Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning

TL;DR

This study develops a metal oxide sensor array combined with machine learning algorithms to accurately identify and quantify VOCs in complex mixtures, aiding non-invasive disease detection.

Contribution

The paper introduces a novel sensor array with machine learning methods capable of classifying and predicting multiple VOCs simultaneously in mixtures.

Findings

KNN and RF achieved over 99% classification accuracy.

KNN provided R2 > 0.99 for concentration prediction.

LOD values for VOCs ranged from 0.012 to 0.025 PPM.

Abstract

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.