The use of LS–PLS for improved understanding, monitoring and prediction of cheese processing

In this paper we demonstrate both a design strategy and a set of analysis techniques for a designed experiment from an industrial process (cheese making) with multivariate responses (sensory data). The design strategy uses two-level factorial design for the factors that can be controlled, and blocking on the raw material to cover other non-designed variation in the raw material. We measure both the raw materials and on several points during the process with FT-IR spectroscopy. The methods of analysis complement each other to give more understanding and better modelling. The 50–50 MANOVA method provides multivariate analysis of variance to test for significance of effects for the design variables. Ordinary PLS2 analysis gives an overview of the data and generates hypotheses about relations. Finally, the orthogonal LS–PLS method is extended to multivariate responses and used to identify the source of the observed block effect and to build models that can be used for statistical process control at several points in the process. In these models, the information at one point is corrected for information that has already been described elsewhere.