# An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Muhammad Ali Akbar, Amine Ait Si Ali, Abbes Amira, Faycal Bensaali, Mohieddine Benammar, Muhammad Hassan, Amine Bermak

Research output: Contribution to journalArticle

27 Citations (Scopus)

### Abstract

Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated $4 \times 4$ tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the $4 \times 4$ array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz.

Original language English 7467450 5734-5746 13 IEEE Sensors Journal 16 14 https://doi.org/10.1109/JSEN.2016.2565721 Published - 15 Jul 2016

### Keywords

• Electronic nose
• Feature reduction
• Gas identification
• LDA
• PCA
• Zynq SoC

### ASJC Scopus subject areas

• Electrical and Electronic Engineering
• Instrumentation

### Cite this

Akbar, M. A., Ait Si Ali, A., Amira, A., Bensaali, F., Benammar, M., Hassan, M., & Bermak, A. (2016). An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification. IEEE Sensors Journal, 16(14), 5734-5746. [7467450]. https://doi.org/10.1109/JSEN.2016.2565721