Paper-based SERS chip with adaptive attention neural network for pathogen identification.

Journal: Journal of hazardous materials
Published Date: May 21, 2025

Abstract

High-speed and accuracy identification of pathogens has become increasingly critical in both individual patient care and public health. Artificial intelligence (AI)-assisted surface-enhanced Raman scattering (SERS) biosensors enable simultaneous identification of multiple pathogens. However, there are still problems such as low accuracy and limited diversity in bacterial fingerprints. To this end, we present a novel multi-branch adaptive attention convolutional neural network (MBAA-CNN)-assisted paper-based SERS chip for prompt and reliable pathogen discrimination. In the approach, we employed a dual-function molecule 4-mercaptophenylboronic acid (4-MPBA) to capture bacteria and enhance Raman spectra diversity, referring as 4-MPBA labeled mode (label mode). Meanwhile, we utilized the K-means algorithm to identify pathogens in the label mode, producing much higher accuracy compared to label-free mode (n = 2000). Furthermore, we acquired 98.6 % accuracy at all pathogen species and 99.5 % accuracy at the antibiotic-resistant and sensitive strains (n = 10,000) using MBAA-CNN. The superior performance of MBAA-CNN was further validated through comparisons with traditional machine learning models, particularly in terms of loss value, speed and accuracy. We envision the developed approach has potential for early culture-free diagnosis of pathogens and real-time monitoring of microbial contamination in water environment.

Authors

  • Liyan Bi
    School of Special Education and Rehabilitation, Binzhou Medical University, Yantai 264003, China; Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai, Yantai 264005, China. Electronic address: liyan_bi@bzmc.edu.cn.
  • Huangruici Zhang
    School of Special Education and Rehabilitation, Binzhou Medical University, Yantai 264003, China.
  • Chenyu Mu
    School of Electronic Engineering, Xidian University, Xi'an 710071, China.
  • Kaidi Sun
    Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin 150081, PR China.
  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Zhiyang Zhang
    Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
  • Lingxin Chen
    Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China. Electronic address: lxchen@yic.ac.cn.

Keywords

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