Abstract
Abstract Wegscheider, W. and Walner, U., 1993. Sparse experimentation for sparse effects in a mixed variable mixture model. Chemometrics and Intelligent Laboratory Systems , 19: 169–174. It is of general interest to describe the properties of a mixture with as few experiments as possible. Special models are necessary for the analysis of mixture data, due to the mixture constraint. The partial least squares model was found to be useful in interpreting mixture data. A case is treated in which additional factors are involved, apart from those related to composition. Only a fraction (14%) of the original number of experiments may be necessary in order to draw adequate conclusions.
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