Coffee Data Science
Diving into the Log Sampling of Coffee Particle Distributions
A different sampling
Particle distributions are a measure of the size of particles resulting from a coffee grind, particularly to quantify how many particles are a given size. However, when using laser diffraction to measure size, the bin size is hardware constrained to be logarithmic. Usually, the output is viewed without taking this into account, and we should definitely understand this technology better.
To understand how they work, Shimadzu has a document explaining the process. A laser illuminates the particles and a diffraction pattern results onto a sensor.
This sensor accumulates how many particles fall into certain ranges allowing a measurement of particle size. If you look at the sensor layout, lines distinguish cells, and those cells grow with size as they are further from the center. So with these techniques, the output is restricted to logarithmic sampling.
Typically, we look at a plot like this:
There are usually two peaks for coffee, and we always see this main peak as being very large. Is it that large though?
The bin size at the peak of 325 um is around 50 um while the bin size at 40 um is 5 um. The second peak is at 10%, but the total coverage is much larger.
Let’s fix this: let’s normalize all the percentages to a 5 um bin size. This is what the curve should look like if the sampling bins were sampled linearly.
Tools are very important to understanding coffee, but the tools themselves need to be understood as well. I have given particle distributions a lot of thought after working through imaging coffee particles without the restriction of logarithmically increasing bin sizes. So I wanted to better understand how to compare my results to laser bin size results.
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Further readings of mine:
My Second Book: Advanced Espresso
My First Book: Engineering Better Espresso
