An optimization of optical fiber salicylic acid sensor using artificial neural network

Han Chern Loh, Musa Ahmad, Mohd Nasir Taib

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 x 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.

Original languageEnglish
Pages (from-to)35-44
Number of pages10
JournalAnalytical Letters
Volume38
Issue number1
DOIs
Publication statusPublished - 2005

Fingerprint

Salicylic acid
Optical Fibers
Salicylic Acid
Optical fibers
Neural networks
Sensors
Immobilization
Calibration
Neurons
Spectrophotometers
Backpropagation
Nitrates
Dynamic response

Keywords

  • Artificial neural network
  • Ferric(III) nitrate
  • Optical fiber sensor
  • Salicylic acid

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry, Genetics and Molecular Biology(all)
  • Biochemistry

Cite this

An optimization of optical fiber salicylic acid sensor using artificial neural network. / Loh, Han Chern; Ahmad, Musa; Taib, Mohd Nasir.

In: Analytical Letters, Vol. 38, No. 1, 2005, p. 35-44.

Research output: Contribution to journalArticle

Loh, Han Chern ; Ahmad, Musa ; Taib, Mohd Nasir. / An optimization of optical fiber salicylic acid sensor using artificial neural network. In: Analytical Letters. 2005 ; Vol. 38, No. 1. pp. 35-44.
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