A New Approach for Liquid Scanners to Determine Flammable Liquid Concentration in Solutions

Abstract

Strong liquid explosives were obtained by mixing some chemical liquids and these explosives were used in many terrorist attacks in crowded places such as airports, railway stations and shopping malls. They were also used to cause sabotage to facilities that produce, store or use hazardous chemicals in their processes. For this reason, it is very important to take the necessary measures to prevent sabotage and terrorist attacks that may occur in such places in order to ensure public and environmental safety. In this study, a machine learning based liquid control system that can be used in airports, railway stations and shopping malls as well as in places with high fire probability is proposed. The difference of the proposed system from traditional liquid scanner systems is that it can detect the hazardous liquid concentration in the solutions as well as the detection of pure flammable liquids. Linear Discriminant Analysis and Quadratic Discriminant Analysis are used as classifiers and the performances of these techniques are compared. The results show that Quadratic Discriminant Analysis offers higher accuracy and lower error rates compared to Linear Discriminant Analysis.