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Issue 1, 2019
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Amino-functionalized mesoporous silica nanospheres (MSN-NH2) as sorbent for extraction and concentration of synthetic dyes from foodstuffs prior to HPLC analysis

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Abstract

In this work, amino-functionalized mesoporous silica nanospheres (MSN-NH2) were synthesized and demonstrated as useful sorbents of dispersive solid-phase extraction (dSPE) for extraction and preconcentration of synthetic dyes (tartrazine, amaranth, sunset yellow, allura red, and orange II) from foodstuffs prior to high-performance liquid chromatography (HPLC) analysis. The MSN-NH2 was characterized by transmission electron microscopy, Fourier-transform infrared spectroscopy, nitrogen adsorption and desorption isotherms, and elemental analysis. To achieve optimized extraction efficiency, several critical factors including amount of MSN-NH2, sample pH, adsorption time, adsorption temperature, desorption time, and desorption solvents, were systematically investigated. Under the optimized conditions, the enrichment factors were higher than 120. The limits of detection were found to be 0.10–0.30 ng mL−1. The method showed excellent linearity in the range of 0.45–1000.0 ng mL−1 with correlation coefficients (R2) ≥ 0.9951. The intra-day and inter-day precision (n = 5) were 1.72–3.64% and 1.45–4.52%. The recoveries at three concentration levels ranged from 82.7 to 116.3%. The proposed dSPE by using MSN-NH2 as sorbent coupled with HPLC was successfully applied to the analysis of synthetic dyes in real samples.

Graphical abstract: Amino-functionalized mesoporous silica nanospheres (MSN-NH2) as sorbent for extraction and concentration of synthetic dyes from foodstuffs prior to HPLC analysis

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Supplementary files

Article information


Submitted
10 Oct 2018
Accepted
23 Nov 2018
First published
26 Nov 2018

Anal. Methods, 2019,11, 105-112
Article type
Paper

Amino-functionalized mesoporous silica nanospheres (MSN-NH2) as sorbent for extraction and concentration of synthetic dyes from foodstuffs prior to HPLC analysis

P. Qin, Y. Yang, W. Li, J. Zhang, Q. Zhou and M. Lu, Anal. Methods, 2019, 11, 105
DOI: 10.1039/C8AY02215A

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