Chemometrics is the chemical discipline that uses mathematical and statistical methods,. (a) to design or select optimal measurement procedures / experiments ...
Exploring Raman spectral data using chemometrics L. Duponchel Laboratoire de Spectrochimie Infrarouge et Raman (LASIR), CNRS UMR8516, Bât. C5, Université Lille I, Sciences et Technologies, 59655 Villeneuve d’Ascq Cedex, France.
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Chemometrics is the chemical discipline that uses mathematical and statistical methods, (a) to design or select optimal measurement procedures / experiments and (b) to provide maximum chemical information by analyzing chemical data. In a more general manner, it corresponds to the entire process whereby data (e.g., numbers in a table or spectra as rows in a matrix) are transformed into information used for decision making. There is therefore a huge potential to explore Raman spectral data in a multivariate way by exploiting simultaneously all variables in the considered spectral domain. This presentation will provide first a general overview of major chemometrics tools for the development of quantitative (multivariate regressions) or qualitative models (clustering and classification methods) capable of predicting a concentration or class membership from a spectrum. In the second part, more original algorithms such as multivariate curve resolution or super-resolution will be presented. The current craze for these concepts is explained by their great potential. Multivariate curve resolution is indeed capable of extracting simultaneously spectra and corresponding contributions of all pure compounds from mixture spectra with no a priori. For example, this is of particular importance for spectroscopic imaging for which a chemical image of an unknown compound can even be generated. For its part, the super-resolution approach uses several low resolution images of the same sample (observed in different ways) in order to retrieve a higher resolution chemical image. This issue is especially important because nanosciences force us to analyze always smaller structures or observe always more details on bulk samples.
