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A new approach for calculating uncertainties of analytical results based on the information from the validation process is proposed. This approach complements the existing approaches proposed to date and can be applied to any validated analytical method. The precision estimates generated during the process of assessment of the accuracy take into account the uncertainties of preprocessing steps and analytical measurement steps as long as the different factors that influence these steps are representatively varied in the whole validation process. Since the accuracy of an analytical method should be always assessed before applying it to future working samples, little extra work needs to be done to estimate the final uncertainty. Other sources of uncertainty not previously considered (e.g. uncertainty associated to sampling, to differences between the test-sample and the working sample, etc.) are subsequently included and mathematically combined with the uncertainty arising from the assessment of the accuracy to provide the overall uncertainty. These ideas are illustrated with a case study of the determination of copper in contaminated land.

Several compositional parameters of the main hydrocarbon families are determined in naphtha. This hydrocarbon mixture is traditionally analysed by gas chromatography (GC) but there are several alternative spectroscopic methods, such as near-infrared spectroscopy combined with multivariate calibration. Spectroscopic methods have the advantage that they are faster and more suitable for on-line analysis. Previous studies have determined the global percentage of each hydrocarbon family: linear and branched paraffins, naphthenes and aromatic compounds. Here we present the determination by mid-infrared (MIR) spectroscopy and multivariate calibration of the most detailed compositional parameters provided by gas chromatography. Results were good, that is to say there was no bias and the root mean square error of cross validation (RMSECV) was low, for the determination of all the detailed naphthenes and aromatic compounds, and most n-paraffins and isoparaffins. Several methods of detecting outliers are discussed and the quality of the models is evaluated by sample specific error predictions calculated by two approaches: the Unscrambler expression and the Faber–Kowalski expression. The proposed method enables a more specific analysis of naphtha than other methods and provides the same information as GC.

In order to establish an adequate analytical system for the quality control of industrially produced titanium dioxide white pigments, two multivariate linear calibration techniques, principal component regression (PCR) and partial least squares (PLS), are used to model the relationship between the important pigment property, change of colour, and its chemical composition. The results, in terms of accuracy, precision, suitability for quality control and analysis time are compared to those obtained with artificial neural networks (ANNs). Two multivariate display techniques, principal component analysis (PCA) and correspondence factor analysis (CFA) together with two hierarchical clustering techniques, divisive and Ward's agglomerative hierarchical clustering, are also applied to the X-ray fluorescence data of the pigments samples so as to extract as much information as possible. Correlation coefficients obtained by PCR and PLS are 0.92 and 0.94, respectively. Both of them are higher than the already achieved correlation coefficient by ANNs [1], but the precision of the model derived by ANNs is better. It should also be pointed out that some important additional information about the relations between independent variables (chemical composition of the pigment samples) and about the influence of different oxide concentrations on the pigment property, which could be used in the controlling of the production process, was found out.

Method comparison is one of the most important activities within the domain of method validation. The results of newly developed analytical methods are regressed against those obtained using reference methods for a series of samples with different concentrations of the analyte of interest. The linear model, based on the bivariate least squares (BLS) calibration method, and developed taking into account the comparable errors in both axes, should fit a straight line where the intercept is not significantly different from zero and the slope not significantly different from one. To check these premises, the joint confidence interval for the intercept and the slope is usually applied. This article compares the results of this method with the joint confidence test for the intercept and the slope based on the ordinary least squares (OLS) and weighted least squares (WLS) methods, using two real data sets.

A graphical criterion approach has been developed to examine the quality of a set of sensors in multicomponent analysis. Criteria such as sensitivity and selectivity, used in wavelength selection problems, can be explained in terms of the confidence interval of the estimated concentrations. These confidence intervals describe (hyper)ellipsoids whose volume, shape and orientation are related to the optimization criteria. The effect of sensor selection on these criteria is discussed and guidelines for wavelength selection are given. The usefulness of the graphical criterion is shown in the simultaneous determination of 2-chlorophenol and 2,4-dichlorophenol in water.

