rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea,Journal of Food and Drug Analysis

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rGO-NS SERS-based coupled chemometric prediction of acetamiprid residue in green tea
Journal of Food and Drug Analysis ( IF 2.6 ) Pub Date : 2019-01-01 , DOI: 10.1016/j.jfda.2018.06.004
Md Mehedi Hassan ₁ , Quansheng Chen ₁ , Felix Y H Kutsanedzie ₁ , Huanhuan Li ₁ , Muhammad Zareef ₁ , Yi Xu ₁ , Mingxiu Yang ₁ , Akwasi A Agyekum ₁

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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.

中文翻译：

基于rGO-NS SERS的绿茶中啶虫脒残留的耦合化学计量学预测

近年来，食品中的农药残留备受关注。在本文中，开发了一种快速、灵敏、SERS（表面增强拉曼散射）活性还原氧化石墨烯-金-纳米星（rGO-NS）纳米复合纳米传感器，用于检测啶虫脒（AC）残留物。绿茶。不同浓度的 AC 与 rGO-NS 纳米复合材料静电结合，随着 AC 浓度从 1.0 × 10-4 到 1.0 × 103 μg/mL 的增加线性地产生强 SERS 信号，表明 rGO-NS nano-复合物来检测绿茶中的 AC。遗传算法-偏最小二乘回归 (GA-PLS) 算法用于开发 AC 残留预测的定量模型。GA-PLS 模型的相关系数 (Rc) 为 0.9772，真实样品的回收率为 97.06%-115.88%，RSD 为 5。98% 使用开发的方法。总体结果表明，拉曼光谱结合 SERS 活性 rGO-NS 纳米复合材料可用于测定绿茶中的 AC 残留，以实现质量和安全性。

更新日期：2019-01-01

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