Let's see here.
It's good to see everyone today.
So we can just do some introductions real quick and just see what everybody's working
on.
My name, of course, is Keegan, founder of CanLytics.
And so this past week, a lot came together.
So the software, the API is now up and running and running smooth, testing it in multiple
programming languages at the moment, Python and Node, and working well.
And it's funny how things work, but a piece of CanLytics code, the generation of certificates
has now been used to generate certificates in production.
So congratulations.
Thanks.
It wasn't the anticipated piece that was going to get used first.
That's how things work.
And so a small piece of the code has been used to generate certificates.
And so now I'm just going to expand on that and keep improving that functionality.
Which state?
In Oklahoma.
So there are some licensees out there with certificates that were partially made with
CanLytics software.
That's cool.
So baby steps.
So how about everyone else?
Any other exciting forays into data in the past week or so?
Not data, but quality control issues.
What quality control has come up?
So concentrates shouldn't taste like eggs.
However, if sulfur is not cleaned up in the process, it can become very concentrated.
So when it combusts, it becomes sulfur dioxide.
It's very obvious.
So that's a quality control error that I experienced recently.
But the only challenge is that the people at the dispensaries have no idea what I'm
talking about because the extraction site is many, many miles away.
So anyway, everything was fine.
It's just I find these quality control issues to be exciting and demand attention.
Well, that's an interesting, relevant issue.
So we're looking at some of the data from Washington State, looking at the lab results.
And I thought I would just do what I preach.
And so last week, I was saying, oh, we should look at the failure rates for the different
solvents and microbes, microtoxins.
So I actually went ahead and prepared those statistics today so we can at least look at
them.
A lot of the data looks like a lot of the potentials in cannabinoids, but I'll show
you the failure rates as is.
So I know Charles has done some interesting work here.
Paul, real quick, have you done anything?
Yeah.
So unfortunately, most of my time has been spent writing my paper, but I should be ready
to share some results next week.
So I've gone through the entire process of selecting candidate dispensaries for analysis.
And so I've gone through that process.
I showed you a little bit of that previously.
And I have ran through my market basket analysis for those three dispensaries, looking at the
various combinations that emerge as far as product associations and natural product grouping.
So I'll have something to show next week if I could do that and let you guys see what
I've got up to.
Definitely, definitely.
Always interested to hear about your analysis.
And then even if just this week anything interesting had popped up in your literature review, feel
free to share it with us.
And then Charles, I almost wanted to save it in case things go wrong, but I've done
a bit of prep.
So last week, ran out of memory as I was presenting last week some of the data and things ended
abruptly.
So the recording goes on.
So if that does happen today, then just power on and maybe Charles, you can pick things
up.
However, I'm interested to see, hear what your analysis before I go on a spiel.
So you wouldn't want to talk about some of this analysis that you've done recently.
And hello, by the way, Melody.
Hi.
Hello.
I've got a, I'm sorry to interrupt.
I have an eight year old with really severe epilepsy and I'm a beginning data scientist.
And so she's a little noisy, so I'm going to put myself on mute.
Well, it's awesome to have you here, Melody.
We're all learning.
And so you're in the right place.
So we'll just be talking about analytics coincidentally about cannabis.
So we typically have a fun time.
Perfect.
Thanks.
Thanks for having me.
Oh, by all means.
Welcome aboard.
So just to introduce, Charles has been looking at some of the cannabinoid statistics from
this data set we have in Washington state that has a lot that has essentially all of
the lab results from the traceability system up through the end of 2020.
We'll get the newer data set, but for the time being, we'll just look at this historic
data.
And so Charles, you wouldn't want to introduce what you've done real quick.
So I've been going through the lab results data frame column by column and looking at
the number of missing values, just sort of plotting it all out, getting trends, showing
percentages of failures based on how they correlate to other columns and just kind of
just basically going through it and looking at everything because trying to use a classifier
to figure out which samples are going to fail or not.
There's certainly some issues and it came, no matter what I tried, it always seemed to
turn out to be just a glorified dummy classifier.
So now I'm going through and looking and trying to figure out what data really matters and
what data is just kind of maybe kind of, what data is too messy to use and just kind of
going through the whole thing and just looking at it overall and so that we can look at each
column and see what if any information is really there.
I was doing a bit of exploratory analysis here and maybe I'll start sharing some of
my work here.
