Classification of VOC Vapors Using Machine Learning Algorithms

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 a cobalt phthalocyanine (CoPc) thin film at six different VOC vapors (methanol, ethanol, butanol, isopropyl alcohol, acetone, ammonia) concentrations from 50 450 ppm investigated. sense, it observed that interaction between CoPc surface not selective. It shown using machine learning algorithms present sensor, poorly selective, can be transformed into more efficient one with better detection ability. As feature, 10 seconds responses taken steady state region used without any additional processing technique. Among classification algorithms, k-nearest neighbor (KNN) reaches highest accuracy 96.7%. This feature also compared classical response feature. Classification results indicate based on much than