bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wavelength selection in multicomponent near‐infrared calibration
Authors: Brown Philip J. 
Published: May 2005

AbstractModern scanning (near‐)infrared reflectance/absorption (NIR) spectroscopes measure the absorptions or reflectances at a sequence of around 1000 wavelengths. Training data may consist of 10–100 carefully designed sample mixtures for which the true composition of the mixture is either known by formulation or accurately determined by wet chemistry. In future one wishes to predict the true composition from the spectrum. In this paper we compare a simple wavelength selection approach with methods which retain all the wavelengths. It offers a powerful yet simple technique for choosing those wavelengths that are specific to each pure component as against the other components (including the medium) for the varying compositions. In the presence of a defined range of ingredients in thus chooses wavelengths which are highly selective for each particular component. It has the added advantage of selecting wavelengths which are little effected by interaction effects and consequent non‐linearities.The calibration data used consist of 125 observations of three sugars, each varying at five levels in a full 53design. The validation set consists of 21 further samples specially selected to have compositions outside the range of the training sample. The selection methods perform much better on this prediction set than methods which retain all the wavelengths, 700 in this case. The leave‐one‐out cross‐validation internal to the calibration data would point to the opposite finding and suggests that such crossvalidations may be overly flattering to techniques such as partial least squares and may encourage overfitting. After selection, simple straightforward least squares methods may be used, eschewing the need for ‘shrinkage’ methods such as partial least squares or ridge regression.
Multiple‐correspondence analysis optical microscopy for determination of starch granules
Authors: Devaux M. F. , Qannari E. M. , Gallant D. J. 
Published: May 2005

AbstractRaw starch is composed botanically of characteristic granules of various sizes and shapes, so that each kind of starch may be characterized by the population of its granules. In the present study ten commercial starch species were studied: wheat, rice, manioc, potato, arrowroot, amylomaize, normal maize, waxy maize and two different banana species. Six variables measuring the size and shape of granules were obtained by image analysis. The objective was to find a method to describe and compare the granule populations of the ten species. For such a study, multiple‐correspondence analysis (MCA) was applied. MCA makes it possible to draw similarity maps of categories and objects. For each starch species the frequency distributions (histograms) of the six variables were assessed and each granule was characterized by its species and the classes of histograms to which it belonged. MCA was applied to the granule table and a description of the histogram classes and the granules was obtained. From the variables description a general typology of the granules was deduced. The similarity maps showed considerable scatter of the granules for all species except rice. A particular species could therefore not be identified by a single granule, but the granule distribution seemed to be characteristic. MCA was an appropriate method to analyse these data because it points out non‐linear relationships between quantitative and qualitative variables.
A robust PLS procedure
Authors: Wakelinc I. N. , Macfie H. J. H. 
Published: May 2005

AbstractA robust partial least squares (PLS) regression algorithm is developed. This is achieved by substitution of the univariate regression steps in the iterative PLS2 algorithm by a robust alternative. The angle between loading vectors from both perturbed and unperturbed solutions is used as a measure of robustness. By means of a perturbation study on a structure‐activity data set, it is demonstrated that the stability of the robust method is superior to standard PLS.
Which principal components to utilize for principal component regression
Authors: Sutter Jon M. , Kalivas John H. , Lang Patrick M. 
Published: May 2005

AbstractPrincipal components (PCs) for principal component regression (PCR) have historically been selected from the top down for a reliable predictive model. That is, the PCs are arranged in a list starting with the most informative (PC associated with the largest singular value) and proceeding to the least informative (PC associated with the smallest singular value). PCs are then chosen starting at the top of this list. This paper discusses an alternative procedure of treating PC selection as an optimization problem. Specifically, without any regard to the ordering, the optimal subset of PCs for an acceptable predictive model is desired. Five data sets are analyzed using the conventional and alternative approaches. Two data sets are spectroscopic in nature, two data sets deal with quantitative structure‐activity relationships (QSARs) and one data set is concerned with modeling. All five data sets confirm that selection of a subset without consideration to order secures the best results with PCR. One data set is also compared using partial least squares 1.
Weighted analysis of principal components: Two approximations to statistical weights
Authors: Simeon Vladimir , Pavković Damir 
Published: May 2005

AbstractTwo approximate methods for weighted principal components analysis (WPCA) were devised and tested in numerical experiments using either empirical variances (obtained from replicated data) or assumed variances (derived from unreplicated data). In the first (‘spherical’) approximation each data vector was assigned a weight proportional to the geometrical mean of its variances in all dimensions. The arithmetical mean of variances was used instead in the other approximation. Both the numerical experiments with artificial data containing random errors of various kinds (constant, proportional, constant plus proportional, Poisson) and the analysis of two sets of Raman spectra clearly indicated the necessity of introducing statistical weights. The spherical approximation was found to be slightly better than the arithmetical one. The application of statistical weighting was found to improve the performance of PCA in estimation problems.
Genetic algorithms as a strategy for feature selection
Authors: Leardi R. , Boggia R. , Terrile M. 
Published: May 2005

