A Method of Examination of Liquids by Neural Network Analysis of Reflectometric and Transmission Time Domain Data From Optical Capillaries and Fibers

This paper presents the construction and working principles of an intelligent fiber-optic sensor used for liquid examination using time domain data. The sensing elements consisted of a length of optical fiber or a short section of optical capillary and worked either on the reflection intensity basis or on transmission intensity basis. The changes of the monitored signal are caused mainly by the variation in light propagation conditions at the interfaces of liquid and gaseous phases and formation of drops of liquids or lenses at liquid-vapor interfaces. The physical effects on which depends the formation of a drop of liquid or a lens are surface tension, viscosity, boiling point, vapor pressure of liquid and its heat capacity. They provide information allowing determining the type of the liquid by a procedure which includes submerging, submersion, emerging and emergence of the sensing head from the examined liquid, or by local heating of the liquid sample. The measured data were analyzed using neural networks.