bluuebunny/crossref_metadata_2025_split huggingface dataset:
Software review
Authors: Brown Steven D. 
Published: May 2005

AbstractSTATA: VERSION 1.5, available from Computing Resources Center, 10801 National Boulevard, Los Angeles, CA 90064, U.S.A. Telex: 263199 STATAPC. Published by the Computing Resources Center, 1986. Basic version, for data management, data presentation, statistical testing, estimation, including linear regression, ANOVA and ANCOVA, with two program collections (written in the STATA programming language ) for (1) parametric and non‐parametric tests and (2) generation of statistical graphs. Price: $395 in North America, $470 elsewhere (academic discount price in North America: $265).STATE GRAPHICS MODULE, for high‐resolution statistical graphics, including one‐way and two‐way scatter plots, scatter‐plot matrices, box plots, bar and pie charts, multiple imaging and overlays. Price: $195 in North America, $270 elsewhere (academic discount price in North America: $130).
Identification and substructure analysis of oligosaccharide chains derived from glycoproteins by computer retrieval of high‐resolution 1H‐NMR spectra
Authors: Bot D. S. M. , Cleij P. , Van 'T Klooster H. A. , Van Halbeek H. , Veldink G. A. , Vliegenthart J. F. G. 
Published: May 2005

AbstractBased on a statistical model of the reproducibility of NMR spectral features, a system for computer retrieval of high‐resolution 1H‐NMR spectra of glycoprotein carbohydrates has been developed. For corresponding peaks in an unknown and a reference spectrum, a similarity index based on the reproducibility of the chemical shifts is calculated. In addition, a second similarity index, based on the probability distribution of the percentage of non‐matching peaks, has been developed. From these two similarity indices, a combined similarity index using the recall–reliability function as the optimizing criterion has been derived.First results indicate that the ‘1H‐NMR reproducibility‐based retrieval’ (‘1HRR’) system offers good perspectives for both identification and substructure analysis.
The effect of interferences and calbiration design on accuracy: Implications for sensor and sample selection
Authors: Lorber Avraham , Kowalski Bruce R. 
Published: May 2005

AbstractMethods of multivariate calibration use models that relate spectral data or sensor array responses to the concentrations of analytes. The goal is to insure that the calibration model can accurately estimate analyte concentrations in unknown samples not contained in the calibration set. The sensors or spectral channels (e.g. wavelengths) selected for incorporation in the model, as well as the samples selected for the calibration step, are known to have an effect on the accuracy of analysis for unknown samples. This work provides a fundamental treatment of this effect and derives criteria for optimal selection. Additionally, a proof is given for the advantage of having more sensors and calibration samples than analytes—the overdetermined case.
Estimation of prediction error for multivariate calibration
Authors: Lorber Avraham , Kowalski Bruce R. 
Published: May 2005

AbstractWhen arrays of non‐selective sensors or overlapping spectra are used for chemical analysis, multivariate calibration must be used to relate the instrument responses to individual analytes. Using a set of carefully selected calibration samples, a multivariate mathematical model is constructed for one or more analytes. If this step is successful, the model can be used to predict the concentrations of these analytes in prospective samples. Previously, the equations required to estimate the errors in the predicted concentrations, and from these the confidence intervals, were not available because the three sources of error (measured responses from calibration data, concentrations of the analytes in the calibration set and measured responses from the unknown sample) propagated in a non‐linear manner not amenable to statistical analysis. A new theory for error propagation is developed. The theory developed herein does not require estimates of the actual three sources of errors mentioned above and therefore is easy to implement. Data from near‐infrared reflectance spectrometry of wheat samples were used to test the equations derived from the theory. Complete agreement between the true prediction errors and those estimated from the theory is demonstrated.
Principal component regression in NIR analysis: Viewpoints, background details and selection of components
Authors: Næs Tormod , Martens Harald 
Published: May 2005

AbstractIn this paper we present formulae for prediction error related to principal component regression (PCR). The difference between PCR and ordinary least‐squares (LS) regression is discussed in relation to these formulae. This discussion is used as a basis for a treatment of PCR in NIR analysis. The theory is illustrated by two examples from NIR analysis.
Tensorial calibration: I. First‐order calibration
Authors: Sanchez Eugenio , Kowalski Bruce R. 
Published: May 2005

