rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea

Authors

M.M. Hassan
Q. Chen
F.Y.H. Kutsanedzie
H. Li
M. Zareef
Y. Xu
M. Yang
A.A. Agyekum

Abstract

Pesticide residue in food is of grave concern in recent years. In this paper, a rapid, sensitive, SERS (Surface-enhanced Raman scattering) active reduced-graphene-oxide-gold-nano-star (rGO-NS) nano-composite nanosensor was developed for the detection of acetamiprid (AC) residue in green tea. Different concentrations of AC combined with rGO-NS nano-composite electro-statically, yielded a strong SERS signal linearly with increasing concentration of AC ranging from 1.0 × 10−4 to 1.0 × 103 μg/mL indicating the potential of rGO-NS nano-composite to detect AC in green tea. Genetic algorithm-partial least squares regression (GA-PLS) algorithm was used to develop a quantitative model for AC residue prediction. The GA-PLS model achieved a correlation coefficient (Rc) of 0.9772 and recovery of the real sample of 97.06%–115.88% and RSD of 5.98% using the developed method. The overall results demonstrated that Raman spectroscopy combined with SERS active rGO-NS nano-composite could be utilized to determine AC residue in green tea to achieve quality and safety. © 2018

Abstract Image

ScienceDirect Link

10.1016/j.jfda.2018.06.004

Recommended Citation

Hassan, M.M.; Chen, Q.; Kutsanedzie, F.Y.H.; Li, H.; Zareef, M.; Xu, Y.; Yang, M.; and Agyekum, A.A. (2019) "rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea," Journal of Food and Drug Analysis: Vol. 27 : Iss. 1 , Article 23.
Available at: https://doi.org/10.1016/j.jfda.2018.06.004

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https://www.sciencedirect.com/science/article/pii/S1021949818301091/pdfft?md5=9322c1b2bc3ba381ecd0c6a91a7861d9&pid=1-s2.0-S1021949818301091-main.pdf