So essentially what I was thinking was, okay, so there's low failure rates.
So it may be just tough to predict failures, but perhaps if a failure occurs, maybe that's
a sign of maybe a larger problem at hand.
So I thought maybe what would be worthwhile is, okay, let's just look at the samples that
fail and like what's going on with them.
So I haven't gone too much further down that repertoire, but I'll show you essentially
how you can find those samples.
And then perhaps Charles, you could maybe help me with this.
If we attach all the data points, right, because we'd want to see, okay, what's the strain,
potentially where, you know, where's, what's the geographic location.
And it would just be interesting to see if we can't pin down some essentially other issues
going on there.
So, you know, do cultivators that fail, do they have, you know, lower than average cannabinoids
like, or do they have above average moisture content, below average moisture content?
So that was sort of my idea was, okay, let's isolate the failures and see if there's anything
else going on with those samples.
So do they have, you know, statistically different X, you know, moisture or what have you.
So long story short, I'll go ahead and just start presenting some of this data.
In doing so, we'll essentially pick back up where we got interrupted last week where we
were replicating a cannabinoid analysis from edibles in Jamaica.
So let's present.
All right.
So we're essentially working with these lab results and just to give you a look at what
a lab result or just an observation looks like.
We essentially have many, many data points, most of them aren't that useful.
So the pesticides aren't reliably entered in.
So what we, the key variables that we do have are we've got the cannabinoids principally
THC, THCA, and CBD.
And we're able to use some other identifiers.
So here we can actually identify the lab.
So this came from lab 12.
And you can identify who, which licensee this was tested for.
This was tested for license 413 to 87.
And then you can even connect this to the inventory item.
So that way you can get the strain information and perhaps more information such as harvest
date.
So we'll need to connect that data.
So since we were already talking about solvents in micro, I'll go ahead and show you the analysis
I've done there.
And then we can move into the cannabinoid analysis where we can finish out replicating
the analysis we were intending to replicate last week.
So we've read in our data and then it's worthwhile to note that, okay, not all of the samples
are the same.
So the main distinction you have here is essentially the intermediate types.
I did a bit more research and so essentially the flower is when it's harvested, then it's
packaged into flower lots, which are then sold as usable marijuana.
So what's sold at the stores is usable marijuana.
However, they're having typically their flower lots tested.
However, sometimes you have the flower tested immediately after the harvest, I think, before
it's sub-lotted.
So I think those are what those lab results are for.
So what would be worthwhile is still, I think, to connect the lab results to what's actually
sold at the store and maybe only look at those lab results
because at the moment we may be including things in analysis that aren't even ever sold
at a store.
So right now we're just looking at the entire, essentially the entire population of cannabis
that was tested, whether it was sold or not.
So just to look at the concentrates real quick, essentially just grouped everything that appeared
to be any sort of concentrated product.
So the main ones are hydrocarbon concentrates and you also have concentrates for inhalation.
And then I even included the non-solvent-based concentrates almost as a sanity check, as
well as food-grade solvents, and then of course CO2 concentrates and ethanol concentrate.
The main salt, well, not the main, all of the solvents are listed here.
So everything from acetone down to your xylenes.
And then I collected the limits.
So last week I mentioned that you can find the Washington State mandated limits in their
administrative code.
And so essentially I got the limits that they have listed here and I put them in this spreadsheet
using the keys provided.
So that way we can match up, okay, acetone has a limit of 5,000 parts per million, so
on and so forth.
So I collected the limits and so now we've got all the data we need.
And so for starters, I said, okay, let's just look at the average concentration of solvents
in each concentrate.
We'll need to define the concentrate types.
And so we can now start to just look at essentially the baseline concentration in all the concentrates
just to get an idea of what may or may not be present.
And so keep in mind that certain samples may have high parts per million, whereas many
other samples would have none.
So you start to see that, okay, things like concentrates for inhalation, they never test
for solvents, hydrocarbon concentrates, they're coming out across the board, low concentrations,
but you're getting hits for the various solvents.
What's interesting here, you are getting a few hits with the non-solvent-based concentrates.
I included infused mix, it doesn't look like those, those may not get tested for residual
solvents.
And so we're just essentially just trying to just start building some conditional averages
here just to see, okay, what solvents may or may not be present.
So now, as I mentioned in prior meetups, that, okay, let's look at the failure rate for each
solvent.
