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Updated: Jul 15, 2026

Aseptic Laboratory Techniques: Plating Methods

Aseptic Laboratory Techniques: Plating Methods

Published on: May 11, 2012

Strategies for automating analytical and bioanalytical laboratories.

Kerstin Thurow¹

¹Center for Life Science Automation, University of Rostock, Rostock, Germany. Kerstin.Thurow@celisca.de.

Analytical and Bioanalytical Chemistry

|May 12, 2023

Summary

Automating (bio)analytical laboratory processes enhances throughput and result quality. Future flexible automation will leverage collaborative robots for complex sample analysis, despite current low automation levels.

Keywords:

Analytical chemistry Automation strategies Automation systems Laboratory automation Robotics Systems engineering

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Last Updated: Jul 15, 2026

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Published on: May 11, 2012

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Published on: December 1, 2017

Area of Science:

Analytical Chemistry
Laboratory Automation
Biotechnology

Background:

Analytical measurement methods are crucial across production, quality control, diagnostics, environmental monitoring, and research.
Manual laboratory processing of offline samples is common when direct inline or online methods are not feasible.
Despite increasing automation, (bio)analytical laboratories exhibit lower automation degrees due to process complexity, stringent conditions, and intricate sample matrices.

Purpose of the Study:

To explore automation strategies for (bio)analytical laboratory processes.
To address the challenges hindering higher automation levels in these labs.
To discuss the evolution of automation concepts, including robotic systems and future distributed models.

Main Methods:

Review of existing automation strategies in (bio)analytical laboratories.
Analysis of factors influencing the selection of automation concepts.
Discussion of classical liquid handler systems, central robot systems, and emerging collaborative robot-based distributed automation.

Main Results:

Automation significantly enhances throughput and improves the quality of results in analytical measurements.
The complexity of processes, required conditions, and sample matrices are key barriers to automation.
Advancements in collaborative robots offer potential for more flexible, distributed automation systems.

Conclusions:

Automation is essential for improving efficiency and reliability in (bio)analytical measurements.
Overcoming challenges related to process complexity and sample matrices is key to increasing automation.
Future automation will likely involve more flexible, adaptable systems, including distributed models enabled by collaborative robots.