bluuebunny/crossref_metadata_2025_split huggingface dataset:
The performance of least squares and robust regression in the calibration of analytical methods under non‐normal noise distributions
Authors: Wolters R. , Kateman G. 
Published: May 2005

AbstractBy means of Monte Carlo simulations a comparison has been made between ordinary least squares regression and robust regression. The robust regression procedure is based on the Huber estimate and is computed by means of the iteratively reweighted least squares algorithm. The performance of both procedures has been evaluated for estimation of the parameters of a calibration function and for determination of the concentration of unknown samples. The influence of the distributional characteristics skewness and kurtosis has been studied, and the number of measurements used for constructing the calibration curve has also been taken into account. Under certain conditions robust regression offers an advantage over least squares regression.
Use of replication and signal‐to‐noise ratios in the identification and estimation of the composition of lubricant basestock mixtures using13C nuclear magnetic resonance spectroscopy and projection into principal component/canonical variates space
Authors: Deane John M. , MacFie Halliday J. H. , King Alexander G. 
Published: May 2005

AbstractThe theory of experimental error in analysis of mixture experiments by abstract factor analysis or target transformation factor analysis is considered. The theoretical implications of using signal‐to‐noise ratios (as weights) or canonical variates analysis to reduce the level of imbedded error in the factor model are examined. The approach is illustrated by application to13C NMR spectra of lubricant basestock mixtures.
Condition numbers, iterative refinement and error bounds
Authors: Kalivas John H. , Lang Patrick 
Published: May 2005

AbstractConcentration estimates of components present in a sample mixture can be obtained using matrix mathematics. In the past, the condition number of the calibration matrix has been used to give an amplification factor by which uncertainties in data can work through to errors in the concentration estimates. This paper explores an additional interpretation of condition numbers with regards to significant figures and rounding errors. A procedure is suggested which will always give the most accurate concentration estimates provided the calibration matrix is not too ill‐conditioned. Condition numbers have also been used by analytical chemists to discuss the error bounds for concentration estimates. Unfortunately, only one representative error bound can be approximated for all the components. This paper will show how to compute bounds for individual concentration estimates obtained as solutions to a system of m equations and n unknowns. The procedure is appropriate when calibration data and sample responses are inaccurate.
Testing for redundancy in product quality control test criteria: An application to aviation turbine fuel
Authors: Deane John M. , MacFie Halliday J. H. 
Published: May 2005

AbstractGiven a set of test criteria that determine a quality specification, the question often arises whether any of the tests are redundant because of intercorrelations Simple selection of tests on the basis of partial correlations with the other tests is rejected on the basis that the random error in the data may be causing spurious correlations.One method is to use cross‐validation to define the systematic principal components and examine the correlation structure in this reduced space. It is shown that the presence of principal components dominated by individual tests (‘variable specific’ PCs), which are indicated by cross‐validation as being non‐systematic, must be taken into account. Having defined the dimensionality, a variable reduction method based on Procrustes rotation selects subsets of tests that preserve the structure of the samples in multivariate space. This is an attractive proposition in the context of maintaining a quality control specification. It is also shown that the variable reduction technique can be used to aid the identification of the true dimensionality of the data space. This approach is applied to a number of routine tests carried out on aviation turbine fuel.
The problem of adequate sample size in pattern recognition studies: The multivariate normal case
Authors: Han J. H. , Ward A. J. I. , Lavine B. K. 
Published: May 2005

AbstractBecause many pattern recognition techniques are predicated on the assumption of mutivariate normal data, Monte Carlo simulation studies were performed to determine the number of samples that are necessary to describe a multivariate normal population adequately. From these studies we have learned that hundreds of samples are required. These results suggest that parametric procedures should only be used to analyze very large data sets.
Recursive parameter estimation
Authors: Rutan Sarah C. 
Published: May 2005

AbstractThe use of recursive filtering techniques for parameter estimation in a variety of areas is reviewed. In particular, the Kalman filter algorithm is described, along with several variations, including square‐root, UDUT and information filters. The solution to parameter estimation problems is discussed for both linear and non‐linear models. Applications described include calibration, curve resolution in spectroscopy, chromatography, electrochemistry, kinetic analysis and process monitoring.
Quantifying similarity for handling information in knowledge bases
Authors: Bandemer Hans 
Published: May 2005

AbstractWhen constructing diagnostic systems or using knowledge‐based systems, e.g. in analytical chemistry, features of different type and character, represented by numbers, trajectories or linguistic variables such as intensities or colours, must be considered. To find neighbourhoods or to fill in missing values, the notion of similarity is of essential importance. The paper presents a new fuzzy‐set‐theory‐based approach to quantifying similarity and provides a system of rules to be implemented into the diagnostic part of the knowledge base to be used.
Latent features with maximal correlation between two equal sets of variables
Authors: Henrion René , Henrion Günter 
Published: May 2005