Okay, so here, essentially just, essentially given a sample, so let's just, you know, given
say a sample, so this one wouldn't have acetone, but given a sample's acetone level, we can
compare that to the limit for acetone.
And if it's above 5,000, it's a failure, if it's below, then it passes.
So we'll just manually compare these to the limits and essentially try to calculate the
failure rate.
One second, I'm going to suppress these warnings just so it's slightly neater for us.
So what I can show you here is there's quite, as Charles was noting in his general analysis,
the failure rate is low.
So your failure across the board, so your highest for hydrocarbon concentrate, your
highest risk is butanes, and you have a less than 1% chance of failing for butane on average.
And so here are just the conditional averages for each solvent, for each method of extraction.
And as you'll see, I don't see any with above a 1% chance of failure, but so, so long story
short is it may be, as Charles was noting, it may be tricky trying to predict failures,
so it may be just more worthwhile looking at specific failures and trying to figure
out, OK, what went wrong in this situation.
Just to continue in this vein, we're also going to look at mycotoxins, which we also
have limits for, and I was just going to look at some of the microbials.
So in particular, these correspond to the enterobacteria.
So this is what I would just generally describe as an uncleanly cultivation site.
So I think that's what would lead to you failing for microbes.
And so I think it's worth looking at one way or the other, so that way we at least know
what they are.
So if we look at mycotoxins, we'll actually see that there's really almost, I guess, almost
no failures for mycotoxins.
So you're seeing very low percentages.
You're seeing a handful of flowers that are failing, as well as you've got some non-solvent
base that may fail for okratoxin, marijuana mix.
So essentially what I was thinking was, OK, why don't you just identify these flower lots
and potentially see what's happening there.
So for example, I'll write this snippet here in a bit potentially.
We'll just do it right now.
So essentially what we're looking for here.
So here is all the flower data.
And so you can essentially find all the mycotoxin failures of flower.
You basically say, OK, let's find all the flower data where the aflatoxin was greater
than or equal to 20.
So you'll see, OK, so there's only 45 failures for mycotoxins.
And so essentially what I was getting, starting to come around to is, OK, it may not be worthwhile
trying to predict a mycotoxin failure.
And why don't we look at these samples here and see if there's something that they have
statistically different in some way.
Do they have above or below average moisture?
So here they have an average moisture of 7%.
And so we can just say, OK, what's the average moisture content on a whole?
So you're seeing, OK, the moisture content as a whole has an average maybe around 5%.
So the standard deviation here is high, so it probably wouldn't be statistically significant.
However, you could do a test and say, OK, is this mean statistically different than
the average?
In this case, it actually does just eyeballing it.
Looking at the high standard deviation, it doesn't look like it's going to be statistically
significant.
However, if you go through the various variables and see, is there anything, is there any pattern?
Keegan, I have a general question.
A couple of weeks ago, you provided some analysis and some thinking behind there's a work that
you were working with another gentleman and it was for outdoor cultivation.
And I think there was a higher failure rates for the outdoor cultivation tests as opposed
to what we're looking at for Washington State, which I believe we're looking at like the
indoor grows here.
Have you noticed between, and there's two different ways of growing, but have you noticed
between different states just kind of more systemic failure rates, you know, like looks
like here in Washington State is pretty low.
Do they all tend to be pretty low for the various states you've seen or?
Anecdotally, I've heard various things.
So I think it varies state by state.
So it matters and the regulations matter.
So it's not a state, but for example, I think I've heard that failure rates may be high
in Canada.
And so a lot of it depends on, okay, what are you testing or screening for?
And what is the threshold, the limit for failure?
So the lower your limit and the more, you know, pathogens or analytes that you're screening
for, the higher your failure rates are going to be.
Yeah, it seems like there's a lot of, from a public health perspective, it seems like
there's a lot of wiggle room for variation across all the different states, right?
It seems like that there's, I don't know, not a single standard, obviously, the states
run it their own way, but I don't know, it seems like there's a lot of areas where things
it's going to be hard to really know from a consumer perspective, just, you know, how
safe is my product going from state to state?
Well, I was going to give them a shout out real quick, but we'll have to revisit that.
So I think that's fruitful analysis.
