Food Engineering / Gıda Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/12
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Article Citation - WoS: 9Citation - Scopus: 11Prediction of Vinegar Processing Parameters With Chemometric Modelling of Spectroscopic Data(Elsevier, 2021) Çavdaroğlu, Çağrı; Çavdaroğlu, Çağrı; Özen, Banu; Özen, Fatma BanuSpectroscopic methods have the advantages of being rapid and environmentally friendly and can be used in measurement and control of processing parameters during food production. It was aimed to predict several quality and chemical parameters of vinegar processing from UV-visible and mid-infrared spectroscopic profiles. Two processing lines of both traditional and submerged vinegar production from 2 separate grape varieties (green and red grapes) were monitored. Some of the important markers of the fermentation processes; pH, brix, total acidity, total flavonoid content, total and individual phenolic contents, organic acid, sugar, ethanol concentrations as well as UV-visible and mid-infrared spectra were obtained during both types of vinegar processing and quality and chemical parameters were predicted from spectroscopic data using chemometric methods. Individual UV-visible and mid-infrared spectral profiles along with low level of data fusion were used in building of chemometric prediction models. Accurate, reliable and robust prediction models (R(2)cal and R(2)val >0.9) were obtained for quality parameters mostly with combination of two spectroscopic datasets. Predictive models used for phenolic components were below average except for p-coumaric and syringic acids. Citric and acetic acids were the most accurately estimated ones among organic acids along with ethanol. Close agreements between reference and predicted values were obtained during the monitoring of changes of some quality parameters for vinegar fermentation process through rapid and simultaneous spectroscopic measurements.Article Citation - WoS: 75Citation - Scopus: 78Classification of Turkish Olive Oils With Respect To Cultivar, Geographic Origin and Harvest Year, Using Fatty Acid Profile and Mid-Ir Spectroscopy(Springer Verlag, 2008) Gürdeniz, Gözde; Özen, Fatma Banu; Tokatlı, FigenFatty acid composition and mid-infrared spectra of olive oils in combination with chemometric techniques were used in the classification of Turkish olive oils with respect to their varieties, growing location and harvest year. In particular, olive oil samples belonging to five different cultivars were obtained from the same orchard in the middle part of Aegean region and two of these varieties were also received from another orchard in northern part of the same region of Turkey in two consecutive harvest years. Evaluation of nine different fatty acid compositions with principal component analysis revealed clear differentiation with respect to variety, geographical origin and harvest year. On the other hand, mid-infrared spectra also achieved varietal and seasonal discrimination to some extent, but differentiation is not as clear as that obtained using fatty acid compositions. © 2008 Springer-Verlag.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; Özen, Fatma BanuFourier 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.