The entire process could be completed within a half hour. 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 results were concentration-dependent, allowing the identification of a dominant species from bacterial mixtures. Each peptide's contribution to correct classification was evaluated. 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). A wireless, pocket fluorescence microscope (interfaced with a smartphone) counted such particle aggregations. Peptides were crosslinked to submicron particles, and peptide-bacteria interactions on paper microfluidic chips caused the particle aggregation. 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). 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. ![]() Specific bioreceptors or selective growth media are necessary for most bacterial detection methods. ![]() ![]() Numerous bacteria can cause water- and foodborne diseases and are often found in bacterial mixtures, making their detection challenging.
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