I'm not 100% certain how you would frame your research question, but essentially, what I
think you'd be trying to uncover is, okay, are like the regulations, I guess, guiding,
I guess, I guess like the argument put forth has been, okay, so say in California, they
have strict regulations on microbe levels and pesticide levels.
The idea is that cultivators in those states get skilled at keeping their microbes low
in their pesticides low.
So I know at the time, you know, the recreational marijuana was legalized in Oregon, Oregon
was supposed to have the strictest standards.
And certainly, you know, there were much higher standard than what was in Washington and Colorado
at the time.
So I don't know, I mean, a lot of other states have legalized it since then.
So I don't know if those states have enacted stricter standards or if they're more lax
or if they're the same.
But I guess I would actually, that's probably easily obtainable information.
And that might be a good comparison.
Yeah, it's just was, I don't want to derail the conversation too much, but it just the
analysis that you're showing, Keegan, just got me thinking and just the standardization,
I guess, is the word I'm thinking of here.
Well, you brought it up in the prime time, because here we are essentially talking about
microbes.
And this is what the microbiologists are stressing.
So you've got people in a lot of different camps here.
And so there are people in the camp that think that we're not really screening cannabis enough
or for the right pathogens.
And so there are people in the camp that think, okay, we need to be screening more essentially.
And then there are essentially some people in the camp, I met them, so they're basically
saying like, okay, it doesn't necessarily make sense to be screening everything for,
I don't want to put words in people's mouths, but for all of these pathogens.
And so I'm not certain which ones per se.
So like I said, you do see failures for certain ones of them.
But it seems that, okay, maybe we don't necessarily need to be testing for microtoxins.
And like I said, I don't want to put words in people's mouths, but I'm not certain which
ones, but the microtoxins are potentially some of the microbes like salmonella.
I don't think you really see samples failing for those.
Whereas perhaps there are other contaminants that may be worthwhile screening for like
perhaps, so in Washington state, they don't really have the yeast and molds to my understanding.
So I think just from what I've picked up, it seems that, okay, maybe we're screening
for the wrong contaminants and not across the board, but some of them may be excessively,
we may be doing excessive tests for microbes that aren't really expected to be there.
And we may not be testing for microbes that are worthwhile to be screening for.
Yeah, it seems like time will tell what the consensus is on a lot of these things and
whether the federal government gets involved on overall standards and that sort of thing.
Exactly.
And so perhaps even for next week, since I always try to pick up sort of an interesting
topic in the week that I don't actually know enough about and try to do to research it
for the next week.
So I could start looking into some of people's perspectives on, especially in the lab space
on microbes.
So what should maybe be tested more, what should maybe be tested less, what do we have
right?
What states have it right?
What states have it wrong?
And so then we can almost do a state by state comparison of the various regulations and
see and even how those regulations have made may have changed over time, because in Washington
State, they changed which microbes were being screened for.
And this may have happened in other states.
So I think.
Yeah, I could see if you went down that path.
And I don't know if other analytical outfits are doing this type of work.
But if you were able to pull that information together to a certain level, it almost be
like, can it could be in an advisory position based on that knowledge that you've compiled,
right?
Because I just see, you know, not that I'm not very knowledgeable in this area, but I
could see that the force is pushing in that direction.
And if you if you position yourself with that information, it might be a good resource for
others.
That's definitely something that we want to incorporate in the analytics platform.
So essentially, we see the lab testing community as really just one big community of labs,
right?
We've got labs all across the country, and a lot of them are doing the very similar things,
right?
So here you've got labs in Oklahoma, and they're testing cannabinoids, they're testing pesticides,
just like people in Washington State, or just like people in Colorado, just like people
in Michigan.
And exactly.
And so we essentially have a platform here that can handle, okay, yes, the limits may
vary state by state, but you're still doing the same process.
So yeah, so in general terms, in any kind of market, the folks that develop a platform,
you can think of, you know, Uber and search engines and anything else you can come up
with.
But those who have the platform that connects all the different groups together based on
what it is they're trying to accomplish usually ends up being in a pretty good position.
And just to to toot our horn real quick, since it is up and running, can essentially show
you essentially what that would look like, just its simplest version.
So essentially, you know, you'd have your set of analytes.
And so, you know, for example, let's see if we can't find a nice microbe.
So aspergillus, so, you know, for example, you know, this limit, you know, I've got to
set it one part per million.
So I think so.
So I think they've got, you know, one part per million restriction on aspergillus in
Oklahoma.
