A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures.

ABSTRACT: Enumeration of Campylobacter from environmental waters can be difficult due to its low concentrations, which can still pose a significant health risk. Spectrophotometry is an approach commonly used for fast detection of water-borne pollutants in water samples, but it has not been used for pathogen detection, which is commonly done through a laborious and time-consuming culture or qPCR Most Probable Number enumeration methods (i.e., MPN-PCR approaches). In this study, we proposed a new method, MPN-Spectro-ML, that can provide rapid evidence of Campylobacter detection and, hence, water concentrations. After an initial incubation, the samples were analysed using a spectrophotometer, and the spectrum data were used to train three machine learning (ML) models (i.e., supported vector machine - S

SUBMITTER: Zhang K 

PROVIDER: S-EPMC11379395 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature