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Leaf morphological and anatomical features of some cultivars of hybrid tea roses and their drought resistance when grown on the southern coast of the Crimea
... Currently, the most widely used methods for determining AOA include: photocolorimetric and fluorimetric methods; methods based on electron paramagnetic resonance spectroscopy; chemiluminescent and amperometric methods [10][11][12][13][14][15][16]. The phenolic substances that form the phenolic profile of wines exhibit a high antioxidant capacity due to the high reactivity of the hydroxyl groups that are a part of them [11,17]. ...
This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F9.5 = 19.10 and p = 0.0023) can be fairly accurately predicted with an R² of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272.
Data are presented on changes in the total water content in leaf tissues and the level of real water deficiency in 7 genotypes of garden roses (Rosa gallica L., R. odorata var. gigantea × R. multiflora, R. hugonis Hemsl., R. chinensis var. minima (Sims) Voss., R. bracteata J.C. Wendl., and R. foetida var. persiana (Lem.) Rehder) and cv. ‘Borisfen’ during the summer seasons of 2022–2023 on the Southern Coast of Crimea. Most genotypes showed a sublethal limit of water deficiency with the loss of 22–26% of water in their leaves. The leaf water regime was described for the studied genotypes under the extreme drought of 2023. For R. gallica, the critical limit of water loss was 10–15%. Simulating conditions close to the hot dry wind (t = 27°C, Rh = 30%) resulted in a more intensive loss of moisture by the leaves of the studied roses, while only R. odorata var. gigantea × R. multiflora showed a reduction in water consumption. The threshold for sublethal moisture deficiency in the leaves of relatively resistant plants (cv. ‘Borisfen’ and R. hugonis) dropped to 20–24%.
Analyzing main parameters of chlorophyll fluorescence induction proved that the development of water deficiency in the range of 20–25% under low air humidity caused an irreversible inactivation of PS II in R. bracteata, R. gallica, and R. odorata var. gigantea × R. multiflora. Disturbances in the functioning of the photosynthetic apparatus in cv. ‘Borisfen’, R. hugonis, and R. foetida var. persiana were reversible. The variable fluorescence level and the fluorescence decay rate are informative for determining the degree of drought resistance in garden rose plants.
Physiological and Biophysical Methods Fruit Crop Breeding
- A Lischuk
Nikitskiy botanicheskiy sad kak nauchnoe uchrezhdenie.
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