Food Engineering / Gıda Mühendisliği

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

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Institution Author: search.filters.institutionAuthor.Kadiroğlu, Pınar

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Now showing 1 - 4 of 4
  • 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.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 9
    Quantification of Staphylococcus Aureus in White Cheese by the Improved Dna Extraction Strategy Combined With Taqman and Lna Probe-Based Qpcr
    (Elsevier Ltd., 2014) Kadiroğlu, Pınar; Korel, Figen; Ceylan, Çağatay
    Four different bacterial DNA extraction strategies and two different qPCR probe chemistries were studied for detection of Stapylococcus aureus from white cheeses. Method employing trypsin treatment followed by a commercial kit application and TaqMan probe-based qPCR was the most sensitive one detecting higher counts than standards in naturally contaminated samples.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 16
    Classification of Turkish Extra Virgin Olive Oils by a Saw Detector Electronic Nose
    (John Wiley and Sons Inc., 2011) Kadiroğlu, Pınar; Korel, Figen; Tokatlı, Figen
    An electronic nose (e-nose), in combination with chemometrics, has been used to classify the cultivar, harvest year, and geographical origin of economically important Turkish extra virgin olive oils. The aroma fingerprints of the eight different olive oil samples [Memecik (M), Erkence (E), Gemlik (G), Ayvalik (A), Domat (D), Nizip (N), Gemlik-Edremit (GE), Ayvalik-Edremit (AE)] were obtained using an e-nose consisting a surface acoustic wave detector. Data were analyzed by principal component analysis (PCA) and discriminant function analysis (DFA). Classification of cultivars using PCA revealed that A class model was correctly discriminated from N in two harvest years. The DFA classified 100 and 97% of the samples correctly according to the cultivar in the 1st and 2nd harvest years, respectively. Successful separation among the harvest years and geographical origins were obtained. Sensory analyses were performed for determining the differences in the geographical origin of the olive oils and the preferences of the panelists. The panelists could not detect the differences among olive oils from two different regions. The cultivar, harvest year, and geographical origin of extra virgin olive oils could be discriminated successfully by the e-nose.