Compare Cross-Run Bioprocess Data & Golden Batch Analysis

SIMCA is a paid Sartorius/Umetrics product with seat licences typically in the thousands of euros per year. Free alternatives exist for common multivariate batch analysis workflows: R (FactoMineR, mixOmics, ropls), Python (scikit-learn PLSRegression, NIPALS implementations), MetaboAnalyst for web-based PCA, and this free browser tool for paste-and-compare bioreactor batch comparison with golden batch envelope construction. None require a licence key or login.

R with FactoMineR, mixOmics, and ropls covers PCA, PLS, and OPLS. Python's scikit-learn includes PLSRegression and PCA. MetaboAnalyst offers a web interface for metabolomics-style PCA. For bioprocess batch-comparison specifically, overlaying multiple bioreactor runs against a golden batch envelope without coding, this free tool runs entirely in your browser and exports cohort statistics as CSV.

Plot each variable across all runs on a common time axis, then either compute the mean and standard deviation envelope from a reference set of good batches to flag deviations visually, or align time axes on biological events (induction, glucose depletion) when run durations differ. This tool does both in one workflow: paste your CSV, pick a reference run as the golden batch, see all other runs as deviation from the cohort mean plus or minus 1 or 2 sigma.

A golden batch is built by selecting a set of historical batches that all met product quality and yield targets, then computing the time-series mean and confidence interval at each timestep. New batches are compared against this envelope, and contributing variables are flagged whenever the new run drifts outside the band. The minimum reference set is three batches but five to ten is more reliable. This tool computes the envelope live as you toggle batches into or out of the reference set.

Normalise the failing batch to the golden batch envelope, compute the per-variable z-score at each timestep, and rank variables by the magnitude of their deviation. The top contributors point to the likely root cause: substrate concentration, dissolved oxygen, pH, temperature, agitation, or feed rate. This tool exposes z-score contribution as a horizontal bar chart at any selected timepoint, replacing the contribution-plot workflow that SIMCA and Aspen ProMV charge thousands of euros for.

Raw clock-time overlays are misleading when batches differ in duration. The standard solution is dynamic time warping, which stretches or compresses each run's time axis to match a reference. A lighter workflow that solves 90 percent of real cases is event-anchored alignment: subtract a per-batch offset so all runs start at a common biological event such as inoculation, induction, or first glucose-zero. This tool offers four event-alignment modes (first biomass rise, glucose depletion, lactate peak, clock) plus manual t-zero entry per batch.

Dynamic time warping (DTW) finds the optimal local stretch and compression of each run's time axis against a reference so corresponding process events line up. Robust derivative DTW matches the shape of the derivative rather than absolute values, which helps when sensor calibration drifts between runs. Free implementations exist in dtw-python on PyPI and the dtw package on CRAN. For visual batch trajectory analysis without true DTW, event-anchored alignment in this tool covers most workflows.

Run a multivariate batch analysis (PCA, PLS, or OPLS) across all historical batches, identify the scores that cluster high-performing versus low-performing groups, then trace contribution back to specific process variables. For non-statisticians, the same insight is reachable visually by overlaying time-series data with a golden batch envelope and inspecting which variables drift outside the band. Both approaches expose the same underlying variability, the second is faster for routine monitoring.

Scale-dependent shifts in CHO behaviour usually trace to one of mixing time, dissolved oxygen gradients, carbon dioxide stripping, or shear stress. The fastest diagnostic is to overlay small-scale and large-scale runs of VCD, glucose, lactate, pH, and DO on a common time axis and visually identify where the divergence starts. Use the deviation contribution panel in this tool to rank which variable contributed most at the divergence timepoint.

A batch evolution model (BEM) is the SIMCA-specific term for a time-resolved PLS or OPLS model fitted to a cohort of historical batches, used for real-time monitoring of an ongoing run. The model expresses each variable as a trajectory through batch maturity and computes Hotelling T-squared and DModX control limits. This free tool implements the visual and univariate equivalent: the golden batch envelope and z-score contribution chart cover the same diagnostic workflow without requiring a SIMCA seat or statistical training.

Use this Golden Batch Analysis tool when you have three or more runs and want cross-run comparison: golden batch envelope, z-score deviation, batch alignment on biological events. Use the Bioreactor Data Dashboard when you have a single run and want full process state visualisation plus derived rate metrics (specific growth rate μ, specific glucose consumption qGlc, specific lactate production qLac, specific productivity qP, IVC). Both tools share the same five organism demo datasets so you can move between single-run microscope and cohort-level overview with the same mental model.