An Optimization of Optical Fiber Salicylic Acid Sensor Using Artificial Neural Network

Abstract

The application of an artificial neural network (ANN) in optimizing the response of an optical fiber salicylic acid (SA) sensor is presented in this paper. This sensor is fabricated based on immobilization of ferric(III) nitrate on Dowex‐50 × 8. The reflectance spectra of the sensor were measured by using an optical fiber spectrophotometer. A backpropagation (BP) ANN was used to analyze the response of the sensor developed. The results showed that the ANN technique was effective and useful in broadening the limited dynamic response range of the SA sensor (0.02 – 0.50 g/L) to an extensive calibration response (0.02–2.00 g/L). It was found that a network with 15 hidden neurons was highly accurate in predicting the response of the optical fiber SA sensor. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.014 g/L and 0.032 g/L, respectively. Scholarship of National Science Fellowship (NSF) toward Han Chern Loh from the Ministry of Science, Technology and Environment (MOSTE), Malaysia is greatly acknowledged.