M. Cruz Ortiz

Genetic-algorithm-based wavelength selection in multicomponent spectrometric determinations by PLS: application on indomethacin and acemethacin mixture

A genetic algorithm is a suitable method for selecting wavelengths for PLS (partial least squares) calibration of mixtures with almost identical spectra without loss of prediction capacity. In the calibration of acemethacin and indomethacin, the proposed procedure eliminates the matrix effect due to the solvent which causes greater variability of the calibration spectra than that due to the difference in the concentration of the two drugs. For the calibration samples, the concentrations calculated using the wavelengths selected are, significantly, equal to those obtained with the full spectrum (significance level above 0.7 in the Student t-test for differences) and differ from the true ones in their average value — less than 1.8% relative error — for the four pH values used in the analysis.

Advances in methodology for the validation of methods according to the International Organization for Standardization. Application to the determination of benzoic and sorbic acids in soft drinks by high-performance liquid chromatography.

Robust chemometric techniques such as least median of squares regression, H15 Huber estimator and Lenths method are fundamental tools in the validation of analytical methods since they contribute the strategies needed to estimate efficiently parameters such as robustness, linear range, selectivity, accuracy (trueness and precision) and the capability of detection. In addition, the capability of discrimination defined as a generalisation of the capability of detection for any nominal concentration is evaluated. The new strategy proposed is applied to the validation of a chromatographic method for use in systematic analysis.

Robust regression techniques: A useful alternative for the detection of outlier data in chemical analysis

The validation of an analytical procedure means the evaluation of some performance criteria such as accuracy, sensitivity, linear range, capability of detection, selectivity, calibration curve, etc. This implies the use of different statistical methodologies, some of them related with statistical regression techniques, which may be robust or not. The presence of outlier data has a significant effect on the determination of sensitivity, linear range or capability of detection amongst others, when these figures of merit are evaluated with non-robust methodologies. In this paper some of the robust methods used for calibration in analytical chemistry are reviewed: the Huber M-estimator; the Andrews, Tukey and Welsh GM-estimators; the fuzzy estimators; the constrained M-estimators, CM; the least trimmed squares, LTS. The paper also shows that the mathematical properties of the least median squares (LMS) regression can be of great interest in the detection of outlier data in chemical analysis. A comparative analysis is made of the results obtained by applying these regression methods to synthetic and real data. There is also a review of some applications where this robust regression works in a suitable and simple way that proves very useful to secure an objective detection of outliers. The use of a robust regression is recommended in ISO 5725-5.

Determination and identification, according to European Union Decision 2002/657/EC, of malachite green and its metabolite in fish by liquid chromatography-tandem mass spectrometry using an optimized extraction procedure and three-way calibration.

This paper reports a multiresponse optimization of an extraction procedure in the simultaneous determination of malachite green (MG) and its metabolite (leucomalachite green, LMG) in fish by liquid chromatography with triple quadrupole mass spectrometry (LC-MS/MS). Prior to optimization, the active factors of the extraction procedure were determined by a screening experimental design. Then, in the optimal experimental conditions of the extraction, MG and LMG have been determined by using a three-way calibration model based on parallel factor analysis (PARAFAC). The procedure fulfils the performance requirements for a confirmatory method established by the European Union Decision 2002/657/EC. This norm establishes maximum permitted tolerances for relative abundance of the precursor/product ion pairs. There is a reported contradiction in the literature related to the fact that there are standard samples whose concentration is greater than CCalpha but the maximum permitted tolerances are not fulfilled in the identification of the analytes. In this work, it is shown that with the information provided by PARAFAC this contradiction is avoided. The figures of merit for PARAFAC and univariate calibration procedures were evaluated under optimal conditions in the extraction step. The figures of merit obtained were in the range of 0.13-0.23 microg kg(-1) for the decision limit, CCalpha, (alpha=0.01) and 0.22-0.39 microg kg(-1) for the detection capability, CCbeta, (beta=0.05), whereas mean relative errors in absolute value were in the range of 2.8-4.6% for MG and LMG with PARAFAC calibration. The proposed optimized extraction procedure using a PARAFAC calibration was also applied in the determination of MG and LMG in gilthead bream samples: the decision limit was in the range of 0.45-0.55 microg kg(-1), the detection capability was in the range of 0.76-0.92 microg kg(-1) for MG and LMG. Trueness was likewise confirmed and the mean of the absolute values of relative errors were between 4.2% and 7.2%.