AbstractIf in two data tables X and Y objects are characterized by the same variables (measured at different occasions), then looking for common latent features should be more appropriate than choosing latent features in X and Y separately (e.g. as in canonical correlation or PLS). The procedure to be proposed here is a slight modification of the method of linear characteristics (according to De Groot and Li) by disclaiming the assumption of equal inner‐block covariance matrices. In order to find weights defining a latent feature with maximal correlation between X and Y, a system of non‐linear equations has to be solved. The procedure is applied to the investigation of heavy metal concentrations in different human tissues.
Cross‐validation, influential observations and selection of variables in chemometric studies of wines by principal components analysis
Authors: Scarponi Giuseppe , Moret Ivo , Capodaglio Gabriele , Romanazzi Mario 
Published: May 2005

AbstractCross‐validatory estimation of the bilinear model based on principal components is reviewed and Krzanowski's modification of Wold's procedure is described. Two different types of residuals useful for checking model adequacy are defined and indices measuring the influence of each observed unit on the estimates of the parameters are discussed. A method for the selection of variables derived from Procrustes analysis is described. Results arising from the study of two sets of enological data are given.
Comments on the power method
Authors: Seasholtz Mary Beth , Pell Randy J. , Gates Kevin E. 
Published: May 2005

AbstractIn a recent short communication, Miyashita et al. have commented on the weakness of the NIPALS algorithm (equivalently the power method) for calculating the eigenvalues out of order. They offer a diagnostic to ascertain when this may have occurred and suggested a modification to the NIPALS algorithm to avoid this situation. Further comments regarding the use of the power method and Miyashita's presentation of its weakness are warranted. The general inadequacies of methods for decomposing a matrix with degenerate eigenvalues and their relationship to the orthogonal design of experiments are discussed.
The start and early history of chemometrics: Selected interviews. Part 1
Authors: Geladi Paul , Esbensen Kim 
Published: May 2005

AbstractThis is a first foray into the historical start and early years of chemometrics from about 1972 onwards. We have gathered interviews with three originators (Kowalski, Wold and Massart) as well as with a selected group of six other well‐known chemometricians who gradually became active in the 1970s (Christie, Clementi, Hopke, Martens, Brown and Deming). The interviews include amongst a host of subjective recollections a succinct record of the key historical literature as highlighted by the interviewees' own rankings of ‘earliest’ and ‘best’.A discussion of the most general commonalities in these interviews together with other historical material is presented in the second part of the paper.
Factor analysis of a simulated data matrix involving aqueous complex equilibria
Authors: Conny Joseph M. , Meglen Robert R. 
Published: May 2005

AbstractAbstract factor analyses were performed on databases consisting of simulated samples from aqueous equilbria. The program COMPLEX was used to generate equilibrium species in a system of three reactant metals and five reactant bases. Reactant concentrations and pH were drawn from random–normal distributions so that sample data vectors comprised a multivariate log–normal distribution of equilibrium concentrations. In addition, sample groups were created containing different distributions for pH and reactant concentrations.Equilibrium species were shown to contain variance contributed by change in pH among samples as well as change in reactant concentrations. Factor modelling revealed the qualitative relationships among the species and how the relationships change with pH. Factors also revealed those reactants containing variance in the data matrix. In some cases, reactant variance obscured relationships between pH and the equilibrium species.Since factor modelling of a simulated data matrix revealed the expected chemical equilibrium interactions, a potentially powerful tool exists for investigating the effects of outliers and error.
Variations in essential oils of mandarine related to processing and ripening
Authors: Cotroneo Antonella , Dugo Giovanni , Favretto Luciano , Favretto Luciana Gabrielli 
Published: May 2005

AbstractMandarine essential oils are extracted from green unripened fruits as well as from red fruits at ripening, both oils having specific uses as natural additives in the food industry. Two processes of production, pressing and peeling, are currently adopted in their production. Capillary gas chromatography with flame ionization detection has been suggested as a sensitive method for the fractionation of volatile components of the essential oils. Principal component analysis was proposed as an exploratory chemometric method for the differentiation of essential oils from fruits at different degrees of ripening, taking into account the processes of production. Product–moment correlations between variables (concentrations of 17 components) were used as starting matrices and the explained variance was adopted as a criterion for eigenvalue selection. Bi‐plots and three‐dimensional plots of unrotated principal component scores were systematically used as well as those of orthogonally rotated factor scores. The 17 variables were reduced to four principal components, which explained 87% of the total variance. Projection on the first three eigenvectors of all data as unrotated component scores allowed for a tentative differentiation of 59 oils according to their degree of ripening. The two processes of production were also differentiated in a sample of 55 oils.
Dependence between fusion temperatures and chemical components of a certain type of coal using classical, non‐parametric and bootstrap techniques
Authors: González‐Manteiga W. , Prada‐Sánchez J. M. , Fiestras‐Janeiro M. G. , García‐Jurado I. 
Published: May 2005

