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4. Quality Control and Batch Analytics

In traditional pharma, QC happens after production. In AI-
enabled plant platforms, it happens during.

Using:

• Multispectral imaging, to detect chlorophyll
degradation or stress markers.
• Noninvasive spectroscopy, to estimate protein
concentration in living tissue.
• Real-time analytics, to flag outlier plants or
batches before processing.

This transforms quality control from a reactive to a
preventive process—crucial for regulatory approval and
global scaling.

From Bioprocessing to Biologistics

AI doesn’t just grow the drug—it moves it.

• Inventory forecasting: AI predicts demand based
on seasonality, outbreak modeling, and population
demographics.
• Stability modeling: It anticipates degradation
patterns under different temperatures and humidity
levels, guiding shelf-life labeling for global
deployment.
• Cold-chain bypass modeling: By simulating
protein degradation kinetics, AI validates room-
temperature storage for months or years—slashing
global logistics costs.

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