Rosemary (Rosmarinus officinalis L.) is a plant worldwide cultivated mainly for essential oils, extracts, and as a spice. Up-to-date results showed diversity in composition of the essential oils, which may influence their quality, biological activity, and thermal properties. Therefore, the aim of this study was to investigate the chemical composition, antimicrobial activity, and thermal properties of the rosemary essential oils originating from Serbia and Russia. Additionally, oils were added to the sunflower oils in order to investigate possible antioxidant activity during the frying. Investigation of the chemical profile marked α-pinene, eucalyptol, and camphor as the most abundant compounds in both oils. However, overall composition influenced in such manner that Russian oil showed significantly higher antimicrobial activity, while Serbian oil proved to be better antioxidant agent in case of frying of sunflower oil. This would significantly influence possible application of the oils, which could be used as an antioxidant agent for extension of the food shelf life, or antimicrobial agent for protection against different microbial strains.

Polyphenolic compounds are of great interest in today’s science. Naturally, they occur in plants and other sources in many different forms. Their wide range of biological activity has attracted the attention of the scientific community. One of the sources of phenolic compounds is stinging nettle (Urtica dioica L.), a common plant in almost all parts of the world. A long tradition of utilization and an interesting chemical profile make this plant a fascinating and extensive object of study. The chemical profile also allows this plant to be used as a food and a pigment source in the food, pharmaceutical, and cosmetic industries. Previously conducted studies found phenolic acids and polyphenolic compounds in root, stalk, and stinging nettle leaves. Different extraction techniques were usually used to isolate them from the leaves. Obtained extracts were used to investigate biological activity further or formulate different functional food products. This study aimed to collect all available knowledge about this plant, its chemical composition, and biological activity and to summarize this knowledge with particular attention to polyphenolic compounds and the activity and mechanisms of their actions.

Phenolic compounds may be useful in food processing due to their high antioxidative activities and abundance in the plant kingdom. They are of particular interest for applications in the areas of functional foods and nutraceuticals because they act as preserving agents to protect the human body system against degenerative diseases caused by oxidative damage. Among the aromatic and medicinal plants available to the food industry, basil (Ocimum basilicum L.) has promising benefitial properties. This review summarizes our knowledge of available phytonutrients from Ocimum basilicum, antioxidant activity and their health-benefits in prevention of some modern diseases.

Air pollution is one of the most challenging and complex problems of our time. This research presents the prediction of air pollutant values based on using an R program with linear regression. The research sample consists of obtained values of air pollutants such as sulphur dioxide (SO₂), particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrite oxides (NO, NO₂, and NO_X), atmospheric data pressure (p), temperature (T), and relative humidity (rh). The research data were collected from the city of Belgrade air quality monitoring reports, published by the Environmental Protection Agency of the Republic of Serbia. The report data were transformed into a form suitable for processing by the R program and used to derive prediction functions based on linear regression upon pairs of air pollutants. In this paper, we describe the R program that was created to enable the correlation of air pollutants with linear regression, which results in functions that are used for the prediction of pollutant values. The correlation of pollutants is presented graphically with diagrams created within the R GUI environment. The predicted data were categorized according to air pollution standard ranges. It has been shown that the derived functions from linear regression enable predictions that are well correlated with the data obtained by automatic acquisition from air quality monitoring stations. The R program was created by using R language statements without any additional packages, and, therefore, it is suitable for multiple uses in a diversity of application domains with minor adjustments to appropriate data sets.