| Abstrakt: |
Machine learning modeling can help overcome some of the limitations of gas sensors, such as high operational conditions, drift errors, limited selectivity, the need for a large amount of labeled data, and cost and fabrication challenges. In this research, an electronic nose system was developed for the detection of sulfur dioxide and acetic acid. three treatments, including sunny, acetic, and sulfuric, were prepared in three repetitions, and each was exposed to olfactory sensors for 60 minutes to record the sensor responses to each treatment. Then, the data obtained from the sensor responses were examined by machine learning models to determine the modeling accuracy of each method. The results showed that the utilized Gradient Boost Regression model with a determination coefficient of 0.9972, root mean square error of 0.0209, mean absolute error of 0.0026, and relative root mean square error of 0.0209 was able to model the gas sensor responses well for the introduced treatments. Furthermore, by analyzing the results, the type and degree of correlation between the sensor responses to each other and over time were determined to evaluate their behavior prediction. Then, based on the conducted modeling, it was revealed that MQ9, MQ3, MQ5, and TGS2620 sensors, with determination coefficients of 0.8668, 0.8786, 0.9458, and 0.9074, and root mean square errors of 0.0163, 0.0168, 0.0083, and 0.0227, respectively, provided more accurate and predictable responses compared to MQ135, TGS822, TGS810, and MQ4 sensors. [ABSTRACT FROM AUTHOR] |
