Application of the Agilent 7900 ICP-MS with Method Automation function for the routine determination of trace metallic components in food CRMs
Applications | 2014 | Agilent TechnologiesInstrumentation
Food safety and nutritional monitoring require reliable identification of trace element levels in complex matrices. Globalization of supply chains and stricter regulatory limits for toxic elements (e.g. Pb, Cd, As) drive the need for fast, sensitive, multi-element methods. Accurate elemental profiling also supports fraud detection by fingerprinting origin through trace element patterns.
This application note evaluates the performance of the Agilent 7900 ICP-MS equipped with the Method Automation function in ICP-MS MassHunter 4.1. The primary goal is to develop and validate a fully automated, routine method for determining trace metals in fish Certified Reference Materials (CRMs) (DORM-4, 7402-a, 7403-a), comparing automatic method generation with manual expert setup.
Sample Preparation:
The automatically generated method achieved excellent agreement with certified values for all major and trace elements in three fish CRMs. Measured concentrations for elements such as Na, Mg, P, S, K, Ca, Cr, Mn, Fe, Cu, Zn, As, Se, Cd, Hg and Pb matched reference data within stated uncertainties. Spike recoveries for 26 analytes fell between 90 % and 110 %, confirming method accuracy. Settings selected by Method Automation—plasma mode, cell gas (He, H2, HEHe), isotopes, integration times and internal standards—closely mirrored those chosen by an experienced ICP-MS specialist, demonstrating robustness of automated development.
Advances in collision-reaction cell chemistries and automation will further streamline multi-element analysis. Integration with artificial intelligence for adaptive method optimization, real-time data validation and cloud-based workflows is expected to boost laboratory productivity. Coupling inline separation or digestion modules may expand capabilities to handle more complex matrices.
The Agilent 7900 ICP-MS Method Automation function reliably generates fully optimized methods for multi-element trace analysis in food CRMs, delivering accuracy equivalent to expert manual setup. This automation streamlines routine testing, ensures consistent data quality and supports regulatory compliance in food safety monitoring.
1. Kaoru Ariyama, Akemi Yasui, JARQ, 40(4), 333–339 (2006)
2. Cinzia Benincasa et al., Anal. Chim. Acta, 585, 366–370 (2007)
3. Erik H. Larsen, Stefan Sturup, J. Anal. At. Spectrom., 9(10), 1099–1105 (1994)
4. Maurizio Pettine et al., Anal. Chim. Acta, 599(2), 191–198 (2007)
ICP/MS
IndustriesFood & Agriculture
ManufacturerAgilent Technologies
Summary
Importance of the Topic
Food safety and nutritional monitoring require reliable identification of trace element levels in complex matrices. Globalization of supply chains and stricter regulatory limits for toxic elements (e.g. Pb, Cd, As) drive the need for fast, sensitive, multi-element methods. Accurate elemental profiling also supports fraud detection by fingerprinting origin through trace element patterns.
Objectives and Study Overview
This application note evaluates the performance of the Agilent 7900 ICP-MS equipped with the Method Automation function in ICP-MS MassHunter 4.1. The primary goal is to develop and validate a fully automated, routine method for determining trace metals in fish Certified Reference Materials (CRMs) (DORM-4, 7402-a, 7403-a), comparing automatic method generation with manual expert setup.
Methodology and Instrumentation
Sample Preparation:
- Microwave digestion of 0.5 g CRM samples with HNO3/HCl in closed vessels at 210 °C.
- Spike recovery tests for method accuracy verification.
- Agilent 7900 ICP-MS with Ultra High Matrix Introduction (UHMI) and H2 collision cell options.
- ICP-MS MassHunter 4.1 software with Method Automation (Method Wizard) in fully automatic mode.
- Standard sample introduction: concentric nebulizer, quartz spray chamber, quartz torch, Ni-tipped cones, ASX-520 autosampler.
Main Results and Discussion
The automatically generated method achieved excellent agreement with certified values for all major and trace elements in three fish CRMs. Measured concentrations for elements such as Na, Mg, P, S, K, Ca, Cr, Mn, Fe, Cu, Zn, As, Se, Cd, Hg and Pb matched reference data within stated uncertainties. Spike recoveries for 26 analytes fell between 90 % and 110 %, confirming method accuracy. Settings selected by Method Automation—plasma mode, cell gas (He, H2, HEHe), isotopes, integration times and internal standards—closely mirrored those chosen by an experienced ICP-MS specialist, demonstrating robustness of automated development.
Benefits and Practical Applications
- Rapid method development reduces time and resource demands.
- Minimizes reliance on operator expertise for interference management.
- Delivers high throughput with reproducible low detection limits for routine food safety testing.
- Applicable to diverse matrices including environmental, geological, clinical and pharmaceutical samples.
Future Trends and Opportunities
Advances in collision-reaction cell chemistries and automation will further streamline multi-element analysis. Integration with artificial intelligence for adaptive method optimization, real-time data validation and cloud-based workflows is expected to boost laboratory productivity. Coupling inline separation or digestion modules may expand capabilities to handle more complex matrices.
Conclusion
The Agilent 7900 ICP-MS Method Automation function reliably generates fully optimized methods for multi-element trace analysis in food CRMs, delivering accuracy equivalent to expert manual setup. This automation streamlines routine testing, ensures consistent data quality and supports regulatory compliance in food safety monitoring.
Reference
1. Kaoru Ariyama, Akemi Yasui, JARQ, 40(4), 333–339 (2006)
2. Cinzia Benincasa et al., Anal. Chim. Acta, 585, 366–370 (2007)
3. Erik H. Larsen, Stefan Sturup, J. Anal. At. Spectrom., 9(10), 1099–1105 (1994)
4. Maurizio Pettine et al., Anal. Chim. Acta, 599(2), 191–198 (2007)
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