rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea
Md Mehedi Hassan, Quansheng Chen*, Felix Y.H. Kutsanedzie, Huanhuan Li, Muhammad Zareef, Yi Xu, Mingxiu Yang, Akwasi A. Agyekum
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
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 x10-4 to 1.0 x103 µg/mL indicating the potential of rGO-NS nanocomposite 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 nanocomposite could be utilized to determine AC residue in green tea to achieve quality and safety.
Keywords: Acetamiprid residue, Chemometrics, Green tea, Reduced graphene oxide-gold
Nanostar, Surface-enhanced Raman scattering