The application of municipal sewage sludge as fertilizer in the production of non-food energy crops is an environmentally and economically sustainable approach to sewage sludge management. In addition, the application of municipal sewage sludge to energy crops such as Miscanthus x giganteus is an alternative form of recycling nutrients and organic material from waste. Municipal sewage sludge is a potential source of heavy metals in the soil, some of which can be removed by growing energy crops that are also remediation agents. Therefore, the objective of the research was to investigate the effect of municipal sewage sludge applied at three different rates of 1.66, 3.22 and 6.44 t/ha on the production of Miscanthus. Based on the analyses conducted on the biomass of Miscanthus fertilized with sludge from the wastewater treatment plant in three fertilization treatments, it can be concluded that the biomass of Miscanthus is a good feedstock for the process of direct combustion. Moreover, the application of the largest amount of municipal sewage sludge during cultivation had no negative effect on the properties of Miscanthus biomass. Moreover, the cellulose and hemicellulose content of Miscanthus is ideal for the production of second-generation liquid biofuels. Fertilizer treatments had no effect on the content of cellulose and lignin, while a significant statistical difference was found for hemicellulose.

Invasive plant species (IAS), with their numerous negative ecological, health, and economic impacts, represent one of the greatest conservation challenges in the world. Reducing the negative impacts and potentially exploiting the biomass of these plant species can significantly contribute to sustainable management, protect biodiversity, and create a healthy environment. Therefore, the main objective of this study was to evaluate the nutritional potential, phytochemical status, and antioxidant capacity of nine alien invasive plant species: Abutilon theophrasti, Amaranthus retroflexus, Ambrosia artemisiifolia, Datura stramonium, Erigeron annuus, Galinsoga ciliata, Reynoutria japonica, Solidago gigantea, and Sorghum halepense. Multivariate statistical methods such as cluster and PCA were performed to determine possible connections and correlations among selected IAS depending on the phytochemical content. According to the obtained results, R. japonica was notable with the highest content of vitamin C (38.46 mg/100 g FW); while E. annuus (1365.92 mg GAE/100 g FW) showed the highest values of total polyphenolic compounds. A. retroflexus was characterized by the highest content of total chlorophylls (0.26 mg/g) and antioxidant capacity (2221.97 µmol TE/kg). Therefore, it can be concluded that the selected IAS represent nutrient-rich plant material with significant potential for the recovering of bioactive compounds.

Honeybees (Apis mellifera) produce propolis by collecting material from plant resins, leaves, buds or exudates, and mixing it with beeswax and their saliva. This mixture is placed in the hive to stabilize the temperature, glue the frames together, fix gaps, and protect the bee family from outside threats. The origin, local flora, collection time and bee genetics influence the organoleptic, chemical and biological properties of propolis. Propolis is rich in phenolic compounds such as phenolic acids, tannins, flavonoids, stilbens and lignans and in other secondary plant metabolites. It is used in medicines, cosmetics and food. Clinical studies show that propolis can be used to treat various health problems. The present review provides a comprehensive overview of the main chemical compounds and flavonoid content of propolis. It highlights the need for further research to understand how environmental conditions influence the organoleptic properties and composition of propolis, and discusses the potential of propolis in food production and preservation. Graphical

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation.

The aim of this study was to investigate the potential of using structural analysis parameters for estimating the higher heating value (HHV) of biomass by obtaining information on the composition of cellulose, lignin, and hemicellulose. To achieve this goal, several nonlinear mathematical models were developed, including polynomials, support vector machines (SVMs), random forest regression (RFR) and artificial neural networks (ANN) for predicting HHV. The performed statistical analysis “goodness of fit” showed that the ANN model has the best performance in terms of coefficient of determination (R² = 0.90) and the lowest level of model error for the parameters X² (0.25), RMSE (0.50), and MPE (2.22). Thus, the ANN model was identified as the most appropriate model for determining the HHV of different biomasses based on the specified input parameters. In conclusion, the results of this study demonstrate the potential of using structural analysis parameters as input for HHV modeling, which is a promising approach for the field of biomass energy production. The development of the model ANN and the comparative analysis of the different models provide important insights for future research in this field.