bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemiluminescent immunoassay (CLIA) for the detection of brucellosis and tularaemia antigens
Authors: Vidžiūnaité R. , Mikulskis P. , Kulys J.
Published: May 2005

The detection of brucellosis and tularaemia infection agents is of particular interest for medical practice. The possibility of using enhanced chemiluminescence reactions for the determination of these agents is studied in this work.

Light intensity depends on both the conjugate concentration used and the conditions at which the adsorption was performed. Optimal conditions for these test‐systems were: ∼ 20 μg/mL of Ig and 200 μg/mL (titre 1:20) of conjugate. As is seen from the chemiluminescent and spectrophotometric results the lowest determined concentrations are 10 and 30 ng/mL (for brucellosis) and 1 and 5 ng/mL (for tularaemia), respectively. Calibration curves in the antigen concentrations ranging from 10 to 2500 ng/mL (for brucellosis) and from 1 to 500 ng/mL (for tularaemia) are observed. Optical density depends linearly on the logarithm of the antigen concentration from 30 to 5000 ng/mL (for brucellosis) and from 5 to 250 ng/mL (for tularaemia).

The results obtained permit the conclusion that the chemiluminescence method can be used in enzyme immunoanalysis for brucellosis and tularaemia antigens.
A blue chemiluminescence in the reaction of umbelliferone with hypochlorite
Authors: Slawinska Danuta , Slawinski Janusz
Published: May 2005

A strongly fluorescing 7‐hydroxycoumarin (umbelliferone, U) oxidized in dilute (10 μmol/L‐0, 1 mol/L) aqueous solution with CIO− or CIO− + H2O2 (but not with H2O2 alone) produces a strong chemiluminescence (CL). Light emission kinetics depends on the pH of solution (4.0–10.5) and the reaction has a low activation energy Ea = 31 ± 2 kJ/mol (285–310 K). The spectrum covers the fluorescence of umbelliferone (400–550 nm, λmax 460nm). No red emission typical of 1Δg, 1Σ+g (O2)2 is observed either in the umbelliferone +CIO− or the umbelliferone +CIO− + H2O2 solution. The possible mechanism of CL and concomitant degradative oxidation of umbelliferone is discussed.
Evaluation of Jansson's method for resolving overlapped gas chromatographic peaks
Authors: Crilly Paul Benjamin
Published: May 2005

In a previous paper,1 Jansson's method was used to deconvolve overlapped gas chromatographic peaks. It was found that this method gave improved performance over conventional techniques such as perpendicular drop and shoulder quantitation.2,3 This paper will present the results of a comprehensive test of Jansson's method using several sample mixtures. These results will be compared to what can be obtained by using shoulder quantitation, perpendicular drop or an instrument with a longer column. A relatively large number of tests will be performed for each of the four mentioned methods of peak resolution, so there will be a reasonable statistical basis for the goodness of Jansson's method to resolve fused gas chromatographic peaks for a given set of instrument conditions. A brief review of Jansson's method and the modeling of a gas chromatographic process will also be presented.
Partial least squares analysis with cross‐validation for the two‐class problem: A Monte Carlo study
Authors: Ståhle Lars , Wold Svante
Published: May 2005

A method for statistical analysis of two independent samples with respect to difference in location is investigated. The method uses the partial least squares projections to latent structures (PLS) with cross‐validation. The relation to classical methods is discussed and a Monte Carlo study is performed to describe how the distribution of the test‐statistic employed depends on the number of objects, the number of variables, the percentage variance explained by the first PLS‐component and the percentage missing values. Polynomial approximations for the dependency of the 50 per cent and the 5 per cent levels of the test‐statistic on these factors are given. The polynomial for the 50 per cent level is complicated, involving several first‐, second‐ and third‐degree terms, whereas the polynomial for the 5 per cent level is dependent only on the number of objects and the size of the first component. A separate Monte Carlo experiment indicates that a moderate difference in sample size does not affect the distribution of the test‐statistic. The multi‐sample location problem is also studied and the effect of increasing the number of samples on the test‐statistic is shown in simulations.
An evaluation of SIMCA. Part 2 — classification of pyrolysis mass spectra of pseudomonas and serratia bacteria by pattern recognition using the SIMCA classifier
Authors: Dröge J. B. M. , Rinsma W. J. , Van 'T Klooster H. A. , Tas A. C. , Van Der Greef J.
Published: May 2005

As part of a critical evaluation of the pattern recognition method SIMCA, three data sets containing pyrolysis mass spectra from bacteria were analysed using the SIMCA classifier. Each set consisted of two classes, Pseudomonas and Serratia bacteria, each class containing ten mass spectra and each mass spectrum having 285 spectral features.

The results indicate that for these py‐MS data sets, with low object/feature ratio, the SIMCA classifier produces satisfactory results at the first classification level. At the second level, however, the classification results are not reliable, even after deleting outliers. A comparison of the cross‐validation method and Malinowski's indicator function for the determination of the number of significant principal components showed that the cross‐validation method is less stable and therefore less reliable than the indicator function.
Software review
Authors: Brown Steven D.
Published: May 2005

STATA: 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

Based 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

Methods 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

When 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

In 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

Many 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

A 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

An 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

A 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

One 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

Analysis 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

When 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

The 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

The 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

Some 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

Information 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

Dedicated 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

The 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

A 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

This 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

Continuous 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

Regression 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

Evaluation 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

Window 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

This 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.
Wavelength selection in multicomponent near‐infrared calibration
Authors: Brown Philip J.
Published: May 2005

Modern 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

Raw 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

A 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.
MARS: A tutorial
Authors: Sekulic Sonja , Kowalski Bruce R.
Published: May 2005

This tutorial paper presents a simplified view of one of the more recently published multivariate calibration methods particularly suited to dealing with non‐linear data sets. The method is referred to as MARS and stands for multivariate adaptive regression splines. Simple examples are provided to explain the workings of the method.
Which principal components to utilize for principal component regression
Authors: Sutter Jon M. , Kalivas John H. , Lang Patrick M.
Published: May 2005

Principal 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

Two 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

Genetic 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

We 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

The 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

A 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

Window 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

Regularized 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

A 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

A 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

PLS1 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

New 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

We 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

Profficiency 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

The 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

Presented 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.
PLS shrinks
Authors: De Jong Sijmen
Published: May 2005

An algebraic proof is given that in partial least squares (PLS) regression the Euclidean length of the estimator is shrunk in comparison with the ordinary least squares estimator or with PLS estimators based on a larger number of dimensions.