Electronic mucosa system for complex odour recognition

• 第一著者: Azahar, Nur Syazana
• フォーマット: 学位論文
• 言語: 英語
• 出版事項: 2015
• 主題: TK Electrical engineering. Electronics Nuclear engineering
• オンライン･アクセス: http://eprints.utm.my/53839/1/NurSyazanaAzaharMFKE2015.pdf

Over the years, electronic nose technology has been researched extensively and used in a wide range of applications. Commercial electronic nose system has been in the market for quite some time and is being used in several applications such as environmental, food, medical and perfume industries. However, sensitivities and selectivities of current electronic nose have still not achieved the biological olfactory system. Motivated to improve the performance of current electronic nose, this research discusses the development of electronic mucosa system which is an extention of the electronic nose system. Electronic mucosa system refers to an electronic nose system combined with gas chromatography column to produce extra information, which is spatial and temporal information of odour profile. The main components in electronic mucosa system are four sets of sensor arrays. Each sets containing five metal oxide chemo resistive sensors also known as e2v sensors. Each e2v sensor in an array has different sensitivity. Three gas chromatography columns coated with polar, mid polar and non-polar compound are added in the system to aid the ability of the system to discriminate between simple and complex odour. This electronic mucosa system mimics nasal chromatography effect in human mucous. This system produces spatial and temporal information also known as spatio-temporal information. The spatio-temporal data aid pattern recognition technique and enhances the ability to distinguish more complex odour compared to conventional electronic nose. The electronic mucosa system has successfully discriminated complex odours: lavender, lemon and floral essential oils with small number of sensors with high accuracy. This electronic mucosa system may improve electronic nose technology in the future.