化学计量学在电分析化学中的应用

本文对化学计量学各种方法，诸如多元校正，因子分析，信号处理，参数估计，模式识别等电分析化学中的应用作了回顾及评述，指出了化学计量学电分析化学中应用的良好前景。     

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.     

Methodology of chemometric modeling of spectrometric signals in the analysis of complex samples

A generalized algorithm of the multivariate simulation of spectrometric data is considered for solving typical analytical problems, like the determination of the concentration of a particular analyte and the assignment of a sample to one of predefined classes. In particular, we considered preliminary data processing, exploratory analysis, optimization of a chemometric model, calculation of performance characteristics, transfer of the model to other spectrometers, and automation of chemometric processing for the routine analysis of samples. To illustrate the potential of the method, we selected a system of bovine and porcine heparin, mixtures of soy and sunflower lecithin, and a set of red and white wine samples as test samples. Partial least squares and discriminant analysis were used as chemometric methods. We used proton nuclear magnetic resonance (1H NMR) to record signals. Using the MATLAB environment, chemometric programs were developed for automated data processing in the context of problems under consideration and for the transfer of multivariate models to other spectrometers. Based on the results obtained, a methodology is proposed for the multivariate analysis of spectrometric data, which can be used in the analysis of various types of matrices and spectrometric signals.     

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: an important tool for the modern chemist, an example from wood-processing chemistry

This study briefly outlines the idea of principal component analysis and cross-correlation calculations (applied chemometrics) and presents an illustrative example from wood-processing chemistry. The applicability of chemometric data analysis was demonstrated by investigating the various structural changes that take place in dissolved and degraded lignin ("kraft lignin") during laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) and silver birch (Betula pendula). The structural data (31P NMR and size exclusion chromatographic data) on kraft lignin were further processed by chemometric multivariate techniques (PCA and 2DCC), confirming, for example, that the cleavage of beta-aryl ether structures, the most prominent linkages between monomeric units, is directly related to the decrease in the average molecular mass of lignin.     

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.     

Ultrasonic Mastering of Filter Flow and Antifouling of Renewable Resources

Inadequate access to pure water and sanitation requires new cost‐effective, ergonomic methods with less consumption of energy and chemicals, leaving the environment cleaner and sustainable. Among such methods, ultrasound is a unique means to control the physics and chemistry of complex fluids (wastewater) with excellent performance regarding mass transfer, cleaning, and disinfection. In membrane filtration processes, it overcomes diffusion limits and can accelerate the fluid flow towards the filter preventing antifouling. Here, we outline the current state of knowledge and technological design, with a focus on physicochemical strategies of ultrasound for water cleaning. We highlight important parameters of ultrasound for the delivery of a fluid flow from a technical perspective employing principles of physics and chemistry. By introducing various ultrasonic methods, involving bubbles or cavitation in combination with external fields, we show advancements in flow acceleration and mass transportation to the filter. In most cases we emphasize the main role of streaming and the impact of cavitation with a perspective to prevent and remove fouling deposits during the flow. We also elaborate on the deficiencies of present technologies and on problems to be solved to achieve a wide‐spread application.     

Harnessing the complexity of metabolomic data with chemometrics

Because of the ever‐increasing number of signals that can be measured within a single run by modern platforms in analytical chemistry, life sciences datasets become not only gradually larger but also more intricate in their structures. Challenges related to making use of this wealth of data include extracting relevant elements within massive amounts of signals possibly spread across different tables, reducing dimensionality, summarising dynamic information in a comprehensible way and displaying it for interpretation purposes. Metabolomics constitutes a representative example of fast‐moving research fields taking advantage of recent technological advances to provide extensive sample monitoring. Because of the wide chemical diversity of metabolites, several analytical setups are required to provide a broad coverage of complex samples. The integration and visualisation of multiple highly multivariate datasets constitute key issues for effective analysis leading to valuable biological or chemical knowledge. Additionally, high‐order data structures arise from experimental setups involving time‐resolved measurements. These data are intrinsically multiway, and classical statistical tools cannot be applied without altering their organisation with the risk of information loss. Dedicated modelling algorithms, able to cope with the inherent properties of these metabolomic datasets, are therefore mandatory for harnessing their complexity and provide relevant information. In that perspective, chemometrics has a central role to play. Copyright © 2013 John Wiley & Sons, Ltd.     

Research applications of the Cambridge Structural Database (CSD)

Crystal structure data are of fundamental importance in a wide spectrum of scientific activities. This tutorial review summarises the principal application areas, so far, for the data from more than 300,000 crystal structures of small organic and metal-organic compounds that are stored in the Cambridge Structural Database (CSD). Direct use of the accumulated data is valuable in establishing standard molecular dimensions, determining conformational preferences and in the study of intermolecular interactions, all of which are crucial in structural chemistry and rational drug design. More recently, information derived from the CSD has been used to construct two dynamic libraries of structural knowledge: Mogul, which stores intramolecular information, and IsoStar, which stores information about intermolecular interactions. These electronic libraries provide information "at the touch of a button". In their turn, the libraries also serve as sources of structural knowledge for applications software that address specific problems in small-molecule and biological chemistry.     

