Detection of organic solvent compounds using optical fiber interferometer array and neural network pattern recognition

Organic solvent compounds are widely used as production raw materials in the field of chemical industry. Organic compounds are easily changed from liquid to gas conditions at room temperature. Organic solvent compounds are commonly found as gases or vapors, which are flammable, toxic, and explosive. The identification of the gas sensor is required in identifying and classifying some gases of volatile organic compounds, especially to monitor the condition of the organic solvent vapor pollutants in the environment. The latest development of gas sensor was designed based on the optical field by using Fabry-Perot interferometer which is applied to optical fiber to increase the sensitivity of gas sensor. The gas sensor was designed by coating chemical membranes on the tip of the optical fiber to increase the sensor selectivity. Three different types of chemical membranes are coated on the same three optical fibers placed in the sensor chamber. In this study, sensor output data are interpreted into digital form through analog-to-digital converter, while data processing and identification are performed by computer. The identification process of organic solvent is done by using artificial neural network algorithm. The results show that the sensor array could produce a different pattern for each of the gas vapor samples. The Neural network pattern recognition system can identify the type of vapor with 100% accuracy rate. Identification of organic solvent compound types, may be used to detect low-vapor gas vapor exposure applied in monitoring activities and analysis of organic solvent vapor.