Showing papers in "Chemometrics and Intelligent Laboratory Systems in 2002"

TL;DR: An overview of the different methods used for calibration transfer and a critical assessment of their validity and applicability is presented, focusing on methods for transfer of near-infrared (NIR) spectra.

TL;DR: Methods for estimating the rotational ambiguity in any specific result are discussed, and it is emphasized that application of these techniques must be based on some external information about acceptable or desirable shapes of factors.

TL;DR: A faster two-step algorithm that is more stable numerically is constructed and illustrated on a data set with four dimensions and on two chemometrical data sets with 1200 and 600 dimensions.

TL;DR: A comparison between Wold's R criterion and AIC for the selection of the number of latent variables to include in a PLS model that will form the basis of a multivariate statistical process control representation is undertaken based on a simulation study.

TL;DR: Support vector machines (SVM) as a recent approach to classification implement classifiers of an adjustable flexibility, which are automatically and in a principled way optimised on the training data for a good generalisation performance.

TL;DR: In this paper, a new approach was developed to determine the best substitution methods when dealing with the detection limits (DL) values for a given data set Monte Carlo simulation experiments, using a mixture multivariate model, were performed to test the effects of substitution of the "When "

TL;DR: In this paper, the combination of GC×GC with ANOVA-based feature selection was found to be a useful tool to enhance the chemical selectivity, and thus the classification power of the analytical procedure, when coupled with PCA.

TL;DR: The results showed that the proposed method successfully identified structure-bearing variables in both data sets and that it leads to a better subset of variables than other studied feature selection methods.

TL;DR: In this paper, the authors proposed a robust estimation method for contaminated left-censored data sets with potential outliers, which is based on a PROP influence function and showed that the robust estimates obtained with or without the outliers are in close agreement with the corresponding classical estimates.

TL;DR: In this article, the adequacy of two approximate expressions when using unfold-or tri-PLS for the calibration of second-order data is examined, under the assumption that the errors in the predictor variables are homoscedastic, i.e., of constant variance.

TL;DR: In this article, a series of recently synthesized nifedipine analogues containing the nitroimidazolyl group at the C-4 position and different ester substituents at C-3 and C-5 positions of the 1,4-dihydropyridine (DHP) ring were modeled as a function of theoretically derived descriptors.

TL;DR: In this paper, a QSPR study was performed to develop models that relate the structures of 856 organic compounds to their critical temperatures using molecular descriptors derived solely from structure.

TL;DR: A general introduction to some of the topics is given and supplemented by recent literature.

TL;DR: A panoply of multivariate receptor models applied to the analysis of air quality data with a view of identifying common challenges and projecting future developments is looked at.

TL;DR: The proposed method of data ordering and display is simple, informative and fulfils fundamental objectives of the data visualisation techniques and is applied for exploratory analysis of an environmental data set.

TL;DR: In this paper, the authors used the coupling of IR and fluorescence spectroscopies for studying the modifications affecting proteins during cheese ripening, which was performed using appropriate chemometric methods: Common Components and Specific Weights Analysis and Canonical Correlation Analysis.

TL;DR: The combination of the convex hull and the uncertainty estimation offers a practical way for detecting outliers in prediction by adding the potential function method, inliers can also be detected.

TL;DR: Experiments with the cluster size insensitive FCM (csiFCM) on different numerical datasets, synthetic and real multivariate images for different number of clusters and cluster sizes show the improvement compared to FCM and FMLE.

TL;DR: A new nonlinear partial least squares (NLPLS) algorithm that embeds the Takagi–Sugeno–Kang (TSK) fuzzy model into the regression framework of the Partial least squares method to give a more favorable modeling environment in which the knowledge of experts can be easily applied.

TL;DR: In this paper, a piecewise orthogonal signal correction (OSC) algorithm was proposed to remove orthogonality from the response matrix by considering localized spectral features, and the results showed that performing POSC prior to calibration yields regression models that are more parsimonious and with better predictive power than models obtained by the Wise and Fearn OSC algorithms.

TL;DR: In this article, five methods are compared for assessing the uncertainty in multivariate regression coefficients, namely, an approximate variance expression and four resampling methods (jack-knife, bootstrapping objects, bootstrap residuals, and noise addition).

TL;DR: In this paper, a Bayesian approach is proposed to deal with model uncertainties in multivariate receptor models by using Markov chain Monte Carlo (MCMCMC) schemes, which can simultaneously estimate parameters (compositions and contributions), parameter uncertainties, and model uncertainties (number of sources and identifiability conditions).

TL;DR: In this paper, positive matrix factorization (PMF) and convenient factor analysis (CFA) models have been tested using a large aerosol database measured in Hong Kong, and the test results of the two models mentioned above were assessed qualitatively by analyzing factor characteristics, and quantitatively by comparing factor mass profiles.

TL;DR: A multidimensional system of parallel coordinates (abbreviated as ∥-coords) is constructed which induces a one-to-one mapping between subset of N -space and subsets of 2-space, resulting in a rigorous methodology for doing and seeing N -dimensional geometry.

TL;DR: In this paper, the authors proposed a direct method for calculating the parameter sensitivities and applied it to the design of experiments for estimating the order of chemical reactions, where the derivatives of the response with respect to the parameters were calculated.

TL;DR: The WTDS shows an improvement in calibration transfer performance, although wavelet coefficients are compressed, which offers increased robustness against testing errors, with no significant loss of performance.

TL;DR: The use of rough set theory (RST) to construct reducts in a supervised way for reducing the number of features in unsupervised clustering is described.

TL;DR: In this article, the authors compare the performance of least squares regression, ridge regression, and partial least square regression in the context of varying calibration data sizes. But, they focus on the accuracy rather than robustness.

TL;DR: This algorithm is based on the concepts of genetic algorithms for descriptor hyperspace exploration and combined with a stepwise approach to get local convergence and its selection power was evaluated by a fitness function derived from a fuzzy clustering method.

TL;DR: In this paper, a new algorithm, the partial least squares (PLS) improved by genetic algorithm-genetic programming (GA-GP) is applied to deal with functions for inner relationship in quantitative structure-activity relationship (QSAR).