Trends in kinetic methods of analysis

Artificial neural network assisted kinetic spectrophotometric technique for simultaneous determination of paracetamol and p-aminophenol in pharmaceutical samples using localized surface plasmon resonance band of silver nanoparticles

Spectrophotometric analysis method based on the combination of the principal component analysis (PCA) with the feed-forward neural network (FFNN) and the radial basis function network (RBFN) was proposed for the simultaneous determination of paracetamol (PAC) and p-aminophenol (PAP). This technique relies on the difference between the kinetic rates of the reactions between analytes and silver nitrate as the oxidizing agent in the presence of polyvinylpyrrolidone (PVP) which is the stabilizer. The reactions are monitored at the analytical wavelength of 420nm of the localized surface plasmon resonance (LSPR) band of the formed silver nanoparticles (Ag-NPs). Under the optimized conditions, the linear calibration graphs were obtained in the concentration range of 0.122-2.425μgmL(-1) for PAC and 0.021-5.245μgmL(-1) for PAP. The limit of detection in terms of standard approach (LODSA) and upper limit approach (LODULA) were calculated to be 0.027 and 0.032μgmL(-1) for PAC and 0.006 and 0.009μgmL(-1) for PAP. The important parameters were optimized for the artificial neural network (ANN) models. Statistical parameters indicated that the ability of the both methods is comparable. The proposed method was successfully applied to the simultaneous determination of PAC and PAP in pharmaceutical preparations.

Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method

A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes.

A novel flow-based strategy for implementing differential kinetic analysis

Differential kinetic analysis can be implemented in a multi-pumping flow system, and this was demonstrated in relation to an improved spectrophotometric catalytic determination of iron and vanadium in Fe-V alloys. The method exploited the influence of Fe(II) and V(IV) on the rate of iodide oxidation by Cr(VI) under acidic conditions; therefore the Jones reductor was needed. The sample was inserted into an acidic KI stream that acted also as carrier stream, and a Cr(VI) solution was added by confluence. Successive measurements were performed during sample passage through the detector, each one related to a different yet reproducible condition for reaction development. Data treatment involved multivariate calibration by the PLS algorithm. The proposed system is very simple and rugged, allowing about 50 samples to be run per hour, meaning 48 mg KI per determination. The first two latent variables carry ca. 94% of the analytical information, pointing out that the intrinsic dimensionality of the data set is two. Results are in agreement with inductively coupled argon plasma-optical emission spectrometry.