bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short communication Classification methods for multivariate quality parameters
Authors: Weihs C. , Baumeister W. , Schmidli H. 
Published: May 2005

AbstractThere are many chemical products where product conformity is decided upon by qualitative human judgements of overall product quality. Nowadays, quantitative instrumentally determined quality parameters become available which are intended to replace such qualitative judgements by means of automatic decision rules using multivariate specification limits. Six classification methods to derive such limits are compared in terms of their power to predict corresponding human judgemets on overall color conformity of 17 dyestuffs based on historical quality data. Standard statistical classification methods turned out to be unacceptable for the routine generation of decision rules because of the frequent distinct suboptimality of their predictive power. Instead, a simple non‐statistical classification method utilizing a priori knowledge about the underlying data structure yielded uniformly satisfactory decision rules.
Non‐linear mapping for structure‐activity and structure‐property modelling
Authors: Domine D. , Devillers J. , Chastrette M. , Karcher W. 
Published: May 2005

AbstractFrom a review of the theoretical aspects of non‐linear mapping and the different algorithms available in the literature, the methodological and practical problems linked to the use of this multivariate method in structure‐activity and structure‐property relationship studies are underlined. Useful tools for the graphical display of the outputs and the interpretation of the obtained clusters are presented. Statistical parameters estimating the quality of the graphical representation of each individual are also introduced. An example of application on a data matrix of 37 acaricides described by four physicochemical descriptors (π, F, R, MR) is presented.
Graphics for linearity and selectivity and prediction diagnostics for multicomponent spectra
Authors: Brown Philip J. 
Published: May 2005

AbstractModern scanning infrared reflectance/absorption spectroscopes measure absorptions or reflectance at a sequence of around 1000 wavelengths. Training data may consist of 10–100 carefully designed sample mixtures whose true compositions are either known by formulation or accurately determined by wet chemistry. In future, one wishes to predict the true composition of a newly presented sample from its spectrum. Varying compositions of a mixture of three sugars in water are used for illustration of several different graphical techniques; the spectral measurements here are near‐infrared (NIR) absorbances, but there is nothing exclusively infrared about the methodology. Graphs display the adequacy of a linear explanation of absorbance variability at each wavelength by wavelength linearity plots. These highlight regions of the spectrum where non‐linearities and interaction effects are substantial. By selecting out these substantially non‐linear regions, one can concentrate on linear formulae for prediction with resultant robust linear modelling. Such selections are further aided by plots which identify the component sugar for which each wavelength is most selective. Such plots offer rather natural pre‐screening as an alternative or supplement to the wavelength selection method of Brown.We also display prediction diagnostics (R, Rx) which on a sample‐by‐sample basis may diagnose a particularly unusual presented spectrum. These diagnostics are shown to have predictive import for a validation data set.
Multivariate exploratory data analysis and graphics: A tutorial
Authors: Weihs C. 
Published: May 2005

AbstractExploratory data analysis (EDA) is a toolbox of data manipulation methods for looking at data to see what they seem to say, i.e. one tries to let the data speak for themselves. In this way there is hope that the data will lead to indications about ‘models’ of relationships not expecteda priori. In this respect EDA is a pre‐step to confirmatory data analysis which delivers measures of how adequate a model is. In this tutorial the focus is on multivariate exploratory data analysis for quantitative data using linear methods for dimension reduction and prediction. Purely graphical multivariate tools such as 3D rotation and scatterplot matrices are discussed after having introduced the univariate and bivariate tools on which they are based. The main tasks of multivariate exploratory data analysis are identified as ‘search for structure’ by dimension reduction and ‘model selection’ by comparing predictive power. Resampling is used to support validity, and variables selection to improve interpretability.
Optimization in organic synthesis. An approach to obtaining kinetic information by sequential response surface modelling. Outline of the principles
Authors: Carlson Rolf , Axelsson Anna‐Karin , Nordahl Åke , Barth Tanja 
Published: May 2005

AbstractA method is presented by which it is possible to estimate the initial rate of chemical reactions when the experimental conditions are varied according to a response surface design. The method is intended as a complementary method for analysing data obtained from experiments in synthetic chemistry when the objective is to optimize the yield of the reaction.Data obtained by simulations have been used to develop the method. From the simulated reactions it is shown that sequential analysis of the chemical yield of the reaction makes it possible to estimate models which describe how the parameters of the response surface of the yield vary over time. The derivatives of these time functions of the response surface parameters can be used to define a rate function which describes how the variations in the experimental conditions influence the rate of the reaction.It is shown how such rate functions can be used to afford reasonable estimates of the initial rates of the reaction. The initial reaction rates thus estimated can be used to determine the kinetic order of the reactants and also to provide estimates of the activation energy of the reaction.A thorough discussion of how canonical analysis of the rate function may assist in the elucidation of reaction mechanisms is given.
Multivariate selection of variables in industrial quality control: Optimizing aviation fuel final control
Authors: Andrade J. M. , Prada D. , Muniategui S. , Gomez B. , Pan M. 
Published: May 2005

