DETECTION METHODS FOR FAECAL CONTAMINATION EVENTS: THE GAP FOR AUSTRALIA

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD₆₀₀ . Typically, modern UV-Vis spectrophotometers can scan in the region of approximately 200-1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV-Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV-Vis spectral data and generate machine learning "proof of principle" for the UV-Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD₆₀₀ method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.

Monitoring Approaches for Faecal Indicator Bacteria in Water: Visioning a Remote Real-Time Sensor for E. coli and Enterococci

A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, environmental awareness regarding necessary reagent release or other pollution sources, in situ monitoring capability, and portability. Findings showed that several methods are precise and sophisticated but cannot be performed in real-time or remotely. This is mainly due to their laboratory testing requirements, such as lengthy sample preparations, the requirement for expensive reagents, and fluorescent tags. This study determined that portable fluorescence sensing, combined with advanced modelling methods to compensate readings for environmental interferences and false positives, can lay the foundations for a hybrid FIB sensing approach, allowing remote field deployment of a fleet of networked FIB sensors that can collect high-frequency data in near real-time. Such sensors will support proactive responses to sudden harmful faecal contamination events. A method is proposed to enable the development of the visioned FIB monitoring tool.