Time-adjusted performance evaluation (TAPE) of predictive multivariate models for bioprocess data

Abstract Cell culture bioprocess data are typically collected across many timepoints and batches, where numerous analytes covary with each other and, critically, with elapsed process time. This time dependence can inflate performance metrics and compromise the validity of multivariate models. We introduce time-adjusted performance evaluation (TAPE), a regression-agnostic validation technique that quantifies and separates time-driven from time-independent predictivity. TAPE pairs leave-one-group-out cross-validation with per-timepoint centering to decompose performance into between-timepoint (time-dependent) and within-timepoint (time-decoupled) parts by comparing predicted and observed deviations from each timepoint mean. Applying TAPE to orthogonal partial least squares models across five Chinese hamster ovary cell culture datasets (three Raman spectroscopy, one metabolomics, and one transcriptomics), several ostensibly strong models’ predictivity was largely explained by timepoint means alone. After removing between-timepoint variation, only models with sample–response relationships independent of time retained good predictivity. For Raman, only models for Raman-active analytes (glucose, lactate) remained predictive, whereas Raman-inactive ones (K + , NH 4 + ) did not. In the omics studies, the models for titer, viable cell density, growth rate, and death rates were predominantly time-driven. By quantifying time’s contribution to model performance, TAPE helps prevent misleadingly good performance metrics and supports more reliable multivariate modeling of time-series bioprocess data.