Improving the detection accuracy of the dual SERS aptasensor system with uncontrollable SERS "hot spot" using machine learning tools.

Journal: Analytica chimica acta
PMID: 38719408

Abstract

BACKGROUND: Simultaneous detection of food contaminants is crucial in addressing the collective health hazards arising from the presence of multiple contaminants. However, traditional multi-competitive surface-enhanced Raman scattering (SERS) aptasensors face difficulties in achieving simultaneous accurate detection of multiple target substances due to the uncontrollable SERS "hot spots". In this study, using chloramphenicol (CAP) and estradiol (E2) as two target substances, we introduced a novel approach that combines machine learning methods with a dual SERS aptasensor, enabling simultaneous high-sensitivity and accurate detection of both target substances.

Authors

  • Junlin Chen
    State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, 266003, China.
  • Hong Lin
    Wenzhou Medical University, Wenzhou, China.
  • Minqiang Guo
    State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, 266003, China; College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang, 843100, China.
  • Limin Cao
    State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, 266003, China.
  • Jianxin Sui
    State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong, 266003, China.
  • Kaiqiang Wang

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