bluuebunny/crossref_metadata_2025_split huggingface dataset:
PCR/PLSR optimization based on noise covariance estimation and Kalman filtering theory
Authors: Ergon Rolf , Esbensen Kim H. 
Published: October 2002

AbstractThe theoretical connection between principal component regression (PCR) and partial least squares regression (PLSR) on one hand and Kalman filtering (KF) on the other is known from earlier work. In the present paper we investigate the possibilities to use latent variables modeling and KF theory as means for optimization of ordinary PLSR and PCR predictors, based on the prerequisite of prior X noise covariance estimates facilitated e.g. by more X than y observations. The result is a new PLSR optimization method, while the PCR optimization turns out to be identical with an earlier known method. A simulation example and two real‐world data examples supporting the theoretical development are presented. The treatment is limited to cases with only one response variable, although an extension to multiresponse cases is also possible. Copyright © 2002 John Wiley & Sons, Ltd.
Batch statistical processing of 1H NMR‐derived urinary spectral data
Authors: Antti H. , Bollard M. E. , Ebbels T. , Keun H. , Lindon J. C. , Nicholson J. K. , Holmes E. 
Published: October 2002

AbstractMultivariate statistical batch processing (BP) analysis of 1H nuclear magnetic resonance (NMR) urine spectra was employed to establish time‐dependent metabolic variations in animals treated with the model hepatotoxin hydrazine. Hydrazine was administered orally to rats (at 90 mg kg−1), and urine samples were collected from dosed rats and matched control animals (n = 5 per group) at time points up to 168 h post‐dose. Urine samples were analysed via 1H NMR spectroscopy and partial least squares‐based batch processing analysis, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile was established for the control group, and samples obtained from hydrazine‐treated animals were assessed using this model. Time‐dependent deviations from the control model were evident in all hydrazine‐treated animals, and hepatotoxicity was manifested by increased urinary excretion of taurine, creatine, 2‐aminoadipate, citrulline and β‐alanine together with depletion of urinary levels of citrate, succinate and hippurate. The experiment was repeated at six different pharmaceutical centres in order to assess the robustness of the technology and to establish the natural variability in the data. Results were consistent across the data for all centres. BP plots showed a characteristic pattern for each toxin, allowing the time points at which there were maximum metabolic differences to be determined and providing a means of visualizing the net toxin‐induced metabolic movement of urinary metabolism. BP may prove to be a powerful metabonomic tool in defining time‐dependent metabolic consequences of toxicity and is an efficient means of visualizing inter‐animal variations in response as well as defining multivariate statistical limits of normality in terms of biofluid composition. Copyright © 2002 John Wiley & Sons, Ltd.
Maximum likelihood fitting using ordinary least squares algorithms
Authors: Bro Rasmus , Sidiropoulos Nicholaos D. , Smilde Age K. 
Published: October 2002

AbstractIn this paper a general algorithm is provided for maximum likelihood fitting of deterministic models subject to Gaussian‐distributed residual variation (including any type of non‐singular covariance). By deterministic models is meant models in which no distributional assumptions are valid (or applied) on the parameters. The algorithm may also more generally be used for weighted least squares (WLS) fitting in situations where either distributional assumptions are not available or other than statistical assumptions guide the choice of loss function. The algorithm to solve the associated problem is called MILES (Maximum likelihood via Iterative Least squares EStimation). It is shown that the sought parameters can be estimated using simple least squares (LS) algorithms in an iterative fashion. The algorithm is based on iterative majorization and extends earlier work for WLS fitting of models with heteroscedastic uncorrelated residual variation. The algorithm is shown to include several current algorithms as special cases. For example, maximum likelihood principal component analysis models with and without offsets can be easily fitted with MILES. The MILES algorithm is simple and can be implemented as an outer loop in any least squares algorithm, e.g. for analysis of variance, regression, response surface modeling, etc. Several examples are provided on the use of MILES. Copyright © 2002 John Wiley & Sons, Ltd.
Screening for dioxin contamination in fish oil by PARAFAC and N‐PLSR analysis of fluorescence landscapes
Authors: Pedersen Dorthe Kjœr , Munck Lars , Engelsen Søren Balling 
Published: October 2002

