Coffee Data Science

Initial Testing on Batch Size for Coffee Roasting

A sprinkle of data

Robert McKeon Aloe
4 min readSep 3, 2024

I normally don’t adjust my batch size when roasting because I want to keep the variable constant. However, while discussing the Roest with a few others, there was a discussion about where the bean temperature sensors are located and how batch size affected temperature read. Part of this discussion was also about how closely the Roest is to a drum roaster or a fluid bed roaster. The current thought among that group is that it acts like a drum roaster for higher batch sizes (above 150g) and acts like a fluid bed around 50g.

So I went on to experiment with three batch sizes to aim for the drum roaster side of the equation:

  1. 120g, which is my baseline batch size
  2. 150g as an intermediate step to 180g
  3. 180g instead of 200g because 200g is the maximum, and I didn’t want to run into boundary conditions.

I used my Thermal Pulsing with Fan High on Down for all the roasts, and I did a 1 minute development time. The overall time for the 3 roasts was 7, 10, and 12 minutes, so there is a variable of concern where the batch size impacts the roast. This is difficult to adjust. It could mean doing a longer slower roast to have similar overall roast times or change the development time to be a percentage of the total. I figured I would run the test this way and see how it went before making adjustments.

Post-Roast Statistics

Weight loss showed a larger difference.

Even based on moisture, the larger batch had less moisture.

Oddly enough, the larger roast had a higher water activity. I’m not sure how important that is or what it means as it is a more recent variable for post-roast.

For bean color, they were a bit closer, particularly the 150g batch and the 180g batch, but both are darker than the 120g baseline.

Density decreased as expected based on the weight loss.

Tasting Equipment/Technique

Espresso Machine: Kim Express, Thermal Pre-infusion

Coffee Grinder: Niche Zero and Momentem

Coffee: Home Roasted Coffee, medium (First Crack + 1 Minute) on the Roest

Pre-infusion: Long, ~25 seconds, 30 second ramp bloom, 0.5 ml/s flow during infusion

Filter Basket: 20 Wafo Spirit

Other Equipment: Acaia Pyxis Scale, DiFluid R2 TDS Meter

Metrics of Performance

I used two sets of metrics for evaluating the differences between techniques: Final Score and Coffee Extraction.

Final score is the average of a scorecard of 7 metrics (Sharp, Rich, Syrup, Sweet, Sour, Bitter, and Aftertaste). These scores were subjective, of course, but they were calibrated to my tastes and helped me improve my shots. There is some variation in the scores. My aim was to be consistent for each metric, but some times the granularity was difficult.

Total Dissolved Solids (TDS) is measured using a refractometer, and this number combined with the output weight of the shot and the input weight of the coffee is used to determine the percentage of coffee extracted into the cup, called Extraction Yield (EY).

Data

The baseline (120g) tasted better than the 150g. There could be a bias because I’m more used to that batch size, and I am stating the bias just in case. The 180g batch did not taste as good. It seemed to lack some body and sweetness, and I think some would describe it as baked.

In terms of extraction, the larger roast didn’t extract quite as well.

This is a snippet of data, to be clear, there is not enough for statistical significance tests. The aim was for intuition building, and it seems the larger batch roast hits differently. It could also be that my profile is optimized for 120g batches, and if I modify it, a larger batch would work just as well.

If you like, follow me on Twitter, YouTube, and Instagram where I post videos of espresso shots on different machines and espresso related stuff. You can also find me on LinkedIn. You can also follow me on Medium and Subscribe.

Further readings of mine:

My Second Book: Advanced Espresso

My First Book: Engineering Better Espresso

My Links

Collection of Espresso Articles

A Collection of Work and School Stories

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Robert McKeon Aloe

I’m in love with my Wife, my Kids, Espresso, Data Science, tomatoes, cooking, engineering, talking, family, Paris, and Italy, not necessarily in that order.