Amino Acid Analysis using Andrew+ Automated Preparation

Significance of the Topic

Amino acids are fundamental building blocks of proteins and critical components in cell culture media and food products. Precise quantification of amino acid profiles ensures optimal growth conditions in bioprocessing and compliance with nutritional standards in food quality control. Routine analysis of these compounds demands reliable, high-throughput workflows to support research, quality assurance, and industrial applications.

Objectives and Study Overview

This study evaluates an automated derivatization and sample preparation workflow using the Andrew+ liquid handling robot combined with the AccQ•Tag Ultra Derivatization Automation Kit. The primary goals are to compare the automated protocol against a conventional manual method in terms of accuracy, precision, and linearity, and to demonstrate the efficiency gains and robustness of automation for amino acid analysis.

Methodology and Instrumentation Used

Samples of cell culture and food/feed amino acid standards were prepared both manually and on the Andrew+ platform using 32-, 64-, and 96-sample protocols.

The analytical workflow included derivatization with the AccQ•Tag Ultra reagents followed by UPLC separation and UV detection. Data acquisition and processing were performed in Empower chromatography software.

Key Instrumentation

• Andrew+ liquid handling robot with Bluetooth-enabled electronic Pipette+
• AccQ•Tag Ultra Derivatization Automation Kit (3×32 format)
• 96-well LoBind PCR plates
• ACQUITY UPLC H-Class PLUS Bio System with Tunable UV Detector
• OneLab cloud-based protocol management software
• Shaker+, Peltier+, and gripper modules for automated labware handling

Main Results and Discussion

Precision was assessed at 10 µM, 200 µM, and 400 µM levels for both sample types. The automated method achieved maximal mean %CV values of 2.0% (cell culture) and 1.7% (food/feed), closely matching manual preparation (%CV up to 2.8%).

Accuracy, expressed as percent recovery, remained within ±10% of target concentrations across all amino acids and levels for both methods. Linearity over the full calibration range (0.5 µM–500 µM; cystine 0.25 µM–250 µM) yielded correlation coefficients (r2) above 0.995, with no significant deviations between manual and automated protocols.

Benefits and Practical Applications

The automated workflow offers:

• Significant time savings—complete calibration and sample preparation in under one hour
• Hands-free operation—Bluetooth pipette switching and automated labware movement free analysts for other tasks
• Enhanced reproducibility—elimination of analyst-to-analyst variability supports method standardization and site-to-site transfer
• Remote monitoring—OneLab software enables run supervision from any internet-connected device

Future Trends and Opportunities

Advancements in amino acid analysis may include integration with high-resolution mass spectrometry for expanded analyte coverage, scalable automation for higher sample throughput, and incorporation of machine-learning algorithms for predictive maintenance and real-time data interpretation. The continued development of cloud-based workflows and interoperable lab automation platforms will further streamline routine analyses.

Conclusion

The Andrew+ automated derivatization workflow demonstrates equivalence to manual preparation in accuracy, precision, and linearity while delivering considerable efficiency and reproducibility benefits. Adoption of this automated approach can enhance laboratory throughput, reduce hands-on time, and ensure consistent data quality across diverse analytical settings.