I just use that as a model for these limits.
Let's see if there's any other.
So you would be able to look across all the various different companies that are using
your platform and and then across one particular analyte here, you would be able to see all
the different limits that they're using.
Exactly.
And so we would even want to try to help out a little.
But essentially, you know, the limits are going to vary state by state.
Everybody's essentially doing the same thing.
And yes, it would be then interesting to then measure like, OK, do those limits then have
an effect?
So are people failing samples more or less in some states or others, depending on the
limit?
And I think the reason that would be significant is there's a truncation effect that they talk
about in economics.
So whenever you whenever you're running right up to the threshold, that can affect your
analysis for starters.
And so the reason I feel like that's relevant here is OK.
So you're actually let's pull up this.
The Midwestern.
And database here real quick, because I think this is a good example of exactly what I'm
talking about here.
So.
So here, essentially, this red the red line is you can think about that as your failure
rate for for cannabinoids and hemp.
And so you would expect that this would be similar for.
So let's just pretend that this is for for various other compounds.
So to say this is for salmonella or something.
So in this case, and I think at the federal level, they are looking at adjusting the threshold
to THC.
So in hemp.
So this would be a limit where I think it would lead to misleading outcomes, because
you're basically saying that, OK, something on this side of the red line is passing hemp.
And then something on the exact other side of the line is failing hemp.
And so.
You can you can get misleading results, essentially.
And my concern is, you know, what if this was happening with like you wouldn't want
to see this happening with a microbe?
So if you had something like this with your microbe where there's some.
You know, a lot on one side and not many on the other.
So your point is you're basically going to have things that they look like they passed,
but they're really just shy of failing.
Right.
Yeah, there you go.
So hard, hard concept I'm trying to convey.
But basically.
How is that?
You know, how do states want to set their thresholds?
And so, like, obviously, in Washington state, it looks like the bar for microbes is really
high.
And so let's just kind of throw these axes out the window.
But basically, you know, the red bar is not capturing any of the.
Of anything, even if it may have high microbes, whereas in other states, they may lower the
threshold.
But then you may wind up in this scenario where you're essentially failing samples that
look quite similar to samples that pass.
Yeah.
Yeah, there's it's not not subjectivity, but there's.
Certain level of reasonableness that you have to apply to these thresholds.
Exactly.
I mean, I just I guess maybe this is because I coming from the economics point of view,
I think, OK, well, it just comes down to a cost benefit analysis.
You know, what's the you know, obviously, there is a cost of increased testing and,
you know, there's a benefit as well.
So, you know, they said essentially the, you know, the public safety.
And so from the economics point of view, I think it would just come down to a cost benefit
analysis where you just try to measure the benefit to the public and try to balance that
with the cost of.
Essentially more testing and then the cost to cultivators for having to jump through all
the hoops necessary to keep.
Right.
To keep your failures low.
So right.
But that's that's where I'm coming from.
Different people come from this from different places.
Right.
So you've got the regulators looking at this situation and they weigh the public safety
highly.
And then, you know, the businesses are coming from it from their point of view and they're
they're going to be focused about the costs.
So good stuff.
So anyways, we went down that rabbit hole.
So just to get back to the data real quick.
And then in the last bit of time here, I'll just kind of wrap up that cannabinoid analysis
because I thought that was kind of interesting.
But here's just a look at the microbes real quick by by sample type.
What I noticed was interesting here.
So we'll come back to this in a second.
So if you look across the board, you'll see, OK, you know, you're having some some flower
lots fail for microbes.
And then you're actually having, you know, above, you know, slightly higher marijuana
mix failing for microbes.
And from my experience, this is essentially what you'd expect.
So a lot of the times the marijuana mix just ends up being some of the anecdotally I've
heard ends up just being some of the like the the loose bits that may be kind of left
over after after trimming or after packaging.
So they may get everything packaged and they just may package up all the loose bits as
marijuana mix.
And so when you would expect when you do that, things are going to there's just going to
be more microbes in in that mix.
So they tend to fail slightly higher than average for microbes.
And then the other thing I was going to point out that I thought was interesting in this,
you may need to drill down a bit because summary statistics can be misleading.
But I thought, oh, like there's a you know, there's, you know, a non negligible portion of non mandatory
samples failing for for micro.
And so what that looks like me is like maybe maybe people may suspect that they may have
a dirty environment.
