Showing papers in "Chemometrics and Intelligent Laboratory Systems in 1997"

TL;DR: The multi-way decomposition method PARAFAC is a generalization of PCA to higher order arrays, but some of the characteristics of the method are quite different from the ordinary two-way case.

TL;DR: Positive matrix factorization (PMF) is a recently published factor analytic technique where the left and right factor matrices (corresponding to scores and loadings) are constrained to non-negative values as mentioned in this paper.

TL;DR: Examples of the use of multi-layer feed-forward neural networks for prediction of carbon-13 NMR chemical shifts of alkanes is given and advantages and disadvantages of multilayer feed- forward neural networks are discussed.

TL;DR: A time-efficient algorithm PMF3 is presented for solving the three-way PARAFAC (CANDECOMP) factor analytic model, in contrast to the usual alternating least squares, which typically leads to convergence in 40–100 iteration steps.

TL;DR: The principles of the Kohonen and counterpropagation artificial neural network (K-ANN and CP-ANN) learning strategy is described and the use of both methods is explained with several examples from analytical chemistry.

TL;DR: The wavelet transform has similarities to the short time Fourier transform (STFT) but partitions the time-frequency space differently in order to obtain better resolutions along time and frequency/scales as discussed by the authors.

TL;DR: Different approaches to the WPT coefficients selection aiming at signal compression and denoising are described and Uniform compression of the set of signals is discussed as well.

TL;DR: In this article, two data sets of near infrared (NIR) spectra that are now available for general use are described. And the results from a recent wavelength selection study using the two datasets are summarized.

TL;DR: In this paper, it was shown that only one experiment is needed in the case where the contribution of the components in the mixture spectra is of a decaying exponential character, which is called DECRA (direct exponential curve resolution algorithm).

TL;DR: In this paper, the authors provide a brief review of the history of detection limits in chemistry, illustrating the critical need for the development of a sound and uniform system of terms and symbols, and the ensuing harmonized position on concepts and nomenclature.

TL;DR: In this article, the authors describe a contribution to Elsevier's data base of files, which includes Raman spectra of a reaction followed in time, FTIR microscopy spectra and time resolved mass spectra for a three-component mixture.

TL;DR: The orthogonal projection approach (OPA), a stepwise approach developed for the determination of the number of compounds present in a multicomponent system, has been extended by including a step that allows the chromatographic and spectroscopic pure compound profiles to be determined using an alternating least-squares procedure.

TL;DR: A Matlab PCA program which efficiently deals with all kinds of data sets was developed and it is recommended to use the kernel EVD for wide data sets whether all PCs or only the first few are required.

TL;DR: For a total resolution of the system full rank is necessary, however, resolution and quantitative determinations of individual nucleic bases in mixtures in the presence of interferences can be achieved (with a prediction error lower than 2% in most cases) even in the case of rank deficiency.

TL;DR: In this article, a new standardization method based on transferring near-infrared (NIR) spectra in the wavelet domain is proposed, which is applied to two different data sets, and its performance is compared with the piecewise direct standardization (PDS) method.

TL;DR: An attempt to reduce the number of iterations that PARAFAC spends in a swamp, a particular method of stabilization is employed and results are presented which suggest that the number iterations can often be greatly reduced.

TL;DR: Four PCA algorithms, namely NIPALS, the power method (POWER), singular value decomposition (SVD) and eigenvalue decomposing (EVD), and their kernel versions are systematically applied to three NIR data sets from the pharmaceutical industry.

TL;DR: In this paper, principal component analysis (PCA) was applied to a very simple case of a tempera panel painted with four known pigments (cinnabar, malachite, yellow ochre and chromium oxide).

TL;DR: The authors demonstrates the application of PLS regression, balanced realization, and canonical variate (CV) state space modeling techniques in identifying stationary vector autoregressive moving average (VARMA) type of time series models in state space.

TL;DR: A new approach to best-basis selection for a set of signals, enabling significant and uniform data compression in the time-frequency domain, is proposed and compared with Wickerhauser's approach of fast approximate PCA.

TL;DR: In this article, a generalized least squares approach to target transformation factor analysis has been shown to have large-sample performance properties that are robust against violations of distributional assumptions, and it is shown how to obtain point estimates and credible intervals for source contributions (either in absolute terms or as proportions of total contributions from all sources).

TL;DR: In this paper, Hotelling's T2 statistic based on PCA is used for the development of multivariate control charts and significant principal components (PCs) are used to develop the T2-chart and the remaining PCs contribute to the ρSPE-chart.

TL;DR: The simplex simulated annealing enhances further stronger stochastic and evolution character of this method.

TL;DR: In this paper, partial least squares (PLS) and principal component analysis (PCA) have been widely used with these kind of processes because they both can be used with redundant data sets.

TL;DR: The Simple-to-use interactive self-modelling mixture analysis approach (SIMPLISMA) as mentioned in this paper is an interactive selfmodeling curve resolution method which differs slightly in the criterion used for the selection of the right number of components present in the mixture data set and also in the calculation of individual concentration profiles and the pure component spectra.

TL;DR: Of all the standardization techniques assayed, PDS gives the lowest mean squared error of prediction, probably due to the inclusion of more variability in the mapping of the spectra.

TL;DR: This paper describes a strategy for developing rugged calibration models using artificial neural networks and demonstrates the method on several NIR process data sets.

TL;DR: A new sequential uniform design procedure is developed and five DTC compounds, which failed to be separated by CE using the technique with changing one variable one time, were completely separated by the proposed procedure.

TL;DR: Theoretical bias and covariance matrix of the parameter vector in the linearized model are given for this method if the original errors are normally distributed, independent and have a constant variance as discussed by the authors.