한빛사논문
- 조회310
Yan Liang a,1, Min Hee Lee b,1, Avory Zhou c, Bradley Khanthaphixay c, Dong Soo Hwang b, Jeong-Yeol Yoon a,c
aDepartment of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States
bDivision of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongsangbuk-do, 37673, Republic of Korea
cDepartment of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United State
1These authors contributed equally.
Corresponding authors: Dong Soo Hwang, Jeong-Yeol Yoon
Abstract
Numerous bacteria can cause water- and foodborne diseases and are often found in bacterial mixtures, making their detection challenging. Specific bioreceptors or selective growth media are necessary for most bacterial detection methods. In this work, we collectively used five quorum sensing-based peptides identified from bacterial biofilms to identify 10 different bacterial species (Bacillus subtilis, Campylobacter jejuni, Enterococcus faecium, Escherichia coli, Legionella pneumophila, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella Typhimurium, Staphylococcus aureus, Vibrio parahaemolyticus) and their mixtures in water and milk. Four different machine learning classification methods were used: k-nearest neighbors (k-NN), decision tree (DT), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Peptides were crosslinked to submicron particles, and peptide-bacteria interactions on paper microfluidic chips caused the particle aggregation. A wireless, pocket fluorescence microscope (interfaced with a smartphone) counted such particle aggregations. XGBoost showed the best accuracy of 83.75% in identifying bacterial species from water samples using 320 different datasets and 91.67% from milk samples using 140 different datasets (5 peptide features per dataset). Each peptide's contribution to correct classification was evaluated. The results were concentration-dependent, allowing the identification of a dominant species from bacterial mixtures. Using XGBoost and the previous milk database, we tested 14 blind samples of various bacterial mixtures in milk samples, with an accuracy of 81.55% to predict the dominant species. The entire process could be completed within a half hour. The demonstrated system can provide a handheld, low-cost, easy-to-operate tool for potential hygiene spot-checks, public health, or personal healthcare.
논문정보
- Research article
- 2023년 05월 (BRIC 등록일 2023-04-19)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 바이오소재
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