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    A comprehensive study on the quality characteristics of sweet red pepper paste and the evaluation of near-infrared spectroscopy as a rapid alternative tool
    (Springer Science and Business Media Deutschland GmbH, 2023) Ayvaz, Hüseyin; Temizkan, Rıza; Menevşeoğlu, Ahmed; Doğan, Muhammed Ali; Nazlım, Burak Alptuğ; Günay, Ezgi; Pala, Ciğdem Uysal
    Red pepper (Capsicum annuum L. cv. Capia) is an important vegetable obtained from a 1-year cultivated plant belonging to the Solanaceae family. Among the products of red pepper, its paste is very similar to tomato paste in appearance; however, it differs in terms of flavor properties. The wide-ranging quality parameters (dry matter, soluble solids, pH, titration acidity, salt, insoluble ash, total reducing sugars, color values, total carotenoid, antioxidant properties, browning index, and 5-hydroxymethylfurfural) of commercial sweet pepper pastes (n = 52) were determined. To the best of our knowledge, this study is the most comprehensive study on determining the quality parameters of red pepper paste, and its in-depth results will help food authorities issue registrations related to commercial pepper paste in countries such as Türkiye. Additionally, near-infrared (NIR) spectroscopy-based machine-learning algorithms were evaluated for the first time in paste samples as a rapid alternative or complementary to the conventional analytical methods. Regarding the conditional entropy-based models, successful predictions with high accuracy were developed for all the quality parameters of pepper paste. Except for the coefficient of determination value of 0.69 and 0.85 in the prediction of HMF and a/b color values in the developed models, respectively, 0.96 or higher coefficients of determination were obtained for all other parameters. Therefore, NIR spectroscopy, along with the conditional entropy-based predictions, enables simple, fast, multiparametric and accurate quantitative prediction of the numerous quality parameters for commercial sweet pepper pastes and has the potential to be used in routine quality control analyses by pepper paste producers.
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    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.
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    Near- and Mid-Infrared Spectroscopy Combined with Machine Learning Algorithms to Determine Minerals and Antioxidant Activity in Commercial Cheese
    (2023) Menevşeoğlu, Ahmed; Güneş, Nurhan; Ayvaz, Huseyin; Sarıkaya, Sevim Beyza Öztürk; Zehiroğlu, Cuma
    Erzincan Tulum Cheese (ETC) holds a significant place among the most popular cheeses in Türkiye. It has been awarded Protected Geographical Indication status, which restricts the allowable milk species, its production area, and specific sheep breed used in its production. Mineral content and antioxidant activity of ETC were aimed to be predicted using conventional FT-NIR and a portable FT-MIR spectrometer combined with partial least square regression (PLSR) and machine learning algorithms based on conditional entropy. Seventy ETC samples were analyzed for their mineral (Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, and P) content using ICP-MS. The samples' antioxidant activity was measured using the DPPH•+ scavenging activity method. PLSR combined with FT-NIR spectral data correlated with antioxidant activity (r=0.89) and minerals (as low as r=0.83) except for Cr and Fe. FT-MIR data provided a good correlation for minerals (as low as r=0.82) except for Cr and Mn and a moderate correlation with antioxidant activity (r=0.64). Information theory was applied to select wavenumbers used in machine learning algorithms, and better results were obtained compared to PLSR. Overall, FT-NIR and FT-MIR spectroscopy provided rapid (~ 1 min), non-destructive, sensitive, and reliable output for mineral and antioxidant activity predictions in commercial cheese samples.