Marina Vives Mestres
Compositional data are defined as vectors of components that represent parts of a whole and contain relative information (e.g. %, ppm, mg/l...). CoDa are widely found in chemical, pharmaceutical and food industries among others. This thesis proposes a control chart based on the T2 statistic to monitor processes in which the quality characteristic is a composition. The thesis shows how traditional approaches are not consistent with this type of data, i.e. do not follow the principle of subcompositional coherence and result in control regions outside the restricted sample space. We propose a new control chart (compositional T2, T2C) based on a representation of CoDa into coordinates on real space by the use of log ratios of components. The thesis also proposes an algorithm to identify the components that are responsible of the anomaly. We have applied the T2C to a grit manufacturing process and to control the impurity profile of a drug substance
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