

Better Beer Through GPUs: How GPUs and AI Help Brewers Improve - JasonCEC
http://blogs.nvidia.com/blog/2015/09/02/beer/

======
Itaxpica
Very cool! I thought it was interesting that you're getting good, usable data
despite using untrained tasters - have you ever experimented with using some
kind of calibrated tasters, like BJCP judges or something? Do you think it
would lead you to get the same kind of data, but with a smaller sample size?
Or would it not really make a difference?

EDIT: never mind, I just saw in another comment that you're using your own,
trained staff - I miss-read the article.

~~~
JasonCEC
We use our own staff for R&D (training the models), but semi-trained tasters
are reviewing the product at the brewery;

we weight individuals on a verity of factors and have a pre-processing method
that controls for experience level in expected flavor perception per flaw.

So experience level is important, but we often have to work around it. Most of
the employees of a brewery or other beverage manufacturing are relatively
experienced anyway!

------
deutronium
Sounds a very interesting idea!

I'm curious what kind of volume of tasting data you're processing out of
interest?

Also do you use textual reviews from beer rating sites too?

If the main idea is to detect flaws in beers, I'm wondering if you could also
use mass spectrometer data, [https://nextglass.co/science-of-
satisfaction/](https://nextglass.co/science-of-satisfaction/) seem to be using
them to compare the similarity of beers, although thinking about it, people's
perceptions of different chemicals may vary widely.

~~~
JasonCEC
We have a couple thousand reviews per style, so its really fat-data not big
data. (in the tens of thousands for some styles)

The main difference is that we take a complete sensory profile from every
review, and join that with environmental and personal effects, such as time of
day, day of week, temperature, altitude, etc and learn the reviewers
preferences and sensitivities over time. We take about 600 variables per
review in total.

From that data, we're able to generate a much more actuate and actionable
analysis (at a cheaper cost) than a GC/MS or HPLC could - just consider, your
mouth is a perfect tool for tasting the compounds that matter to you when
deciding what you like and dislike. This is modified by your environment, and
we need to control for how your perception of flavor will change over time
with age, exposure, and experience.

I believe that a GC is a waste of money for 90% of breweries.

~~~
unwind
Is "GC" short for gas chromatography?

If so, "MS" is probably "mass spectrometry" and "HPLC" means "high performance
liquid chromatography".

Those were rather opaque to me, so I had to look them up, perhaps it saves
someone the time.

------
JasonCEC
Wow, 2 articles published about us in a day!

also being discussed here:
[https://news.ycombinator.com/item?id=10159852](https://news.ycombinator.com/item?id=10159852)

------
sprocket
How much of what you do is limited to being beer-centric? I run a small
artisan cheese company, and having data like this would also be helpful at
refining our make processes, as well as detecting seasonal preferences in both
the milk we produce, and the cheeses that are derived from it.

~~~
JasonCEC
Hey Sprocket!

We can do any homogeneous product made in batches - so cheese may be a good
fit!

We've done some yogurt and cheese reviews in the past to test the system but
nothing extensive.

Send me an email at JasonCEO [at] Gastrograph [dot] com and we'll see what we
can do?

Cheers! \- Jason

------
gballard
JasonCEC -- Just a question about the tasting protocols: where does the
sampling and tasting take place? Is it local to the brewery and/or
distribution areas?

Really fantastic idea and looks like a great implementation!

~~~
JasonCEC
Hi gballard!

The tastings are done in-house by the companies production team. There are no
samples to ship, and the sensory system is easy enough that just about anyone
in the industry picks it up there first 4 - 8 times (its often more in-depth
then they're used to!).

Thank you for the kind words!