AbstractThis paper deals with a typical question encountered in all industrial analytical laboratories: are all the tests performed in the laboratory to characterize the final product really necessary? In this work the cross‐validation technique, Procrustes rotation techniques and statistical variable selection have been used to reduce the 26 initial British Petroleum and ASTM kerosene specification test to ten ‘essential’ ones. Statistical as well as chemical considerations were used to select the optimum subset of original variables to be retained from all the possible ones.
Comments on the residual bilinearization method
Authors: Wang Yongdong , Borgen Odd S. , Kowalski Bruce R. 
Published: May 2005

AbstractThrough theoretical analysis and computer simulation, this short communication comments on the residual bilinearization (RBL) method and compares it with non‐bilinear rank annihilation (NBRA) for the treatment of second‐order calibration with non‐bilinear data. It is found that these two methods are mathematically equivalent but have different noise propagation properties. The second‐order advantage, namely quantitation in the presence of unknown interferences, can be carried over to non‐bilinear data only if there exists a net analyte rank (NAR) for the analyte of interest.
Statistical thinking and technique for QSAR and related studies. Part I: General theory
Authors: Stone Mervyn , Jonathan Philip 
Published: May 2005

AbstractThe two parts of this paper form a critique of a variety of statistical techniques of actual or potential use in quantitative structure‐activity relationship (QSAR) studies and related fields. Part I explores the statistical thinking that is needed to underpin those techniques. Emphasis as placed on (a) the role of ‘exchangeability’ as an alternative to unrealistic statistical modelling and (b) the use of cross‐validation to limit self‐deception in the use of any particular technique. The problem of the almost unlimited range of molecular descriptors is seriously addressed. (Part II provides a concise critical review of methods‐some well‐established and some new.)
Robust principal component analysis by projection pursuit
Authors: Xie Yu‐Long , Wang Ji‐Hong , Liang Yi‐Zeng , Sun Li‐Xian , Song Xin‐Hua , Yu Ru‐Qin 
Published: May 2005

AbstractPrincipal component analysis (PCA) is a widely used technique in chemometrics. The classical PCA method is, unfortunately, non‐robust, since the variance is adopted as the objective function. In this paper, projection pursuit (PP) is used to carry out PCA with a criterion which is more robust than the variance. In addition, the generalized simulated annealing (GSA) algorithm is introduced as an optimization procedure in the process of PP calculation to guarantee the global optimum. The results for simulated data sets show that PCA via PP is resistant to the deviation of the error distribution from the normal one. The method is especially recommended for use in cases with possible outlier(s) existing in the data.
Generalized rank annihilation method. II: Bias and variance in the estimated eigenvalues
Authors: Faber N. M. , Buydens L. M. C. , Kateman G. 
Published: May 2005

AbstractRank annihilation factor analysis (RAFA) is a method for multicomponent calibration using two data matrices simultaneously, one for the unknown and one for the calibration sample. In its most general form, the generalized rank annihilation method (GRAM), an eigenvalue problem has to be solved. In this second paper expressions are derived for predicting the bias and variance in the eigenvalues of GRAM. These expressions are built on the analogies between a reformulation of the eigenvalue problem and the prediction equations of univariate and multivariate calibration. The error analysis will also be performed for Lorber's formulation of RAFA. It will be demonstrated that, depending on the size of the eigenvalue, large differences in performance must be expected. A bias correction technique is proposed that effectively eliminates the bias if the error in the bias estimate is not too large. The derived expressions are evaluated by Monte Carlo simulations. It is shown that the predictions are satisfactory up to the limit of detection. The results are not sensitive to an incorrect choice of the dimension of the factor space.
Quantitative analysis of latex in paper coatings by ATR‐FTIR spectroscopy
Authors: Dupuy N. , Duponchel L. , Amram B. , Huvenne J. P. , Legrand P. 
Published: May 2005

AbstractAttenuated total reflectance Fourier transform infrared spectrometry (ATR‐FTIR) has been used to determine the amount of styrene–butadiene latex on the surface of coated papers and to predict the composition of the polymer. Spectrum recording was performed on the sample in its usual form without any modification.For quantitative analysis, partial least squares (PLS) regression, principal component regression (PCR) and multi‐linear regression (MLR) were used to calculate models for prediction. The best result is obtained with PLS.We analysed two series of paper samples. The first analysis concerns the measurement of the quantity of latex of a constant quality on the coating surface. For 15 samples the concentration varied between 5 and 25 parts (grams per 100g of mineral pigments). We compared the predictive results at various resolutions. We obtained a relative error of 0.15 parts in latex at 4 cm−1resolution. The second analysis concerns the measurement of the styrene/butadiene ratio in various types of latex. We obtained a relative error of 0.156 parts for styrene determination and 0.161 parts for butadiene determination.
Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms
Authors: Lindgren Fredrick , Geladi Paul , Rännar Stefan , Wold Svante 
Published: May 2005

