Simultaneous kinetic determination of Fe(II) and Fe(III) based on their reactions with NQT4S in micellar media by using PLS and PCR methods

Online spectrophotometric determination of Fe(II) and Fe(III) by flow injection combined with low pressure ion chromatography

A simple and new low pressure ion chromatography combined with flow injection spectrophotometric procedure for determining Fe(II) and Fe(III) was established. It is based on the selective adsorption of low pressure ion chromatography column to Fe(II) and Fe(III), the online reduction reaction of Fe(III) and the reaction of Fe(II) in sodium acetate with phenanthroline, resulting in an intense orange complex with a suitable absorption at 515nm. Various chemical (such as the concentration of colour reagent, eluant and reductive agent) and instrumental parameters (reaction coil length, reductive coil length and wavelength) were studied and were optimized. Under the optimum conditions calibration graph of Fe(II)/Fe(III) was linear in the Fe(II)/Fe(III) range of 0.040-1.0mg/L. The detection limit of Fe(III) and Fe(II) was respectively 3.09 and 1.55μg/L, the relative standard deviation (n=10) of Fe(II) and Fe(III) 1.89% and 1.90% for 0.5mg/L of Fe(II) and Fe(III) respectively. About 2.5 samples in 1h can be analyzed. The interfering effects of various chemical species were studied. The method was successfully applied in the determination of water samples.

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.

Simultaneous spectrophotometric determination of trace amount of malachite green and crystal violet in water after cloud point extraction using partial least squares regression

In this work, a new method has been proposed to simultaneously determine the trace amount of malachite green and crystal violet from aqueous solution by spectrophotometry after cloud point extraction (CPE) using partial least squares regression. The optimal extraction and operating conditions, such as pH, reagents concentration and effect of time and temperature, and so on, have been investigated using the non-ionic surfactant Triton X-114. The maximum absorption wavelength for malachite green and crystal violet is 624 and 579 nm, respectively; linearity is obeyed in the range of 9.9-800 and 16-1000 ng mL(-1) with detection limit of 2.9 and 4.8 ng mL(-1), and the root mean square error of prediction (RMSEP) are 0.0197 and 0.0343, respectively. The proposed method has been applied successfully to simultaneously determine the trace amount of malachite green and crystal violet in real matrix samples with the recoveries of 92.45-102.5%.

A comparative study between PCR and PLS in simultaneous spectrophotometric determination of diphenylamine, aniline, and phenol: Effect of wavelength selection

Partial least squares (PLS) and principal component regression (PCR) have received considerable attention in the chemometrics for multicomponent analysis where superiority of one over another is a challenging problem yet. Considering the effect of wavelength selection, a comparison was made between PCR and PLS methods by application those to simultaneous spectrophotometric determination of diphenylamine (DPA), a compound from the third European Union list of priority pollutants, and its environmentally related products aniline and phenol. The UV absorbance spectra of the methanolic solutions of the analytes were measured in the concentration ranges of 1.0-10.0 microg mL(-1) and then subjected to PCR and PLS. The models refinement procedure and validation was performed by cross-validation. A modified changeable size moving windows strategy, where optimized the intervals between the sensors in a selected windows, was also proposed to select the more informative spectral regions for each of the analytes. It was found that wavelength selection improved the quality of predictions for both regression methods whereas more reliable results were obtained by removing of the highly collinear neighboring wavelengths. The resultant data explained that PLS produced more or less better results when whole spectral data were used but in the case of selected wavelength regions both methods produced similar results and no comments could be given about the superiority of one against another. The major difference was obtaining the higher number of factors for PCR, which is not a significant problem.