International Congress on Fundamental and Applied Sciences, Antalya, Türkiye, 19 - 21 Ekim 2021, ss.158
Detection of volatile organic compound (VOC) vapors, which are known to have carcinogenic
effects, is extremely important and necessary in many areas. In this work, the sensing properties of
a CoPc thin film at 6 different VOC vapors (methanol, ethanol, butanol, isopropyl alcohol, acetone,
and ammonia) concentrations from 50 to 450 ppm are investigated and it is observed that the
interaction between the VOC vapors and CoPc surface is not selective enough. Then, it is showed
that using machine learning algorithms the present sensor, which is poorly selective, can be
transformed into a more efficient one with higher sensing ability. As a feature, 10 seconds of raw
response values taken from steady state region is used without any additional feature extraction
technique. Among classification algorithms, 97.1% accuracy is obtained by k-nearest neighbor
(KNN) algorithm. The considered feature is also compared with classical maximum response value
feature. Classification results indicate that the feature based on 10 seconds of raw response values
taken from steady state region is much better than that based on classical maximum response value
feature.