AbstractGenetic algorithms have been created as an optimization strategy to be used especially when complex response surfaces do not allow the use of better‐known methods (simplex, experimental design techniques, etc.). This paper shows that these algorithms, conveniently modified, can also be a valuable tool in solving the feature selection problem. The subsets of variables selected by genetic algorithms are generally more efficient than those obtained by classical methods of feature selection, since they can produce a better result by using a lower number of features.
Quadratic PLS regression
Authors: Höskuldsson Agnar 
Published: May 2005

AbstractWe treat here an extension of linear PLS regression to include regression on quadratic PLS components. The quadratic regression can be viewed as a natural extention of linear PLS regression to quadratic PLS according to the H‐principle of mathematical modelling. The numerical implementation is treated in detail. It is shown that this approach can be used for models with large numbers of variables. Some modelling strategies are discussed depending on the purpose of the modelling. Applications of this approach are treated.
Predictive ability of regression models. Part I: Standard deviation of prediction errors (SDEP)
Authors: Cruciani Gabriele , Baroni Massimo , Clementi Sergio , Costantino Gabrielle , Riganelli Daniela , Skagerberg Bert 
Published: May 2005

AbstractThe standard deviation of prediction errors (SDEP) is used to evaluate and compare the predictive ability of some regression models, namely MLR, ACE and linear and non‐linear PLS, the last being the best one. The parameter is determined by a cross‐validation approach as an average of several runs obtained on forming groups in a random way. The variation in SDEP with the number of latent variables in PLS is also discussed.
The kernel algorithm for PLS
Authors: Lindgren Fredrik , Geladi Paul , Wold Svante 
Published: May 2005

AbstractA fast and memory‐saving PLS regression algorithm for matrices with large numbers of objects is presented. It is called the kernel algorithm for PLS. Long (meaning having many objects, N) matrices X (N × K) and Y (N × M) are condensed into a small (K × K) square ‘kernel’ matrix XTYYTX of size equal to the number of X‐variables. Using this kernel matrix XTYYTX together with the small covariance matrices XTX (K × K), XTY (K × M) and YTY (M × M), it is possible to estimate all necessary parameters for a complete PLS regression solution with some statistical diagnostics. The new developments are presented in equation form. A comparison of consumed floating point operations is given for the kernel and the classical PLS algorithm. As appendices, a condensed matrix algebra version of the kernel algorithm is given together with the MATLAB code.
Investigation of copper(II)‐Ethylenediaminetetraacetate complexation by window factor analysis of ultraviolet spectra
Authors: Den Wei , Malinowski Edmund R. 
Published: May 2005

AbstractWindow factor analysis (WFA) is a self‐modeling chemometric technique for obtaining the concentration profiles of components from evolutionary processes such as chromotography, titration and reaction kinetics. By specifying the ‘window’, i.e. the region along the evolutionary axis indigenous to a component, the concentration profile of the component can be obtained without recourse to any information concerning the other components. Mathematical expressions required to perform such computations are derived. The method is applied to the investigation of copper(II) complexation with ethylenediaminetetraacetate (EDTA) by recording and factor analyzing the ultraviolet spectra of aqueous solutions containing a fixed amount of the disodium salt of EDTA and varying amounts of CuCl2. Evidence for four different species of EDTA is obtained. Clues concerning the stoichiometry of the species are garnered from the concentration profiles.
Improvements to the classification performance of RDA
Authors: Aeberhard Stefan , Coomans Danny , Vel Olivier De 
Published: May 2005

AbstractRegularized discriminant analysis has proven to be a most effective classifier for problems where traditional classifiers fail because of a lack of sufficient training samples, as is often the case in highdimensional settings. However, it has been shown that the model selection procedure of regularized discriminant analysis, determining the degree of regularization, has some deficiencies associated with it. We propose a modified model selection procedure base on a new appreciation function. By means of an extensive simulation it was shown that the new model selection procedure performs better than the original one. We also propose that one of the control parameters of regularized discriminant analysis be allowed to take on negative values. This extension leads to an improved performance in certain situations. The results are confirmed using two chemical data sets.
Representation of spectra by continuous functions
Authors: Alsberg Bjørn K. 
Published: May 2005

