ANALYSIS OF MIXTURE DATA WITH PARTIAL LEAST-SQUARES

The analysis of mixture data is a common problem in industrial research and development, particularly in chemical and related industries, e.g. pharmaceuticals, cosmetics, oil, and biotechnology. Analyzing mixture data with multiple regression necessitates special model forms due to the mixture constraint. The canonical polynomials of Scheffe and of Cox will be discussed, as well as the limitation of multiple regression with data in constrained regions. For the analysis of mixture data, partial least squares (PLS) has been found to be practical. In particular when both mixture and process variables are involved, it offers a flexible and simple approach which works well in practice. The analysis of mixture data using PLS and multiple regression are compared, with case studies from the scientific literature.