view(s)

28
checked on Nov 28, 2021

Download(s)

36
checked on Nov 28, 2021

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.