The main characteristics of a Matlab program to select D-optimal subsets of calibration samples for multiple linear regression are described. The performance of Fedorov's exchange algorithm to select samples is compared with the Kennard-Stone algorithm and the random selection of samples into training and test sets.

The development of a solid-contact potentiometric sensor based on conducting rubbers using a carbon nanotubes ink is described here. Commercial rubbers are turned into conductive ones by a simple and versatile method, i.e. painting an aqueous dispersion of single-walled carbon nanotubes on the polymer surface. On this substrate, both the working ion-selective electrode and the reference electrode are built in order to form an integrated potentiometric cell. As a proof-of-principle, selective potassium electrodes are fully characterized giving comparable performances to conventional electrodes (sensitivity, selectivity, stability, linear range, limit of detection and reproducibility). As an application of the rubber-based electrodes, a bracelet was constructed to measure potassium levels in artificial sweat. Since rubbers are ubiquitous in our quotidian life, this approach offers great promise for the generation of chemical information through daily objects.

A new solid-state reference electrode using a polymeric membrane of polyvinyl butyral (PVB), Ag/AgCl and NaCl to be used in decentralized chemical measurements is presented. The electrode is made by drop-casting the membrane cocktail onto a glassy carbon (GC) substrate. A stable potential (less than 1 mV dec(-1)) over a wide range of concentrations for the several chemical species tested is obtained. No significant influence to changes in redox potential, light and pH are observed. The response of this novel electrode shows good correlation when compared with a conventional double-junction reference electrode. Also good long-term stability (90±33 μV/h) and a lifetime of approximately 4 months are obtained. Aspects related to the working mechanisms are discussed. Atomic Force Microscopy (AFM) studies reveal the presence of nanopores and channels on the surface, and electrochemical impedance spectroscopy (EIS) of optimized electrodes show low bulk resistances, usually in the kΩ range, suggesting that a nanoporous polymeric structure is formed in the interface with the solution. Future applications of this electrode as a disposable device for decentralized measurements are discussed. Examples of the utilization on wearable substrates (tattoos, fabrics, etc) are provided.

5 pages, 3 Figures.-- Supplementary Data, In this communication we present a potentiometric aptasensor based on chemically modified graphene (transducer layer of the aptasensor) and aptamers (sensing layer). Graphene oxide (GO) and reduced graphene oxide (RGO) are the basis for the construction of two versions of the aptasensor for the detection of a challenging living organism such as Staphylococcus aureus. In these two versions, DNA aptamers are either covalently (in the GO case) or non-covalently (in the RGO case) attached to the transducer layer. In both cases we are able to selectively detect a single CFU/mL of S. aureus in an assay close to real time, although the noise level associated to the aptasensors made with RGO is lower than the ones made with GO. These new aptasensors, that show a high selectivity, are characterized by the simplicity of the technique and the materials used for their construction while offering ultra-low detection limits in very short time responses in the detection of microorganisms., Spanish Ministry of Science and Innovation (MICINN) through project grants CTQ2010-18717, MAT2010-15026, Spanish Higher Research Council CSIC under project 201080E124, Regional Government of Aragon and the European Social Fund under project DGA-FSE-T66 CNN

10.1016/j.bios.2013.06.007 A new paper-based chemiresistor composed of a network of single-wall carbon nanotubes (SWCNTs) and anti-human immunoglobulin G (anti-HIgG) is reported herein. SWCNTs act as outstanding transducers because they provide high sensitivity in terms of resistance changes due to immunoreaction. As a result, the resistance-based biosensor reaches concentration detection as low as picomolar. The resulting paper-based biosensor is sensitive, selective and employs low-cost substrate and simple manufacturing stages. Since chemiresistors require low-power equipment and are able to detect low concentrations with inexpensive materials, the present approach may pave the way for the development of resistive biosensors at very low-cost with high performances.