AbstractA preliminary investigation of fish oils demonstrates that fluorescence excitation–emission landscapes evaluated by three‐way chemometric methods may be a candidate for an inexpensive screening method to indicate the level of contamination by dioxins and PCBs, which are normally analysed by expensive and time‐consuming physicochemical separation techniques such as GC‐MS. Fluorescence landscapes of 88 fish oils have been investigated and showed great variation due to species, season and treatment, depicting a variation in natural fluorescent components. The fluorescence landscapes were analysed by PARAFAC. Samples with similar fluorescence fingerprints were selected from a PARAFAC score plot, and local significant prediction models with PARAFAC/MLR, N‐PLSR and PLSR were established with correlation coefficients in the range from r = 0.69 (n = 10) to r = 0.97 (n = 75) for dioxin and of r = 0.92 (n = 12) for PCB. Application of PARAFAC/MLR and N‐PLSR to fluorescence landscapes of fish oils resulted in local regression models for dioxin determination with prediction errors below 1 ng kg−1, which is comparable to the reference method. In the PARAFAC model, two of the modes give the excitation and emission spectra of the pure underlying fluorophores, while the third mode gives their individual concentrations. Excitation and emission optima for three or four PARAFAC components in each data set were identified, representing both positive and negative (quenching) correlation components. It is hypothesized that the quenching correlation may be effected by the joint contribution of chlorinated organic compounds in the fish oil, including dioxins and PCBs. Other explanations for the results are discussed. Copyright © 2002 John Wiley & Sons, Ltd.
Omeprazole and analogue compounds: a QSAR study of activity against Helicobacter pylori using theoretical descriptors
Authors: Bruni Aline Thais , Ferreira Márcia Miguel Castro 
Published: October 2002

AbstractOmeprazole and analogues were studied with respect to their activity as inhibitors of urease Helicobacter pylori. Conformational analysis was performed according to the method proposed by Bruni et al. Theoretical descriptors were calculated by an ab initio method (6–31G** basis set). Since several minimum energy structures were obtained for each compound, and the calculated descriptors proved to be sensitive to the structural conformation, different criteria were proposed for conformation selection. Three data sets were generated wherein conformations were grouped according to minimum heat of formation, minimum electronic energy and structural similarity. For these three sets, experimental per cent of control was used to develop quantitative structure–activity models by PLS. Their cross‐validation and correlation coefficients were very good (Q2 = 0.97 and R2 = 0.99 on average) and the standard error of validation was much smaller in comparison with results from the literature. Copyright © 2002 John Wiley & Sons, Ltd.
Hierarchical experimental design exemplified by QSAR evaluation of a chemical library directed towards the melanocortin 4 receptor
Authors: Andersson Per M. , Lundstedt Torbjörn 
Published: October 2002

AbstractA design strategy for how to select a smaller subset of compounds to synthesize, test for activity and finally establish a QSAR model was investigated. The biological target of interest was the melanocortin 4 receptor (MC4R), which is assumed to have an important role in the control of feeding behaviour and is therefore of great interest as a drug target. A subset of our in‐house compound collection was used to investigate and illustrate the proposed design strategy. The selected compound set has a phenyl core motif in common with five substituents in the ring, which could be varied independently and therefore represented a suitable design scaffold to investigate. By using statistical molecular design in combination with the proposed hierarchical design approach, a training set of compounds was selected. As design variables for the substituents, principal properties based on a set of 110 substituents characterized with σ, π and Verloop parameters were used. By using measured affinity data towards MC4R as a response variable together with the principal properties, a QSAR model was established which proved to have good predictive ability. Two important conclusions were that none of the compounds in the test set were predicted as false negatives by the QSAR model and that the proposed design approach appears to work well. The strategy is implemented in an ongoing project, and the usefulness of hierarchical design of experiments will be explored further. Copyright © 2002 John Wiley & Sons, Ltd.
Estimation of uncertainty of concentration estimates obtained by image analysis
Authors: Korpelainen M. , Reinikainen S.‐P. , Laukkanen J. , Minkkinen P. 
Published: October 2002

