The Beer Bread that Eats Like a Meal

Today I heeded a call for bread. Not just any bread, but a meaty bread. I’ve been wanting to make a high-protein bread and a beer bread for some time now, so I decided to get creative and mash-up the two ideas and make this hearty bread. I’m calling for your favourite beer, spicy chili oil, and some Italian herbs. I had on hand some Grenville Island Robson St. hefeweizen (half-white beer). This beer has a hint of banana, a mild aroma and a tart, crisp finish, so it will only flavour the bread and not overpower the taste.

Dry:

  • 1 tsp. salt (I used iron pink salt)
  • 3 cup bread flour
  • 1/3 cup non-fat dry milk powder
  • 1 tsp. hot chili oil
  • 1/4 tsp. thyme
  • 1/4 tsp. basil
  • 1/4 tsp. oregano
  • 1/16 tsp. ground nutmeg (just a hint)

Wet:

  • 1 tbsp. olive oil
  • 12-oz bottle of beer (Grenville)
  • 3 tbsp. honey
  • 1/3 cup melted butter, luke warm

Yeast:

  • 2 1/4 tsp. bread machine yeast

Prep time: 10 minutes; Cook time: 3 hours.

Mix the ingredients according to your model of bread make (mine takes wet + salt, dry then yeast). Program it for sweet bread, 1-1.5 lb. loaf.

For the next 3 hours while the bread bakes, go find some geocaches to work up your appetite. When you come back, you should have at least found a few micro hides. Maybe even a multi-stage.

Crack open the bread pan door and feast your senses on the aroma of your spicy, savoury, slightly inebriated protein bread.

Results:

Here what mine looks like.

My bread-machine beer bread

The bread was fairly dense (as expected) but did not have the structural integrity to maintain a loaf shape, and so it collapsed in on itself just a little. Now it’s a soufflé bread.

The crust was firm but not brittle, and had a nice crumb. All that butter certainly kept the bread moist lending a nice texture. The chili oil did not show through enough which was a little disappointing, so next time I will up the spice factor. The beer did show through the perfect amount as I was hoping so I’m pleased with the subtle flavour. The added herbs are a little more obvious above the beer background, and I think next time I can omit the nutmeg and possibly increase the oregano/basil combo. My first attempt at a beer bread was quite pleasant, and I think I’ll work to improve the recipe.

by Leonardo Posted on May 16, 2012
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Bacon

I’d like to take a moment to publicly acknowledge the awesomeness that is Heather and her wonderful birthday gift of cooking demonstration classes. These have been amazing evenings to relax, enjoy mouth-watering food, and meet new people and the chef-du-jour. The central theme to last night’s class, taught by Paul Harding, was bacon, and contrasting flavours to savoury and salty tastes. I enjoyed the evening so much that I decided I would write one of my Toastmaster speeches on the subject, How to Say It. A speech about the sensory experience of cooking and eating was a perfect fit for this task.

Bacon. It’s the food that inspires the culinary creations of chefs, and the grumbling guts of hungry people.

Last evening I had the good fortune to take a cooking class. It was a gift, from my girlfriend Heather meant to inspire me to cook new meals that we could both savour.

Let me set the scene for you. The class chef was Paul Harding of The Only on King. The place was Jill’s Table test kitchen. Paul is preparing the evening’s ingredient as bacon-scented steam rises from out of the oven. A large mirror overhead lets the audience see the raw ingredients from the eyes of the chef.

Let’s imagine that we’re all in the store together, watching Paul work his magic. Tonight’s meal is 4 courses:

  1. The soup: Nettle soup with house cured bacon and potato pancake
  2. The appetizer: Angels and devils on horseback
  3. The entrée: Maple chili glazed bacon slab with asparagus and grainy mustard
  4. The dessert: Lemon creme brulée

All of tonight’s courses feature _bacon_ as a central ingredient, except for the dessert. The bacon was not the ordinary salty variety you can buy from the store, but home cured in a salt/sugar mix, and maple smoked using a barbecued wood chips. While the bacon sizzles and steams on the skillet, the nettle greens and rice simmer in soup stock. The soup is served with a little island floating in the centre made of fried potato pancake, with little bits of bacon floating like tiny icebergs. The nettle soup was silky, and had a little tartness to enhance the savoury, salty bacon, against the contrast of earthy, almost spinach-like, nettle soup.

The appetizer has a unique name, angels and devils on horseback. When I heard the name, it conjured up an image of white-on-black, black-on-white contrast. The devil in the dish is a cooked date, stuffed with blue cheese. This again creates a savoury, bitter taste from the cheese, in contrast to the soft-texture of the sweet dates. The angels are fresh Pacific oysters, lightly cooked. I have never eaten oysters, so I can now tell you these oysters were good. They have a mild salty taste, and when cooked, had a texture softer than cooked squid. Both of the horseback companions, the angels and devils, were wrapped in cured bacon, and served over a bed of mixed arugula and cabbage sprout greens.

