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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