Journal of Chemometrics

Cover image for Vol. 26 Issue 11-12

November-December 2012

Volume 26, Issue 11-12

Pages i–iii, 557–603

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Research Articles
    1. Issue Information (pages i–iii)

      Version of Record online: 17 DEC 2012 | DOI: 10.1002/cem.2483

  2. Research Articles

    1. Top of page
    2. Issue Information
    3. Research Articles
    1. A real-time Mooney-viscosity prediction model of the mixed rubber based on the Independent Component Regression-Gaussian Process algorithm (pages 557–564)

      Kai Song, Fang Wu, Tuo-peng Tong and Xiao-jing Wang

      Version of Record online: 23 SEP 2012 | DOI: 10.1002/cem.2478

      The ICR-GP algorithm is applied to predict Mooney viscosity with the online-obtained rheological parameters for the first time. The measurement time delay of Mooney viscosity can be dramatically decreased from about 240 to 2 min by the Mooney-viscosity prediction model. The highest prediction accuracy was achieved at M = 0.8765 (the root mean square error), which was high enough considering the measuring accuracy (M = ±0.5) of the Mooney viscometer.

    2. Slice transform-based weight updating strategy for PLS (pages 565–575)

      Yiming Bi, Qiong Xie, Silong Peng and Weiying Lu

      Version of Record online: 30 SEP 2012 | DOI: 10.1002/cem.2479

      A modified partial least squares (PLS) algorithm is presented on the basis of a novel weight updating strategy. The new weight can handle situations with directions in X space having large variance unrelated to Y, whereas the linear PLS may not work well. In the proposed algorithm, the slice transform technique is introduced to provide a piecewise linear representation of the weight vectors. An optimal piecewise linear replacements of the PLS weights are achieved by the proposed method. Experimental results show that the proposed method can achieve simpler models, whereas the model performances are at least comparable with PLS and other methods.

    3. Accounting of ligand–receptor interactions to explore and design novel architecture for PTP 1B inhibition: a legitimate approach (pages 576–584)

      Priyanka Malla, Rajnish Kumar and Manoj Kumar

      Version of Record online: 21 NOV 2012 | DOI: 10.1002/cem.2480

      This study is aimed to elucidate the three-dimensional (3D) structural features of compounds to act as protein tyrosine phosphatase 1B (PTP 1B) inhibitors and to obtain predictive 3D quantitative structure–activity relationship (QSAR) models that may guide in the rational design and development of novel PTP 1B inhibitors. Taken together, pharmacophore modeling, atom-based 3D-QSAR and docking studies provided a 3D topological view of the active site that can be used for the rational modifications for development and optimization of highly selective and potent PTP 1B inhibitors.

    4. Monitoring chemical impacts on cell cultures by means of image analyses (pages 585–597)

      Morgan B. McConico, Rebecca B. Horton, Kendhl K. Witt and Frank Vogt

      Version of Record online: 14 NOV 2012 | DOI: 10.1002/cem.2481

      It has been hypothesized that chemical environments of living cells have impacts on the cells' physical appearance. To investigate whether ambient chemical parameters are reflected in the distributions of cell size and shape, a novel image analysis technique has been developed for noninvasive measurements. As proof-of-principle application, microalgae cells have been chosen as a novel means for environmental sensing. Results are reported, which demonstrate that nonlinear multivariate modeling of these size and shape distributions facilitate quantitative measurements of microalgae nutrients.

    5. Application of modified particle swarm optimization as an efficient variable selection strategy in QSAR/QSPR studies (pages 598–603)

      Aboozar Khajeh, Hamid Modarress and Hamed Zeinoddini-Meymand

      Version of Record online: 22 NOV 2012 | DOI: 10.1002/cem.2482

      In this work, we present a novel method combining modified particle swarm optimization (MPSO) and partial least squares (PLS) for selecting a subset of relevant descriptors and building the optimal linear model for QSAR/QSPR studies. The proposed method is applied to developing a valid QSPR model for predicting the entropy of formation with high accuracy for a structurally wide variety of organic compounds.