Food Engineering / Gıda Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/12

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Institution Author: search.filters.institutionAuthor.Kadiroğlu, PınarSubject: search.filters.subject.Machine vision system

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Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Chemometric Analysis of Chemo-Optical Data for the Assessment of Olive Oil Blended With Hazelnut Oil
    (Stazione Sperimentale per le Industrie, 2019) Kadiroğlu, Pınar; Korel, Figen; Pardo, Matteo
    The main objective of this study was to determine different hazelnut oil concentrations in extra virgin olive oil (EV00) belonging to different geographical regions inside Turkey using the combination of a SAW sensor based electronic nose (e-nose) and a machine vision system (MVS). We leveraged the oil characterisation given by the two easy-to-use and complementary experimental techniques through the adoption of conventional PCA for data exploration and random forests (RF) for supervised learning. The e-nose/MVS combination allows significantly better results both in adulteration detection independently of EVOO's geographical provenance and in EVO0 geographical provenance determination, independently of the adulteration level, with respect to the single characterisation method. RF analysis also produces feature ranking, permitting to shed light on which oils' characteristics influence the learning result. We found that EV00 geographical provenance discrimination is mainly due to yellowness and guaiacol content, while (E)-2-hexenal chiefly determines the prediction of the hazelnut level.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Chemometric Studies on Znose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils
    (John Wiley and Sons Inc., 2015) Kadiroğlu, Pınar; Korel, Figen
    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.