Paper-based SERS chip with adaptive attention neural network for pathogen identification
文献类型：期刊论文
| 作者 | Bi, Liyan2,4; Zhang, Huangruici2; Mu, Chenyu1; Sun, Kaidi2; Chen, Hao5; Zhang, Zhiyang3; Chen, Lingxin2,3 |
| 刊名 | JOURNAL OF HAZARDOUS MATERIALS
 |
| 出版日期 | 2025-08-15 |
| 卷号 | 494页码:12 |
| 关键词 | Surface-enhanced Raman scattering Paper-based chip Deep learning Pathogen identification |
| ISSN号 | 0304-3894 |
| DOI | 10.1016/j.jhazmat.2025.138694 |
| 通讯作者 | Bi, Liyan(liyan_bi@bzmc.edu.cn) ; Chen, Lingxin(lxchen@yic.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | BACTERIA ; INACTIVATION |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001504617200001 |
| 资助机构 | Key R & D Program of Shandong Province, China ; Shandong Science and Technology Foundation ; Science Fund of Shandong Laboratory of Advanced Materials and Green Manufacturing (Yantai) ; National Natural Science Foundation of China ; Funds for scientific and technological development of China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/41115]  |
| 专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 |
| 通讯作者 | Bi, Liyan; Chen, Lingxin |
| 作者单位 | 1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Binzhou Med Univ, Sch Special Educ & Rehabil, Yantai 264003, Peoples R China 3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Coastal Zone Ecol Environm Monitoring Technol & Eq, Shandong Key Lab Coastal Environm Proc,Key Lab Coa, Yantai 264003, Peoples R China 4.Shandong Lab Adv Mat & Green Mfg Yantai, Yantai 264005, Peoples R China 5.YanTai Univ, Sch Environm & Mat Engn, Yantai 264003, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bi, Liyan,Zhang, Huangruici,Mu, Chenyu,et al. Paper-based SERS chip with adaptive attention neural network for pathogen identification[J]. JOURNAL OF HAZARDOUS MATERIALS,2025,494:12. |
| APA | Bi, Liyan.,Zhang, Huangruici.,Mu, Chenyu.,Sun, Kaidi.,Chen, Hao.,...&Chen, Lingxin.(2025).Paper-based SERS chip with adaptive attention neural network for pathogen identification.JOURNAL OF HAZARDOUS MATERIALS,494,12. |
| MLA | Bi, Liyan,et al."Paper-based SERS chip with adaptive attention neural network for pathogen identification".JOURNAL OF HAZARDOUS MATERIALS 494(2025):12. |
入库方式： OAI收割
来源：烟台海岸带研究所
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