Yazar "Korel, F." seçeneğine göre listele

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    Öğe
    Chemometric analysis of chemo-optical data for the assessment of olive oil blended with hazelnut oil
    (Innovhub Ssi-Area Ssog, 2019) Kadiroglu, P.; Korel, F.; Pardo, M.
    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.
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    Öğe
    Effect of different raw materials on aroma fingerprints of 'boza' using an e-nose and sensory analysis
    (Wageningen Academic Publishers, 2019) Kemahlioglu, K.; Kendirci, P.; Kadiroglu, P.; Yucel, U.; Korel, F.
    Boza is a Turkish traditional beverage produced by fermentation of maize, rice, wheat, millet, cracked wheat, and durum clear flour. The aim of this study was to determine the effect of different raw material combinations on the aroma fingerprints of boza samples using an electronic nose equipped with surface acoustic wave detector in combination with sensory analysis. According to flavour profile analysis of boza samples, significant differences were obtained among the samples. Hierarchical clustering analysis of e-nose and sensory analyses indicated that boza samples were clustered based on their aroma profiles, odour and taste properties revealing the effect of different cereals as raw materials. Rheological analysis showed that all boza samples exhibited pseudoplastic flow behaviour as the apparent viscosity decreased with increasing shear rate. This revealed that differences in raw materials did not change flow behaviour of boza samples. The results indicated that e-nose could be used as a fast and non-destructive method to assess the influence of raw material formulation on aroma profiles of boza samples in correlation with sensory analysis.