AbstractImages from the scanning electron microscope (SEM) and the optical microscope are widely used in mineral processing to estimate the concentrations of mineral species. The aim of this study was to optimize the procedure used for the determination of concentration estimates from SEM images of ore and rock samples. The goal was especially to develop a method that would allow the estimation of the concentration uncertainty when the image analysis is still in progress. For this study, samples consisting of chromite (FeCr2O4, density 4.7 g cm−3) and quartz (SiO2, density 2.7 g cm−3) were prepared. The chromite concentration in the sample materials varied from 0.1 to 90 wt%. Four replicate microscopic samples for each chromite concentration were prepared and analysed. The total number of samples was 36. Two hundred images and 20 000–30 000 particles per microscopic sample were analysed. Estimation of the sampling error was based on Pierre Gy's sampling theory. Several error components were studied and the error estimates were found to be relatively reliable when they were based on the fundamental sampling error. Copyright © 2002 John Wiley & Sons, Ltd.
Development of a software sensor for phosphorus in municipal wastewater
Authors: Jansson Åsa , Röttorp Jonas , Rahmberg Magnus 
Published: October 2002

AbstractDifferent multivariate techniques were applied to data from a wastewater treatment plant in order to develop a software sensor for phosphorus. The predictive power, measured as root mean square error of cross‐validation (RMSECV) and root mean square error of prediction (RMSEP), of partial least squares (PLS) models was found to be superior to that of multiple linear regression (MLR) and principal component regression (PCR) models. The result from using a static as well as a finite impulse response (FIR) model structure was also investigated. Good estimates of phosphorus were obtained for both kinds of model structure, but the FIR models were slightly better than the static ones. Out of the two different fractions of phosphorus that were modelled, i.e. total phosphorus and phosphate in solution, the best predictions were obtained for soluble phosphorus. Copyright © 2002 John Wiley & Sons, Ltd.
Decomposition using maximum autocorrelation factors
Authors: Larsen Rasmus 
Published: October 2002

AbstractThis paper presents methods for the analysis and decomposition of multivariate data sets where a given ordering/structure of the observations or the variables exists. Examples of such data sets are found in remote sensing imagery, where observations (pixels) each consisting of a reflectance spectrum are organized in a two‐dimensional grid. Another example is biological shape analysis. Here each observation (e.g. human bone, cerebral ventricle) is represented by a number of landmarks, the co‐ordinates of which are the variables. Here we do not have an ordering of the observations (individuals). However, normally we have an ordering of landmarks (variables) along the contour of the objects. For the case with observation ordering, the maximum autocorrelation factor (MAF) transform was proposed for multivariate imagery (Switzer P. InComputer Science and Statistics, Billard L (ed.). Elsevier: Amsterdam, 1985; 13–16). This corresponds to an R‐mode analysis of the data matrix. We propose to extend this concept to situations with variable ordering. This corresponds to a Q‐mode analysis of the data matrix. We call this method the Q‐MAF decomposition. It turns out that in many situations the new variables resulting from MAF and Q‐MAF analyses can be interpreted as a decomposition of (spatial) frequency. However, contrary to Fourier decomposition, these new variables are located in frequency as well as location (space, time, wavelength, etc.). Copyright © 2002 John Wiley & Sons, Ltd.
Applicational aspects of support vector machines
Authors: Belousov A. I. , Verzakov S. A. , von Frese J. 
Published: October 2002

