Research Projects

New Calibration Methods to Improve Accuracy in Trace Element Analysis

Analytical spectrochemical methods such as ICP-OES, ICP-MS and MIP-OES have increasingly become fundamental tools in modern quantitative analysis, with applications in such a broad range of fields as environmental, materials science, medicine and ecology. Despite all their success, accuracy issues, usually associated to the analysis of complex matrices, can lead to unreliable results.

Several projects in our lab involve the development of new calibration strategies that are capable of minimizing matrix and/or spectral interfering effects to significantly improve accuracy in ICP-OES, ICP-MS and MIP-OES determinations. Calibration methods such as the interference standard (IFS), standard dilution analysis (SDA), multi-energy calibration (MEC), multi-isotope calibration (MICal), and multispecies calibration (MSC)are part of our efforts to improve the performance of modern analytical instrumentation. These are simple strategies, which require no expensive instrument modifications nor laborious sample preparation procedures, and can significantly improve accuracy and sample throughput in trace element analysis.

References

Donati, G. L and Amais, R. S. Fundamentals and new approaches to calibration in atomic spectrometry. J. Anal. At. Spectrom., 34(12), 2019, 2353-2369.

Carter, J. A.; Barros, A. I.; Nóbrega, J. A. and Donati, G. L. Traditional calibration methods in atomic spectrometry and new calibration strategies for inductively coupled plasma mass spectrometry. Front. Chem., 6, 2018, Art. 504, 25p.

Jones, W. B.; Donati, G. L., Calloway Jr., C. P. and Jones, B. T. Standard dilution analysis. Anal. Chem., 87(4), 2015, 2321-2327.

Virgilio, A.; Gonçalves, D. A.; McSweeney, T.; Gomes Neto, J. A.; Nóbrega, J. A. and Donati, G. L. Multi-energy calibration applied to atomic spectrometry. Anal. Chim. Acta, 982, 2017, 31-36.

Williams, C. B. and Donati, G. L. Multispecies calibration: a novel application for inductively coupled plasma tandem mass spectrometry. J. Anal. At. Spectrom., 33 (5), 2018, 762-767.

Sloop, J. T.; Bonilla, H. J. B.; Harville, T.; Jones, B. T. and Donati, G. L. Automated matrix-matching calibration using standard dilution analysis with two internal standards and a simple three-port mixing chamber. Talanta, 205, 2019, 120160.

Jones, W. B.; Donati, G. L.; Calloway Jr., C. P. and Jones, B. T. Automated standard dilution analysis. J. Anal. At. Spectrom., 35(1), 2020, 178-187.

Jones, W. B.; Jones, A. M.; Sutton, A. M.; Donati, G. L.; Calloway Jr., C. P. and Jones, B. T. Standard dilution analysis: an introduction to accurate and precise quantitative analysis with no tedious solution preparation. Chem. Educator, 25, 2020, 98-102.

Virgilio, A.; Silva, A. B. S.; Nogueira, A. R. A.; Nóbrega, J. A. and Donati, G. L. Calculating limits of detection and defining working ranges for multi-signal calibration methods. J. Anal. At. Spectrom., 35(8), 2020, 1614-1620.

Sloop, J. T.; Gonçalves, D. A.; O’Brien, L. M.; Carter, J. A.; Jones, B. T. and Donati, G. L. Evaluation of different approaches to applying the standard additions calibration method. Anal. Bioanal. Chem., 413(5), 2021, 1293-1302.

Naturally Occurring Plasma Species Combined with Supervised and Unsupervised Learning to Improve Accuracy in Spectrochemical Analysis

In a few of our projects, we study the use of plasma native species such as Ar, H, N, N2+, O and OH to identify changes in the atomization/ionization/excitation environment and use that information to correct for matrix effects. Unsupervised machine learning based on principal component analysis (PCA) and affinity propagation clustering (AP) is used to find subtle patterns in the background species data, which is then used to identify and quantify matrix effects, contributing to informed decisions on calibration strategies that will ensure accurate results. Supervised machine learning based on random forest, support vector machine with a radial basis function kernel, k-nearest neighbors, among others is then used to correct for signal bias caused by matrix effects.

This approach has the potential to improve accuracy on the fly. Background species are monitored during the analysis and then used to automatically correct for discrepancies between the calibration standards and the sample, which may significantly improve the performance of important methods such as ICP-OES, MIP-OES and ICP-MS.

References

Lowery, K. L.; McSweeney, T.; Adhikari, S. P.; Lachgar, A. and Donati, G. L. Signal correction using molecular species to improve biodiesel analysis by microwave-induced plasma optical emission spectrometry. Microchem. J., 129, 2016, 58-62.

Williams, C. B.; Jones, B. T. and Donati, G. L. Naturally occurring molecular species used for plasma diagnostics and signal correction in microwave-induced plasma optical emission spectrometry. J. Anal. At. Spectrom., 33 (7), 2018, 1224-1232.

Carter, J. A.; Sloop, J. T.; McSweeney, T.; Jones, B. T. and Donati, G. L. Identifying and assessing matrix effect severity in inductively coupled plasma optical emission spectrometry using non-analyte signals and unsupervised learning. Anal. Chim. Acta, 1062, 2019, 37-46.

Carter, J. A.; Sloop, J. T.; Harville, T.; Jones, B. T. and Donati, G. L. Non-analyte signals and supervised learning to evaluate matrix effects and predict analyte recoveries in inductively coupled plasma optical emission spectrometry. J. Anal. At. Spectrom., 35(4), 2020, 679-692.

Carter, J. A.; O’Brien, L. M.; Harville, T.; Jones, B. T. and Donati, G. L. Machine learning tools to estimate the severity of matrix effects and predict analyte recovery in inductively coupled plasma optical emission spectrometry. Talanta, 223(2), 2021, 121665.