Showing papers in "Critical Reviews in Analytical Chemistry in 2006"
TL;DR: This paper discusses the uses of the correlation coefficient r, either as a way to infer correlation, or to test linearity, and recommends the use of z Fisher transformation instead of r values because r is not normally distributed but z is (at least in approximation).
Abstract: Correlation and regression are different, but not mutually exclusive, techniques. Roughly, regression is used for prediction (which does not extrapolate beyond the data used in the analysis) whereas correlation is used to determine the degree of association. There situations in which the x variable is not fixed or readily chosen by the experimenter, but instead is a random covariate to the y variable. This paper shows the relationships between the coefficient of determination, the multiple correlation coefficient, the covariance, the correlation coefficient and the coefficient of alienation, for the case of two related variables x and y. It discusses the uses of the correlation coefficient r, either as a way to infer correlation, or to test linearity. A number of graphical examples are provided as well as examples of actual chemical applications. The paper recommends the use of z Fisher transformation instead of r values because r is not normally distributed but z is (at least in approximation). For eithe...
649 citations
TL;DR: This work is mainly oriented to give an overview of the progress of multivariate curve resolution methods in the last 5 years with the latest trends in theoretical contributions and in the field of analytical applications.
Abstract: This work is mainly oriented to give an overview of the progress of multivariate curve resolution methods in the last 5 years. Conceived as a review that combines theory and practice, it will present the basics needed to understand what is the use, prospects and limitations of this family of chemometric methods with the latest trends in theoretical contributions and in the field of analytical applications.
583 citations
TL;DR: This article critically examines the use of factorial and response surface methodology in modern experimental design and optimization in biological, environmental and pharmaceutical analysis, food technology and industrial-related processes.
Abstract: This article critically examines the use of factorial and response surface methodology in modern experimental design and optimization. A survey of important screening and optimization techniques in the literature since 2000 are presented. Current applications in biological, environmental and pharmaceutical analysis, food technology and industrial-related processes are examined.
233 citations
TL;DR: Multiway analysis started to take off in chemistry in the 1980s, but only in recent years has it been broadly applied to many diverse kinds of data as mentioned in this paper, and it is evident from this review that multiway analysis is presently a generally accepted and used tool whose full potential is far from reached.
Abstract: This review describes advances in multiway analysis during the period 2000–2005. Multiway analysis started to take off in chemistry in the 1980s, but only in recent years has it been broadly applied to many diverse kinds of data. This review reflects how the field has matured and how the methods have been applied to more and more difficult types of data in new research areas. Multiway analysis is described in terms of different types of data, different areas of applications as well as more fundamental and theoretical results throughout the period. It is evident from this review that multiway analysis is presently a generally accepted and used tool whose full potential is far from reached.
212 citations
TL;DR: This review article highlights the advantages, recent developments, applications and future perspectives of sol–gel immobilized biomolecules, which includes enzymes, antibodies, microorganisms, plant and animal cells.
Abstract: The encapsulation or generation of new surfaces that can fix biomolecules firmly without altering their original conformations and activities is still challenging for the utilization of biochemical functions of active biomolecules. Presently, sol–gel chemistry offers new and interesting possibilities for the promising encapsulation of heat-sensitive and fragile biomolecules (enzyme, protein, antibody and whole cells of plant, animal and microbes); mainly, it is an inherent low temperature process and biocompatible. The typical sol–gel process initiates by the hydrolysis of M(OR) 4 and is performed in the presence of the active biomolecule. Hydrolysis and condensation of the M-monomers in the presence of an acid or base catalyst trigger cross-linking with formation of amorphous MO 2 , a porous inorganic matrix that grows around the biomolecule in a three-dimensional manner. This class of sol–gel matrices possesses chemical inertness, physical rigidity, negligible swelling in aqueous solution, tunable poros...
212 citations
TL;DR: Support Vector Machines are a new generation of classification method that attempts to produce boundaries between classes by both minimising the empirical error from the training set and also controlling the complexity of the decision boundary, which can be non-linear.
Abstract: Support Vector Machines (SVMs) are a new generation of classification method. Derived from well principled Statistical Learning theory, this method attempts to produce boundaries between classes by both minimising the empirical error from the training set and also controlling the complexity of the decision boundary, which can be non-linear. SVMs use a kernel matrix to transform a non-linear separation problem in input space to a linear separation problem in feature space. Common kernels include the Radial Basis Function, Polynomial and Sigmoidal Functions. In many simulated studies and real applications, SVMs show superior generalisation performance compared to traditional classification methods. SVMs also provide several useful statistics that can be used for both model selection and feature selection because these statistics are the upper bounds of the generalisation performance estimation of Leave-One-Out Cross-Validation. SVMs can be employed for multiclass problems in addition to the traditional two ...
