Neural network processing of scattered light measurements in the detection of immiscible water pollutants

Using optoelectronic instrumentation, a smart sensing system for the detection and characterisation of oil pollution in water is demonstrated. Near infrared optics are used to derive sensor signatures in response to changes in background type and pollution levels. The signatures consist of scattered light intensities measured at 5 different angles. The measurements are used as input training sets for a multilayer perceptron, trained using the back propagation algorithm. Output neurons indicate the pollution level in parts per million, including multispecies detection and characterisation