AUTOMATE STANDARD PREPARATIONS FOR FOOD ANALYSES – A REAL-WORLD EVALUATION

Importance of Automating Sample Preparation

Automating the dilution and mixing steps in food analysis significantly reduces manual workload, minimizes human error, and ensures consistent sample preparation quality. This is crucial for maintaining accuracy and precision in laboratory workflows where reproducibility and throughput are key drivers for analytical success.

Objectives and Study Overview

This evaluation aimed to assess the performance of the Andrew+ pipetting robot and OneLab software platform for routine food testing assays. Key goals included:

• Comparing robot-assisted serial dilutions with manual procedures
• Implementing automated workflows across diverse analytes and detection techniques
• Quantifying accuracy, precision, and potential bias in real-world food analyses

Methodology and Instrumentation

The study covered common food analytes—ions, vitamins, amino acids, sugars, and nutrients—using techniques such as IC-CD, LC-FLR, LC-UV/Vis, LC-MS/MS, electrochemical detection, and turbidity assays. The Andrew+ system utilized:

• Tip Insertion System Domino and various rack dominos for 2 mL to 50 mL vessels
• Single-channel pipettes spanning 10 µL to 10 mL
• OneLab cloud-native software for protocol design and execution

Main Results and Discussion

1. Accuracy of Automated Serial Dilutions

• Bias ranged from –2.8 % to +3.0 %, comparable to manual variability of –5.0 % to +4.2 %.
• Example: Carnitine LC-MS/MS dilutions showed within ±1.1 % for most samples.

2. Real-World Food Analysis Performance

• Analytes such as folic acid, cysteine, methionine, retinol, vitamin D3, and galactose yielded results within ±3 % of manual prep.
• No systematic bias was introduced by automation across multiple analytical techniques.

3. Precision of Automated Sample Preparation

• Relative standard deviations for eight replicate water sample dilutions remained below 2 %.

Benefits and Practical Applications

Automated workflows deliver:

• Improved throughput—parallel handling of multiple samples and dilutions
• Enhanced traceability—digital protocol records and audit trails
• Greater lab safety—reduced exposure to volatile solvents (e.g., hexane)
• Consistent precision—minimized operator-dependent variability

Future Trends and Applications

Emerging directions include:

• Integration with artificial intelligence for adaptive dosing and error detection
• Expanded protocol libraries covering complex matrices and advanced sample cleanup
• Remote monitoring and cloud-based analytics for multi-site standardization
• Coupling with on-line detection for end-to-end automated workflows

Conclusion

The Andrew+ robot and OneLab platform demonstrated robust performance in standard and sample dilutions for food analyses, matching manual accuracy and precision while enhancing productivity and safety. This approach can be readily deployed across diverse analytical methods to streamline laboratory operations.

Reference

Liu K., Pointer B., Yang J., Skinner N., Toerber S., Karote D. Automate Standard Preparations for Food Analyses – A Real-World Evaluation. Waters Corporation, 2021.