In this paper we present a new generation of potentiometric biosensors based on carbon nanotubes (transducer layer of the biosensor) and aptamers (sensing layer of the biosensor) for the ultralow and selective detection of microorganisms. We show that with these aptasensors we were able to detect a few CFU of the target bacteria almost in real-time, both in buffered and in real samples.

A comparison study on the performance characteristics and surface characterization of two different solid-contact selective potentiometric thrombin aptasensors, one exploiting a network of single-walled carbon nanotubes (SWCNTs) and the other the polyaniline (PANI), both acting as a transducing element, is described in this work. The molecular properties of both SWCNT and PANI surfaces have been modified by covalently linking thrombin binding aptamers as biorecognition elements. The two aptasensors are compared and characterized through potentiometry and electrochemical impedance spectroscopy (EIS) based on the voltammetric response of multiply charged transition metal cations (such as hexaammineruthenium, [Ru(NH3)6]3+) bound electrostatically to the DNA probes. The surface densities of aptamers were accurately determined by the integration of the peak for the reduction of [Ru(NH3)6]3+to [Ru(NH3)6]2+. The differences and the similarities, as well as the transduction mechanism, are also discussed. The sensitivity is calculated as 2.97 mV/decade and 8.03 mV/decade for the PANI and SWCNTs aptasensors, respectively. These results are in accordance with the higher surface density of the aptamers in the SWCNT potentiometric sensor.

A simple and generalized approach to build electrochemical sensors for wearable devices is presented. Commercial cotton yarns are first turned into electrical conductors through a simple dyeing process using a carbon nanotube ink. These conductive yarns are then partially coated with a suitable polymeric membrane to build ion-selective electrodes. Potentiometric measurements using these yarn-potentiometric sensors are demonstrated. Examples of yarns that can sense pH, K+ and NH4+ are presented. In all cases, these sensing yarns show limits of detection and linear ranges that are similar to those obtained with lab-made solid-state ion-selective electrodes. Through the immobilization of these sensors in a band-aid, it is shown that this approach could be easily implemented in a wearable device. Factors affecting the performance of the sensors and future potential applications are discussed.

The development of ion-selective electrodes (ISEs) using solid-state transducer materials is of great interest for advanced potentiometric detection systems. At present, conducting polymers are the most used solid-state transducing materials. However, their reliability is strongly related to their chemical stability and the formation of internal water films. Here we report on the use of reduced graphene oxide (RGO) films of different thicknesses as transducer materials in potentiometric all-solid-state ISEs. First, the transduction mechanism is fully analyzed, revealing that RGO films act as asymmetric capacitors where their electron density is in contact with ions of the electrolyte solution, creating a capacitance due to the constant phase elements present in the system. Second, as a proof of concept, RGO films are used in a calcium ISE showing highly reproducible sensing responses and outstanding increased signal-to-noise ratios with drifts of only 10 μV/h. These performance parameters are among the best compared to those of other ISE transducer materials so far. With its ease of fabrication and processing into reproducible films of controlled thickness and ease for further tailoring chemical composition and tailoring electrical properties, RGO offers great promise as a reliable high-performance transducer material for solid-state ISE sensors.

In this paper we report the first biosensor that is able to detect Staphylococcus aureus in real-time. A network of single-walled carbon nanotubes (SWCNTs) acts as an ion-to-electron potentiometric transducer and anti-S. aureus aptamers are the recognition element. Carbon nanotubes were functionalized with aptamers using two different approaches: (1) non-covalent adsorption of drop-casted pyrenil-modified aptamers onto the external walls of the SWCNTs; and (2) covalent bond formation between amine-modified aptamers and carboxylic groups previously introduced by oxidation at the ends of the SWCNTs. Both of these approaches yielded functional biosensors but there were large differences in the minimum detectable bacteria concentration and sensitivity values. With covalent functionalization, the minimum concentration detected was 8×10(2)colony-forming units (CFU)/mL and the sensitivity was 0.36 mV/Decade. With the non-covalent approach, the sensitivity was higher (1.52 mV/Decade) but the minimum concentration detected was greatly affected (10(7) CFU/mL). In both cases, potential as a function of Decade of bacteria concentration was linear. Functional biosensors were used to test real samples from freshly excised pig skin, contaminated with the target microorganism, as a surrogate for human skin.