AbstractMany analytical instruments now produce one‐, two‐ orn‐dimensional arrays of data that must be used for the analysis of samples. An integrated approach to linear calibration of such instruments is presented from a tensorial point of view. The data produced by these instruments are seen as the components of a first‐, second‐ ornth‐order tensor respectively. In this first paper, concepts of linear multivariate calibration are developed in the framework of first‐order tensors, and it is shown that the problem of calibration is equivalent to finding the contravariant vector corresponding to the analyte being calibrated. A model of the subspace spanned by the variance in the calibration must be built to compute the contravarian vectors. It is shown that the only difference between methods such as least squares, principal components regression, latent root regression, ridge regression and partial least squres resides in the choice of the model.
Principal components regression for routine multicomponent UV determinations: A validation protocol
Authors: Gemperline Paul J. , Salt Alger 
Published: May 2005

AbstractA validation protocol for multicomponent spectroscopic assays based on principal components regression is described. Factorial design and hypothesis tests are used to establish the linearity and absence of interaction between components in the regression model. Testing considers multiple response variables simultaneously so that correlation between residuals is properly treated. Assay reproducibility and sensitivity to related substances are evaluated.
Expert system for the characterization of laboratory sample input signals: AUTOCORR
Authors: Mijland P. , Klaessens J. , Vandeginste B. , Kateman G. 
Published: May 2005

AbstractAn expert system is presented for automated time series analysis of laboratory sample input signals. The system, AUTOCORR, builds a model of the time series by identifying the processes that are present. These are an uncorrelated random process and, underlying this, possibly one or more of the following: a first‐order autoregressive process, a trend and a periodic process. AUTOCORR has a knowledge base of 44 rules and 41 facts for this purpose. The employed shell, INFER, allows the use of algorithmic procedures. Elaborate tests with simulated signals show that AUTOCORR has a very low false positive score and is successful in describing time series for laboratory simulation models.
Calibration: Regression models for concentration problems
Authors: Ruskin H. J. 
Published: May 2005

AbstractA brief review of the literature on point estimators in linear calibration problems is undertaken. Supportive evidence for the relative merits of the classical and inverse regression models, drawn in general from the classical inferential and Bayesian approaches, is considered and the criteria for comparison of the estimators are discussed with respect to their suitability for certain classes of problems. the performance of the estimators is assessed with respect to determining the current value of ‘x’, the percentage concentration of administered drug levels in blood in this example. No single ‘best’ method of estimation appears to hold for all values of the unknown concentration when performance is assessed by criteria based on the mean square error (MSE). However, the inverse estimator would appear to be superior to the classical for those values of unknownXclose tox̄.
Mixture analysis using factor analysis I: Calibration and quantitation
Authors: Gemperline Paul J. 
Published: May 2005

AbstractOne of the major application areas of factor analysis, multivariate calibration and quantitation, is covered in this review. The algorithms, methodologies and applications covered include principal component regression, target transformation factor analysis, singular value decomposition and rank annihilation factor analysis. Many important areas of research having relevance to multivariate calibration and quantitation problems are also covered in this review, including background correction, measurement error, rank determination, cross‐validation, figures of merit, detection of invalid samples, experimental design, sample selection, statistical inference and wavelength selection.
Multivariate response surface modelling by principal components analysis
Authors: Bratchell N. 
Published: May 2005

AbstractAnalysis of multivariate response data by modelling the principal components of the response has been applied to two sets of data. In both cases principal components analysis revealed the relationships among the response variables and exploited them to simplify the problem of modelling and optimizing the multivariate response. The models and optima obtained from the principal components compared favourably with the individual models and simultaneous optima.
Discriminant principal components analysis
Authors: Yendle Peter W. , MacFie Halliday J. H. 
Published: May 2005

AbstractWhen the number of variables exceeds the number of samples, one method of multivariate discrimination is to use principal components analysis to reduce the dimensionality and then to perform canonical variates analysis (PC–CVA). This paper proposes an alternative approach in which discriminant analysis is carried out by a weighted principal component analysis of the group means (DPCA). This method does not require prior data reduction and produces discriminant factors that are orthogonal in the original data space.The theory and performance of the two methods are compared. Although the individual factors of DPCA are found to be less discriminating than PC–CVA, the overall discrimination, calculated by multivariate analysis of variance, and the predictive value, estimated by the leaving‐one‐out error rate, are broadly comparable.
Comments on the NIPALS algorithm
Authors: Miyashita Yoshikatsu , Itozawa Toshiaki , Katsumi Hiroyuki , Sasaki Shin‐Ichi 
Published: May 2005

