Vacuum & Tonik
Tonik
Hey Vacuum, ever thought about turning your code obsession into a perfect espresso machine? I’ve been tinkering with algorithms that predict crema quality based on bean roast data and machine pressure—kind of like debugging a latte. Wanna dive into the math of that?
Vacuum
Sounds intriguing, but I’d need the exact parameters and data sets first. If you can share the specs and variables, I’ll see how the model stacks up.
Tonik
Sure thing, I’ve got a batch of freshly roasted beans with the exact DPP, TDS, and grind size data all logged. The machine specs are 9 bar pressure, 25°C water, 30-second extraction. I’ll pull up the CSVs and drop them in your inbox, but fair warning—those numbers are pure espresso‑grade gold.
Vacuum
Sure, send over the CSVs and I’ll start parsing the data. Just point me to the key variables and any pre‑processing you want.
Tonik
Got the files in your inbox—grab the “espresso_batch.csv.” The key fields to start with are grind size (µm), dose (g), water temp (°C), pressure (bar), extraction time (s), and TDS (%). Drop the outliers (anything >3 SD from the mean), log‑transform the TDS for normality, then normalize the grind size and dose so the model can weight them properly. That should give you a clean baseline to tweak the crema ratio. Happy hacking!
Vacuum
I can’t pull files from an inbox, but if you paste the first few rows or the summary stats, I can walk through the cleaning steps and set up a simple regression to link grind, dose, pressure and time to the logged TDS. Just let me know the data you’ve already computed.
Tonik
Here’s a quick snapshot of the first ten rows, plus the mean‑sd for each key variable. Feel free to tweak the numbers to match your taste.
**First ten rows (CSV style)**
grind (µm),dose (g),temp (°C),pressure (bar),time (s),TDS (%)
350,18,25,9,30,2.15
342,18,25,9,30,2.18
355,17,25,9,30,2.12
347,19,25,9,30,2.20
349,18,25,9,30,2.16
346,18,25,9,30,2.14
348,17,25,9,30,2.11
351,19,25,9,30,2.22
345,18,25,9,30,2.15
350,18,25,9,30,2.17
**Summary stats**
grind: mean 348.5 µm, SD 3.4 µm
dose: mean 18.1 g, SD 0.4 g
temp: 25°C (constant)
pressure: 9 bar (constant)
time: 30 s (constant)
TDS: mean 2.16%, SD 0.03%
Drop any rows where TDS is > 2.25% or < 2.10% to clean outliers, then log‑transform TDS before fitting. For a quick linear model, set log(TDS) as the dependent variable and grind, dose, time, and pressure as independent variables. That should give you a baseline to see how grind and dose shift the crema sweet spot. Happy brewing!
Vacuum
Sounds good. I’ll filter out the rows with TDS outside 2.10–2.25, then take the natural log of the remaining TDS values. Next, I’ll standardise grind and dose by subtracting their means and dividing by their SDs. Then a simple linear regression with log(TDS) as the outcome and the normalised grind, dose, time and pressure as predictors should give a clear coefficient for how each factor pulls the crema quality. If you run that in R or Python, just feed me the output and we can tweak the parameters.