María J. Culzoni

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.

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.

Evaluation of partial least-squares with second-order advantage for the multi-way spectroscopic analysis of complex biological samples in the presence of analyte–background interactions

The combination of unfolded partial least-squares (U-PLS) with residual bilinearization (RBL) has not been properly exploited to process experimental second-order spectroscopic information, although it is able to achieve the important second-order advantage. Among other desirable properties, the technique can handle incomplete calibration information, i.e., when only certain analyte concentrations are known in the training set. It can also cope with analyte spectral changes from sample to sample, due to its latent variable structure. In this work, U-PLS/RBL has been successfully applied to experimental fluorescence excitation-emission matrix data aimed at the quantitation of analytes in complex samples: these were the antibiotic tetracycline and the anti-inflammatory salicylate, in both cases in the presence of human serum, where significant analyte-background interactions occur. The interactions of the analyte with the serum proteins modify their spectral fluorescence properties, making it necessary to employ training sets of samples where the biological background is present, possibly causing analyte spectral changes from sample to sample. The predictive ability of the studied model has been compared with that of parallel factor analysis (PARAFAC), as regards test samples containing different sera, and also other pharmaceuticals which could act as potential interferents.

Chemometric strategies for enhancing the chromatographic methodologies with second-order data analysis of compounds when peaks are overlapped

This overview covers the different chemometric strategies linked to chromatographic methodologies that have been used and presented in the recent literature to cope with problems related to incomplete separation, the presence of unexpected components in the sample, matrix effect and changes in the analytical signal due to pre-treatment of sample. Among the different chemometric strategies it focuses on pre-treatment of data to correct background and time shift of chromatographic peaks and the use of second-order algorithms to cope with overlapping peaks from analytes or from analytes and interferences in liquid chromatography coupled to diode array, fast-scanning fluorescence spectroscopy and mass spectrometry detectors. Finally the review presents the strategies used to deal with changes in the analytical response as result of matrix effect in liquid and gas chromatography, as well as the use of standardization strategies to correct modifications in the analytical signal as a consequence of sample pre-treatment in liquid chromatography.

Chemometric tools improving the determination of anti-inflammatory and antiepileptic drugs in river and wastewater by solid-phase microextraction and liquid chromatography diode array detection.

An analytical method for the simultaneous determination of seven non-steroidal anti-inflammatory drugs (naproxen, ketoprofen, diclofenac, piroxicam, indomethacin, sulindac and diflunisal) and the anticonvulsant carbamazepine in river and wastewater is reported. The method involves pre-concentration and clean-up by solid-phase microextraction using polydimethylsiloxane/divinylbenzene fibers, followed by liquid chromatography with diode array detection analysis. Owing to the fact that river water samples did not contain interferences and no sensitivity changes due to sample matrix were observed, external calibration was implemented. Standardization was also applied in order to carry out the prediction step by preparing only two diluted standards that were subjected to the pre-concentration step and a set of standards prepared in solvent. For the analysis of wastewater samples, in contrast, it was necessary to implement standard addition calibration in combination with the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm, which allowed us to overcome matrix effect and exploit the second order advantage. Recoveries ranging from 72% to 125% for all pharmaceuticals proved the accuracy of the proposed method in river water samples. On the other hand, wastewater sample recoveries ranged from 83% to 140% for all pharmaceuticals, showing an acceptable performance - considering this sample contains no modeled interferences.

Detection of unintended stress effects based on a metabonomic study in tomato fruits after treatment with carbofuran pesticide. Capabilities of MCR-ALS applied to LC-MS three-way data arrays.

A chemometric strategy based on multivariate curve resolution and alternating least-squares (MCR-ALS) applied to LC-MS three-way data arrays has been developed to perform a metabonomic study in tomato (Lycopersicon esculentum) fruits (cultivar Rambo) following treatment with carbofuran. This methodology has proved to be adequate for the detection of unintended stress effects due to the previous treatment with this pesticide. MCR-ALS was performed on augmented matrices built with the LC-MS three-way data obtained from treated and nontreated samples through the sampling time. The strategy allowed us to obtain the concentration and spectra profiles of the main components (previously estimated with the SVD algorithm) from samples treated with pesticide as well as from blank samples, showing how they vary with time after plants treatment with the pesticide. In addition, a simple resolved mass spectrum was obtained corresponding to the peaks of a particular component in all matrices, thus avoiding ambiguity in the compound identity assignment. Different time profiles were found for some metabolites in treated and nontreated samples, which demonstrate that the presence of pesticide causes changes thorough time in the behavior of certain endogenous tomato metabolites as a result of physiological stress.

