Automate Standard Preparations for Food Analyses – A Real-World Evaluation

Significance of the Topic

Automation in sample preparation is critical to enhancing analytical throughput and consistency in food testing laboratories. By reducing manual pipetting steps, laboratories can minimize human error, improve data quality, and free skilled staff from repetitive tasks.

Objectives and Study Overview

This study evaluated the performance of the Andrew+ liquid handling robot and OneLab cloud-native software in routine food analysis workflows. The focus was on accuracy and precision in serial dilutions and mixing for standard and sample preparations across various assays. Comparative assessments against manual operations were conducted under real-world laboratory conditions.

Methodology and Instrumentation

The evaluation comprised two main phases:

1. Accuracy assessment of serial dilutions performed by the robot compared with standard operating procedures.
2. Implementation in actual food analysis methods, comparing results using robot-prepared reagents versus manual preparation.

Used Instrumentation:

• Andrew+ pipetting robot.
• OneLab software platform for protocol scripting and traceability.

Analytical techniques covered included IC-CD, LC-FLR, LC-UV/Vis, LC-MS/MS, electrochemical detection, and microbiological turbidity assays. Solvents ranged from water and common organic solvents to volatile hexane.

Main Results and Discussion

Accuracy in automated serial dilutions ranged between -2.8% and 3.0%, outperforming manual accuracy of -5.0% to 4.2%. In real-world analyses, relative differences between robot and human results were generally within ±3%, with a single case at 6.7%. Precision tests showed relative standard deviations below 2.0% across a range of volumes. The intuitive interface allowed method development within ten minutes, and the scripted workflow ensured full traceability.

Benefits and Practical Applications

• Comparable or improved accuracy and precision relative to manual pipetting.
• Reduced operator fatigue and risk of repetitive strain injuries.
• Increased productivity by reallocating staff to higher-value tasks.
• Decreased solvent consumption through accurate volume dispensing and miniaturization of protocols.
• Enhanced traceability and reproducibility via software-controlled scripts.

Future Trends and Potential Applications

Integration of automated liquid handling with advanced analytics and data platforms is expected to drive further improvements in laboratory efficiency. Expansion into more complex sample preparation workflows, such as digestion and derivatization, could extend automation benefits. Cloud-native architectures will facilitate remote monitoring and batch processing across multiple sites.

Conclusion

The Andrew+ robot and OneLab software demonstrate robust performance in food analysis sample preparation. They meet stringent accuracy and precision requirements while improving lab safety, traceability, and operator productivity. Their ease of use and compact design make them suitable for adoption in a wide range of analytical environments.