In this paper, a new technique for assessing the accuracy of analytical methods using linear regression is reported. The results of newly developed analytical methods are regressed against the results obtained using reference methods. The new test is based on the joint confidence interval for the slope and the intercept of the regression line, which is calculated taking the uncertainties in both axes into account. The slope, intercept, and variances which are associated with the regression coefficients are calculated with bivariate least-squares regression (BLS). The new technique was validated using three simulated and five real data sets. The Monte Carlo method was applied to obtain 100 000 data sets for each of the initial simulated data sets to show the correctness of the new technique. The application of the new technique to five real data sets enables differences to be detected between the results of the joint confidence interval based on the BLS method and the results of the commonly used tests based on ordinary least-squares or weighted least-squares regression.

Surfaces made of entangled networks of single-walled carbon nanotubes (SWCNTs) display a strong adsorption affinity for aromatic hydrocarbons. Adsorption of these compounds onto the walls of SWCNTs changes the electrical characteristics of the SWCNT-solution interface. Using these features, we have developed a potentiometric sensor to detect neutral aromatic species. Specifically, we can detect online aromatic hydrocarbons in industrial coolant water. Our chromatographic results confirm the adsorption of toluene onto the walls of carbon nanotubes, and our impedance spectroscopy data show the change in the double layer capacitance of the carbon nanotube-solution interface upon addition of toluene, thus confirming the proposed sensing mechanism. The sensor showed a toluene concentration dependent EMF response that follows the shape of an adsorption isotherm and displayed an immediate response to the presence of toluene with a detection limit of 2.1 ppm. The sensor does not respond to other nonaromatic hydrocarbons that may coexist with aromatic hydrocarbons in water. It shows a qualitative sensitivity and selectivity of 100% and 83%, respectively, which confirms its ability to detect aromatic hydrocarbons in aqueous solutions. The sensor showed an excellent ability to immediately detect the presence of toluene in actual coolant water. Its operational characteristics, including its fast response, low cost, portability, and easy use in online industrial applications, improve those of current chromatographic or spectroscopic techniques.

A novel concept is proposed for the encapsulation of components within polyacrylate microspheres, prior to their incorporation into a membrane phase. Thus finer and better controlled dispersion of heterogeneous membrane components can be achieved. This concept was verified by using a poly(n-butyl acrylate) membrane-based reference electrode as an example. In this example the proper dispersion of solid constituents of the heterogeneous membrane and prevention of their leakage are both of primary importance. Potassium chloride-loaded poly(n-butyl acrylate) microspheres were prepared and then left in contact with silver nitrate to convert some of the KCl into AgCl. The material obtained was introduced into a poly(n-butyl acrylate) membrane. The reference electrode membranes obtained in this way were characterized with much more stable potential (both in different electrolytes and over time) compared with electrodes prepared by the direct introduction of KCl and AgCl to the membrane.

We describe in detail the different steps involved in the construction of a carbon nanotube field-effect transistor (CNTFET) based on a network of single-walled carbon nanotubes (SWCNTs), which can selectively detect human immunoglobulin G (HIgG). HIgG antibodies, which are strongly adsorbed onto the walls of the SWCNTs, are the basic elements of the recognition layer. The nonspecific binding of proteins or other interferences are avoided by covering the nonadsorbed areas of the SWCNTs with Tween 20. The CNTFET is a reagentless device that does not need labels to detect HIgG.

