# Enhanced Gas Classification in Electronic Nose Systems Using an SMOTE-Augmented Machine Learning Framework

## TL;DR

This paper presents a machine learning framework that improves gas classification accuracy in electronic nose systems using data augmentation and noise reduction techniques.

## Contribution

The novel use of SMOTE data augmentation and PCA optimization in SVM for gas classification in e-nose systems.

## Key findings

- The SMOTE-augmented SVM model achieved 0.93 ± 0.08 recognition accuracy for most target gases.
- The SVM model outperformed decision tree and ANN classifiers by 19% and 7%, respectively.
- The ANN regression model showed 99.55% correlation between predicted and measured values in mixed-gas experiments.

## Abstract

Electronic nose systems are widely used in environmental monitoring and other related fields. In recent years, systems based on gas sensor arrays have attracted considerable attention. However, relying solely on improvements in gas-sensitive materials has struggled to break through the bottleneck in recognition accuracy. To address this challenge, this study designs and validates an integrated machine learning framework for enhanced gas identification in electronic nose systems. Specifically, (1) a Butterworth low-pass filter is combined with principal component analysis (PCA) to suppress sensor noise; (2) the synthetic minority over-sampling technique (SMOTE) is utilized for training set data augmentation to further enhance the classification accuracy of the support vector machine (SVM); and (3) the relationship between single-component and mixed-gas responses is analyzed to construct an artificial neural network (ANN) regression model. Experimental results demonstrate that the SMOTE-augmented, PCA-optimized SVM model achieves a recognition accuracy of 0.93 ± 0.08 for most target gases, representing improvements of 19% and 7% over decision tree and ANN classifiers, respectively, and that the ANN regression model attains a correlation coefficient of 99.55% between predicted and measured values in mixed-gas experiments. Overall, the construction and optimization of this system demonstrate significant practical value for intelligent gas identification and the development of advanced e-nose devices.