Simultaneous Quantitative Determination of Cadmium,Lead, and Copper on Carbon‐Ink Screen‐Printed Electrodes by Differential Pulse Anodic Stripping Voltammetry and Partial Least Squares Regression

Water is a vital commodity for every living entity on the planet. However, water resources are threatened by various sources of contamination from pesticides, hydrocarbons and heavy metals. This has resulted in the development of concepts and technologies to create a basis for provision of safe and high quality drinking water. This paper focuses on the simultaneous quantitative determination of three common contaminants, the heavy metals cadmium, lead and copper. Multivariate calibration was applied to voltammograms acquired on in‐house printed carbon‐ink screen‐printed electrodes by the highly sensitive electrochemical method of differential pulse anodic stripping voltammetry (DPASV). The statistically inspired modification of partial least squares (SIMPLS) algorithm was employed to effect the multivariate calibration. The application of data pretreatment techniques involving range‐scaling, mean‐centering, weighting of variables and the effects of peak realignment are also investigated. It was found that peak realignment in conjunction with weighting and SIMPLS led to the better overall root mean square error of prediction (RMSEP) value. This work represents significant progress in the development of multivariate calibration tools in conjunction with analytical techniques for water quality determination. It is the first time that multivariate calibration has been performed on DPASV voltammograms acquired on carbon‐ink screen‐printed electrodes.     

Interpreting Analytical Chemistry Data: Recent Advances in Curve Resolution with the Aid of Chemometrics

This overview summarizes the application and impact of chemometrics on the extraction and interpretation of analytical data with the use of curve resolution methods from about 2005 onward. The development and usage of well-known and novel chemometric methods have been described and approximately 85 papers have been referenced. Many suggested improvements to some well-known methods, for example, multivariate curve resolution, have been noted as well as the growing software for such methods. Also, these high dimensional resolution methods have found significant application and, arguably, have opened up a new perspective in calibration, that is, extraction of otherwise unobtainable analytical information from strongly overlapping profiles in the presence of interferences. Recent literature suggests that the use of chemometric methods in analytical chemistry for data extraction and interpretation provides indispensable tools for multivariate data processing and extraction of hidden information, which otherwise would be difficult to obtain.     

Photochemistry and chemometrics—An overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics.To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics’ interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided.     

Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products

Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils.     

Simultaneous spectrophotometric determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric methods

In this study, the simultaneous determination of paracetamol, ibuprofen and caffeine in pharmaceuticals by chemometric approaches using UV spectrophotometry has been reported as a simple alternative to using separate models for each component. Spectra of paracetamol, ibuprofen and caffeine were recorded at several concentrations within their linear ranges and were used to compute the calibration mixture between wavelengths 200 and 400 nm at an interval of 1 nm in methanol:0.1 HCl (3:1). Partial least squares regression (PLS), genetic algorithm coupled with PLS (GA-PLS), and principal component-artificial neural network (PC-ANN) were used for chemometric analysis of data and the parameters of the chemometric procedures were optimized. The analytical performances of these chemometric methods were characterized by relative prediction errors and recoveries (%) and were compared with each other. The GA-PLS shows superiority over other applied multivariate methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity. Although the components show an important degree of spectral overlap, they have been determined simultaneously and rapidly requiring no separation step. These three methods were successfully applied to pharmaceutical formulation, capsule, with no interference from excipients as indicated by the recovery study results. The proposed methods are simple and rapid and can be easily used in the quality control of drugs as alternative analysis tools.     

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.     

Interpretation of analytical chemical information by pattern recognition methods-a survey

Since the late sixties, pattern recognition techniques have been used by analytical chemists to facilitate the interpretation of multivariate analytical information. Most research within the field has focused on adapting pattern recognition methods to chemical data. This has been necessary since chemical data are often complicated by the fact that distributions are unknown. Through the first decade of chemical pattern recognition, promising results have been obtained even though the data sets studied have frequently been rather small for statistical analysis. The past few years have shown that an increasing number of analytical chemists are interested in the sheer utility of pattern recognition. This can be taken as a valid sign of a useful approach. The present communication surveys this development. Those methods which have proved most useful for analytical chemical data are described in some detail, and applications within the various fields of analytical chemistry are reviewed.     

