# Looking for advice: roast optimization through data



## Lovethybean (Nov 29, 2020)

Hi everyone,

After reading through CFUK to research for my start-up, I am thoroughly impressed with the positive constructive environment of experts so I thought this might be the chance for some valuable feedback to my questions:

*--> In your expert opinion: How would you approach coffee roasters with the goal of helping these experts to always optimize their roasts to spec through data analysis? What would be important for you?*

I believe that technology can never replace our human intuition and know-how. But technology can enrich us with abilities that we humans are not so good at. From what I experienced and saw is that always roasting to a specific taste of an established brand is extremley challenging due to the large number of varing influences on the roast-profile over the year (Beantype, -density,-moisture,-size, -age, -origin, roaster conditions, temperature, air-moisture, storage & roasting conditions,....).

I am developing a data driven aid for roasters to navigate those varying influences to always achieve their desired roast despite all those varying parameters. How would you go about approaching roasters? What questions would you want to be answered? What challenges do you see?

Greetings and particularly in current times: good health!

Pascal


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## BlackCatCoffee (Apr 11, 2017)

As a roaster and someone that studied computer science at university I find this intriguing but the task seems absolutely gargantuan.

You would need a heck of a lot of input data to start with such as bean moisture content, environmental humidity, environmental temperature, roaster temperature, roster thermal mass etc.

Then a way to quantify the results of the roast. Colour is relatively easy with the right equipment but how would tasting notes be measured and interpreted? Then I assume the program would suggest/make changes to the roast profile to align the roast to the desired taste. This in itself is a massive challenge as all roasters vary and different gas/fan/drum rpm changes mean different things on different roasters. Due to differing design/probe placement/probe thickness etc a roast profile is pretty much a unique data set to a specific roasting machine. The program would need to know this and account for the specific roaster in order to make meaningful changes.

If you can pull this off though, I willowy certainly be keen.


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## Lovethybean (Nov 29, 2020)

Thank you for your constructive reply  and nice!: you target the challenge at its technological core: I expect that many influences on the roast are difficult to measure reliably or economically, if possible at all. Furthermore I expect that the available data can differ widely from roaster to roaster (machine, equipment, set-up, process,...).

Hence my solution shifts the perspective from the end-result towards intuitively empowering the roaster. More specifically the solution is designed to work with large, patchy, missing and messy data to order& structure it such that we humans intuitively gain additional insight into the complex interdependent impacts on a roast. The idea for this came to me after recently re-thinking a particular manually executed but still data-driven roaster optimization project I did a couple of years ago. (FYI technology directions: soft-sensors, correlation methods, data-driven (physics)-modelling, swarm-intelligence).

From the customer or user side:
Developing solutions for experts and getting the chance to demonstrate its benefits is, from what I experienced in the past, extremely challenging as it could be perceived as change. And the perceived suggestion of change to methods, or worse, questioning of expertise, can understandably generate resistance & doubt. And for good reason: There are so many marketing promises out there that simply don't hold up in practical implementation.
That is why I emphasize that my solution does not replace the expert and will not magically lead to the perfect roast. But rather, it helps the expert much in the same way as the bean temperature probe or a software like artisan, just with more "automated analytics" in the background.
That is why I am asking for insight on which questions arise from the roaster-business perspective to learn how to address these rightful questions constructively. Because I believe that we can progress through augmenting our unique human capabilities through technology to improve our work & results.

Maybe I can frame my question better:

-> As a user of a data-analytics based roast support software: What would be your questions be before trying such a new approach?


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## MediumRoastSteam (Jul 7, 2015)

BlackCatCoffee said:


> As a roaster and someone that studied computer science at university I find this intriguing but the task seems absolutely gargantuan.


 I also studied computer science at University. And I personally found roasting extremely challenging and complex. i tried, and I gave up as an amateur home roaster after 1 year. I think anyone can follow a recipe, computers better than humans to be honest. However, human input is key in terms of feeling and correction, something which computers are not quite there yet. Like a good baker. 
i think there should be a mix so roasts can be reproduced easily. But it certainly requires some degree of human intervention to make the difference between the "meh" and the sublime. And that's why we have excellent roasters, who can understand the inputs and outputs of those machines.

Ps: I'm just expanding on BlackCatCoffee here. Not segueing anything. I pretty much take my hat off to anyone who can produce an excellent roast, so my hat is off to him! 👍


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## Lovethybean (Nov 29, 2020)

I fully agree with MediumRoastSteam: Coffee roasting done right is an artform; just looking at the chemistry thats going on during a roast is a dazzling insight to the complexity involved. A complexity that, to some exend, expert humans can intuitively deal with through what we like to call "experience" and "know-how". And I believe that it's also the tools that moved us from pan-roasting over a campfire to the toolchain involved in getting perfectly from bean to pressure-extracted espresso.

Good point towards the influence on quality and human intervention -> It probably makes sense to segment the coffee market reagarding the individuality of a product for this discussion -> on the one end of the spectrum is the highly specialized individualized roast where each small batch is tuned to perfection and on the other extreme the mass product optimized for affordability.
--> Are the roasting tools used by roasters in principle that different between the two ends of the spectrum? When roasting commercially in the high end segment, is taste consistency / achieving the same taste at different times throughout the year important? How is that achieved with a varying raw product and without wastage?


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## Three Hills Coffee Co (Feb 22, 2021)

This sounds really interesting, I'm a coffee roaster with a scientific background but know little about computers.

Data is always useful provided it is good data and you have enough of it. My initial thoughts would be how to standardise the data you collect, for instance different makes of coffee roaster will have temp probes in different spots so the readings for bean temp will vary ( I use several roasters for different jobs and they need slightly different techniques). Mass, time etc are relatively constant so should be trouble free but important points like first crack are again subjective. One roaster might call first crack on the very first pop that they hear but another may decide to call it slightly later for instance - I am also deaf so will inevitably call it later than someone younger than me

Last of all you have the problem of reducing the resulting beans to data, that's probably the trickiest bit and I have no immediate answers.

Like most experiments - designing it is the hardest bit, I'd be happy to share what I know though, get in touch if you'd like a chat.


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