A facile, solid-contact selective potentiometric aptasensor exploiting a network of single-walled carbon nanotubes (SWCNT) acting as a transducing element is described in this work. The molecular properties of the SWCNT surface have been modified by covalently linking aptamers as biorecognition elements to the carboxylic groups of the SWCNT walls. As a model system to demonstrate the generic application of the approach, a 15-mer thrombin aptamer interacts with thrombin and the affinity interaction gives rise to a direct potentiometric signal that can be easily recorded within 15 s. The dynamic linear range, with a sensitivity of 8.0 mV/log a(Thr) corresponds to the 10(-7)-10(-6) M range of thrombin concentrations, with a limit of detection of 80 nM. The aptasensor displays selectivity against elastase and bovine serum albumin and is easily regenerated by immersion in 2 M NaCl. The aptasensor demonstrates the capacity of direct detection of the recognition event avoiding the use of labels, mediators, or the addition of further reagents or analyte accumulation.

In the present study, we have used carbon nanotube field effect transistors (FET) that have been functionalized with protein G and IgG to detect Aspergillus flavus in contaminated milled rice. The adsorbed protein G on the carbon nanotubes walls enables the IgG anti-Aspergillus antibodies to be well oriented and therefore to display full antigen binding capacity for fungal antigens. A solution of Tween 20 and gelatine was used as an effective blocking agent to prevent the non-specific binding of the antibodies and other moulds and also to protect the transducer against the interferences present in the rice samples. Our FET devices were able to detect at least 10 μg/g of A. flavus in only 30 min. To evaluate the selectivity of our biosensors, Fusarium oxysporum and Penicillium chrysogenum were tested as potential competing moulds for A. flavus. We have proved that our devices are highly selective tools for detecting mycotoxigenic moulds at low concentrations in real samples.

We describe in detail the different steps involved in the construction of a carbon nanotube field-effect transistor (CNTFET) based on a network of single-walled carbon nanotubes (SWCNTs), which can selectively detect human immunoglobulin G (HIgG). HIgG antibodies, which are strongly adsorbed onto the walls of the SWCNTs, are the basic elements of the recognition layer. The nonspecific binding of proteins or other interferences are avoided by covering the nonadsorbed areas of the SWCNTs with Tween 20. The CNTFET is a reagentless device that does not need labels to detect HIgG.

A solid contact ion-selective electrode using for the first time multi-walled carbon nanotubes (MWCNT) for the transducer material was developed for detecting perchlorate in water. To demonstrate the excellent ion-to electron transducer ability of the MWCNTs, a 15 microm thick layer of carboxylated MWCNT was deposited between an acrylic membrane selective to perchlorate ions and a glassy carbon rod used as the substrate and electrical conductor. The electrodes showed a Nernstian response of 57 mV decade(-1) (standard deviation of 3 mV decade(-1) over time and different electrodes) across a wide linear range of 10(-6) to 10(-2) M. The limit of detection was 10(-7.4) M of perchlorate. The response time was less than 10 s for activities higher than 10(-6) M and the intermediate-term potential stability shows a small drift of 0.22 mV h(-1) recorded over 5 hours. The electrode displays a selectivity comparable to liquid-contacted ISEs containing the same membrane.

A new solid-contact ion-selective electrode has been developed for determining choline and derivatives in aqueous solutions. The backbone of this new potentiometric sensor is the conjunction of the cavitand receptor, as the molecular recognition element, and a network of non-carboxylated single-walled carbon nanotubes, acting as a solid transducer material. The octaamide cavitand, a synthetic receptor that is highly selective for biologically important trimethyl alkylammonium cations such as choline, acetylcholine or carnitine, makes the selective determination of these compounds possible for the first time. The guest-host interaction takes place in the acrylate ion-selective membrane of the solid-contact electrode. The sensor was characterized by electrochemical impedance spectroscopy and environmental scanning electron microscopy. The new electrode displays a nearly Nernstian slope (57.3+/-1.0 mV/decade) and very stable behaviour (DeltaE/Deltat=224 muVh(-1)) throughout the dynamic range (10(-5) to 10(-1)M). The limit of detection of 10(-6.4)M and the high selectivities obtained will enable choline and derivatives to be determined in biological samples. Finally, the stability of the electrical potential of the new solid-contact electrode was examined by performing current-reversal chronopotentiometry and the influence of the interfacial water film was evaluated by the potentiometric water layer test.

