Title :
Detection of oil pollution in seawater: Biosecurity prevention using electronic nose technology
Author :
Chandler, Rob ; Das, Aruneema ; Gibson, Tim ; Dutta, Ritaban
Author_Institution :
AutoNose Manuf. Ltd., Leeds, UK
Abstract :
In this paper-conducting polymer gas sensor based AutoNose electronic nose (E-Nose) technology has been used for detection of oil contamination in seawater samples. AutoNose E-nose is a headspace analyzer based on six conducting polymer sensors. Seawater samples with known (or induced) oil contamination were tested and classified against the unpolluted seawater samples using machine learning based ensemble classifiers with very high accuracy. We show that a simple headspace sensing E-Nose could be used to rapidly detect oil pollution in seawater for early biosecurity prevention.
Keywords :
conducting polymers; electronic noses; learning (artificial intelligence); marine safety; oil pollution; pattern classification; seawater; AutoNose e-nose; AutoNose electronic nose; biosecurity prevention; conducting polymer gas sensor; electronic nose technology; headspace analyzer; machine learning based ensemble classifiers; oil contamination detection; oil pollution detection; unpolluted seawater; Chemical sensors; Chemicals; Electronic noses; Neural networks; Nose; Polymers; Sensors; biosecurity; conducting polymer gas sensor; electronic nose; ensemble classifier; machine learning;
Conference_Titel :
Data Engineering Workshops (ICDEW), 2015 31st IEEE International Conference on
Conference_Location :
Seoul
DOI :
10.1109/ICDEW.2015.7129554