And they they're trying to do just some some almost like research and development to try
to get things cleaned up proactively.
But but don't want to attach too much of a narrative to that because I think further
drilling is needed.
So the non mandatory plant sample is makes up zero point zero zero point zero one two percent of the samples.
Exactly. So it may even it's not it may almost be an insignificant amount of the sample.
So so yeah.
So I mean, one failure could, you know, you could make it look really statistically significant
when there might only be one or two or maybe like 10 samples or something.
It's not a whole lot.
Exactly. And so it's an outlier.
And so I think, yeah, instead of chasing chasing that, you I think what's worthwhile here is,
OK, maybe you may want to look at the flowers and the marijuana mixes and see if there's
any pattern that you could uncover, because basically what are you trying to do here?
You're trying to find some factor that may help the cultivators have a cleaner environment.
So if you just if you just look at all the failures and you notice they're all in this,
like this is like the classic example of statistics. And I unfortunately don't remember.
The. The name of the statistician, but.
But basically, they were looking at, I think, cholera outbreaks and they were looking at wells
and they said, oh, they realized, oh, many people that frequent this well, you know,
they're they're suffering. And so you could essentially try to do that with cannabis.
So you could say, OK, let's look at all the failures for microbes and try to uncover a pattern.
So are all the microbial failures, are they right beside cow pastures or.
What? So there may be there may be just being nothing going on there,
but I think it's worthwhile trying to find out because.
Let's say you discover something and you're like, OK, people beside cow pastures
have a 15 percent greater chance of failing for microbes.
Well, if cultivators knew that, then they may not want to set up shop right beside a cow pasture.
And so it's just, you know, the more you know, the better operation you can run.
That's my spiel there. And so now let's go ahead and finish out the cannabinoids from from the priority.
So we're Heather and Paul, just to show you what we're doing here.
Basically, just look in the literature, trying to find an analysis that people were doing.
Saw that there was a recent paper here, July 10th.
About people looking at edibles in Jamaica.
And what jumped out to me was, OK, they actually only have, you know, 45, a sample of 45,
which is better than nothing.
And so, you know, they were just looking at the THC CBD concentrations.
And just to show you a chart.
They're just measuring these in the lab.
And so then they get their THC CBD ratio in the edibles.
And so essentially thought, OK, we can replicate their study in Washington state and just look at the THC CBD ratio in edibles here in Washington.
Just to, you know, just a demonstration of what we can do and how powerful our analysis is.
So we can go ahead and isolate the solid edibles.
And, you know, just to toot our horns a little bit.
So this is what's cool about Washington state's public data is over the prior years.
And this is where we crashed last week was we were basically saw, OK, you know, from 2018 to 2021, we have almost 9000.
Solid edible samples. And so, you know, we can do some some some good analysis here.
Quick note.
Inevitable are coded as either percent or milligrams per gram. It looks like a handful, more than a handful, 460 samples are coded with THC percent.
And the rest were coded with milligrams per gram.
And so you can actually convert percent to milligrams per gram.
So when you think about it, there's a thousand.
Milligrams in a gram. So, you know, if something is 10 percent.
THC, well, then that's going to be a hundred milligrams.
Per a thousand milligrams or per gram.
So that would be a hundred milligrams per gram.
Ten percent. So you can convert.
Percentages. So say you've got 10 percent here.
You've got 10 percent there. You can convert 10 percent to milligrams per gram by just multiplying by 10.
So now you just take your 10 percent, you multiply it by 10.
You now have a hundred milligrams per gram.
So. The only thing that hung me up was just coding it up real quick, but we can basically convert everything that's missing.
Milligrams per gram, but does have the percentage and we can just put everything in milligrams per gram.
Just for the sake of time, I have not actually solved this yet, so if anyone wants to try to solve this line of code, then by all means.
Otherwise, I'll look at it for next week. So long story short.
We can look at the THC and CBD in edibles here.
And so. Only. Probably going to need this chart up here.
And then hopefully this just works.
So. Just we're quick. Essentially, we were just saying, OK, can we essentially replicate their study, but do it in Washington?
And essentially we have so.
Keep in mind here, I'm dropping outliers, so I'm dropping the top 5 percent because you see things that.
Have. Potentially unrealistic levels of THC and CBD, so I was just dropping the outliers.