Multivariate calibration: What is in chemometrics for the analytical chemist?

Chemometrics has been used for some 30 years but there is still need for disseminating the potential benefits to a wider audience. In this paper, we claim that proper analytical chemistry (1) must in fact incorporate a chemometric approach and (2) that there are several significant advantages of doing so. In order to explain this, an indirect route will be taken, where the most important benefits of chemometric methods are discussed using small illustrative examples. Emphasis will be on multivariate data analysis (for example calibration), whereas other parts of chemometrics such as experimental design will not be treated here. Four distinct aspects are treated in detail: noise reduction; handling of interferents; the exploratory aspect and the possible outlier control. Additionally, some new developments in chemometrics are described.     

Chemometrics and food chemistry: data validation

Chemometrics has often been applied in food chemistry to cluster and classify samples or to produce models for food quality. In recent years, data on food composition have become important for public health protection and food trades. The quality of the available chemical information on foods is a problem; data obtained with the newer analytical methods is scarce and general knowledge about food composition is poor, judged by published tables on food composition. Moreover, agreement between results obtained by different analytical methods is very poor. To overcome this critical problem, several countries have decided to create data banks on food composition. The analytical results to be stored must be validated. Chemometric modelling is useful for this purpose. Interlaboratory studies allow standardization of methods and the preparation of food reference materials. The classical computation of repeatability and reproducibility does not extract all the available information so that a multivariate approach is necessary to improve the quality of a data bank on food composition.     

Challenges in preparing water-matrix reference materials for PAHs and pesticides: examples from SWIFT-WFD proficiency-testing schemes

In accordance with Article 8 of the European Union (EU) Water Framework Directive (WFD), EU Member States are required to establish monitoring programs for the quality of the surface water and the groundwater within each river-basin district. As such data are the basis for regulatory decisions and measures required to achieve WFD environmental objectives, appropriate analytical quality-assurance and quality-control tools have to be implemented by the monitoring laboratories. In this respect, reference materials (RMs) play a key role. Within the framework of the SWIFT-WFD project (Screening methods for Water data InFormation in support of the WFD), several approaches to the preparation of matrix RMs for the analysis of polycyclic aromatic hydrocarbons and pesticides in water have been used in wide-scale proficiency-testing (PT) schemes. We present the different strategies employed in preparing water-matrix RMs for organic analytes. By reviewing the results from the SWIFT-WFD PT schemes, we reflect on the applicability and the suitability of the different approaches.     

Interpolation and extrapolation problems of multivariate regression in analytical chemistry: benchmarking the robustness on near-infrared (NIR) spectroscopy data

Modern analytical chemistry of industrial products is in need of rapid, robust, and cheap analytical methods to continuously monitor product quality parameters. For this reason, spectroscopic methods are often used to control the quality of industrial products in an on-line/in-line regime. Vibrational spectroscopy, including mid-infrared (MIR), Raman, and near-infrared (NIR), is one of the best ways to obtain information about the chemical structures and the quality coefficients of multicomponent mixtures. Together with chemometric algorithms and multivariate data analysis (MDA) methods, which were especially created for the analysis of complicated, noisy, and overlapping signals, NIR spectroscopy shows great results in terms of its accuracy, including classical prediction error, RMSEP. However, it is unclear whether the combined NIR + MDA methods are capable of dealing with much more complex interpolation or extrapolation problems that are inevitably present in real-world applications. In the current study, we try to make a rather general comparison of linear, such as partial least squares or projection to latent structures (PLS); "quasi-nonlinear", such as the polynomial version of PLS (Poly-PLS); and intrinsically non-linear, such as artificial neural networks (ANNs), support vector regression (SVR), and least-squares support vector machines (LS-SVM/LSSVM), regression methods in terms of their robustness. As a measure of robustness, we will try to estimate their accuracy when solving interpolation and extrapolation problems. Petroleum and biofuel (biodiesel) systems were chosen as representative examples of real-world samples. Six very different chemical systems that differed in complexity, composition, structure, and properties were studied; these systems were gasoline, ethanol-gasoline biofuel, diesel fuel, aromatic solutions of petroleum macromolecules, petroleum resins in benzene, and biodiesel. Eighteen different sample sets were used in total. General conclusions are made about the applicability of ANN- and SVM-based regression tools in the modern analytical chemistry. The effectiveness of different multivariate algorithms is different when going from classical accuracy to robustness. Neural networks, which are capable of producing very accurate results with respect to classical RMSEP, are not able to solve interpolation problems or, especially, extrapolation problems. The chemometric methods that are based on the support vector machine (SVM) ideology are capable of solving both classical regression and interpolation/extrapolation tasks.     

