Extending the response range of an optical fibre pH sensor using an artificial neural network

Abstract An artificial neural network (ANN) has been applied for the analysis of the response of an optical fibre pH sensor. The optrode was based on a 3,4,5,6-tetrabromophenol sulphonephthalein indicator, which was covalently bound onto aminopropyl glass beads, then packed at the tip of a bifurcated fibre-optic bundle. A three layer feed forward network was used and network training was performed using the recursive prediction error (RPE) algorithm. It was found that an optimised network with 13 hidden neurons was highly accurate in predicting the response of the optical pH sensor, with the worst interpolation error of 0.08 pH for test data set and 0.07 pH for measuring unknown buffer solutions. Overall, the application of ANN enabled the extension of the useful pH response range of the sensor from its narrow linear range (pH 5.0–7.25) to the full calibration response (pH 2.51–9.76).

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