info:eu-repo/semantics/article
Multi-response optimization of a green solid-phase extraction for the analysis of heterocyclic aromatic amines in environmental samples
Fecha
2020-02Registro en:
Canales, Maria Romina; Marino Repizo, Leonardo; Reta, Mario Roberto; Cerutti, Soledad; Multi-response optimization of a green solid-phase extraction for the analysis of heterocyclic aromatic amines in environmental samples; Royal Society of Chemistry; Analytical Methods; 2-2020
1759-9660
CONICET Digital
CONICET
Autor
Canales, Maria Romina
Marino Repizo, Leonardo
Reta, Mario Roberto
Cerutti, Soledad
Resumen
A multi-response optimization of a green and efficient solid phase extraction (SPE) sample treatment using non-modified multi-walled carbon nanotubes combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed for the quantification of ten heterocyclic aromatic amines (HAAs) in river and reservoir surface water samples. The proposed methodology was evaluated with the employment of experimental designs, which provided to greening the approach. Ultra-trace amounts of HAAs were retained into the SPE cartridge. Then, these analytes were removed from the carbon nanotubes with 0.8 mL of a mixture of acetonitrile/water with 0.1 % of formic acid. Under the optimal conditions, linearity was achieved for concentration levels ranging from 0.20 µg L-1 to 500 µg L-1, with regression coefficients (R2) from 0.990 to 0.998. Limits of detection varying from 0.06 µg L-1 and 0.23 µg L-1 were attained, the relative standard deviations (n=3) varied from 1.7 to 6.4, and extraction recoveries ranged from 90.6 % to 106.0 % for all the analytes. Results of the presence of HAAs found in the river samples demonstrated levels from 0.16 µg L-1 to 0.53 µg L-1; meanwhile, in the reservoir, the levels were higher, from 0.37 µg L-1 to 0.93 µg L-1. Finally, a comparative discussion was applied in order to assess the greenness of approaches for the determination of heterocyclic aromatic amines in surface water using the available metrics.