TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Coffee Data Science

Salting Green Coffee Beans before Roasting

Robert McKeon Aloe
TDS Archive
Published in
4 min readOct 28, 2022

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I have been thinking about water. I have tried experimenting with my water recipe, but I still don’t have a sense of which direction to go because my filtered water always had won out. In the water discussions, there is talk about using distilled water and then adding minerals after the shot. It reminded me of cooking. I used to add salt after food was done, but then I learned how to cook and found out you’re supposed to season food before you cook it.

When brewing coffee, it has a certain amount of salt in the water, and some have added salt to espresso to reduce bitterness. I wondered if salt seasoned the coffee during brewing, why not try adding it to the green beans prior to roasting? I did a single roast to better understand salting green coffee.

All images by author

I started with a small amount of salt, just a pinch. The salt weighted 0.36g, and then I added it to 6g of water. Once it was dissolved, I mixed that in the green beans and waited overnight. I also mixed 6g of water into my control sample because I know adding moisture will affect the roast.

Both roasts (salted and unsalted) had similar weight loss (87.6% and 88.5%) and similar densities (0.395 and 0.404).

Equipment/Technique

Espresso Machine: Decent Espresso Machine

Coffee Grinder: Niche Zero

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

Shot Preparation: Staccato Tamped

Pre-infusion: Long, ~25 seconds

Infusion: Pressure Pulsing

Filter Basket: 20g VST

Other Equipment: Atago TDS Meter, Acaia Pyxis Scale

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).

Intensity Radius (IR) is defined as the radius from the origin on a control chart for TDS vs EY, so IR = sqrt( TDS² + EY²). This metric helps normalize shot performance across output yield or brew ratio.

Data

I ended up with 9 pairs of shots, and they were pretty similar for TDS/EY/IR. The salted ones looked slightly higher, but taste told a different story.

In terms of taste, most of the salted shots tasted worse. They tasted over-salted or muted in flavor. They were weird tasting shots.

I looked at individual score metrics, and unsalted was better in all categories.

The direction to go in this experiment is less salt, but I didn’t notice anything in this experimental data to suggest salted coffee would have improved a specific flavor. I am glad I tried salting coffee just in case it made a huge difference.

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 Book

My Links

Collection of Espresso Articles

A Collection of Work and School Stories

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Robert McKeon Aloe
Robert McKeon Aloe

Written by 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.

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