The Use of Artificial Neural Network to Optimize The pH Response Range of Chlorophenol Red

Abstract

Artificial Neural Network (ANN) had been used in this study to extend the response range of the pH indicator. The input from absorbance values of the absorbance spectra of chlorophenol red at different pH was used to train the ANN. During the training process, the coefficient values of the ANN will be adjusted to obtain the desire output. In this research, back propagation algorithm had been used for optimizing the response range of the pH indicator chlorophenol red in solution. The result indicates that the use of ANN enable the pH response range to be extended from 4.8-6.8 to 1.0-10.0.Keywords : Artificial neural network, back-propagation algorithm, pH indicator,chlorophenol red. INTRODUCTIONChlorophenol red is a pH indicator, which gradually changes its colour over a range called "visual transition interval" 1 . Most of the pH indicators determine the pH changes indirectly in limited linear dynamic range, often 2-4 pH units only. Chlorophenol red indicator changes its colour from yellow to purple in a narrow interval of 4.0-7.0 pH units. To solve this problem especially in optical fiber optic pH sensor research, a lot of approach has been used such as using multiple pH indicators 2,3 , the use of indicators with multiple steps of acid dissociation and ANN technique 4-6 .The original works on ANN were published more than 50 years ago by McCulloch and Pitts 7,8 and Webb 9 . Lately, ANN has been shown to provide a superior alternative mechanism for modeling non-linear systems [10][11][12][13][14] . Application of ANNs is similar to conventional non-linear modeling techniques that require a model-structure design and parameter-estimating cycle to calibrate the model but ANNs use more general modeling approach, i.e. model structure need not to be defined explicitly 12 .