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

详细信息    查看全文

• 作者：Jon Gabrielsson ; Hans Jonsson ; Christian Airiau ; Bernd Schmidt ; Richard Escott ; Johan Trygg
• 关键词：Multi-block strategies ; Pre-processing ; UV-data ; OPLS ; O2PLS ; Batch process
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2006
• 出版时间：1 December 2006
• 年：2006
• 卷：84
• 期：1-2
• 页码：153-158
• 全文大小：367 K

文摘

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.