Chemometrics applied to unravel multicomponent processes and mixtures

Juan, Anna de; Tauler, Romà

doi:10.1016/s0003-2670(03)00724-4

Cited by 494 publications

(365 citation statements)

References 116 publications

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“…However, equation (2) can often be solved in terms 266 of true constituent profiles if a proper set of initial values for B aug or C are found in 267 order to start the bilinear decomposition, and suitable constraints are applied during the 268 fit, to limit the number of possible solutions, and to ensure they are chemically 269 reasonable. A popular algorithm accomplishing this goal is multivariate curve resolution 270 coupled to ALS (MCR-ALS) [26][27][28]. MCR-ALS is usually employed for quantitative 271 analytical purposes in the so-called extended mode [29], which decomposes an 272 augmented data matrix created from calibration and unknown matrices.…”

Section: Standards (Gram Dtld) or Do Not Achieve The Second-order Admentioning

confidence: 99%

Second- and higher-order data generation and calibration: A tutorial

Escandar

Goicoechea

Peña

et al. 2014

Analytica Chimica Acta

150

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Section: Standards (Gram Dtld) or Do Not Achieve The Second-order Admentioning

confidence: 99%

Second- and higher-order data generation and calibration: A tutorial

Escandar

Goicoechea

Peña

et al. 2014

Analytica Chimica Acta

150

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“…[12,13,14,15,16]). Generally, these algorithms resemble ICA in that they perform blind recovery of spectra and concentrations from the spectra of mixtures only, using no or little a priori information about mixture composition and/or about pure spectra.…”

Section: Introductionmentioning

confidence: 99%

“…[27]. While the above are general purpose BSS methods, there exist more specialized chemometrics (MCR) algorithms designed to use non-negativity (see, e.g., [12,13,14,15,16,24,28]). In many of them, some variant of principle component analysis is used to first prewhiten the data, i.e.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition

et al. 2006

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We propose a simulated annealing algorithm (called SNICA for "stochastic non-negative independent component analysis") for blind decomposition of linear mixtures of non-negative sources with non-negative coefficients. The de-mixing is based on a Metropolis type Monte Carlo search for least dependent components, with the mutual information between recovered components as a cost function and their non-negativity as a hard constraint. Elementary moves are shears in two-dimensional subspaces and rotations in three-dimensional subspaces. The algorithm is geared at decomposing signals whose probability densities peak at zero, the case typical in analytical spectroscopy and multivariate curve resolution. The decomposition performance on large samples of synthetic mixtures and experimental data is much better than that of traditional blind source separation methods based on principal component analysis (MILCA, FastICA, RADICAL) and chemometrics techniques (SIMPLISMA, ALS, BTEM).

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“…MCR-ALS is an algorithm that fits the requirements for image resolution [28][29][30][31] .This method decompose the unfolded hyperspectral data cube, the matrix X (xy × λ), into the product of two matrices, C (xy × K), containing the concentration profiles and S T (K × λ), containing the spectral profiles for each K component (Figure 1 and equation 1). In this case, xy is the spatial image dimensions and λ the number of spectral data points.…”

Section: Introductionmentioning

confidence: 99%

Characterization of sildenafil citrate tablets of different sources by near infrared chemical imaging and chemometric tools

Sabin

Lozano

Rocha

et al. 2013

Journal of Pharmaceutical and Biomedical Analysis

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In this paper the chemical imaging technique by near infrared spectroscopy was applied for characterization of drug formulations in tablets of sildenafil citrate of different sources. This work is original and has never been published because it was used the sildenafil citrate, including in the commercial one called "Viagra".In the first stage of the study, a chemometric method based on multivariate curve resolution was properly chosen for the development of the distribution map of concentrations of the active ingredient in tablets. In the second step, the normalized histograms of images from active ingredient were classified by hierarchical cluster analysis. Finally it was possible to recognize the patterns of homogeneity of images in sildenafil citrate tablet. This concept can be used to improve the knowledge of industrial products and processes, as well as, for characterization of counterfeit drugs.

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Chemometrics applied to unravel multicomponent processes and mixtures

Cited by 494 publications

References 116 publications

Second- and higher-order data generation and calibration: A tutorial

Second- and higher-order data generation and calibration: A tutorial

Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition

Characterization of sildenafil citrate tablets of different sources by near infrared chemical imaging and chemometric tools

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