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利用纳米等离子体传感器阵列同时检测和鉴定噻虫啉、丙硫磷和丙环唑农药。

Simultaneous detection and identification of thiometon, phosalone, and prothioconazole pesticides using a nanoplasmonic sensor array.

机构信息

Department of Chemistry, Sharif University of Technology, Tehran, 11155-9516, Iran.

Department of Nanotechnology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education, and Extension Organization (AREEO), Karaj, 3135933151, Iran.

出版信息

Food Chem Toxicol. 2021 May;151:112109. doi: 10.1016/j.fct.2021.112109. Epub 2021 Mar 12.

DOI:10.1016/j.fct.2021.112109
PMID:33716053
Abstract

In this work, a colorimetric sensor array has been designed for the identification and discrimination of thiometon (TM) and phosalone (PS) as organophosphate pesticides and prothioconazole (PC) as a triazole pesticide. For this purpose, two different plasmonic nanoparticles including unmodified gold nanoparticles (AuNPs) and unmodified silver nanoparticles (AgNPs) were used as sensing elements. The principle of the proposed strategy relied on the aggregation AuNPs and AgNPs through the cross-reactive interaction between the target pesticides and plasmonic nanoparticles. Therefore, these aggregation-induced UV-Vis spectra changes were utilized to discriminate the target pesticides with the help of linear discriminant analysis (LDA). Besides, we have employed the bar plots and the heat maps as visual non-statistical methods to differentiate the pesticides in a wide range of concentrations (i.e., 20-5000 ng mL). Multivariate calibration plots from partial least squares (PLS)- regression indicated that the responses linearly depend on the pesticide concentrations in the range of 100-1000 ng mL with the limit of detections (LOD) of 66.8, 68.3, and 41.4 ng mL, for TM, PS, and PC, respectively. Finally, the potential applicability of the proposed sensor array has been evaluated for the detection and identification of the pesticides in the mixtures, water samples, and cucumber fruit.

摘要

在这项工作中,设计了一个比色传感器阵列,用于识别和区分作为有机磷农药的噻虫嗪(TM)和丙硫磷(PS)以及作为三唑类农药的丙硫菌唑(PC)。为此,使用了两种不同的等离子体纳米粒子,包括未经修饰的金纳米粒子(AuNPs)和未经修饰的银纳米粒子(AgNPs)作为传感元件。所提出策略的原理依赖于目标农药与等离子体纳米粒子之间的交叉反应相互作用导致 AuNPs 和 AgNPs 的聚集。因此,利用这些聚集诱导的紫外-可见光谱变化,借助线性判别分析(LDA)来区分目标农药。此外,我们还采用条形图和热图作为非统计视觉方法,在较宽的浓度范围内(即 20-5000ng mL)区分农药。偏最小二乘(PLS)回归的多元校准图表明,在 100-1000ng mL 的范围内,响应与农药浓度呈线性相关,TM、PS 和 PC 的检出限(LOD)分别为 66.8、68.3 和 41.4ng mL。最后,评估了所提出的传感器阵列在混合物、水样和黄瓜果实中检测和识别农药的潜在适用性。

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