OPLS methodology for analysis of pre-processing effects on spectroscopic data

Pre-processing of spectroscopic data is commonly applied to remove unwanted systematic variation. Possible loss of information and ambiguity regarding discarded variation are issues that complicate pre-treatment of data. In this paper, OPLS methodology is applied to evaluate different techniques for pre-processing of spectroscopic data gathered from a batch process. The objective is to present a rational scheme for analysis of preprocessing in order to understand the influence and effect of pre-treatment. O2PLS uses linear regression to divide the systematic variation in X and Y into three parts; one part with joint X-Y covariation, i.e. related to both X and Y, one part of X with Y-orthogonal variation and one part of Y with X-orthogonal variation. All of the investigated pre-treatment methods removed an additive baseline as expected. In the analysis of raw and differentiated data variation associated with the baseline was found in the Y-orthogonal part of X. Orthogonal information was also found in Y, which suggests that this preprocessing procedure not only removed variation. This would have been more difficult to detect without the O2PLS model since both raw and differentiated data must be analysed simultaneously. Development of a knowledge based strategy with OPLS methodology is an important step towards eliminating trial and error approaches to pre-processing. (c) 2006 Elsevier B.V. All rights reserved.