The brewery QC lab runs on a manual of hundreds of methods — acrospire counts, extract by pycnometer, diacetyl by GC, sterility checks, fill-height verification, a tasting panel. It is high-volume, repetitive, pattern-heavy measurement, which is exactly the shape where machine learning earns its keep, and exactly the kind of work a confident model gets subtly wrong if you let it off the leash.
This 6-part series walks the lab bench by bench. Every use case lands in one of three jobs — risk triage, soft sensing, or machine vision — and every one keeps the same rule: the reference method stays the arbiter, and the model is a way to test smarter, not a replacement for testing. No hype, as always — where the model earns its keep I’ll show you, and where it confidently misleads I’ll show you that too.
The series
- AI in the Brewery Lab: What’s Real and What’s Hype — the map of the whole lab and the three jobs AI actually does.
- On the Malt Bench: Vision, NIR and Incoming QC — acrospires, colour and foreign material by camera, and predicting extract before the mash.
- Soft Sensors in the Beer-Chemistry Lab — diacetyl forecasting, dissolved-oxygen anomaly detection, and shelf-life survival models.
- From Plate Counts to Risk Triage in Microbiology — colony counting by vision and contamination-risk models across the process.
- Machine Vision on the Packaging Line — fills, crowns, labels and pasteurisation units, inspected inline.
- AI in Sensory and the QA System — taster notes as data, predictive calibration, and deviation triage.
Read them in order — each builds on the map in the first. Every post in the series is tagged #brewery-lab-ai.
When you’re ready to apply it, the Brewing Science & AI track is full of concrete brewing problems to learn against.