Multivariate data analysis of quality parameters in drinking water

The quality of water destined for human consumption has been treated as a multivariate property. Since most of the quality parameters are obtained by applying analytical methods, the routine analytical laboratory (responsible for the accuracy of analytical data) has been treated as a process system for water quality estimation. Multivariate tools, based on principal component analysis (PCA) and partial least squares (PLS) regression, are used in the present paper to: (i) study the main factors of the latent data structure and (ii) characterize the water samples and the analytical methods in terms of multivariate quality control (MQC). Such tools could warn of both possible health risks related to anomalous sample composition and failures in the analytical methods.     

Techniques in chemometrical modeling for micro-pollutants assessment

Summary Chemometrics have been described as effective tools for exploring chemical data, and many software packages are now available on micro-computers. This work evaluate their suitability for environmental analytical chemistry. Guidelines for multivariate method selection are proposed. They are based on the type of variables and the goal of the study. Two examples are proposed to illustrate these methods and their efficiency. Firstly it is shown that a global assessment of Rhine basin mercuric pollution in the Alsace region is possible with Multiple Correspondence Factor Analysis. Several goals are simultaneously reached: a mapping of pollution, a detection of pollution changes during the study period and an evaluation of bio-accumulation as a function of fish species. Secondly the modeling of industrial soil pollution by heavy metals is studied by Multiple Linear Regression. It demonstrates that chemometrics provide us with necessary tools for environmental analytical chemistry but also for toxicological studies or ecosystem modeling.     

Screening Brazilian commercial gasoline quality by hydrogen nuclear magnetic resonance spectroscopic fingerprintings and pattern-recognition multivariate chemometric analysis

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance (¹H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the ¹H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.     

Interpretation of geochemical data by the use of multivariate data analysis

Different strategies of multivariate data analysis are used to interpret a data base from geological samples. Cluster and correspondence analysis are applied to classify properly 34 chemical elements from 10 representative rock samples (volcanic series from Borovitsa, Rhodopa mountains, Bulgaria). Principal components analysis is also used as display method to visualize the relation between the variables and objects of interest. The multivariate data analysis applied makes it possible to interpret the origin and orogenesis of the samples.     

Element analysis of inhomogeneous samples by laser ablation ICP mass spectrometry

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been used for the determination of selected elements in heterogeneous powdered soils. The precision of the method essentially depends on the homogeneity of the samples. Therefore the influence of sample inhomogeneity on the precision of analytical results has been checked. The statistical evaluation was carried out using a correlation function. In the context of the described experiments, it is necessary to analyze a volume of about 20 mm³ in order to obtain reliable information on the bulk composition of the heterogeneous samples. This corresponds to about 50 craters, each with a depth of 1 mm. The results can be used to calculate certain analytical parameters, particularly the sample homogeneity, depending on the necessary analytical volume and the representative mass for the quantitative analysis under previously defined conditions. In addition to these calculations, analytical experiments have been carried out which confirm the applicability of this approach.     

A review of multivariate calibration methods applied to biomedical analysis

The determination of the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples is usually performed by using high-performance liquid chromatography (HPLC). Modern multivariate calibration methods constitute an attractive alternative, even when they are applied to intrinsically unselective spectroscopic or electrochemical signals. First-order (i.e., vectorized) data are conveniently analyzed with classical chemometric tools such as partial least-squares (PLS). Certain analytical problems require more sophisticated models, such as artificial neural networks (ANNs), which are especially able to cope with non-linearities in the data structure. Finally, models based on the acquisition and processing of second- or higher-order data (i.e., matrices or higher dimensional data arrays) present the phenomenon known as “second-order advantage”, which permits quantitation of calibrated analytes in the presence of interferents. The latter models show immense potentialities in the field of biomedical analysis. Pertinent literature examples are reviewed.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives

One of the most important physicochemical parameters of a molecule that determines its bioactivity is its lipophilicity. Cluster analysis (CA), principal component analysis (PCA), and sum of ranking differences (SRD) were used to compare the lipophilic parameters of twenty phenylacetamide derivatives, obtained experimentally as chromatographic retention data in the presence of different solvents and calculated by different mathematical methods. All the applied methods of multivariate analysis gave approximately similar grouping of the studied lipophilic parameters. In the attempt to group the investigated compounds in respect of their lipophilicity, the obtained results appeared to be dependent on the applied chemometric method. The CA and PCA, grouped the compounds on the basis of the nature of the substituents R₁ and R₂, indicating that they determine to a great extent the lipophilicity of the investigated molecules. Unlike them, the SRD method could not be used to group the studied compounds on the basis of their lipophilic character.     

