Abstract
A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more practical if each individual odorant could be determined directly. This paper proposes two methods for such odor components analysis for electronic nose systems. First, a K-nearest neighbor (KNN)-based local weighted nearest neighbor (LWNN) algorithm is proposed to determine the components of an odor. According to the component analysis, the odor training data is firstly categorized into several groups, each of which is represented by its centroid. The examined odor is then classified as the class of the nearest centroid. The distance between the examined odor and the centroid is calculated based on a weighting scheme, which captures the local structure of each predefined group. To further determine the concentration of each component, odor models are built by regressions. Then, a weighted and constrained least-squares (WCLS) method is proposed to estimate the component concentrations. Experiments were carried out to assess the effectiveness of the proposed methods. The LWNN algorithm is able to classify mixed odors with different mixing ratios, while the WCLS method can provide good estimates on component concentrations.
Highlights
An electronic nose is a biomimetic olfactory system developed based on chemical sensor principles, electronic system design and data analysis techniques
The testing air was infused into the glass chamber, which connects to a commercial Cyranose 320 electronic nose, which consists of 32 carbon black composite sensors
This section reports the performance of the proposed methodology that uses a weighted and constrained least-squares method to estimate the concentration of each component present in an odor mixture
Summary
An electronic nose is a biomimetic olfactory system developed based on chemical sensor principles, electronic system design and data analysis techniques. Different odors are recognized by different combinations of odorant receptors [1,2]. Learning from this mechanism, an array of different chemical sensors is used in the design of an electronic nose. One type of data analysis methods is classification, which aims to group an object into one of the predefined class. K-Nearest Neighbor classifier (KNN) is one of the widely applied classification method that classifies an item according to the majority voting of the K nearest items. Instead of setting a global value for K, Locally Adaptive Nearest Neighbor (Local KNN)
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