Food Engineering / Gıda Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/12
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Article Citation - WoS: 9Citation - Scopus: 8White Bean and Hazelnuts Flours: Application in Gluten-Free Bread(Academic Press, 2023) Tuna, Ayça; Cappa, Carola; Tokatlı, Figen; Alamprese, CristinaThis study investigated the effects of white bean and hazelnut flour addition (15–30% alone or in combination) to a rice flour-corn starch mixture in gluten-free (GF) breads formulated according to a mixture design. The chemical composition of flours and pasting properties of their mixtures were investigated, as well as the spectroscopic characteristics and leavening performance of doughs. Physical properties of fresh and stored (up to 48 h) bread samples were analyzed. Bean and hazelnut flours had higher protein and fiber contents, and lower carbohydrates content than rice flour and corn starch. Although the reference bread made of rice flour-corn starch mixture (STD) resulted in the highest specific volume (7.0 mL/g) and the lowest hardness (0.43 N), the sample enriched with 15% hazelnut flour (H15) approached these characteristics the most (3.8 mL/g and 1.59 N, respectively). After 48 h of storage, H15 also showed lower hardness than STD. This study paves the way for new applications of white bean and hazelnut flours and showed as a simple reformulation can help to develop healthier bread: the European legal constraint for “fiber source” claim was achieved for breads with 15 or 30% hazelnut flour, and 30% bean-hazelnut mixture, with a fiber content of 3.34, 4.48, and 3.27 g/100g, respectively. © 2023 The AuthorsArticle Citation - WoS: 13Citation - Scopus: 14Uv-Vis Spectroscopy for the Estimation of Variety and Chemical Parameters of Olive Oils(Springer, 2021) Jolayemi, Olusola Samuel; Tokatlı, Figen; Özen, BanuOlive oils produced in different years from different varieties were studied with UV-Vis spectroscopy for classification and prediction. Multivariate models were created with second derivative spectral data, and tested with external validation sets. For varietal classification, orthogonal partial least square discriminant analysis resolved oil samples into various classes with correct classification rate more than 89% for validation set (n = 20). A sample of fresh and stored oils were also classified with a correct classification rate more than 90% for validation set (n = 20). In the predictions of chemical parameters (70 for calibration, 30 for validation), the combination of UV-Vis spectroscopy with orthogonal partial least square regression models showed potential for simultaneous quantification of chlorophylls (0.6-5.6 mg/kg; R-val(2) , 0.79; RPD, 1.97); carotenoids (0.6-3.3 mg/kg; R-val(2), 0.80; RPD, 2.38); ratio of mono to polyunsaturated fatty acids (3.6-8.8; R-val(2) , 0.77; RPD, 1.90), oleuropein derivatives (1.2-62.3 mg/kg; R-val(2) , 0.66; RPD, 1.77), and total phenol content (62.2-505 mg/kg; R-val(2) , 0.67; RPD, 1.74), although showed poor to moderate results for the quantification of free fatty acid (0.3-5.4%; R-val(2), 0.67; RPD, 1.64); monounsaturated fatty acids (66-76.5%; R-val(2) , 0.71; RPD, 1.67); polyunsaturated fatty acids (8.6-18.2%; R-val(2) , 0.73; RPD, 1.65). The models were unable to estimate oxidative stability, saturated fatty acids, and individual phenolics such as hydroxytyrosol, pinoresinol, luteolin, total phenolic acids (R-val(2) , 0.26-0.64; RPD, 0.60-1.52). Results showed the capacities of UV-Vis spectroscopy for classification of olive oils, and prediction of total pigments and phenol content and ratio of mono to polyunsaturated fatty acids.Article Citation - WoS: 61Citation - Scopus: 70Use of Ftir and Uv-Visible Spectroscopy in Determination of Chemical Characteristics of Olive Oils(Elsevier, 2019) Uncu, Oğuz; Özen, Banu; Tokatlı, FigenIt was aimed to predict fatty acid ethyl ester (FAEE), wax, diacylglycerol (DAG) and color pigment contents of olive oils by using rapid and non-destructive spectroscopic techniques (FTIR and UV-vis) individually and in combination. Prediction models were constructed by using partial least squares (PLS) regression with cross and external validation. FAEEs were estimated best with FTIR + UV-Vis spectroscopy (R-cv.(2) = 0.84, R-pred(2) = 0.90, and RPD = 3.0). PLS model with R-cv.(2) = 0.79, R-pred(2) = 0.71, and RPD = 1.9 was obtained for the estimation of 1,2 DAG using FTIR spectral data. Major pigments, lutein, pheophytin a and their derivatives and total xanthophylls were quantified successfully by FTIR + UV-Vis with a range of R-cv.