Determination of concentrations at hydrolytic potentiometric titrations with models made by artificial neural networks

Brodnjak-Vončina, Darinka; Dobčnik, Danilo; Novič, Marjana; Zupan, Jure

doi:10.1016/s0169-7439(98)00160-9

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…The proposed method was applied for the simultaneous determination of these metal ions in synthetic mixtures with low values of RPE (in the range of 0.1-0.55%). Brodnjak-Voncina et al [32] applied ANN to resolve the potentiometric precipitation data from sulfate in river and drinking waters (titrant: Ba 2+ ) and calcium in wine samples (titrant: oxalate). The results indicated that ANN could successfully predict the concentration of compounds from the titration curves with an error of ±10%, which is acceptable for rapid screening of waters and wines.…”

Section: Complexometric and Precipitation Titrationsmentioning

confidence: 99%

Does chemometrics enhance the performance of electroanalysis?

Kokot

2008

Analytica Chimica Acta

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Section: Complexometric and Precipitation Titrationsmentioning

confidence: 99%

Does chemometrics enhance the performance of electroanalysis?

Kokot

2008

Analytica Chimica Acta

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“…Different methods are applied to realize these models, starting with simple linear or multilinear methods (Hemmateenejad et al 2002), through partial least squares methods (Wold et al 2001) and finishing with artificial intelligence methods (Hemmateenejad 2005). (Lek and Guégan 1999), weather forecasting (Ramírez et al 2005), prediction of volcanic activity (Luongo et al 2004), pharmacology (Turner et al 2004), toxicology (Hemmateenejad 2005) and chemistry (Dohnal et al 2003, Brodnjak-Vončina et al 1999 (Farková et al 1999), analytical method development (Hameda et al 2005) or for QSAR modeling (Hemmateenejad 2005).…”

Section: Fig 1 Inhibition Of Acetylcholinesterase By Tabunmentioning

confidence: 99%

Prediction of a new broad-spectrum reactivator capable of reactivating acetylcholinesterase inhibited by nerve agents

Dohnal¹,

Kuča²,

Jun³

2005

J Appl Biomed

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A methodology combining molecular structure represented by fragments, and artificial neural network (ANN) was applied for the prediction of a new acetylcholinesterase (AChE; EC 3.1.1.7) reactivator. We searched for a new structure of the AChE reactivator with the capability of reactivating AChE inhibited by almost all actual nerve agents. For this purpose, we have tested in vitro seventeen potential AChE reactivators for reactivation of AChE inhibited by sarin, cyclosarin, agent VX and tabun. The results obtained were used as input data for prediction by ANN. Using ANN we have predicted new AChE reactivators.

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“…These procedures require a previous training process with standards that generates the necessary information for acquiring the predictive ability. The exploited detection systems have been usually the pH-ISE, used in combination with different mathematical models to solve complex acid-base mixtures after NaOH titration (Moisio 1996;Ni 1998;Shamsipur 2002) or metal mixtures after complexometric titrations (Ni 1995); sulphate, or calcium, were separately determined employing an hydrolytic potentiometric titration in conjunction with an ANN model (Brodnjak-Vonina 1999). Also, the acid-base flow injection titration of mixtures of acids in juice samples is described in the literature, for which an ANN model was developed (Zampronio 2004).…”

Section: Introductionmentioning

confidence: 99%

Multicomponent Titration of Calcium + Magnesium Mixtures Employing a Potentiometric Electronic‐Tongue

2007

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A fast novel potentiometric titration procedure is proposed, in which the detection system is formed by a potentiometric electronic tongue. The titration consists in a reduced number of fixed titrant additions to the sample and the recording of the potentials of an array of Ion Selective Electrodes. The obtained data matrix is entered to an Artificial Neural Network response model, previously trained to furnish concentrations of a multicomponent mixture. The principle is demonstrated with automated EDTA titration of mixtures of Ca 2þ and Mg 2þ at fixed pH 8.5. In this case, five 2 ml fixed volume additions plus the readings of four sensors were adequate for the resolution of the mixture. In the conditions used, titration was feasible up to 3.3 mM total ion concentration, the precision was estimated as 3.20% RSD for Ca 2þ and 2.76% RSD for Mg 2þ (n ¼ 5), and the detection limits 0.16 mM Ca 2þ and 0.26 mM Mg 2þ . The procedure was applied to mineral waters and compared with reference methods (correlation ¼ 0.92 for Ca 2þ and correlation ¼ 0.89 for Mg 2þ , n ¼ 14).

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Determination of concentrations at hydrolytic potentiometric titrations with models made by artificial neural networks

Cited by 8 publications

References 18 publications

Does chemometrics enhance the performance of electroanalysis?

Does chemometrics enhance the performance of electroanalysis?

Prediction of a new broad-spectrum reactivator capable of reactivating acetylcholinesterase inhibited by nerve agents

Multicomponent Titration of Calcium + Magnesium Mixtures Employing a Potentiometric Electronic‐Tongue

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