Carrying on with the fermentation theme, my long term goal is to use machine learning to design an (possibly) awesome beer recipe. However, in order to train a model, we need some data linking to recipes to consumer opinions. While there are lots of online resources for opinions, commercial beer recipes are often guarded secrets, and it's especially rare that they are shared following a structured data format. One (sort of) exception to this rule is the BrewDog DIY Dog. The catalogue of all BrewDog recipes is published anually in pdf format allowing homebrewers to have a go themselves. While recipes are largely written in a consistent way, some challenges existed in parsing the data to a machine readable format - and a couple of different libraries (PyPDF2 and tabula-py) were required.

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Here's the first post of a few planned within a fermentation series. One of the key ingredients to any drink, whether beer, kombucha, hop water etc is of course water, and not all waters are created equal. If water is the base of a drink, some flavours are better complimenteted by a blank canvas, while minerality can be used to make certain flavours pop. A common analogy is when cooking, flavours are brought out using salt, vinegar and other condiments. Getting this balance right can make all difference. Conversely if you're starting point is too rich in these properties, it's probably worth considering diluting, or even switching out for low-minerality bottled water. A common recommendation is to look in to the properties of your water at home, investigate what the properties of your water are, and see where you can go from there to tweak your water profile using common additives such as gypsum, baking soda and salt.

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