Liquid Handling Automation System Streamlines Sample Preparation for Nutritional Analysis

Significance of the Topic

Automating sample preparation in nutritional analysis addresses common challenges in high-throughput laboratories, such as repetitive pipetting steps, manual errors, and resource constraints. By integrating liquid-handling robots, laboratories can enhance data quality and consistency, accelerate turnaround times, and free analysts to focus on advanced method development and data interpretation.

Study Objectives and Overview

This case study examines how Eurofins Scientific’s Nutrition Analysis Center in Des Moines, Iowa, implemented the Andrew Alliance Andrew+ pipetting robot and OneLab software to streamline workflows for amino acid and micronutrient assays. Key goals included reducing manual handling, minimizing errors, and achieving measurable productivity gains to support ISO 17025-accredited methods and USDA-inspected operations.

Methodology and Instrumentation

Sample preparation methods for vitamins and amino acids, previously performed manually, were evaluated and transferred to the Andrew+ system. Performance tests covered a range of solvents, analyte volumes (0.4 µL to 10 mL), serial dilutions, and direct dispensing into HPLC vials. Specific tests included folic acid micro-vitamin assays with programmed blow-outs, air cushions, and mixing steps. Comparative assessments between manual and automated protocols were conducted to verify equivalence or improvement in accuracy and repeatability.

Used Instrumentation:

• Andrew Alliance™ Andrew+ pipetting robot
• OneLab cloud-native software
• Waters ACQUITY UPLC H-Class and Arc HPLC systems
• ACQUITY UPC2™ system
• Xevo™ TQ-XS triple quadrupole mass spectrometer
• ACQUITY QDa single quadrupole MS detector
• Empower™ and MassLynx™ software

Main Results and Discussion

Validation trials demonstrated that the Andrew+ robot matched or exceeded manual performance. For folic acid calibration, five samples (A–E) showed near-identical concentrations (e.g., Sample A: 0.37226 vs. 0.37895 mg/100 g) and improved repeatability at low concentration levels. Analysts saved 30–60 minutes per day, while minimizing error risk. Minimal training time (approximately 20 minutes for programming and 5 minutes for operations) facilitated rapid deployment in a busy laboratory environment.

Benefits and Practical Applications

• Increased throughput: Improved sample capacity by up to 50% by reclaiming analyst time.
• Enhanced consistency: Precise pipetting reduces variability in quantitative assays.
• Lean laboratory integration: Supports ISO 17025 workflows and lean principles by optimizing resource use.
• Traceability: OneLab software provides graphical protocol design and audit trails for method transfer across sites.
• Scalability: Modular Dominos platform allows integration of gripper tools and diverse consumables.

Future Trends and Applications

Expansion plans include full integration of the Andrew+ system into LC, GC, and LC-MS workflows, enabling automation of additional assays such as bound and free amino acid analyses. Ongoing validation (6–12 months) will formalize the robot’s use in standard operating procedures. Emerging trends point to increased adoption of cloud-native laboratory informatics, modular automation platforms, and remote monitoring to further accelerate analytical services.

Conclusion

The adoption of the Andrew Alliance Andrew+ pipetting robot at Eurofins Nutrition Analysis Center exemplifies how targeted automation delivers significant time savings, consistent data quality, and operational flexibility. By aligning with lean laboratory practices and leveraging established vendor partnerships, the center achieved a clear return on investment and set the stage for broader workflow automation in routine nutritional analysis.

References

1. Eurofins Scientific, “Eurofins Fact Sheet,” accessed 2021.
2. Liu K., Pointer B., Yang J., Skinner N., Toerber S., Karote D., “Automate Standard Preparations for Food Analyses – A Real World Evaluation,” Waters Application Note, 2021.