New and old trends in chemometrics. How to deal with the increasing data volumes in R&D&P (research, development and production) - with examples from pharmaceutical research and process modeling
2002 (English)In: Journal of Chemometrics: Special Issue: Proceedings of the 7th Scandinavian Symposium on Chemometrics . Issue Edited by Lars Nørgaard, Vol. 16, no 8-10, 377-86 p.Article in journal (Refereed) Published
Chemometrics was started around 30 years ago to cope with and utilize the rapidly increasing volumes of data produced in chemical laboratories. The methods of early chemometrics were mainly focused on the analysis of data, but slowly we came to realize that it is equally important to make the data contain reliable information, and methods for design of experiments (DOE) were added to the chemometrics toolbox. This toolbox is now fairly adequate for solving most R&D problems of today in both academia and industry, as will be illustrated with a few examples. However, with the further increase in the size of our data sets, we start to see inadequacies in our multivariate methods, both in their efficiency and interpretability. Drift and non-linearities occur with time or in other directions in data space, and models with masses of coefficients become increasingly difficult to interpret and use. Starting from a few examples of some very complicated problems confronting chemical researchers today, possible extensions and generalizations of the existing chemometrics methods, as well as more appropriate preprocessing of the data before the analysis, will be discussed. Criteria such as scalability of methods to increasing size of problems and data, increasing sophistication in the handling of noise and non-linearities, interpretability of results, and relative simplicity of use will be held as important. The discussion will be made from a perspective of the evolution of the scientific methodology as driven by new technology, e.g. computers, and constrained by the limitations of the human brain, i.e. our ability to understand and interpret scientific and data analytical results. Quilt-PCA and Quilt-PLS presented here address and offer a possible solution to these problems.
Place, publisher, year, edition, pages
2002. Vol. 16, no 8-10, 377-86 p.
chemometrics, large data sets, PLS, non-linearities, interpretability of results
IdentifiersURN: urn:nbn:se:umu:diva-9140DOI: 10.1002/cem.746OAI: oai:DiVA.org:umu-9140DiVA: diva2:148811