Identification of Multiple Analytes Using an Optical Sensor Array and Pattern Recognition Neural Networks

Abstract

The further development of a vapor-sensing device utilizing an array of broadly distributed optical sensors is detailed. Data from these optical sensors provided input to pattern-recognizing neural networks, which successfully identified and quantified a collection of 20 analyte vapors. The optical sensor array consisted of 19 optical fibers whose tips were coated with Nile Red immobilized in various polymer matrices. Responses consisted of the changes in fluorescence with time resulting from the presentation of a vapor to the sensor array. Numerical descriptors calculated from these responses were then used to highlight important temporal and spatial features. Learning vector quantization neural network models were constructed using subsets of these descriptors, and they accurately identified and quantified each of the presented analytes. Successful classification was achieved for both the training set data (89%) and for the external prediction set data (90%). Relative concentrations were correctly assigned for 90% of the prediction set data.