Porous carbon materials and carbon nanotubes were recently used as solid contacts in ion-selective electrodes (ISE), and the signal transduction mechanism of these carbon-based materials is therefore of great interest. In this work the ion-to-electron transduction mechanism of carbon nanotubes is studied by using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Single-walled carbon nanotubes (SWCNT) are deposited on glassy carbon (GC) disk electrodes by repetitive spraying, resulting in SWCNT layers with thicknesses of 10, 35, and 50 mum. The impedance spectra of these GC/SWCNT electrodes in contact with aqueous electrolyte solution show a very small resistance and a large bulk capacitance that is related to a large effective double layer at the SWCNT/electrolyte interface. Interestingly, the impedance response of GC/SWCNT is very similar to that of poly(3,4-ethylenedioxythiophene) (PEDOT) film electrodes studied earlier under the same experimental conditions. The same equivalent circuit is valid for both types of materials. The reason is that both materials can be described schematically as an asymmetric capacitor where one side is formed by electronic charge (electrons/holes) in the SWCNT wall or along the conjugated polymer chain of PEDOT and the other side is formed by ions (anions/cations) in the solution (or in the ion-selective membrane when used as a solid contact in ISE).

We report a new type of ion-sensitive field effect transistor (ISFET). This type of ISFET incorporates a new architecture, containing a network of single-walled carbon nanotubes (SWCNTs) as the transduction layer, making an external reference electrode unnecessary. To show an example of its application, the SWCNT-based ISFET is able to detect at least 10(-8) M of potassium in water using an ion-selective membrane containing valinomycin.

Mesure des concentrations en mercure, plomb, cadmium, fer, nickel, cuivre et zinc dans les eaux de la zone cotiere des Iles Canaries

Gas detection is important for controlling industrial and vehicle emissions, household security and environmental monitoring. In recent decades many devices have been developed for detecting CO(2), CO, SO(2), O(2), O(3), H(2), Ar, N(2), NH(3), H(2)O and several organic vapours. However, the low selectivity or the high operation temperatures required when most gas sensors are used have prompted the study of new materials and the new properties that come about from using traditional materials in a nanostructured mode. In this paper, we have reviewed the main research studies that have been made of gas sensors that use nanomaterials. The main quality characteristics of these new sensing devices have enabled us to make a critical review of the possible advantages and drawbacks of these nanostructured material-based sensors.

Soft- and hard-modelling strategy was applied to near-infrared spectroscopy data obtained from monitoring the reaction between glycidyloxydimethylphenyl silane, a silicon-based epoxy monomer, and aniline. On the basis of the pure soft-modelling approach and previous chemical knowledge, a kinetic model for the reaction was proposed. Then, multivariate curve resolution-alternating least squares optimization was carried out under a hard constraint, that compels the concentration profiles to fulfil the proposed kinetic model at each iteration of the optimization process. In this way, the concentration profiles of each species and the corresponding kinetic rate constants of the reaction, unpublished until now, were obtained. The results obtained were contrasted with 13C NMR. The joint interval test of slope and intercept for detecting bias was not significant (alpha=5%).

Nanoscience and nanotechnology deal with the study and application of structures of matter of at least one dimension of the order of less than 100 nm (1 nm=one millionth of a millimetre). However, properties related to low dimensions are more important than size. Nanotechnology is based on the fact that some very small structures usually have new properties and behaviour that are not displayed by the bulk matter with the same composition. This overview introduces and discusses the main concepts behind the development of nanosensors and the most relevant applications in the field of environmental analysis. We focus on the effects (many of which are related to the quantum nature) that distinguish nanosensors and give them their particular behaviour. We will review the main types of nanosensors developed to date and highlight the relationship between the property monitored and the type of nanomaterial used. We discuss several nanostructures that are currently used in the development of nanosensors: nanoparticles, nanotubes, nanorods, embedded nanostructures, porous silicon, and self-assembled materials. In each section, we first describe the type of nanomaterial used and explain the properties related to the nanostructure. We then briefly describe the experimental set up and discuss the main advantages and quality parameters of nanosensing devices. Finally, we describe the applications, many of which are in the environmental field.

