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Svante Wold's New Year Editorial 2005 PDF Print E-mail
Written by Svante Wold, Umetrics & Umea University   
Another year has passed too quickly, and we now read January 2005 on our calendars. In 2004, good things happened and terrible, happy and sad, as usual. However, chemometrics does well, spreading deeper into biology where the data sets are larger than ever with thousands of variables in, among others, gene arrays, LC-MS profiles, and NIR and IR microscopy. As we all are proudly aware of, Chemometrics has become indispensable in analytical, environmental, and medicinal chemistry, and growing parts of engineering, and finds its way even into inorganic and physical chemistry. As an illustration, the new search engine www.scholar.google with the search phrase "chemometrics inorganic" gives about 500 hits, enough reading for a week or so.
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Editorial flash
Can we use the regression coefficient profile for selecting PLS components?
The linear model y=Xb + f is often used in chemometrics. Unfortunately, we have put too much emphasis on the importance of the regression coefficient profile b, and mistakenly assumed or believed that it should be a good estimate of the "pure profile". This is not true, and I will argue why in later editorials. But this time, I want to address an earlier question that was posed in relation to the regression coefficient profile.
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News flash
Metabolomics 2010 meeting

www.metabolomics2010.com

June 27- July 1, 2010

Amsterdam, The Netherlands

Last chance for Abstract submission for ORAL presentations !!

The deadline for submitting abstracts for oral presentations is approaching rapidly! You have until Friday 23rd April.
We encourtage you all to submit your proposed contributions by then.
After this date you can however still send in abstracts for posters.
As we have a limited space for just 400 posters we encourage everyone to submit their abstracts as soon as possible in order not to miss out.

To register and send in your abstracts for talks and posters click here

The Local Organisers

Thomas Hankemeier and Robert Hall

 

 

Tutorial flash
PLS Regression and the Covariance
The PLS regression is characterized by finding a score vector in X and a score vector in Y that provide with maximal covariance. In the analysis of data the results of this optimization task should be reported. Here it is shown how this should be done. It is also illustrated how this can provide with useful information on the modeling task.


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