AbstractA general framework for manipulating spectra as functions in traditional multivariate methods such as PCA and PLS is described. The functional representation is very convenient for compression, ensuring smoothness and continuity. There are two fundamentally different types of representations: (a) by functions and (b) by function coefficients. The use of coefficients is the most practical way of analysis.
Detecting and adjusting for non‐linearities in calibration of near‐infrared data using principal components
Authors: Oman Samuel D. , Naes Tormod , Zube Anan 
Published: May 2005

AbstractA new regression method for non‐linear near‐infrared spectroscopic data is proposed. The technique is based on a model which is linear in the principal components and simple functions (squares and products) of them. Added variable plots are used to determine which squares and products to incorporate into the model. The regression coefficients are estimated by a Stein estimate which shrinks towards the estimate determined by the first several principal components and the selected non‐linear terms. The technique is not computationally intensive and is appropriate for routine predictions of chemical concentrations. The method is tested on three data sets and in all cases gives more accurate predictions than does linear principal components regression.
PLS fits closer than PCR
Authors: Jong Sijmen De 
Published: May 2005

AbstractPLS1 regression is generally viewed as lying in between PCR and OLS regression. Proof is given that the coefficient of determination,R2, for a PLS multivariate calibration model is at least as high as that for a PCR model with the same number of components. It appears that PLS can be linked to a correlation‐weighted polynomial regression of a constant response on the eigenvalues of the covariance matrix of the predictor variables.
New molecular descriptors for 2D and 3D structures. Theory
Authors: Todeschini Roberto , Lasagni Marina , Marengo Emilio 
Published: May 2005

AbstractNew theoretical molecular indices are defined. They contain information about the whole molecular structure in terms of size, shape, symmetry and atom distribution. These indices are calcualted from the (x, y, z) co‐ordinates of a molecule within different weighting schemes in a straightforward manner and represent a very general approach to describe molecules, molecular fragments, macromolecules and molecular conformations in a unitary conceptual framework. Their interpretability is quite evident and is defined by the same mathematical properties as the algorithm used for their calculation. Examples on the total surface area, toxicity of PCDD and PCDF and reaction rate of catalysed reactions show a high modelling power of these indices.
A combined theory for PCA and PLS
Authors: Höskuldsson Agnar 
Published: May 2005

AbstractWe present here an algorithmic approach to modelling data that includes principal component analysis (PCA) and partial least squares (PLS). In fact, the numerical algorithm presented can carry out PCA or PLS. The algorithm for linear analysis and extensions to non‐linear analysis applies to both PCA and PLS. The algorithm allows for combination of PCA and PLS types of models and therefore extends modelling to new types of models that involve combination of regression models and ‘selection of variation’ models, which is the idea of PCA‐type models. The fact that the algorithm carries out both PCA and PLS shows that PCA and PLS are based on the same theory. This theory is based on the H‐principle of mathematical modelling. The algorithm allows tests for outliers, sensitivity analysis and tests of submodels. These aspects of the algorithm are treated in detail. We compute various measures of sizes, e.g. of components, of the covariance matrix, of its inverse, etc. that show how much the algorithm has selected at each step. The analysis is illustrated by data from practice.
Expression of the reference value for proficiency tests
Authors: Feinberg Max , Bugner Emmanuel , Theiller Gédéon , Houba Victor , Kadijk Fetze 
Published: May 2005

AbstractProfficiency tests raise two types of problems: (i) the homogeneity of a sample must be carefully checked before using it as a reference material; (ii) it is necessary to define a conventional reference value (RV) in order to rank participants. Since these topics are poorly addressed in the literature, a reference material was specially prepared in order to propose a procedure for verifying homogeneity and to establish whether the algorithms classically used to compute the conventional true value have an influence on the conclusion of the test.The homogeneity of the sample was not perfect and univariate and multivariate techniques were used to demonstrate some analytes can be suspected as heterogeneous. However, this was considered adequate for introducing the sample in a large proficiency test organized between four laboratory associations which regularly perform such tests. Altogether more than 3000 measurements were collected and eight algorithms were applied to compute the RV on 14 analytes. Thus is was possible to demonstrate that some of these algorithms are better adapted than others. It is also obvious that some analytes are better suited to proficiency testing.It can be concluded from this work that some effort towards standardization would be profitable to check homogeneity or to compute the RV, especially since the economic weight of proficiency testing is becoming even more important with the development of certification accreditation.
Diagnostics statistics in QSAR
Authors: Mager Peter P. 
Published: May 2005

