# Perfume Classification Using a Low-Cost Electronic Nose with Isolation Forest and Linear Discriminant Analysis
> Jaya Z.

URL kanonis: https://discover.unhas.ac.id/publications/perfume-classification-using-a-low-cost-electronic-nose-with-isolation-forest-an
Jurnal / Konferensi: 2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems Aims 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/AIMS66189.2025.11229818
Citations: 0

## Authors
- Jaya Z.

## Abstract
This research proposes a perfume classification methodology based on a low-cost electronic nose (e-nose) system utilizing MQ gas sensors. The proposed methodology incorporates a preprocessing pipeline featuring Z-score standardization for data normalization, Isolation Forest for efficient outlier detection, Linear Discriminant Analysis (LDA) for effective feature extraction, and Radial Basis Function Support Vector Machine (RBF-SVM) for classification. Experimental evaluations conducted on ten distinct perfume samples using six MQ sensors (MQ-2, MQ-3, MQ-4, MQ-5, MQ-7, and MQ-135) demonstrate that the LDA-based approach achieves a classification accuracy of 95%, outperforming other dimensionality reduction methods like Principal Component Analysis (PCA) at 85%, Uniform Manifold Approximation and Projection (UMAP) at $75 \%$, and t-Distributed Stochastic Neighbor Embedding (t-SNE) at 85%. The robustness of LDA approach is further validated through 5-fold cross-validation, yielding a mean accuracy of 0.944 with a standard deviation of 0.023. These results highlight the effectiveness of LDA and Isolation Forest in overcoming the challenges posed by low-cost gas sensors, thus enhancing the accuracy and robustness of perfume classification.

## Keywords
- Electronic nose
- Linear discriminant analysis
- Pattern recognition (psychology)
- Principal component analysis
- Robustness (evolution)
- Dimensionality reduction
- Artificial intelligence
- Outlier
- Support vector machine
- Random forest
- Preprocessor
- Computer science
- Mathematics
- Projection pursuit
- Feature vector
- Data mining
- Feature extraction
- Mahalanobis distance
- Projection (relational algebra)
- Standard deviation
- Independent component analysis
- Curse of dimensionality
- Local outlier factor
- Statistical classification
- Discriminant
- Statistics
- Optimal discriminant analysis
- Machine learning
- Discriminant function analysis
- Steganalysis
- Nonlinear dimensionality reduction

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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