TY - JOUR

T1 - On the Possibility of Universal Chemometric Calibration in X-ray Fluorescence Spectrometry: Case Study with Ore and Steel Samples

AU - Selivanovs, Z.

AU - Panchuk, V.

AU - Kirsanov, D.

N1 - Export Date: 28 November 2023 Адрес для корреспонденции: Kirsanov, D.; Institute of Chemistry, Russian Federation; эл. почта: d.kirsanov@gmail.com Сведения о финансировании: Russian Science Foundation, RSF, RSF 23-23-00108 Текст о финансировании 1: This study was funded by the Russian Science Foundation, grant number RSF 23-23-00108. Пристатейные ссылки: Taraškevičius, R., Motiejūnaitė, J., Zinkutė, R., Eigminienė, A., Gedminienė, L., Stankevičius, Ž., Similarities and differences in geochemical distribution patterns in epiphytic lichens and topsoils from kindergarten grounds in Vilnius (2017) J. Geochem. Explor, 183, pp. 152-165; Moreno-Santos, A., Rios-Hurtado, J.C., Flores-Villaseñor, S.E., Esmeralda-Gomez, A.G., Guevara-Chavez, J.Y., Lara-Castillo, F.P., Escalante-Ibarra, G.B., Hydroxyapatite Growth on Activated Carbon Surface for Methylene Blue Adsorption: Effect of Oxidation Time and CaSiO3 Addition on Hydrothermal Incubation (2023) Appl. Sci, 13; Revenko, A.G., X-Ray Fluorescence Analysis in Pharmacology (2022) X-Ray Fluorescence in Biological Sciences, pp. 475-488. , Singh V.K., Kawai J., Tripathi D.K., (eds), John Wiley & Sons, Ltd., Hoboken, NJ, USA; Ruschioni, G., Micheletti, F., Bonizzoni, L., Orsilli, J., Galli, A., FUXYA2020: A Low-Cost Homemade Portable EDXRF Spectrometer for Cultural Heritage Applications (2022) Appl. Sci, 12; Barago, N., Pavoni, E., Floreani, F., Crosera, M., Adami, G., Lenaz, D., Larese Filon, F., Covelli, S., Portable X-ray Fluorescence (pXRF) as a Tool for Environmental Characterisation and Management of Mining Wastes: Benefits and Limits (2022) Appl. Sci, 12; Vanhoof, C., Bacon, J., Fittschen, U., Vincze, L., Atomic spectrometry update: Review of advances in X-ray fluorescence spectrometry and its special applications (2022) J. Anal. At. Spectrom, 37, pp. 1761-1775; Richard, M.R., Corrections for matrix effects in X-ray fluorescence analysis—A tutorial (2006) Spectrochim. Acta B At. Spectrosc, 61, pp. 759-777; Wang, Y., Zhao, X., Kowalski, B.R., X-Ray Fluorescence Calibration with Partial Least-Squares (1990) Appl. Spectrosc, 44, pp. 998-1002; Kaniu, M.I., Angeyo, K.H., Mwala, A.K., Mangala, M.J., Direct rapid analysis of trace bioavailable soil macronutrients by chemometrics-assisted energy dispersive X-ray fluorescence and scattering spectrometry (2012) Anal. Chim. Acta, 729, pp. 21-25. , 22595429; Panchuk, V., Yaroshenko, I., Legin, A., Semenov, V., Kirsanov, D., Application of chemometric methods to XRF-data—A tutorial review (2018) Anal. Chim. Acta, 1040, pp. 19-32. , 30327110; Aidene, S., Khaydukova, M., Pashkova, M., Chubarov, V., Savinov, S., Semenov, V., Kirsanov, D., Panchuk, V., Does chemometrics work for matrix effects correction in X-ray fluorescence analysis? (2021) Spectrochim. Acta B At. Spectrosc, 185, p. 106310; Aidene, S., Khaydukova, M., Savinov, S., Semenov, V., Kirsanov, D., Panchuk, V., Partial least squares assisted influence coefficients concept improves accuracy in X-ray fluorescence analysis (2022) Spectrochim. Acta B At. Spectrosc, 193, p. 106452; Kawai, J., Yamasaki, K., Tanaka, R., Fundamental Parameter Method in X-Ray Fluorescence Analysis (2019) Encyclopedia of Analytical Chemistry, pp. 1-14. , Meyers R.A., (ed), John Wiley & Sons, Ltd., Hoboken, NJ, USA; Bro, R., Smilde, A.K., Principal component analysis (2014) Anal. Methods, 6, pp. 2812-2831; (2022) R: A Language and Environment for Statistical Computing, , https://www.R-project.org/, R Foundation for Statistical Computing, Vienna, Austria, Available online; Kucheryavskiy, S., mdatools—R package for chemometrics (2020) Chemometr. Intell. Lab. Syst, 198, p. 103937; Jeremy, F., Jens, H., Chad, J.H., Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls) (2017) J. Stat. Softw, 79, pp. 1-26; Hainmueller, J., Hazlett, C., Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach (2014) Polit. Anal, 22, pp. 143-168; Andersen, C.M., Bro, R., Variable selection in regression—A tutorial (2010) J. Chemom, 24, pp. 728-737

