Browsing by Author "Tunc, Yazgan"

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Evaluation of Genetic Diversity in Some Hybrid Individuals of Honeyberry (Lonicera Caerulea L.) Based on Fruit Characteristics, Leaf Morphology, Vitamin C, Antioxidant Activity, and Biochemical and Nutritional Contents
(Bmc, 2024) Gurcan, Kahraman; Yilmaz, Kadir Ugurtan; Tunc, Yazgan; Yaman, Mehmet; Gunes, Adem; Yildiz, Ercan; Khadivi, Ali
BackgroundGenetic diversity is a prerequisite for breeding programs, and one of the main goals here is to obtain quality products. Therefore, this study aims to evaluate the genetic diversity in some hybrid individuals of honeyberry (Lonicera caerulea L.) based on fruit characteristics, leaf morphology, vitamin C, antioxidant activity, biochemical, and nutritional content. In this context, superior quality individuals have been identified based on the 42 variables examined in our study. These hybrid individuals can be economically incorporated into production after the registration stages, and their sustainability for use in breeding programs can also be ensured.ResultsThe fruit weight ranged from 0.71 ('H11') to 1.66 g ('H6'). The ascorbic acid varied between 17.13 ('H7') and 20.64 mg AAE/100 g ('H15'). The antioxidant activity changed between 12.59 ('Store') and 15.03 mu mol Trolox g-1 ('Aurea'). The total anthocyanins were found to be highest in 'Borrel Beast' (163.79 mg cyn-3-gluc 100 g-1), followed by 'H8' (163.20 mg cyn-3-gluc 100 g-1). The highest nutrient levels in the fruits were found in the 'H10' individual, with calcium (2445.77 mg kg-1), potassium (2274.36 mg kg-1), phosphorus (2123.27 mg kg-1), magnesium (1263.95 mg kg-1), and sulfur (859.62 mg kg-1), respectively. The highest nutrient levels in the leaves were found in the 'H14' individual for calcium (19,493.21 mg kg-1), 'H5' for magnesium (5643.52 mg kg-1), 'H8' for sulfur (2312.11 mg kg-1), 'H6' for phosphorus (2007.51 mg kg-1), and 'H6' for potassium (1099.32 mg kg-1). In general, the nutrients in the fruit exhibited significant correlations among themselves at different levels (*, **, ***). Within the scope of principal component analysis, the first 8 principal components explained 80.69% of the total variance. According to the cluster and population analyses, it was determined that there was a high variation in subgroup B2. Additionally, although honeyberry is a relatively new fruit in T & uuml;rkiye, efforts have begun to develop new cultivars through hybrid breeding.ConclusionsWhen 42 variables were evaluated together to determine genetic diversity, hybrid individuals 'H14', 'H5', 'H8', and 'H1' were identified as superior individuals, respectively.
Refinement of Surface Sterilization Protocol for in Vitro Olive (Olea Europaea L.) Shoot Proliferation and Optimizing by Machine Learning Techniques
(Korean Soc Horticultural Science, 2025) Palaz, Esra Bulunuz; Demirel, Serap; Popescu, Gheorghe Cristian; Demirel, Fatih; Ugur, Remzi; Yaman, Mehmet; Tunc, Yazgan
The olive tree (Olea europaea L.) is one of the most ancient fruit species grown throughout history. Given the challenges and costs associated with propagating olive cultivars by cuttings and grafting, it is crucial to identify a method for efficient and widespread propagation. Micropropagation is especially advantageous for propagating plants that are conventionally challenging to propagate or for producing virus-free seedlings or plants with specified traits. This work aimed to improve the in vitro shoot proliferation of O. europaea L. 'Sultani' cultivated in T & uuml;rkiye. Machine learning (ML) techniques were used to predict the efficiency of surface sterilization treatments. The explants were subjected to varied concentrations and durations of five disinfectants: hydrogen peroxide (H2O2), silver nitrate (AgNO3), mercuric chloride (HgCl2), sodium hypochlorite (NaOCl), and chlorine dioxide (ClO2). Each disinfectant was assigned three treatment levels (T1, T2, T3), which varied in concentration and exposure duration. The measured variables were contamination rate, survival rate, growth rate, shoot diameter, shoot length, and leaf number. ClO2 and NaOCl were the most efficient disinfection agents for the growth of explants. ClO2 showed particularly excellent results in terms of shoot diameter (0.765 mm), shoot length (43.733 mm), and leaf number (14.578). NaOCl treatment resulted in the greatest growth percentage (70.55%). AgNO3 treatment performed moderately performance in most parameters, but the lowest contamination rate (13.556%) was observed. Ultimately, the selection of chemical and treatment techniques substantially impacted the efficacy of in vitro olive shoot proliferation. The support vector regression, random forest, extreme gradient boosting (XGBoost), elastic net, and Gaussian processes algorithms were used to model and forecast the optimal sterilizing settings. The XGBoost provided the most accurate (R2) for survival rate, growth rate, shoot diameter, shoot length, and leaf number variables; 0.587, 0.959, 0.843, 0.894, and 0.900, respectively. The XGBoost algorithm was used to predict and optimize surface sterilization. The optimal circumstances for survival and development were projected to include explants sterilized with a 30% solution of NaOCl for 20 min. Moreover, it was projected that explants treated with a 15% concentration of ClO2 for 30 min would be possibly ideal in terms of shoot diameter, shoot length, and leaf number values. ML algorithms could further optimize these protocols for better outcomes, reducing the number of treatments needed and improving efficiency.