A design and analysis strategy for situations with uncontrolled raw material variation

To be able to control an industrial process, it is necessary to know the relationship between raw materials, process settings and end‐product results. In many situations the raw materials are highly complex and difficult to vary in a systematic way. This makes the use of standard experimental design techniques with a systematic variation in the variables difficult. To solve this, one might measure the raw materials at hand, but then the problem is to know what to measure. In this paper we present a general approach for such situations based on an experimental design in factors that are possible to control, a set up of raw material in blocks in combination with multivariate measurements (using FT‐IR) of the raw material. To analyse the results, we include these measurements as principal components of the spectra. The usefulness of the approach is demonstrated with an example from cheese production. It is shown that it is possible to obtain a model for the amount of ‘cheese fines’ (a yield loss parameter) based on this approach. The final model contains readily measurable information about the raw materials but is obtained without any prior hypothesis about their contribution.