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    Detection of seagull meat in meat mixtures using real-time PCR analysis
    (ELSEVIER SCI LTD, 2013) Kesmen, Zulal; Celebi, Yasemin; Gulluce, Ayten; Yetim, Hasan
    The possibility of the adulteration of meat products with seagull meat disturbs people living in coastal cities. In order to eliminate the suspicions of consumers a sensitive and reliable method is needed for the detection of seagull meat. In order to identify and quantify seagull meat in meat mixtures a real-time polymerase chain reaction (PCR) assay, using species-specific primers and a TaqMan probe was designed on the mitochondrial NADH dehydrogenase subunit 2 gene. In addition, it was possible to detect the template DNA of seagull at the level of 100 pg without any cross-reactivity with non-target species (bovine, ovine, donkey, pork, horse, chicken, turkey, goose, duck). Also, the method was capable of detecting seagull meat at the level of 0.1% in raw and heat-treated test mixtures, prepared by mixing seagull meat with beef and chicken at different levels (0.01-10%). In conclusion, it can be suggested that the real-time PCR assay used in this research could be a rapid and sensitive method for the routine identification of seagull meat in raw or cooked meat products. (C) 2013 Elsevier Ltd. All rights reserved.
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    Differentiation of black tea according to country of origin using the μ-CTE/TD/GC-MS method combined with decision tree-optimizable neural network analysis
    (Wiley, 2025) Okuyan, Nurullah; Yetim, Hasan; Kesmen, Zulal
    BACKGROUNDAccurate discrimination of the country of origin of teas is critical to determine their actual commercial value, to meet consumer preferences, and to ensure compliance with labeling regulations. Therefore, in this study, we developed a new approach to accurately discriminate the country of origin of teas in the Turkish market.RESULTSA thermal desorption/gas chromatographic-mass spectrometric (TD/GC-MS) method combined with optimizable neural networks (ONN) was developed to analyze the volatile organic compounds (VOCs) of tea samples subjected to infusion or grinding pretreatments. Prior to GC-MS analysis, the conventional thermal desorption method was applied to VOCs in the powdered teas, while VOCs in the infused teas were adsorbed on Tenax-TA sorbent tubes attached to a micro-chamber/thermal extractor (mu-CTE) and then thermally desorbed. Using a feature selection technique, a total of 11 VOCs from infused tea samples, 21 VOCs from ground tea samples, and 18 VOCs from both groups were identified as specific VOCs that critically affect the classification of the teas. As a result of ONN classification of selected VOCs from only ground tea samples and infused tea samples, 95.51% and 96.7% accuracy was obtained, respectively, while 100% classification accuracy was achieved by ONN classification of VOCs from both sample groups.CONCLUSIONThe results showed that different pretreatments applied to Turkish and Ceylon teas caused the release of different volatile compounds, resulting in more specific VOC profiles. In addition, the developed mu-CTE/TD/GC-MS method allowed a more accurate classification of the black tea samples than the TD/GC-MS system alone. (c) 2025 Society of Chemical Industry.
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    Multi fragment melting analysis system (MFMAS) for one-step identification of lactobacilli
    (Elsevier, 2020) Kesmen, Zulal; Kilic, Ozge; Gormez, Yasin; Celik, Mete; Bakir-Gungor, Burcu
    The accurate identification of lactobacilli is essential for the effective management of industrial practices associated with lactobacilli strains, such as the production of fermented foods or probiotic supplements. For this reason, in this study, we proposed the Multi Fragment Melting Analysis System (MFMAS)-lactobacilli based on high resolution melting (HRM) analysis of multiple DNA regions that have high interspecies heterogeneity for fast and reliable identification and characterization of lactobacilli. The MFMAS-lactobacilli is a new and customized version of the MFMAS, which was developed by our research group. MFMAS-lactobacilli is a combined system that consists of i) a ready-to-use plate, which is designed for multiple HRM analysis, and ii) a data analysis software, which is used to characterize lactobacilli species via incorporating machine learning techniques. Simultaneous HRM analysis of multiple DNA fragments yields a fingerprint for each tested strain and the identification is performed by comparing the fingerprints of unknown strains with those of known lactobacilli species registered in the MFMAS. In this study, a total of 254 isolates, which were recovered from fermented foods and probiotic supplements, were subjected to MFMAS analysis, and the results were confirmed by a combination of different molecular techniques. All of the analyzed isolates were exactly differentiated and accurately identified by applying the single-step procedure of MFMAS, and it was determined that all of the tested isolates belonged to 18 different lactobacilli species. The individual analysis of each target DNA region provided identification with an accuracy range from 59% to 90% for all tested isolates. However, when each target DNA region was analyzed simultaneously, perfect discrimination and 100% accurate identification were obtained even in closely related species. As a result, it was concluded that MFMAS-lactobacilli is a multi-purpose method that can be used to differentiate, classify, and identify lactobacilli species. Hence, our proposed system could be a potential alternative to overcome the inconsistencies and difficulties of the current methods.