Optimising an Electronic Nose for Microbial Detection

• Autor(a) principal: Cebola, Inês de Andrade Baeta Guerreiro
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/147190
• topic: Electronic nose; Gas sensing materials; Relative humidity; Bacterial discrimination; Odorant binding protein; Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias

Bacterial infections are one of the top 10 global health threats according to the WHO. However, the methods used in clinical practice to identify the microorganisms can take days. This leads to the overuse of broad-spectrum antibiotics, one of the causes of increasing bacterial resistance to antibiotics. This opened the possibility to use electronic noses. These devices mimic biological olfaction by using sensing elements that interact with volatile biomarkers associated to bacteria and produce a signal that can be further processed and analysed, having the potential to be fast methods as a clinical diagnostics tool. This work aims to investigate two electronic nose prototype devices (optical and electrical) for microbial detection. A first step in the work was the implementation of a strategy to control relative humidity variations during gas sensing experiments. In a second step, this optimised set-up was used to evaluate its potential in discriminating distinct bacteria cultures. Three case studies were designed to obtain preliminary results regarding the microbial detection performance of the prototype electronic nose using machine learning methods. Those studies were the distinction between the headspace of liquid bacterial culture samples of (i) methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA), (ii) MSSA and Staphylococcus epidermidis (S.epidermidis); and (iii) MSSA and Escherichia coli (E.coli). The overall classification accuracies for the best sensors formulations and best dataset splitting method were 63-75%, 60% and 65-70% for the discrimination between MSSA and MRSA, MSSA and S.epidermidis and MSSA from E.coli, respectively. In a third step, an odorant binding protein was recombinantly expressed and purified with the final goal of increasing the selectivity of the electronic nose sensors. It was achieved a production yield of 110 mg protein per liter of expression, with a purity of 95%.