A new solvent polymeric membrane electrode based on Schiff base complexes of Co(II) is described which demonstrates excellent selectivity toward the iodide ion. The resulting electrode exhibits fairly low detection limits and good selectivity properties. The selectivity sequence observed is iodide>thiocyanate～nitrite>perchlorate～bromide>nitrate>chloride>sulfate. The excellent selectivity for iodide is related to the unique interaction between the central Co(II) ion and iodide. The response mechanism of the electrode was also studied with the ac impedance and spectroscopic techniques.

Anew phosphate ion-sensitive electrode based on a cobalt matrix has been prepared. The electrode showed a relalively selective potentiometric response toward H2PO4-which seems to originate from the COO layer covering the electrode surface. The response mechanism of the electrode is discussed in terms of the host-guest chemistry of a nonstoichiometric compound of COO.

Morphological analysis (MA) is proposed to determine the local chemical rank of two-way data from hyphenated chromatography in the presence of heteroscedastic noise, based on local least squares regression of each spectrum on its neighboring spectra. The MA method uses an approach different from ordinary analysis of variance to identify the different patterns of the structural and noisy spectra. It employs a morphological factor to distinguish different patterns of the spectral signal and the noise. The morphological factor possesses the property of scale invariance, being unaffected by heteroscedastic noise. A fast algorithm is also proposed based on the Gram-Schmidt orthogonalization technique for the local least squares regression. Both numerical simulation and real analytical data are used to illustrate the feasibility of the proposed method.

The chaotic dynamical system is introduced in g enetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (1(A1) and 2(E)) of halides [MX4]. The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples.\

In quantitative structure-activity relationship (QSAR) modeling, when compounds in a training set exhibit a significant structural distinction between each other, in particular when chemicals of biological interest interacting on the receptor involve a different mechanism, it might be difficult to construct a single linear model for the whole population of compounds of interest with desired residuals. Developing a piecewise linear local model can be effective to circumvent the aforementioned problem. In this paper, piecewise modeling by the particle swarm optimization (PMPSO) approach is applied to QSAR study. The minimum spanning tree is used for clustering all compounds in the training set to form a tree, and the modified discrete PSO is applied to divide the tree to find satisfactory piecewise linear models. A new objective function is formulated for searching the appropriate piecewise linear models. The proposed PMPSO algorithm was used to predict the antagonism of angiotensin II. The results demonstrated that PMPSO is useful for improvement of the performance of regression models.