您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Development of surface molecular-imprinted electrochemical sensor for palmitic acid with machine learning assistance

文献类型： 外文期刊

作者： Zhang, Heng 1 ; Luo, Bin 1 ; Liu, Ke 1 ; Wang, Cheng 1 ; Hou, Peichen 1 ; Zhao, Chunjiang 1 ; Li, Aixue 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

2.Hebei Univ Engn, Sch Landscape & Ecol Engn, Handan 056038, Peoples R China

关键词： Molecularly imprinted polymer; Palmitic acid; Electrochemical sensor; Artificial neural network

期刊名称：TALANTA （ 2022影响因子：6.1； 五年影响因子：5.4 ）

ISSN： 0039-9140

年卷期： 2024 年 275 卷

页码：

收录情况： SCI

摘要： Palmitic acid (PA) is a kind of saturated high fatty acid, which is involved in physiological safety and food quality. A surface molecularly imprinted polymer (MIP) electrochemical sensor was prepared on MXene surface using dopamine (DA) as functional monomer. The electrode was modified with gold nanoparticles (AuNPs), ferrocene-graphene oxide-multiwalled carbon nanotubes (Fc-GO-MWCNT) composite to enhance the electroactive area and conductivity. The sensor was characterized by scanning electron microscope (SEM), energydispersive X-ray spectroscopy (EDS), electrochemical impedance spectroscopy (EIS) and Differential pulse voltammetry (DPV), respectively. The parameters concerning this assay and various regeneration conditions have been carefully studied. The sensor can detect PA in the range of 1 nM-1 mM (R2 = 0.995), the limit of detection (LOD) is 0.48 nM (S/N = 3), and the limit of quantification (LOQ) is 1.61 nM. The artificial neural network (ANN) model in machine learning is further used to analyze the data collected by the sensor. The results show that the back propagation (BP) neural network in ANN is more suitable for the intelligent analysis of PA. The practicality of the sensor was confirmed by detecting PA in pork samples. This is the first MIP-based electrochemical sensor for PA, and it has great potential in practical applications.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台