Development of caprine liver quality control materials for trace element analysis of biological tissues

Certified reference materials (CRMs) are used in analytical chemistry for method validation studies in order to establish measurement accuracy, traceability, and long-term stability throughout repeated analyses. Quality control (QC) during routine analysis requires access to stable materials appropriate for the sample matrix being analyzed. However, it may be difficult to find representative, low-cost QC materials, especially for specific analytes in biological tissue matrices. Here, four caprine liver pools are prepared for use as internal QC materials for trace element measurements in biological tissue. Analytes of interest include essential and nonessential trace elements and the lanthanide series elements. The suitability of caprine liver to serve as a secondary reference material (RM), as well as for routine QC purposes, is demonstrated through homogeneity and stability measurements, and the acquisition of precision and uncertainty data. Traceability is established for selected analytes for which available CRMs can provide an unbroken chain of calibrations.     

Multivariate analysis of matrix-assisted laser desorption/ionization mass spectrometric data related to glycoxidation products of human globins in nephropathic patients

To clarify the possible pathogenetic role of oxidation products originated from the glycation of proteins, human globins from nephropathic patients have been studied by matrix-assisted laser desorption/ionization mass spectrometry (MALDI), revealing not only unglycated and monoglycated globins, but also a series of different species. For the last ones, structural assignments were tentatively done on the basis of observed masses and expectations for the Maillard reaction pattern. Consequently, they must be considered only propositive, and the discussion which will follow must be considered in this view. In our opinion this approach does not seem to compromise the intended diagnostic use of the data because distinctions are valid even if the assignments are uncertain. We studied nine healthy subjects and 19 nephropathic patients and processed the data obtained from the MALDI spectra using a multivariate analysis. Our results showed that multivariate analytical techniques enable differential aspects of the profile of molecular species to be identified in the blood of end stage nephropathic patients. A correct grouping can be achieved by principal component analysis (PCA) and the results suggest that several products involved in carbonyl stress exist in nephropathic patients. These compounds may have a relevant role as specific markers of the pathological state.     

Multivariate processing of atomic-force microscopy images for detection of the response of plasticized polymeric membranes

The possibility of using the atomic-force microscopy as a method for detection of the analytical signal from plasticized polymeric sensor membranes was analyzed. The surfaces of cadmium-selective membranes based on two polymeric matrices were examined. The digital images were processed with multivariate image analysis techniques. A correlation was found between the surface profile of an ion-selective membrane and the concentration of the ion in solution.     

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied.     

Trends in product analysis for research in the pharmaceutical industry

Characterization of substances and formulations with respect to purity and stability is a major analytical chemical task during product development in the pharmaceutical industry. The importance of separation methods, especially liquid chromatography, is shown with some examples from work with omeprazole. The role of mass spectrometry (MS) for structure elucidation is emphasized. Dissolution testing comprises a large number of samples and adaptation to standardized procedures is important. Work is exemplified with dissolution testing of felodipine. Automation and robots with multivariate calibration are necessary approaches. Macromolecular characterization is exemplified from the fields of biomolecules and macromolecular excipients. Separation methods and MS are important tools. New challenges in the area of process analytical chemistry are discussed and chemometrics will play an important role. In all fields method validation is stressed, but should not require more power than necessary in a specification.     