So excluding the outliers so we can actually get a decent looking chart.
Let's compare this to.
Oh, actually. I may be able to do is I may have printed this off.
OK, so it doesn't look like it printed correctly, so.
Just trying to get these charts side by side to a decent.
So bear with me here.
OK. So. We've got we have similar charts, so we're both seeing that, OK, the THC to CBD ratio, it's typically less than 50.
And so their mean was 29 was around 30.
Milligrams of THC to one milligram of CBD, we're seeing that it's around 20 to one.
There's standard deviation 54 hours is 34 or.
Around 33.
So sorry, Keegan, I'm sorry to interrupt you. I do have to drop for some work related activities, but I'll give you a call later today if you're available.
I wanted to talk to you about a couple of things about the graduate project.
Definitely, definitely. And we'll be wrapping up here in a second, but thank you for attending, Paul.
And definitely, and if anyone else has questions about some of the analytics we're doing here, just always, always happy to talk.
Hot cannabis data. All right. Thanks guys. We'll talk to you soon. Bye bye.
Thank you. Bye.
Just for everyone else to go ahead and bring this home here.
So essentially. Essentially, what we're just doing is we're just saying, OK, yes, you know, not only.
Can we look at so basically we can start looking at analyses that other people have done and we can potentially replicate them in Washington state with.
A greater amount of data. Of course, it's different. You know, they're looking at Jamaica and we're looking at Washington state.
However, we're both looking at cannabis edibles, so there could be things to uncover.
So this was just a simple analysis here where we're just, you know, we're just looking at the THC to CBD ratio.
But now we know. And so so how is this valuable?
Well, so now if you're a.
A processor, an edible manufacturer, well, now you can know that, OK, most people are producing edibles that are less than, you know, 50 to one THC to CBD.
And, you know, dialing in your ideal ratio is is often part of the challenge for the edible manufacturers.
So it's a tricky thing to do. And so.
So, so I think this could could be worthwhile, worthwhile analysis.
And like I said, this is just a start. So, you know, now you have a whole nother variable here, you know, your THC to CBD ratio.
So now you can once again, you can do analysis and OK, do any of these other factors we're looking at, you know, do those influence.
Influence ratio. No, so.
There is a lot more to be done here with.
Analytics of cannabinoids, so I think I'll go ahead and conclude there because.
There's so much to do that. You know, you may have to start looking through some of this on your own.
So.
But. Real quick in the last few minutes, any questions, comments, ideas?
I just I guess I don't mean to be dramatic, I just feel a little moved by the last graph that you showed with the red line and the.
The lack of what I think is a suitable distribution around the failure point.
You know, all your points being clustered, I mean, that's extremely unsettling.
I'm going to say that I've been privy to that in the lab directly, but not when it really matters like this.
Like, well, I don't know. Keep in mind that was hemp we were looking at.
So that was hemp. I understand. But it is it is something that I'm sure the hemp producers would appreciate you being up in arms about that because.
It's just tough, right? Because like, say you're a hemp producer and you tested it point three one percent.
And then you're the guy down the road or the girl down the road, tested at point two nine percent.
And you've got to destroy your harvest and then they get to go on and make a small fortune.
Yeah, that can that can hurt. So now you need to start setting up cameras like if I'm going to be growing my own stuff,
test it just before you chop or as you're chopping better be on camera.
Like, when are you testing? You know, I just there's just so many things to consider.
Maryland state, for example, flower test super high in Maryland. And I don't know why.
It's also very, very dry. So when the numbers that you're referring to at times, they seem low.
But that's because of the numbers that we're looking at in Maryland that are very standard.
So it's it's not to say good or bad. It's just what gives rise to that.
The method of testing. There's so many things that go into the testing.
You're referring to testing edibles, testing the center of it. Oh, no.
Like, it's so complicated. I'm not saying it's overwhelming, but I'm I'm intrigued more and I just want to keep on listening.
Oh, you bring up interesting points in the mid to edibles. That's what people are always asking.
So typically what they have to do is send in the entire edible and then they'll they'll they'll they'll they'll dilute or they'll dissolve it.
Create a dilution, then test that. And so, you know, people are always what it's always a question.
Do I test the inputs? And then you raise interesting point with hemp.
When do you send in the sample? So maybe some people have techniques that they know when to sample.
To to make sure that they're they're staying below the permitted THC levels.
