Identification of 24 Unknown Substances (NIAS/IAS) from Food Contact Polycarbonate by LC-Orbitrap Tribrid HRMS-DDMS3: Safety Assessment

Twenty-four substances, mainly NIAS, have been tentatively identified in food contact polycarbonate through the application a new, fast, and automated analytical strategy for the investigation of unknowns in food contact materials. Most of the identified compounds were plasticizers, slip agents, antioxidants, and ultraviolet stabilizers and fragrances, and the majority of them have not been previously identified in PC food contact materials. The workflow setup includes an intelligent data acquisition applied using LC-Orbitrap Tribrid-HRMS (MS3), with an automated data processing using Compound DiscovererTM. To obtain a high confidence identification of unknown substances, a very strict criterion has been established, which comprises exact mass, isotopic profile, MS2 match, retention time, and MS3 match. To check for the safety of the migration from the food contact polycarbonate, a risk assessment was achieved using the threshold of the toxicological concern (TTC) approach. Except for the slip agent hexadecanamide, the compounds tentatively identified do not represent a risk.

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This editorial article briefly outlines the objectives and achieved goals of the Special Issue on “Convergence of Intelligent Data Acquisition and Advanced Computing Systems” running between September 2019 and September 2020 in the Sensors journal [...]

Free-running data acquisition system for the AMBER experiment

Triggered data acquisition systems provide only limited possibilities of triggering methods. In our paper, we propose a novel approach that completely removes the hardware trigger and its logic. It introduces an innovative free-running mode instead, which provides unprecedented possibilities to physics experiments. We would like to present such system, which is being developed for the AMBER experiment at CERN. It is based on an intelligent data acquisition framework including FPGA modules and advanced software processing. The system provides a triggerless mode that allows more time for data filtering and implementation of more complex algorithms. Moreover, it utilises a custom data protocol optimized for needs of the free-running system. The filtering procedure takes place in a server farm playing the role of the highlevel trigger. For this purpose, we introduce a high-performance filtering framework providing optimized algorithms and load balancing to cope with excessive data rates. Furthermore, this paper also describes the filter pipeline as well as the simulation chain that is being used for production of artificial data, for testing, and validation.