Multivariate Calibration Tool Research Articles

Simultaneous determination of vitamins C, B6 and PP in pharmaceutics using differential pulse voltammetry with a glassy carbon electrode and multivariate calibration tools

In this work, the artificial neural networks (ANN) and partial least squares (PLS) were applied to data obtained by differential pulse voltammetry for the determination of vitamins in synthetic and pharmaceutical samples. For calibration purposes, both synthetic and commercial samples were employed as standards. From the results it was possible to verify that ANN is the best method for modeling the data due to the fact that interactions among electro-active components result in non-linear response on the glassy carbon electrode. The results achieved for the determination of vitamins in pharmaceutical samples using ANN method provided a maximum value for relative error of 0.40% for VC, 8.3% for VPP and 9.1% for VB6. The proposed methodology is simple, rapid and can be easily used to control quality laboratories as an alternative analysis method.

Enhanced application of square wave voltammetry with glassy carbon electrode coupled to multivariate calibration tools for the determination of B 6 and B 12 vitamins in pharmaceutical preparations

Vitamins B 6 (VB 6) and B 12 (VB 12) were simultaneously determined in pharmaceutical preparations by using square wave voltametry (SWV) together with artificial neural networks (ANNs). Supporting electrolyte solution, pH and voltametric technique were optimised. The calibration set was built with several artificial samples containing both active ingredients and excipients. Deviations from linearity were observed for both analytes. It is probably due to interactions among the electro active components and competition by the electrode surface, fact that supports the use of ANNs. Recoveries when analysing a nine sample validation set, of 100.2 and 96.4 were calculated for VB 6 and VB 12, respectively. Commercial samples were analysed with reasonably good results considering the complexity of the mixture studied.

Artificial neural networks applied to potentiometric acid–base flow injection titrations

Artificial neural network (ANN) was applied for data treatment as a multivariate calibration tool in a potentiometric acid–base flow injection titration. A multilayer feed-forward ANN model, with Levenberg–Marquardt weight error correction was used for data modeling. The neural network parameter architecture was optimized to establish a relationship between the titration profile and the acid concentration. Citric and malic acids in synthetic sample mixtures and in orange juices were analyzed and the performance of ANN was compared with that of partial least squares (PLS) regression.

Genetic algorithms applied to the selection of factors in principal component regression

Using principal component regression (PCR) as a multivariate calibration tool, always brings up the question what subset of factors, i.e. principal components (PCs) gives the best calibration model. Normally factor selection is based on deterministic methods like top–down procedures, forward–backward-stepwise variable selection or correlated principal component regression (CPCR). In contrast to this, we applied a stochastic method, i.e. a genetic algorithm (GA) for factor selection in this paper. A new kind of fitness function was applied which combined the prediction error of the calibration and an independent validation set. The performance of eigenvalue and correlation ranking was compared. A general statistical criterion for judging the significance of differences between individual calibration models is introduced. In this context it could be shown that for the uncertainties of the standard deviations representing the prediction errors a very simple approximation formula holds which only includes the number of standards. For the current applications it is shown that the GA gives a result very close to CPCR-solutions.

Simultaneous determination of phenol isomers in binary mixtures by differential pulse voltammetry using carbon fibre electrode and neural network with pruning as a multivariate calibration tool

Neural networks with pruning were applied to model overlapped peaks obtained in differential pulse voltammetry (DPV) with modified carbon fibre electrode with TiO 2 of binary mixtures of catechol and hydroquinone. The best condition for electrochemical response was obtained with 0.05 mol l −1 Tris–HCl buffer at pH 6.0 and T-800 sized carbon fibre electrode. Initially the voltammograms were processed using Fourier transform filter and principal component analysis (PCA) to noise reduction and data compression, respectively. The scores of these principal components were the input into the neural network and the optimal brain surgeon (OBS) was the procedure employed for pruning the neural network. The results obtained with pruning procedure were slightly better in relation to hydroquinone in comparison to the PLS1 and PLS2. However, the similar errors were obtained to catechol when using PLS or neural networks models. Using neural networks with pruning was possible to determine catechol and hydroquinone by DPV using carbon fibre electrode, in concentration range of 1.0×10 −4 up to 6.0×10 −4 mol l −1 with root mean square errors of predictions (%RMSEP) of 7.42 and 8.02, respectively. The good results show that the proposed methodology is a good alternative to simultaneous determination of catechol and hydroquinone in binary mixtures.

Development of a potentiometric flow cell with a stainless steel electrode for pH measurements. Determination of acid mixtures using flow injection analysis

In this work a stainless steel electrode was prepared, characterised and used as indicator electrode in a potentiometric flow cell. Due to its versatility, it was possible to study different electrode geometries and flow cell arrangements. After optimising the system, mixtures of succinic and oxalic acids were determined by titration. Partial least squares (PLS) regression as multivariate calibration tool was applied for data treatment. The predicted results obtained in a test set showed a relative error of 4.3% for succinic acid and 5.5% for oxalic acid.

Chemometrics for on-line spectroscopy applications?theory and practice

In today's chemical industry there is a great need to both improve the effectiveness of existing assets and generate new prospects for business renewal. In addition, the fast pace of the current ‘information age’ makes it necessary to bring about both of these in a rapid and responsive manner in order to be competitive. On-line spectroscopic techniques, which provide rapid and representative information on a chemical process, are inherently capable of facilitating both asset productivity and business renewal. Chemometric ‘tools’, in principle, are particularly well suited for improving the effectiveness of on-line spectroscopic techniques through two main functions: (1) extracting the wealth of useful, yet convoluted information that is available from such techniques; and (2) facilitating the automation of on-line analytical techniques, at a time when manufacturing plant support resources are being ‘cut back’. Multivariate calibration tools can be used to improve the performance of existing methods, as well as to prepare more stable methods for new applications. More ‘soft’ modeling tools, such as multivariate curve resolution, are particularly well suited for providing timely information during the early stages of new process development, where very little is known about the process chemistry and dynamics. In practice, however, there is a wide range of issues, both technical and non-technical, that limit and even prevent the successful application of chemometric techniques for on-line spectroscopy. This paper will illustrate both the means by which chemometric tools can make a difference in the chemical industry, and the issues that are preventing its successful implementation, using examples from actual process development projects and on-line measurements. Copyright © 2000 John Wiley & Sons, Ltd.

Evaluation of principal component selection methods to form a global prediction model by principal component regression

Most situations using principal component regression (PCR) as a multivariate calibration tool use the conventional top-down selection procedure to determine the number of principal components (PCs) to generate a global model, i.e., the regression model is established by including PCs in sequence according to variances related to the PCs. This model is then used to predict future samples adequately spanned by the corresponding calibration set. Recently, some alternative procedures have been proposed for PC selection with respect to multivariate calibration. These include optimization (selection) by generalized simulated annealing and correlation principal component regression (CPCR) where PCs are ordered according to correlations with the dependent variable (concentration). The PCs are then selected one by one to form the global model based on a prediction criterion. In this paper, a forward selection procedure PCR (FSPCR) is evaluated and compared to CPCR and top-down selection. Four spectroscopic data sets are analyzed for the comparison study. In essence, results reveal that PCs selected based on a top-down approach generates the most stable global model. That is, top-down selection generally performs best for prediction of numerous future samples sets compared to CPCR and FSPCR. Reasons for such differences in performances of these procedures have been analyzed.

Artificial neural networks as a multivariate calibration tool: modeling the FeCrNi system in x-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the CrNiFe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K α and K β lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges.