Informative PLS score-loading plots for process understanding and monitoring

• Published in: Journal of process control Vol. 14; no. 8; pp. 889 - 897
• Main Author: Ergon, Rolf
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2004
• Subjects: Biplot; PLS; Process understanding and monitoring; Score-loading correspondence; Biplot; Score-loading correspondence; PLS; Process understanding and monitoring
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2004.02.004

Principal component regression (PCR) based on principal component analysis (PCA) and partial least squares regression (PLSR) are well known projection methods for analysis of multivariate data. They result in scores and loadings that may be visualized in a score-loading plot (biplot) and used for process monitoring. The difficulty with this is that often more than two principal or PLS components have to be used, resulting in a need to monitor more than one such plot. However, it has recently been shown that for a scalar response variable all PLSR/PCR models can be compressed into equivalent PLSR models with two components only. After a summary of the underlying theory, the present paper shows how such two-component PLS (2PLS) models can be utilized in informative score-loading biplots for process understanding and monitoring. The possible utilization of known projection model monitoring statistics and variable contribution plots is also discussed, and a new method for visualization of contributions directly in the biplot is presented. An industrial data example is included.