Handling complex effects in slurry-sampling-electrothermal atomic absorption spectrometry by multivariate...

Area of Science:

• Analytical Chemistry
• Environmental Science

Background:

• Slurry-sampling electrothermal atomic absorption spectrometry (SS-ETAAS) analysis of solid samples faces challenges from spectral and chemical interferences.
• Concomitants in solid matrices can cause interferences that are difficult to resolve with traditional methods like stabilized temperature platform furnace (STPF) conditions or sample ashing.

Purpose of the Study:

• To present a novel approach for managing interferences in SS-ETAAS using multivariate calibration techniques.
• To determine antimony (Sb) concentrations in solid matrices (soils, sediments, coal fly ash) as slurries by ETAAS, overcoming common analytical challenges.

Main Methods:

• Application of multivariate calibration methods, specifically partial least squares (PLS) and artificial neural networks (ANN).
• Utilized experimental designs to develop calibration models and identify key interfering ions (Na+, Ca2+).
• Validated the method by analyzing certified reference materials (CRMs) and assessing robustness.

Main Results:

• Demonstrated that PLS and ANN can effectively handle complex interferences (displacement, depletion, broadening) caused by Na+ and Ca2+ in SS-ETAAS.
• Achieved reliable, fast, and cost-effective prediction of Sb concentrations in diverse solid matrices.
• Showed that linear PLS models are comparable to more complex non-linear ANN models in performance.

Conclusions:

• Multivariate calibration offers a robust and efficient solution for interference management in SS-ETAAS analysis of solid samples.
• The developed method provides accurate and reliable determination of Sb in environmental and industrial solid matrices.
• This approach offers a cost-effective alternative to traditional, time-consuming interference mitigation techniques.