Second order advantage obtained by spectroelectrochemistry along with novel carbon nanotube modified mesh electrode: Application for determination of acetaminophen in Novafen samples.

A matrix-augmentation multivariate curve resolution-alternating least-squares (MA-MCR-ALS) has been conducted on the spectroelectrochemical data of acetaminophen oxidation in order to quantify acetaminophen in Novafen capsule in the presence of unknown interferences. The experiments were carried out using new cheap mesh electrode, namely carbon nanotube modified mesh electrode (CNMME) as optically transparent thin layer electrode (OTTLE). For each sample, a second order spectroelectrochemical data was obtained and MA-MCR-ALS method was applied to analyze these data. Unlike full trilinear models such as PARAFAC, MCR-ALS is flexible in applying trilinearity constraint for each component, a fact which makes it manage deviations from trilinearity of data effectively. This method was employed in both spectral and kinetic augmentation mode of data under examining different trilinear components. However, spectral augmentation was the only setting which allows MA-MCR-ALS to solve the analytical problem achieving the second order advantage. Therefore, here the results of the augmentation in this mode have been described. In order to obtain the best analytical figures of merit in the analysis, different constraints were investigated. The results indicated the accuracy of the proposed method.

Analysis of variation matrix array by bilinear least squares-residual bilinearization (BLLS-RBL) for resolving and quantifying of foodstuff dyes in a candy sample.

Bilinear least square (BLLS) method is one of the most suitable algorithms for second-order calibration. Original BLLS method is not applicable to the second order pH-spectral data when an analyte has more than one spectroscopically active species. Bilinear least square-residual bilinearization (BLLS-RBL) was developed to achieve the second order advantage for analysis of complex mixtures. Although the modified method is useful, the pure profiles cannot be obtained and only the linear combination will be obtained. Moreover, for prediction of analyte in an unknown sample, the original algorithm of RBL may diverge; instead of converging to the desired analyte concentrations. Therefore, Gauss Newton-RLB algorithm should be used, which is not as simple as original protocol. Also, the analyte concentration can be predicted on the basis of each of the equilibrating species of the component of interest that are not exactly the same. The aim of the present work is to tackle the non-uniqueness problem in the second order calibration of monoprotic acid mixtures and divergence of RBL. Each pH-absorbance matrix was pretreated by subtraction of the first spectrum from other spectra in the data set to produce full rank array that is called variation matrix. Then variation matrices were analyzed uniquely by original BLLS-RBL that is more parsimonious than its modified counterpart. The proposed method was performed on the simulated as well as the analysis of real data. Sunset yellow and Carmosine as monoprotic acids were determined in candy sample in the presence of unknown interference by this method.