Yazar "Dogan, Muhammed Ali" seçeneğine göre listele

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    Infrared spectroscopy combined with chemometrics as a convenient method to detect adulterations in cooking/stretching process in commercial cheese
    (Elsevier Sci Ltd, 2022) Ozturk, Mustafa; Dogan, Muhammed Ali; Menevseoglu, Ahmed; Ayvaz, Huseyin
    Stretching and kneading of the curd during fresh Kashar cheese manufacturing must take place in hot water; dry cooking/stretching of the curd with the assistance of emulsifying salts is not allowed. However, some producers in recent years tend to label their processed cheese as Kashar cheese resulting in unfair economic gain and consumer deception. Near-infrared (NIR) diffuse reflectance and mid-infrared (MIR) attenuated total reflectance (ATR) spectroscopy were assessed for the fast and convenient identification of these two types of cheese. Soft independent modeling of class analogy (SIMCA) models of both NIR and MIR-ATR spectra were developed; the latter gave superior separation due to the greater inter-class distance originating from better-resolved peaks associated with phosphates (6.7 for MIR-ATR versus 3.2 for NIR). Information Theory determined two and three variables were enough for MIR and NIR spectral data classification, respectively; quadratic discriminant and support vector machine provided 100% accuracy for class prediction. (C) 2021 Published by Elsevier Ltd.
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    Multiparametric analysis of cheese using single spectrum of laser-induced breakdown spectroscopy
    (Elsevier Sci Ltd, 2019) Ayvaz, Huseyin; Sezer, Banu; Dogan, Muhammed Ali; Bilge, Gonca; Atan, Mustafa; Boyaci, Ismail Hakki
    The potential of using laser-induced breakdown spectroscopy (LIBS) for prediction of chemical quality/control parameters in a cheese matrix was investigated. Traditional methods are usually time-consuming and require complex pre-treatments, the use of toxic chemicals and laboratories and personnel equipped with these analytical skills. In this study, 11 different chemical analyses commonly performed on cheese samples were conducted using traditional methods on 82 full-fat, white pickled and ripened cheese samples. Additionally, these parameters of interest were correlated with multi-elemental spectra of LIBS using partial least squares regression. The results obtained for moisture, dry matter, salt, total ash, total protein, pH, fat, acidity, water soluble nitrogen, trichloroacetic acid soluble nitrogen, and phosphotungstic acid soluble nitrogen (R-cal(2) = 0.964, 0.959, 0.970, 0.973, 0.952, 0.971, 0.733, 0.762, 0.714, 0.633, and 0.707, respectively) indicated that LIBS could be used as an alternative or complementary method in quality control of cheese. (C) 2018 Elsevier Ltd. All rights reserved.
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    Quantification of phenolics and antioxidant properties in dried grape pomace by near infrared spectroscopy
    (2025) Ayvaz, Huseyin; Dogan, Muhammed Ali
    This study evaluated the potential of near-infrared (NIR) spectroscopy, combined with partial least squares regression (PLSR) to quantify bioactive properties in dried grape pomace powders. Twenty-four samples of red-pink and purple-black grapes were examined for total monomeric anthocyanins, phenolics, flavonoids, proanthocyanidins, and antioxidant capacity utilizing CUPRAC (cupric reducing antioxidant capacity) and DPPH (2,2-diphenyl-1-picrylhydrazyl) assays. Chemical analyses verified higher phenolic and antioxidant contents in purple-black pomace than red-pink. PLSR models from NIR spectra produced promising results, showing strong calibration and cross-validation for all analytes. These outcomes indicate that NIR spectroscopy provides a simple, nondestructive, and eco-friendly alternative to conventional wet chemistry methods. Moreover, the approach offers potential for monitoring bioactive compounds in grape pomace, thereby supporting the valorization of agro-industrial residues as sustainable sources of phenolics and antioxidants for food, nutraceutical, and cosmetic applications.