Who can guess what was in the entrée? The star of the main course was a chunk of pork belly, slow cooked to remove a lot of the belly fat, leaving behind a crispy, and just a bit jiggly, piece of bacon. Over top of the bacon was a glaze of maple syrup and chili glaze. With just a hint of heat, the pork has a texture that starts off crispy and brown-glazed on the outside, and as you eat into the centre, the favour becomes softer, more savoury. The pork sat on a bed of asparagus, and simple mustard sauce. At this point, the entire store is being smoked by the bacon, infusing the wonderful smell into our clothes.

Dessert was equally delicious, though unfortunately bacon was not in the dish. To get the hard crust on the dessert, Paul blow-torched a little sugar on top of the custard. After some cooling, your spoon breaks through the hard crust, and the scoops up a bright lemon flavoured custard. It was the perfect way to end an adventure in bacon.

After all that food, it certainly sounds rich. Almost too rich. There was bacon in every dish, and cream was the next common ingredient. The flavour of the entire meal was deep, contrasting savoury with sweet or acidity. I left the meal feeling light, not stuffed, having enjoyed a range of concentrated flavours from bacon. I think I’ll be starting breakfast tomorrow with some bacon, and taking my cooking to a new level.

I hope now you go away and cook up some bacon. Go. Now. Do it!

by Leonardo Posted on May 7, 2012
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Pretty Terminals

Lately I’m spending a lot of time in an SSH terminal to securely connect to remote servers. This process works just fine, but it’s a really ugly eye-sore. I either get to look at black text on bright white background, or the inverse. Well, enough eye strain!

Making Putty Pretty

I did a quick Google search to look for custom colour themes for Putty and came across two pretty designs by Ilya Grigorich. After a quick download, executing the scripts installs the theme into the registry so the next time Putty runs, it loads them by default.

Quicker, slicker Putty

Now, with some pretty colours installed, I wanted a quicker way of running ssh commands. This is where Launchy triumphs, by offering a pop-up window that can launch programs or execute commands. With a quick install of the Putty Plugin for Launchy, we’re off the races, and I can execute an ssh session in a second.

Here’s a picture of the plugin in action.

Putty Plugin for Launchy

Update:

Using Console2, there are two equally pretty themes found here when you just want a pretty looking console. I’m using the dark Solarized theme.

by Leonardo Posted on May 3, 2012
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Predicting The Weather with Hidden Markov Models

This is the last post in an introductory series about Markov chains, Bayes networks and now Hidden Markov models. Hidden Markov models, or HMM, work pretty much like Markov chains, a system moves from state to state with finite probabilities, and each state produces a possible outcome. HMM are different because they model a hidden layer of states in the Markov chain responsible for the outcomes, and only these outcomes are observable. The Markov chain observations are identical to the (observable) states, while HMM has hidden (unobservable) states and observable outcomes.

HMM Weather ModelLet’s consider the weather for a concrete example. For simplicity, the weather conditions are either sunny, cloudy, or raining. These conditions are observable, and will serve as the observation sequence. However, many factors influence the weather, so hidden states will represent possible causes for the different weather. Specifically, I will only consider high and low pressure to affect the weather. The graphic on the right represents the HMM we will consider. In this model, high and low pressure are hidden states (we don’t own a barometer), but we can observe the weather. There is a 0.7 chance of starting in a high pressure state. Red arrows correspond to high pressure state transition probabilities, and blue arrows correspond to low pressure. For example, in the high pressure hidden state, the output probabilities are 0.6 for sun, 0.3 for rain and 0.1 for clouds, and a low pressure state transition probability of 0.7.

Continue reading →

by Leonardo Posted on Apr 27, 2012
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Bayesian Networks and Inferences

In my earlier post about Markov models, I introduced the simple Markov model called a Markov chain. Before I move on to discuss Hidden Markov models, I want to diverge a bit to first introduce Bayesian networks. This will help us

Bayesian networks (or Bayes networks, Bayes nets) are a probabilistic graphical model representing random variables linked by their conditional dependencies. For example, if you stepped outdoors and saw the ground was wet and the sky was cloudy, you would infer that it has recently rained, and may soon rain again. The water on the ground is conditionally dependent on the sky being cloudy and that it has rained recently, but if the sky was sunny and clear, the ground might be wet for another reason. Generally speaking, Bayes nets are a tool to show causality between events and update our beliefs about events given some information about the events.

Bayesian networks and Markov models are similar in that they are useful graphical representations of nodes with an implied directed order. They differ in that Markov chains represent temporal relations between events (nodes), and Bayesian networks represent causal relations.

Continue reading →

by Leonardo Posted on Apr 21, 2012
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