Antibiotic resistance in microorganisms is an important problem of modern medicine which can be solved by searching for antimicrobial preparations of the new generation. Nanoparticles (NPs) of metals and their oxides are the most promising candidates for the role of such preparations. In the last few years, the number of studies devoted to the antimicrobial properties of silver oxide NPs have been actively growing. Although the total number of such studies is still not very high, it is quickly increasing. Advantages of silver oxide NPs are the relative easiness of production, low cost, high antibacterial and antifungal activities and low cytotoxicity to eukaryotic cells. This review intends to provide readers with the latest information about the antimicrobial properties of silver oxide NPs: sensitive organisms, mechanisms of action on microorganisms and further prospects for improving the antimicrobial properties.

The potential of the method of fluorescence spectroscopy for the detection of damage and diseases of fruits and vegetables was studied. For this purpose, the spectra of fluorescence of healthy and rotten apples and potatoes have been investigated. Excitation of samples was carried out using a continuous semiconductor laser with a wavelength of 405 nm and a pulsed solid-state laser with a wavelength of 527 nm. Peaks in the region of 600–700 nm in rotten samples were shifted towards shorter wavelengths for most samples in both modes of spectroscopy. The differences in the fluorescence spectra of a healthy and rotten apple surface have been revealed to be in the spectral range of 550–650 nm for 405 nm continuous excitation. When exposed to a laser in a pulsed mode (527 nm), the contribution of the 630 nm peak in the spectrum increases in rotten samples. The observed differences make it possible to use this method for separating samples of healthy and rotten fruits and vegetables. The article paid attention to the influence of many factors such as sample thickness, time after excitation, contamination by soil and dust, cultivar, and location of the probing on fluorescence spectra.

In this paper, iron oxide nanoparticles coated with trisodium citrate were obtained. Nanoparticles self-assembling stable clusters were ~10 and 50–80 nm in size, consisting of NPs 3 nm in size. The stability was controlled by using multi-angle dynamic light scattering and the zeta potential, which was −32 ± 2 mV. Clusters from TSC-IONPs can be destroyed when interacting with a hen egg-white lysozyme. After the destruction of the nanoparticles and proteins, aggregates are formed quickly, within 5–10 min. Their sizes depend on the concentration of the lysozyme and nanoparticles and can reach micron sizes. It is shown that individual protein molecules can be isolated from the formed aggregates under shaking. Such aggregation was observed by several methods: multi-angle dynamic light scattering, optical absorption, fluorescence spectroscopy, TEM, and optical microscopy. It is important to note that the concentrations of NPs at which the protein aggregation took place were also toxic to cells. There was a sharp decrease in the survival of mouse fibroblasts (Fe concentration ~75–100 μM), while the ratio of apoptotic to all dead cells increased. Additionally, at low concentrations of NPs, an increase in cell size was observed.

The influence of laser radiation of a typical surgical laser on the physicochemical properties of the Bovine Serum Albumin (BSA) protein was studied. It was established that the physicochemical characteristics of optical breakdown weakly depend on the concentration of protein molecules. At the same time, the patterns observed for an aqueous solution of BSA irradiated with a laser for different time periods were extremely similar to the classical ones. It was established that after exposure to laser radiation, the optical density of protein solutions increases. At the same time, the intensity of BSA fluorescence due to aromatic amino acid residues decreases insignificantly after exposure to laser radiation. In this case, the position of the excitation and emission maximum does not change, and the shape of the fluorescence spot on 3D maps also does not change significantly. On the Raman spectrum after exposure to laser radiation, a significant decrease in 1570 cm⁻¹ was observed, which indicates the degradation of α-helices and, as a result, partial denaturation of BSA molecules. Partial denaturation did not significantly change the total area of protein molecules, since the refractive index of solutions did not change significantly. However, in BSA solutions, after exposure to laser radiation, the viscosity increased, and the pseudoplasticity of aqueous solutions decreased. In this case, there was no massive damage to the polypeptide chain; on the contrary, when exposed to optical breakdown, intense aggregation was observed, while aggregates with a size of 400 nm or more appeared in the solution. Thus, under the action of optical breakdown induced by laser radiation in a BSA solution, the processes of partial denaturation and aggregation prevail, aromatic amino acid residues are damaged to a lesser extent, and fragmentation of protein molecules is not observed.

The application of plasma-activated water (PAW) in agriculture has gained the attention of researchers and practitioners. In particular, treatment with PAW is a promising method for increasing scion and rootstock survival as well as augmenting the mineral nutrition applicable to tree fruit crops. However, the applications of PAW are hampered by the lack of information about the effects of PAW on apple tree condition and yield. The increase in survival rate by PAW is believed to stem from the general stimulation of physiological processes in the plant tissue. To assess the actual effect of the PAW treatments, one needs to consider an important indicator of young tree quality such as their vegetative growth. We conducted field experiments to study the possibility of use of PAW for increase in primary nutrient contents in fruits and leaves in an orchard, as well as to assess the scion survival rate and vegetative growth of young grafts in a nursery. The application of PAW influenced the fruitset, yield, leaf nitrogen (N) and potassium (K), fruit phosphorus (P), calcium (Ca) ascorbic acid (AA) and titratable acidity (TA). Treatment with PAW did not significantly reduce the negative impact of the rootstock thickness on the survival rate of bench grafts and their subsequent development. At the same time, scion survival tended to increase in the case when the scions and the rootstocks were of compatible thickness. Further studies of the PAW treatment effects are needed to better understand its applicability in diverse fields of horticulture.

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits.