Problems In Analytical Chemistry Research Articles

Least-squares support vector machines for simultaneous voltammetric determination of lead and tin: A comparison between LS-SVM and PLS in voltammetric data

A differential pulse stripping method for the simultaneous determination of lead and tin is proposed. The procedure involves accumulation with dissolving of lead and tin on a hanging mercury drop electrode (HMDE), followed by oxidation of dissolve lead and tin by voltammetric scan using differential pulse modulation. The optimum experimental conditions are: 0.2 mol L −1 HNO 3, accumulation potential of −900 mV versus Ag/AgCl, accumulation time of 200 s, scan rate of 20 mV s −1 and pulse height of 80 mV. Lead and tin peak currents were observed in the same potential region at about −400 mV. The simultaneous determination of lead and tin by using voltammetry is a difficult problem in analytical chemistry, due to voltammogram interferences. The resolution of mixture of lead and tin by the application of least-squares support vector machines (LS-SVM) was performed. The linear dynamic ranges were 0.003–0.35 and 0.008–0.50 μg mL −1 and detection limits were 1 and 3 ng mL −1 for lead and tin, respectively. The capability of the method for the analysis of real samples was evaluated by the determination of lead and tin in real matrix samples with satisfactory results.

Simultaneous spectrophotometric determination of cobalt, copper and nickel using 1-(2-thiazolylazo)-2-naphthol by chemometrics methods

The simultaneous determination of cobalt, copper and nickel using 1-(2-thiazolylazo)-2-naphthol (first figure of this article) by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS) regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 550–750-nm range for 21 different mixtures of cobalt, copper and nickel. Calibration matrices were formed from samples containing 0.05–1.05, 0.05–1.30 and 0.05–0.80 μg mL −1 for cobalt, copper and nickel, respectively. The root mean square error of prediction (RMSEP) for cobalt, copper and nickel with OSC and without OSC were 0.007, 0.008, 0.011 and 0.031, 0.037, 0.032 μg mL −1, respectively. This procedure allows the simultaneous determination of cobalt, copper and nickel in synthetic and real samples and good reliability of the determination was proved.

Simultaneous Voltammetric Determination of Lead and Tin by Adsorptive Differential Pulse Stripping Method and Orthogonal Signal Correction‐Partial Least Squares in Water Samples

AbstractAn adsorptive differential pulse stripping method for the simultaneous determination of lead and tin is proposed. The procedure involves an adsorptive accumulation of lead and tin on a hanging mercury drop electrode (HMDE), followed by oxidation of adsorbed lead and tin by voltammetric scan using differential pulse modulation. The optimum experimental conditions are: 0.2 mol L−1 HNO3, accumulation potential of −900 mV versus Ag/AgCl, accumulation time of 200 s, scan rate of 20 mV s−1 and pulse height of 80 mV. Lead and tin peak currents were observed in the same potential region at about −400 mV. The simultaneous determination of lead and tin by using voltammetry is a difficult problem in analytical chemistry, due to voltammogram interferences. The resolution of a mixture of lead and tin by the application of orthogonal signal correction‐partial least squares (OSC‐PLS) was performed. The linear dynamic ranges were 0.003‐0.35 and 0.008‐0.50 μg mL−1 and detection limits were land 3 ng mL−1 for lead and tin, respectively. The RMSEP for lead and tin with OSC and without OSC were 2.8737, 6.0557 and 8.0941, 9.5151, respectively. The capability of the method for the analysis of real samples was evaluated by the determination of lead and tin in water samples with satisfactory results.

A comparative study between least-squares support vector machines and partial least squares in simultaneous spectrophotometric determination of cypermethrin, permethrin and tetramethrin

Least-squares support vector machines (LS-SVM) and partial least squares (PLS) have received considerable attention in the chemometrics for multicomponent analysis. A comparison was made between LS-SVM and PLS methods by applying them to simultaneous spectrophotometric determination of cypermethrin, permethrin and tetramethrin. The UV absorbance spectra of the methanolic solutions of the analytes were measured in the concentration ranges, 0.1-12.9, 0.1-24.9 and 0.1-13.8 µg mL-1 for cypermethrin, permethrin and tetramethrin, respectively. The simultaneous determination of cypermetrin, permetrin and tetrametrin by UV spectrophotometry is a difficult problem in analytical chemistry, due to spectra interferences. The resolution of mixture of cypermetrin, permetrin and tetrametrin by chemometrics methods was performed. The root means square error of prediction (RMSEP) for cypermetrin, permetrin and tetrametrin by PLS and LS-SVM models were 0.1629, 0.2828, 0.1984 and 0.0766, 0.0907, 0.0467, respectively. The capability of the method for the analysis of synthesis and real matrix samples were evaluated by the determination of cypermethrin, permethrin and tetramethrin with satisfactory results. The resultant data explained that LS-SVM produced better results than PLS.

Orthogonal signal correction–partial least squares method for simultaneous spectrophotometric determination of cypermethrin and tetramethrin

The simultaneous determination of cypermethrin and tetramethrin mixtures by using spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS) regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200–350 nm range for 25 different mixtures of cypermethrin and tetramethrin. Calibration matrices were containing 0.1–12.9 and 0.1–13.8 μg mL −1 for cypermethrin and tetramethrin, respectively. The RMSEP for cypermethrin and tetramethrin with OSC and without OSC were 0.0884, 0.0614 and 0.2915, 0.2309, respectively. This procedure allows the simultaneous determination of cypermethrin and tetramethrin in synthetic and real samples good reliability of the determination was proved.

Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines

An adsorptive differential pulse stripping method for the simultaneous determination of morphine and noscapine is proposed. The procedure involves an adsorptive accumulation of morphine and noscapine on a hanging mercury drop electrode (HMDE), followed by oxidation of adsorbed morphine and noscapine by voltammetric scan using differential pulse modulation. The optimum experimental conditions are: pH 10.0, accumulation potential of −100 mV versus Ag/AgCl, accumulation time of 150 s, scan rate of 40 mV s −1 and pulse height of 100 mV. Morphine and noscapine peak currents were observed in same potential region at about +0.25 V. The simultaneous determination of morphine and noscapine by using voltammetry is a difficult problem in analytical chemistry, due to voltammogram interferences. The resolution of mixture of morphine and noscapine by the application of least-squares support vector machines (LS-SVM) was performed. The linear dynamic ranges were 0.01–3.10 and 0.015–2.75 μg mL −1 and detection limits were 3 and 7 ng mL −1 for morphine and noscapine, respectively. The capability of the method for the analysis of real samples was evaluated by the determination of morphine and noscapine in addict's human plasma with satisfactory results.

Spectrophotometric Simultaneous Determination of Manganese(II) and Iron(II) in Pharmaceutical by Orthogonal Signal Correction‐Partial Least Squares

The simultaneous determination of manganese(II) and iron(II) mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used to remove the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 450-600 nm range for 21 different mixtures of manganese(II) and iron(II). Calibration matrices were containing 0.05-1.2 and 0.1-2.3 microg mL(-1) Mn(II) and Fe(II), respectively. The RMSEP for manganese(II) and iron(II) with OSC and without OSC were 0.0316, 0.0291, and 0.0907, 0.115, respectively. This procedure allows the simultaneous determination of manganese(II) and iron(II) in synthetic and real matrix samples with good reliability of the determination.

Quantitative study of the structure–retention index relationship in the imine family

The Kováts retention index is one of the most popular descriptors of the performance of organic compounds in gas chromatography (GC). The mathematical modeling of this index is an interesting and open problem in analytical chemistry. In this paper, two models for the prediction of the Kováts retention index are presented. Topologic, topographic and quantum-chemical descriptors were used as structural descriptors. Multiple linear regression (MLR) analysis provides the first model using the forward stepwise procedure for the variable selection. For the second one, an ensemble of artificial neural network (ANN) was constructed using the pruning algorithm. Both methods were validated by an external set of compounds, by the Golbraikh and Tropsha method and by the leave-one-out (LOO) and the leave many out (LMO) procedures. The R 2, RMScv and Q 2, values for the training sets were 0.884, 0.589 and 0.830 for NN and 0.974, 0.417 and 0.970 for MLR models, respectively. The robustness of both models was demonstrated. Both portrait the chromatographic performance of the sample but in this case, the results of MLR equation are better than the NN ones. The MLR model is recommended because of its simplicity.

Determination of complex isotopomer patterns in isotopically labeled compounds by mass spectrometry.

A classic problem in analytical chemistry has been determination of individual components in a mixture without availability of the pure individual components. Measurement of the distribution of isotopomers in a labeled compound or mixture of labeled compounds is an example of this problem that is commonly encountered when stable isotopically labeled metabolites are used to determine in vivo kinetics and metabolism. We present a method that uses the measured mass spectral data of the unlabeled material to represent any and all combinations of isotopomer variations of that material and to determine abundances of these isotopomers. Although examples of the method are presented for gas chromatography/mass spectrometry, the method is applicable to any type of mass spectrometry data. The method also accounts for errors induced by mass spectrometer ionization and resolution effects. To demonstrate this method, we determined the isotopomer distributions of samples of 13C-labeled leucine and glucose for both highly enriched isotopomers and labeled isotopomers present in low abundance against a natural isotopic abundance background. The method accurately and precisely determined isotopomer identity and abundance in the labeled materials without adding noise or error that was not inherent in the original mass spectral data. In examples shown here, isotopomer uncertainties were calculated with relative standard errors of <1% from good quality mass spectral data.

