Yazar "Mortaş, Mustafa" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Anthocyanins from Agro-Industrial Food Waste: Geographical Approach and Methods of Recovery-A Review
    (MDPI, 2023) Diaconeasa, Zorita; Iuhas, Cristian I.; Ayvaz, Hüseyin; Mortaş, Mustafa; Farcas, Anca; Mihai, Mihaela; Danciu, Corina; Stanila, Andreea
    Drastic growth in the amount of global food waste produced is observed every year, not only due to incessant population growth but also economic growth, lifestyle, and diet changes. As a result of their increasing health awareness, people are focusing more on healthy diets rich in fruits and vegetables. Thus, following worldwide fruit and vegetable consumption and their processing in various industries (juice, jams, wines, preserves), significant quantities of agro-industrial waste are produced (pomace, peels, seeds) that still contain high concentrations of bioactive compounds. Among bioactive compounds, anthocyanins have an important place, with their multiple beneficial effects on health; therefore, their extraction and recovery from food waste have become a topic of interest in recent years. Accordingly, this review aims to summarize the primary sources of anthocyanins from food waste and the novel eco-friendly extraction methods, such as pulsed electric field extraction, enzyme-assisted extraction, supercritical fluid extraction, pressurized liquid extraction, microwave-assisted extraction, and ultrasonic-assisted extraction. The advantages and disadvantages of these techniques will also be covered to encourage future studies and opportunities focusing on improving these extraction techniques.
  • Küçük Resim
    Öğe
    Machine Learning-Assisted Near- and Mid-Infrared spectroscopy for rapid discrimination of wild and farmed Mediterranean mussels (Mytilus galloprovincialis)
    (Elsevier Inc., 2024) Ayvaz, Hüseyin; Temizkan, Rıza; Kaya, Burcu; Salman, Merve; Menevşeoğlu, Ahmed; Ayvaz, Zayde; Güneş, Nurhan; Doğan, Muhammed Ali; Mortaş, Mustafa
    The objective of this study was to investigate the ability to discriminate between wild and farmed Mediterranean mussels (Mytilus galloprovincialis) using machine learning-assisted near-infrared (NIR) and mid-infrared (MIR) spectroscopy. Mussels are of significant global importance in aquaculture due to their nutritional characteristics, encompassing a rich source of protein, essential fatty acids, various vitamins, and abundant minerals. Additionally, their ease of farming adds to their value as a desirable aquaculture species. The mussels' capacity to reflect environmental quality attributes makes them valuable as biomonitoring agents. However, differences in nutritional composition may arise between wild mussels harvested from natural marine hard-bottoms and those farmed in open artificial systems in the sea. In this study aimed at distinguishing between the two types of mussels, the classification models were created, and the most accurate results were achieved using the FT-MIR spectral data extracted from the interior part of the mussels, while the performance of FT-MIR data obtained from the mussels' shells was slightly lower, with the accuracy of 92% and R2 of 0.87. Still, the accuracies of all the classification models were over 90%. The Ensemble model, trained using FT-MIR spectra from the interior part of the mussel, achieved an accuracy of 98.4%, surpassing the performance of other variable sets. In both NIR and MIR models, spectra from the mussels' interior provide better discrimination than spectra from the outer shell.