Life science areas require specific sample pretreatment to increase the concentration of the analytes and/or to convert the analytes into an appropriate form for the detection and separation systems. Various workstations are commercially available, allowing for automated biological sample pretreatment. Nevertheless, due to the required temperature, pressure, and volume conditions in typical element and structure-specific measurements, automated platforms are not suitable for analytical processes. Thus, the purpose of the presented investigation was the design, realization, and evaluation of an automated system ensuring high-precision sample preparation for a variety of analytical measurements. The developed system has to enable system adaption and high performance flexibility. Furthermore, the system has to be capable of dealing with the wide range of required vessels simultaneously, allowing for less cost and time-consuming process steps. However, the system's functionality has been confirmed in various validation sequences. Using element-specific measurements, the automated system was up to 25% more precise compared to the manual procedure and as precise as the manual procedure using structure-specific measurements.
In life science process development, optimized manual protocols are converted to semi-automated processes to address high throughput and accuracy demands and to promote technician safety. However, little research has been conducted on technician workload assessment as a basis for identifying and prioritizing automation targets. The objectives of this study were to: 1) assess technician workload in a manual protocol and identify automation “targets” (for load reduction); and 2) compare workload with prototype automation vs. purely manual performance. Three expert technicians performed a mercury analysis process for three replications. Perceived workload was collected for each task using the NASA-Task Load index (TLX). Results on the manual process indicated “pipetting” and “measuring/recording” tasks to pose significantly higher perceived workload. The pipetting task posed the highest mental demand and risk of repetitive strain injuries, and was identified as a priority automation target. An automated pipetting system was prototyped and integrated in the manual protocol. The technician’s role was changed to transporting materials and programming tasks. In general, findings indicate that perceived workload assessment can be used to effectively identify target tasks for automation in life science processes. Technicians perceived significantly lower workload when performing automated pipetting, as compared with manual performance. However, there may be other factors (e.g., task time, number of steps) that influence workload and such factors may represent other targets for automation.
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