The proper characterization of a commercial qualitative method for determining aflatoxin B1 in some nuts is described. A qualitative method that provides binary responses of the yes/no type means that the performance parameters have been properly adapted and defined. Performance characteristics such as the cut-off limit, the detection limit, sensitivity, specificity, false-positive and negative rates, and the unreliability or uncertainty region are defined and then estimated by means of the performance characteristic curves. The commercial test kit showed the cut-off limit at 1.6 ng/g, with a sensitivity rate of 95% and a false-negative rate of zero. A modification can be performed to shift the cut-off to 2.0 ng/g, keeping the same values for the sensitivity and false-negative rate.

For determining low levels of pesticides and phenolic compounds in river and wastewater samples by high performance liquid chromatography (HPLC), solid phase extraction (SPE) is commonly used before the chromatographic separation. This preconcentration step is not necessarily selective for the analytes of interest and it may retain other compounds of similar characteristics as well. In this case, we present, humic and fulvic acids caused a large baseline drift and overlapped the analytes to be quantified. The inaccurate determinations of the area of the peaks of these analytes made it difficult to quantify them with univariate calibration. Here we compare three second-order calibration algorithms (generalized rank annihilation method (GRAM), parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least squares (MCR-ALS)) which efficiently solve this problem. These methods use second-order data, i.e., a matrix of responses for each peak, which is easily obtained with a high performance liquid chromatography-diode array detector (HPLC-DAD). With these methods, the area does not need to be directly measured and predictions are more accurate. They also save time and resources because they can quantify analytes even if the peaks are not resolved. GRAM and PARAFAC require trilinear data. Biased and imprecise concentrations (relative standard deviation, %R.S.D. = 34) were obtained without correcting the time-shift. Hence, a time-shift correction algorithm to align the peaks was needed to obtain accurate predictions. MCR-ALS was the most robust to the time-shift. All three algorithms provided similar mean predictions, which were comparable to those obtained when sulfite was added to the samples. However, the predictions for the different replicates were more similar for the second-order algorithms (%R.S.D. = 3) than the ones obtained by univariate calibration after the sulfite addition (%R.S.D. = 13).

In this paper we shall discuss the concept of method validation, describe the various elements and explain its close relationship with fitness for purpose. Method validation is based on the assumption that a series of requirements are fulfilled and we shall explain how these requirements are selected, the way in which evidence is supplied and what work has to be carried out in the laboratory. The basic principles of method validation and the different ways to validate a methodology, by inter-laboratory comparison or performing an in-house validation, are also described.En este artículo se discute el concepto de validación del método, se describen los elementos que la componen y se explica la fuerte relación entre la validación y las características de ajuste. El método de validación se basa en el cumplimiento de una serie de requerimientos, se explica como seleccionar esos requerimientos, la forma en que se suministran evidencias, y que trabajo se debe llevar a cabo en el laboratorio. También se describen, los principios básicos del método de validación y los diferentes caminos para validar una metodología, tanto en la comparación entre laboratorios o como cuando se lleva a cabo una validación dentro del laboratorio.

The bias of an analytical procedure is calculated in the assessment of trueness. If this experimental bias is not significant, we assume that the procedure is unbiased and, consequently, the results obtained with this procedure are not corrected for this bias. However, when assessing trueness there is always a probability of incorrectly concluding that the experimental bias is not significant. Therefore, non-significant experimental bias should be included as a component of uncertainty. In this paper, we have studied if it is always necessary to include this term and which is the best approach to include this bias in the uncertainty budget. To answer these questions, we have used the Monte-Carlo method to simulate the assessment of trueness of biased procedures and the future results these procedures provide. The results show that nonsignificant experimental bias should be included as a component of uncertainty when the uncertainty of this bias represents at least a 30% of the overall uncertainty.

