Free access to 2015 Kowalski Prize winning paper

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  • Author: Statistics Views
  • Date: 22 October 2015

The Journal of Chemometrics is pleased to announce that the 2015 Kowalski Prize in Chemometrics has been awarded to: Barry K. Lavine, Ayuba Fasasi, Nikhil Mirjankar, Mark Sandercock and Steven D. Brown for their paper Search prefilters for mid-infrared absorbance spectra of clear coat automotive paint smears using stacked and linear classifiers.

thumbnail image: Free access to 2015 Kowalski Prize winning paper

The paper, which was published in the Journal's Kowalski special issue in May 2014, has been given free access for a limited period.

The Kowalski Prize is awarded annually, and alternates between the "best theoretical paper" and "best applied paper" published in the Journal. This year was the turn of the “Best Applied Paper" published in 2013 and 2014.

In this paper, Lavine et al demonstrate that by using stacked partial least squares classifiers and genetic algorithms for feature selection and classification, search prefilters can be developed to extract investigative lead information from clear coat paint smears. The results obtained in this study also show that identifying specific wavelengths or wavelet coefficients in IR spectral data is superior to identifying informative wavelength windows when applying pattern recognition techniques to IR spectra from the paint data query (PDQ) database when differentiating paint samples by assembly plant. Search prefilters developed using specific wavelengths or wavelet coefficients outperformed search prefilters that utilized spectral regions. Clear coat paint spectra from the PDQ database may not be well suited for stacking as there are few spectral intervals that can reliably distinguish the different sample groups (i.e., assembly plants) in the data. The information contained in the IR spectra about assembly plant may not be highly compartmentalized in an interval, which also works against stacking. The similarity of the IR spectra within a plant group and the noise present in the IR spectra may also be obscuring information present in spectral intervals.

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