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Contaminant Analysis by DPiMS™-2020 (2): Detection of Agricultural Chemicals in Beverages

Applications | 2021 | ShimadzuInstrumentation
LC/MS, DART, LC/SQ
Industries
Food & Agriculture
Manufacturer
Shimadzu

Summary

Significance of the Topic


The rapid and reliable detection of agricultural chemical residues in beverages is critical for food safety, regulatory compliance and consumer protection. Traditional analysis often requires complex preparation steps and lengthy chromatography runs. A streamlined screening approach can enhance throughput and enable timely decisions in quality control and contamination investigations.

Objectives and Study Overview


This work aimed to establish a simple and high sensitivity screening method for detecting glyphosate based herbicides and malathion based insecticide in various beverages. The study evaluated a direct probe electrospray ionization mass spectrometry approach using the DPiMS-2020 system, focusing on both active ingredients and common surfactant additives. Spike and recovery experiments in multiple drink matrices assessed detection limits and the feasibility of rapid contamination judgments.

Methodology and Instrumentation


A probe electrospray ionization (PESI) interface was employed to collect sample material directly from a dedicated sample plate. A small volume (9 microliters) of beverage or standard solution was applied and subjected to alternating negative and positive ion scans. Prior LC MS analysis identified characteristic surfactants in each agricultural formulation. Monitoring ions included deprotonated glyphosate at m/z 168 and adduct ions for both glyphosate surfactants and malathion species. Quantitative thresholds were defined by a signal ratio of three relative to blank matrices.

Instrumentation Used


  • DPiMS-2020 direct probe ionization mass spectrometer
  • PESI sample probe assembly
  • LC MS system for preliminary surfactant identification

Key Results and Discussion


Glyphosate residues were detected in carbonated and sports drinks at levels down to 0.001 percent (10 microliters per liter), with consistent detection of surfactant additives even when the active ingredient fell below the limit. In barley tea and milk tea, glyphosate detection was less sensitive, but surfactant ions provided reliable contamination indicators at concentrations of 0.01 percent or higher. Malathion spikes were detected at 0.001 percent in most beverages except milk tea, where surfactant signals allowed identification at 0.02 percent. These findings demonstrate that targeting both active compounds and formulation additives yields robust screening across diverse matrices.

Benefits and Practical Applications


  • Minimal sample preparation and solvent use
  • Rapid analysis time under two minutes per sample
  • High throughput screening capability for quality control labs
  • Reliable detection of formulation surfactants as alternative markers

Future Trends and Potential Applications


Advancements could include integration of automated sample handling, expansion of target panels to other pesticide classes and mycotoxins, and development of portable PESI MS platforms for field testing. Coupling with data analytics and machine learning may improve pattern recognition and quantitation, further enhancing screening workflows in food safety and environmental monitoring.

Conclusion


This study confirms that PESI MS on the DPiMS-2020 system provides a fast and sensitive approach for detecting agricultural chemical contamination in beverages. By monitoring both active ingredients and surfactant additives, the method achieves detection limits as low as 200 microliters per liter. The simplicity and speed of the workflow make it a promising tool for routine contaminant screening and rapid response scenarios.

References


  1. Nakano S, Kamata H, Sasaki N et al J Mass Spectrom Soc Jpn 67(2) 53-63 2019
  2. Wada M et al Application of probe electrospray ionization mass spectrometry to the analysis of poisons and drugs in adulterated foods and beverages Jpn J Forens Sci Technol

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