Yazar "Uysal, Reyhan Selin" seçeneğine göre listele

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    A promising electrochemical sensor based on gold deposited-reduced graphene oxide sheets for the detection of Cd(II) and Pb(II)
    (Springer Int Publ Ag, 2024) Uysal, Reyhan Selin
    This study presents electrochemical-based detection of heavy metal ions in mineral water samples. The aim of this research was to design a low-cost and highly sensitive disposable sensor to quantify Cd(II) and Pb(II). To this end, we have modified a pencil graphite electrode coated with graphene oxide (GO), electrochemically reduced graphene oxide and deposited metallic Au particles using square wave anodic stripping voltammetry. The findings revealed that the combined effect of GO and activation of the surface with auric acid improved electrical conductivity, thus facilitating deposition of Cd(II) and Pb(II) onto the electrode's surface. Under optimal conditions, a linear correlation was observed between current values and the concentrations of Cd(II) and Pb(II) within the ranges of 0.6-1.6 mu M and 0.4-1.6 mu M, where the limit of detection values were obtained as 0.36 mu M and 0.24 mu M for Cd(II) and Pb(II), respectively. According to the experimental results, the developed electrode can achieve a considerably high recovery rates for detection of Cd(II) (98.5%) and Pb(II) (93.5%) in gaseous natural mineral water samples.
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    Effects of Aging in Wood Casks on Anthocyanins Compositions, Volatile Compounds, Colorimetric Properties, and Sensory Profile of Jerez Vinegars
    (Mdpi, 2024) Uysal, Reyhan Selin
    The Jerez (Sherry) vinegars, including Vinagre de Jerez, Reserva, and Gran Reserva, are crafted from Sherry wines and are protected under the Denomination of Origin in Spain. The aim of this study was to (i) characterize the physicochemical properties and composition; (ii) investigate the impact of the aging process on color properties, phenolics, volatile compounds, and sensorial profiles; and (iii) find a marker for tracing the authenticity of non-aged (Apto) and aged Jerez vinegars. The phenolic components were identified through LC-MS/MS, whereas the volatile compounds were examined using the GC-MS/MS technique. As the aging progressed, a decrease was observed in the levels of flavonol and phenolic acids, with anthocyanin components being undetectable in non-aged and aged samples. In the Gran Reserva variety, 2-methylbutyl acetate, acetic acid, and ethanol emerged as the predominant volatile substances. The presence of oaklactone and ethyl butanoate components served as marker substances to authenticate the Gran Reserva. Additionally, alterations in color properties were noted, marked by a decrease in yellow content and an increase in the red component depending on aging. Furthermore, novel sensory descriptors, such as vanilla, clove, woody, and nutty notes, and winy character emerged in the samples with prolonged aging.
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    Exploring Ultrasound and Microwave-Assisted Accelerated Aging of Jerez Vinegar: Impacts on Phenolic, Volatile, Colorimetric, and Sensory Properties
    (Mdpi, 2025) Uysal, Reyhan Selin; Issa-Issa, Hanan; Carbonell-Barrachina, Angel A.; Sendra, Esther
    Jerez vinegar is a high-quality wine vinegar produced in the Vinagre de Jerez denomination of origin (Spain) and is traditionally aged in wood barrels for over 10 years. Considering the long aging process, a practical technique to accelerate the aging process was simulated. This study aimed to evaluate ultrasound and microwave treatments as alternative aging methods for fresh Jerez vinegars with oak chips, and to investigate their effects on phenolic content, volatile compounds, and colorimetric and sensory properties. Fresh control samples with oak chips were treated using ultrasound (US) in an ultrasonic bath three times: 0.5 h (US1), 2 h (US2), and 10 h (US3). Microwave (MW) treatments were performed using a domestic microwave oven with three power/time combinations: 640 W for 10 min (MW1), 640 W for 20 min (MW2), and 800 W for 10 min (MW3). Compared with the fresh control (4230 mu g/kg), US- and MW-treated samples showed a significant reduction in total phenolic content, decreasing to 3943 mu g/kg in the US1 sample and to 3988 mu g/kg in the MW2 treatment. Moreover, volatile substances significantly decreased from 1019 mg/L in the fresh control to 623 mg/L in the US3 treatment and 716 mg/L in the MW1 sample. Regarding sensory properties, US3 and MW1 treatments exhibited marked distinctions in certain odor and flavor attributes when compared with the fresh control. As a result, both techniques modified the phenolic, volatile and sensory profiles. Further research is needed to fully mimic the aging process, but US has proven to be a promising technique.
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    From Pixels to Spectra: Predicting Wine Colorimetric Characteristics Through Machine Learning Models
    (Springer, 2026) Erdemir, Naz; Sinop, Celal Deniz; Uysal, Reyhan Selin; Dalyan, Tugba
    This study employs digital image processing and machine learning techniques to predict all colorimetric characteristics (color intensity, density, tonality, and color index percentages) of colored wines in a novel, cost-effective, and rapid manner. To determine the values of colorimetric characteristics, ultraviolet-visible (UV-Vis) absorbance was measured at three key wavelengths (A420, A520, and A620) using UV-Vis spectrophotometry, corresponding to the yellow, red, and blue color percentages, respectively. Simultaneously, the pictures of 86 wine samples were acquired, and the corresponding RGB and HSV color values were extracted from the images to serve as input features for multiple regression models. The models developed included principal component regression, k-nearest neighbors, linear regression, decision tree, random forest, and partial least squares (PLS). Among the models, random forest outperformed PLS in predicting A620 absorbance value due to its ability to capture non-linear patterns, whereas PLS demonstrated greater accuracy (R2 > 0.95) in predicting the A420 and A520 absorbance values. According to feature selection, hue and saturation had the biggest impact on prediction accuracy. By determining absorbance values using the developed models, the complete colorimetric characteristics of the wine samples can be calculated, enabling the evaluation of their physicochemical parameters during the fermentation process or post-fermentation. As a result, all the models, improved, offer a promising alternative for quick, easy, and scalable prediction methods by reducing measurement time, eliminating the need for laboratory instruments, and introducing a new methodology to complement conventional spectroscopic techniques, with potential applications in consumer-level analysis and the process of wine quality control.