To celebrate Philips end of service from the Army – he threw a party, southern style! (FYI – this was before all the restrictions went into effect with the pandemic)
We thought this was the perfect opportunity to serve the first batch of Danger Shed beer and see what people thought. Philip, John, and I raised our glasses high and took a first taste of our finished beer. Overall….not bad for not brewing in almost a year. But definitely needs some tweaking. Here’s the AleProof analysis:
Let’s take a look at the stats provided by AleProof:
Compiled and calculated within the Batch Analysis function
- OG – 1.046
- FG – 1.003
- Final pH – 4.3
- ABV – 5.6%
- Attenuation – 94%
Based on Subjective Analysis forms submitted by seven people. The form is based on the BJCP scoresheet.
The “Impression” analysis looks at five different qualities:
- Aroma (0 to 12)
- Appearance (0 to 3)
- Flavor (0 to 20)
- Mouthfeel (0 to 5)
- Overall (0 to 10)
Based on the data and graphics, it looks like Flavor received low scores across the board, meaning it was a general consensus that this characteristic could use some work.
The “Descriptors” analysis looks at the presence of various qualities (mostly bad) in your beer. The pie chart shows the descriptors that were noted by evaluators, and their frequency. I’m still tweaking the graphic to be more specific, but you can see that each slice of the pie corresponds to a particular descriptor, in this case, seven different descriptors were checked one time. If any descriptor had been checked more than once, that would be note worthy. We will still take these results into consideration when brewing the next batch, but the analysis shows that there were no descriptors prevalent enough to merit multiple votes.
Style Analysis – Blonde Ale
Using Batch Analysis tools we plotted gravity in blue and activated the style OG and FG ranges. The upper pink field is the style OG range and the lower pink field is the style FG range. We were right in the middle of the OG range but fell below the style FG range.
Next we plotted the ABV over time (this is all done with only a few mouse clicks by the way!) and activated the style ABV range. The alcohol content of this batch was slightly over the style range for a Blonde Ale.
Again, using Batch Analysis tools, we plotted the fermentation temperature and activated the yeast strain (Wyeast 1056) temperature range (light blue field). Our system can cool beer using an Ss Brewtech glycol chiller but unfortunately we have no way to warm it up at the moment. Since the temperatures at night are still too cool to ferment ales, this batch of beer had to be fermented inside with no temperature control during primary fermentation (Day 0 to 6). We were able to keep the temp in range (mostly) but as you can see, we rode the top of that range. The warm fermentation temperatures likely were a factor in the high attenuation.
Next we looked at the attenuation range of Wyeast 1056. The blue line is attenuation over time and the yellow field is the attenuation range given by Wyeast for this strain. This batch had a surprisingly high attenuation value (94% !!!). With a value that high, a brewer may suspect something went wrong. While this batch definitely was not infected (we are pretty sure of that after finishing off all 5 gallons) we have other theories as to why the attenuation was out the roof.
We figure it’s a combination of several things: our fermentation temp was slightly warm, and the mash temp was far lower than we intended – making the wort more fermentable. It was interesting to see the correlation between high attenuation (which dries out the beer, leaving less flavor) and the low flavor scores.
The “Timeline” tool in Batch Analysis allows you to see what you did to the beer throughout fermentation. It may be a little hard to see, so here’s the timeline for this batch:
- Day 0, fill Ss Conical 1 with 8 Stitches – 03-01-20
- Day 6, mark end of primary fermentation
- Day 7, set temp to 45
- Day 9, transfer to Corney 1
- Day 9, set temp to 32
- Day 11, carbonate
- Day 13, Corney 1 is vacant
Each event is given a vertical line on the X-Axis and corresponding color. Based on our timeline, the beer fermented for 6 days before we crashed it for 2 days. Then we transferred it to a keg and carbonated it 2 days later. On day 13 we served it at a party to friends who happily finished all 5 gallons, and on the same day we marked the keg as vacant.
We learned so much from this first batch. Not only did we learn to work as a team in a new environment, but we learned how valuable it is to visualize fermentation as it happens using AleProof. With the results from this analysis, we are heading into our next brew fully armed with data-driven insights that can only improve our beer. Next up – a stout using the same yeast strain. Stay tuned!
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