Issue 26, 2022, Issue in Progress

Determination of macro, micro and toxic element concentrations in peanuts from main peanut producing areas of China by ICP-MS: a pilot study on the geographical characterization

Abstract

Peanut is an important crop grown worldwide. The geographic origin of peanuts has been a topic of substantial attention since their prices can vary according to their geographic origins. This study evaluated the main macro (K, Ca, Mg, Na, and Al), micro (Fe, Zn, Mn, Ni, Sr, Mo, Cu, Se, V, Co), and toxic (As, Cd, Cr, and Pb) element concentrations in peanuts collected from six different Chinese provinces. Multi-element analysis of peanuts from different regions was carried out to develop a reliable method to trace the origin of peanuts. After microwave digestion, the element concentrations were determined through inductively coupled plasma mass spectrometry (ICP-MS). Certified reference material (CRM, GBW10011) was used to ensure accurate results. The profile of contents of major elements obtained in the current study showed the order: K > Mg > Ca > Al > Na > Zn > Fe > Mn > Ni > Sr > Mo. The average concentrations of toxic elements such as Pb, Cd, As, and Cr were very low and within the safe limits. Correlation analysis showed that there was a strong correlation between individual elements in peanut samples. The data were processed by means of the chemometric approach of linear discriminant analysis (LDA) and 97.0% of samples were correctly predicted.

Graphical abstract: Determination of macro, micro and toxic element concentrations in peanuts from main peanut producing areas of China by ICP-MS: a pilot study on the geographical characterization

Article information

Article type
Paper
Submitted
03 Apr 2022
Accepted
23 May 2022
First published
07 Jun 2022
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2022,12, 16790-16797

Determination of macro, micro and toxic element concentrations in peanuts from main peanut producing areas of China by ICP-MS: a pilot study on the geographical characterization

L. Chen, M. Ding, Z. Li, X. Li and L. Deng, RSC Adv., 2022, 12, 16790 DOI: 10.1039/D2RA02148J

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