Routine Measurement of Multiple Elements in Food using an ICP-OES Fitted with a Switching Valve

Importance of the topic

Food products can accumulate trace elements from soil, water, air, and processing equipment, posing potential health risks when consumed regularly. Reliable, low-level quantification of these elements is essential for regulatory compliance and public safety. China's GB5009.268-2016 standard requires analysis of 16 elements in food matrices, highlighting the need for methods that achieve required sensitivity, accuracy, and throughput.

Objectives and study overview

This work sought to develop a cost-effective, high-throughput ICP-OES method fulfilling GB5009.268-2016. Using a rice flour reference material, the study aimed to determine levels of aluminum, boron, barium, calcium, copper, iron, potassium, magnesium, manganese, sodium, nickel, phosphorus, strontium, titanium, vanadium, and zinc with accuracy and precision, while optimizing sample cycle time and argon consumption.

Instrumentation and methodology

An Agilent 5110 Synchronous Vertical Dual View ICP-OES with an Advanced Valve System (AVS 6) was employed. Key features include:

• Plug-and-play vertical torch, triple argon mass flow controllers, and Dichroic Spectral Combiner for simultaneous axial/radial detection
• High-speed positive displacement pump in the AVS 6 switching valve for rapid sample uptake and rinse
• Inert PTFE double-pass cyclonic spray chamber and PEEK MiraMist nebulizer with a 1.8 mm quartz injector to minimize boron contamination
• Fitted background correction (FBC) and Fast Automated Curve Fitting (FACT) for robust correction of spectral interferences

Sample preparation involved microwave digestion of 0.50 g rice flour in 5 mL HNO₃ followed by dilution to 50 mL. Calibration standards covered method ranges, and method detection limits (MDLs) were established from 120 blanks across three days.

Main results and discussion

All 16 elements exhibited linear calibration (R²>0.9998). MDLs were below GB5009.268-2016 requirements for each analyte. Analysis of NIST 1568b rice flour yielded recoveries within ±10% of certified values. Spike recoveries in digested samples ranged from 95–109%, confirming absence of boron memory effects. Six-hour stability tests with QC checks every 10 samples showed recoveries within ±10% and RSDs below 3%. Sample cycle time was reduced to 48 s and argon usage to 17 L per sample, versus 37 L without AVS 6.

Benefits and practical applications

The proposed ICP-OES method offers:

• High sensitivity and extended dynamic range via SVDV configuration
• Automated, accurate background correction for complex matrices
• Reduced sample-to-sample carryover and memory effects
• Enhanced productivity with sub-minute analysis times
• Lower operating costs through minimized argon consumption

These features support routine monitoring of food safety in quality assurance laboratories, regulatory testing, and agricultural research.

Future trends and opportunities

Emerging directions include further optimization of gas flows and valve timing to reduce costs, integration with ICP-MS for elements requiring ultra-trace detection, and deployment of automated sample preparation robots. Advances in spectral deconvolution algorithms and cloud‐based data analytics will enhance throughput and data quality. Adaptation to other complex food matrices and real-time monitoring platforms presents additional growth areas.

Conclusion

The Agilent 5110 SVDV ICP-OES with AVS 6 switching valve meets the performance criteria of China's GB5009.268-2016 for determination of 16 elements in food. The approach delivers accurate, precise, and stable results, combined with rapid analysis and reduced argon usage, making it a robust solution for routine elemental screening in food safety applications.

Reference

• GB5009.268-2016 Determination of Multiple Elements in Food, Dec. 2016.
• Agilent Technologies. Synchronous Vertical Dual View (SVDV) for Superior Speed and Performance, publication 5991-4853EN.
• Agilent Technologies. Fitted Background Correction (FBC) – Fast, Accurate and Fully Automated Background Correction, publication 5991-4836EN.
• Agilent Technologies. Real-time Spectral Correction of Complex Samples using FACT Spectral Deconvolution Software, publication 5991-4837EN.