Coffee Data Science

Flat vs Convex vs Concave Coffee Roasting Profiles

Simplified roast profiles

Robert McKeon Aloe
4 min readJun 18, 2024

I have been doing more complex roasting, but when I started roasting coffee ten years ago, my profiles were simpler. They followed a shape. I knew a flat profile wasn’t necessarily the best, so I looked at slowly bringing the coffee to temperature (concave) compared to quickly bringing the roast to temperature (convex). Concave could taste a bit earthy while convex could taste burnt.

I decided to explore these shapes again with small roasts. It is a small amount of data, but I wanted to start somewhere. I used Bean Temperature/Inlet Temperature (BT/IT) profiles which vary the inlet temperature based on the bean temperature.

Roast Data

The resulting roast curves were not surprising. Concave roasts took longer, and convex was faster.

The Rate of Rise (RoR) shape was interesting because the concave had a narrow peak before stabilizing at a near constant.

The detected cracks also changed quite a bit.

Post-Roast Metrics

Most of the metrics didn’t show much. They lost a similar amount of water.

Their moisture was pretty similar.

The coffee color was not. I would like to try this again to see if I could better match the color because there should be a taste difference between 52 and 60 which may not be due to the roasting curve.

They all had similar density too. With weight loss, this may give better indication that the color difference was the result of the profile.

Tasting Equipment/Technique

Espresso Machine: Decent Espresso Machine, Thermal Pre-infusion

Coffee Grinder: Zerno

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

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).


I pulled 3 espresso shots for each roast. The second shot used an ultrasonic mixer, which improved taste. The third show was a lower dose, so I used a puck screen.

Let’s re-sort these shots. There is not a clear trend winner here. I was surprised there wasn’t a big difference. I wonder how much differences show up when using a higher grade coffee because that could be influencing this test.

EY was pretty similar across the board, so there wasn’t some extraction benefit.

I looked at a few different curves and didn’t find a difference in taste. I’m not sure what to make of it, but I might need to try again with a lower grade coffee. One challenge in this test was the roast color being off between roasts, but even then, there wasn’t some large taste gap.

I want to separate the variable of development time and curve shape, and it might be better possible to do that by only modifying the beginning or the end of the curve in the profiles.

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



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.