148 citations
TL;DR: An overview of robust chemometrical/statistical methods which search for the model fitted by the majority of the data, and hence are far less affected by outliers, is presented.
Abstract: In analytical chemistry, experimental data often contain outliers of one type or another. The most often used chemometrical/statistical techniques are sensitive to such outliers, and the results may be adversely affected by them. This paper presents an overview of robust chemometrical/statistical methods which search for the model fitted by the majority of the data, and hence are far less affected by outliers. As an extra benefit, we can then detect the outliers by their large deviation from the robust fit. We discuss robust procedures for estimating location and scatter, and for performing multiple linear regression, PCA, PCR, PLS, and classification. We also describe recent results concerning the robustness of Support Vector Machines, which are kernel-based methods for fitting non-linear models. Finally, we present robust approaches for the analysis of multiway data.
142 citations
TL;DR: A review of application of ion chromatography for the determination of inorganic anions (F−, Cl−, NO2 −, NO3 −, BrO3 − and ClO2 −) and cations (Li+, Na+, NH4 +, K+, Mn2+, Ca2+, Mg2+, Sr2+, Ba2+) in water and wastewater is presented in this paper.
Abstract: Water analysis is an important part of the chemical analysis of environmental samples. The development of new methods of water analysis and improvement of existing ones is a major task for analytical chemists. Analysis of common inorganic anions and cations in water is mandatory. Ion chromatography has almost replaced most of the wet chemical methods used in water analysis. The demands from regulators for justifiable analytical results and from laboratories for validated methods have led to necessity to standardize ion chromatography methods. The paper is a review of application of ion chromatography for the determination of inorganic anions (F−, Cl−, NO2 −, NO3 −, BrO3 −, ClO2 −, ClO3 −, PO4 3−, SO3 2−, SO4 2−, CrO4 2−, I−, SCN−, and S2O3 2−) and cations (Li+, Na+, NH4 +, K+, Mn2+, Ca2+, Mg2+, Sr2+, Ba2+) in water and wastewater.
84 citations
TL;DR: Field-flow fractionation is presented as a versatile and powerful analytical technique for separation and characterization of different sample types and its exemplary application and the future trends in development of FFF techniques are presented.
Abstract: In this review, field-flow fractionation (FFF) is presented as a versatile and powerful analytical technique for separation and characterization of different sample types. The underlying principles of FFF separation theory including sources of the obstacles and the difficulties as well as the new approaches is also introduced. This paper describes FFF sub-techniques, presents its exemplary application and the future trends in development of FFF techniques.
77 citations
TL;DR: The scope of this paper is to demonstrate that multivariate, data based statistical methods can play a critical role in process understanding, multivariate statistical process control, abnormal situation detection, fault diagnosis, process control and process scale-up, as linked to process analytical technology.
Abstract: Process analytical chemistry was recognized by Callis et al. (Analytical Chemistry, 59 (1987): 624A–635A) (1) as a field that extends well beyond real time measurements of process parameters. Process Analytical Technology is taking central stage with the 2004 guidance from the Food and Drug Administration, with a mandate much wider than real time measurements. The pharmaceutical industry is entering a new era. Chemometrics has played an integral part for the real time development of process analytical measurements (multivariate calibration) and it is ready to face the challenge of Process Analytical Technology in this wider definition. The scope of this paper is to demonstrate that multivariate, data based statistical methods, can play a critical role in process understanding, multivariate statistical process control, abnormal situation detection, fault diagnosis, process control and process scale-up, as linked to process analytical technology.
69 citations
TL;DR: Methods for data from coupled chromatographic methods, which have found increasing use and where data pre-processing is a prerequisite for multivariate modeling, are included and a diverse set of applications are presented.
Abstract: This review covers the area of multivariate calibration; from pre-processing of data prior to modeling and applications of regression methods for calibration and prediction. The importance of pre-treatment of data is highlighted with many of the recently developed methods together with traditional methods. Several articles provide comparisons between different pre-processing methods. Methods for data from coupled chromatographic methods, which have found increasing use and where data pre-processing is a prerequisite for multivariate modeling, are also included. Many of the novel chemometric methods deal with model complexity and interpretation. A diverse set of applications are also presented and references are also given to early papers, making it possible to acquire a deeper knowledge of methods of interest.
TL;DR: In this paper, the authors present a review of the analytical methods used to determine organic acids of honey components, and the advantages and disadvantages of all the procedures described are discussed, as well as a comparison of the three methods.
Abstract: Although organic acids represent less than 0.5% of honey's constituents, they make important contributions to organoleptic, physical, and chemical properties of honey. They could be used as fermentation indicators, for the treatment of Varroa infestation, and to discriminate among honeys according to their botanical and/or geographical origins. This article reviews the current literature related to the analytical methods (enzymatic, chromatographic and electrophoretic) that have been applied recently to the determination of honey's organic acids. The advantages and disadvantages of all the procedures described are also discussed. This review has been written to make the study of these interesting honey component easier.
