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Multivariate optimization of the feather sample cleaning procedure prior to ICP-MS analysis
Authors: Piroutková Martina | Husáková Lenka | Kováčik Jozef | Patočka Jan | Vydra Marek
Year: 2024
Type of publication: ostatní - článek ve sborníku
Name of source: The Czech-Slovak Spectroscopic Conference & Mössbauer Spectroscopy in Materials Science : book of abstracts
Publisher name: Spektroskopická společnost Jana Marca Marci
Place: Praha
Page from-to: 150
Titles:
Language Name Abstract Keywords
eng Multivariate optimization of the feather sample cleaning procedure prior to ICP-MS analysis This study aimed to develop an effective washing procedure for feather samples prior to multielement ICP-MS analysis. Various washing schemes, were tested using different cleaning solutions, including deionized water with detergent, ethanol, acetone, nitric acid, ethylenediaminetetraacetic acid tetrasodium salt (EDTA), and citric acid (CA). For this purpose, a multivariate design was employed, allowing for optimization in a short period, generation of a large amount of information from a small number of experiments, and the evaluation of the interaction between variables. Specifically, a two-level fractional factorial design with central points was utilized to identify critical factors and their optimal levels, such as concentrations, volumes of analytical agents, and extraction time using ultrasonication. The optimized cleaning procedure involved sequential use of deionized water with detergent followed by a mixture of acetone, EDTA, and diluted nitric acid with sonication, which yielded the best results for multianalyte ICP-MS analysis. This method was successfully applied to analyze certified reference materials for human hair (GBW07601 and NCS ZC 81002b) and various feather samples collected from polluted and control locations in Slovakia, demonstrating its efficacy in determining metal load. The integration of multivariate statistical analysis in this context proved invaluable for understanding the effects of pollution on the elemental profiles of samples in different environmental settings. Multivariate optimization; Feather cleaning; ICP-MS; Machine learning

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