Smartphone-based colorimetric sensor array using gold nanoparticles for rapid distinguishment of multiple pesticides in real samples
Smartphone-based colorimetric sensor array using gold nanoparticles for rapid distinguishment of multiple pesticides in real samples
复制标题
基于智能手机的比色传感器阵列，使用金纳米粒子快速区分真实样品中的多种农药
DOI：
10.1016/j.foodchem.2022.134768
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发表时间：
2022-11-03
期刊：
影响因子：
8.8
通讯作者：
Zhou, Haibo
中科院分区：
文献类型：
--
作者：
Zhao, Ting;Liang, Xiaochen;Zhou, Haibo
提出了一种简单、灵敏的基于金纳米颗粒（AuNPs）的农药检测方法。乙酰胆碱酯酶（AChE）水解碘化乙酰硫代胆碱（ATCh）的能力受不同农药的影响，产生不同浓度的硫代胆碱。硫代胆碱可以很容易地通过Au -S共价键连接到AuNPs，并且AuNPs经历聚集，由于表面等离子体共振性质的改变而导致可见的颜色变化。基于这些结果，我们成功地区分了8种农药（草甘膦，福美双，吡虫啉，苯磺隆甲基，烟嘧磺隆，噻吩磺隆甲基，敌敌畏，和非禾草灵）利用5种不同的金纳米粒子的比色测定。该目视法对所有农药的检测限（LOD）均小于1.5 × 10（-7）M，比美国环境保护署法规规定的灵敏度（1.18类似于3.91 × 10（-6）M）更高。通过将便携式智能手机设备与使用（颜色名称AR）和RGB（红色、绿色、蓝色）值的颜色拾取应用程序相结合，进一步改进了该方法。将该方法应用于真实的样品中农药残留的线性判别分析，取得了较好的效果。
A simple, sensitive method for pesticide distinguishment based on a colorimetric sensor array using diverse gold nanoparticles (AuNPs) at room temperature is presented in this study. Acetylcholinesterase (AChE) hydrolysis ability was influenced by different pesticides and produced different concentrations of thiocholine by hydro-lyzing acetylthiocholine iodide (ATCh). Thiocholine could be easily linked to the AuNPs through an Au -S covalent bond, and AuNPs underwent aggregation, resulting in a visible color change due to alteration of surface plasmon resonance properties. Based on these results, we successfully distinguished eight pesticides (glyphosate, thiram, imidacloprid, tribenuron methyl, nicosulfuron, thifensulfuron methyl, dichlorprop, and fenoprop) uti-lizing five different AuNPs by colorimetric assay. The limit of detection (LOD) of this visual method for all pesticides was less than 1.5 x 10(-7) M, which was more sensitive than the U.S. Environmental Protection Agency regulations specify (1.18 similar to 3.91 x 10(-6) M). This method was further improved by combining a portable smartphone device with a color picking application using (color name AR) and RGB (red, green, blue) values. The method was successfully applied to pesticide residue distinguishment in real samples by linear discriminant analysis (LDA).