Optimization of the derivatization reaction and the solid-phase microextraction conditions using a D-optimal design and three-way calibration in the determination of non-steroidal anti-inflammatory drugs in bovine milk by gas chromatography―mass spectrometry

An experimental design optimization is reported of an analytical procedure used in the simultaneous determination of seven non-steroidal anti-inflammatory drugs (NSAIDs) in bovine milk by gas chromatography with mass spectrometry detection (GC-MS). This analytical procedure involves a solid-phase microextraction (SPME) step and an aqueous derivatization procedure of the NSAIDs to ethyl esters in bovine milk. The following NSAIDs are studied: ibuprofen (IBP), naproxen (NPX), ketoprofen (KPF), diclofenac (DCF), flufenamic acid (FLF), tolfenamic acid (TLF) and meclofenamic acid (MCL). Three kinds of SPME fibers - polyacrylate (PA), polydimethylsiloxane/divinylbenzene (PDMS/DVB) and polydimethylsiloxane (PDMS) - are compared to identify the most suitable one for the extraction process, on the basis of two steps: to determine the equilibrium time of each fiber and to select the fiber that provides the best figures-of-merit values calculated with three-way PARAFAC-based calibration models at the equilibrium time. The best results were obtained with the PDMS fiber. Subsequently, 8 experimental factors (related to the derivatization reaction and the SPME) were optimized by means of a D-optimal design that involves only 14 rather than 512 experiments in the complete factorial design. The responses used in the design are the sample mode loadings of the PARAFAC decomposition which are related to the quantity of each NSAID that is extracted in the experiment. Owing to the fact that each analyte is unequivocally identified in the PARAFAC decomposition, a calibration model is not needed for each experimental condition. The procedure fulfils the performance requirements for a confirmatory method established in European Commission Decision 2002/657/EC.

Advantages of PARAFAC calibration in the determination of malachite green and its metabolite in fish by liquid chromatography-tandem mass spectrometry.

This paper reports the properties and advantages of the three-way calibration models based on parallel factor analysis (PARAFAC) in the simultaneous determination of malachite green (MG) and its metabolite (leucomalachite green, LMG) in trout. A recently method proposed by community reference laboratory AFSSA-LERMVD (Fougères, France) has been used. The method is based on liquid chromatography-triple quadrupole mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode. The validation of the method has been carried out taking into account the Decision 2002/657/EC. The figures of merit for PARAFAC and univariate calibration models of six non-consecutive days analyzed during a month were evaluated. With the samples of the first 3 days, calibration models were built and the fish fortification samples of the other days were predicted. Decision limits (CCalpha, alpha=0.01), detection capabilities (CCbeta, beta=0.05) and mean relative errors in absolute value (in calibration and with test samples) obtained with PARAFAC calibrations were more homogeneous than the ones obtained with the univariate calibrations, especially in LMG. These figures of merit were in the range of 0.2-0.83 microg kg(-1) (CCbeta) and 0.2-0.49 microg kg(-1) (CCalpha), whereas mean relative errors in absolute value were in the range of 1.1-7.4% in calibration and 3-12% in test samples for MG and LMG with PARAFAC calibrations. The PARAFAC calibrations allow detecting the test samples which are not similar to the calibration samples and in this way their wrong quantification is avoided.

Qualitative and quantitative aspects of the application of genetic algorithm-based variable selection in polarography and stripping voltammetry

Abstract A genetic algorithm (GA) is successfully applied as a variable selection method in the multivariate analysis with partial least squares (PLS) regression of several polarographic and stripping voltammetric data sets, where different interferences are present (coupled reactions, formation of intermetallic compounds, overlapping signals and matrix effect). In most cases, the results corresponding to this variable selection method are better than those obtained when all the variables are considered. Such is the case in the determination of benzaldehyde, where a dimerization reaction occurs simultaneously to the electrochemical reactions. In general, an improvement in the precision is achieved for the test samples by using the GA. On the other hand, the GA provides valuable qualitative information that, in every case, provides a significant tool to detect and understand the chemical phenomena related to each analysis.

Modelling the background current with partial least squares regression and transference of the calibration models in the simultaneous determination of Tl and Pb by stripping voltammetry

With the aim of carrying out a calibration transfer for routine analysis, partial least squares (PLS) regression was successfully applied to simultaneously determine thallium and lead by stripping voltammetry when an interfering background current is present. The presence of a significant blank signal that overlaps the thallium peak, together with the overlapping thallium and lead signals were both suitably modelled by this multivariate regression technique. Moreover, once the PLS models are built, the piecewise direct standardization (PDS) method can be used to transfer these models over time in such a way that the number of calibration samples that will be needed in future determinations is reduced from 25 to 9, without a loss of quality in the analyses. The mean of the relative errors (in absolute values) obtained for thallium and lead is below 4.94% and 3.19%, respectively.

Typification of alcoholic distillates by multivariate techniques using data from chromatographic analyses

Multivariate chemometric techniques were used to classify alcoholic distillates and to develop a typification model for Galician liquors, on the basis of percentage data obtained from nine chromatographic peaks. By using the Bayesian model, the probability of a genuine Galician liquor being rejected is 0.11 and that of a false one being accepted is practically nil. Partial least squares was used as a modelling method, taking the liquor category as response variable. This method enables a confidence interval (95%) to be constructed that does not include any of the other distillates.

Genetic-algorithm-based potential selection in multivariant voltammetric determination of indomethacin and acemethacin by partial least squares

A procedure was proposed for the resolution of strongly overlapping voltammetric signals from mixtures of indomethacin and acemethacin. In this procedure a partial least squares regression (PLS) used the full voltammogram. The application of a genetic algorithm to select some of the predictor variables (potentials of the voltammogram) allows one to reduce by up to one tenth the number of experimental variables, but does not diminish the prediction of the PLS model constructed with these selected variables. The relative error in absolute value is less than 2% when concentrations of several mixtures are calculated.