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Pseudo optimization of e-nose data using region selection with feature feedback based on regularized linear

Gu-Min Jeong¹, Nguyen Trong Nghia², Sang-Il Choi³

¹Electrical Engineering, Kookmin University, 861-1, Jeongeung-dong, Songbuk-gu, Seoul 136-702, Korea. gm1004@kookmin.ac.kr.

Sensors (Basel, Switzerland)

|January 7, 2015

Summary

This summary is machine-generated.

This study introduces a new method for optimizing electronic nose (e-nose) systems by selecting key sensor channels and time periods. This approach enhances e-nose performance and cost-effectiveness for practical applications.

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Area of Science:

* Sensor Technology
* Data Analysis
* Chemical Sensing

Background:

* Electronic nose (e-nose) systems require optimization for performance and cost-effectiveness.
* Key parameters like the number of channels, sampling time, and sensing time significantly impact e-nose functionality.
* Existing methods may not fully address the multi-faceted optimization needs of e-nose systems.

Purpose of the Study:

* To develop a pseudo-optimization method for e-nose data analysis.
* To enhance the performance and cost functions of e-nose systems through intelligent feature selection.
* To identify optimal sensor channels and time horizons for improved e-nose operation.

Main Methods:

* Utilized region selection with feature feedback based on regularized linear discriminant analysis (R-LDA).
* Developed a two-dimensional discriminant information map by reverse mapping feature space to data space.
* Employed heuristic selection of optimal channels and time units based on the discriminant map.

Main Results:

* The proposed R-LDA based method effectively identifies important channels and time horizons.
* A two-dimensional discriminant information map was generated for channel and time unit analysis.
* Experimental validation demonstrated significant improvements in e-nose performance and cost-effectiveness for various volatile organic compounds.

Conclusions:

* The presented pseudo-optimization method offers a cost- and performance-effective solution for e-nose systems.
* The discriminant information map provides a valuable tool for optimizing e-nose channel and time selection.
* This approach is highly effective for the real-world implementation of enhanced e-nose systems.