ORIGINAL PAPER Analyses of the temperature and pH effects on the complexation of magnesium and calcium in human blood plasma: an approach using artificial neural networks J. C. D. Conway & A. Liparini & J. R. de Oliveira Jr & J. C. Belchior Received: 30 May 2007 / Revised: 28 July 2007 / Accepted: 2 August 2007 / Published online: 15 September 2007 # Springer-Verlag 2007 Abstract The temperature and pH effects on the equilibri- um of a blood plasma model have been studied on the basis of artificial neural networks. The proposed blood plasma was modeled considering two important metals, calcium and magnesium, and six ligands, namely, alanate, carbonate, citrate, glycinate, histidinate and succinate. A large data set has been used to simulate different concentrations of magnesium and calcium as a function of temperature and pH and these data were used for training the neural network. The proposed model allowed different types of analyses, such as the effects of pH on calcium and magnesium concentrations, the competition between calcium and mag- nesium for ligands and the effects of temperature on calcium and magnesium concentrations. The model developed was also used to predict how the variation of calcium concen- tration can affect magnesium concentrations. A comparison of neural network predictions against experimental data produced errors of about 3%. Moreover, in agreement with experimental measurements (Wang et al. in Arch. Pathol. 126:947–950, 2002; Heining et al. in Scand. J. Clin. Lab. Invest. 43:709–714, 1983), the artificial neural network predicted that calcium and magnesium concentrations decrease when pH increases. Similarly, the magnesium concentrations are less sensitive than calcium concentrations to pH changes. It is also found that both calcium and magnesium concentrations decrease when the temperature increases. Finally, the theoretical model also predicted that an increase of calcium concentrations will lead to an increase of magnesium concentration almost at the same rate. These results suggest that artificial neural networks can be efficiently applied as a complementary tool for studying metal ion complexation, with especial attention to the blood plasma analysis. Keywords Artificial neural networks . Metal ions . Calcium . Magnesium . Blood plasma Introduction Minerals are partially or totally present in human blood plasma in ionic form, and, in general, as dissociated ions that are electrically charged. The reactions between ions and enzymes form organometallic complexes (metalloenzymes, metalloproteins) and are fundamental to the correct working of the human body. Complexes consist of mineral ions and ligands and, generally, are attached to one another. Mostly, low molecular weight complexes are important in many biochemical and physiologic processes. They are involved in the transfer of metal ions to the proteins and enzymes, in the transfer of ions through the cellular membrane and in the conservation of essential ions in solution [1]. Particu- larly, divalent metals such as manganese, iron, zinc and cobalt are important for cellular metabolism [2]. Similarly, calcium and magnesium contribute to the plasma equilibri- um owing to their complexation, for example, with citrate ligand [3]. Therefore, metal ions, such as calcium and magnesium, also play an important role in human blood plasma equilibrium. Among many other physical and Anal Bioanal Chem (2007) 389:1585–1594 DOI 10.1007/s00216-007-1544-0 J. C. D. Conway : A. Liparini : J. R. de Oliveira Jr : J. C. Belchior (*) Departamento de Química - ICEx, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Pampulha, 31270-901 Belo Horizonte, Minas Gerais, Brazil e-mail: jadson@ufmg.br J. C. D. Conway Pontifícia Universidade Católica de Minas Gerais, Av. Dom José Gaspar 500, Coração Eucarístico, 30535-9011 Belo Horizonte, Minas Gerais, Brazil