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UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions

This paper reports the results for the quantitative determination of ammonia (NH3) in aqueous solution by a UV-vis spectrophotometric method and artificial neural network (ANN) intelligence tool. Quantitation of NH3 was based on the chemical reaction of NH3 with cobalt(II) (Co2+) ions in basic mediu...

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Main Authors: Tan Ling, Ling, Lee Yook, Heng, Musa Ahmad
Format: Article
Language:en_US
Published: Royal Soc Chemistry 2015
Subjects:
Sensors
Waters
Online Access:http://ddms.usim.edu.my/handle/123456789/8461
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spelling my.usim-84612017-06-15T07:26:46Z UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions Tan Ling, Ling Lee Yook, Heng Musa Ahmad Sensors Waters This paper reports the results for the quantitative determination of ammonia (NH3) in aqueous solution by a UV-vis spectrophotometric method and artificial neural network (ANN) intelligence tool. Quantitation of NH3 was based on the chemical reaction of NH3 with cobalt(II) (Co2+) ions in basic medium to form a blue hexamminecobaltate(II) ([Co(NH3)(6)](2+)) complex. Characterizations of Co2+ ion in solution included photostability, pH effect, response time, Co2+ ion concentration effect, dynamic linear range and reproducibility, which were performed using a UV-vis spectrophotometer. The pink cobalt species gradually changed to blue with increasing NH3 concentration. The absorption calibration curve was linear over the NH3 concentration range of 0.6-3.5 mM at optimum pH 8 with a reproducibility relative standard deviation (RSD) of <4.0%. The interference effect was found to be negligible for a number of foreign ions present in the reaction medium during NH3 determination in an aqueous environment. A set of absorbance data for the [Co(NH3)(6)](2+) complex at selected wavelengths was input for ANN training using a back-propagation algorithm. The trained network with 22 hidden neurons, a 28 500 epoch number and 0.001% learning rate has extended the dynamic NH3 concentration range to 0.6-5.9 mM with a calibration error as low as 0.0649 x 10(-3). The proposed ANN electronic sensor shows promise for NH3 estimation in unknown water samples based on pattern recognition. 2015-06-19T06:49:29Z 2015-06-19T06:49:29Z 2013-01-01 Article 1759-9660 1759-9679 http://ddms.usim.edu.my/handle/123456789/8461 en_US Royal Soc Chemistry
institution Universiti Sains Islam Malaysia
building USIM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universit Sains Islam i Malaysia
content_source USIM Institutional Repository
url_provider http://ddms.usim.edu.my/
language en_US
topic Sensors
Waters
spellingShingle Sensors
Waters
Tan Ling, Ling
Lee Yook, Heng
Musa Ahmad
UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions
description This paper reports the results for the quantitative determination of ammonia (NH3) in aqueous solution by a UV-vis spectrophotometric method and artificial neural network (ANN) intelligence tool. Quantitation of NH3 was based on the chemical reaction of NH3 with cobalt(II) (Co2+) ions in basic medium to form a blue hexamminecobaltate(II) ([Co(NH3)(6)](2+)) complex. Characterizations of Co2+ ion in solution included photostability, pH effect, response time, Co2+ ion concentration effect, dynamic linear range and reproducibility, which were performed using a UV-vis spectrophotometer. The pink cobalt species gradually changed to blue with increasing NH3 concentration. The absorption calibration curve was linear over the NH3 concentration range of 0.6-3.5 mM at optimum pH 8 with a reproducibility relative standard deviation (RSD) of <4.0%. The interference effect was found to be negligible for a number of foreign ions present in the reaction medium during NH3 determination in an aqueous environment. A set of absorbance data for the [Co(NH3)(6)](2+) complex at selected wavelengths was input for ANN training using a back-propagation algorithm. The trained network with 22 hidden neurons, a 28 500 epoch number and 0.001% learning rate has extended the dynamic NH3 concentration range to 0.6-5.9 mM with a calibration error as low as 0.0649 x 10(-3). The proposed ANN electronic sensor shows promise for NH3 estimation in unknown water samples based on pattern recognition.
format Article
author Tan Ling, Ling
Lee Yook, Heng
Musa Ahmad
author_facet Tan Ling, Ling
Lee Yook, Heng
Musa Ahmad
author_sort Tan Ling, Ling
title UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions
title_short UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions
title_full UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions
title_fullStr UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions
title_full_unstemmed UV-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(II) ions
title_sort uv-vis spectrophotometric and artificial neural network for estimation of ammonia in aqueous environment using cobalt(ii) ions
publisher Royal Soc Chemistry
publishDate 2015
url http://ddms.usim.edu.my/handle/123456789/8461
_version_ 1645152421402378240
score 13.222552

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