AbstractThe special emphasis of support vector machines (SVMs) on generalization ability makes this approach particularly interesting for real‐world applications with limited amounts of training data. In this paper we analyse the applicational aspects of SVMs, illustrating them with the step‐by‐step construction of a classifier for polymers by means of their mid‐infrared spectra. With this example we show how the main difficulties of a typical industrial classification task can be addressed using SVMs. Copyright © 2002 John Wiley & Sons, Ltd.
New and old trends in chemometrics. How to deal with the increasing data volumes in R&D&P (research, development and production)—with examples from pharmaceutical research and process modeling
Authors: Wold Svante , Berglund Anders , Kettaneh Nouna 
Published: October 2002

AbstractChemometrics was started around 30 years ago to cope with and utilize the rapidly increasing volumes of data produced in chemical laboratories. The methods of early chemometrics were mainly focused on the analysis of data, but slowly we came to realize that it is equally important to make the data contain reliable information, and methods for design of experiments (DOE) were added to the chemometrics toolbox. This toolbox is now fairly adequate for solving most R&D problems of today in both academia and industry, as will be illustrated with a few examples. However, with the further increase in the size of our data sets, we start to see inadequacies in our multivariate methods, both in their efficiency and interpretability. Drift and non‐linearities occur with time or in other directions in data space, and models with masses of coefficients become increasingly difficult to interpret and use. Starting from a few examples of some very complicated problems confronting chemical researchers today, possible extensions and generalizations of the existing chemometrics methods, as well as more appropriate preprocessing of the data before the analysis, will be discussed. Criteria such as scalability of methods to increasing size of problems and data, increasing sophistication in the handling of noise and non‐linearities, interpretability of results, and relative simplicity of use will be held as important. The discussion will be made from a perspective of the evolution of the scientific methodology as driven by new technology, e.g. computers, and constrained by the limitations of the human brain, i.e. our ability to understand and interpret scientific and data analytical results. Quilt‐PCA and Quilt‐PLS presented here address and offer a possible solution to these problems. Copyright © 2002 John Wiley & Sons, Ltd.
Non‐linear partial least squares. Estimation of the weight vector
Authors: Hassel Per A. , Martin Elaine B. , Morris Julian 
Published: October 2002

AbstractA key issue in non‐linear partial least squares (PLS) is the calculation of the weight vectors. The weight vector can be derived in a number of ways; for example, as in linear PLS, through an optimization routine as in error‐based non‐linear PLS, or through the covariance criterion as in Spline‐PLS. An alternative approach to the estimation of the weight vector in non‐linear PLS is proposed based on the theory of the weighted average, the reciprocal variance criterion. Unlike the covariance‐based criterion that gives equal consideration to both the response and the process variables in the calculation of the weight vector, the proposed approach gives emphasis to the response variables. The new weighting criterion performs well where the variability in the response variables can be captured by a few underlying phenomena. The methodology is illustrated through its application to simulated spectral data and an industrial NIR data set. It is shown that for the case studies the proposed approach outperforms the covariance criteria of PLS in terms of performance on a validation data set. Copyright © 2002 John Wiley & Sons, Ltd.
Generalization of pair correlation method (PCM) for non‐parametric variable selection
Authors: Héberger Károly , Rajkó Róbert 
Published: October 2002

AbstractThe pair correlation method (PCM) has been developed for choosing between two correlated predictor variables (factors) provided that the scatter is caused not only by random effects. The distinction between two variables can be made using an arrangement into a 2 × 2 contingency table. Further on, suitable test statistics can be used to decide the significance of differences between factors. PCM can easily be generalized (GPCM) for variable selection purposes using more than two variables. The comparison of factors can be made pairwise in all possible combinations. If a given statistical test indicates a significant difference between the factors, the following terms are used for the overwhelming and subordinate factors: superior–inferior or winner–loser respectively. Every comparison can mark a factor as superior, inferior or no decision can be made. The following step is ranking of predictor variables. Three ways of ranking have been elaborated: (i) simple ranking, (ii) ranking based on differences and (iii) ranking according to probability‐weighted differences. (Difference here means number of wins minus number of losses.) Suitable examples are presented to show the usefulness and applicability of the method in various conditions. Copyright © 2002 John Wiley & Sons, Ltd.
PARAFAC with splines: a case study
Authors: Reis Marlon M. , Ferreira Márcia M. C. 
Published: October 2002

