In this paper (open access) the authors trained a Machine Learning model to differentiate between Top, Bottom and Spontaneous fermented bottled beers. Data were collected using a non-invasive hand held NIR scanner pointed directly through the unopened bottle using a customised foam attachment. The model was trained on 25 samples of major brands purchased online, rather than reference samples of verified traceability, but the training samples covered a wide range of beer types from stouts to light ales, and a wide range of bottle types and colours.
The authors report good classification based on fermentation method. They consider that evidence of a wrong fermentation method could be one quick and easy check that could flag counterfeits. They also correlated the NIR data with sensory panel assessments and SPME-GC-MS data and concluded that non-invasive NIR has the potential to classify beers based on their aroma profiles.
Image from the paper
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