A new maximum entropy‐based method for deconvolution of spectra with heteroscedastic noise

AbstractBroadening of spectral lines combined with large and heteroscedastic noise contributions constitutes an important problem in analytical chemistry. Reduced interpretability and artefacts in further data analysis make deconvolution methods necessary. A new robust deconvolution method (RHEMEM) based on the principle of maximum entropy is proposed in order to effectively handle the presence of heteroscedastic noise. Other deconvolution methods such as Jansson's method, Fourier self‐deconvolution and LOMEP are also studied with respect to their ability to handle heteroscedastic noise. A systematic simulation study is used to compare the performance of the new method with the reference methods. They are evaluated according to reconstruction performance, robustness and the ability to work without manual input. Copyright © 2005 John Wiley &amp; Sons, Ltd.

Spectrophotometric simultaneous determination of nitroaniline isomers by orthogonal signal correction–partial least squares

The simultaneous determination of nitroaniline isomer mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removes the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200–500 nm range for 21 different mixtures of nitroaniline isomers. Calibration matrices were containing 1–21, 1–15 and 1–18 μg ml −1 of m-nitroaniline, o-nitroaniline and p-nitroaniline, respectively. The RMSEP for m-nitroaniline, o-nitroaniline and p-nitroaniline with OSC and without OSC were 0.6567, 0.2692, and 0.3134, and 1.3818, 1.2181, and 0.3953, respectively. This procedure allows the simultaneous determination of nitroaniline isomers in real matrix samples and good reliability of the determination was proved.

Simulation of Asymmetric Peak-Shaped Analytical Signals by the Frame Representation of Their Shape Using Stripping Voltammetry as an Example

To simulate peak-shaped analytical signals, the shape characteristics and geometrical properties of four asymmetric basic functions (the derivative of the logistic function, the Gaussian function, the Cauchy function modified by Fraser and Suzuki, and the Poisson function with the Stromberg correction) are systematically studied. The frame representation, which was proposed earlier by the two authors of this paper, is used to characterize the peak shape. Some regularities are found in peak properties expressed in terms of the frame parameters and considered as functions of the coefficient of skewness b. Peak shapes are compared using two approaches to obtaining dimensionless frame parameters: one using relative parameters and another using parameters of the unit frame. It is shown that the frame representation of the peak shape can be used in the phenomenological simulation of metal peaks in stripping voltammetry. The practical use of information on the shape of the analytical signal in the solution of various problems of analytical chemistry is discussed.

The effect of influential data, model and method on the precision of univariate calibration

Building a calibration model with detection and quantification capabilities is identical to the task of building a regression model. Although commonly used by analysts, an application of the calibration model requires at first careful attention to the three components of the regression triplet (data, model, method), examining (a) the data quality of the proposed model; (b) the model quality; (c) the LS method to be used or a fulfillment of all least-squares assumptions. This paper summarizes these components, describes the effects of deviations from assumptions and considers the correction of such deviations: identifying influential points is the first step in least-squares model building, the calibration task depends on the regression model used, and finally the least squares LS method is based on assumptions of normality of errors, homoscedasticity, independence of errors, overly influential data points and independent variables being subject to error. When some assumptions are violated, the ordinary LS is inconvenient and robust M-estimates with the iterative method of reweighted least-squares must be used. The effects of influential points, heteroscedasticity and non-normality on the calibration precision limits are also elucidated. This paper also considers the proper construction of the statistical uncertainty expressed as confidence limits predicting an unknown concentration (or amount) value, and its dependence on the regression triplet. The authors’ objectives were to provide a thorough treatment that includes pertinent references, consistent nomeclature, and related mathematical formulae to show by theory and illustrative examples those approaches best suited to typical problems in analytical chemistry. Two new algorithms, calibration and linear regression written in s-plus and enabling regression triplet analysis, the estimation of calibration precision limits, critical levels, detection limits and quantification limits with the statistical uncertainty of unknown concentrations, form the goal of this paper.

Review of books of problems in analytical chemistry for higher educational institutions

The structure and content of main Russian books of problems in analytical chemistry were analyzed. It was revealed that they incompletely correspond to the standard curriculum (not enough problems on instrumental methods, separation, and preconcentration), to the content of main textbooks, and to conventional didactic requirements (lack or small number of complex or nonstandard problems). The mathematical tools known to students are insufficiently employed. Recommendations are given on the use of previously published books of problems and on the development of new ones.

