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

• 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

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.