Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification.

ABSTRACT: Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the

SUBMITTER: Kim S 

PROVIDER: S-EPMC8192483 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature