Yazar "Mortas, Mustafa" seçeneğine göre listele

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    Adulteration detection technologies used for halal/kosher food products: an overview
    (Springernature, 2022) Mortas, Mustafa; Awad, Nour; Ayvaz, Huseyin
    In the Islamic and Jewish religions, there are various restrictions that should be followed in order for food products to be acceptable. Some food items like pork or dog meat are banned to be consumed by the followers of the mentioned religions. However, illegally, some food producers in various countries use either the meat or the fat of the banned animals during food production without being mentioned in the label on the final products, and this considers as food adulteration. Nowadays, halal or kosher labeled food products have a high economic value, therefore deceiving the consumers by producing adulterated food is an illegal business that could make large gains. On the other hand, there is an insistent need from the consumers for getting reliable products that comply with their conditions. One of the main challenges is that the detection of food adulteration and the presence of any of the banned ingredients is usually unnoticeable and cannot be determined by the naked eye. As a result, scientists strove to develop very sensitive and precise analytical techniques. The most widely utilized techniques for the detection and determination of halal/kosher food adulterations can be listed as High-Pressure Liquid Chromatography (HPLC), Capillary Electrophoresis (CE), Gas Chromatography (GC), Electronic Nose (EN), Polymerase Chain Reaction (PCR), Enzyme-linked Immuno Sorbent Assay (ELISA), Differential Scanning Calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Near-infrared (NIR) Spectroscopy, Laser-induced Breakdown Spectroscopy (LIBS), Fluorescent Light Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy and Raman Spectroscopy (RS). All of the above-mentioned techniques were evaluated in terms of their detection capabilities, equipment and analysis costs, accuracy, mobility, and needed sample volume. As a result, the main purposes of the present review are to identify the most often used detection approaches and to get a better knowledge of the existing halal/kosher detection methods from a literature perspective.
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    Improving the screening of potato breeding lines for specific nutritional traits using portable mid-infrared spectroscopy and multivariate analysis
    (Elsevier Sci Ltd, 2016) Ayvaz, Huseyin; Bozdogan, Adnan; Giusti, M. Monica; Mortas, Mustafa; Gomez, Rene; Rodriguez-Saona, Luis E.
    Efficient selection of potato varieties with enhanced nutritional quality requires simple, accurate and cost effective assays to obtain tuber chemical composition information. In this study, 75 Andean native potato samples from 7 Solanum species with different colors were characterized and quantified for their anthocyanin, phenolics and sugar content using traditional reference methods. IR (infrared) spectra of potato extracts were collected using a portable infrared system and partial least squares regression (PLSR) calibration models were developed. These models were validated using both full cross-validation and an independent sample set giving strong linear correlation coefficients of prediction (rPred) > 0.91 and standard error of prediction (SEP) of 24 mg/100 g phenolics, 7 mg/100 g monomeric anthocyanins, 0.1 g/100 g reducing sugars and 0.12 g/100 g sucrose. Overall, portable infrared system with PLSR showed great potential to facilitate potato breeding and certain aspects of crop management, material selection for potato processing and related research by providing alternative prediction models. (C) 2016 Elsevier Ltd. All rights reserved.
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    Near- and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species
    (Springer, 2021) Ayvaz, Hüseyin; Mortas, Mustafa; Doğan, Muhammed Ali; Atan, Mustafa; Yıldız Tiryaki, Gülgün; Karagül Yüceer, Yonca
    This study aimed to evaluate the performance of both near-infrared (NIR) diffuse reflectance and mid-infrared-attenuated total reflectance (MIR-ATR) in determining some quality parameters of a commercial white cheese made of unknown ratios of various milk species. For this purpose, 81 commercial Ezine cheese samples, a special ripened cheese produced in Turkey, containing unknown ratios of bovine, caprine, and ovine milk, were used. Reference analyses, including textural properties, protein content, nitrogen fractions, ripening index coefficients, fat, salt, dry matter-moisture, and ash contents as well as pH and titratable acidity levels, were conducted in the samples following the traditional gold standards. For NIR applications, the spectra of both intact cubes and hand-crushed cheese samples were collected, whereas the spectra of only hand-crushed cheese samples were collected for MIR-ATR. PLSR (Partial Least Squares Regression) calibration models were developed for each parameter (n = 61) and then validated using both cross-validation (leave-one-out approach) and an external validation set (n = 20). Overall, PLSR models developed for total protein, fat, salt, dry matter, moisture, and ash content, as well as pH and titratable acidity, yielded satisfactory performance statistics in the complementary use of NIR and MIR spectroscopy. However, PLSR models of the other parameters, including textural properties, nitrogen fractions, and the ripening index, could only separate high and low values and were not able to make accurate quantitative predictions. NIR spectroscopy was found to be more accurate than that of MIR-ATR spectroscopy for almost all the parameters except for pH and titratable acidity, for which MIR-ATR spectroscopy was superior.
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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 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.