Differentiation between microcystin contaminated and uncontaminated fish by determination of unconjugated MCs using an ELISA anti-Adda test based on receiver-operating characteristic curves threshold values: application to Tinca tinca from natural ponds.

The aim of this study was to evaluate whether the enzyme-linked immunosorbent assay (ELISA) anti-Adda technique could be used to monitor free microcystins (MCs) in biological samples from fish naturally exposed to toxic cyanobacteria by using receiver operating characteristic (ROC) curve software to establish an optimal cut-off value for MCs. The cut-off value determined by ROC curve analysis in tench (Tinca tinca) exposed to MCs under laboratory conditions by ROC curve analysis was 5.90-µg MCs/kg tissue dry weight (d.w.) with a sensitivity of 93.3%. This value was applied in fish samples from natural ponds (Extremadura, Spain) in order to asses its potential MCs bioaccumulation by classifying samples as either true positive (TP), false positive (FP), true negative (TN), or false negative (FN). In this work, it has been demonstrated that toxic cyanobacteria, mainly Microcystis aeruginosa, Aphanizomenon issatchenkoi, and Anabaena spiroides, were present in two of these ponds, Barruecos de Abajo (BDown) and Barruecos de Arriba (BUp). The MCs levels were detected in waters from both ponds with an anti-MC-LR ELISA immunoassay and were of similar values (between 3.8-6.5-µg MC-LR equivalent/L in BDown pond and 4.8-6.0-µg MC-LR equivalent/L in BUp). The MCs cut-off values were applied in livers from fish collected from these two ponds using the ELISA anti-Adda technique. A total of 83% of samples from BDown pond and only 42% from BUp were TP with values of free MCs higher than 8.8-µg MCs/kg tissue (d.w.).

Use and misuse of supervised pattern recognition methods for interpreting compositional data.

In this paper, the critical aspects of supervised learning pattern recognition techniques are examined in order to apply the suitable methodology avoiding its abuse and misuse that could provide pitfalls and misleading results.

Estimation of dietary intake and target hazard quotients for metals by consumption of wines from the Canary Islands.

This paper describes the impact of mineral content on wines and assesses the potential health risk from consuming these wines from Canary Islands. The metal content (B, Ca, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Zn) of red wines belonging to different regions in the Canary Islands was determined by ICP-OES. The studied wine regions were Valle de la Orotava, Tacoronte-Acentejo, Ycoden-Daute-Isora, Abona and Valle de Güimar in Tenerife Island and only one in La Gomera and La Palma Islands. According to the content found, elements could be classified in two categories: the main group including Ca, K, Mg, Na, and the ''minor'' set consisting of B, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb and Zn. Once calculated the metal intake through red wines consumption, we can conclude that Canarian drinkers are not exposed to unsafe levels of the metals studied, actually, the safety intake limits (daily) ranges between 0.9% in Zn and 2% in Cu, for normal drinkers. And also it has been demonstrated the good quality of Canarian red wines and there is no reason for health concern through the THQ calculation being the highest values determined in La Gomera wines.

Supervised pattern recognition procedures for discrimination of whiskeys from gas chromatography/mass spectrometry congener analysis.

The volatile congener analysis of 52 commercialized whiskeys (24 samples of single malt Scotch whiskey, 18 samples of bourbon whiskey, and 10 samples of Irish whiskey) was carried out by gas chromatography/mass spectrometry after liquid-liquid extraction with dichloromethane. Pattern recognition procedures were applied for discrimination of different whiskey categories. Multivariate data analysis includes linear discriminant analysis (LDA), k nearest neighbors (KNN), soft independent modeling of class analogy (SIMCA), procrustes discriminant analysis (PDA), and artificial neural networks techniques involving multilayer perceptrons (MLP) and probabilistic neural networks (PNN). Classification rules were validated by considering the number of false positives (FPs) and false negatives (FNs) of each class associated to the prediction set. Artificial neural networks led to the best results because of their intrinsic nonlinear features. Both techniques, MLP and PNN, gave zero FPs and zero FNs for all of the categories. KNN is a nonparametric method that also provides zero FPs and FNs for every class but only when selecting K = 3 neighbors. PDA produced good results also (zero FPs and FNs always) but only by selecting nine principal components for class modeling. LDA shows a lesser classification performance, because of the building of linear frontiers between classes that does not apply in many real situations. LDA led to one FP for bourbons and one FN for scotches. The worse results were obtained with SIMCA, which gave a higher number of FPs (five for both scotches and bourbons) and FNs (six for scotchs and two for bourbons). The possible cause of these findings is the strong influence of class inhomogeneities on the SIMCA performance. It is remarkable that in any case, all of the methodologies lead to zero FPs and FNs for the Irish whiskeys.

Study of catechin and xanthine tea profiles as geographical tracers.

The contents of gallic acid, epigallocatechin gallate, epigallocatechin, epicatechin, epicatechin gallate, catechin, caffeine, theophylline, and theobromine were determined in a set of 45 tea samples, including fermented (black and red) and nonfermented (green) teas of different geographical origins (i.e., China, Japan, Kenya, Sri Lanka, and India). A reversed-phase high performance liquid chromatographic method with gradient elution and photometric detection at 275 nm was used to carry out the analysis. Before the HPLC determination, an extraction step was developed using a mixture of acetonitrile and water (60:40, v/v). Pattern recognition techniques involving principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to differentiate the tea samples according to their geographical origins. Catechins, gallic acid, and tea alkaloids are adequate chemical descriptors to distinguish between fermented and nonfermented tea samples cultivated in different geographical areas.

Cylindrospermopsin determination in water by LC-MS/MS: optimization and validation of the method and application to real samples.

