Raman spectroscopy is applied for characterizing paintable displays. Few other options than Raman spectroscopy exist for doing so because of the liquid nature of functional materials. The challenge is to develop a method that can be used for estimating the composition of a single display
cell on the basis of the collected three-dimensional Raman spectra. A classical least squares (CLS) model is used to model the measured spectra. It is shown that spectral preprocessing is a necessary and critical step for obtaining a good CLS model and reliable compositional profiles. Different
kinds of preprocessing are explained. For each data set the type and amount of preprocessing may be different. This is shown using two data sets measured on essentially the same type of display cell, but under different experimental conditions. For model validation three criteria are introduced:
mean sum of squares of residuals, percentage of unexplained information (PUN), and average residual curve. It is shown that the decision about the best combination of preprocessing techniques cannot be based only on overall error indicators (such as PUN). In addition, local residual analysis
must be done and the feasibility of the extracted profiles should be taken into account.
Biosystems Data Analysis Group, Swammerdam Institute for Life Sciences, Faculty of Science, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands
Publication date: March 1, 2005
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The Society publishes the internationally recognized, peer reviewed journal, Applied Spectroscopy, which is available both in print and online. Subscriptions are included with membership or can be purchased by institutional or corporate organizations. Abstracts may be viewed free of charge. Previously published as Bulletin (Society for Applied Spectroscopy)