AbstractA statistical study of the dependence between various critical fusion temperatures of a certain kind of coal and its chemical components is carried out. As well as using classical dependence techniques (multiple, stepwise and PLS regression, principal components, canonical correlation, etc.) together with the corresponding inference on the parameters of interest, non‐parametric regression and bootstrap inference are also performed.
Tutorial to robust statistics
Authors: Rousseeuw Peter J. 
Published: May 2005

AbstractIn this tutorial we first illustrate the effect of outliers on classical statistics such as the sample average. This motivates the use of robust techniques. For univariate data the sample median is a robust estimator of location, and the dispersion can also be estimated robustly. The resulting ‘z‐scores’ are well suited to detect outliers. The sample median can be generalized to very large data sets, which is useful for robust ‘averaging’ of curves or images. For multivariate data a robust regression procedure is described. Its standardized residuals allow us to identify the outliers. Finally, a survey of related approaches is given. (This review overlaps with earlier work by the same author, which appeared elsewhere.)
Splitting of calibration data by cluster analysis
Authors: Naes Tormod , Isaksson Tomas 
Published: May 2005

AbstractThe topic of the present paper is the splitting of calibration data into subgroups with improved linearity in each group. The method proposed is based on a criterion which is a weighted average of a Mahalanobis distance and a squared regression residual. The algorithm used to find the solution is based on fuzzy clustering. Two examples are given to illustrate the theory.
A new serial method for searching the optimum operating conditions of a technological process
Authors: Tran V. , Morat J.‐L. , Bertrand D. 
Published: May 2005

AbstractA new program, named ANTICOMPLEX, has been specially written for searching the optimum conditions of a technological process. The algorithm is based on an iterative procedure with, at each step, a random sampling for a better exploration of the hyperspace of the parameters. Since the process is considered as a ‘black box’, the program operates by successive reductions of the variation domains with a strategy derived from some function optimization (COMPLEX). This approach is very flexible and the experimenter can stop the investigation at any series of experiments according to the desired accuracy for the optimum region. This program has been successfully tested on several real processes. This paper gives a complete description of the approach together with two illustrations on simulated processes.
Correspondence analysis and adsorbate selection for chemical sensor arrays
Authors: Avila Fernando , Myers D. E. , Palmer Chris 
Published: May 2005

AbstractCarey et al. utilized principal components analysis (PCA) to analyze frequency shift data obtained from piezoelectric sensors formed by coating quartz crystals with 27 different GC stationary phases and tested using 14 analytes. The objective of the analysis was to determine an optimal reduced set of coatings for detection of the analytes. The results were correlated with those obtained from cluster analysis. In this paper the data are re‐analyzed using correspondence analysis (CA). The advantage of using CA include a symmetric treatment of sensor coatings and analytes and better identification of the representation of the analytes in terms of the detection components. The results obtained by the conjunctive use of PCA, a varimax rotation and cluster analysis were obtained by CA.
Mathematical model for the optimization of the reaction between 2,3‐bis(m‐tolyl)quinolizinium bromide and piperidine using a factorial design
Authors: Martin M. A. , Frutos G. , Del Castillo B. , Alvarez‐Builla J. , Martin P. J. 
Published: May 2005

AbstractA number of nucleophiles have been successfully identified and quantified using the Kröhnke reaction with 2,3‐diphenyl derivatives of quinolizinium salts. Optimization of the reaction conditions by means of a mathematical model involving analysis of the response surface has led to a better understanding of the factors exerting an influence on the above reaction.The parameters chosen were temperature, reaction time, base concentration, water concentration and nucleophile concentration. The reaction was developed in polar aprotic solvents (acetone, acetonitrile). To facilitate the nucleophilic attack, the presence of an organic base (triethylamine) was necessary, although concentrations in excess of 0·15M did not alter the development of the reaction. Since pharmaceutical solutions are often aqueous, the influence of water on the reaction was studied. Low water volumes have no significant influence on the opening of the quinolizinium ring by the nucleophilic reagent. However, when the water proportion exceeded that of the organic solvent, the fluorescence intensity was lower than expected. Development of the fluorescent reaction product was first detected 5 min after the reaction started. The fluorescence intensity reached its optimum value after 138 min.
Ranking principal components to reflect group structure
Authors: Krzanowski W. J. 
Published: May 2005

