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CAC-2008 Poster Prize Winners

Jul 4, 2008

Eigenvector was pleased to sponsor the “Best Poster” prize at CAC-2008. The top three poster presenters all received a certificate good for a copy of PLS_Toolbox or Solo (well, OK, it wasn’t exactly a certificate, it was one of my business cards with “Good for one PLS_Toolbox” written on the back!). The top poster also got $500USD, which equates to 320€.

There were 160 posters presented at CAC, so this was quite a contest! The winners, selected by the CAC scientific committee, represent some exceptional efforts selected from a very large body of good work.

The third place poster was “Drift compensation of gas sensor array data by Orthogonal Signal Correction” by M. Padilla, A. Perera, I. Montoliu, A. Chaudry, K. Persaud and S. Marco. This is a nice application of OSC. We’ve used it for spectroscopic instrument standardization and found it to work well in that application. It makes sense that it would work well for electronic noses as well.

Second place went to Pat Wiegand, Randy Pell and Enric Comas, all of Dow, for “Simultaneous Variable and Sample Selection for PLS Calibrations Using a Robust Genetic Algorithm.” This work addressed the problem where one has both samples and variables that are irrelevant for building a predictive model for a given property. Most previous work address either the variable selection or the sample selection problem, but not both. The robustness of their algorithm comes, in part, from a robust PLS algorithm from the LIBRA Toolbox, developed by Sabine Verboven and Mia Hubert. This toolbox is what provides the robust options for PCA and PLS in PLS_Toolbox, so of course we think that was a very good choice!

Emma Peré-Trepat accepted the first place prize on behalf of herself and co-workers I. Montoliu, F.P. Martin, S. Rezzi and S. Kochhar, all of Nestlé Research Center. They presented “Data fusion strategies for nutrimetabonomics.” Nutrimetabonomics, the application of metabonomics to nutritional sciences, is the study of metabolic responses to the consumption of specific foods and ingredients. Their approach used hierarchical modeling to fuse NMR and meta-data.

Congratulations again to the winners!