Journal of Chemometrics

Cover image for Vol. 28 Issue 1

January 2014

Volume 28, Issue 1

Pages i–v, 1–70

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Cover Image
    4. Special Feature
    5. Perspective
    6. Research Articles
    1. Issue Information (pages i–iii)

      Article first published online: 20 JAN 2014 | DOI: 10.1002/cem.2537

  2. Cover Image

    1. Top of page
    2. Issue Information
    3. Cover Image
    4. Special Feature
    5. Perspective
    6. Research Articles
    1. Cover Image (page iv)

      Article first published online: 20 NOV 2013 | DOI: 10.1002/cem.2571

      Thumbnail image of graphical abstract
  3. Special Feature

    1. Top of page
    2. Issue Information
    3. Cover Image
    4. Special Feature
    5. Perspective
    6. Research Articles
    1. Harnessing the complexity of metabolomic data with chemometrics (page v)

      Julien Boccard and Serge Rudaz

      Article first published online: 21 NOV 2013 | DOI: 10.1002/cem.2572

      Thumbnail image of graphical abstract

      Metabolomics constitutes a representative example of fast moving research fields taking advantage of recent technological advances to provide extensive sample monitoring. Challenges related to making use of the wealth of data generated include extracting relevant elements within massive amounts of signals possibly spread across different tables, reducing dimensionality, summarising dynamic information in a comprehensible way and displaying it for interpretation purposes. The added-value of most metabolomic models is not restricted to their statistical significance but also strongly related to their biological relevance. Understandable metabolic information unravelling the role of each variable is therefore needed. Dedicated modelling algorithms, able to cope with the inherent properties of these metabolomic datasets are mandatory for harnessing their complexity and provide relevant information. In that perspective, chemometrics has a central role to play.

  4. Perspective

    1. Top of page
    2. Issue Information
    3. Cover Image
    4. Special Feature
    5. Perspective
    6. Research Articles
    1. Harnessing the complexity of metabolomic data with chemometrics (pages 1–9)

      Julien Boccard and Serge Rudaz

      Article first published online: 12 NOV 2013 | DOI: 10.1002/cem.2567

      Metabolomics constitutes a representative example of fast-moving research fields taking advantage of recent technological advances to provide extensive sample monitoring. Challenges related to making use of this wealth of data include extracting relevant elements within massive amounts of signals possibly spread across different tables, reducing dimensionality, summarising dynamic information in a comprehensible way and displaying it for interpretation purposes. In that perspective, chemometrics has a central role to play.

  5. Research Articles

    1. Top of page
    2. Issue Information
    3. Cover Image
    4. Special Feature
    5. Perspective
    6. Research Articles
    1. Bilinear modeling of batch processes. Part III: parameter stability (pages 10–27)

      Jose Maria González-Martínez, Jose Camacho and Alberto Ferrer

      Article first published online: 20 NOV 2013 | DOI: 10.1002/cem.2562

      A paramount aspect in the development of a model for a monitoring system is the so-called parameter stability. There is no sound study on the parameter stability in batch multivariate statistical process control. The aim of this paper is to investigate the parameter stability associated to the most used synchronization and principal component analysis-based batch multivariate statistical process control methods. Results are discussed in connection with previous conclusions in the first two papers of the series

    2. Wavelet-based self-organizing maps for classifying multivariate time series (pages 28–51)

      Pierpaolo D'Urso, Livia De Giovanni, Elizabeth Ann Maharaj and Riccardo Massari

      Article first published online: 11 NOV 2013 | DOI: 10.1002/cem.2565

      We suggest a time series clustering method that combines the benefits connected to the interpretative power of wavelet representations of the time series and the informational gain of clustering and vector quantization connected to the adopted self-organizing map technique. Our clustering method considers composite wavelet-based information that combines and tunes information connected to the wavelet variance and wavelet correlation. To assess the effectiveness of the proposed clustering approach, results of simulation studies and an empirical application are given.

    3. Bayesian predictive modeling and comparison of oil samples (pages 52–59)

      Paul Blomstedt, Romain Gauriot, Niina Viitala, Tapani Reinikainen and Jukka Corander

      Article first published online: 22 NOV 2013 | DOI: 10.1002/cem.2566

      We propose a Bayesian predictive approach for evaluating the similarity between the chemical compositions of two oil samples. We derive the underlying statistical model from basic assumptions on modeling assays in analytical chemistry, and, to further facilitate and improve numerical evaluations, develop analytical expressions for the key elements of Bayesian inference for this model. The approach is illustrated with both simulated and real data and is shown to have appealing properties in comparison with both standard frequentist and Bayesian approaches.

    4. 3D CoMFA, CoMSIA, topomer CoMFA and HQSAR studies on aromatic acid esters for carbonic anhydrase inhibitory activity (pages 60–70)

      Shrikant S. Nilewar and Muthu K. Kathiravan

      Article first published online: 18 DEC 2013 | DOI: 10.1002/cem.2574

      From molecular modelling studies [comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), topomer CoMFA and hologram quantitative structure-activity relationship (HQSAR)], SAR has been derived. It was found that electropositive groups are favoured at R1 and meta positions while electronegative groups are favoured at ortho and para positions. Bulky groups are favoured at R1 and ortho positions. Hydrophobic group substitution on the R1 position will produce a potent molecule.

SEARCH

SEARCH BY CITATION