TL;DR: In this contribution the essential aspects of the complete process are discussed, these include data acquisition, modelling of the concentration profiles and the actual fitting algorithms which are identical for both types of investigation.
Abstract: Equilibrium and kinetic data usually can be described quantitatively by a chemical model that is based on the law of mass action. In such instances parameters of interest like rate and equilibrium constants and, depending on the nature of the data, also spectral information can be determined by model-based analysis of the appropriate data sets. In this contribution the essential aspects of the complete process are discussed, these include data acquisition, modelling of the concentration profiles and the actual fitting algorithms which are identical for both types of investigation. An overview of recent developments like globalisation of the analysis and attempts to analyse industrially relevant data incorporating corrections for non-ideal behaviour are also given.
TL;DR: An overview on the application of chemometrics to electroanalytical data is presented in this paper, with special attention to the contributions of the last decade, especially for multianalyte calibration and modelling in multicomponent dynamic systems.
Abstract: The use of chemometrics in electroanalytical chemistry is not as popular as in spectroscopy, although recently, application of these methods for mathematical resolution of overlapping signals, calibration and model identification have been increasing. Self-modelling curve resolution and multivariate analysis have been shown to be very powerful for in the analysis of electroanalytical data, especially for multianalyte calibration and modelling in multicomponent dynamic systems. In this paper, an overview on the application of chemometrics to electroanalytical data is presented, with special attention to the contributions of the last decade.
TL;DR: The application of PQQ-dependent enzymes is predicted as one of alternative and promising ways for detection of glycerol in complex biological samples and future trends in application are predicted.
Abstract: This review focuses on the enzymatic glycerol detection methods. Importance of glycerol as biologically active compound, chemical glycerol determination methods, and current trends in enzymatic glycerol determination are reviewed. Application of FAD-dependent glycerol oxidases, NAD-dependent dehydrogenases, lipases and multi-enzymatic systems for glycerol and polyglyceride detection by optical and electrochemical methods are analyzed in detail. The application of PQQ-dependent enzymes is predicted as one of alternative and promising ways for detection of glycerol in complex biological samples. Future trends in application of PQQ-dependent glycerol dehydrogenases are predicted.
TL;DR: In this paper, a review paper systematizes available information connected with the occurrence and the analysis of PAHs and PCBs in natural waters and points out error sources, which can appear in every step of the analytical procedures.
Abstract: Today, special attention is paid in environmental analysis to sample preparation methods. Isolation and/or preconcentration of analytes from water samples characterized by complex composition of the matrices constitutes an essential step of analytical procedures used for determination of trace organic components. Collection, transport and storage of water samples are also important. This review paper systematizes available information connected with the occurrence and the analysis of PAHs and PCBs in natural waters. Special attention is paid to point out error sources, which can appear in every step of the analytical procedures.
TL;DR: The calibration procedures related to the standard addition method and used in flow analysis are critically reviewed and those of extrapolative character are distinguished as they are exclusively able to compensate the multiplicative interference effect in wide range of the interferences.
Abstract: The calibration procedures related to the standard addition method and used in flow analysis are critically reviewed. All examples met in the literature are considered with respect to their facilities for overcoming the interferences. It is disclosed that the flow techniques give a chance to add the standard(s) to a sample by different manners allowing the analytical result to be calculated by either interpolative or extrapolative way. However, from among various calibration procedures those of extrapolative character are distinguished as they are exclusively able to compensate the multiplicative interference effect in wide range of the interferent(s) concentration. It is also shown how the flow standard addition approaches can be employed to solve different analytical problems and—on the other hand—why some of them reveal limited usefulness for calibration purposes. The particular groups of calibration procedures are compared with each other and discussed in terms of their analytical performance.
TL;DR: In this paper, inhibition experiments are mimicked inhibiting enzyme activity to 10% of its original value, and the number of metabolites switching clusters under the influence of heteroscedastic noise is lower if bagging is used.
Abstract: Clustering of metabolomics data can be hampered by noise originating from biological variation, physical sampling error and analytical error. Using data analysis methods which are not specially suited for dealing with noisy data will yield sub optimal solutions. Bootstrap aggregating (bagging) is a resampling technique that can deal with noise and improves accuracy. This paper demonstrates the possibilities for bagged clustering applied to metabolomics data. The metabolomics data used in this paper is computer-generated with the human red blood cell model. Perturbing this model can be done in several ways. In this paper, inhibition experiments are mimicked inhibiting enzyme activity to 10% of its original value. Comparing bagged K-means clustering to ordinary K-means, the number of metabolites switching clusters under the influence of heteroscedastic noise is lower if bagging is used. This favors bagged K-means above ordinary K-means clustering when dealing with noisy metabolomics data. A special validati...