Application of Multiway Principal Component Analysis for Identification of Process Improvements in Pharmaceutical Manufacture

This paper describes the application of batch trajectory alignment, outlier detection, and multiblock multiway principal component analysis (MPCA) to data from an industrial active pharmaceutical ingredient manufacturing process. The process data routinely collected from historical batches, including temperatures, pressures, and controller outputs, has been used to improve process operation and understanding. MPCA highlighted questionable batches from which plant issues were identified. Variable contributions to the MPCA scores were used to identify the process variables potentially causing the variation in batch drying time.