Norwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: [email protected]
Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: [email protected]
There has been much debate about which program can predict NMR spectra the best. It is well known within the NMR community that spectra prediction strongly depends on the “quality” of the starting data sets for those systems which use real data as a knowledge base. It has become a hot topic in some blogs, although disappointingly most of the authors tend to have affiliations to one software vendor or another.
The starting point for this column is the last one from last year! In that column TD began what was intended to be a two-part story but has, with this column, become a story in four parts. There were required topics we had not covered and we need to begin this column by completing the discussion of removing multiplicative effects by describing what the two most popular methods do to NIR spectra.
When we set up ALIS GmbH one of the first major “discoveries” was probably the most embarrassing for me. Having worked on analytical data standards for so long, I seem to have successfully generated a blind spot for the developments which have taken place in the structure, standardisation and functionality of the Portable Document Format (PDF).2 Maybe it’s due to a subconscious aversion to what I had for a long time seen as a simplistic “get out” solution for those too lazy to convert data into a long-term, stable, vendor-neutral format. How often have we...
The last TD column showed the effect of calculating second derivatives on a set of 100 spectra, which will be the starting point for this column.
This column is about the most basic of pre-treatments, which has been used in spectroscopy well before the word "Chemometrics" was invented.
Tony Davies investigates eMolecules. The system is essentially a clearing-house for metadata and chemical structure metadata indexing of numerous third-party chemical information providers.
Norwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. [email protected]
aChemometric Consultant, Rubensstraat 7, 6717 VD Ede, The Netherlands. [email protected]
bDepartment of Unit Operations and Food Engineering, Szeged College of Food Engineering, University of Szeged, H-6701 Szeged, POB 433, Hungary
Norwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK
aExternal Professor, University of Glamorgan, UK, Director, ALIS Ltd, Analytical Laboratory Informatics Solutions
bPhaseFour Informatics Limited, [email protected]
cDepartment of Analytical Chemistry, Institute of Chemical Technology, Technika 5, 166 28 Prague 6–Dejvice, The Czech Republic
aNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK
bDepartment of Statistical Science, University College London, Gower Street, London, UK
aExternal Professor, University of Glamorgan, UK, Director, ALIS Ltd, Analytical Laboratory Informatics Solutions
bInstitute for Organic Chemistry, University of Vienna, Austria
cModgraph Consultants, 1 Oakland View, Welwyn AL6 0RJ, UK
aNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK
bDepartment of Statistical Science, University College London, Gower Street, London, UK
aExternal Professor, University of Glamorgan, UK
c/o Waters Informatics, Europaallee 27–29, 50226 Frechen, Germany
bUniversity of West Indies, Mona Campus, Jamaica
cISAS, Institute for Analytical Science, Bunsen-Kirchoff-Strasse 11, 44139 Dortmund, Germany
aNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK
bDepartment of Statistical Science, University College London, Gower Street, London, UK
External Professor, University of Glamorgan, UK
c/o Waters Informatics, Europaallee 27–29, 50226 Frechen, Germany
Norwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK
External Professor, University of Glamorgan, UK, c/o Waters Informatics, Europaallee 27–29, 50226 Frechen, Germany
PCA is a mathematical method of reorganising information in a data set of samples. It can be used when the set contains information from only a few variables but it becomes more useful when there are large numbers of variables, as in spectroscopic data.