AbstractThe formal application of a Hansch analysis to a series of 3‐quinuclidinyl benzylates (QNBs) led to a ‘statistically significant’ QSAR equation. In contrast, the application of the MASCA model has shown that the design matrix is unsuitable for each QSAR analysis: one sample member is an outlier but not a high‐leverage or influential point; another one is an influential point, a high‐leverage point and an extra‐carrier point. The regressors of the design matrix are multicollinear without predictive model power. The result of such flagged observation and this type of multicollinearity is a multiple cluster correlation. The QNB series is a good example for ‘sampling artifacts’ where no practically important but artificial QSARs can be found.
A second‐order standard addition method with application to calibration of a kinetics–spectroscopic sensor for quantitation of trichloroethylene
Authors: Booksh Karl , Henshaw John M. , Burgess Lloyd W. , Kowalski Bruce R. 
Published: May 2005

AbstractPresented here is an algorithm for analysis of second order data by the method of standard additions. The method of standard additions is applicable when matrix effects make traditional calibration unreliable. The algorithm employs a generalized eigenproblem to mathematically separate the instrument response of the analyte from the instrument response of any interfering species. A scheme for determining the eigenvectors (and hence the concentration estimate) that uniquely correspond to the analyte of interest is given. These eigenvectors can readily be distinguished from any eigenvector that corresponds to the spectrum of the interferents or both the interferents and analyte. The stability of the estimated analyte concentration is verified by Monte Carlo simulations. The algorithm is applied to the analysis of trichloroethylene in samples that have matrix effects caused by an interaction with chloroform.
A simple iteration algorithm for PLS regression
Authors: Zhu Eryi , Barnes Ramon M. 
Published: May 2005

AbstractA simple iteration algorithm that is faster and less memory‐intensive than the NIPALS iteration algorithm for PLS regression is presented. The iteration algorithm is obtained by treating the orthogonal expansion or decomposition of a matrix X as an extremum problem subject to normalization and orthogonality constraint conditions and then solving the problem by use of the method of Lagrange multipliers. The main idea in this method is to find the transformation vector r. The latent variable t is expressed exactly as the linear combination of X‐variables with the vector r so that the final regression coefficients can be conveniently provided. In the algorithm the recursion of the orthogonal projection is needed, which is derived by use of a matrix inverse formula. Algorithms are established from the equation for calculating the vector r that are suitable for dealing with three cases of large data sets. The first case is when the number of objects is very large, the number of variables is relatively small and the number of Y‐variables is equal to or greater than the number of X‐variables. The second case is when the number of objects is very large, the number of variables is relatively small and the number of X‐variables is greater than the number of Y‐variables. The last case is when the number of variables, either X‐ or Y‐variables, or both, is very large and the number of objects is small.
An extended strategy for exploratory multivariate image analysis including noise considerations
Authors: Pedersen F. , Bengtsson E. , Nordin B. 
Published: May 2005

AbstractMultivariate image analysis (MIA) is a powerful tool for many image segmentation and classification problems, but the interpretation and understanding of the original and resulting multidimensional (multivariate) data are not always easy. A strategy for MIA has been proposed which describes its usage on multivariate images for segmentation tasks. MIA starts with principal component analysis (PCA) and then continues with interactive analysis of the output from PCA. In this paper a number of extensions to MIA are proposed. The extensions are the suggestion to incorporate preprocessing of the multivariate image in MIA, the suggestion to use synthetic multivariate image models which create a clear‐cut situation, and new visualization tools which improve the interactivity and understanding of the results. Extended MIA is applied on synthetic multivariate image data simulating a possible application with large noise, positron emission tomography (PET). As a result of the interactive analysis, suggestions for preprocessing emerge. The developed methodology for handling the noise is then applied on real PET image data with good results.
Application of regression analysis for motor gasoline classification
Authors: Khots M. S. , Nazarov V. I. , Emelyanov V. E. 
Published: May 2005

AbstractA procedure for the classification of motor gasolines by their trend to carburettor icing has been proposed. For this goal a set of regression models was employed. Motor gasoline classification was carried out in terms of the isopropyl equivalent using seven physicochemical properties.
A PLS kernel algorithm for data sets with many variables and few objects. Part II: Cross‐validation, missing data and examples
Authors: Rännar Stefan , Geladi Paul , Lindgren Fredrik , Wold Svante 
Published: May 2005

AbstractThis is Part II of a series concerning the PLS kernel algorithm for data sets with many variables and few objects. Here the issues of cross‐validation and missing data are investigated. Both partial and full crossvalidation are evaluated in terms of predictive residuals and speed and are illustrated on real examples. Two related approaches to the solution of the missing data problem are presented. One is a full EM algorithm and the second a reduced EM algorithm which applies when the number of missing values is small. The two examples are multivariate calibration data sets. The first set consists of UV–visible data measured on mixtures of four metal ions. The second example consists of FT‐IR measurements on mixtures consisting of four different organic substances.