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    Development and Validation of Confirmatory Method for Analysis of Nitrofuran Metabolites in Milk, Honey, Poultry Meat and Fish By Liquid Chromatography-Mass Spectrometry
    (Univ Sv Kiril & Metodij Skopje, Fak Veterinarna Medicina, 2016) Alkan, Fatih; Kotan, Arzu; Özdemir, Nurullah
    In this study we have devoloped and validated a confirmatory analysis method for nitrofuran metabolites, which is in accordance with European Commission Decision 2002/657/EC requirements. Nitrofuran metabolites in honey, milk, poultry meat and fish samples were acidic hydrolised followed by derivatisation with nitrobenzaldehyde and liquid-liquid extracted with ethylacetate. The quantitative and confirmative determination of nitrofuran metbolites was performed by liquid chromatography/electrospray ionisation tandem mass spectrometry (LC/ESI-MS/MS) in the positive ion mode. In-house method validation was performed and reported data of validation (specificity, linearity, recovery, CC alpha and CC beta). The advantage of this method is that it avoids the use of clean-up by Solid-Phase Extraction (SPE). Furthermore, low levels of nitrofuran metabolites are detectable and quantitatively confirmed at a rapid rate in all samples.
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    Oak Leaf Classification: An Analysis of Features and Classifiers
    (IEEE, 2019) Kaya, Heysem; Keklik, İlhan; Ensari, Tolga; Alkan, Fatih; Biricik, Yağmur
    Automatic classification of trees from leaves is a popular computer vision/machine learning task and has important applications in monitoring of forest wealth. While the final aim is preparing an application, which is capable of visual signal processing and classification, in this paper we present a new oak leaf dataset and preliminary results for classification of 8 types of oak trees. The novelties include comparative analysis of a small set of hand-crafted geometric features and popularly used high-dimensional appearance features, such as Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG). We further compare commonly used Support Vector Machines (SVM) classifier with a recently popular, fast and robust learner called Extreme Learning Machines (ELM). Results indicate that a small set of geometric features reach an accuracy of 75%, while high dimensional appearance features can boost the performance up to92%.