Food Engineering / Gıda Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/12
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Article Citation - WoS: 5Citation - Scopus: 6Chemometric 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; Korel, Figen; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThe 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: 35Citation - Scopus: 40Flavour of Natural and Roasted Turkish Hazelnut Varieties (corylus Avellana L.) by Descriptive Sensory Analysis, Electronic Nose and Chemometrics(John Wiley and Sons Inc., 2012) Alasalvar, Cesarettin; Korel, Figen; Bahar, Banu; Korel, Figen; Ölmez, Hülya; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyA total of eighteen natural and roasted hazelnut varieties (amongst which only Tombul variety is classified as prime quality), grown in the Giresun province of Turkey, were compared for their differences in descriptive sensory analysis (DSA), electronic nose (e-nose) data and chemometrics. Differences in some descriptive of DSA between natural and roasted hazelnuts as well as within the varieties were observed. Although Tombul hazelnut was selected as one of the best varieties in terms of flavour attributes and received the highest intensities in general, no significant differences (P>0.05) existed among hazelnut varieties except in certain flavour attributes ('after taste' and 'nutty'). DSA and e-nose data of natural and roasted hazelnuts were also evaluated for discrimination using principal component analysis (PCA) and cluster analysis. Results of PCA using e-nose data showed that extracted principal components explained 99.7% and 99.8% of the total variance of the data for natural and roasted hazelnut varieties, respectively. Both DSA and e-nose can be used for discrimination of natural and roasted hazelnuts. © 2011 The Authors. International Journal of Food Science and Technology © 2011 Institute of Food Science and Technology.Article Citation - WoS: 68Citation - Scopus: 76Differentiation of Mixtures of Monovarietal Olive Oils by Mid-Infrared Spectroscopy and Chemometrics(John Wiley and Sons Inc., 2007) Gürdeniz, Gözde; Tokatlı, Figen; Tokatlı, Figen; Özen, Fatma Banu; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyFourier transform infrared (FT-IR) spectroscopy in combination with chemometric techniques has become a useful tool for authenticity determination of extra-virgin olive oils. Spectroscopic analysis of monovarietal extra-virgin olive oils obtained from three different olive cultivars (Erkence, Ayvalik and Nizip) and mixtures (Erkence-Nizip and Ayvalik-Nizip) of monovarietal olive oils was performed with an FT-IR spectrometer equipped with a ZnSe attenuated total reflection sample accessory and a deuterated tri-glycine sulfate detector. Using spectral data, principal component analysis successfully classified each cultivar and differentiated the mixtures from pure mono-varietal oils. Quantification of two different monovarietal oil mixtures (2-20%) is achieved using partial least square (PLS) regression models. Correlation coefficients (R2) of the proposed PLS regression models are 0.94 and 0.96 for the Erkence-Nizip and Ayvalik-Nizip mixtures, respectively. Cross-validation was applied to check the goodness of fit for the PLS regression models, and R 2 of the cross-validation was determined as 0.84 and 0.91, respectively, for the two mixtures.