AbstractThe Non‐linear Iterative Partial Least Squares (NIPALS) algorithm is used in principal component analysis to decompose a data matrix into score vectors and eigenvectors (loading vectors) plus a residual matrix. NIPALS starts with some guessed starting vector. The principal components obtained by NIPALS depends on the starting vector; the first principal component could not always be computed. Wold has suggested a starting vector for NIPALS, but we have found that even if this starting vector is used, the first principal component cannot be obtained in all cases. The reason why such a situation occurs is explained by the power method. A simple modification of the original NIPALS procedure to avoid getting smaller eigenvalues is presented.
Residual bilinearization. Part 2: Application to HPLC—diode array data and comparison with rank annihilation factor analysis
Authors: Öhman Jerker , Geladi Paul , Wold Svante 
Published: May 2005

AbstractThe partial least squares–residual bilinearization (PLS–RBL) approach to background correction presented in Part 1 of this work is demonstrated with an example from HPLC with diode array detection. Data are also evaluated with generalized rank annihilation factor analysis (GRAFA) and results are compared.
A role for covariance stabilization in the construction of the classical mixture surface
Authors: Rayens William S. 
Published: May 2005

AbstractSome well‐established ideas from classical covariance stabilization and shrinkage estimation are extended to the context of physical mixtures through the use of a model developed by Burdick and Rayens. A cross‐validation techique is proposed and associated computational issues are addressed. The methodology is evaluated in the context of real polychlorinated biphenyl data.
A review of information theory in analytical chemometrics
Authors: Eckschlager Karel , Štěpánek Vladimír , Danzer Klaus 
Published: May 2005

AbstractInformation theory makes it possible to judge and evaluate methods and results in chemical analysis. The obtained information can be expressed in different ways. One way is to define information as the decrease of uncertainity after analysis. Conditional probabilities are therefore considered when evaluating the information provided by qualitative analyses. However, the use of other information measures, such as the information gain, is often preferable. In multicomponent analysis the translation of information from signals to the amounts of the analytes has been investigated along with the relevance of individual components. Information theory can also be applied to find the optimum experimental conditions. The evaluation of the properties of analytical methods by information theory has been proposed.
Dedicated principal properties for peptide QSARs: Theory and applications
Authors: Skagerberg Bert , Sjöström Michael , Wold Svante 
Published: May 2005

AbstractDedicated principal properties (DPPs) are presented. The DPP approach is an iterative process based on PLS wherein principal properties are calibrated on QSARs with improved predictive capabilities as a consequence. The DPP approach is illustrated by two examples. In the first example thez‐scales developed for structural description in peptide QSARs are optimized according to the DPP approach. In the second example DDPs based on simulated pentapeptide‐like sequences are calculated and evaluated.A comparison is made of the predictive power of peptide QSAR models based on structural description of the peptides with (a) 29 physico‐chemical variables with (b) three principal properties (z‐scales) and (c) three sets of DPPs. The results show that models based on all 29 variables give better predictions than models based on principal properties. Modelling based on DPPs improves the predictive power to the same level as models based on all 29 variables.
A similarity measure for chemical data: Applications to cluster analysis
Authors: Kolossváry István , Wegscheider Wolfhard 
Published: May 2005

AbstractThe results of unsupervised pattern recognition methods are critically dependent on the measure of similarity used for clustering objects. There is little a priori information available on the relative utility of various similarity measures. We introduce here an alternative similarity measure based on the metric tensor measure (MTM). Two standard clustering strategies are tested with the proposed similarity measure: hierarchical clustering and the K‐median method. As data we use the ARCH obsidian data, a data set on Hungarian coal, and trace element data on Hungarian paprika. Differences from the Mahalanobis distance measure are described for intraclass relations.
U. K. usage of chemometrics and artificial intelligence in QSAR analysis
Authors: Rose V. S. , Hyde R. M. , Macfie H. J. H. 
Published: May 2005

