Nowadays, special importance is given to quality control and food safety. Food quality currently creates significant problems for the industry and implicitly for consumers and society. The effects materialize in economic losses, alterations of the quality and organoleptic properties of the commercial products, and, last but not least, they constitute risk factors for the consumer’s health. In this context, the development of analytical systems for the rapid determination of the sanitary quality of food products by detecting possible pathogenic microorganisms (such as Escherichia coli or Salmonella due to the important digestive disorders that they can cause in many consumers) is of major importance. Using efficient and environmentally friendly detection systems for identification of various pathogens that modify food matrices and turn them into food waste faster will also improve agri-food quality throughout the food chain. This paper reviews the use of metal nanoparticles used to obtain bio nanosensors for the purpose mentioned above. Metallic nanoparticles (Au, Ag, etc.) and their oxides can be synthesized by several methods, such as chemical, physical, physico-chemical, and biological, each bringing advantages and disadvantages in their use for developing nanosensors. In the “green chemistry” approach, a particular importance is given to the metal nanoparticles obtained by phytosynthesis. This method can lead to the development of good quality nanoparticles, at the same time being able to use secondary metabolites from vegetal wastes, as such providing a circular economy character. Considering these aspects, the use of phytosynthesized nanoparticles in other biosensing applications is also presented as a glimpse of their potential, which should be further explored.

The paper describes the development of an optimization model for the layout of an industrial robot relative to known locations of served machines and operations to be performed. Robotized material handling applications, defined by trajectories (paths, speed profiles) and final points, are considered in this research. An energy-monitoring framework set up by joint velocities provides input data that are fed to the optimization model. The physical placement of the robot base stands for the decisional variables, while the objective function is represented by the total distance covered by individual joints along established task routes transposed into energy consumption. The values of the decisional variables are restricted by trajectory constraints (waypoints on paths), joint operating values and link dimensions. Modelling technique and practical results using the Microsoft Solver optimization tool from Excel for Microsoft 365, Version 2312 are reported for SCARA-type robots. The performance of the optimization model is compared with actual measurements of consumed energy on an Adept Cobra S600 SCARA robot.