L.A. Sarabia

Capability of detection of an analytical method evaluating false positive and false negative (ISO 11843) with partial least squares

Analytical techniques based on soft multivariate calibrations (as those which provide first and second order analytical signals necessarily are) remain outside the field of application of the ISO norms related to capability of detection. In this work, a complete solution for the problem of applying ISO norm 11843 to soft calibration (for instance, one or multi-way partial least squares (PLS)) is provided. The methodological procedure is applied to different case studies which implies different analytical techniques.

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%.

Handling intrinsic non-linearity in near-infrared reflectance spectroscopy

Abstract The relationship between absorption in the near-infrared (NIR) spectral region and the target analytical parameter is frequently of the non-linear type. The origin of the non-linearity can be widely varied and difficult to identify. In some cases, the relationship between absorption and the analytical parameter of interest is intrinsically non-linear owing to the very chemical nature of the sample or analyte concerned. In this work, various multivariate calibration procedures were tested with a view to overcoming intrinsic non-linearity in NIR reflectance. An approach to solving the problem is suggested. Calibration was done, after transformation of spectra, by using linear and non-linear techniques. The linear calibration techniques used are partial least squares (PLS) regression (with and without variable selection), linear PLS with X projection (LP-PLS) and stepwise polynomial principal component (SWP-PC) regression. Non-linear calibration methods included polynomial PLS (PPLS) and artificial neural networks (ANNs). Results were compared on the basis of NIR spectra for ampicillin trihydrate samples, where the simultaneous presence of crystallization water and surface moisture gives rise to intrinsic non-linearity that affects the determination of the total water content in the sample. The best results were obtained by using the non-linear calibration techniques.

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.

A study of robustness with multivariate calibration. Application to the polarographic determination of benzaldehyde

A procedure to evaluate the robustness of an analytical method when there are changes in some experimental variables, when using multivariate calibration, is proposed. The procedure consists of analysing the root mean square error of prediction (RMSEP) as a response to a Plackett-Burman experimental design, through which the influence of several experimental factors on the prediction capability of the multivariate partial least squares (PLS) models built is studied. Two different ways of analysing the experimental design response are considered: establishing the residual variance with replicates and using Lenths method. The proposed methodology has been applied to estimate the robustness of the polarographic determination of benzaldehyde when PLS calibration is used.

Analysis of protein chromatographic profiles joint to partial least squares to detect adulterations in milk mixtures and cheeses

To prevent possible frauds and give more protection to companies and consumers it is necessary to control that the types of milk used in the elaboration of dairy products correspond to those appearing in their label. Therefore, it is greatly interesting to have efficient, quick and cheap methods of analysis to identify them. In the present work, the multivariate data are the protein chromatographic profiles of cheese and milk extracts, obtained by high-performance liquid chromatography with diode-array detection (HPLC-DAD). These data correspond to pure samples of bovine, ovine and caprine milk, and also to binary and ternary mixtures. The structure of the data is studied through principal component analysis (PCA), whereas the percentage of each kind of milk has been determined by a partial least squares (PLS) calibration model. In cheese elaborated with mixtures of milk, the procedure employed allows one to detect 3.92, 2.81 and 1.47% of ovine, caprine and bovine milk, respectively, when the probability of false non-compliance is fixed at 0.05. These percentages reach 7.72, 5.52 and 2.89%, respectively, when both the probability of false non-compliance and false compliance are fixed at 0.05.