Reference material for improving the quality of nutritional composition data for foods

Summary Analytical values for the nutritional composition of foods are required for a range of purposes, in particular, for the construction of nutritional data bases. These provide the basis for estimating nutrient intakes in population groups, and for the nutritional labelling of foods. Both of these uses requires analytical values that accurately represent the composition of the foods. The requisite quality can only be achieved by the institution of a sound quality assurance programme in which reference materials have a vital role.The special features of nutritional analyses and their control with reference materials are reviewed and the criteria for the production of these reference materials discussed.     

Three-way Principal Components Analysis for fluorescence spectroscopic classification of algae species

Excitation-emission matrices (EEM) from fluorescence spectroscopy may contain characteristic information about different algae species. As a result of measurements, one gets a whole stack of EEMs each of them corresponding to one species. Such a stack of matrices has to be understood as a cubic data array spanned by the dimensions ‘excitation’, ‘emission’ and ‘species’. The interpretation of higher dimensional data arrays requires efficient tools from multivariate data analysis. In this paper, it is illustrated how Three-way Principal Components Analysis as the appropriate generalization of conventional Principal Components Analysis may serve as a powerful method for classification of algae species. Received: 31 May 1996 / Revised: 1 July 1996 / Accepted: 4 Juli 1996     

Three-way Principal Components Analysis for fluorescence spectroscopic classification of algae species

Excitation-emission matrices (EEM) from fluorescence spectroscopy may contain characteristic information about different algae species. As a result of measurements, one gets a whole stack of EEMs each of them corresponding to one species. Such a stack of matrices has to be understood as a cubic data array spanned by the dimensions ‘excitation’, ‘emission’ and ‘species’. The interpretation of higher dimensional data arrays requires efficient tools from multivariate data analysis. In this paper, it is illustrated how Three-way Principal Components Analysis as the appropriate generalization of conventional Principal Components Analysis may serve as a powerful method for classification of algae species. Received: 31 May 1996 / Revised: 1 July 1996 / Accepted: 4 Juli 1996     

A coarse graining method for the identification of transition rates between molecular conformations

The coarse graining method to be advocated in this paper consists of two main steps. First, the propagation of an ensemble of molecular states is described as a Markov chain by a transition probability matrix in a finite state space. Second, we obtain metastable conformations by an aggregation of variables via Robust Perron Cluster Analysis (PCCA+). Up to now, it has been an open question as to how this coarse graining in space can be transformed to a coarse graining of the Markov chain while preserving the essential dynamic information. In this article, we construct a coarse matrix that is the correct propagator in the space of conformations. This coarse graining procedure carries over to rate matrices and allows to extract transition rates between molecular conformations. This approach is based on the fact that PCCA+ computes molecular conformations as linear combinations of the dominant eigenvectors of the transition matrix.     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

Investigations of the chemical homogeneity of solids

Chemical homogeneity is a relative property of solid-state materials; it depends not only on their ultimate purposes, but also on the parameters of the analytical procedure applied in the investigation. These features are often given inadequate attention in the characterization of solid materials. A statistical model for representative sampling is used here as the starting point to derive relationships which demonstrate quantitatively the increasing rigour of the homogeneity test with increasing spatial resolution and precision of the analytical procedure. It is also shown that chemical homogeneity can be confirmed at a calculated risk, when the tests do not show the material to be significantly inhomogeneous and when maximum permissible deviations in concentration can be given for a practical application.     

Sulfation Patterns of Saccharides and Heavy Metal Ion Binding

Sulfated saccharides are an essential part of extracellular matrices, and they are involved in a large number of interactions. Sulfated saccharide matrices in organisms accumulate heavy metal ions in addition to other essential metal ions. Accumulation of heavy metal ions alters the function of the organisms and cells, resulting in severe and irreversible damage. The effect of the sulfation pattern of saccharides on heavy metal binding preferences is enigmatic because the accessibility to structurally defined sulfated saccharides is limited and because standard analytical techniques cannot be used to quantify these interactions. We developed a new strategy that combines enzymatic and chemical synthesis with surface chemistry and label-free electrochemical sensing to study the interactions between well-defined sulfated saccharides and heavy metal ions. By using these tools we showed that the sulfation pattern of hyaluronic acid governs their heavy metal ions binding preferences.