High Speed Simultaneous Analysis of Amino Acids in Foods Using Automatic Pretreatment Function

Significance of the Topic

Amino acids play critical roles in nutrition and sensory properties of food products. Rapid and reliable quantification of amino acids is essential for quality control in food manufacturing, nutritional analysis, and research applications. Traditional post-column derivatization methods are time-consuming and require complex instrumentation, while manual pre-column labeling poses challenges in handling and reproducibility. The automatic pretreatment function of the Nexera XR system addresses these limitations by integrating derivatization steps directly into the analysis sequence, enabling high-throughput and robust workflows.

Study Objectives and Overview

This work aims to demonstrate a fast and simple method for simultaneous analysis of 20 proteinogenic amino acids in diverse food matrices using pre-column derivatization with an automated pretreatment module. Key objectives include:

• Optimization of reagent mixing and derivatization within the autosampler.
• Evaluation of separation performance on a Shim-pack XR-ODS II column under a low-pressure gradient.
• Application of the method to various foods, including consommé, sauces, beverages, and fermented products.
• Assessment of matrix effects via spike-and-recovery tests.

Methodology and Instrumentation

An automated pre-column derivatization sequence was programmed in the Nexera XR autosampler. Sample and reagents (borate buffer, MPA, OPA, FMOC, phosphoric acid) were automatically aspirated and mixed in the needle, followed by a 2-minute reaction before injection. Separation was achieved on a Shim-pack XR-ODS II column (100 mm × 3.0 mm, 2.2 μm) with a three-component mobile phase: acetate buffer (pH 6), water/acetonitrile (1:9), and acetate buffer (pH 5) containing EDTA. A low-pressure gradient (0–45% B over 14 min) and a flow rate of 1.0 mL/min at 35 °C enabled baseline resolution of all 20 amino acids in 14 min. Dual-channel fluorescence detection (Ex/Em 350/450 nm and 266/305 nm) provided sensitive quantification.

Used Instrumentation

• Nexera XR Ultra High Performance Liquid Chromatograph with automatic pretreatment function
• Shim-pack XR-ODS II column, 100 mm × 3.0 mm I.D., 2.2 μm
• Fluorescence detector with dual excitation/emission channels
• Autosampler with mixing capability in the injection needle

Main Results and Discussion

The optimized method achieved complete separation of 20 amino acids at 100 μmol/L each within 14 min. Representative chromatograms from standard mixtures and food samples (consommé, Japanese soup base, ketchup, miso, gochujang, peanut butter, energy drink, peach juice, sake, soy sauce) demonstrated consistent retention times and peak shapes across matrices. Spike-and-recovery experiments yielded recoveries typically between 90% and 110%, indicating minimal matrix interference and high derivatization efficiency under automated conditions.

Benefits and Practical Applications

The automated pre-column derivatization approach offers several advantages:

• Significant reduction in analysis time compared to post-column methods.
• Simplified instrument configuration without the need for separate derivatization modules.
• Reproducible and hands-free derivatization process, reducing operator workload.
• Applicability to a wide range of food matrices with reliable quantification.

This workflow suits routine food analysis laboratories, quality control in food and beverage production, and research studies on amino acid profiles.

Future Trends and Opportunities

Advancements may include integration with mass spectrometric detection for expanded scope and sensitivity, development of new rapid derivatization chemistries, miniaturization of pretreatment modules for reduced reagent consumption, and implementation of high-throughput autosamplers for large sample sets. The platform could also be adapted for non-food applications, such as clinical and environmental amino acid analysis.

Conclusion

The Nexera XR system with automatic pretreatment enables fast, robust, and user-friendly quantification of amino acids in complex food matrices. By automating pre-column derivatization, the method combines the speed of pre-column labeling with the reproducibility of automated systems, supporting efficient routine analyses and high sample throughput.