Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 3Citation - Scopus: 3Development of a Yeast-Free Bread Using Legume and Nut Flours in a Gluten-Free Flour: Techno-Functional Characteristics and Sensory Evaluation(Wiley, 2024) Tuna, Ayca; Başer, Filiz; Ortiz-Sola, Jordi; Tokatlı, Figen; Lopez-Mas, Laura; Baser, Filiz; Kallas, Zein; Aguilo-Aguayo, Ingrid; Tokatli, FigenThis study aimed to investigate the effect of combined use of legume and nut flours on physical, nutritional and sensory properties of yeast-free bread by substituting gluten-free flour with hazelnut and white bean flours. Yeast-free bread containing a mixture of 30% hazelnut and white bean flours was found to have the lowest hardness (9.04 N) and the largest specific volume (1.51 mL g-1) compared to the reference gluten-free bread (18 N and 1.43 mL g-1) using a mixture design. Hazelnut and bean flours improved the in vitro starch digestion, reducing rapidly digestible starch by 29% and increasing resistant starch compared to the reference bread. Free choice profiling sensory analysis revealed that the developed breads containing nuts and legumes differed from the standard gluten-free formulation and a commercial product available on the market. The combined use of bean and hazelnut flours was demonstrated as functional ingredients for enhancement of nutritional, sensory and textural aspects. Gluten- and yeast-free bread formulated using combination of white bean and hazelnut flours had significantly better textural and nutritional properties. Free choice profiling showed the different characteristics of the new product compared to standard gluten-free breads based on rice flour and corn starch.dagger imageArticle Citation - WoS: 15Citation - Scopus: 16Formulation of Gluten-Free Cookies Utilizing Chickpea, Carob, and Hazelnut Flours Through Mixture Design(MDPI, 2023) Doğruer, Ilgın; Başer, Filiz; Güleç, Şükrü; Tokatlı, Figen; Özen, BanuLegume flours, which offer high nutritional quality, present viable options for gluten-free bakery products. However, they may have an objectionable flavor and taste for some consumers. In this study, it was aimed to improve the gluten-free cookie formulation by incorporating carob and hazelnut flours to pre-cooked chickpea flour and to investigate the techno-functional properties of the formulated cookies. The flours used in the formulations were assessed for their chemical and physical properties. This study employed a mixture design (simplex-centroid) to obtain the proportions of the flours to be used in the cookie formulations. The rheological characteristics of the doughs and the technological attributes of the baked cookies were determined. The addition of the hazelnut and carob flours had the overall effect of reducing the rheological characteristics of the cookie doughs. Furthermore, the textural attribute of the hardness of the baked cookies decreased as the ratio of hazelnut flour in the formulations was raised. The analysed results and sensory evaluation pointed to a formulation consisting of 30% pre-cooked chickpea/30% carob/30% hazelnut flours, which exhibited improved taste and overall acceptability scores. A total of 16.82 g/100 g of rapidly digestible starch, 5.36 g/100 g of slowly digestible starch, and 8.30 g/100 g of resistant starch exist in this particular cookie. As a result, combinations of chickpea, hazelnut, and carob flours hold promise as good alternatives for gluten-free cookie ingredients and warrant further exploration in the development of similar products.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: 13Citation - Scopus: 13Authentication of Turkish Olive Oils by Using Detailed Pigment Profile and Spectroscopic Techniques(John Wiley and Sons Inc., 2020) Uncu, Oğuz; Uncu, Oğuz; Özen, Banu; Özen, Fatma Banu; Tokatlı, Figen; Tokatlı, FigenBACKGROUND Minor compounds of olive oil could have discriminatory characteristics in the authentication of this product. It was aimed to determine the detailed pigment profiles of Turkish olive oils and use them in differentiation of the samples in comparison to fast, reliable, and environmentally friendly Fourier-transform infrared (FTIR) and ultraviolet (UV)-visible spectroscopic techniques. Pigment contents of 91 olive oils obtained from different locations for two consecutive harvesting years were determined with chromatographic analysis and FTIR and UV-visible spectra of these samples were also obtained. All data were analyzed with orthogonal partial least-squares discriminant analysis to investigate the differentiation ability of these methods with regard to their detailed pigment and spectroscopic profiles. RESULTS Pheophytin a (2.78-8.98 mg kg(-1)) and lutein (1.19-4.07 mg kg(-1)) were the major pigments in all samples. Pigment profiles provided successful classification of olive oils with respect to their designated origins and harvesting year with average correct classification rates of 97%. UV-visible spectroscopy has quite similar results with pigment profiles in terms of its discriminatory power. In addition, FTIR and fused data were slightly better in discrimination of the samples, and the fused dataset has the highest correct classification rate of 100%. CONCLUSION Use of detailed pigment profiles is quite promising in authentication of olive oils. However, UV-visible and FTIR spectroscopic techniques could be reliable alternatives for the same purposes. All of the techniques studied have great potential in 'protected designation of origin' certification studies. (c) 2020 Society of Chemical IndustryArticle 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.