PY - 2023/4/26

Y1 - 2023/4/26

N2 - The accuracy of X-ray fluorescence spectrometry in quantitative element analysis depends on the particular sample composition (so-called matrix effects). Counteracting these effects requires a large number of calibration samples similar in composition to those under analysis. Application of the model constructed for a particular type of samples is not possible for the analysis of samples having a different matrix composition. A possible solution for this problem can be found in the construction of universal calibration models. We propose the development of these universal models using chemometric tools: influence coefficients—partial least squares regression (IC-PLS) and nonlinear kernel regularized least squares regression. We hypothesize that the application of these methods for constructing calibration models would allow embracing the samples of different types in the framework of a single model. We explored this approach for the case of two substantially different types of samples: ores and steels. The performance of these methods was compared with the fundamental parameters (FP) method, which takes into account matrix effects using theoretical equations and allows handling samples of different elemental composition. IC-PLS significantly outperforms traditional FP in terms of accuracy for predicting the content of Al (root mean squared error of prediction 0.96% vs. 3.87%) and Ti (0.05% vs. 0.09%) and yields comparable results for Si and Mn quantification in ores and steels. © 2023 by the authors.

AB - The accuracy of X-ray fluorescence spectrometry in quantitative element analysis depends on the particular sample composition (so-called matrix effects). Counteracting these effects requires a large number of calibration samples similar in composition to those under analysis. Application of the model constructed for a particular type of samples is not possible for the analysis of samples having a different matrix composition. A possible solution for this problem can be found in the construction of universal calibration models. We propose the development of these universal models using chemometric tools: influence coefficients—partial least squares regression (IC-PLS) and nonlinear kernel regularized least squares regression. We hypothesize that the application of these methods for constructing calibration models would allow embracing the samples of different types in the framework of a single model. We explored this approach for the case of two substantially different types of samples: ores and steels. The performance of these methods was compared with the fundamental parameters (FP) method, which takes into account matrix effects using theoretical equations and allows handling samples of different elemental composition. IC-PLS significantly outperforms traditional FP in terms of accuracy for predicting the content of Al (root mean squared error of prediction 0.96% vs. 3.87%) and Ti (0.05% vs. 0.09%) and yields comparable results for Si and Mn quantification in ores and steels. © 2023 by the authors.

KW - chemometrics

KW - EDX

KW - fundamental parameters

KW - IC-PLS

KW - KRLS

KW - multidimensional calibration

UR - https://www.mendeley.com/catalogue/df184922-21f3-342c-b1df-4575ef9118ec/

U2 - 10.3390/app13095415

DO - 10.3390/app13095415

M3 - статья

VL - 13

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

IS - 9

ER -