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Sensors 2010, 10(11), 10467-10483; doi:10.3390/s101110467
Article

A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems

1,* , 2
 and 2
Received: 1 November 2010; in revised form: 11 November 2010 / Accepted: 15 November 2010 / Published: 18 November 2010
(This article belongs to the Special Issue Direct and Indirect Sensing of Odor and VOCs and Their Control)
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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.
Keywords: electronic nose; mixed odor analysis; weighted nearest neighbor; weighted and constrained least-squared method electronic nose; mixed odor analysis; weighted nearest neighbor; weighted and constrained least-squared method
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Tang, K.-T.; Lin, Y.-S.; Shyu, J.-M. A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems. Sensors 2010, 10, 10467-10483.

AMA Style

Tang K-T, Lin Y-S, Shyu J-M. A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems. Sensors. 2010; 10(11):10467-10483.

Chicago/Turabian Style

Tang, Kea-Tiong; Lin, Yi-Shan; Shyu, Jyuo-Min. 2010. "A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems." Sensors 10, no. 11: 10467-10483.


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