Afanador, N.L., Tran, T.N. and Buydens, L.M.C. (2013). Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression. Analytica Chimica Acta, 768(1), 49–56. Duvigneau, S., Dürr, R., Laske, T., Bachmann, M., Dostert, M. and Kienle, A. (2020). Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing—Application to influenza vaccine production. PLoS computational biology, 16(6), e1007810 Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikström, C. and Wold, S. (2006). Multi-and megavariate data analysis. Umetrics Ab, Umea. Facco, P., Zomer, S., Rowland-Jones, R.C., Marsh, D., Diaz-Fernandez, P., Finka, G., Bezzo, F. and Barolo, M. (2020). Using data analytics to accelerate biopharmaceutical process scale-up. Biochemical Engineering Journal, 164(April), 107791. Food and Drug Administration. (2004). Guidance for Industry, PAT-A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. Frederick, D.W., McDougal, A.V., Semenas, M., Vappiani, J., Nuzzo, A., Ulrich, J.C., Becherer, J. D., Preugschat, F., Stewart, E.L., Sévin, D.C. and Kramer, H.F. (2020). Complementary NAD+ replacement strategies fail to functionally protect dystrophin-deficient muscle, Skeletal Muscle, 10, 30. Fuhrer, T., Heer, D., Begemann, B. and Zamboni, N. (2011). High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry, Analytical Chemistry, 83(18), 7074-7080. Geladi, P. and Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17. Gregersen, L. and Jørgensen, S.B. (1999). Supervision of fed-batch fermentations, Chemical Engineering Journal, 75(1), 69-76. Hong, M.S., Severson, K.A., Jiang, M., Lu, A.E., Love, J.C. and Braatz, R.D. (2018). Challenges and opportunities in biopharmaceutical manufacturing control. Computers and Chemical Engineering, 110, 106–114. Karst, D.J., Scibona, E., Serra, E., Bielser, J.M., Souquet, J., Stettler, M., Broly, H., Soos, M., Morbidelli, M. and Villiger, T. K. (2017). Modulation and modeling of monoclonal antibody N-linked glycosylation in mammalian cell perfusion reactors. Biotechnology and Bioengineering, 114(9), 1978–1990. Li, F., Vijayasankaran, N., Shen, A.Y., Kiss, R. and Amanullah, A. (2010) Cell culture processes for monoclonal antibody production, mAbs, 2(5), 466-479. Mehmood, T., Liland, K.H., Snipen, L. and Sæbø, S. (2012). A review of variable selection methods in Partial Least Squares Regression. Chem. Int. Lab. Sys., 118, 62-69. Nomikos, P. and MacGregor, J. F. (1995). Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems, 30(1), 97–108. Ramaker, H.J., Van Sprang, E.N.M., Westerhuis, J.A. and Smilde, A.K. (2005). Fault detection properties of global, local and time evolving models for batch process monitoring. Journal of Process Control, 15(7), 799–805. Rameez, S., Mostafa, S.S., Miller, C. and Shukla, A.A. (2014). High-throughput miniaturized bioreactors for cell culture process development: Reproducibility, scalability, and control. Biotechnology Progress, 30(3), 718–727. Wold, S., Sjöström, M. and Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. Zhou, B., Xiao, J.F., Tuli, L. and Ressom, H.W. (2012). LC-MS-based metabolomics. Molecular BioSystems, 8(2), 470–481. Zürcher, P., Sokolov, M., Brühlmann, D., Ducommun, R., Stettler, M., Souquet, J., Jordan, M., Broly, H., Morbidelli, M. and Butté, A. (2020). Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction. Biotechnology Progress, (December 2019), 1–11.