AbstractThe PARAFAC model has been used in several applications in chemistry, e.g. for overlapped spectra resolution and second‐order calibration. In general, the PARAFAC method uses a vector space approach by considering the matrices resulting from the decomposition as a collection of vectors. This paper presents a PARAFAC application where the factors resulting from the decomposition are considered as functions. The functional objects used for this are spline functions. The methodology used performs the Spline‐PARAFAC decomposition based on the Bro–Sidiropoulos approach for the unimodality constraint. One of the advantages of using splines is the possibility of achieving a controlled degree of smoothing on the decomposed components. The amount of smoothing applied on the components in the presented methodology is controlled by a penalty parameter or by the number of basis functions. Thus Spline‐PARAFAC requires the calculation of the parameter λ and the number of basis functions, which were determined in this work by using ordinary cross‐validation (OCV). Spline‐PARAFAC was applied to a carbon monoxide data set comprising concentrations measured every hour during the years 1997 and 1999 in the city of São Paulo, Brazil. Each data set was arranged in a three‐way array of dimension (24 hours × 5 days × 52 weeks). Spline‐PARAFAC showed a good performance, producing smoothed profiles describing the daily variations in emitted gas and the seasonal effects during the year. Copyright © 2002 John Wiley & Sons, Ltd.
Dealing with missing data in MSPC: several methods, different interpretations, some examples
Authors: Arteaga Francisco , Ferrer Alberto 
Published: October 2002

AbstractThis paper addresses the problem of using future multivariate observations with missing data to estimate latent variable scores from an existing principal component analysis (PCA) model. This is a critical issue in multivariate statistical process control (MSPC) schemes where the process is continuously interrogated based on an underlying PCA model. We present several methods for estimating the scores of new individuals with missing data: a so‐called trimmed score method (TRI), a single‐component projection method (SCP), a method of projection to the model plane (PMP), a method based on the iterative imputation of missing data, a method based on the minimization of the squared prediction error (SPE), a conditional mean replacement method (CMR) and various least squared‐based methods: one based on a regression on known data (KDR) and the other based on a regression on trimmed scores (TSR). The basis for each method and the expressions for the score estimators, their covariance matrices and the estimation errors are developed. Some of the methods discussed have already been proposed in the literature (SCP, PMP and CMR), some are original (TRI and TSR) and others are shown to be equivalent to methods already developed by other authors: iterative imputation and SPE methods are equivalent to PMP; KDR is equivalent to CMR. These methods can be seen as different ways to impute values for the missing variables. The efficiency of the methods is studied through simulations based on an industrial data set. The KDR method is shown to be statistically superior to the other methods, except the TSR method in which the matrix to be inverted is of a much smaller size. Copyright © 2002 John Wiley & Sons, Ltd.
Resolution of on‐flow LC/NMR data by multivariate methods — a comparison
Authors: Shen Hailin , Airiau Christian Y. , Brereton Richard G. 
Published: October 2002

AbstractData from LC/NMR have some distinct characteristics, differing from those of LC/MS and LC/DAD. This paper compares several approaches for resolution on both a three‐component and a seven‐component case study. Non‐iterative methods based on principal component analysis (EFA, WFA, HELP) and iterative approaches using constraints (ALS, Gentle) are compared with a new proposed method, constrained key variable regression (CKVR), which involves refining key variables and then using constraints of non‐negativity and equality to obtain optimized spectral resolution. In LC/NMR the reconstruction of detailed spectral profiles is important, requiring specific refinement of existing tools. Copyright © 2002 John Wiley & Sons, Ltd.