Resolution of two-way data: theoretical background and practical problem-solving Part 1: Theoretical background and methodology

This paper reviews recent progress in the resolution of two-way data obtained from hyphenated instruments. Special emphasis is placed on the solution of practical problems. Methods for estimating the number of chemical components both statistically and visually (the first step in solving the resolution problem) and methods for resolving the pure profiles (the second step in solving the resolution problem) are discussed in detail. To deal with real-world problems, pitfalls in the chemometric analysis of the two-way data from the instrumental measurements are also pointed out. Applications of methods for solving some difficult practical problems in environmental chemistry, pharmaceutical chemistry, and physical chemistry will be discussed in the second part of this paper.     

Methyl Complexes of the Transition Metals

Organometallic chemistry can be considered as a wide area of knowledge that combines concepts of classic organic chemistry, that is, based essentially on carbon, with molecular inorganic chemistry, especially with coordination compounds. Transition‐metal methyl complexes probably represent the simplest and most fundamental way to view how these two major areas of chemistry combine and merge into novel species with intriguing features in terms of reactivity, structure, and bonding. Citing more than 500 bibliographic references, this review aims to offer a concise view of recent advances in the field of transition‐metal complexes containing M?CH₃ fragments. Taking into account the impressive amount of data that are continuously provided by organometallic chemists in this area, this review is mainly focused on results of the last five years. After a panoramic overview on M?CH₃ compounds of Groups 3 to 11, which includes the most recent landmark findings in this area, two further sections are dedicated to methyl‐bridged complexes and reactivity.     

Resolution of two-way data: theoretical background and practical problem-solving. Part 1: theoretical background and methodology

This paper reviews recent progress in the resolution of two-way data obtained from hyphenated instruments. Special emphasis is placed on the solution of practical problems. Methods for estimating the number of chemical components both statistically and visually (the first step in solving the resolution problem) and methods for resolving the pure profiles (the second step in solving the resolution problem) are discussed in detail. To deal with real-world problems, pitfalls in the chemometric analysis of the two-way data from the instrumental measurements are also pointed out. Applications of methods for solving some difficult practical problems in environmental chemistry, pharmaceutical chemistry, and physical chemistry will be discussed in the second part of this paper.     

Chemometrics in near-infrared spectroscopy

Spectroscopy methods of chemical analysis are excellent for the application of chemometric methods, because the measurements at many different wavelengths provide inherently multivariate data. The chemist generally requires three categories of information from specimens under investigation: quantitative data, qualitative data, and fundamental information on the properties of the material. Spectroscopy has long been used for all three purposes; the recent application of chemometric algorithms has assisted greatly in these endeavors. Although there is some overlap, three chemometric methods correspond to the three types of information: multiple regression, discriminant analysis, and principal components analysis. The basis of these chemometric methods and some of their strengths and limitations in application to near-infrared spectroscopy are discussed.     

The Application of Chemometric Methods in the Experimental Teaching of Analytical Chemistry

In order to train the students' abilities to deal with the actual problems of analytical chemistry, the synthetic experiment of "The simultaneous determination of mixture of pigments by multivariant calibration and spectrophotometry" was opened to the undergraduate and the chemometric method was employed to evaluate the experimental data and assess the skills of the practical experimental operation. Some significant results have been achieved.     

Chemometric approaches in environmental problems concerning PCDD and PCDF. Data interpretation and source correlation. Mechanisms of formation and destruction in MSW combustion process

Summary The role of multivariate analysis methods in evaluating, rationalizing, and working out complex environmental problems is discussed. The discussion is organized in two sections; a literature analysis of the application of chemometric methods to PCDD/PCDF data interpretation and source correlation and a review of the role of chemometric methods in analysing the results obtained by the Authors studying PCDD/PCDF formation and destruction mechanisms in MSW combustion processes.     

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.     

Simple methods for the optimization of complex‐valued kurtosis as a projection index

With the advancement of modern techniques, complex‐valued data have become more important in chemistry and many other areas. The data collected are often multi‐dimensional. This imposes an increasing demand on the tools used for the analysis of complex‐valued data. In multivariate data analysis, projection pursuit is a useful and important technique that in many cases gives better results than principal component analysis. One important projection pursuit variant uses the real‐valued kurtosis as its projection index and has been shown to be a powerful approach to address different problems. However, using the complex‐valued kurtosis as a projection index to deal with complex‐valued data is rare. This is, to a great extent, due to the lack of simple and fast optimization algorithms. In this work, simple and rapidly executed optimization algorithms for the complex‐valued kurtosis used as a projection index are proposed. The developed algorithms have a variety of advantages: no requirement for sphering or strong‐uncorrelation transformation of the data in advance, no assumption for the latent components (source signals) to be circular or non‐circular, search for maxima or minima on users' requirements, and users having the option to choose uncorrelated scores or orthogonal projection vectors. The mathematical development of the algorithms is described and simulated and real experimental data are employed to demonstrate the utility of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

Investigation of the equality constraint effect on the reduction of the rotational ambiguity in three-component system using a novel grid search method

The obtained results by soft modeling multivariate curve resolution methods often are not unique and are questionable because of rotational ambiguity. It means a range of feasible solutions equally fit experimental data and fulfill the constraints. Regarding to chemometric literature, a survey of useful constraints for the reduction of the rotational ambiguity is a big challenge for chemometrician. It is worth to study the effects of applying constraints on the reduction of rotational ambiguity, since it can help us to choose the useful constraints in order to impose in multivariate curve resolution methods for analyzing data sets. In this work, we have investigated the effect of equality constraint on decreasing of the rotational ambiguity. For calculation of all feasible solutions corresponding with known spectrum, a novel systematic grid search method based on Species-based Particle Swarm Optimization is proposed in a three-component system.