AbstractA modified PLS algorithm is introduced with the goal of achieving improved prediction ability. The method, denoted IVS‐PLS, is based on dimension‐wise selective reweighting of single elements in the PLS weight vectorw. Cross‐validation, a criterion for the estimation of predictive quality, is used for guiding the selection procedure in the modelling stage. A threshold that controls the size of the selected values inwis put inside a cross‐validation loop. This loop is repeated for each dimension and the results are interpreted graphically. The manipulation ofwleads to rotation of the classical PLS solution. The results of IVS‐PLS are different from simply selectingX‐variables prior to modelling. The theory is explained and the algorithm is demonstrated for a simulated data set with 200 variables and 40 objects, representing a typical spectral calibration situation with four analytes. Improvements of up to 70% in external PRESS over the classical PLS algorithm are shown to be possible.
On the use of conditional maximization in chemometrics
Authors: Tu Xin Ming , Meng Xiao‐Li , Pagano Marcello 
Published: May 2005

AbstractThe purpose of this short communication is to illustrate the use of conditional maximization (CM) in chemometric applications. The CM algorithm is useful in reducing the computational complexity when a high‐dimensional and complicated maximization problem arises from fitting chemometric models. It can also be efficiently combined with the expectation‐maximization (EM) algorithm for handling incomplete data, a problem that sometimes arises when only a part of the intended data can be collected. Three models from fluorescence spectroscopy are used for illustration.
Kernel‐based PLS regression; Cross‐validation and applications to spectral data
Authors: Lindgren Fredrik , Geladi Paul , Wold Svante 
Published: May 2005

AbstractMultivariate images are very large data structures and any type of regression for their analysis is very computer‐intensive. Kernel‐based partial least squares (PLS) regression, presented in an earlier paper, makes the calculation phase more rapid and less demanding in computer memory. The present paper is a direct continuation of the first paper. In this study the kernel PLS algorithm is extended to include cross‐validation for determination of the optimal model dimensionality. To show the applicability of the kernel algorithm, two examples from multivariate image analysis are used. The first example is an image from an airborne scanner of size 9 × 512 × 512. It consists of nine images which are regressed against a constructed dependent image to test the accuracy of the kernel algorithm when used on large data structures. The second example is a satellite image of size 7 × 512 × 512. Several different regression models are presented together with a comparison of their predictive capabilities. The regression models are also used as examples for showing the use of cross‐validation.
Adaptive, global, extended Kalman filters for training feedforward neural networks
Authors: Blank Thomas B. , Brown Steven D. 
Published: May 2005

AbstractFinding methods for the optimization of weights in feedforward neural networks has become an ongoing developmental process in connectionist research. The current focus on finding new methods for the optimization of weights is mostly the result of the slow and unreliable convergence properties of the gradient descent optimization used in the original back‐propagation algorithm. More accurate and computationally expensive second‐order gradient methods have displaced earlier first‐order gradient optimization of the network connection weights. The global, extended Kalman filter is among the most accurate and computationally expensive of these second‐order weight optimization methods. The iterative, second‐order nature of the filter results in a large number of calculations for each sweep of the training set. This can increase the training time dramatically when training is conducted with data sets that contain large numbers of training patterns. In this paper an adaptive variant of the global, extended Kalman filter that exhibits substantially improved convergence properties is presented and discussed. The adaptive mechanism permits more rapid convergence of network training by identifying data that contain redundant information and avoiding calculations based on this redundant information.
A frequency deconvolution procedure using a conjugate gradient minimization method with suitable constraints
Authors: Bramanti Emilia , Bramanti Mauro , Stiavetti Paolo , Benedetti Enzo 
Published: May 2005

