title: Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels.
abstract: A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non-linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures. Conductivity measurements and the concept of charge balance are incorporated into the ANN structure, resulting in (1) removal of estimation bias typically seen with use of ISEs in mixtures of unknown composition and (2) improvement of signal estimation by an order of magnitude or more for both major and minor constituents relative to use of ISEs as stand-alone sensors and error reduction by 30-50% relative to use of standard ANN models. This method is suggested as an alternative to parameterization of traditional models (e.g., Nikolsky-Eisenman), for which parameters are strongly dependent on both analyte concentration and temperature, and to standard ANN models which have no mechanism for incorporation of system knowledge. Network architecture and weighting are presented for the base case where the dot product can be used to relate ion concentrations to both conductivity and charge balance as well as for an extension to log-normalized data where the model can no longer be represented in this manner. While parameterization in this case study is analyte-dependent, the architecture is generalizable, allowing application of this method to other environmental problems for which mathematical constraints can be explicitly stated.
authors.affiliation: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave. 48-102, Cambridge, MA 02139, United States. Electronic address: amym@alum.mit.edu.
authors.forename: Amy V
authors.initials: AV
authors.lastname: Mueller
authors.forename: Harold F
authors.initials: HF
authors.lastname: Hemond
mesh_terms: D000838:Anions / Q000032:analysis*; D002412:Cations / Q000032:analysis*; D004553:Electric Conductivity; D017736:Ion-Selective Electrodes; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012996:Solutions; D014867:Water / Q000737:chemistry*
chemical_list: D000838:Anions; D002412:Cations; D012996:Solutions; D014867:Water
keywords: Artificial Neural Network (ANN); Charge balance; Chemometrics; Electronic tongue; In-situ environmental sensors; Ion Selective Electrode (ISE) array; Real-time chemical measurements