Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control

Abstract The optimization and control of cell free protein synthesis (CFPS) presents an ongoing challenge due to the complex synergies nonlinearities that cannot be fully explained in first principle models. This article explores use multivariate statistical tools for analyzing data sets collected from CFPS Cereulide monoclonal antibodies. During collection these sets, several process parameters were modified investigate their effect on end‐point product (yield). Through application principal component analysis partial least squares (PLS), important correlations could identified. For example, yield had a positive correlation with pH NH 3 negative CO 2 dissolved oxygen. It was also found PLS able provide long‐term prediction yield. presented work illustrates techniques insights can help support operation processes.