Multivariate curve resolution methods in imaging spectroscopy: influence of extraction methods and instrumental perturbations

Imaging spectroscopy is becoming a key field of analytical chemistry. In the face of more and more complex samples, we actually need accurate microscopic insight. Nowadays, the methods used to produce concentration maps of the pure compounds from spectral data sets are based on the classical univariate approach although multivariate approaches are sometimes investigated. But in any case, the analytical quality of the chemical images thus provided cannot be discussed since no reference methods are at our disposal. Thus the proposed research focuses on the application of multivariate methods such as Orthogonal Projection Approach (OPA), SIMPLE-to-use Self-modeling Mixture Analysis (SIMPLISMA), Multivariate Curve Resolution - Alterning Least Squares (MCR-ALS), and Positive Matrix Factorization (PMF) for imaging spectroscopy. A systematic and quantitative characterization of the accuracy of spectra and images extraction is investigated on mid-infrared spectral data sets. Of special interest is the influence of instrumental perturbations such as noise and spectral shift on the extraction ability to access the algorithm's robustness.     

Application of flowing-stream techniques to water analysis Part II. General quality parameters and anionic compounds: halogenated, sulphur and metalloid species

In the first part of this review [Talanta 60 (2000) 867], flowing-stream methods (namely, segmented flow analysis (SFA), continuous-flow analysis (CFA), flow-injection analysis (FIA), sequential-injection analysis (SIA), multicommuted flow-injection analysis (MCFIA) and multisyringe flow-injection analysis (MSFIA)) were presented as powerful analytical tools for nutrient determination in water samples when coupled to photometric/fluorimetric detection, flow-through ion-selective electrodes or amperometric sensors.In the present paper, relevant flow methods applied to the monitoring of anionic species as well as to the determination of general parameters for water quality evaluation (such as pH, alkalinity, chemical and biochemical oxygen demand, conductivity and total ionic content) are reviewed, and their background, detection technique and noteworthy analytical features are detailed. Furthermore, other techniques, such as flow systems connected to hydride-generation atomic absorption spectrometry, should be highlighted as practical approaches for metalloid determination since a series of speciation schemes are demonstrated to be readily adaptable.     

Chemometrics-Based Process Analytical Technology (PAT) Tools: Applications and Adaptation in Pharmaceutical and Biopharmaceutical Industries

Process analytical technology (PAT) is used to monitor and control critical process parameters in raw materials and in-process products to maintain the critical quality attributes and build quality into the product. Process analytical technology can be successfully implemented in pharmaceutical and biopharmaceutical industries not only to impart quality into the products but also to prevent out-of-specifications and improve the productivity. PAT implementation eliminates the drawbacks of traditional methods which involves excessive sampling and facilitates rapid testing through direct sampling without any destruction of sample. However, to successfully adapt PAT tools into pharmaceutical and biopharmaceutical environment, thorough understanding of the process is needed along with mathematical and statistical tools to analyze large multidimensional spectral data generated by PAT tools. Chemometrics is a chemical discipline which incorporates both statistical and mathematical methods to obtain and analyze relevant information from PAT spectral tools. Applications of commonly used PAT tools in combination with appropriate chemometric method along with their advantages and working principle are discussed. Finally, systematic application of PAT tools in biopharmaceutical environment to control critical process parameters for achieving product quality is diagrammatically represented.     