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    Rapid detection of milk fat adulteration in yoghurts using near and mid-infrared spectroscopy
    (Elsevier Sci Ltd, 2020) Temizkan, Riza; Can, Aygul; Dogan, Muhammed Ali; Mortas, Mustafa; Ayvaz, Huseyin
    Both Fourier-transform near-infrared (FT-NIR) and mid-infrared (FT-MIR) spectroscopy with chemometrics were used for the fast detection of milk fat adulteration in yoghurts without sample preparation. Soft independent modelling of class analogy models of both NIR and MIR spectra showed successful detection of milk fat adulteration and identification of the type of adulterant oils. Partial least squares regression models of a representative adulterant yielded high correlation coefficients (above 0.98), low standard error of prediction (lower than 7.12%) and high residual predictive deviation values (above 4.35) for both NIR and MIR spectra. Additionally, regardless of the source of adulterant oils used, separate NIR and MIR PLSR models were developed for quantification of milk fat ratio (%) in yoghurts as an alternative approach to measuring the level of adulterant oil (%); NIR spectroscopy was found to be superior in these models. (C) 2020 Elsevier Ltd. All rights reserved.
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    Rapid detection of the presence, activity and concentration of microbial transglutaminase in yogurt using infrared spectroscopy combined with chemometrics
    (Elsevier Sci Ltd, 2025) Sicramaz, Hatice; Ayvaz, Huseyin; Menevseoglu, Ahmed; Yaaqob, Mysa Ahmed Hasan Ayash; Dogan, Muhammed Ali; Ozturk, Mustafa
    The goal of this study was to develop a rapid method by using near-infrared (NIR) diffuse reflectance and midinfrared (MIR) spectroscopy to detect the use, status (active or inactive), and concentration of microbial transglutaminase (mTGase) in yogurt. Control samples were manufactured without mTGase. Two different levels of mTGase concentration were employed: 1 and 2 units. Half of the enzyme-added samples were inactivated after yogurt manufacture to detect the active/inactive status of mTGase. Both for NIR and MIR, analyzed via the soft independent modeling of class analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) approaches were able to classify the control sample from active mTGase-containing yogurts and enzyme status, but could not differentiate enzyme concentrations. Machine learning effectively determined mTGase presence, activity, and concentrations. In conclusion, NIR and MIR spectroscopy, combined with chemometric methods, successfully detected mTGase in yogurt, with machine learning outperforming SIMCA and PLS-DA in identifying enzyme levels.
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    Rapid discrimination of Turkish commercial hazelnut (Corylus avellana L.) varieties using Near-Infrared Spectroscopy and chemometrics
    (Elsevier, 2022) Ayvaz, Huseyin; Temizkan, Riza; Genis, Huseyin Efe; Mortas, Mustafa; Genis, Duygu Ozer; Dogan, Muhammed Ali; Nazlim, Burak Alptug
    Classification of hazelnuts as per their varieties is traditionally made based on subjective phenotypic observations. However, various factors easily affect these observations, including environmental conditions and the growth phase of hazelnuts, making it difficult to separate the varieties. Furthermore, the distinction among the varieties becomes far more challenging in the case of shelled hazelnut kernels, where trade is much more common than hazelnuts in-shell. Nevertheless, literature studies imply that there are apparent differences among the chemical compositions of different hazelnut varieties along with the visual appearances. Thus, developing an analytical method allowing these differences to be determined quickly and objectively would be valuable. Accordingly, the potential of using Near-Infrared Spectroscopy (NIRS) with chemometrics as a fast, convenient, and reliable method in discriminating commercially important hazelnut varieties grown in Turkey has been investigated in this research. A total of 280 hazelnut samples belonging to seven distinct varieties in Turkey were employed in the study. NIR spectra of the hazelnuts were collected from three physical states of hazelnuts as (1) in-shell, (2) shelled, and (3) white interior of split hazelnut kernels. Using the collected spectra and chemometric methods (Partial Least Squares-Discriminant Analysis (PLS-DA) and Logistic Models), calibration models were developed using a training set (n = 210) and validated with a test set (n = 70). The results have indicated that all NIR spectra-based PLS-DA and Logistic Models with MultiClass Fisher's Linear Discriminant Analysis (FLDA) pre-processing technique could accurately determine the variety of unknown hazelnuts at 100 % in either of the three tested physical states of the samples. Accordingly, these approaches can be utilized by the sector that is concerned about economic loss as more reliable and objective methods compared to the traditional analysis of hazelnut varieties, which are generally made via subjective visual assessment.