Improved Data Quality Through Automated Sample Preparation

Importance of Automated Sample Preparation

Automating routine sample preparation steps addresses key challenges in analytical workflows by improving reproducibility, reducing human error, and cutting reagent consumption. Time-intensive tasks such as sample dilution, calibration standard generation, internal standard addition, and derivatization are common sources of variability. Implementing an automated platform can boost data quality and free laboratory personnel for higher-value activities.

Study Objectives and Overview

This application note evaluates the performance of the Agilent 7696 Sample Prep WorkBench for three standard procedures: sample dilution with internal standard addition for GC and LC, calibration curve preparation, and fatty acid derivatization via silylation. Each automated method was compared side-by-side with manual techniques to assess accuracy, precision, and resource efficiency.

Applied Instrumentation

The automated workflows were executed on the Agilent 7696 Sample Prep WorkBench, which features two liquid-dispensing modules, a single-vial heater (up to 80 °C), a vortex-style vial mixer, and a barcode reader. The system accommodates heating or cooling of individual racks and operates offline alongside GC and LC instruments. Manual methods employed standard laboratory equipment such as volumetric flasks, serological and automatic pipettes, and heated blocks.

Methodology

1. Sample Dilution and ISTD Addition (n=10)

• GC: Isooctane, four-analyte standard, and a 0.5 µL internal standard were dispensed into 2 mL vials and mixed.
• LC: Acetonitrile, pesticide standard, and ISTD were combined similarly.

2. Calibration Curve Generation (triplicate sets)

• Manual: Linear dilution of a five-analyte stock in 10 mL volumetric flasks to produce 50–500 ppm standards.
• Automated: Corresponding volumes pipetted into 2 mL vials yielding identical concentration range.

3. Fatty Acid Derivatization (n=3)

• Silylation reagent added to fatty acid solutions, followed by heating to 70 °C; comparison of manual pipetting versus automated dispensing.

Main Results and Discussion

• Dilution Precision and Accuracy: Automated dispensing achieved <0.5% RSD gravimetrically and within 1–2% accuracy based on peak areas for both GC and LC methods. The internal standard addition exhibited ≤2% RSD when a smaller syringe was used.
• Calibration Reproducibility: Individual calibration curves from both approaches had R² ≈ 0.999. However, when superimposed, manual curves fell to R² = 0.934, whereas automated curves maintained R² = 0.997. Relative response factor RSD averaged 16% for manual versus 4% for automated standards.
• Derivatization Consistency: Fatty acid silylation produced identical normalized peak ratios. Automated RSD was 0.7% compared to 0.9% for manual preparation.

Practical Benefits and Applications

• Significant solvent savings: Automation used ~600 µL versus >60 mL for manual standard preparation.
• Time efficiency: Calibration standard setup required approximately half the manual time.
• Enhanced data quality: Superior reproducibility and reduced error rates bolster confidence in quantitative results.
• Operator productivity: Automation frees analysts to focus on experimental design, data interpretation, and other critical tasks.

Future Trends and Opportunities

• Integration with laboratory information management systems (LIMS) for full traceability.
• Expansion of automated protocols to include solid-phase extractions, protein digestions, and novel derivatization chemistries.
• Adoption of smaller-volume syringes and microfluidic approaches to further reduce reagent use.
• Implementation of software-driven workflow optimization and AI-guided method development.

Conclusion

The Agilent 7696 Sample Prep WorkBench consistently outperforms manual techniques in accuracy, precision, and resource efficiency for sample dilution, calibration standard generation, and derivatization. Automation delivers higher data quality, significant solvent savings, and improved laboratory productivity.

Reference

• Susanne Moyer, Dale Snyder, Rebecca Veeneman, and Bill Wilson, “Typical Injection Performance for the Agilent 7693A Autoinjector,” Agilent Technologies Publication 5990-4606EN.