AbstractIn a variety of spectroscopic techniques the fundamental problem exists of determination of the individual spectral components, intrinsically overlapped in the measured spectrum. This is a typical deconvolution problem and several methods and techniques have been proposed for its solution in the technical literature, but suggestions of new approaches are still of interest. A new deconvolution procedure is presented here based on the use of the conjugate gradient minimization algorithm with the addition of sutiable constraints directly obtained by the application to the measured spectrum of the second‐derivative operator or more sophisticated resolution enhancement procedures. Since in the examined case deconvolution essentially requires the minimization of a non‐convex function, the use of such constraints is extremely important to supply suitable input parameters to the conjugate gradient algorithm to avoid obtaining minimum points which have no physical meaning. In our case each spectral compoent used for deconvolution has been assumed to have a Gaussian analytical definition fully identified by three parameters (amplitude, central frequency, spectral bandwidth), so that the input values required to start the deconvolution process are the number M of Gaussian components and 3M suitable initial approximations of the parameters above. It is shown that all this information can be obtained from the measured data. The deconvolution procedure was implemented by a FORTRAN Microsoft Version 5.1 program and experimental results relative to spectroscopic data obtained by FT‐IR analysis of human serum albumin are reported. The results are discussed and compared with data obtained by the use of other techniques.
Deconvolution algorithms for instrumental applications: A comparative study
Authors: Morawski Roman Z. , Szczecinski Leszek , Barwicz Andrzej 
Published: May 2005

AbstractDeconvolution algorithms for measurand reconstruction are considered. Their metrological and numerical properties are briefly characterized. Six algorithms most frequently used for instrumental applications are selected for closer analysis. Their comparative study is based on the use of spectrometric‐type synthetic data, calorimetric‐type synthetic data and spectrometric real‐world data. Conclusions concerning computational complexity and accuracy of the compared algorithms as well as their metrological applicability are drawn.
A correlation principal component regression analysis of NIR data
Authors: Sun Jianguo 
Published: May 2005

AbstractThe use of principal component regression (PCR) as a multivariate calibration method has been discussed by a number of authors. In most situations principal components are included in the regression model in sequence based on the variances of the components, and the principal components with small variances are rarely used in regression. As pointed out by some authors, a low variance for a component does not necessarily imply that the corresponding component is unimportant, especially when prediction is of primary interest. In this paper we investigate a different version of PCR, correlation principal component regression (CPCR). In CPCR the importance of principal components in terms of predicting the response variable is used as a basis for the inclusion of principal components in the regression model. Two typical examples arising from calibrating near‐infrared (NIR) instruments are discussed for the comparison of the two different versions of PCR along with partial least squares (PLS), a commonly used regression approach in NIR analysis. In both examples the three methods show similar optimal prediction ability, but CPCR performs better than standard PCR and PLS in terms of the number of components needed to achieve the optimal prediction ability. Similar results are also seen in other NIR examples.
Selectivity, local rank, three‐way data analysis and ambiguity in multivariate curve resolution
Authors: Tauler Romà , Smilde Age , Kowalski Bruce 
Published: May 2005

AbstractA new multivariate curve resolution method is presented and tested with data of various levels of complexity. Rotational and intensity ambiguities and the effect of selectivity on resolution are the focus. Analysis of simulated data provides the general guidelines concerning the conditions for uniqueness of a solution for a given problem. Multivariate curve resolution is extended to the analysis of three‐way data matrices. The particular case of three‐way data where only one of the orders is common between slices is studied in some detail.
Distance and class space in the UNEQ class‐modeling technique
Authors: Forina M. , Lanteri S. , Sarabia L. 
Published: May 2005

AbstractDifferent estimators of the Mahalanobis distance (such as that based on the Defrise—Gussenhoven correction) are studied and compared with respect to the bias on the distance and the characteristics (sensitivity and specificity) of the class model.Results obtained using estimators with critical values from χ2‐statistics are compared with those obtained using estimators with critical values from β‐statistics (training set) and Hotelling statistics (evaluation set).Tables are reported for D‐statistics (useful for simulating populations of two categories with selectable theoretical sensitivity and specificity) and for critical values of the Mahalanobis distance obtained from β‐statistics.For objects of the training set the estimator of the Mahalanobis distance based on the estimate of the covariance matrix produces models with the optimum sensitivity. The same model has too low a sensitivity for objects of the model category in the evaluation set, but good specificity for objects of outer categories.The estimator with the Defrise–Gussenhoven correction produces enlarged models with too high a sensitivity for objects in the training set, good sensitivity for objects of the model category in the evaluation set and low specificity for objects of outer categories.
Application of procrustean methods to mid‐and near‐infrared spectral data
Authors: Vigneau E. , Devaux M. F. , Safar M. 
Published: May 2005

AbstractProcrustean methods allow the fitting of a given matrix to another given matrix observed on the same objects. In the traditional approach orthogonal constraints are imposed upon the transformation matrix, whereas in the alternative approach Procrustean analysis may be performed without such constraints. The two methods (with and without constraints) were compared on data dealing with mid‐ and near‐infrared spectra of oil. The aim was to reconstruct the mid‐infrared spectral information using data from the near‐infrared spectra. Unconstrained Procrustean analysis proved to be the more efficient for both the calibration and verification sets. Furthermore, the analysis of the transformation matrix between the two infrared ranges made it possible to indicate wavelengths and wave numbers corresponding to the same chemical groups.