(2) of 0.71-0.85, R-pred(2) of 0.70-0.84, and RPD = 1.5-2.5 values but the prediction of the rest of the pigments were poor (R-cv(2) = 0.60-0.76, R-pred(2) = 0.42-0.62, and RPD = 1.2-1.5). Combination of two spectral data resulted in average prediction of wax content of oils (R-cal(2) = 0.95, R-pred(2) = 0.75, and RPD = 1.9). FTIR and UV-vis spectroscopic techniques in combination with PLS regression provided promising results for the prediction of several chemical parameters of olive oils; therefore, they could be alternatives to traditional analysis methods.Editorial Citation - Scopus: 1Novel Methodologies for Food Quality and Provenance Fingerprints Assessment(Hindawi Publishing Corporation, 2019) Ceto, Xavier; Diaz-Cruz, Jose M.; Tokatlı, Figen; Lucci, Paolo; Moret, SabrinaThe development of novel reliable methodologies that allow the control, assessment, and prediction of the characteristics of food products is a field under expansion nowadays, especially those that allow their characterization, classification, and authentication. On the one side, the highly competitive global environment in food industry requires continuous innovation and a better sustainable usage of our natural resources in order to improve the high standards of food producers, leading to high value-added products. The linkage of new research ideas with food production provides a competitive advantage to food makers to fulfil the competitive market challenges.Article Citation - WoS: 12Citation - Scopus: 12Mid-Infrared Spectroscopic Detection of Sunflower Oil Adulteration With Safflower Oil(CSIC Consejo Superior de Investigaciones Cientificas, 2019) Uncu, Oğuz; Özen, Banu; Tokatlı, FigenThe oil industry is in need of rapid analysis techniques to differentiate mixtures of safflower-sunflower oils from pure oils. The current adulteration detection methods are generally cumbersome and detection limits are questionable. The aim of this study was to test the capability of a mid-infrared spectroscopic method to detect the adulteration of sunflower oil with safflower oil compared to fatty acid analysis. Mid-infrared spectra of pure oils and their mixtures at the 10-60% range were obtained at 4000-650 cm(-1) wavenumber and fatty acid profiles were determined. Data were analyzed by multivariate statistical analysis techniques. The lowest level of detection was obtained with mid-infrared spectroscopy at 30% while the fatty acid profile could determine adulteration at around 60%. Adulteration levels were predicted successfully using PLS regression analysis of infrared data with R-2 (calibration) = 0.96 and R-2 (validation) = 0.93. As a rapid and minimum waste generating technique, mid-infrared spectroscopy could be a useful tool for the screening of raw material to detect safflower-sunflower oil mixtures.Book Part Citation - Scopus: 3Infrared Spectroscopy for the Detection of Adulteration in Foods(John Wiley and Sons Inc., 2012) Özen, Banu; Tokatlı, FigenIR spectroscopy in combination with chemometric techniques is an effective tool for the detection of adulteration of high economic value food products such as wine, dietary supplements and olive oil. It provides practical and quick alternative to other commonly used analytical methods.Article Citation - WoS: 30Citation - Scopus: 30Discriminative Capacities of Infrared Spectroscopy and E-Nose on Turkish Olive Oils(Springer Verlag, 2017) Jolayemi, Olusola Samuel; Tokatlı, Figen; Buratti, Susanna; Alamprese, CristinaThe potentials of Fourier transform (FT) near- (NIR) and mid-infrared (IR) spectroscopy, and electronic nose (e-nose) on varietal classification of Turkish olive oils were demonstrated. A total of 63 samples were analyzed, comprising Ayvalik, Memecik, and Erkence oils. Spectra were pretreated with standard normal variate and second derivative. Classification models were built with orthogonal partial least square-discriminant analysis (OPLS-DA), considering the single data sets and also the combined FT-NIR-IR spectra. OPLS-DA models were validated both by cross validation and external prediction. All the models gave good results, being the average correct classification percentages in prediction higher than 90% for spectroscopic data and equal to 82% for e-nose data. The combined FT-NIR-IR data set gave the best results in terms of coefficients of determination (0.95 and 0.67). Different e-nose sensors discriminated Ayvalik, Memecik, and Erkence oils, explaining their distinct aromatic profiles.Article Citation - WoS: 27Citation - Scopus: 29Differentiation