A new method for determining dissolved cylindrospermopsin (CYN) in waters using solid-phase extraction (SPE) with graphitized carbon cartridges and quantification by liquid chromatography coupled with tandem mass spectrometry is described and discussed. The method has been suitably validated: the linear range covered is from 0.900 to 125 µg CYN/L. Limits of detection and quantification were 0.5 and 0.9 µg CYN/L, respectively, and allow CYN determination at concentrations below the guideline proposed of 1 µg CYN/L in natural waters. The method exhibits mean recoveries from 83 to 95%, and intermediate precision (relative standard deviation (%)) values from 5 to 12%, ensuring adequacy against the Association of Official Analytical Chemists guidelines. The method is robust against the following three influential factors considered in the cleanup stage: the batch of the graphitized carbon cartridges, the flow rate of the water sample through the cartridge, and the final redissolved water volume after SPE treatment. The method has been successfully applied to detection and quantification of CYN in water samples from aquaria of a toxicological in vivo laboratory experiment.

Intra-laboratory assessment of method accuracy (trueness and precision) by using validation standards.

Assessment of accuracy of analytical methods is a fundamental stage in method validation. The use of validation standards enables the assessment of both trueness and precision of analytical methods at the same time. Procedures of intra-laboratory testing of method accuracy using validation standards are outlined and discussed.

Differentiation of two Canary DO red wines according to their metal content from inductively coupled plasma optical emission spectrometry and graphite furnace atomic absorption spectrometry by using Probabilistic Neural Networks.

The metal content of 54 commercialized wines (30 samples from Tacoronte-Acentejo DO (class T) and 24 Valle de la Orotava DO (class O) wines) was performed by ICP-OES (Al, Ba, Cu, Fe, Mn, Sr, Zn, Ca, K, Na and Mg) and GF-AAS (Ni and Pb). Wine samples were processed by dry ashing followed by solution with 5% nitric acid. Metals were considered as suitable descriptors to differentiate between T and O classes. Supervised learning pattern recognition procedures were applied. Linear discriminant analysis (LDA) led to good results up to about 90% of correct classification. In order to improve the results, another kind of algorithms able to model non-linear separation between classes was considered: Probabilistic Neural Networks. Accordingly, excellent results were obtained, leading to sensitivities and specificities higher than 95% for the two classes.

Accuracy profiles from uncertainty measurements.

Accuracy profiles of chemical assays are introduced and derived from the uncertainty of the analytical result. The calculation of accuracy profiles is based on the estimation of the measurement uncertainty of the analytical assay from validation data. For the sake of illustration, a case study dealing with the spectrofluorimetric determination of quinine in tonic water is explained in detail.

Practical digest for evaluating the uncertainty of analytical assays from validation data according to the LGC/VAM protocol.

The estimation of the measurement uncertainty of analytical assays based on the LGC/VAM protocol from validation data is fully revisited and discussed in the light of the study of precision, trueness and robustness.

Enzymatic-spectrophotometric determination of sucrose in coffee beans.

A spectrophotometric method for determining sucrose is proposed. Sucrose is hydrolyzed by invertase into glucose and fructose. Then, glucose is oxidized in presence of glucose oxidase and the produced hydrogen peroxide reacts with phenol-4-sulfonic acid sodium salt and 4-aminoantipyrine in presence of peroxidase, yielding a pink dye with an absorption maximum at 505 nm. This method was validated following the EURACHEM and VAM project guidelines for method validation. Trueness, precision, robustness, sensitivity and linearity were considered. The method was applied to the determination of sucrose in green and roasted coffee beans. A comparison with the HPLC method with pulsed amperometric detection was carried out.

Potentiometric titrations in acetonitrile-water mixtures: evaluation of aqueous ionisation constant of ketoprofen.

Non ideality of acetonitrile-water mixtures was studied from data on the excess of molar volumes and viscosities. pH and autoprotolisis constants were evaluated at the standard state of the mixed solvent from titrations of a strong acid with a strong base. In order to illustrate the evaluation of the aqueous ionisation constant of water insoluble compounds from pH titrations in ACN-water mixtures, a typical insoluble arylpropionic acid, ketoprofen, was chosen. Ketoprofen was titrated in mixtures from 10 to 70% w/w of acetonitrile against a strong base. From the titration data, the ionisation constant of ketoprofen was evaluated at the standard state of the solvent mixture (pK(a)(*)). Aqueous pK(a) was determined by extrapolation, as the intercept of the plot of pK(a)(*) versus ACN mole fraction.

Evaluation of measurement uncertainty in analytical assays by means of Monte-Carlo simulation.

The main limitations of the Guide to the expression of Uncertainty Measurement (GUM) approach for evaluating the measurement uncertainty of analytical assays are presented and explained. The advantages of using Monte-Carlo simulation against the GUM approach are outlined and discussed and the principle of propagation of distributions is explained. The procedure of Monte-Carlo analysis is illustrated by two case studies. A first simple example quoted from the EURACHEM Guide and dealing with the preparation of a calibration standard is used to present the technique with detail in a step-by-step way. In this case the results obtained by both approaches are very similar. A second example deals with the calibration of mass according to a strong non-linear model. In this case, the Monte-Carlo analysis leads to better results.

Multivariate characterisation of beers according to their mineral content.

In the present paper, a study on the characterisation of beer samples according to their mineral content has been carried out. Zn, P, B, Mn, Fe, Mg, Al, Sr, Ca, Ba, Na and K were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES) in 32 beer samples. Lager, dark and low alcoholic content beers were considered. The analysed elements were considered as chemical descriptors in order to apply pattern recognition procedures, including display and supervised learning methods such as linear discriminant analysis and artificial neural networks.