Héctor C. Goicoechea

Experimental design and multiple response optimization. Using the desirability function in analytical methods development

A review about the application of response surface methodology (RSM) when several responses have to be simultaneously optimized in the field of analytical methods development is presented. Several critical issues like response transformation, multiple response optimization and modeling with least squares and artificial neural networks are discussed. Most recent analytical applications are presented in the context of analytLaboratorio de Control de Calidad de Medicamentos (LCCM), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, C.C. 242, S3000ZAA Santa Fe, ArgentinaLaboratorio de Control de Calidad de Medicamentos (LCCM), Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, C.C. 242, S3000ZAA Santa Fe, Argentinaical methods development, especially in multiple response optimization procedures using the desirability function.

Rhodamine and BODIPY chemodosimeters and chemosensors for the detection of Hg2+, based on fluorescence enhancement effects

Fluorescent sensors for Hg2+ are demonstrating their potential in a variety of fields such as environmental and biological applications. The review focuses on the recent development of rhodamine derivatives in which the spirolactam (non-fluorescent) to ring-opened amide (fluorescent) process was utilized and on the development of BODIPY derivatives in which the photoinduced electron transfer (PET) process was utilized. New trends in the immobilization of the molecular probes on solid supports, as polymers and/or nanostructures, have been emphasized. The different recognition mechanisms used for the signal responses have been analyzed. The spectroscopic properties, reaction media, analytical parameters, interferences by other ions and practical applications have been summarized.

Second- and higher-order data generation and calibration: a tutorial.

An introduction to multi-way calibration based on second- and higher-order data generation and processing is provided, with emphasis on practical experimental aspects. After a discussion concerning a proper nomenclature scheme, a suitable classification of the obtainable data, and the general features of the available algorithms and their underlying models, a series of examples is discussed in detail, with the purpose of illustrating the great potentiality of the field for the analytical community. Emphasis is directed toward the most popular multi-way data, i.e., second-order or matrix data, which can be conveniently measured in a variety of instruments. Third-order data are being increasingly studied and are also discussed, along with the less explored field of fourth-order data. The estimation of figures of merit, which analysts need to report during method development, is now sufficiently mature to be provided for the general audience.

A comparison of orthogonal signal correction and net analyte preprocessing methods. Theoretical and experimental study

Abstract A comparison is presented between orthogonal signal correction (OSC) and net analyte signal (NAS) calculations. It was shown that the latter can be used as a preprocessing method comparable to the former, before the application of partial least-squares (PLS) to the filtered data. When the number of factors used in the net analyte preprocessing (NAP) procedure increases, the subsequent application of PLS requires progressively less factors, a behavior comparable to OSC. If enough factors are extracted by either NAP or OSC methods, the remaining calibration problem is amenable to a classical least-squares solution, giving rise to two multivariate calibration methods named NAP/CLS and OSC/CLS. All methods are illustrated from cross-validation and external validation results for two experimental examples: (1) the determination of the antibiotic tetracycline in human serum, and (2) the quantitation of the nasal decongestant naphazoline in multicomponent pharmaceutical solutions.

Simultaneous spectrophotometric-multivariate calibration determination of several components of ophthalmic solutions: phenylephrine, chloramphenicol, antipyrine, methylparaben and thimerosal

The use of multivariate spectrophotometric calibration for the simultaneous determination of several active components and excipients in ophthalmic solutions is presented. The resolution of five-component mixtures of phenylephrine, chloramphenicol, antipyrine, methylparaben and thimerosal has been accomplished by using partial least-squares (PLS-1) and a variant of the so-called hybrid linear analysis (HLA). Notwithstanding the presence of a large number of components and their high degree of spectral overlap, they have been determined simultaneously with high accuracy and precision, with no interference, rapidly and without resorting to extraction procedures using non aqueous solvents. A simple and fast method for wavelength selection in the calibration step is presented, based on the minimisation of the predicted error sum of squares (PRESS) calculated as a function of a moving spectral window.

Fast chromatographic method for the determination of dyes in beverages by using high performance liquid chromatography—Diode array detection data and second order algorithms

A fast chromatographic methodology is presented for the analysis of three synthetic dyes in non-alcoholic beverages: amaranth (E123), sunset yellow FCF (E110) and tartrazine (E102). Seven soft drinks (purchased from a local supermarket) were homogenized, filtered and injected into the chromatographic system. Second order data were obtained by a rapid LC separation and DAD detection. A comparative study of the performance of two second order algorithms (MCR-ALS and U-PLS/RBL) applied to model the data, is presented. Interestingly, the data present time shift between different chromatograms and cannot be conveniently corrected to determine the above-mentioned dyes in beverage samples. This fact originates the lack of trilinearity that cannot be conveniently pre-processed and can hardly be modelled by using U-PLS/RBL algorithm. On the contrary, MCR-ALS has shown to be an excellent tool for modelling this kind of data allowing to reach acceptable figures of merit. Recovery values ranged between 97% and 105% when analyzing artificial and real samples were indicative of the good performance of the method. In contrast with the complete separation, which consumes 10 mL of methanol and 3 mL of 0.08 mol L(-1) ammonium acetate, the proposed fast chromatography method requires only 0.46 mL of methanol and 1.54 mL of 0.08 mol L(-1) ammonium acetate. Consequently, analysis time could be reduced up to 14.2% of the necessary time to perform the complete separation allowing saving both solvents and time, which are related to a reduction of both the costs per analysis and environmental impact.

Multiple response optimization applied to the development of a capillary electrophoretic method for pharmaceutical analysis.

Multiple response simultaneous optimization by using the desirability function was used for the development of a capillary electrophoresis method for the simultaneous determination of four active ingredients in pharmaceutical preparations: vitamins B(6) and B(12), dexamethasone and lidocaine hydrochloride. Five responses were simultaneously optimized: the three resolutions, the analysis time and the capillary current. This latter response was taken into account in order to improve the quality of the separations. The separation was carried out by using capillary zone electrophoresis (CZE) with a silica capillary and UV detection (240 nm). The optimum conditions were: 57.0 mmol l(-1) sodium phosphate buffer solution, pH 7.0 and voltage=17.2 kV. Good results concerning precision (CV lower than 2%), accuracy (recoveries ranged between 98.5 and 102.6%) and selectivity were obtained in the concentration range studied for the four compounds. These results are comparable to those provided by the reference high performance liquid chromatography (HPLC) technique.

Enhanced application of square wave voltammetry with glassy carbon electrode coupled to multivariate calibration tools for the determination of B6 and B12 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.

Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration.

The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.

Wavelength selection for multivariate calibration using a genetic algorithm: a novel initialization strategy.

Genetic algorithms and other procedures mimicking natural processes are being increasingly used for variable selection, to improve the predictive ability of partial least-squares multivariate calibration. Two issues are critical for the success of genetic algorithms: initialization (setting the first candidates for solving the problem at hand) and overfitting (the tendency to produce excellent results when training, but poor predictions toward fresh samples). A new procedure is presented for sensor selection problems, involving iterative reinitialization based on a statistical analysis of the included sensors. It is shown to give excellent results without the requirement of preparing independent test data sets. Monte Carlo simulations using a theoretical three-component example illustrate how partial least-squares regression greatly benefits from variable selection when the analyte of interest is diluted, and how the new initialization method compares with other strategies. The new genetic algorithm was applied to five experimental data sets. The target parameters were the concentrations of diluted analytes in four pharmaceutical mixtures studied by UV-visible spectrophotometry and the octane number in gasolines analyzed by near-infrared spectroscopy.