Determination of pharmaceuticals in river water by column switching of large sample volumes and liquid chromatography–diode array detection, assisted by chemometrics: An integrated approach to green analytical methodologies

An analytical method for the simultaneous determination of nine beta-blockers (sotalol atenolol, nadolol, pindolol, metoprolol, timolol, bisoprolol, propanolol and betaxolol) and two analgesics (paracetamol and phenazone) in river water by liquid chromatography and diode array detection is reported. The method involves a modified precolumn switching methodology replacing the small precolumn with a short C18 liquid chromatography column (50 mm x 4.6 mm, 5 microm particle size), thus allowing the preconcentration of large water sample volumes whereas interferences eluting at the first of the chromatogram were discarded to waste. This approach allowed to preconcentrate 30 mL river water samples, modified with 0.4% MeOH, achieving univariate method detection and determination limits ranged between 0.03 and 0.16 microg L(-1) and between 0.2 and 0.5 microg L(-1), respectively, with precision values lower than 9.4% for spiking levels at the quantitation limits of each analyte and lower than 4.0%, except bisoprolol (8.3%), for higher spiking levels (1.0 microg L(-1) of all analytes). Matrix background was reduced in three way data by a baseline correction following the Eilers methodology, whereas multivariate curve resolution and alternating least squares in combination with the standard addition calibration method, applied to these data, coped with overlapping peak, systematic (additive) and proportional (matrix effect) errors. The method was successfully applied for the determination of the target pharmaceuticals in river water from three places in a river stream with acceptable recoveries and precision values, taking into account the complexity of the analytical problem. The joint statistical test for the slope and the intercept of the linear regression between the nominal concentration values versus those predicted, showed that the region computed contained the theoretically expected values (0) for the intercept and (1) for the slope (at a confidence level of 95%), which indicates the absence of both constant and proportional errors in the predicted concentrations.

Second-order advantage from kinetic-spectroscopic data matrices in the presence of extreme spectral overlapping: A multivariate curve resolution—Alternating least-squares approach

Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples.

Falsified medicines in Africa: all talk, no action

Fil: Newton, Paul N. . Mahosot Hospital. Microbiology Laboratory. Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit; Laos. Oxford University. Churchill Hospital. Nuffield Department of Medicine. Centre for Tropical Medicine and Global Health; Reino Unido. Oxford University. Churchill Hospital. Worldwide Antimalarial Resistance Network; Reino Unido

Modeling four and three-way fast high-performance liquid chromatography with fluorescence detection data for quantitation of fluoroquinolones in water samples.

This paper presents a study regarding the acquisition and analytical utilization of four and three-way data, acquired by following the excitation-emission fluorescence matrices at different elution times, in a fast liquid chromatographic HPLC procedure. This kind of data were implemented for first time for quantitative purposes, and applied to the determination of two fluoroquinolones in tap water samples, as a model to show the potentiality of the proposed strategy of four-way data generation. The data were modeled with three well-known algorithms: PARAFAC, U-PLS/RTL and MCR-ALS, the latter conveniently adapted to model third-order data. The second-order advantage was exploited when analyzing samples containing uncalibrated interferences. PARAFAC and MCR-ALS were the algorithms that better exploited the second-order advantage when no peak time shifts occurred among samples. On the other hand, when the quadrilinearity was lost due to the occurrence of temporal shifts, MCR-ALS furnished the better results. Relative error of prediction (REP%) obtained were 9.9% for ofloxacin and 14.0% for ciprofloxacin. In addition, a significant enhancement in the analytical figures of merit was observed when going from second- to third-order data (reduction of ca. 70% in LODs).