69. Nailing down pastry, using big data.

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Suggested toolkit for shortcrust work

There’s only one proper way to resign. Drive your Lotus 7 at high speed into Central London, thump your fist on the boss’s desk a few times and storm out, before the end of the opening credits. The downside is getting kidnapped and imprisoned on a mysterious island, being known only by a number, interrogated weekly, escaping only when the series is finally pulled by the TV company.

My own recent resignation failed to follow ‘The Prisoner’ guidelines and in fact was quite accidental. HR had forgotten somehow to renew my contract, it was a sunny day, the long holidays loomed ahead and I just had that end of term feeling that gets imprinted during all those years of school and university. So, regarding renewing the contract, I found myself thinking, ‘nah’.

So, today, instead of treating people, I’m making pastry. But that doesn’t mean my skills are totally wasted. I’m coming into pastry from a very scientific point of view. In particular I’ve recognised there are a large number of ‘confounders’ or variables that aren’t easily recognised and controlled.

Our old next door neighbour, Mrs Perks, made the best pastry I ever tasted. I could never get her to reveal her secret recipe. I just knew she’d take that secret gooseberry pie formula to the grave. She said there was no particular magic ingredient, but was she telling the truth? Was that white powder dusting really only icing sugar? Sadly, I never got Mrs Perks to talk.

It’s one thing to follow a recipe, but, for brevity, recipe books don’t give a lot of detail about the precise environmental conditions. For instance, what music should be playing in the background? Should the room be colder? Should ultra violet light be restricted? What does ‘light kneading’ mean, in terms of Newtons per square metre? How much iodine is in the salt? Mrs Perks had a solid fuel Aga, whereas I am using nuclear electricity made in France – does that matter?.

All this suggests a Big Data approach, where every conceivable variable is measured and recorded as we go along. Finally we need a valid and reliable rating scale; let’s call it the Perks Scale. The scientists are here now, and the measuring devices are set up. I’m going with James Martin Rich Shortcrust as a starting point. Martin’s recipe is not referenced or annotated, except to say use hands, not machinery. 21 degrees, moderate humidity, silicone rolling pin and pastry board are in the freezer, background music is also chilled, Cleanbandit I think.

Just wondering, if I add some betnovate cream, will it be less flaky?

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How it turned out

 

 

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