Monitoring of pigmented and wooden surfaces in accelerated ageing processes by FT-Raman spectroscopy and multivariate control charts

Two of the most suitable analytical techniques used in the field of cultural heritage are NIR (near-infrared) and Raman spectroscopy. FT-Raman spectroscopy coupled to multivariate control charts is applied here for the development of a new method for monitoring the conservation state of pigmented and wooden surfaces. These materials were exposed to different accelerated ageing processes in order to evaluate the effect of the applied treatments on the goods surfaces. In this work, a new approach based on the principles of statistical process control (SPC) to the monitoring of cultural heritage, has been developed: the conservation state of samples simulating works-of-art has been treated like an industrial process, monitored with multivariate control charts, owing to the complexity of the spectroscopic data collected.The Raman spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart and cumulative sum (CUSUM) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surfaces. CUSUM charts however proved to be more effective in the identification of the exact beginning of the applied treatment. In the case of wooden boards, where a sufficient number of PCs were available, simultaneous scores monitoring and residuals tracking (SMART) charts were also investigated. The exposure to a basic attack and to high temperatures produced deep changes on the wooden samples, clearly identified by the multivariate Shewhart, CUSUM and SMART charts. A change on the pigment surface was detected after exposure to an acidic solution and to the UV light, while no effect was identified on the painted surface after the exposure to natural atmospheric events.     

Photoinduced spectrofluorimetric determination of fluoroquinolones in human urine by using three- and two-way spectroscopic data and multivariate calibration

Multivariate methods comprise of a group of chemometric tools allowing the analysis of different analytical data, i.e., spectroscopic, chromatographic obtained from multichannel detector systems. Second-way data are widely used in analytical applications in combination with multivariate calibration methods, but three- and higher-way data are yet not as widely applied. In complex biological samples, the employment of the three-way data is of special interest, as they may be combined with methods that exploit the second-order advantage allowing calculating individual concentrations of the analytes of interest in the presence of unknown interferences in untreated samples. A very sensitive and selective method is proposed, by coupling photoinduced fluorescence and multivariate analysis of the three-way data excitation-emission fluorescence matrices (EEMs), of the photoproducts obtained from UV irradiation of three fluoroquinolones: enoxacin (ENO), norfloxacin (NOR) and ofloxacin (OFLO). The application of a previous photoirrradiation process allows the determination of mixtures of ENO, NOR and OFLO, in urine samples at biological levels without sample pretreatments. The resolution ability of N-way partial least squares (N-PLS), parallel factor analysis (PARAFAC) and self weighted alternating trilinear decomposition (SWATLD), is compared with partial least squares (PLS) and unfolded-PLS (U-PLS), in the analysis of ENO, NOR and OFLO in human urine samples.     

NMR on emulsions: characterisation of liquid dispersed systems

Pulsed field gradient NMR (PFG-NMR) is an important method for the characterisation of emulsions. Apart from its application in quality control and process development, especially high-field NMR methods can be applied to investigate emulsions properties on the molecular level. Meanwhile, complex emulsion structures such as double emulsions have been developed and require analytical tools especially for the determination of droplet size distributions. This contribution provides an overview on the possibilities and methods of PFG-NMR referring to measurement, data processing and interpretation of droplet size distributions. Comparison of techniques and measurements on double emulsions are presented.     

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure.     

Determination of molecular formulas of natural organic matter molecules by (ultra-) high-resolution mass spectrometry: Status and needs

Electrospray ionization (ESI) combined with ultra-high-resolution mass spectrometry on a Fourier transform ion cyclotron resonance mass spectrometer has been shown to be a very powerful tool for the analysis of fulvic and humic acids and of natural organic matter (NOM) at the molecular level. With this technique thousands of ions can be separated from each other and their m/z ratio determined with sufficient accuracy to allow molecular formula calculation. Organic biogeochemistry, water chemistry, and atmospheric chemistry greatly benefit from this technique. Methodical aspects concerning the application of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) to NOM isolated from surface water, groundwater, marine waters, and soils as well as from secondary organic aerosol in the atmospheric are reviewed. Enrichment of NOM and its chromatographic separation as well as possible influences of the ionization process on the appearance of the mass spectra are discussed. These steps of the analytical process require more systematic investigations. A basic drawback, however, is the lack of well defined single reference compounds of NOM or fulvic acids. Approaches of molecular formula calculation from the mass spectrometric data are reviewed and available graphical presentation methods are summarized. Finally, unsolved issues that limit the quality of data generated by FTICR-MS analysis of NOM are elaborated. It is concluded that further development in NOM enrichment and chromatographic separation is required and that tools for data analysis, data comparison and data visualization ought to be improved to make full use of FTICR-MS in NOM analysis.