AbstractA survey of members of the U.K. QSAR Discussion Group has been made to determine the extent of use and development of chemometric and artificial intelligence (AI) methods in the analysis of multivariate quantitative structure–activity relationship (QSAR) data in the U.K. Chemometric methods were found to be well established in both industrial and educational establishments and there was significant method development occurring. AI methods were not employed to any great extent and the general level of interest in these techniques was low compared to chemometric methods. A requirement for more education in multivariate statistical methods and regression methods was indicated. A need for a user‐friendly, comprehensive, commercially available multivariate statistical package containing multivariate stability testing and regression diagnostic methods was identified.
The start and early history of chemometrics: Selected interviews. Part 2
Authors: Esbensen Kim , Geladi Paul 
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.
Stepwise canonical discriminant analysis of continuous digitalized signals: Application to chromatograms of wheat proteins
Authors: Bertrand Dominique , Courcoux Philippe , Autran Jean‐Claude , Meritan Regis , Robert Paul 
Published: May 2005

AbstractContinuous digitalized signals such as spectra, electrophoregrams or chromatograms generally have a large number of data points and contain redundant information. It is therefore troublesome performing discriminant analysis without any preliminary selection of variables. A procedure for the application of canonical discriminant analysis (CDA) on this kind of data is studied. CDA can be presented as a succession of two principal component analyses (PCAs). The first is performed directly on the raw data and gives PC scores. The second is applied on the gravity centres of each qualitative group assessed on the normalized PC scores. A stepwise procedure for selection of the relevant PC scores is presented. The method has been tested on an illustrative collection of 165 size‐exclusion high‐performance (SE‐HPLC) chromatograms of proteins of wheat belonging to 55 genotypes and grown in three locations. The discrimination of the growing locations was performed using seven to nine PC scores and gave more than 86% accurate classifications of the samples both in the training sets and the verification sets. The genotypes were also rather well identified, with more than 85% of the samples correctly classified. The studied method gives a way of assessing relevant mathematical distances between digitalized signals according to qualitative knowledge of the samples.
Regression on multivariate images: Principal component regression for modeling, prediction and visual diagnostic tools
Authors: Geladi Paul , Esbensen Kim 
Published: May 2005

AbstractRegression between two blocks (usually called ‘dependent’ or Y and ‘independent’ or X) of data is a very important scientific and data‐analytical tool. Regression on multivariate images is possible and constitutes a meaningful addition to existing univariate and multivariate techniques of image analysis. The regression can be used as a modeling tool or for prediction. The form of the regression equation chosen is dependent upon problem specification and information at hand. This paper describes the use of principal component regression (PCR). Both model building and prediction are presented for continuous Y‐variables. The final goal is to supply new image material that can be used for visual inspection on a screen. Also, visual tools for diagnosis of model and prediction are provided, often based on derived image material. Examples of modeling and prediction are given for six channels in a seven‐channel satellite image.
Rapid estimation of the number of coeluting components in liquid chromatography by factor analysis
Authors: Shen Chengbo , Vickers Thomas J. , Mann Charles K. 
Published: May 2005

AbstractEvaluation of the results of factor analysis of sets of spectroscopically detected chromatograms is carried out by examining the shapes of the abstract factors. This is done either by visual inspection or by analysis of the power density spectra produced from them. Owing to constraints imposed by the column function and the spectroscopic instrument function, the information content of the chromatograms necessarily occurs at low spatial frequencies. As a consequence, it appears as relatively broad features in the abstract chromatograms and as a peak in the low‐frequency region of the corresponding power density plot. On the basis of examination of the power density distribution, a well‐defined distinction is made between primary and secondary abstract factors. The major uncertainty encountered in determining the number of chemical components appears to arise from effects of contaminants in reagents.
Window factor analysis: Theoretical derivation and application to flow injection analysis data
Authors: Malinowski Edmund R. 
Published: May 2005

AbstractWindow factor analysis (WFA) is a self‐modeling method for extracting the concentration profiles of individual components from evolutionary processes such as flow injection, chromatography, titrations and reaction kinetics. The method takes advantage of the fact that each component lies in a specific region along the evolutionary axis, called the window. Theoretical equations are derived. The method is used to extract the concentration profiles and spectra of seven bismuth species from data obtained by Gemperline and Hamilton, who injected bismuth perchlorate into a flowing stream of hydrochloric acid.
The relationship of closure, mean centering and matrix rank interpretation
Authors: Pell Randy J. , Seasholtz Mary Beth , Kowalski Bruce R. 
Published: May 2005

AbstractThis paper describes an investigation into the relationship of closure, a baseline offset and mean centering to the interpretation of matrix rank. The equivalence of a certain type of closure to a constant baseline (i.e. a simple numerical offset which may vary between response channels but is constant over all samples) is demonstrated. A systematic approach to the interpretation of the rank of a matrix is given.