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

• Published in: Chemometrics and intelligent laboratory systems Vol. 84; no. 1; pp. 153 - 158
• Main Authors: Gabrielsson, Jon, Jonsson, Hans, Airiau, Christian, Schmidt, Bernd, Escott, Richard, Trygg, Johan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2006
• Subjects: Batch process; Multi-block strategies; O2PLS; OPLS; Pre-processing; UV-data; O2PLS; UV-data; OPLS; Pre-processing; Multi-block strategies; Batch process
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2006.03.013

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 pre-processing 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 pre-processing 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.