Issue 19, 2020

Multivariate optimization of an ultrasound-assisted extraction procedure for the determination of Cu, Fe, Mn, and Zn in plant samples by flame atomic absorption spectrometry

Abstract

In this work, a simple, easy, and fast ultrasound-assisted extraction procedure for the determination of Cu, Fe, Mn, and Zn in plant samples was developed. Sample preparation conditions were optimized in a multivariate manner using mixture design and Box–Behnken. The extraction conditions adequate to obtain the maximum response were 90% power, 10 minutes, 40 °C temperature, and using an extraction solution composed of HNO3 and HCl at a concentration of 0.5 and 1.1 mol L−1, respectively, for a volume of 10 mL and 250 mg of samples. Accuracy was verified from the analysis of a certified reference material (Apple leaves, NIST 1515) yielding recoveries between 89 and 98% for the procedure. Precision was evaluated in a repeatability study (n = 10), yielding RSDs less than 6.7%, and the limits of quantification (mg kg−1) were 0.95, 2.42, 1.31, and 1.18 for Cu, Fe, Mn, and Zn, respectively. The concentrations determined in the stem and leaf of the mangrove species varied according to the collection point and the tissue, being consistent with other studies. The proposed procedure was efficient and reliable for elemental determination in plant samples, with the advantages of low cost and reagent consumption besides short analysis time as a valid alternative to the conventional sample preparation procedure.

Graphical abstract: Multivariate optimization of an ultrasound-assisted extraction procedure for the determination of Cu, Fe, Mn, and Zn in plant samples by flame atomic absorption spectrometry

Article information

Article type
Paper
Submitted
17 Mar 2020
Accepted
13 Apr 2020
First published
14 Apr 2020

Anal. Methods, 2020,12, 2509-2516

Multivariate optimization of an ultrasound-assisted extraction procedure for the determination of Cu, Fe, Mn, and Zn in plant samples by flame atomic absorption spectrometry

F. S. Costa, R. V. P. Leal, C. S. V. Pacheco, F. A. C. Amorim, R. M. de Jesus, L. N. Santos and E. G. P. da Silva, Anal. Methods, 2020, 12, 2509 DOI: 10.1039/D0AY00554A

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