Predominance zone diagrams and their application to solvent extraction techniques

Predominance zone diagrams have been useful tools in solving problems in analytical chemistry. They can be used to establish the best conditions for separation of mixtures or to optimize recovery procedures for a given species. The few reports on predominance zone diagrams for the participant species in liquid–liquid extraction systems, describe their construction as diagrams of the Pourbaix type (ε/pH). With the generalized species and equilibria method (GSEM) it is possible to elaborate Predominance zone diagrams for extraction (PZDE) in proper spaces and with parameters strictly related to these processes such as pH and the volume ratio, r. Therefore, using the GSEM, PZDE that allow us to determine the best conditions for the extraction of a given substance have been elaborated. The stoichiometry of the species been extracted can also be determined from the experimental conditions. It has been demonstrated that with the GSEM, PZDE can be constructed for systems of one and two components. In this work, we intend to demonstrate that the algorithm is valid for the elaboration of PZDE in systems of three and four components. Examples of analytical interest are presented such as lead (II) extraction with diphenyltiocarbazone (dithizone) and that for cadmium (II) with 8-hydroxyquinolein (oxine) in chloroform. The influence of a masking agent, the etilendiaminotetraacetic acid (EDTA) over the extraction of both metals was also assessed.

Spectrophotometric determination of organic dye mixtures by using multivariate calibration

The simultaneous determination of organic dye mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By using multivariate calibration methods such as partial least-squares regression (PLSR), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration stage. In this study, the calibration model is based on absorption spectra in the 350–650-nm range for a set of 16 different mixtures of reactive red 195, reactive yellow 145 and reactive orange 122 dyes, and made the determination of the dye concentrations possible in a validation set with significantly greater accuracy than the conventional univariate calibration method. By using the developed model it was possible to monitor the decolorization kinetic of one dye (reactive orange 122), when the mixture of the three dyes was previously submitted to an ozonation process.

Pre-concentration of rare earths using silica gel loaded with 1-(2-pyridylazo)-2-naphthol (PAN) and determination by energy dispersive X-ray fluorescence

The determination of a single rare earth element in a mixture with other species of this family is a very challenging problem in analytical chemistry due to the close similarity of their chemical properties. In this work, a liquid–solid extraction procedure for praseodymium, neodymium, samarium and yttrium mixtures and subsequent determination by energy dispersive X-ray fluorescence spectrometry is described. The pre-concentration procedure, which involves the use of silica modified with 1-(2-pyridylazo)-2-naphthol, permits complete recovery of the rare earths and significant sensitivity enhancement in comparison with direct determination in the aqueous phase. Determinations in quaternary mixtures show typical precisions and accuracies of 3% and 5%, respectively.

Aspects of network training and validation on noisy data: Part 1. Training aspects

Multi-layered feed-forward (MLF) neural networks are commonly trained by the generalized Delta learning rule. The training method has shown to be robust and easy to implement for various chemical problems in analytical chemistry. However, the slow and unpredictable learning behavior puts considerable limitations to the interpretation and validation of neural network models. In this two-part paper, both training and validation aspects are addressed. The first part of this paper focuses to the generalized Delta learning rule and some important aspects of network training. It is shown that the use of quasi-Newton training on unfolded MLF networks, accelerates the training time to an order of magnitude. The learning behavior of MLF-networks trained by the generalised Delta rule and the quasi-Newton learning rule are compared by means of simulated and real data from analytical chemistry. Some conclusions about the general applicability of the discussed methods are drawn.

Chemometric alternatives for resolution of classical analytical problems: Spectrophotometric determination of lanthanide mixtures

The simultaneous determination of lanthanide family elements is one of the greatest problems in analytical chemistry, due to the close similarity of their chemical properties. Spectrophotometric methods are generally of limited use, due to the various mutual spectral interferences involved. By using multivariate calibration methods (partial least-squares regression, PLSR), it was possible to obtain a model that adjusts itself perfectly to the values of the mixture concentrations used in the calibration. The model used absorption spectra in the 290–800 nm range for a set of 20 different mixtures of Ce, Pr, Nd and Sm, and made possible the determination of Ce, Pr and Nd concentrations of a commercial rare-earth product, with significantly greater precision than the conventional univariate calibration method. Determination of the Sm concentrations was not possible, since its concentration was below the concentrations used in the model definition.

Decision‐theoretical formulation of thecalibration problem

The choice of calibration policy is of basic importance in analytical chemistry. A prototype of the practical calibration problem is formulated as a mathematical task and a Bayesian solution of the resulting decision problem is presented. The optimum feedback calibration policy can then be found by dynamic programming. The underlying parameter estimation and filtering are solved by updating relevant conditional distributions. In this way: the necessary information is specified (for instance, the need for knowledge of the probability distribution of unknown samples is clearly recognized as the conceptually unavoidable informational source); the relationship of the information gained from a calibration experiment to the ultimate goal of calibration, i.e., to the estimation of unknown samples, is explained; an ideal solution is given which can serve for comparing various ways of calibration; and a consistent and conceptually simple guideline is given for using decision theory when solving problems of analytical chemistry containing uncertain data. The abstract formulation is systematically illustrated by an example taken from gas chromatography.