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.