And maybe some people are naive and they may not know. And so it's a tricky dance to dance.
And my only consolidation to your consolation to to help producers is I as far as like I thought they had raised it for a second,
but it looks like they may not have. But there's definitely noise about them raising the the hemp requirement to I think point six or point seven five.
And so I mean, you saw the chart. And so if they did raise the hemp.
Permitted THC to point six or point seven five, then that would allow a lot more hemp into the market.
They know the conversion rate is a like point oh three percent that you're talking about with THC for certain states.
Is that based on the conversion rate that they know?
So that's after essentially you apply conversion rate to THC, a because THC decarboxylates into THC.
So technically your total THC, which is Delta nine THC plus zero point eight seven seven times your THC.
The combination of those is your total THC. So your total THC for hemp has to be less than zero point three percent.
Meaning to say the their concern is that if there's beyond a certain level, that it will the THC level will go up beyond a certain point.
Do they know that potential?
I think. Yeah, sorry.
As you're picking up, there's a lot of things that may not make entire entire sense, right?
Because, OK, say you've got it tested. OK, your cannabinoids, they may change over time.
So they're exposed to light photons. I mean, these these compounds must be sensitive to light.
And I know that they are. So exactly.
So perhaps your crops CBTA heavy and it passes.
And then over time, that turns into. So it's.
There's a lot of there's not a lot of nuance taken into consideration in the rules.
It's just the rules are just pretty flat. It's just zero point three percent.
This is how you calculate THC and that's what it is.
So. And that's why and that's why we're why we're here to help is we're just trying to one, just let people know, OK, these are the rules.
And then to provide them with analytics to see, OK, given the rules, how can we say we want to grow hemp?
How can we grow hemp to make sure our probability of growing legal hemp is is high?
You know, just because that reduces your costs.
You know, we're just just trying to help people create value.
Yeah, I'd like to know what sort of data I can get a hold of in my state that's free or anything.
Yeah. So we are medical only.
So we have three certified labs to test cannabis.
So when you whoever that I mean, well, we have quite the monopoly here because we're not fully recreational.
So there are companies that like to apply for their certification and they get it and they get spots number one out of six or whatever it is.
So unfortunately, the monopoly is just getting out of control.
But we our program is pretty decent.
We just the all samples, they go to that certified one of those three testing centers.
And yes, I've applied to those jobs.
So I put a link in last week.
Read me up. I'll send send the link to you.
So there is some Maryland data primarily with sales.
Oh, that would be interesting.
Yeah, so you can at least look at the sales data.
Oh, I'll I'll try again to see if there's anything else I can scrounge up for Maryland.
I think it would just be useful in comparison to is like in a comparison study.
So what I'm curious about what I'm becoming curious about is maybe look at per capita.
So, OK, does maybe Maryland have above below or typical consumption per capita?
So maybe not even.
But what any data points we can get, look at it in comparison to other states and see if there's any any juice we can squeeze from this rock.
Yeah, I'm interested. I like state to state stuff.
You know, people in Maryland are just itching to get their specific strain of flour from a different strain, a different state, even if it means to travel.
So from a customer perspective, our priority is the strain that we like.
That's our medication. I'm not speaking for everyone, but it's it's a general sentiment that people have here.
So I love the state to state combat.
OK, well, that came up early with regulations, so maybe we can try to combine this next week.
May just do a deep dive into regulations state by state.
So just be just OK. Testing regulations. What's going on?
How is it affecting people? So that could be a good topic.
Yeah, or, you know, lack of testing like I'm not saying that, no, we have our stuff is tested.
It's just every now and then we get a little strange QC error.
Oh, yes. And that's what we're talking about.
And that's something I want to leave you today with is be careful what you measure.
Right. Because if you're only measuring.
Acetone or butane, then that's all you're measuring.
And so in Heather's case, then, yeah, it looks like everything's fine.
It looks like pass this big put a big green pass on it.
But you just may not have measured your your silver.
Oh, oh, my God. So so be careful what you measure.
So that's the lesson of the day.
And with that, I want to thank everybody for coming.
Thank you, Melody. Thank you, Charles. Thank you, Heather.
Thank you.
Enjoy your day. Have a productive week now.
Yeah, I'm going to try.
Definitely. Well, until next week, keep your nose to the grindstone.
Right on. Bye now.
Well, thanks again. Bye.