Non-covalent functionalization of multi-walled carbon nanotubes (MWCNTs) by a pyrene based benzo-18-crown-6 ether 1 leads to nanostructure assemblies that play both the role of an ion-to-electron transducer and a selective recognition element in solid-contact ion-selective-electrodes (SC-ISEs). The high loading capacity (36 wt%) as well as the suitable dispersion character of the MWCNT hybrid in the ion-selective membrane (ISM) confirmed the benefit of this approach over the covalent one. The sensor has been applied successfully to the detection of potassium. Nernstian response (56.9 ± 0.9 mV per decade) was obtained (10(-5) and 10(-2) M K(+)) and the selectivity pattern was not altered by the immobilization of the ionophore on the MWCNTs. Leakage of the ionophore from the polymeric matrix is therefore avoided while the sensor construction was simplified and the analytical performances were maintained.

Instrumental approaches to remotely and wirelessly monitoring chemical species are increasingly needed. Together with the electronic developments, efforts to optimize and validate the performance of these new devices are required. In this work, the analytical performance of a recently developed potentiometer-radiofrequency tag connected to ion- selective electrodes is evaluated. This credit card sized and extremely low power consumption device yield results that are comparable to those obtained with more sophisticated, lab-based tools. Advantages such as portability and autonomy, together with unique features, such as the ability to be read through the walls in a closed vessel are demonstrated. Future perspectives opened by this new generation of devices, such as their use in wearable devices and in decentralized settings are discussed.

The synthesis and full characterization of new hybrid materials, i.e. benzo-18-crown-6 covalently linked to multi-wall carbon nanotubes – incorporating first and second generation dendrimer approaches – are reported here. Furthermore, the benefit of using these new nanostructured assemblies is demonstrated for solid-contact ion-selective sensor configuration. Both selective recognition and ion-to-electron transduction events take place at the same hybrid nanostructured material. Functionalization of multi-wall carbon nanotubes (MWCNTs) plays a key role in the development of such devices not only allowing an improvement of the analytical parameters such as selectivity and stability but also providing simplicity in the manufacturing stages.

Five multivariate data analysis methods, namely principal components analysis, non-linear mapping, hierarchical clustering, minimal spanning tree and mode analysis, were used to analyse the natural structure of red wines from Tarragona (Catalonia, Spain). Seventeen parameters were analysed for each sample to determine the multi-dimensional spectrum in which the wines are studied. In spite of the homogeneity of the wine samples which resulted from the similarity of the edaphic, varietal factors as well as the cultural conditions of the producing areas, two different clusters can be distinguished on the basis of all the methods used. The groups of wine samples obtained coincide with two viticultural zones that differ with regard to climate and geographical conditions. Therefore, a natural distinction can bei made between the wines produced in each adjacent zone, forming the basis for future differentiation according to brand name.

In this paper, we describe how to assess the trueness of analytical procedures using spiked samples and regression techniques. We show then how to calculate the uncertainty of future samples using the information generated in this process. Two types of bias are calculated in the assessment of trueness: a proportional bias (normally expressed in terms of recovery) and a constant bias. Since in many cases blank samples are not available, we propose a method for assessing trueness when samples that already contain the native analyte are spiked at several levels of concentration. Only proportional bias can be estimated from spiked samples, while constant bias must be estimated by the Youden method, i.e. by taking different amounts of sample. We propose two ways of assessing trueness. The first expresses results as instrumental responses and the second expresses results as concentration. We then present expressions for calculating uncertainty that cover these two situations. Finally, we use the expressions to calculate uncertainty from validation data of the analysis of esters in wine by SPME-GC.