of Wines With the Use of Combined Data of Uv-Visible Spectra and Color Characteristics(Academic Press Inc., 2016) Şen, İlknur; Tokatlı, FigenUV-visible spectra and color parameters of monovarietal wines with orthogonal partial least square-discriminant analysis (OPLS-DA) were shown to be practical and rapid methods for classification purposes. Red and white wines from the 2006-2009 vintages were characterized in terms of color, anthocyanin content and UV-visible spectra. Syrah and Cabernet Sauvignon wines had high color density and intensity. Kalecik Karasi wines had the highest CIELab parameters and the lowest color density. Boğazkere and Öküzgözü wines showed similarities with respect to their high red color parameters and were distinct from other wines. Merlot, Syrah and Öküzgözü wines had the highest total anthocyanin content (61.9-55. mg/L as median values). White wines made from Chardonnay, Muscat and Emir grapes were found to have different color characteristics. The vintage-based discrimination of red wines was mostly apparent in total anthocyanin contents. Different UV wavelength regions were found to be effective in classification with respect to variety and vintage. Correct classification rates in the validation set were 100% and 75%, for varietal and vintage classifications, respectively. This study demonstrated the potential of combination of UV-visible spectra and color characteristics to be used in the authentication of wines.Article Citation - WoS: 42Citation - Scopus: 47Effects of Malaxation Temperature and Harvest Time on the Chemical Characteristics of Olive Oils(Elsevier Ltd., 2016) Jolayemi, Olusola Samuel; Tokatlı, Figen; Özen, BanuThe aim of the study was to determine the effects of harvest time and malaxation temperature on chemical composition of olive oils produced from economically important olive varieties with a full factorial experimental design. The oils of Ayvalik and Memecik olives were extracted in an industrial two-phase continuous system. The quality parameters, phenolic and fatty acid profiles were determined. Harvest time, olive variety and their interaction were the most significant factors. Malaxation temperature was significant for hydroxytyrosol, tyrosol, p-coumaric acid, pinoresinol and peroxide value. Early and mid-harvest oils had high hydroxytyrosol and tyrosol (maximum 20.7 mg/kg) and pigment concentrations (maximum chlorophyll and carotenoids as 4.6 mg/kg and 2.86 mg/kg, respectively). Late harvest oils were characterized with high peroxide values (9.2-25 meq O2/kg), stearic (2.4-3.1%) and linoleic acids (9.3-10.4%). Multivariate regression analysis showed that oxidative stability was affected positively by hydroxytyrosol, tyrosol and oleic acid and negatively by polyunsaturated fatty acids.Article Citation - WoS: 27Citation - Scopus: 30Combination of Visible and Mid-Infrared Spectra for the Prediction of Chemical Parameters of Wines(Elsevier Ltd., 2016) Şen, İlknur; Öztürk, Burcu; Tokatlı, Figen; Özen, BanuRapid and environmentally friendly methods for the prediction of chemical compositions have been an interest in the wine industry. The objective of the study was to show the potentials of combined use of visible and mid-infrared (MIR) spectroscopies to improve the prediction of various chemical compounds of wine as opposed to using mid-infrared range only. Wine samples of twelve grape varieties from two harvest years were analyzed. The chemical composition of wine samples was related to MIR and visible spectra using orthogonal partial least square (OPLS) regression technique. The prediction abilities were tested with crossvalidation and independent validation sets. The coefficient of determination of validation (R2 val) for anthocyanin compounds of red wines were between 0.76 and 0.90, and that for total phenol content was 0.90. Range of R2 val for glycerol, glycerol/ethanol ratio, malic acid, o-coumaric acid and °Brix were between 0.77 and 0.96. The spectral ranges that played significant roles in the predictions were also determined. The validations with independent data sets showed that the combination of visible and MIR ranges with multivariate methods improved the prediction of anthocyanin compounds and total phenols; produced comparable results for the rest of the parameters as MIR. This is the first study in the literature that shows the practical use of visible spectra along MIR. The combined use of these spectral ranges with multivariate models can be applied for the rapid, on-line determination of quality parameters and chemical profiles of wines.