量子点
呋喃丹
纳米传感器
氨基甲酸酯
西维因
偏最小二乘回归
荧光
纳米-
检出限
化学
RGB颜色模型
色谱法
农药残留
杀虫剂
碲化镉光电
分析化学(期刊)
环境化学
纳米技术
材料科学
计算机科学
生物
有机化学
物理
人工智能
复合材料
农学
机器学习
量子力学
作者
Hengye Chen,Ou Hu,Yao Fan,Lu Xu,Lei Zhang,Wei Lan,Ying Hu,Xingrui Xie,Lixia Ma,Yuanbin She,Haiyan Fu
出处
期刊:Food Chemistry
[Elsevier BV]
日期:2020-05-15
卷期号:327: 127075-127075
被引量:120
标识
DOI:10.1016/j.foodchem.2020.127075
摘要
The needing of rapid and sensitive detection method for pesticides is increasing, to facilitate its detection without complicated instruments. Herein, a novel paper-based senor was developed for the visual detection of three carbamate pesticides (metolcarb, carbofuran, and carbaryl) based on CdTe quantum dots (QDs) and nano zinc 5, 10, 15, 20-tetra(4-pyridyl)-21H-23H-porphine (nano ZnTPyP) with a turn-off-on mode. This fluorescence sensing model could be applied in the highly selective and sensitive detection of carbamate pesticides both by fluorescence spectrometry or paper-based sensors. Based on the extracted RGB color values of paper, the partial least squares regression (PLSR) was used to accurately quantify the concentrations of carbamate pesticides in different food matrices (apple, cabbage and tea water). This method featured in high speed, low price and high accuracy, and provided a new strategy for the detection of food safety.
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