AbstractCanonical variate analysis is the appropriate descriptive technique for multivariate data which have an a priori group structure, but problems arise with this technique when there are more variables than within‐group degrees of freedom because of singularity of matrices. In such cases it is shown through illustrative examples that principal component analysis is a viable substitute provided that the principal components are ranked according to the canonical variate criterion (ratio‐ of between‐ to within‐group variances) rather than the usual criterion of total variance. This ranking can also be used to select components for subsequent discriminant analysis.
Classification of mass spectra
Authors: Drabløs Finn 
Published: May 2005

AbstractIn order to investigate correlations between the functionality of compounds and the classification of their mass spectra, low‐resolution spectra of monofunctional compounds have been classified by four supervised classification methods. The new classes are characterized by structural features and the correlation between functionality and classification is explained by fragmentation rules. Systematic misclassifications show that low‐resolution mass spectra alone are not suitable for reliable identification of functionality.
Estimation of prediction error for samples within the calibration range
Authors: Karstang Terje V. , Toft Jostein , Kvalheim Olav M. 
Published: May 2005

AbstractA modification of a technique proposed by Lorber and Kowalski for the estimation of prediction errors is presented. The method is applied to five data sets. The results show that for some data sets the estimated prediction errors are close to the actual prediction errors for samples within the calibration range, while samples outside the calibration range must be background corrected before quantification of the prediction error.
On the integration of kinetic models using a high‐order taylor series method
Authors: Baeza Juan José Baeza , Plá Francisco Pérez , Ramos Guillermo Ramis 
Published: May 2005

AbstractA general equation to derive kinetic models up to any order is given. This equation greatly facilitates the application of the Taylor series method to the integration of kinetic models up to very high orders. When dealing with non‐stiff models, computing time is always reduced by increasing the integration order, at least up to the 20th order. When the model is stiff, the integration order should be optimized; however, a twelfth order is recommended to integrate weakly stiff models. The use of an algorithm which permits the immediate calculation of the integration step size required to maintain a given accuracy leads to further reductions in computing time. When implemented as recommended here, a high‐order Taylor series method is more rapid and accurate than Runge–Kutta and predictor‐corrector methods and can be advantageously used in combination with optimization methods to perform mechanism studies and in multicomponent kinetic determinations.
A review of three‐dimensional chemical structure retrieval systems
Authors: Willett Peter 
Published: May 2005

AbstractThis paper presents a review of the rapid developments that have taken place over the last few years for the searching of databases of three‐dimensional (3D) molecules. The geometric arrangement of the atoms in a 3D molecule is described by an interatomic distance matrix. This is a form of labelled graph that can thus be searched using the subgraph‐isomorphism algorithms that are widely used for searching databases of two‐dimensional (2D) molecules. Several in‐house and commercial systems have been developed for 3D database searching that are based on such techniques. These systems are reviewed and their effectiveness demonstrated by examples of their use in the discovery of novel, biologically active molecules. Current systems represent a molecule by one or a small number of low‐energy conformations and there is hence much interest in the development of representational techniques and searching algorithms that account for the full set of geometric arrangements that can be adopted by a flexible molecule.
Predictive ability of regression models. Part II: Selection of the best predictive PLS model
Authors: Baroni Massimo , Clementi Sergio , Cruciani Gabriele , Costantino Gabriele , Riganelli Daniela , Oberrauch Ermanno 
Published: May 2005

AbstractA procedure called GOLPE is suggested in order to detect those variables which increase the predictivity of PLS models. The procedure is based on evaluating the predictive power of a number of PLS models built by different combinations of variables selected according to a factorial design strategy. Examples are given of the efficiency of this variable selection procedure, which shows how these predictive PLS models are better than those obtained by all variables and better than the corresponding ordinary regression models.
Identification of finite impulse response models with continuum regression
Authors: Wise B. M. , Ricker N. L. 
Published: May 2005

AbstractThe use of continuum regression (CR) for the identification of finite impulse response (FIR) dynamic models is investigated. CR encompasses the methods of principal component regression (PCR), partial least squares (PLS) and multiple linear regression (MLR). PCR and MLR are at the two extremes of the continuum. In PCR and PLS, cross‐validation is used to determine the optimum number of factors or ‘latent variables’ to retain in the regression model. CR allows one to vary themethodin addition. Cross‐validation then determines both the optimum method and the number of latent variables. The CR ‘prediction error surface’—a function of the method and number of latent variables—is elucidated. The optimal model is defined as the minimum of this surface. Among the cases studied, the optimal model usually comes from the region of the continuum between PCR and PLS. Few derive from the region between PLS and MLR. It is also demonstrated that FIR models identified by CR have frequency domain properties similar to those identified by PCR.