Yazar "Menevseoglu, Ahmed" 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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    Öğe
    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.