Applications of artificial neural network on signal processing of optical fibre pH sensor based on bromophenol blue doped with sol–gel film

In this paper, the applications of artificial neural network (ANN) in signal processing of optical fibre pH sensor is presented. The pH sensor is developed based on the use of bromophenol blue (BPB) indicator immobilized in a sol–gel thin film as a sensing material. A three layer feed-forward network was used and the network training was performed using the back-propagation (BP) algorithm. Spectra generated from the pH sensor at several selected wavelengths are used as the input data for the ANN. The bromophenol blue indicator, which has a limited dynamic range of 3.00–5.50 pH units, was found to show higher pH dynamic range of 2.00–12.00 and with low calibration error after training with ANN. The enhanced ANN could be used to predict the new measurement spectra from unknown buffer solution with an average error of 0.06 pH units. Changes of ionic strength showed minor effect on the dynamic range of the sensor. The sensor also demonstrated good analytical performance with repeatability and reproducibility characters of the sensor yield relative standard deviation (R.S.D.) of 3.6 and 5.4%, respectively. Meanwhile the R.S.D. value for this photostability test is 2.4% and it demonstrated no hysteresis when the sensor was cycled from pH 2.00–12.00–2.00 (acid–base–acid region) of different pH. Performance tests demonstrated a response time of 15–150 s, depending on the pH and quantity of the immobilized indicator.