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 January 1st and April 31st 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.

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The paper presents an original research regarding the risk for human health in the area of Ploiesti city using SADA software - Spatial Analysis and Decision Assistance. Due to the high level of toxicity, international legislation provides the list of PCBs compounds to be monitored: PCB28, 52, 101, 118, 138, 153, 180. Sample collection was made in 22 points including public green gardens, residential areas, roadsides and industrial areas. The chemical analyses were conducted in the Laboratory of National Research and Development Institute for Soil Science, Agro-Chemistry and Environment from Bucharest, according to an own analytical method adapted after EPA. According to the Romanian standards, PCBs were elevated across industrial regions near urban and industrial sources. The concentrations of PCBs overcome the normal values in the most sampling points and the area presents a potential of risk for people. Local authorities should address the human health threats from urban and industrial soils in Ploiesti city.