An Artificial Olfactory System for Toxic Compounds Classification Using Machine Learning Techniques

The long-term exposure to nitrogen dioxide produces harmful effects for humans and any living being. Thus, in security applications, sensor arrays are required for detecting nitrogen dioxide by interfering gas classification. In this work, a compact and intelligent electronic nose (e-nose) based on a Shear-Horizontal Surface Acoustic Wave (SH-SAW) sensor array is proposed for sensing, classifying, and calibrating toxic chemicals. Different carbon-based nanostructured materials are deposited as sensitive layers providing excellent outcomes by mass and elastic changes in this type of sensors. The HS-SAW sensors achieve a high sensitivity, fast response, and reproducibility to different toxic gases such as nitrogen dioxide, carbon monoxide, ammonia, benzene and acetone. The gas flows were controlled by an automated system that consists of four mass flow controllers to obtain the desired concentrations. The e-nose provides an efficient performance with supervised machine learning techniques. Outcomes indicate that Linear Discrimination Analysis (LDA) performs a 90% precise discrimination on test dataset and provides a clear discrimination of NO₂ with interfering toxic compounds. On the other hand, K-Nearest Neighbors (KNN) and Logistic Regression (LR) also achieve excellent classification scores (95% and 79% respectively). Decision surface for toxic compounds of different classification algorithms were also performed achieving good classification. An evaluation and comparison of the prediction methods: Partial Least Square (PLS), Artificial Neural Networks (ANNs) and cascade of ANNs are accomplished. The ANN cascade results show that this technique is an excellent candidate for an accurate prediction and classification of NO₂. Therefore, the designed and validated e-nose is a promising on-line tool of analysis for environmental applications.